system

Abstract

We provide the system. [Solution] A method for analyzing user order requests using natural language processing technology and extracting their needs, A generation AI means that generates the optimal service plan based on extracted requests, A means of presenting the generated service plan to the user's terminal and receiving confirmation and modification requests from the user, A means of recording user-confirmed orders in a database and managing their progress, A means of collecting user feedback on completed orders and using it to improve the service, A system that includes this.

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, and includes 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 the conventional data line order reception process, it is often performed manually, the work is complicated and time-consuming, and there is a risk of human error. In addition, the response to customer requests and questions may be delayed, leading to a decrease in customer satisfaction. Furthermore, it takes a great deal of effort and time to confirm the order content, manage the progress status, and respond when problems occur, and there is a problem that efficient operation is difficult.

Means for Solving the Problems

[0005] This invention solves this problem by analyzing user order requests using natural language processing technology and extracting their needs. Furthermore, it generates an optimal service plan based on the extracted needs using generative AI and presents it to the user's terminal, significantly reducing the effort required for manual confirmation and correction. In addition, the server automatically manages the progress of orders, automatically detects problems when they occur, and provides solutions. Finally, it collects feedback after order completion and uses it to improve services, thereby improving operational efficiency and customer satisfaction.

[0006] "Natural language processing technology" refers to the technology that enables computers to understand, analyze, and generate human language.

[0007] "Generative AI" is an artificial intelligence technology that uses machine learning to generate new information by utilizing large amounts of data.

[0008] A "user terminal" is a device used by a user to operate, and includes computers, smartphones, tablets, and other similar devices.

[0009] A "service plan" refers to a document that outlines the details and terms of the communication and data-related services provided to a customer.

[0010] "Order progress status" refers to information that shows the progress of each stage from order acceptance to completion.

[0011] "Feedback" refers to the opinions and evaluations received from users regarding the services provided, and is information that is useful for improving the services. [Brief explanation of the drawing]

[0012] [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] 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.

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is an AI agent system for streamlining order processing for enterprise data lines. The system is based on a server, user terminals, and natural language processing and generative AI technologies.

[0034] First, the system initializes natural language processing technology on the server and prepares to receive orders from users. Users input their requests in natural language using a terminal, such as "We need high-bandwidth internet in our new office." These inputs are sent to the server and analyzed by natural language processing technology. The requests are extracted from the analysis results, and the server uses AI technology to generate the optimal service plan based on this information.

[0035] The generated service plan is sent from the server to the user's terminal, where the user can review the presented plan. The user can then approve the plan or request further modifications. This interaction takes place between the terminal and the server, and the confirmed order is recorded on the server. The server manages the progress of the order and provides progress information to the user as needed.

[0036] One example is a user case requiring a high-bandwidth plan for a new office. In this case, the user inputs their desired specifications, and the AI ​​proposes the optimal plan. If the proposed plan is suitable, the user can easily confirm the plan, and the server processes the order quickly. This automated process prevents human error, significantly reduces customer waiting times, and achieves high customer satisfaction.

[0037] Finally, the server collects user feedback on completed orders to help continuously improve the service. In this way, the present invention enables companies to provide efficient data line services.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The server starts up and initializes the natural language processing model and the generative AI model. This prepares it to analyze user input.

[0041] Step 2:

[0042] The user uses their device to input requests regarding the data connection in natural language. The input requests are sent to the server in real time.

[0043] Step 3:

[0044] The server analyzes the user input it receives using natural language processing technology. Specifically, it extracts keywords and important information related to the request from the input.

[0045] Step 4:

[0046] Based on the information extracted by the server, the system searches the database for a corresponding service plan. The optimal plan is selected and customized by the generating AI.

[0047] Step 5:

[0048] The server generates a service plan and sends it to the user's terminal, presenting the plan details to the user. The user can then review the details and request revisions or approve the plan.

[0049] Step 6:

[0050] When a user requests approval or modification of a plan, their input is sent again from the terminal to the server. The server then regenerates or finalizes the plan as requested.

[0051] Step 7:

[0052] The server records confirmed orders in the database and begins managing the order's progress. The progress is also notified to the user's terminal in a timely manner.

[0053] Step 8:

[0054] Once the order process is complete, the server sends a completion notification to the user. Subsequently, feedback from the user is collected and the data is analyzed to help improve future services.

[0055] (Example 1)

[0056] 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."

[0057] In modern information technology, there is a demand for the rapid and efficient delivery of service plans that best meet user needs. However, traditional methods require a significant amount of time for requirements analysis and plan generation, which hinders the ability to adequately enhance user satisfaction. Furthermore, there are challenges in promptly updating information on ongoing instructions and quickly providing solutions when problems arise.

[0058] 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.

[0059] In this invention, the server includes means for rapidly and accurately analyzing user requests using natural language processing technology, means for dynamically generating diverse plans using generative AI technology, and means for repeatedly presenting the generated plans to the user using information presentation means to facilitate two-way communication. This enables the provision of an optimal service plan tailored to the user's needs, rapid updates of progress information, and presentation of countermeasures when problems occur.

[0060] "Natural language processing technology" is a technology that analyzes text data from users and understands its meaning and intent.

[0061] A "generative AI method" is an algorithm that automatically generates new plans and proposals based on past data and patterns.

[0062] An "information display device" is a device that uses a computer or other related equipment to visually present information or plans to users.

[0063] "Information storage device" refers to a part of a computer or related device used to store digital data for extended periods.

[0064] "Feedback gathering" is the process of collecting evaluations and suggestions from users regarding completed plans and instructions.

[0065] "Two-way communication" is a process in which the information provider and the information recipient interact with each other to share and confirm information.

[0066] This invention is a system for efficiently understanding user needs and providing services based on those needs. At the heart of the system are a server, terminals, and natural language processing and generative AI technologies. The server prepares itself to receive user input by initializing natural language processing technologies such as BERT or GPT models.

[0067] Users input their needs in natural language using a terminal. This input is sent to a server and analyzed. Specific natural language processing techniques include multiple transformer models to understand the meaning of the text data. After information is extracted based on the analysis, a suitable service plan is generated using generative AI technology. The generative AI model includes algorithms that automatically design new service plans based on historical data.

[0068] The generated plan is sent from the server to the user's terminal, allowing the user to review and request modifications. For example, the user might enter a prompt such as, "Please suggest the best internet plan for my new office."

[0069] After an order is confirmed, the server records the information in its data storage device and manages its progress. The ongoing process involves two-way communication with the user, which is greatly aided by generative AI technology. Feedback is collected from completed orders and used to improve future services. This process ensures a system that provides fast and efficient service tailored to user needs.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The server initializes its natural language processing technology. The server prepares BERT or GPT models as its analysis engine. This preparation enables accurate analysis of natural language input from users. The input consists of natural language sentences, and the output generates a parseable data format.

[0073] Step 2:

[0074] The user enters their request using a terminal. On the terminal, the user enters a prompt message such as, "I need high-speed internet in my new office." This input data is sent to the server in natural language format.

[0075] Step 3:

[0076] The server analyzes the user's input. The server analyzes the received natural language input using initialized natural language processing techniques. Specifically, it tokenizes the text data, analyzes its meaning, and then extracts the request. The output of this step includes data that indicates the user's specific needs.

[0077] Step 4:

[0078] The server generates service plans using generative AI technology. Based on the analyzed needs, the server runs a generative AI model (e.g., GPT-3®) to generate the optimal service plan. This process can refer to past successful plans, and the output becomes proposed plan information.

[0079] Step 5:

[0080] The server sends the generated plan to the terminal. The generated service plan is sent to the terminal and can be viewed on the user's screen. The user can review the presented plan and request modifications if necessary. In this step, the modification request is sent back to the server.

[0081] Step 6:

[0082] The user reviews the plan and submits an approval or modification request. If the user approves the reviewed plan, that information is sent back to the server. If modifications are needed, return to step 4 and perform the regeneration process.

[0083] Step 7:

[0084] The server records confirmed orders and manages their progress. Confirmed orders are recorded in the information storage device, and the server manages their subsequent progress. Progress reports are provided to users as needed.

[0085] Step 8:

[0086] The server collects feedback on completed orders. Upon completion of a service, the server collects feedback from users and uses this information to improve future services. The collected feedback is analyzed and used to develop new operational rules and plans.

[0087] (Application Example 1)

[0088] 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."

[0089] When users engage in diverse online activities, selecting the optimal payment method is complex, requiring individual comparisons of information regarding fees and convenience. This process is time-consuming and laborious, potentially leading to incorrect decisions. This invention aims to solve the problem of improving user convenience by quickly and accurately proposing the optimal payment plan that meets the user's needs.

[0090] 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.

[0091] In this invention, the server includes means for analyzing user requests using natural language processing technology and extracting the payment method required by the user; means for generating an optimal payment plan based on the extracted payment method; and means for presenting the generated payment plan to the user's terminal and receiving confirmation and modification requests from the user. This makes it possible for the user to easily and quickly select the optimal payment method.

[0092] "Natural language processing technology" is the technology that allows computers to understand, analyze, and generate human language.

[0093] "Generative AI methods" refer to systems that use artificial intelligence to automatically generate optimal plans and solutions based on user requests.

[0094] "User terminal" refers to the equipment or device used by service users to operate the service, and typically includes smartphones and personal computers.

[0095] An "information recording device" is a device that is responsible for storing data over a long period of time, and includes databases and the like.

[0096] "Means of collecting opinions" refers to a system for collecting, recording, and analyzing feedback and requests from users.

[0097] In this invention, the server analyzes user requests using natural language processing technology and extracts the necessary payment methods. Users input their requests in natural language using terminals such as smartphones or personal computers. The input is sent to the server and analyzed using natural language processing technology. For this analysis, natural language processing libraries such as spaCy or NLTK can be used.

[0098] Subsequently, the server uses a generative AI model to generate the optimal payment plan based on the analysis results. For example, OpenAI's GPT can be used as the generative AI model. The generated plan is sent to the user's terminal, where the user can view it on the screen. The user can then approve the generated plan or request modifications. This information is sent back to the server, which stores the final decision in an information recording device.

[0099] For example, if a user enters "I'm looking for a low-fee payment method for online shopping," the server analyzes this request and generates and presents the most suitable payment plan. An example of a prompt to the generating AI model might be, "The user is looking for a low-fee credit card payment method. Please suggest the best plan."

[0100] This system allows users to quickly and accurately select a payment plan that suits their needs, improving convenience.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user uses a terminal to input a request, such as "I want a low-fee payment method for online shopping." The terminal sends this request to the server as natural language text. The input is natural language text data.

[0104] Step 2:

[0105] The server analyzes the received natural language text using natural language processing techniques. Libraries such as spaCy and NLTK can be used for this analysis. In this step, payment requests are extracted from the input natural language text and output as structured data. Specifically, the server extracts key phrases and identifies intent.

[0106] Step 3:

[0107] The server generates the optimal payment plan using a generative AI model based on the extracted requests. OpenAI's GPT, for example, can be used for the model. The input here is structured request data, which is fed into the AI ​​model as prompts. The output is the generated payment plan in text format. Specifically, the server inputs prompt text into the generative AI model and receives a response.

[0108] Step 4:

[0109] The server sends the generated payment plan to the terminal. The terminal presents the plan to the user, who then confirms it. The input is a payment plan in text format, and the output is a confirmation of the plan displayed on the screen. Specifically, the terminal visually displays the plan through its user interface.

