System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a review request entered by a user into an interface; means for analyzing the received review request through natural language processing and initiating a review process based on the analysis; means for reporting the progress of the review process to the user; means for requesting additional information from the user during the review process; and means for completing the review process and notifying the user of a final review result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The traditional screening process requires manual judgment, which is not only time-consuming but also prone to errors and misjudgments. Therefore, the challenge is to improve the efficiency of screening while minimizing errors. [Means for solving the problem]

[0005] The present invention is a system including a means for receiving a review request entered by a user into an interface, a means for analyzing the received review request using natural language processing and starting a review process based on the analysis result, a means for reporting the progress of the review process to the user, a means for requesting additional information from the user if necessary during the review process, and a means for completing the review process and notifying the user of the final review result, and further including a means for retrieving initial data from a database based on the analysis result before starting the review process, and a means for receiving additional information sent by the user and continuing the review process. This configuration makes it possible to automate and streamline the review process and reduce human error.

[0006] "User" refers to the person who operates the system and enters review requests.

[0007] "Interface" refers to the screens and functions for input and output that allow a user to interact with a system.

[0008] "Review Request" means the instructions and information a User enters into the System to initiate a particular Review Process.

[0009] "Means for receiving" refers to a function for obtaining user input through an interface and processing it within the system.

[0010] "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0011] "Analysis" refers to the process of interpreting received information and understanding its necessary meaning and intent.

[0012] "Review process" refers to the series of steps the system performs on a review request.

[0013] "Means to initiate" refers to the functionality for executing a review process based on the analysis results.

[0014] "Progress" refers to the current stage or execution of the review process.

[0015] "Means for reporting" refers to a function for notifying the user of the progress.

[0016] "Additional Information" refers to any further input information from the user that is required to complete the review process.

[0017] "Means for requesting" refers to the functionality for requesting additional information from the user.

[0018] "Means of Completion" refers to the function of gathering all necessary information and concluding the review process.

[0019] "Final review outcome" refers to the decision or conclusion generated as a result of the review process.

[0020] "Means of notification" refers to the function for informing the user of the final review results.

[0021] "Initial Data" refers to the basic information required to begin the review process.

[0022] A "database" refers to a system that stores information in an organized manner and allows it to be retrieved as needed.

[0023] "Means of acquisition" refers to the function for pulling out the necessary initial data from the database.

[0024] "Submitted Additional Information" means any additional data or information provided by a User in response to a request.

[0025] "Means to continue" refers to the ability to continue the review process with additional information. [Brief explanation of the drawings]

[0026] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0027] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0028] First, the terms used in the following description will be explained.

[0029] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0030] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0031] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0032] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0033] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0034] [First embodiment]

[0035] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0036] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0037] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0038] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0039] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0040] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0041] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0042] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0043] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0044] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0045] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0046] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0047] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[0048] System configuration

[0049] The system includes the following main components:

[0050] 1. User Interface

[0051] A screen or application where users can enter review requests.

[0052] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[0053] 2. Server

[0054] Receives user input and parses it using a natural language processing engine.

[0055] Based on the analysis results, the necessary initial data is retrieved from the database to begin the review process.

[0056] Updates and reports progress to the user.

[0057] Example: If the server receives a request saying "Start reviewing new customer, Taro Tanaka," it uses natural language processing to identify "Taro Tanaka" and retrieves his information from the database.

[0058] 3. Database

[0059] Stores initial data and information required for review.

[0060] The server retrieves the necessary data based on the user's input.

[0061] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[0062] 4. Reporting and Notification System

[0063] Notify users of the progress and final outcome of the review process.

[0064] If any additional information is needed, it will be requested from the user.

[0065] Example: During the screening process, you may request additional information from the user, such as "Please tell me Taro Tanaka's place of employment."

[0066] Processing flow

[0067] Receiving a review request

[0068] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[0069] Request analysis and initiation of review process

[0070] The server analyzes the received request using a natural language processing engine and retrieves the initial data required to start the review process based on the analysis results from the database, allowing the review process to proceed automatically.

[0071] Review process and reporting

[0072] The server tracks the progress of the review process and reports the status to the user as needed, for example, "Taro Tanaka's review data has been obtained. The review is in progress."

[0073] Request for additional information

[0074] If additional information is required during the review process, the server sends a request to the user, and the terminal displays it to the user. The additional information entered by the user is then sent back to the server, and the review process continues.

[0075] Completion of the review process and notification of the final result

[0076] Once all necessary information is available, the server completes the review process, generates a final review result, and notifies the user, for example, "Taro Tanaka's review has been approved."

[0077] This system will streamline the review process, allowing it to proceed quickly and accurately, minimizing human error and providing highly reliable review results.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] A user logs in to a dedicated user interface and enters a new review request, for example, "Start a review for a new customer."

[0081] Step 2:

[0082] The terminal receives the user's input and sends it to the server, packaging the request data in an appropriate format.

[0083] Step 3:

[0084] The server receives the user's request and passes the input data to a natural language processing engine to begin parsing it, which determines the intent of the request and any initial data required.

[0085] Step 4:

[0086] Based on the analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[0087] Step 5:

[0088] The server notifies the user that the review process has started. For example, it sends a message to the terminal saying, "Taro Tanaka's initial data has been acquired. Review is in progress."

[0089] Step 6:

[0090] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the review.

[0091] Step 7:

[0092] If the server needs additional information as it goes through the screening process, it will request it from the user. For example, it may send a request such as "Please tell me Taro Tanaka's place of employment information" to the terminal.

[0093] Step 8:

[0094] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[0095] Step 9:

[0096] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[0097] Step 10:

[0098] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[0099] Step 11:

[0100] The server obtains all necessary information, completes the review process, generates a final review result (e.g., "Taro Tanaka's review has been approved"), and sends it to the device.