[0110] Step 5:

[0111] The user either approves the presented plan or enters a request for modification. The terminal sends this response to the server. The input consists of data related to the user's selections and requested modifications.

[0112] Step 6:

[0113] The server saves the finalized information to the information recording device. The input is the user's confirmed or modified payment plan, and the output is the payment order recorded in the database. Specifically, the server performs a write operation to the database.

[0114] 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.

[0115] This invention is a system that further enhances order acceptance for enterprise data lines by utilizing an AI agent incorporating an emotion engine. The basic processing flow is similar to conventional processes that use natural language processing technology, but it is enhanced by recognizing the user's emotions using the emotion engine during interaction with the user.

[0116] The server analyzes the user's data line order request using natural language processing technology and extracts the user's needs. Simultaneously, an emotion engine analyzes the user's input data and recognizes emotions such as positive, negative, or neutral. Based on this information, the server uses generative AI technology to generate an optimal service plan and presents it to the user's terminal.

[0117] When users review plans on their devices, recognized emotional information is reflected in the plan suggestions, allowing for plan adjustments that take into account the user's potential dissatisfaction and needs. Furthermore, an emotional engine monitors emotions in real time, and a system is in place to send alerts to customer service representatives as needed. This significantly improves the customer experience and enables proactive measures before problems arise.

[0118] For example, when a user orders a new internet connection for their office, the server's emotion engine recognizes the urgency of the need for a stable connection and generates a corresponding rapid response plan. If the user reviews this plan on their device and the emotion engine detects any concerns, the server may suggest options to add further guarantees. Through this process, the service is tailored to the user's emotions, leading to improved customer satisfaction.

[0119] This system allows for the proper management of user emotions throughout the order process, from acceptance to completion, thereby improving service quality. Furthermore, by analyzing user emotion data during post-completion feedback collection, further improvements can be made for future orders.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] When the server starts up, it initializes the natural language processing model, the generative AI model, and the emotion engine. This initialization prepares the system to analyze user requests and emotions.

[0123] Step 2:

[0124] The user uses a terminal to input their data connection requirements in natural language. This input includes specific needs such as, "We need high bandwidth for our new office."

[0125] Step 3:

[0126] The server analyzes the received requests using a natural language processing engine to extract specific needs and requirements. Simultaneously, an emotion engine recognizes the user's emotions from the input data, determining their emotional state (positive, negative, neutral, etc.).

[0127] Step 4:

[0128] Based on extracted needs and emotional information, the server uses generative AI to generate the optimal service plan. Emotional information is used as a factor to fine-tune the proposed plan.

[0129] Step 5:

[0130] The generated service plan is sent to the device and presented to the user. The user can then review the plan in detail and request or approve modifications to suit their needs.

[0131] Step 6:

[0132] When a user requests to confirm or modify a plan, that information is sent from the terminal to the server. The server receives this information, regenerates the plan as needed, and confirms the final plan. Confirmed orders are recorded in the database.

[0133] Step 7:

[0134] The server manages the progress of orders, and the sentiment engine monitors user sentiment in real time. If a problem occurs or if the user's sentiment changes, an alert is generated to enable a quick response.

[0135] Step 8:

[0136] Once an order is complete, the server sends a completion notification to the user and finally collects feedback. This feedback includes sentiment information and is stored and analyzed as data to improve the service.

[0137] (Example 2)

[0138] 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".

[0139] Traditional ordering systems process requests without considering the user's emotional state, leading to a failure to accurately grasp user dissatisfaction and potential needs, resulting in a decline in service quality. Therefore, there is a need to appropriately recognize and reflect the user's emotions along with their requests to provide more personalized services.

[0140] 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.

[0141] In this invention, the server includes means for analyzing order requests from users using natural language processing technology and extracting requests; means for analyzing the user's emotional information using emotion recognition technology; and an automatic generation device for generating optimal service content based on the extracted requests and emotional information. This makes it possible to provide personalized services that comprehensively consider the user's requests and emotions.

[0142] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0143] "Users" refers to individuals or corporations that use the system to receive services.

[0144] An "order request" refers to a request made by a user to the system for goods or services.

[0145] "Requests" refer to the specific needs and conditions that users request when placing an order.

[0146] "Emotion recognition technology" is a technology that analyzes a user's words and actions to detect their emotions and state of mind.

[0147] "Emotional information" refers to data about the user's psychological state obtained through emotion recognition technology.

[0148] An "automatic generation device" refers to a device or software that creates optimal service content based on extracted requests and emotional information.

[0149] "Service details" refers to specific information such as the products and contract terms provided to the user.

[0150] A "data storage device" is a device used to record and store information such as a user's order history and progress status.

[0151] "Evaluation information" refers to opinions and feedback on services provided by users.

[0152] "Progress status" refers to information indicating the stage of an order request and the process until completion.

[0153] This invention is a system that uses an AI agent with emotion recognition capabilities to analyze customer order requests and provide personalized services. A server plays a major role in the implementation of this system.

[0154] First, the server analyzes user order requests using natural language processing (NLP) techniques. Specifically, it uses NLP libraries widely used by educational institutions and service providers. For example, it can use industry-renowned open-source NLP toolkits to identify requests embedded in natural language.

[0155] Next, the server analyzes the user's emotional information using emotion recognition technology. The emotion engine analyzes facial expressions and tone of voice within the text and classifies emotions as positive, negative, neutral, etc. This emotion analysis utilizes industry-standard emotion analysis APIs and proprietary algorithms.

[0156] Based on extracted requests and sentiment information, the server utilizes a generative AI model to automatically generate the optimal service content. For the generative AI model, it is recommended to use an open-source generative AI framework known for its excellent text generation capabilities. This model takes prompts that consider the user's specific needs and sentiments as input and provides personalized responses.

[0157] For example, if a user requests "urgent internet installation in a new office," the server will input a prompt message into the AI ​​model such as "We will propose a plan suitable for users who desire a fast and stable connection." This will then present a plan that meets the user's expectations.

[0158] The terminal displays the service details sent from the server to the user, providing the user with the convenience of reviewing the content.

[0159] This invention provides a platform that, through these processes, enables the delivery of services that are attentive to the user's emotions and improves customer satisfaction.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user enters the necessary information into the terminal to order a data service for their business. This information includes the type of service, any special requests, and the purpose of use. This entered data is then sent to the server.

[0163] Step 2:

[0164] The server analyzes requests using natural language processing techniques based on the received input data. Specifically, it extracts keywords and understands the context to identify the user's main requests and necessary conditions. The input is the user's text, and the output is a list of extracted requests.

[0165] Step 3:

[0166] The server applies sentiment recognition technology to the input user information to evaluate the user's emotions. For example, it determines the emotional state (positive, negative, neutral, etc.) from the user's word choice and tone. The input is user text, and the output is sentiment information.

[0167] Step 4:

[0168] Based on the extracted requests and sentiment information, the server uses a generative AI model to generate the optimal service plan. The generative AI model receives prompts such as "The user urgently desires a stable connection," and outputs specific plan details.

[0169] Step 5:

[0170] The server sends the generated service plan to the terminal and presents the proposal to the user. The terminal uses its user interface to view the plan details. At this stage, it is confirmed that the plan meets the user's needs and preferences.

[0171] Step 6:

[0172] Users review the displayed plan and send feedback or modification requests from their device as needed. The server receives this feedback and takes action, such as readjusting the plan, as required.

[0173] Step 7:

[0174] Once the user finally confirms the proposed service, the server records the order information in a data storage device and manages the progress. The input here is user confirmation information, and the output is storage in the database and progress recording.

[0175] (Application Example 2)

[0176] 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."

[0177] Traditional order taking systems have struggled to make suggestions that take user emotions into consideration, and have faced the challenge of not being able to accurately grasp users' potential dissatisfactions and needs. Furthermore, they have been unable to quickly detect negative user emotions and respond appropriately, resulting in insufficient improvement in customer satisfaction.

[0178] 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.

[0179] In this invention, the server includes means for analyzing user order requests using natural language processing technology and extracting requests; generation means equipped with an emotion engine that recognizes the extracted requests and the user's emotions and generates optimal service proposals; and means for presenting the generated service proposals to the user terminal and receiving confirmation and modification requests from the user. This enables user support that takes emotional information into consideration.

[0180] "Natural language processing technology" is a technology that uses computers to understand, analyze, and process human language.

[0181] An "order request" is the action or content of a user requesting a specific service or product.

[0182] An "emotion engine" is a device or program that analyzes a user's emotions in real time and determines their emotional state, such as positive or negative.

[0183] "Generation method" refers to a mechanism for creating appropriate service proposals based on user requests and emotional information.

[0184] A "service proposal" refers to the optimal service plan or offer provided while meeting the user's needs and taking their emotional state into consideration.

[0185] A "user terminal" is an electronic device that users directly operate to confirm orders and service proposals.

[0186] A "recording device" is a device or system used to store order information and retain data for subsequent management and use.

[0187] A "sentiment analysis algorithm" is a mathematical or statistical method for identifying and analyzing a user's emotions from language or other input data.

[0188] The system for implementing this invention is configured as follows: The server analyzes the user's order request using natural language processing technology and extracts the user's needs. Simultaneously with the extracted needs, it utilizes an emotion engine to recognize the user's emotions in real time. This recognition uses general natural language processing libraries and emotion analysis libraries (for example, SpaCy or TextBlob).

[0189] The server utilizes a generative AI model based on requests and emotional states to create optimal service suggestions and present them to the user's device. This generative AI model can utilize, for example, OpenAI's GPT-3. Furthermore, any new comments or feedback from the user during the user review process are also analyzed.

[0190] For example, when a user orders a new service for their office, the server's emotion engine recognizes the urgency, such as "I need a stable connection immediately." Based on this information, it generates and presents a service proposal that emphasizes a rapid response. If the server detects any anxiety regarding the proposal, it can offer a plan with additional guarantees. In this way, it provides proposals that are tailored to the user's emotions.

[0191] A concrete example of a prompt message could be, "Users are experiencing stress during payment. Please generate a message to calm them down." By utilizing such prompt messages and leveraging a generation AI model, it is possible to propose services that enhance user satisfaction.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server receives an order request from a user. This input data is likely to be sent in text format. The server analyzes this data using a natural language processing library (e.g., SpaCy) to extract the user's requests. The extracted requests are used as the basis for the next step.

[0195] Step 2:

[0196] The server uses a sentiment analysis library (e.g., TextBlob) along with the extracted requests to evaluate the user's emotional state from their statements. The input is the user's text data, and the output is emotional information classified as positive, negative, or neutral. This prepares the server to respond based on the user's emotions.

[0197] Step 3:

[0198] The server uses a generative AI model (e.g., OpenAI's GPT-3) based on requests and emotional states to generate optimal service suggestions. The model's input consists of user requests and emotional data, and its output is a specific service plan. This service plan is designed to align with the user's needs and emotions.

[0199] Step 4:

[0200] The server sends the generated service proposal to the user's terminal. At this stage, the user can review the service proposal and enter modification requests if necessary. The user's input is re-evaluated in the next cycle.

[0201] Step 5:

[0202] If there is user feedback or a request for correction, the server analyzes it and performs another sentiment analysis. The input is the user feedback data, and the output is the corrected sentiment and new requests.