[0101] Step 12:

[0102] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[0103] Example 1

[0104] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] Traditional review processes are often manual, which not only takes time but also makes them prone to human error. Furthermore, it is difficult to report review progress in a timely manner, making it difficult for users to grasp the review progress. Furthermore, the need to obtain additional information and the generation and notification of final review results are not always smooth, which can lead to a decline in overall efficiency.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0107] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for generating a final examination result and notifying the user, and means for conducting the examination using an automated algorithm and constantly monitoring the progress. This makes it possible to efficiently automate the examination process and quickly grasp the progress and obtain necessary additional information.

[0108] "User" means a person or entity that uses the System to enter review requests and view the progress and results of the review process.

[0109] An "interface" is a point of contact with a user, including a screen or application through which the user inputs an examination request.

[0110] A "review request" is a request or inquiry entered by a user to initiate a review.

[0111] "Server" means the central processing unit that receives and analyzes review requests and manages and executes the review process.

[0112] "Natural language processing" is a technology that analyzes language data entered by the user and understands and processes its meaning.

[0113] "Analysis results" refers to data and information obtained as a result of the natural language processing engine analyzing the review request.

[0114] The "review process" is a series of evaluations and confirmations carried out based on the analysis results.

[0115] "Progress" refers to the progress made at each stage of the review process.

[0116] "Additional Information" is supplemental information required for further evaluation during the review process.

[0117] "Final review outcome" means the conclusion or decision reached after the entire review process.

[0118] An "automated algorithm" is a program that automatically conducts the review process based on specific rules and calculation procedures.

[0119] "Monitoring" refers to the act of constantly observing and checking the progress of the review process.

[0120] The "database" is a system that stores and manages necessary initial data and information related to the review.

[0121] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[0122] First, a user inputs a review request using a terminal. For example, the user inputs "Start reviewing a new customer" on a dedicated screen or application. This input is sent from the terminal to a server. The server then sends the received request to a natural language processing engine (e.g., Google (registered trademark) Cloud Natural Language API) for analysis. The natural language processing engine analyzes the request and extracts keywords such as "new customer" and "review."

[0123] The server receives the analysis results and retrieves the necessary initial data from a database (e.g., MySQL (registered trademark) or PostgreSQL). This initial data includes, for example, the customer's credit score and past transaction history. The server starts the screening process based on the retrieved initial data and constantly monitors its progress. Specifically, the screening process progresses using an automated algorithm, and reports are made to the user as the process progresses. For example, the server may notify the user, "Taro Tanaka's screening data has been retrieved. The screening is currently underway."

[0124] If the server needs to request additional information during the review process, the request is sent to the user and displayed on the terminal. The user enters the additional information and sends it back to the server from the terminal, allowing the server to continue the review process. Once all the necessary information is gathered, the server completes the review and generates a final review result, which is notified to the user. For example, the server may inform the user of the result, "Taro Tanaka's review has been approved."

[0125] In this way, the review process is automated efficiently and accurately, enabling faster reviews while minimizing human error.

[0126] Specific examples

[0127] For example, if a sales representative wants to begin reviewing a new customer, Taro Tanaka, the process would proceed through the following specific steps:

[0128] 1. User (sales representative): Enters "Start reviewing a new customer" into the terminal.

[0129] 2. Terminal: Sends input to the server.

[0130] 3. Server: Sends a request to the natural language processing engine for analysis.

[0131] 4. Natural language processing engine: Analyzes the request and extracts keywords such as "new customer" and "review."

[0132] 5. Server: Based on the analysis results, obtain initial data about Taro Tanaka from the database.

[0133] 6. Server: Initiates the review process based on the acquired data, monitors the progress and reports back to the user.

[0134] 7. Server: If additional information is needed, the server requests the user, "Please tell me Taro Tanaka's place of employment information."

[0135] 8. Terminal: The server's request is displayed to the user, who enters additional information and sends it back to the server.

[0136] 9. Server: Once all necessary information is gathered, the review is completed and the final result, "Taro Tanaka's review has been approved," is generated and notified to the user.

[0137] Prompt Sentence Examples

[0138] "We'll begin screening new clients."

[0139] "Please tell me Taro Tanaka's place of employment information."

[0140] "Taro Tanaka's review has been approved."

[0141] The system utilizes generative AI models to improve the accuracy of natural language processing and process prompts smoothly, making the entire screening process more efficient and enabling more reliable screening.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1: User enters review request using terminal

[0144] The user enters "Start review of a new customer" into the terminal interface. This input data is sent to the server as a review request. Input: User review request (text). Output: Review request data (e.g., JSON format) from the terminal to the server.

[0145] Step 2: The device sends a request to the server

[0146] The terminal receives the user's input, converts it into an appropriate data format, and sends it to the server. For example, it packages the data in JSON format and sends it to the server using an HTTP request. Input: User's review request (text). Output: Review request received by the server (JSON format).

[0147] Step 3: The server analyzes the received request using a natural language processing engine

[0148] The server passes the received request data to a natural language processing engine for analysis. A generative AI model (e.g., Google Cloud Natural Language API) is used to extract keywords and elements of the review request. Input: Review request data received by the server (JSON format). Output: Parsed request keywords and elements (JSON format).

[0149] Step 4: The server uses the analysis results to retrieve the initial data from the database.

[0150] The server retrieves the necessary initial data from the database based on the analysis results obtained from the natural language processing engine. For example, it queries the database for the credit score and past transaction history of a customer identified as a "new customer." Input: Keywords and elements of the analysis results (JSON format). Output: Initial data (JSON format) retrieved from the database.

[0151] Step 5: The server starts the review process based on the initial data it has obtained.

[0152] The server uses an automated algorithm to start the screening process based on the initial data acquired. For example, it evaluates the credit score and analyzes past transaction history to proceed with the first stage of screening. Input: Initial data (JSON format). Output: Progress of the screening process (JSON format).

[0153] Step 6: The server constantly monitors the progress of the review and reports it to the user.

[0154] The server monitors the progress of the review process in real time and notifies the user of the progress. For example, it sends a message to the user saying, "Taro Tanaka's review data has been obtained. The review is in progress." Input: Progress of the review process (JSON format). Output: Progress notification to the user (text).

[0155] Step 7: Server requests additional information

[0156] If additional information is required during the review, the server sends a request to the user and displays it on the terminal. For example, it sends a message saying "Please tell me Taro Tanaka's place of employment." Input: Progress of the review process (JSON format). Output: Request for additional information from the user (text).

[0157] Step 8: User enters additional information and submits it to the server

[0158] The user checks the request from the server on the device and enters additional information. For example, they enter "Taro Tanaka's workplace information" and send it from the device to the server. Input: User's additional information (text). Output: Additional information sent to the server (JSON format).

[0159] Step 9: The server verifies all necessary information and completes the verification

[0160] The server confirms that all necessary information is gathered and completes the final review process. It generates a review result and notifies the user of the final result. For example, it generates a result such as "Taro Tanaka's review has been approved." Input: All necessary information (JSON format). Output: Final review result (text).

[0161] Step 10: The server notifies the user of the final result

[0162] The server notifies the user of the generated final review result. For example, it may notify the user of the result, "Taro Tanaka's review has been approved." Input: Final review result (text). Output: Notification of the final result to the user (text).

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] Traditional visitor authentication systems require manual verification, which is inefficient and has accuracy issues. While there is a need to automate the process of instantly reviewing and authenticating visitor identities, no effective system exists to achieve this. This has prevented improvements in security and operational efficiency from being achieved.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for completing the examination process and notifying the user of the final examination result, means for photographing the visitor's face with a camera and obtaining the visitor's name by voice input to authenticate the visitor's identity, and means for comparing the photographed face information and the voice-input name with information in a database to examine the visitor's identity. This makes it possible to automate the visitor authentication process and perform examination in real time.

[0168] The "user interface" refers to an input device and a screen through which a user inputs an examination request.

[0169] A "review request" is a review requirement or inquiry submitted by a user to the system.

[0170] "Natural language processing" is a technology that allows a computer to understand text entered by a user and is a means of analyzing and generating responses.

[0171] A "review process" is a series of analysis, judgment, and review actions that the system performs based on a received review request.

[0172] "Progress" means the current status or progress at each stage of the review process.

[0173] "Reporting means" refers to the means by which the system notifies the user of the progress and results of the review.

[0174] "Additional Information" means information not provided in the initial input that is necessary to continue or complete the review process.

[0175] The "notification means" is a means of communication for informing the user of the final review result.

[0176] "Authentication Method" means the method used to verify and authenticate a visitor's identity.

[0177] A "camera" is an image capturing device for capturing images of visitors' faces.

[0178] The "voice input means" is a means for inputting the name of the visitor by voice.

[0179] The "verification means" is a means for comparing the acquired face information and the voice-input name with existing information in a database.

[0180] "Database" means a digital recording device for storing and managing information necessary for visitor screening.

[0181] This invention relates to a visitor authentication system, which provides a series of means for receiving an examination request entered by a user, analyzing it using natural language processing, and reporting the progress and final results of the examination process to the user. Furthermore, the invention aims to efficiently authenticate visitors by verifying their identity using a visitor's facial image and voice input and comparing it with information in a database.

[0182] System configuration

[0183] User Interface

[0184] Provide an input device and screen for users to input review requests, such as a smartphone or smart glasses.

[0185] server

[0186] The server receives the review request sent by the user and analyzes it using natural language processing (NLP). Based on the analysis results, it retrieves the necessary data from the database and starts the review process. The server reports the review progress and final results to the user, and requests additional information if necessary.

[0187] Database

[0188] It stores visitor information (such as facial images, names, and past screening history) and provides the necessary information in response to data matching requests from the server.

[0189] Reporting method

[0190] Smartphones and notification systems are used to notify users of the progress and results of the review.

[0191] Hardware and software used

[0192] camera

[0193] It is used as an image capture device to capture the visitor's face, for example, a built-in camera in a smartphone or smart glasses.

[0194] microphone

[0195] Used as a device for voice input of the visitor's name, such as the built-in microphone of a smartphone or smart glasses.

[0196] Natural Language Processing Engine

[0197] The software used is a natural language processing engine such as SpaCy.

[0198] Facial Recognition Software

[0199] Facial images are captured using image processing libraries such as OpenCV and matched with a database.

[0200] Notification System

[0201] The notification function of smartphones is used as a means of notifying users of the review results and progress in real time.

[0202] Specific examples of processing

[0203] Example 1: Visitor authentication

[0204] 1. The user takes a photo of the visitor's face with their smartphone camera and then voice-inputs the visitor's name following the prompt, "Hello, please enter the visitor's name by voice."

[0205] 2. The server applies natural language processing to the facial image and voice-input name to retrieve relevant information from a database and verify the visitor's identity.

[0206] 3. Within a few seconds, a notification will appear on your smartphone saying, "Visitor authentication completed. The review result is approved."

[0207] Prompt Sentence Examples

[0208] "Hello, please speak your visitor's name."

[0209] The system automates the visitor authentication process and enables real-time screening, improving security and operational efficiency.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] The user takes a photo of the visitor's face with a camera and inputs the visitor's name by voice into the interface. The input at this time is the face image acquired from the camera and the voice data acquired from the microphone. These data are sent to the terminal, which temporarily stores them.

[0213] Step 2:

[0214] The facial image and audio data stored on the device are sent to the server. The server first processes the facial image using OpenCV to extract facial features. The input is the facial image and the output is facial feature data. At the same time, the server converts the audio data into text using the SpeechRecognition library. The input is audio data and the output is audio text.

[0215] Step 3:

[0216] The server analyzes the acquired text data using a natural language processing (NLP) engine (e.g., SpaCy). The input is speech text, and the output is analyzed name data. The server uses the analyzed name data and facial feature data to match it with registration information in a database. The input is facial feature data and name data, and the output is the visitor's identity information.