[0203] Step 6:

[0204] The server registers the final order in the recording device and monitors its progress. This step also collects feedback from the user to identify areas for improvement in future interactions. Furthermore, sentiment data within the feedback is stored for use in future responses.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Second Embodiment]

[0209] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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".

[0221] This invention is an AI agent system for streamlining order processing for enterprise data lines. The system is based on a server, user terminals, and natural language processing and generative AI technologies.

[0222] First, the system initializes natural language processing technology on the server and prepares to receive orders from users. Users input their requests in natural language using a terminal, such as "We need high-bandwidth internet in our new office." These inputs are sent to the server and analyzed by natural language processing technology. The requests are extracted from the analysis results, and the server uses AI technology to generate the optimal service plan based on this information.

[0223] The generated service plan is sent from the server to the user's terminal, where the user can review the presented plan. The user can then approve the plan or request further modifications. This interaction takes place between the terminal and the server, and the confirmed order is recorded on the server. The server manages the progress of the order and provides progress information to the user as needed.

[0224] One example is a user case requiring a high-bandwidth plan for a new office. In this case, the user inputs their desired specifications, and the AI ​​proposes the optimal plan. If the proposed plan is suitable, the user can easily confirm the plan, and the server processes the order quickly. This automated process prevents human error, significantly reduces customer waiting times, and achieves high customer satisfaction.

[0225] Finally, the server collects user feedback on completed orders to help continuously improve the service. In this way, the present invention enables companies to provide efficient data line services.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The server starts up and initializes the natural language processing model and the generative AI model. This prepares it to analyze user input.

[0229] Step 2:

[0230] The user uses their device to input requests regarding the data connection in natural language. The input requests are sent to the server in real time.

[0231] Step 3:

[0232] The server analyzes the user input it receives using natural language processing technology. Specifically, it extracts keywords and important information related to the request from the input.

[0233] Step 4:

[0234] Based on the information extracted by the server, the system searches the database for a corresponding service plan. The optimal plan is selected and customized by the generating AI.

[0235] Step 5:

[0236] The server generates a service plan and sends it to the user's terminal, presenting the plan details to the user. The user can then review the details and request revisions or approve the plan.

[0237] Step 6:

[0238] When a user requests approval or modification of a plan, their input is sent again from the terminal to the server. The server then regenerates or finalizes the plan as requested.

[0239] Step 7:

[0240] The server records confirmed orders in the database and begins managing the order's progress. The progress is also notified to the user's terminal in a timely manner.

[0241] Step 8:

[0242] Once the order process is complete, the server sends a completion notification to the user. Subsequently, feedback from the user is collected and the data is analyzed to help improve future services.

[0243] (Example 1)

[0244] 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."

[0245] In modern information technology, there is a demand for the rapid and efficient delivery of service plans that best meet user needs. However, traditional methods require a significant amount of time for requirements analysis and plan generation, which hinders the ability to adequately enhance user satisfaction. Furthermore, there are challenges in promptly updating information on ongoing instructions and quickly providing solutions when problems arise.

[0246] 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.

[0247] In this invention, the server includes means for rapidly and accurately analyzing user requests using natural language processing technology, means for dynamically generating diverse plans using generative AI technology, and means for repeatedly presenting the generated plans to the user using information presentation means to facilitate two-way communication. This enables the provision of an optimal service plan tailored to the user's needs, rapid updates of progress information, and presentation of countermeasures when problems occur.

[0248] "Natural language processing technology" is a technology that analyzes text data from users and understands its meaning and intent.

[0249] A "generative AI method" is an algorithm that automatically generates new plans and proposals based on past data and patterns.

[0250] An "information display device" is a device that uses a computer or other related equipment to visually present information or plans to users.

[0251] "Information storage device" refers to a part of a computer or related device used to store digital data for extended periods.

[0252] "Feedback gathering" is the process of collecting evaluations and suggestions from users regarding completed plans and instructions.

[0253] "Two-way communication" is a process in which the information provider and the information recipient interact with each other to share and confirm information.

[0254] This invention is a system for efficiently understanding user needs and providing services based on those needs. At the heart of the system are a server, terminals, and natural language processing and generative AI technologies. The server prepares itself to receive user input by initializing natural language processing technologies such as BERT or GPT models.

[0255] Users input their needs in natural language using a terminal. This input is sent to a server and analyzed. Specific natural language processing techniques include multiple transformer models to understand the meaning of the text data. After information is extracted based on the analysis, a suitable service plan is generated using generative AI technology. The generative AI model includes algorithms that automatically design new service plans based on historical data.

[0256] The generated plan is sent from the server to the user's terminal, allowing the user to review and request modifications. For example, the user might enter a prompt such as, "Please suggest the best internet plan for my new office."

[0257] After an order is confirmed, the server records the information in its data storage device and manages its progress. The ongoing process involves two-way communication with the user, which is greatly aided by generative AI technology. Feedback is collected from completed orders and used to improve future services. This process ensures a system that provides fast and efficient service tailored to user needs.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server initializes its natural language processing technology. The server prepares BERT or GPT models as its analysis engine. This preparation enables accurate analysis of natural language input from users. The input consists of natural language sentences, and the output generates a parseable data format.

[0261] Step 2:

[0262] The user enters their request using a terminal. On the terminal, the user enters a prompt message such as, "I need high-speed internet in my new office." This input data is sent to the server in natural language format.

[0263] Step 3:

[0264] The server analyzes the user's input. The server analyzes the received natural language input using initialized natural language processing techniques. Specifically, it tokenizes the text data, analyzes its meaning, and then extracts the request. The output of this step includes data that indicates the user's specific needs.

[0265] Step 4:

[0266] The server generates service plans using generative AI technology. Based on the analyzed needs, the server runs a generative AI model (e.g., GPT-3) to generate the optimal service plan. This process can refer to past successful plans, and the output becomes proposed plan information.

[0267] Step 5:

[0268] The server sends the generated plan to the terminal. The generated service plan is sent to the terminal and can be viewed on the user's screen. The user can review the presented plan and request modifications if necessary. In this step, the modification request is sent back to the server.

[0269] Step 6:

[0270] The user reviews the plan and submits an approval or modification request. If the user approves the reviewed plan, that information is sent back to the server. If modifications are needed, return to step 4 and perform the regeneration process.

[0271] Step 7:

[0272] The server records confirmed orders and manages their progress. Confirmed orders are recorded in the information storage device, and the server manages their subsequent progress. Progress reports are provided to users as needed.

[0273] Step 8:

[0274] The server collects feedback on completed orders. Upon completion of a service, the server collects feedback from users and uses this information to improve future services. The collected feedback is analyzed and used to develop new operational rules and plans.

[0275] (Application Example 1)

[0276] 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."

[0277] When users engage in diverse online activities, selecting the optimal payment method is complex, requiring individual comparisons of information regarding fees and convenience. This process is time-consuming and laborious, potentially leading to incorrect decisions. This invention aims to solve the problem of improving user convenience by quickly and accurately proposing the optimal payment plan that meets the user's needs.

[0278] 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.

[0279] In this invention, the server includes means for analyzing the requests from users using natural language processing technology and extracting the payment methods required by the users, generative AI means for generating an optimal payment plan based on the extracted payment methods, and means for presenting the generated payment plan to the user terminal and accepting confirmation and modification requests from the users. This enables users to easily and quickly select an optimal payment method.

[0280] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0281] "Generative AI means" is a mechanism that automatically generates optimal plans and solutions based on users' requests using artificial intelligence.

[0282] "User terminal" refers to devices or apparatuses operated by service users, usually corresponding to smartphones and personal computers.

[0283] "Information recording device" is a device that plays a role in retaining data over a long period, including databases and the like.

[0284] "Means for collecting opinions" is a mechanism for collecting, recording, and analyzing feedback and requests from users.

[0285] In this invention, the server analyzes the requests from users using natural language processing technology and extracts the necessary payment methods. The user inputs the requests in natural language using a terminal such as a smartphone or a personal computer. The input is sent to the server and analyzed using natural language processing technology. For this analysis, natural language processing libraries such as spaCy and NLTK can be used.

[0286] After that, the server uses the generative AI model to generate an optimal payment plan based on the analysis results. For the generative AI model, for example, GPT of OpenAI can be used. The generated plan is sent to the user terminal, and the user can check it on the screen. The user can approve the generated plan or make a modification request. This information is sent to the server again, and the server saves the final determined content in the information recording device.

[0287] As a specific example, when the user inputs "looking for a low-fee payment method for online shopping", the server analyzes this request and generates and presents the most suitable payment plan. As an example of the prompt text for the generative AI model, the content "The user is looking for a low-fee credit card payment method. Please propose what plan is optimal." can be considered.

[0288] With this system, the user can quickly and accurately select a payment plan that suits their needs, improving convenience.

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The user uses the terminal to input a request such as "want a low-fee payment method for online shopping". The terminal sends this request to the server as natural language text. The input is natural language text data.

[0292] Step 2:

[0293] The server analyzes the received natural language text using natural language processing technology. For this analysis, libraries such as spaCy and NLTK can be used. In this step, the request related to payment is extracted from the input natural language text and output as structured data. As a specific operation, the server performs keyphrase extraction and intention identification.

[0294] Step 3:

[0295] The server generates the optimal payment plan using a generative AI model based on the extracted requests. OpenAI's GPT, for example, can be used for the model. The input here is structured request data, which is fed into the AI ​​model as prompts. The output is the generated payment plan in text format. Specifically, the server inputs prompt text into the generative AI model and receives a response.

[0296] Step 4:

[0297] The server sends the generated payment plan to the terminal. The terminal presents the plan to the user, who then confirms it. The input is a payment plan in text format, and the output is a confirmation of the plan displayed on the screen. Specifically, the terminal visually displays the plan through its user interface.

[0298] Step 5:

[0299] The user either approves the presented plan or enters a request for modification. The terminal sends this response to the server. The input consists of data related to the user's selections and requested modifications.

[0300] Step 6:

[0301] The server saves the finalized information to the information recording device. The input is the user's confirmed or modified payment plan, and the output is the payment order recorded in the database. Specifically, the server performs a write operation to the database.

[0302] 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.

[0303] The present invention is a system that further enhances the order acceptance of enterprise data lines by utilizing an AI agent incorporating an emotion engine. The basic processing flow is the same as that of conventional processes using natural language processing technology, but it is extended in that the emotion of the user is recognized using the emotion engine during the interaction with the user.

[0304] The server analyzes the request for a data line order from the user using natural language processing technology and extracts the user's requirements. At this time, the emotion engine simultaneously analyzes the user's input data and recognizes emotions such as positive, negative, and neutral. Based on this information, the server utilizes generative AI technology to generate an optimal service plan and presents it to the user terminal.

[0305] When the user checks the plan on the terminal, since the recognized emotion information is reflected in the plan proposal, plan adjustment considering the user's potential dissatisfaction and needs is carried out. Also, a mechanism is implemented where the emotion is monitored in real time by the emotion engine, and the customer service staff receives an alert as needed. As a result, the customer experience is significantly improved, and proactive response is possible before problems occur.

[0306] As a specific example, when the user orders a new line for the office, the server recognizes the sense of urgency such as "urgent need for a stable connection" with the emotion engine and generates a rapid response plan accordingly. If the user examines this plan on the terminal and the emotion engine senses uneasiness, the server may also propose an option to add guarantee content. Through such a process, service provision that conforms to the user's emotion is realized, and customer satisfaction is improved.

[0307] With this system, it becomes possible to appropriately manage the user's emotion in the process from order acceptance to completion, and improve the quality of the service. Furthermore, even in the feedback collection after completion, by analyzing the user's emotion data, further improvement in subsequent times is realized.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] When the server starts up, it initializes the natural language processing model, the generative AI model, and the emotion engine. This initialization prepares the system to analyze user requests and emotions.

[0311] Step 2:

[0312] The user uses a terminal to input their data connection requirements in natural language. This input includes specific needs such as, "We need high bandwidth for our new office."

[0313] Step 3:

[0314] The server analyzes the received requests using a natural language processing engine to extract specific needs and requirements. Simultaneously, an emotion engine recognizes the user's emotions from the input data, determining their emotional state (positive, negative, neutral, etc.).

[0315] Step 4:

[0316] Based on extracted needs and emotional information, the server uses generative AI to generate the optimal service plan. Emotional information is used as a factor to fine-tune the proposed plan.

[0317] Step 5:

[0318] The generated service plan is sent to the device and presented to the user. The user can then review the plan in detail and request or approve modifications to suit their needs.

[0319] Step 6:

[0320] When a user requests to confirm or modify a plan, that information is sent from the terminal to the server. The server receives this information, regenerates the plan as needed, and confirms the final plan. Confirmed orders are recorded in the database.

[0321] Step 7:

[0322] The server manages the progress of orders, and the sentiment engine monitors user sentiment in real time. If a problem occurs or if the user's sentiment changes, an alert is generated to enable a quick response.

[0323] Step 8:

[0324] Once an order is complete, the server sends a completion notification to the user and finally collects feedback. This feedback includes sentiment information and is stored and analyzed as data to improve the service.

[0325] (Example 2)

[0326] 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".

[0327] Traditional ordering systems process requests without considering the user's emotional state, leading to a failure to accurately grasp user dissatisfaction and potential needs, resulting in a decline in service quality. Therefore, there is a need to appropriately recognize and reflect the user's emotions along with their requests to provide more personalized services.

[0328] 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.

[0329] In this invention, the server includes means for analyzing order requests from users using natural language processing technology and extracting requests; means for analyzing the user's emotional information using emotion recognition technology; and an automatic generation device for generating optimal service content based on the extracted requests and emotional information. This makes it possible to provide personalized services that comprehensively consider the user's requests and emotions.

[0330] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0331] "Users" refers to individuals or corporations that use the system to receive services.

[0332] An "order request" refers to a request made by a user to the system for goods or services.

[0333] "Requests" refer to the specific needs and conditions that users request when placing an order.

[0334] "Emotion recognition technology" is a technology that analyzes a user's words and actions to detect their emotions and state of mind.

[0335] "Emotional information" refers to data about the user's psychological state obtained through emotion recognition technology.

[0336] An "automatic generation device" refers to a device or software that creates optimal service content based on extracted requests and emotional information.

[0337] "Service details" refers to specific information such as the products and contract terms provided to the user.

[0338] A "data storage device" is a device used to record and store information such as a user's order history and progress status.

[0339] "Evaluation information" refers to opinions and feedback on services provided by users.

[0340] "Progress status" refers to information indicating the stage of an order request and the process until completion.

[0341] This invention is a system that uses an AI agent with emotion recognition capabilities to analyze customer order requests and provide personalized services. A server plays a major role in the implementation of this system.

[0342] First, the server analyzes user order requests using natural language processing (NLP) techniques. Specifically, it uses NLP libraries widely used by educational institutions and service providers. For example, it can use industry-renowned open-source NLP toolkits to identify requests embedded in natural language.

[0343] Next, the server analyzes the user's emotional information using emotion recognition technology. The emotion engine analyzes facial expressions and tone of voice within the text and classifies emotions as positive, negative, neutral, etc. This emotion analysis utilizes industry-standard emotion analysis APIs and proprietary algorithms.

[0344] Based on extracted requests and sentiment information, the server utilizes a generative AI model to automatically generate the optimal service content. For the generative AI model, it is recommended to use an open-source generative AI framework known for its excellent text generation capabilities. This model takes prompts that consider the user's specific needs and sentiments as input and provides personalized responses.

[0345] For example, if a user requests "urgent internet installation in a new office," the server will input a prompt message into the AI ​​model such as "We will propose a plan suitable for users who desire a fast and stable connection." This will then present a plan that meets the user's expectations.

[0346] The terminal displays the service details sent from the server to the user, providing the user with the convenience of reviewing the content.

[0347] This invention provides a platform that, through these processes, enables the delivery of services that are attentive to the user's emotions and improves customer satisfaction.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The user enters the necessary information into the terminal to order a data service for their business. This information includes the type of service, any special requests, and the purpose of use. This entered data is then sent to the server.

[0351] Step 2:

[0352] The server analyzes requests using natural language processing techniques based on the received input data. Specifically, it extracts keywords and understands the context to identify the user's main requests and necessary conditions. The input is the user's text, and the output is a list of extracted requests.

[0353] Step 3:

[0354] The server applies sentiment recognition technology to the input user information to evaluate the user's emotions. For example, it determines the emotional state (positive, negative, neutral, etc.) from the user's word choice and tone. The input is user text, and the output is sentiment information.

[0355] Step 4:

[0356] Based on the extracted requests and sentiment information, the server uses a generative AI model to generate the optimal service plan. The generative AI model receives prompts such as "The user urgently desires a stable connection," and outputs specific plan details.

[0357] Step 5:

[0358] The server sends the generated service plan to the terminal and presents the proposal to the user. The terminal uses its user interface to view the plan details. At this stage, it is confirmed that the plan meets the user's needs and preferences.

[0359] Step 6:

[0360] Users review the displayed plan and send feedback or modification requests from their device as needed. The server receives this feedback and takes action, such as readjusting the plan, as required.

[0361] Step 7:

[0362] Once the user finally confirms the proposed service, the server records the order information in a data storage device and manages the progress. The input here is user confirmation information, and the output is storage in the database and progress recording.

[0363] (Application Example 2)

[0364] 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 as the "terminal".

[0365] Traditional order taking systems have struggled to make suggestions that take user emotions into consideration, and have faced the challenge of not being able to accurately grasp users' potential dissatisfactions and needs. Furthermore, they have been unable to quickly detect negative user emotions and respond appropriately, resulting in insufficient improvement in customer satisfaction.

[0366] 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.

[0367] In this invention, the server includes means for analyzing user order requests using natural language processing technology and extracting requests; generation means equipped with an emotion engine that recognizes the extracted requests and the user's emotions and generates optimal service proposals; and means for presenting the generated service proposals to the user terminal and receiving confirmation and modification requests from the user. This enables user support that takes emotional information into consideration.

[0368] "Natural language processing technology" is a technology that uses computers to understand, analyze, and process human language.

[0369] An "order request" is the action or content of a user requesting a specific service or product.

[0370] An "emotion engine" is a device or program that analyzes a user's emotions in real time and determines their emotional state, such as positive or negative.

[0371] "Generation method" refers to a mechanism for creating appropriate service proposals based on user requests and emotional information.

[0372] A "service proposal" refers to the optimal service plan or offer provided while meeting the user's needs and taking their emotional state into consideration.

[0373] A "user terminal" is an electronic device that users directly operate to confirm orders and service proposals.

[0374] A "recording device" is a device or system used to store order information and retain data for subsequent management and use.

[0375] A "sentiment analysis algorithm" is a mathematical or statistical method for identifying and analyzing a user's emotions from language or other input data.

[0376] The system for implementing this invention is configured as follows: The server analyzes the user's order request using natural language processing technology and extracts the user's needs. Simultaneously with the extracted needs, it utilizes an emotion engine to recognize the user's emotions in real time. This recognition uses general natural language processing libraries and emotion analysis libraries (for example, SpaCy or TextBlob).

[0377] The server utilizes a generative AI model based on requests and emotional states to create optimal service suggestions and present them to the user's device. This generative AI model can utilize, for example, OpenAI's GPT-3. Furthermore, any new comments or feedback from the user during the user review process are also analyzed.

[0378] For example, when a user orders a new service for their office, the server's emotion engine recognizes the urgency, such as "I need a stable connection immediately." Based on this information, it generates and presents a service proposal that emphasizes a rapid response. If the server detects any anxiety regarding the proposal, it can offer a plan with additional guarantees. In this way, it provides proposals that are tailored to the user's emotions.

[0379] A concrete example of a prompt message could be, "Users are experiencing stress during payment. Please generate a message to calm them down." By utilizing such prompt messages and leveraging a generation AI model, it is possible to propose services that enhance user satisfaction.

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The server receives an order request from a user. This input data is likely to be sent in text format. The server analyzes this data using a natural language processing library (e.g., SpaCy) to extract the user's requests. The extracted requests are used as the basis for the next step.

[0383] Step 2:

[0384] The server uses a sentiment analysis library (e.g., TextBlob) along with the extracted requests to evaluate the user's emotional state from their statements. The input is the user's text data, and the output is emotional information classified as positive, negative, or neutral. This prepares the server to respond based on the user's emotions.

[0385] Step 3:

[0386] The server uses a generative AI model (e.g., OpenAI's GPT-3) based on requests and emotional states to generate optimal service suggestions. The model's input consists of user requests and emotional data, and its output is a specific service plan. This service plan is designed to align with the user's needs and emotions.

[0387] Step 4:

[0388] The server sends the generated service proposal to the user's terminal. At this stage, the user can review the service proposal and enter modification requests if necessary. The user's input is re-evaluated in the next cycle.

[0389] Step 5:

[0390] If there is user feedback or a request for correction, the server analyzes it and performs another sentiment analysis. The input is the user feedback data, and the output is the corrected sentiment and new requests.

[0391] Step 6:

[0392] The server registers the final order in the recording device and monitors its progress. This step also collects feedback from the user to identify areas for improvement in future interactions. Furthermore, sentiment data within the feedback is stored for use in future responses.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is an AI agent system for streamlining order processing for enterprise data lines. The system is based on a server, user terminals, and natural language processing and generative AI technologies.

[0410] First, the system initializes natural language processing technology on the server and prepares to receive orders from users. Users input their requests in natural language using a terminal, such as "We need high-bandwidth internet in our new office." These inputs are sent to the server and analyzed by natural language processing technology. The requests are extracted from the analysis results, and the server uses AI technology to generate the optimal service plan based on this information.

[0411] The generated service plan is sent from the server to the user's terminal, where the user can review the presented plan. The user can then approve the plan or request further modifications. This interaction takes place between the terminal and the server, and the confirmed order is recorded on the server. The server manages the progress of the order and provides progress information to the user as needed.

[0412] One example is a user case requiring a high-bandwidth plan for a new office. In this case, the user inputs their desired specifications, and the AI ​​proposes the optimal plan. If the proposed plan is suitable, the user can easily confirm the plan, and the server processes the order quickly. This automated process prevents human error, significantly reduces customer waiting times, and achieves high customer satisfaction.

[0413] Finally, the server collects user feedback on completed orders to help continuously improve the service. In this way, the present invention enables companies to provide efficient data line services.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The server starts up and initializes the natural language processing model and the generative AI model. This prepares it to analyze user input.

[0417] Step 2:

[0418] The user uses their device to input requests regarding the data connection in natural language. The input requests are sent to the server in real time.