[0217] Step 4:

[0218] The server starts the screening process based on the visitor's identity information obtained from the database. Initial data acquisition and analysis is performed, which includes acquiring and analyzing necessary data. The input is facial feature data and name data, and the output is the screening result.

[0219] Step 5:

[0220] The server tracks the progress of the verification process and reports the results to the user's device. For example, it sends a notification saying, "Visitor authentication has been completed. The verification result is approved." The input is the verification result, and the output is a notification to the user's device.

[0221] Step 6:

[0222] If additional information is required during the review process, the server will request that information from the user. The user inputs the requested additional information and sends it from their terminal to the server. The input is the requested additional information, and the output is the receipt of the additional information.

[0223] Step 7:

[0224] The server receives the additional information and starts the review process again, reacquiring and analyzing the necessary data. The input is the additional information, and the output is the updated review result.

[0225] Step 8:

[0226] Once the final review result is obtained, the server notifies the user of the result. For example, it sends a notification to the user's terminal saying, "Visitor authentication was denied." The input is the final review result, and the output is the final notification to the user's terminal.

[0227] This series of steps makes the visitor authentication process automated, secure, and efficient.

[0228] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0229] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[0230] System configuration

[0231] The system includes the following main components:

[0232] 1. User Interface

[0233] A screen or application where users can enter review requests.

[0234] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[0235] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[0236] 2. Server

[0237] It receives user input and passes the input data to the natural language processing engine and emotion engine to begin analysis. Analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[0238] Initiate the review process and report progress to the user accordingly.

[0239] If additional information is required, it will be requested from the user at the appropriate time.

[0240] Once the review process is complete, the user will be notified of the final outcome.

[0241] Example: If the server receives a request saying "Starting the review of new customer, Taro Tanaka," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[0242] 3. Database

[0243] Stores initial data and information required for review.

[0244] The server retrieves the necessary data based on the user's input.

[0245] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[0246] 4. Reporting and Notification System

[0247] Notify users of the progress and final outcome of the review process.

[0248] If any additional information is needed, it will be requested from the user.

[0249] Make appropriate reports or requests based on the results of sentiment analysis.

[0250] Example: If a request such as "Please tell me Taro Tanaka's place of employment" is required during the screening process, display a message according to the user's emotions.

[0251] Specific examples

[0252] Receiving and analyzing review requests

[0253] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[0254] The server receives the request and begins analyzing it with the natural language processing engine and emotion engine. The natural language processing engine interprets the intent of the request as "starting a new customer screening," and the emotion engine identifies the user as "nervous."

[0255] Obtaining initial data and beginning the review process

[0256] Based on the analysis results, the server retrieves initial data about "Taro Tanaka" (e.g., basic information, credit score) from the database and prepares to begin the screening process.

[0257] Progress Report

[0258] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." It also sends a follow-up message based on the results of the emotion engine, saying, "Don't worry, we will let you know the results soon."

[0259] The terminal receives progress messages from the server and displays them to the user, allowing the user to check the progress of the screening and reduce anxiety.

[0260] Requesting and Receiving Additional Information

[0261] During the review process, if the server needs to request more information about "Taro Tanaka's place of employment," it will send a request for additional information to the terminal. Based on the results of the emotion engine, it will display a polite message such as, "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?"

[0262] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[0263] Completion of the review process and notification of the final result

[0264] The server receives any additional information sent by the user and updates / continues the review process accordingly. Once all necessary information is available, the server completes the review.

[0265] The server generates the final review result and sends it to the device. For example, along with the result "Taro Tanaka's review has been approved," the emotion engine sends a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[0266] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] A user logs in to a dedicated user interface and enters a new review request. For example, they might enter, "I'd like to begin reviewing a new customer." At this time, the emotion engine analyzes the user's input, facial expressions, tone of voice, etc. to identify the user's emotions.

[0270] Step 2:

[0271] The device receives the user's input and the emotion analysis results, and sends them to the server, packaging the request data and emotion data in an appropriate format.

[0272] Step 3:

[0273] The server receives a user request and passes the input data to a natural language processing engine to begin analysis. The analysis identifies the intent of the request and the initial data required. Sentiment analysis results are also taken into account.

[0274] Step 4:

[0275] Based on the analysis results and sentiment analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[0276] Step 5:

[0277] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." Based on the results of the emotion engine, it also sends a reassuring message such as, "Don't worry, we will let you know the results soon."

[0278] Step 6:

[0279] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the screening and feel reassured and understood by the emotionally appropriate messages.

[0280] Step 7:

[0281] As the server progresses through the review process, if additional information becomes necessary, it will request it from the user. For example, a request such as "Please tell me Taro Tanaka's place of employment information" is sent to the terminal. Based on the results of the emotion engine, a polite message such as "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?" is displayed.

[0282] Step 8:

[0283] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[0284] Step 9:

[0285] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[0286] Step 10:

[0287] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[0288] Step 11:

[0289] The server obtains all necessary information and completes the review process. It generates a final review result (e.g., "Taro Tanaka's review has been approved") and sends it to the device. It also sends a follow-up message based on the emotion engine, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[0290] Step 12:

[0291] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[0292] Example 2

[0293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] In the conventional review process, the user's emotions are not taken into consideration when analyzing the request entered by the user, which often leads to anxiety and dissatisfaction. Furthermore, there are cases where progress reports and requests for additional information are not properly made, which reduces the efficiency of the entire review process. Furthermore, there is a need for a method to improve the user experience by communicating with the user in a way that takes emotions into consideration during the review process.

[0295] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0296] In this invention, the server includes means for receiving a review request input by a user to an interface, means for analyzing the received review request using a natural language processing engine and starting a review process based on the analysis result and the result of the sentiment analysis engine, means for reporting the progress of the review process to the user, means for requesting additional information from the user if necessary during the review process, means for sending an appropriate follow-up message to the user based on the sentiment analysis result during the review process, and means for completing the review process and notifying the user of the final review result, thereby enabling an efficient and effective review process while taking user sentiment into consideration.