[0419] Step 3:

[0420] The server analyzes the user input it receives using natural language processing technology. Specifically, it extracts keywords and important information related to the request from the input.

[0421] Step 4:

[0422] Based on the information extracted by the server, the system searches the database for a corresponding service plan. The optimal plan is selected and customized by the generating AI.

[0423] Step 5:

[0424] The server generates a service plan and sends it to the user's terminal, presenting the plan details to the user. The user can then review the details and request revisions or approve the plan.

[0425] Step 6:

[0426] When a user requests approval or modification of a plan, their input is sent again from the terminal to the server. The server then regenerates or finalizes the plan as requested.

[0427] Step 7:

[0428] The server records confirmed orders in the database and begins managing the order's progress. The progress is also notified to the user's terminal in a timely manner.

[0429] Step 8:

[0430] Once the order process is complete, the server sends a completion notification to the user. Subsequently, feedback from the user is collected and the data is analyzed to help improve future services.

[0431] (Example 1)

[0432] 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."

[0433] In modern information technology, there is a demand for the rapid and efficient delivery of service plans that best meet user needs. However, traditional methods require a significant amount of time for requirements analysis and plan generation, which hinders the ability to adequately enhance user satisfaction. Furthermore, there are challenges in promptly updating information on ongoing instructions and quickly providing solutions when problems arise.

[0434] 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.

[0435] In this invention, the server includes means for rapidly and accurately analyzing user requests using natural language processing technology, means for dynamically generating diverse plans using generative AI technology, and means for repeatedly presenting the generated plans to the user using information presentation means to facilitate two-way communication. This enables the provision of an optimal service plan tailored to the user's needs, rapid updates of progress information, and presentation of countermeasures when problems occur.

[0436] "Natural language processing technology" is a technology that analyzes text data from users and understands its meaning and intent.

[0437] A "generative AI method" is an algorithm that automatically generates new plans and proposals based on past data and patterns.

[0438] An "information display device" is a device that uses a computer or other related equipment to visually present information or plans to users.

[0439] "Information storage device" refers to a part of a computer or related device used to store digital data for extended periods.

[0440] "Feedback gathering" is the process of collecting evaluations and suggestions from users regarding completed plans and instructions.

[0441] "Two-way communication" is a process in which the information provider and the information recipient interact with each other to share and confirm information.

[0442] This invention is a system for efficiently understanding user needs and providing services based on those needs. At the heart of the system are a server, terminals, and natural language processing and generative AI technologies. The server prepares itself to receive user input by initializing natural language processing technologies such as BERT or GPT models.

[0443] Users input their needs in natural language using a terminal. This input is sent to a server and analyzed. Specific natural language processing techniques include multiple transformer models to understand the meaning of the text data. After information is extracted based on the analysis, a suitable service plan is generated using generative AI technology. The generative AI model includes algorithms that automatically design new service plans based on historical data.

[0444] The generated plan is sent from the server to the user's terminal, allowing the user to review and request modifications. For example, the user might enter a prompt such as, "Please suggest the best internet plan for my new office."

[0445] After an order is confirmed, the server records the information in its data storage device and manages its progress. The ongoing process involves two-way communication with the user, which is greatly aided by generative AI technology. Feedback is collected from completed orders and used to improve future services. This process ensures a system that provides fast and efficient service tailored to user needs.

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1:

[0448] The server initializes its natural language processing technology. The server prepares BERT or GPT models as its analysis engine. This preparation enables accurate analysis of natural language input from users. The input consists of natural language sentences, and the output generates a parseable data format.

[0449] Step 2:

[0450] The user enters their request using a terminal. On the terminal, the user enters a prompt message such as, "I need high-speed internet in my new office." This input data is sent to the server in natural language format.

[0451] Step 3:

[0452] The server analyzes the user's input. The server analyzes the received natural language input using initialized natural language processing techniques. Specifically, it tokenizes the text data, analyzes its meaning, and then extracts the request. The output of this step includes data that indicates the user's specific needs.

[0453] Step 4:

[0454] The server generates service plans using generative AI technology. Based on the analyzed needs, the server runs a generative AI model (e.g., GPT-3) to generate the optimal service plan. This process can refer to past successful plans, and the output becomes proposed plan information.

[0455] Step 5:

[0456] The server sends the generated plan to the terminal. The generated service plan is sent to the terminal and can be viewed on the user's screen. The user can review the presented plan and request modifications if necessary. In this step, the modification request is sent back to the server.

[0457] Step 6:

[0458] The user reviews the plan and submits an approval or modification request. If the user approves the reviewed plan, that information is sent back to the server. If modifications are needed, return to step 4 and perform the regeneration process.

[0459] Step 7:

[0460] The server records confirmed orders and manages their progress. Confirmed orders are recorded in the information storage device, and the server manages their subsequent progress. Progress reports are provided to users as needed.

[0461] Step 8:

[0462] The server collects feedback on completed orders. Upon completion of a service, the server collects feedback from users and uses this information to improve future services. The collected feedback is analyzed and used to develop new operational rules and plans.

[0463] (Application Example 1)

[0464] 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."

[0465] When users engage in diverse online activities, selecting the optimal payment method is complex, requiring individual comparisons of information regarding fees and convenience. This process is time-consuming and laborious, potentially leading to incorrect decisions. This invention aims to solve the problem of improving user convenience by quickly and accurately proposing the optimal payment plan that meets the user's needs.

[0466] 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.

[0467] In this invention, the server includes means for analyzing user requests using natural language processing technology and extracting the payment method required by the user; means for generating an optimal payment plan based on the extracted payment method; and means for presenting the generated payment plan to the user's terminal and receiving confirmation and modification requests from the user. This makes it possible for the user to easily and quickly select the optimal payment method.

[0468] "Natural language processing technology" is the technology that allows computers to understand, analyze, and generate human language.

[0469] "Generative AI methods" refer to systems that use artificial intelligence to automatically generate optimal plans and solutions based on user requests.

[0470] "User terminal" refers to the equipment or device used by service users to operate the service, and typically includes smartphones and personal computers.

[0471] An "information recording device" is a device that is responsible for storing data over a long period of time, and includes databases and the like.

[0472] "Means of collecting opinions" refers to a system for collecting, recording, and analyzing feedback and requests from users.

[0473] In this invention, the server analyzes user requests using natural language processing technology and extracts the necessary payment methods. Users input their requests in natural language using terminals such as smartphones or personal computers. The input is sent to the server and analyzed using natural language processing technology. For this analysis, natural language processing libraries such as spaCy or NLTK can be used.

[0474] Subsequently, the server uses a generative AI model to generate the optimal payment plan based on the analysis results. For example, OpenAI's GPT can be used as the generative AI model. The generated plan is sent to the user's terminal, where the user can review it on the screen. The user can then approve the generated plan or request modifications. This information is sent back to the server, which stores the final decision in an information recording device.

[0475] For example, if a user enters "I'm looking for a low-fee payment method for online shopping," the server analyzes this request and generates and presents the most suitable payment plan. An example of a prompt to the generating AI model might be, "The user is looking for a low-fee credit card payment method. Please suggest the best plan."

[0476] This system allows users to quickly and accurately select a payment plan that suits their needs, improving convenience.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The user uses a terminal to input a request, such as "I want a low-fee payment method for online shopping." The terminal sends this request to the server as natural language text. The input is natural language text data.

[0480] Step 2:

[0481] The server analyzes the received natural language text using natural language processing techniques. Libraries such as spaCy and NLTK can be used for this analysis. In this step, payment requests are extracted from the input natural language text and output as structured data. Specifically, the server extracts key phrases and identifies intent.

[0482] Step 3:

[0483] The server generates the optimal payment plan using a generative AI model based on the extracted requests. OpenAI's GPT, for example, can be used for the model. The input here is structured request data, which is fed into the AI ​​model as prompts. The output is the generated payment plan in text format. Specifically, the server inputs prompt text into the generative AI model and receives a response.

[0484] Step 4:

[0485] The server sends the generated payment plan to the terminal. The terminal presents the plan to the user, who then confirms it. The input is a payment plan in text format, and the output is a confirmation of the plan displayed on the screen. Specifically, the terminal visually displays the plan through its user interface.

[0486] Step 5:

[0487] The user either approves the presented plan or enters a request for modification. The terminal sends this response to the server. The input consists of data related to the user's selections and requested modifications.

[0488] Step 6:

[0489] The server saves the finalized information to the information recording device. The input is the user's confirmed or modified payment plan, and the output is the payment order recorded in the database. Specifically, the server performs a write operation to the database.

[0490] 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.

[0491] This invention is a system that further enhances order acceptance for enterprise data lines by utilizing an AI agent incorporating an emotion engine. The basic processing flow is similar to conventional processes that use natural language processing technology, but it is enhanced by recognizing the user's emotions using the emotion engine during interaction with the user.

[0492] The server analyzes the user's data line order request using natural language processing technology and extracts the user's needs. Simultaneously, an emotion engine analyzes the user's input data and recognizes emotions such as positive, negative, or neutral. Based on this information, the server uses generative AI technology to generate an optimal service plan and presents it to the user's terminal.

[0493] When users review plans on their devices, recognized emotional information is reflected in the plan suggestions, allowing for plan adjustments that take into account the user's potential dissatisfaction and needs. Furthermore, an emotional engine monitors emotions in real time, and a system is in place to send alerts to customer service representatives as needed. This significantly improves the customer experience and enables proactive measures before problems arise.

[0494] For example, when a user orders a new internet connection for their office, the server's emotion engine recognizes the urgency of the need for a stable connection and generates a corresponding rapid response plan. If the user reviews this plan on their device and the emotion engine detects any concerns, the server may suggest options to add further guarantees. Through this process, the service is tailored to the user's emotions, leading to improved customer satisfaction.

[0495] This system allows for the proper management of user emotions throughout the order process, from acceptance to completion, thereby improving service quality. Furthermore, by analyzing user emotion data during post-completion feedback collection, further improvements can be made for future orders.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] When the server starts up, it initializes the natural language processing model, the generative AI model, and the emotion engine. This initialization prepares the system to analyze user requests and emotions.

[0499] Step 2:

[0500] The user uses a terminal to input their data connection requirements in natural language. This input includes specific needs such as, "We need high bandwidth for our new office."

[0501] Step 3:

[0502] The server analyzes the received requests using a natural language processing engine to extract specific needs and requirements. Simultaneously, an emotion engine recognizes the user's emotions from the input data, determining their emotional state (positive, negative, neutral, etc.).

[0503] Step 4:

[0504] Based on extracted needs and emotional information, the server uses generative AI to generate the optimal service plan. Emotional information is used as a factor to fine-tune the proposed plan.

[0505] Step 5:

[0506] The generated service plan is sent to the device and presented to the user. The user can then review the plan in detail and request or approve modifications to suit their needs.

[0507] Step 6:

[0508] When a user requests to confirm or modify a plan, that information is sent from the terminal to the server. The server receives this information, regenerates the plan as needed, and confirms the final plan. Confirmed orders are recorded in the database.

[0509] Step 7:

[0510] The server manages the progress of orders, and the sentiment engine monitors user sentiment in real time. If a problem occurs or if the user's sentiment changes, an alert is generated to enable a quick response.

[0511] Step 8:

[0512] Once an order is complete, the server sends a completion notification to the user and finally collects feedback. This feedback includes sentiment information and is stored and analyzed as data to improve the service.

[0513] (Example 2)

[0514] 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."

[0515] Traditional ordering systems process requests without considering the user's emotional state, leading to a failure to accurately grasp user dissatisfaction and potential needs, resulting in a decline in service quality. Therefore, there is a need to appropriately recognize and reflect the user's emotions along with their requests to provide more personalized services.

[0516] 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.

[0517] In this invention, the server includes means for analyzing order requests from users using natural language processing technology and extracting requests; means for analyzing the user's emotional information using emotion recognition technology; and an automatic generation device for generating optimal service content based on the extracted requests and emotional information. This makes it possible to provide personalized services that comprehensively consider the user's requests and emotions.

[0518] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0519] "Users" refers to individuals or corporations that use the system to receive services.

[0520] An "order request" refers to a request made by a user to the system for goods or services.

[0521] "Requests" refer to the specific needs and conditions that users request when placing an order.

[0522] "Emotion recognition technology" is a technology that analyzes a user's words and actions to detect their emotions and state of mind.

[0523] "Emotional information" refers to data about the user's psychological state obtained through emotion recognition technology.

[0524] An "automatic generation device" refers to a device or software that creates optimal service content based on extracted requests and emotional information.

[0525] "Service details" refers to specific information such as the products and contract terms provided to the user.

[0526] A "data storage device" is a device used to record and store information such as a user's order history and progress status.

[0527] "Evaluation information" refers to opinions and feedback on services provided by users.

[0528] "Progress status" refers to information indicating the stage of an order request and the process until completion.

[0529] This invention is a system that uses an AI agent with emotion recognition capabilities to analyze customer order requests and provide personalized services. A server plays a major role in the implementation of this system.

[0530] First, the server analyzes user order requests using natural language processing (NLP) techniques. Specifically, it uses NLP libraries widely used by educational institutions and service providers. For example, it can use industry-renowned open-source NLP toolkits to identify requests embedded in natural language.

[0531] Next, the server analyzes the user's emotional information using emotion recognition technology. The emotion engine analyzes facial expressions and tone of voice within the text and classifies emotions as positive, negative, neutral, etc. This emotion analysis utilizes industry-standard emotion analysis APIs and proprietary algorithms.

[0532] Based on extracted requests and sentiment information, the server utilizes a generative AI model to automatically generate the optimal service content. For the generative AI model, it is recommended to use an open-source generative AI framework known for its excellent text generation capabilities. This model takes prompts that consider the user's specific needs and sentiments as input and provides personalized responses.

[0533] For example, if a user requests "urgent internet installation in a new office," the server will input a prompt message into the AI ​​model such as "We will propose a plan suitable for users who desire a fast and stable connection." This will then present a plan that meets the user's expectations.

[0534] The terminal displays the service details sent from the server to the user, providing the user with the convenience of reviewing the content.

[0535] This invention provides a platform that, through these processes, enables the delivery of services that are attentive to the user's emotions and improves customer satisfaction.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] The user enters the necessary information into the terminal to order a data service for their business. This information includes the type of service, any special requests, and the purpose of use. This entered data is then sent to the server.

[0539] Step 2:

[0540] The server analyzes requests using natural language processing techniques based on the received input data. Specifically, it extracts keywords and understands the context to identify the user's main requests and necessary conditions. The input is the user's text, and the output is a list of extracted requests.

[0541] Step 3:

[0542] The server applies sentiment recognition technology to the input user information to evaluate the user's emotions. For example, it determines the emotional state (positive, negative, neutral, etc.) from the user's word choice and tone. The input is user text, and the output is sentiment information.

[0543] Step 4:

[0544] Based on the extracted requests and sentiment information, the server uses a generative AI model to generate the optimal service plan. The generative AI model receives prompts such as "The user urgently desires a stable connection," and outputs specific plan details.

[0545] Step 5:

[0546] The server sends the generated service plan to the terminal and presents the proposal to the user. The terminal uses its user interface to view the plan details. At this stage, it is confirmed that the plan meets the user's needs and preferences.

[0547] Step 6:

[0548] Users review the displayed plan and send feedback or modification requests from their device as needed. The server receives this feedback and takes action, such as readjusting the plan, as required.

[0549] Step 7:

[0550] Once the user finally confirms the proposed service, the server records the order information in a data storage device and manages the progress. The input here is user confirmation information, and the output is storage in the database and progress recording.

[0551] (Application Example 2)

[0552] 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."

[0553] Traditional order taking systems have struggled to make suggestions that take user emotions into consideration, and have faced the challenge of not being able to accurately grasp users' potential dissatisfactions and needs. Furthermore, they have been unable to quickly detect negative user emotions and respond appropriately, resulting in insufficient improvement in customer satisfaction.

[0554] 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.

[0555] In this invention, the server includes means for analyzing user order requests using natural language processing technology and extracting requests; generation means equipped with an emotion engine that recognizes the extracted requests and the user's emotions and generates optimal service proposals; and means for presenting the generated service proposals to the user terminal and receiving confirmation and modification requests from the user. This enables user support that takes emotional information into consideration.

[0556] "Natural language processing technology" is a technology that uses computers to understand, analyze, and process human language.

[0557] An "order request" is the action or content of a user requesting a specific service or product.

[0558] An "emotion engine" is a device or program that analyzes a user's emotions in real time and determines their emotional state, such as positive or negative.

[0559] "Generation method" refers to a mechanism for creating appropriate service proposals based on user requests and emotional information.

[0560] A "service proposal" refers to the optimal service plan or offer provided while meeting the user's needs and taking their emotional state into consideration.

[0561] A "user terminal" is an electronic device that users directly operate to confirm orders and service proposals.

[0562] A "recording device" is a device or system used to store order information and retain data for subsequent management and use.

[0563] A "sentiment analysis algorithm" is a mathematical or statistical method for identifying and analyzing a user's emotions from language or other input data.

[0564] The system for implementing this invention is configured as follows: The server analyzes the user's order request using natural language processing technology and extracts the user's needs. Simultaneously with the extracted needs, it utilizes an emotion engine to recognize the user's emotions in real time. This recognition uses general natural language processing libraries and emotion analysis libraries (for example, SpaCy or TextBlob).

[0565] The server utilizes a generative AI model based on requests and emotional states to create optimal service suggestions and present them to the user's device. This generative AI model can utilize, for example, OpenAI's GPT-3. Furthermore, any new comments or feedback from the user during the user review process are also analyzed.

[0566] For example, when a user orders a new service for their office, the server's emotion engine recognizes the urgency, such as "I need a stable connection immediately." Based on this information, it generates and presents a service proposal that emphasizes a rapid response. If the server detects any anxiety regarding the proposal, it can offer a plan with additional guarantees. In this way, it provides proposals that are tailored to the user's emotions.

[0567] A concrete example of a prompt message could be, "Users are experiencing stress during payment. Please generate a message to calm them down." By utilizing such prompt messages and leveraging a generation AI model, it is possible to propose services that enhance user satisfaction.

[0568] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0569] Step 1:

[0570] The server receives an order request from a user. This input data is likely to be sent in text format. The server analyzes this data using a natural language processing library (e.g., SpaCy) to extract the user's requests. The extracted requests are used as the basis for the next step.

[0571] Step 2:

[0572] The server uses a sentiment analysis library (e.g., TextBlob) along with the extracted requests to evaluate the user's emotional state from their statements. The input is the user's text data, and the output is emotional information classified as positive, negative, or neutral. This prepares the server to respond based on the user's emotions.

[0573] Step 3:

[0574] The server uses a generative AI model (e.g., OpenAI's GPT-3) based on requests and emotional states to generate optimal service suggestions. The model's input consists of user requests and emotional data, and its output is a specific service plan. This service plan is designed to align with the user's needs and emotions.

[0575] Step 4:

[0576] The server sends the generated service proposal to the user's terminal. At this stage, the user can review the service proposal and enter modification requests if necessary. The user's input is re-evaluated in the next cycle.

[0577] Step 5:

[0578] If there is user feedback or a request for correction, the server analyzes it and performs another sentiment analysis. The input is the user feedback data, and the output is the corrected sentiment and new requests.

[0579] Step 6:

[0580] The server registers the final order in the recording device and monitors its progress. This step also collects feedback from the user to identify areas for improvement in future interactions. Furthermore, sentiment data within the feedback is stored for use in future responses.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] [Fourth Embodiment]

[0585] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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).

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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".

[0598] This invention is an AI agent system for streamlining order processing for enterprise data lines. The system is based on a server, user terminals, and natural language processing and generative AI technologies.

[0599] First, the system initializes natural language processing technology on the server and prepares to receive orders from users. Users input their requests in natural language using a terminal, such as "We need high-bandwidth internet in our new office." These inputs are sent to the server and analyzed by natural language processing technology. The requests are extracted from the analysis results, and the server uses AI technology to generate the optimal service plan based on this information.

[0600] The generated service plan is sent from the server to the user's terminal, where the user can review the presented plan. The user can then approve the plan or request further modifications. This interaction takes place between the terminal and the server, and the confirmed order is recorded on the server. The server manages the progress of the order and provides progress information to the user as needed.

[0601] One example is a user case requiring a high-bandwidth plan for a new office. In this case, the user inputs their desired specifications, and the AI ​​proposes the optimal plan. If the proposed plan is suitable, the user can easily confirm the plan, and the server processes the order quickly. This automated process prevents human error, significantly reduces customer waiting times, and achieves high customer satisfaction.

[0602] Finally, the server collects user feedback on completed orders to help continuously improve the service. In this way, the present invention enables companies to provide efficient data line services.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The server starts up and initializes the natural language processing model and the generative AI model. This prepares it to analyze user input.

[0606] Step 2:

[0607] The user uses their device to input requests regarding the data connection in natural language. The input requests are sent to the server in real time.

[0608] Step 3:

[0609] The server analyzes the user input it receives using natural language processing technology. Specifically, it extracts keywords and important information related to the request from the input.

[0610] Step 4:

[0611] Based on the information extracted by the server, the system searches the database for a corresponding service plan. The optimal plan is selected and customized by the generating AI.

[0612] Step 5:

[0613] The server generates a service plan and sends it to the user's terminal, presenting the plan details to the user. The user can then review the details and request revisions or approve the plan.

[0614] Step 6:

[0615] When a user requests approval or modification of a plan, their input is sent again from the terminal to the server. The server then regenerates or finalizes the plan as requested.

[0616] Step 7:

[0617] The server records confirmed orders in the database and begins managing the order's progress. The progress is also notified to the user's terminal in a timely manner.

[0618] Step 8:

[0619] Once the order process is complete, the server sends a completion notification to the user. Subsequently, feedback from the user is collected and the data is analyzed to help improve future services.

[0620] (Example 1)

[0621] 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".

[0622] In modern information technology, there is a demand for the rapid and efficient delivery of service plans that best meet user needs. However, traditional methods require a significant amount of time for requirements analysis and plan generation, which hinders the ability to adequately enhance user satisfaction. Furthermore, there are challenges in promptly updating information on ongoing instructions and quickly providing solutions when problems arise.

[0623] 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.

[0624] In this invention, the server includes means for rapidly and accurately analyzing user requests using natural language processing technology, means for dynamically generating diverse plans using generative AI technology, and means for repeatedly presenting the generated plans to the user using information presentation means to facilitate two-way communication. This enables the provision of an optimal service plan tailored to the user's needs, rapid updates of progress information, and presentation of countermeasures when problems occur.