[0297] An "interface" is a screen or application through which a user inputs a review request.

[0298] A "server" is a central processing unit that receives user input data and performs the analysis and review process.

[0299] A "natural language processing engine" is a software system that analyzes text data entered by a user and interprets their intent.

[0300] An "emotion analysis engine" is a software system that identifies emotions from user input and responses.

[0301] "Analysis results" are information regarding the intent of a review request obtained by a natural language processing engine.

[0302] "Emotion analysis results" are information about the user's emotions obtained by the emotion analysis engine.

[0303] A "review process" is a series of evaluation and judgment steps based on the content of a review request.

[0304] "Progress" is information about the progress of the review process.

[0305] "Additional Information" means any additional data or details required to continue or complete the review process.

[0306] A "follow-up message" is a message of comfort or reassurance that is generated based on the user's emotional state.

[0307] "Final Review Result" means the final evaluation and judgment conclusion reached after the review process is completed.

[0308] "Data storage" means a database or other storage device for storing information necessary for the screening process.

[0309] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[0310] System configuration

[0311] The system includes the following main components:

[0312] 1. User Interface

[0313] Use a screen or application that allows users to enter review requests.

[0314] When a user enters a review request, an emotion engine (e.g., IBM Watson® Tone Analyzer) is used to recognize the user's emotion.

[0315] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[0316] 2. Server

[0317] It receives user input and passes the input data to a natural language processing engine (e.g., Google's Natural Language API) and an emotion engine to begin parsing it. The analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from data storage (e.g., MySQL, PostgreSQL).

[0318] Initiate the review process and report progress to the user accordingly.

[0319] If additional information is required, it will be requested from the user at the appropriate time.

[0320] Once the review process is complete, the user will be notified of the final outcome.

[0321] Example: If the server receives a request saying "We will begin reviewing a new customer, Mr. A," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[0322] 3. Data Storage

[0323] Stores initial data and information required for review.

[0324] The server retrieves the necessary data based on the user's input.

[0325] Example: Accumulate credit scores and past transaction history for "Mr. A" and provide them as needed.

[0326] 4. Reporting and Notification System

[0327] Notify users of the progress and final outcome of the review process.

[0328] If any additional information is needed, it will be requested from the user.

[0329] Make appropriate reports or requests based on the results of sentiment analysis.

[0330] Example: If a request such as "Please tell me Mr. A's place of employment information" is required during the screening process, a message appropriate to the user's emotions will be displayed.

[0331] Specific examples

[0332] Receiving and analyzing review requests

[0333] The terminal receives an examination request entered by the user and sends it to the server. For example, a request may be sent saying, "Please start an examination of a new customer." The server receives the request and begins analyzing it using a natural language processing engine and an emotion engine. Natural language processing interprets the intent of the request as "start an examination of a new customer," and the emotion engine identifies the user as "nervous."

[0334] Review process and reporting

[0335] Based on the analysis results, the server retrieves initial data about "Mr. A" (e.g., basic information, credit score) from data storage and prepares to start the screening process. It then reports the start of the screening process to the user. For example, it sends a message to the device saying, "We have retrieved Mr. A's initial data. The screening is in progress." Based on the results of the emotion engine, it sends a follow-up message saying, "Don't worry, we will report the results to you soon." The device receives progress messages from the server and displays them to the user.

[0336] Requesting and Receiving Additional Information

[0337] During the review process, if the server needs to request more information about "Mr. A's place of employment," it sends a request for the additional information to the terminal. Based on the results of the emotion engine, it generates and displays a polite message, such as "Sorry to trouble you, but could you please tell me Mr. A's place of employment information?" The user enters the required additional information, which is then received by the terminal and sent to the server. For example, the user might enter "Mr. A works for X Co., Ltd."

[0338] Completion of the review process and notification of the final result

[0339] The server receives additional information sent by the user and updates and continues the review process based on that information. Once all necessary information is collected, the server completes the review. The server generates the final review result and sends it to the device. For example, along with the result "Mr. A's review has been approved," the emotion engine may send a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The device receives the final review result from the server and notifies the user. The user can check the final result and take further action based on the results.

[0340] Real-life examples and prompts

[0341] Examples:

[0342] Imagine a process that involves receiving a new customer review request, analyzing and reporting progress, requesting additional information as needed, and reporting the final results.

[0343] Example prompt sentence:

[0344] "Start screening new clients and report back on progress and final results."

[0345] "Please politely request any additional information you need during the review process and communicate the final review result sensitively."

[0346] Based on this explanation, it becomes clear how the system works, and the roles of the user, server, and terminal can be concretely understood. In addition, by specifying the specific names of the hardware and software used, the specific steps for implementing the invention become clear.

[0347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0348] Step 1:

[0349] The user inputs a review request into the interface. For example, they input "I would like to start reviewing a new customer." This input is sent to the terminal. The terminal then sends the input request to the server. The input data also includes the user's emotional information (e.g., facial expression analysis and voice analysis data).

[0350] Step 2:

[0351] The server analyzes the review request and emotional information received from the device. A natural language processing engine (e.g., Google's Natural Language API) and an emotional analysis engine (e.g., IBM Watson Tone Analyzer) are used for the analysis. The input data (review request) is passed to the natural language processing engine, which extracts the intent of "starting a new customer review." At the same time, the emotional information is passed to the emotional analysis engine, which identifies the user as "nervous." The output is the analysis results and the emotional analysis results.

[0352] Step 3:

[0353] The server retrieves initial data from data storage based on the analysis results. In this process, a query is sent to a database (e.g., MySQL, PostgreSQL) to obtain basic information and a credit score for the new customer being screened. The input is the analysis results and a query to the data storage, and the output is the retrieved initial data.