[0625] "Natural language processing technology" is a technology that analyzes text data from users and understands its meaning and intent.

[0626] A "generative AI method" is an algorithm that automatically generates new plans and proposals based on past data and patterns.

[0627] An "information display device" is a device that uses a computer or other related equipment to visually present information or plans to users.

[0628] "Information storage device" refers to a part of a computer or related device used to store digital data for extended periods.

[0629] "Feedback gathering" is the process of collecting evaluations and suggestions from users regarding completed plans and instructions.

[0630] "Two-way communication" is a process in which the information provider and the information recipient interact with each other to share and confirm information.

[0631] This invention is a system for efficiently understanding user needs and providing services based on those needs. At the heart of the system are a server, terminals, and natural language processing and generative AI technologies. The server prepares itself to receive user input by initializing natural language processing technologies such as BERT or GPT models.

[0632] Users input their needs in natural language using a terminal. This input is sent to a server and analyzed. Specific natural language processing techniques include multiple transformer models to understand the meaning of the text data. After information is extracted based on the analysis, a suitable service plan is generated using generative AI technology. The generative AI model includes algorithms that automatically design new service plans based on historical data.

[0633] The generated plan is sent from the server to the user's terminal, allowing the user to review and request modifications. For example, the user might enter a prompt such as, "Please suggest the best internet plan for my new office."

[0634] After an order is confirmed, the server records the information in its data storage device and manages its progress. The ongoing process involves two-way communication with the user, which is greatly aided by generative AI technology. Feedback is collected from completed orders and used to improve future services. This process ensures a system that provides fast and efficient service tailored to user needs.

[0635] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0636] Step 1:

[0637] The server initializes its natural language processing technology. The server prepares BERT or GPT models as its analysis engine. This preparation enables accurate analysis of natural language input from users. The input consists of natural language sentences, and the output generates a parseable data format.

[0638] Step 2:

[0639] The user enters their request using a terminal. On the terminal, the user enters a prompt message such as, "I need high-speed internet in my new office." This input data is sent to the server in natural language format.

[0640] Step 3:

[0641] The server analyzes the user's input. The server analyzes the received natural language input using initialized natural language processing techniques. Specifically, it tokenizes the text data, analyzes its meaning, and then extracts the request. The output of this step includes data that indicates the user's specific needs.

[0642] Step 4:

[0643] The server generates service plans using generative AI technology. Based on the analyzed needs, the server runs a generative AI model (e.g., GPT-3) to generate the optimal service plan. This process can refer to past successful plans, and the output becomes proposed plan information.

[0644] Step 5:

[0645] The server sends the generated plan to the terminal. The generated service plan is sent to the terminal and can be viewed on the user's screen. The user can review the presented plan and request modifications if necessary. In this step, the modification request is sent back to the server.

[0646] Step 6:

[0647] The user reviews the plan and submits an approval or modification request. If the user approves the reviewed plan, that information is sent back to the server. If modifications are needed, return to step 4 and perform the regeneration process.

[0648] Step 7:

[0649] The server records confirmed orders and manages their progress. Confirmed orders are recorded in the information storage device, and the server manages their subsequent progress. Progress reports are provided to users as needed.

[0650] Step 8:

[0651] The server collects feedback on completed orders. Upon completion of a service, the server collects feedback from users and uses this information to improve future services. The collected feedback is analyzed and used to develop new operational rules and plans.

[0652] (Application Example 1)

[0653] 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".

[0654] When users engage in diverse online activities, selecting the optimal payment method is complex, requiring individual comparisons of information regarding fees and convenience. This process is time-consuming and laborious, potentially leading to incorrect decisions. This invention aims to solve the problem of improving user convenience by quickly and accurately proposing the optimal payment plan that meets the user's needs.

[0655] 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.

[0656] In this invention, the server includes means for analyzing user requests using natural language processing technology and extracting the payment method required by the user; means for generating an optimal payment plan based on the extracted payment method; and means for presenting the generated payment plan to the user's terminal and receiving confirmation and modification requests from the user. This makes it possible for the user to easily and quickly select the optimal payment method.

[0657] "Natural language processing technology" is the technology that allows computers to understand, analyze, and generate human language.

[0658] "Generative AI methods" refer to systems that use artificial intelligence to automatically generate optimal plans and solutions based on user requests.

[0659] "User terminal" refers to the equipment or device used by service users to operate the service, and typically includes smartphones and personal computers.

[0660] An "information recording device" is a device that is responsible for storing data over a long period of time, and includes databases and the like.

[0661] "Means of collecting opinions" refers to a system for collecting, recording, and analyzing feedback and requests from users.

[0662] In this invention, the server analyzes user requests using natural language processing technology and extracts the necessary payment methods. Users input their requests in natural language using terminals such as smartphones or personal computers. The input is sent to the server and analyzed using natural language processing technology. For this analysis, natural language processing libraries such as spaCy or NLTK can be used.

[0663] Subsequently, the server uses a generative AI model to generate the optimal payment plan based on the analysis results. For example, OpenAI's GPT can be used as the generative AI model. The generated plan is sent to the user's terminal, where the user can review it on the screen. The user can then approve the generated plan or request modifications. This information is sent back to the server, which stores the final decision in an information recording device.

[0664] For example, if a user enters "I'm looking for a low-fee payment method for online shopping," the server analyzes this request and generates and presents the most suitable payment plan. An example of a prompt to the generating AI model might be, "The user is looking for a low-fee credit card payment method. Please suggest the best plan."

[0665] This system allows users to quickly and accurately select a payment plan that suits their needs, improving convenience.

[0666] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0667] Step 1:

[0668] The user uses a terminal to input a request, such as "I want a low-fee payment method for online shopping." The terminal sends this request to the server as natural language text. The input is natural language text data.

[0669] Step 2:

[0670] The server analyzes the received natural language text using natural language processing techniques. Libraries such as spaCy and NLTK can be used for this analysis. In this step, payment requests are extracted from the input natural language text and output as structured data. Specifically, the server extracts key phrases and identifies intent.

[0671] Step 3:

[0672] The server generates the optimal payment plan using a generative AI model based on the extracted requests. OpenAI's GPT, for example, can be used for the model. The input here is structured request data, which is fed into the AI ​​model as prompts. The output is the generated payment plan in text format. Specifically, the server inputs prompt text into the generative AI model and receives a response.

[0673] Step 4:

[0674] The server sends the generated payment plan to the terminal. The terminal presents the plan to the user, who then confirms it. The input is a payment plan in text format, and the output is a confirmation of the plan displayed on the screen. Specifically, the terminal visually displays the plan through its user interface.

[0675] Step 5:

[0676] The user either approves the presented plan or enters a request for modification. The terminal sends this response to the server. The input consists of data related to the user's selections and requested modifications.

[0677] Step 6:

[0678] The server saves the finalized information to the information recording device. The input is the user's confirmed or modified payment plan, and the output is the payment order recorded in the database. Specifically, the server performs a write operation to the database.

[0679] 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.

[0680] This invention is a system that further enhances order acceptance for enterprise data lines by utilizing an AI agent incorporating an emotion engine. The basic processing flow is similar to conventional processes that use natural language processing technology, but it is enhanced by recognizing the user's emotions using the emotion engine during interaction with the user.

[0681] The server analyzes the user's data line order request using natural language processing technology and extracts the user's needs. Simultaneously, an emotion engine analyzes the user's input data and recognizes emotions such as positive, negative, or neutral. Based on this information, the server uses generative AI technology to generate an optimal service plan and presents it to the user's terminal.

[0682] When users review plans on their devices, recognized emotional information is reflected in the plan suggestions, allowing for plan adjustments that take into account the user's potential dissatisfaction and needs. Furthermore, an emotional engine monitors emotions in real time, and a system is in place to send alerts to customer service representatives as needed. This significantly improves the customer experience and enables proactive measures before problems arise.

[0683] For example, when a user orders a new internet connection for their office, the server's emotion engine recognizes the urgency of the need for a stable connection and generates a corresponding rapid response plan. If the user reviews this plan on their device and the emotion engine detects any concerns, the server may suggest options to add further guarantees. Through this process, the service is tailored to the user's emotions, leading to improved customer satisfaction.

[0684] This system allows for the proper management of user emotions throughout the order process, from acceptance to completion, thereby improving service quality. Furthermore, by analyzing user emotion data during post-completion feedback collection, further improvements can be made for future orders.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] When the server starts up, it initializes the natural language processing model, the generative AI model, and the emotion engine. This initialization prepares the system to analyze user requests and emotions.

[0688] Step 2:

[0689] The user uses a terminal to input their data connection requirements in natural language. This input includes specific needs such as, "We need high bandwidth for our new office."

[0690] Step 3:

[0691] The server analyzes the received requests using a natural language processing engine to extract specific needs and requirements. Simultaneously, an emotion engine recognizes the user's emotions from the input data, determining their emotional state (positive, negative, neutral, etc.).

[0692] Step 4:

[0693] Based on extracted needs and emotional information, the server uses generative AI to generate the optimal service plan. Emotional information is used as a factor to fine-tune the proposed plan.

[0694] Step 5:

[0695] The generated service plan is sent to the device and presented to the user. The user can then review the plan in detail and request or approve modifications to suit their needs.

[0696] Step 6:

[0697] When a user requests to confirm or modify a plan, that information is sent from the terminal to the server. The server receives this information, regenerates the plan as needed, and confirms the final plan. Confirmed orders are recorded in the database.

[0698] Step 7:

[0699] The server manages the progress of orders, and the sentiment engine monitors user sentiment in real time. If a problem occurs or if the user's sentiment changes, an alert is generated to enable a quick response.

[0700] Step 8:

[0701] Once an order is complete, the server sends a completion notification to the user and finally collects feedback. This feedback includes sentiment information and is stored and analyzed as data to improve the service.

[0702] (Example 2)

[0703] 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".

[0704] Traditional ordering systems process requests without considering the user's emotional state, leading to a failure to accurately grasp user dissatisfaction and potential needs, resulting in a decline in service quality. Therefore, there is a need to appropriately recognize and reflect the user's emotions along with their requests to provide more personalized services.

[0705] 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.

[0706] In this invention, the server includes means for analyzing order requests from users using natural language processing technology and extracting requests; means for analyzing the user's emotional information using emotion recognition technology; and an automatic generation device for generating optimal service content based on the extracted requests and emotional information. This makes it possible to provide personalized services that comprehensively consider the user's requests and emotions.

[0707] "Natural language processing technology" is a technology that enables computers to understand and process human language.

[0708] "Users" refers to individuals or corporations that use the system to receive services.

[0709] An "order request" refers to a request made by a user to the system for goods or services.

[0710] "Requests" refer to the specific needs and conditions that users request when placing an order.

[0711] "Emotion recognition technology" is a technology that analyzes a user's words and actions to detect their emotions and state of mind.

[0712] "Emotional information" refers to data about the user's psychological state obtained through emotion recognition technology.

[0713] An "automatic generation device" refers to a device or software that creates optimal service content based on extracted requests and emotional information.

[0714] "Service details" refers to specific information such as the products and contract terms provided to the user.

[0715] A "data storage device" is a device used to record and store information such as a user's order history and progress status.

[0716] "Evaluation information" refers to opinions and feedback on services provided by users.