[0354] Step 4:

[0355] The server prepares to start the review process. Based on the acquired data, review items and procedures are automatically assigned. Data indicating that the review process is ready to start is generated. The input is the initial data, and the output is data indicating that the review process is ready to start.

[0356] Step 5:

[0357] The server reports the progress of the review process to the user. In this process, the server uses a notification API to send a message to the device, such as "We have obtained the initial data of the new customer. The review is in progress." Based on the sentiment analysis results, the server also generates and sends a follow-up message, such as "Don't worry, we will let you know the review results soon." The inputs are the progress data and the sentiment analysis results, and the outputs are the progress message and the follow-up message.

[0358] Step 6:

[0359] The server requests the user for any additional information they require. For example, if it is necessary to request "employment information" during the screening process, it sends a polite message to the terminal saying, "Sorry to trouble you, but please tell us your employment information." The input is the need for the additional information request and the result of sentiment analysis, and the output is the additional information request message.

[0360] Step 7:

[0361] The user uses the terminal to enter additional information, for example, "I work for Company X." The terminal receives this information and sends it to the server. The input is the user's additional information, and the output is the transmission of data from the terminal to the server.

[0362] Step 8:

[0363] The server receives the additional information sent by the user and updates and continues the review process. It analyzes the additional information entered and reflects it in the review process. The input is the user's additional information, and the output is the updated review process data.

[0364] Step 9:

[0365] The server generates the final review result. It evaluates all the information in the review process and makes a final decision. For example, it generates a result such as "the review is approved." The input is all the review process data, and the output is the final review result data.

[0366] Step 10:

[0367] The server notifies the user of the final review result. In this process, a notification API is used to send a message to the device, such as "The review has been approved." Based on the sentiment analysis results, a follow-up message is also sent, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The input is the final review result data and the sentiment analysis results, and the output is the final review result notification and the follow-up message.

[0368] In this way, an efficient and emotionally sensitive screening process is realized through each processing step at the server, terminal, and user.

[0369] (Application example 2)

[0370] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0371] In conventional screening processes, the process proceeds without taking the user's emotions into consideration, which can make the user feel anxious or tense. This leads to a decrease in user satisfaction and a decrease in trust in the screening process. Furthermore, when requesting additional information, the burden felt by the user and unclear communication can cause delays in the process. To solve these issues, a system that analyzes the user's emotions and responds appropriately is needed.

[0372] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a review request entered by a user into an interface; means for analyzing the received review request using natural language processing and starting a review process based on the analysis results and sentiment analysis results; means for reporting the progress of the review process to the user and providing a message appropriate to the user's situation based on the sentiment analysis results; means for making polite requests to the user based on the sentiment analysis results when additional information is needed during the review process; and means for completing the review process and notifying the user of the final review result together with an appropriate follow-up message based on the sentiment analysis results. This makes it possible to proceed with the review process while taking the user's emotions into consideration, reduce the anxiety and tension felt by the user, and improve the efficiency and reliability of the entire review process.

[0373] A "user" is a person who enters a review request into the interface and sends and receives information related to the review process.

[0374] An "interface" is an input or display device that allows a user to input a review request.

[0375] An "audit request" is a request sent by a user to a server through an interface to initiate an audit.

[0376] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[0377] "Emotion analysis" is a technology that analyzes and identifies a user's emotional state from their input.

[0378] The "review process" refers to a series of processes from when the server receives a review request to when it notifies the user of the final result.

[0379] "Status" refers to the current status at each stage of the review process.

[0380] "Message" refers to text or information used to notify users of progress in the review process, requests for additional information, etc.

[0381] "Additional Information" is any additional information the server requests from the user to continue the review process.

[0382] "Results" or "Review Results" means information that informs the user of the final judgment or conclusion of the review process.

[0383] "Database" means a digital storage system for storing the initial data and history required for the review process.

[0384] A "follow-up message" is a message that provides comfort and reassurance to a user as the review process progresses or is completed, taking into account the user's emotional state.

[0385] The present invention is a system for automating a review process that takes into account the emotions of users. This system receives review requests entered by users through an interface and advances the review process while performing natural language processing and emotion analysis. Details are described below.

[0386] System configuration

[0387] The system includes the following main components:

[0388] 1. User Interface

[0389] A screen or application for users to enter review requests.

[0390] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[0391] 2. Server

[0392] The server receives the user's input and passes it to a natural language processing engine (e.g., Google Cloud NLP) and an emotion engine (e.g., IBM Watson Tone Analyzer) to begin analysis. The analysis identifies the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[0393] Initiate the review process and keep you informed of progress. Request any additional information you require from the user in a timely manner.

[0394] After the review process is complete, the final result will be communicated to the user along with a follow-up message based on the sentiment analysis results.

[0395] 3. Database

[0396] The database stores initial data and information required for review.

[0397] The server retrieves the necessary data based on the user's input.

[0398] 4. Reporting and Notification System

[0399] Inform users about the progress and final outcome of the review process, and politely request any additional information required based on sentiment analysis results.

[0400] Example of operation

[0401] An example of the system operation is shown below.

[0402] The worker (user) speaks to the maintenance assistant robot, saying, "This machine needs maintenance."

[0403] The server analyzes the request, obtains initial data such as the machine's condition and past maintenance history, and uses sentiment analysis to identify that the user is nervous.

[0404] Maintenance begins, and the server reports progress by saying, "Maintenance is underway. Please rest assured."

[0405] If additional machine information is needed, the server will politely ask, "Excuse me for asking, but could you please provide more information about your machine?"

[0406] Once the maintenance is complete, the server will report the final result, saying, "Maintenance has been completed successfully. Please rest assured."

[0407] Prompt Sentence Examples

[0408] "The maintenance assistant robot should collect information on machines that need maintenance and report the progress appropriately based on the worker's emotions."