[0717] "Progress status" refers to information indicating the stage of an order request and the process until completion.

[0718] This invention is a system that uses an AI agent with emotion recognition capabilities to analyze customer order requests and provide personalized services. A server plays a major role in the implementation of this system.

[0719] First, the server analyzes user order requests using natural language processing (NLP) techniques. Specifically, it uses NLP libraries widely used by educational institutions and service providers. For example, it can use industry-renowned open-source NLP toolkits to identify requests embedded in natural language.

[0720] Next, the server analyzes the user's emotional information using emotion recognition technology. The emotion engine analyzes facial expressions and tone of voice within the text and classifies emotions as positive, negative, neutral, etc. This emotion analysis utilizes industry-standard emotion analysis APIs and proprietary algorithms.

[0721] Based on extracted requests and sentiment information, the server utilizes a generative AI model to automatically generate the optimal service content. For the generative AI model, it is recommended to use an open-source generative AI framework known for its excellent text generation capabilities. This model takes prompts that consider the user's specific needs and sentiments as input and provides personalized responses.

[0722] For example, if a user requests "urgent internet installation in a new office," the server will input a prompt message into the AI ​​model such as "We will propose a plan suitable for users who desire a fast and stable connection." This will then present a plan that meets the user's expectations.

[0723] The terminal displays the service details sent from the server to the user, providing the user with the convenience of reviewing the content.

[0724] This invention provides a platform that, through these processes, enables the delivery of services that are attentive to the user's emotions and improves customer satisfaction.

[0725] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0726] Step 1:

[0727] The user enters the necessary information into the terminal to order a data service for their business. This information includes the type of service, any special requests, and the purpose of use. This entered data is then sent to the server.

[0728] Step 2:

[0729] The server analyzes requests using natural language processing techniques based on the received input data. Specifically, it extracts keywords and understands the context to identify the user's main requests and necessary conditions. The input is the user's text, and the output is a list of extracted requests.

[0730] Step 3:

[0731] The server applies sentiment recognition technology to the input user information to evaluate the user's emotions. For example, it determines the emotional state (positive, negative, neutral, etc.) from the user's word choice and tone. The input is user text, and the output is sentiment information.

[0732] Step 4:

[0733] Based on the extracted requests and sentiment information, the server uses a generative AI model to generate the optimal service plan. The generative AI model receives prompts such as "The user urgently desires a stable connection," and outputs specific plan details.

[0734] Step 5:

[0735] The server sends the generated service plan to the terminal and presents the proposal to the user. The terminal uses its user interface to view the plan details. At this stage, it is confirmed that the plan meets the user's needs and preferences.

[0736] Step 6:

[0737] Users review the displayed plan and send feedback or modification requests from their device as needed. The server receives this feedback and takes action, such as readjusting the plan, as required.

[0738] Step 7:

[0739] Once the user finally confirms the proposed service, the server records the order information in a data storage device and manages the progress. The input here is user confirmation information, and the output is storage in the database and progress recording.

[0740] (Application Example 2)

[0741] 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".

[0742] Traditional order taking systems have struggled to make suggestions that take user emotions into consideration, and have faced the challenge of not being able to accurately grasp users' potential dissatisfactions and needs. Furthermore, they have been unable to quickly detect negative user emotions and respond appropriately, resulting in insufficient improvement in customer satisfaction.

[0743] 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.

[0744] In this invention, the server includes means for analyzing user order requests using natural language processing technology and extracting requests; generation means equipped with an emotion engine that recognizes the extracted requests and the user's emotions and generates optimal service proposals; and means for presenting the generated service proposals to the user terminal and receiving confirmation and modification requests from the user. This enables user support that takes emotional information into consideration.

[0745] "Natural language processing technology" is a technology that uses computers to understand, analyze, and process human language.

[0746] An "order request" is the action or content of a user requesting a specific service or product.

[0747] An "emotion engine" is a device or program that analyzes a user's emotions in real time and determines their emotional state, such as positive or negative.

[0748] "Generation method" refers to a mechanism for creating appropriate service proposals based on user requests and emotional information.

[0749] A "service proposal" refers to the optimal service plan or offer provided while meeting the user's needs and taking their emotional state into consideration.

[0750] A "user terminal" is an electronic device that users directly operate to confirm orders and service proposals.

[0751] A "recording device" is a device or system used to store order information and retain data for subsequent management and use.

[0752] A "sentiment analysis algorithm" is a mathematical or statistical method for identifying and analyzing a user's emotions from language or other input data.

[0753] The system for implementing this invention is configured as follows: The server analyzes the user's order request using natural language processing technology and extracts the user's needs. Simultaneously with the extracted needs, it utilizes an emotion engine to recognize the user's emotions in real time. This recognition uses general natural language processing libraries and emotion analysis libraries (for example, SpaCy or TextBlob).

[0754] The server utilizes a generative AI model based on requests and emotional states to create optimal service suggestions and present them to the user's device. This generative AI model can utilize, for example, OpenAI's GPT-3. Furthermore, any new comments or feedback from the user during the user review process are also analyzed.

[0755] For example, when a user orders a new service for their office, the server's emotion engine recognizes the urgency, such as "I need a stable connection immediately." Based on this information, it generates and presents a service proposal that emphasizes a rapid response. If the server detects any anxiety regarding the proposal, it can offer a plan with additional guarantees. In this way, it provides proposals that are tailored to the user's emotions.

[0756] A concrete example of a prompt message could be, "Users are experiencing stress during payment. Please generate a message to calm them down." By utilizing such prompt messages and leveraging a generation AI model, it is possible to propose services that enhance user satisfaction.

[0757] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0758] Step 1:

[0759] The server receives an order request from a user. This input data is likely to be sent in text format. The server analyzes this data using a natural language processing library (e.g., SpaCy) to extract the user's requests. The extracted requests are used as the basis for the next step.

[0760] Step 2:

[0761] The server uses a sentiment analysis library (e.g., TextBlob) along with the extracted requests to evaluate the user's emotional state from their statements. The input is the user's text data, and the output is emotional information classified as positive, negative, or neutral. This prepares the server to respond based on the user's emotions.

[0762] Step 3:

[0763] The server uses a generative AI model (e.g., OpenAI's GPT-3) based on requests and emotional states to generate optimal service suggestions. The model's input consists of user requests and emotional data, and its output is a specific service plan. This service plan is designed to align with the user's needs and emotions.

[0764] Step 4:

[0765] The server sends the generated service proposal to the user's terminal. At this stage, the user can review the service proposal and enter modification requests if necessary. The user's input is re-evaluated in the next cycle.

[0766] Step 5:

[0767] If there is user feedback or a request for correction, the server analyzes it and performs another sentiment analysis. The input is the user feedback data, and the output is the corrected sentiment and new requests.

[0768] Step 6:

[0769] The server registers the final order in the recording device and monitors its progress. This step also collects feedback from the user to identify areas for improvement in future interactions. Furthermore, sentiment data within the feedback is stored for use in future responses.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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."

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] The following is further disclosed regarding the embodiments described above.

[0792] (Claim 1)

[0793] A method for analyzing user order requests using natural language processing technology and extracting their needs,

[0794] A generation AI means that generates the optimal service plan based on extracted requests,

[0795] A means of presenting the generated service plan to the user's terminal and receiving confirmation and modification requests from the user,

[0796] A means of recording user-confirmed orders in a database and managing their progress,

[0797] A means of collecting user feedback on completed orders and using it to improve the service,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, which rapidly and accurately analyzes user requests based on natural language processing technology.

[0801] (Claim 3)

[0802] The system according to claim 1, which periodically updates the user's order progress and automatically detects and proposes solutions when problems occur.

[0803] "Example 1"

[0804] (Claim 1)

[0805] A means of analyzing user requests and extracting needs using natural language processing technology,

[0806] A generative AI means that generates an optimal plan based on extracted needs,

[0807] A means of displaying the generated plan on an information display device and receiving confirmation and modification requests from users,

[0808] A means of recording user-confirmed instructions in an information storage device and managing the progress,

[0809] A means of collecting feedback from users regarding completed instructions and using it to improve them,

[0810] A means of repeatedly presenting the generated plan to the user using an information presentation method, and a means of facilitating two-way communication with the user,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, which rapidly and accurately analyzes user needs based on natural language processing technology and dynamically generates diverse plans using generative AI means.

[0814] (Claim 3)

[0815] The system according to claim 1, which periodically updates the progress of user instructions, automatically detects problems when they occur and suggests countermeasures, and optimizes progress management.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A method for analyzing user requests using natural language processing technology and extracting the payment methods that users need,

[0819] A generation AI means that generates the optimal payment plan based on the extracted payment methods,

[0820] A means of displaying the generated payment plan to the user's terminal and receiving confirmation and modification requests from the user,

[0821] A means for storing payment orders confirmed by users in an information recording device and managing their progress,

[0822] We collect feedback from users regarding completed payment orders as a means of contributing to service improvement.

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which rapidly and accurately analyzes user requests based on natural language processing technology.

[0826] (Claim 3)

[0827] The system according to claim 1, which periodically updates the progress of a user's payment order and automatically detects and proposes a solution when a problem occurs.

[0828] "Example 2 of combining an emotion engine"

[0829] (Claim 1)

[0830] A device that uses natural language processing technology to analyze customer order requests and extract their needs,

[0831] An automated generation device that generates optimal service content based on extracted requests and user sentiment information,

[0832] A device that displays the generated service details to the user's terminal and accepts confirmation and correction requests from the user,

[0833] A device that records orders confirmed by users in a data storage device and manages their progress,

[0834] A system including a device that collects user feedback on completed orders and uses it to improve services.

[0835] (Claim 2)

[0836] The system according to claim 1, which rapidly and accurately analyzes the user's requests and emotional state based on natural language processing technology and emotion recognition technology.

[0837] (Claim 3)

[0838] The system according to claim 1, which periodically updates the user's order progress, automatically detects and proposes solutions when problems occur, and improves the user experience based on sentiment information.

[0839] "Application example 2 when combining with an emotional engine"

[0840] (Claim 1)

[0841] A method for analyzing user order requests using natural language processing technology and extracting their needs,

[0842] A generation means equipped with an emotion engine that recognizes extracted requests and user emotions and generates optimal service proposals,

[0843] A means of presenting the generated service proposal to the user's terminal and receiving confirmation and modification requests from the user,

[0844] A means for registering user-confirmed orders in a recording device and managing their progress,

[0845] A means of collecting feedback from users, including sentiment data, regarding completed orders, and using it to improve the service.

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, which uses an emotion analysis algorithm to recognize changes in a user's emotions in real time and reflect them in service proposals.

[0849] (Claim 3)

[0850] The system according to claim 1, which periodically updates the user's order progress and automatically generates countermeasures when negative emotions of the user are detected. [Explanation of symbols]

[0851] 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 method for analyzing user order requests using natural language processing technology and extracting their needs, A generation AI means that generates the optimal service plan based on the extracted requests, A means of presenting the generated service plan to the user's terminal and receiving confirmation and modification requests from the user, A means of recording user-confirmed orders in a database and managing their progress, A means of collecting user feedback on completed orders and using it to improve the service, A system that includes this.

2. The system according to claim 1, which rapidly and accurately analyzes user requests based on natural language processing technology.

3. The system according to claim 1, which periodically updates the user's order progress and automatically detects and proposes solutions when problems occur.

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