[0409] As described above, the present invention provides a system that can efficiently advance the screening process while taking into consideration the user's feelings. This system reduces the user's anxiety and tension and improves the efficiency and reliability of the entire screening process.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1:

[0412] The user inputs an audit request into the interface. The user inputs "This machine needs maintenance" and the terminal receives it. Input: User's audit request. Output: Request data sent by the terminal to the server.

[0413] Step 2:

[0414] The server passes the received review request to the natural language processing engine to begin analysis. Input: Received request data. Output: Request intent and identified emotional state. The server uses the natural language processing engine to interpret the intent of the request and the emotion analysis engine to identify the user's emotion.

[0415] Step 3:

[0416] The server retrieves initial data from the database based on the analysis results. Input: Analysis results (intention and sentiment of the request). Output: Initial data (machine status and past maintenance history). The server queries the database and retrieves the necessary initial data.

[0417] Step 4:

[0418] The server starts the review process and reports progress to the user. Input: Initial data and analysis results. Output: Progress message. The server starts the review process and notifies the user of the progress, saying "Maintenance is in progress. Please rest assured."

[0419] Step 5:

[0420] If the server needs additional information during the review process, it requests it from the user. Input: Additional information required during the review process. Output: Request message for additional information. The server sends a polite message based on the results of sentiment analysis, saying, "Excuse me for bothering you, but could you please tell me more information about your machine?"

[0421] Step 6:

[0422] The user enters additional information, which the device sends to the server. Input: Additional information provided by the user. Output: Additional information data sent to the server. The server continues the review process based on this additional information.

[0423] Step 7:

[0424] The server completes the review process and notifies the user of the final result. Input: All required information and the final result of the review process. Output: A notification message of the final result. Once the review is complete, the server notifies the user of the final result, saying "The maintenance has been completed successfully. Please rest assured," along with a follow-up message based on the sentiment analysis results.

[0425] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0426] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0427] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0431] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0432] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0433] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0434] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0435] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0436] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0437] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0438] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0439] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0440] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0441] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[0442] System configuration

[0443] The system includes the following main components:

[0444] 1. User Interface

[0445] A screen or application where users can enter review requests.

[0446] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[0447] 2. Server

[0448] Receives user input and parses it using a natural language processing engine.

[0449] Based on the analysis results, the necessary initial data is retrieved from the database to begin the review process.

[0450] Updates and reports progress to the user.

[0451] Example: If the server receives a request saying "Start reviewing new customer, Taro Tanaka," it uses natural language processing to identify "Taro Tanaka" and retrieves his information from the database.

[0452] 3. Database

[0453] Stores initial data and information required for review.

[0454] The server retrieves the necessary data based on the user's input.

[0455] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[0456] 4. Reporting and Notification System

[0457] Notify users of the progress and final outcome of the review process.

[0458] If any additional information is needed, it will be requested from the user.

[0459] Example: During the screening process, you may request additional information from the user, such as "Please tell me Taro Tanaka's place of employment."

[0460] Processing flow

[0461] Receiving a review request

[0462] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[0463] Request analysis and initiation of review process

[0464] The server analyzes the received request using a natural language processing engine and retrieves the initial data required to start the review process based on the analysis results from the database, allowing the review process to proceed automatically.

[0465] Review process and reporting

[0466] The server tracks the progress of the review process and reports the status to the user as needed, for example, "Taro Tanaka's review data has been obtained. The review is in progress."

[0467] Request for additional information

[0468] If additional information is required during the review process, the server sends a request to the user, and the terminal displays it to the user. The additional information entered by the user is then sent back to the server, and the review process continues.

[0469] Completion of the review process and notification of the final result

[0470] Once all necessary information is available, the server completes the review process, generates a final review result, and notifies the user, for example, "Taro Tanaka's review has been approved."

[0471] This system will streamline the review process, allowing it to proceed quickly and accurately, minimizing human error and providing highly reliable review results.

[0472] The processing flow will be explained below.

[0473] Step 1:

[0474] A user logs in to a dedicated user interface and enters a new review request, for example, "Start a review for a new customer."

[0475] Step 2:

[0476] The terminal receives the user's input and sends it to the server, packaging the request data in an appropriate format.

[0477] Step 3:

[0478] The server receives the user's request and passes the input data to a natural language processing engine to begin parsing it, which determines the intent of the request and any initial data required.

[0479] Step 4:

[0480] Based on the analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[0481] Step 5:

[0482] The server notifies the user that the review process has started. For example, it sends a message to the terminal saying, "Taro Tanaka's initial data has been acquired. Review is in progress."

[0483] Step 6:

[0484] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the review.

[0485] Step 7:

[0486] If the server needs additional information as it goes through the screening process, it will request it from the user. For example, it may send a request such as "Please tell me Taro Tanaka's place of employment information" to the terminal.

[0487] Step 8:

[0488] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[0489] Step 9:

[0490] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[0491] Step 10:

[0492] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[0493] Step 11:

[0494] The server obtains all necessary information, completes the review process, generates a final review result (e.g., "Taro Tanaka's review has been approved"), and sends it to the device.

[0495] Step 12:

[0496] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[0497] Example 1

[0498] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0499] Traditional review processes are often manual, which not only takes time but also makes them prone to human error. Furthermore, it is difficult to report review progress in a timely manner, making it difficult for users to grasp the review progress. Furthermore, the need to obtain additional information and the generation and notification of final review results are not always smooth, which can lead to a decline in overall efficiency.

[0500] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0501] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for generating a final examination result and notifying the user, and means for conducting the examination using an automated algorithm and constantly monitoring the progress. This makes it possible to efficiently automate the examination process and quickly grasp the progress and obtain necessary additional information.

[0502] "User" means a person or entity that uses the System to enter review requests and view the progress and results of the review process.

[0503] An "interface" is a point of contact with a user, including a screen or application through which the user inputs an examination request.

[0504] A "review request" is a request or inquiry entered by a user to initiate a review.

[0505] "Server" means the central processing unit that receives and analyzes review requests and manages and executes the review process.

[0506] "Natural language processing" is a technology that analyzes language data entered by the user and understands and processes its meaning.

[0507] "Analysis results" refers to data and information obtained as a result of the natural language processing engine analyzing the review request.

[0508] The "review process" is a series of evaluations and confirmations carried out based on the analysis results.

[0509] "Progress" refers to the progress made at each stage of the review process.

[0510] "Additional Information" is supplemental information required for further evaluation during the review process.

[0511] "Final review outcome" means the conclusion or decision reached after the entire review process.

[0512] An "automated algorithm" is a program that automatically conducts the review process based on specific rules and calculation procedures.

[0513] "Monitoring" refers to the act of constantly observing and checking the progress of the review process.

[0514] The "database" is a system that stores and manages necessary initial data and information related to the review.

[0515] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[0516] First, the user inputs a review request using a device. For example, the user might input "Start reviewing a new customer" on a dedicated screen or application. This input is sent from the device to the server. The server then sends the received request to a natural language processing engine (e.g., Google Cloud Natural Language API) for analysis. The natural language processing engine analyzes the request and extracts keywords such as "new customer" and "review."

[0517] The server receives the analysis results and retrieves the necessary initial data from a database (e.g., MySQL or PostgreSQL). This initial data includes, for example, the customer's credit score and past transaction history. The server then starts the screening process based on the retrieved initial data and constantly monitors its progress. Specifically, the screening process progresses using an automated algorithm, and reports are sent to the user as the process progresses. For example, the server may notify the user, "Taro Tanaka's screening data has been retrieved. The screening is currently underway."

[0518] If the server needs to request additional information during the review process, the request is sent to the user and displayed on the terminal. The user enters the additional information and sends it back to the server from the terminal, allowing the server to continue the review process. Once all the necessary information is gathered, the server completes the review and generates a final review result, which is notified to the user. For example, the server may inform the user of the result, "Taro Tanaka's review has been approved."

[0519] In this way, the review process is automated efficiently and accurately, enabling faster reviews while minimizing human error.

[0520] Specific examples

[0521] For example, if a sales representative wants to begin reviewing a new customer, Taro Tanaka, the process would proceed through the following specific steps:

[0522] 1. User (sales representative): Enters "Start reviewing a new customer" into the terminal.

[0523] 2. Terminal: Sends input to the server.

[0524] 3. Server: Sends a request to the natural language processing engine for analysis.

[0525] 4. Natural language processing engine: Analyzes the request and extracts keywords such as "new customer" and "review."

[0526] 5. Server: Based on the analysis results, obtain initial data about Taro Tanaka from the database.

[0527] 6. Server: Initiates the review process based on the acquired data, monitors the progress and reports back to the user.

[0528] 7. Server: If additional information is needed, the server requests the user, "Please tell me Taro Tanaka's place of employment information."

[0529] 8. Terminal: The server's request is displayed to the user, who enters additional information and sends it back to the server.

[0530] 9. Server: Once all necessary information is gathered, the review is completed and the final result, "Taro Tanaka's review has been approved," is generated and notified to the user.

[0531] Prompt Sentence Examples

[0532] "We'll begin screening new clients."

[0533] "Please tell me Taro Tanaka's place of employment information."

[0534] "Taro Tanaka's review has been approved."

[0535] The system utilizes generative AI models to improve the accuracy of natural language processing and process prompts smoothly, making the entire screening process more efficient and enabling more reliable screening.

[0536] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0537] Step 1: User enters review request using terminal

[0538] The user enters "Start review of a new customer" into the terminal interface. This input data is sent to the server as a review request. Input: User review request (text). Output: Review request data (e.g., JSON format) from the terminal to the server.

[0539] Step 2: The device sends a request to the server

[0540] The terminal receives the user's input, converts it into an appropriate data format, and sends it to the server. For example, it packages the data in JSON format and sends it to the server using an HTTP request. Input: User's review request (text). Output: Review request received by the server (JSON format).

[0541] Step 3: The server analyzes the received request using a natural language processing engine

[0542] The server passes the received request data to a natural language processing engine for analysis. A generative AI model (e.g., Google Cloud Natural Language API) is used to extract keywords and elements of the review request. Input: Review request data received by the server (JSON format). Output: Parsed request keywords and elements (JSON format).

[0543] Step 4: The server uses the analysis results to retrieve the initial data from the database.

[0544] The server retrieves the necessary initial data from the database based on the analysis results obtained from the natural language processing engine. For example, it queries the database for the credit score and past transaction history of a customer identified as a "new customer." Input: Keywords and elements of the analysis results (JSON format). Output: Initial data (JSON format) retrieved from the database.

[0545] Step 5: The server starts the review process based on the initial data it has obtained.

[0546] The server uses an automated algorithm to start the screening process based on the initial data acquired. For example, it evaluates the credit score and analyzes past transaction history to proceed with the first stage of screening. Input: Initial data (JSON format). Output: Progress of the screening process (JSON format).

[0547] Step 6: The server constantly monitors the progress of the review and reports it to the user.

[0548] The server monitors the progress of the review process in real time and notifies the user of the progress. For example, it sends a message to the user saying, "Taro Tanaka's review data has been obtained. The review is in progress." Input: Progress of the review process (JSON format). Output: Progress notification to the user (text).

[0549] Step 7: Server requests additional information

[0550] If additional information is required during the review, the server sends a request to the user and displays it on the terminal. For example, it sends a message saying "Please tell me Taro Tanaka's place of employment." Input: Progress of the review process (JSON format). Output: Request for additional information from the user (text).

[0551] Step 8: User enters additional information and submits it to the server

[0552] The user checks the request from the server on the device and enters additional information. For example, they enter "Taro Tanaka's workplace information" and send it from the device to the server. Input: User's additional information (text). Output: Additional information sent to the server (JSON format).

[0553] Step 9: The server verifies all necessary information and completes the verification

[0554] The server confirms that all necessary information is gathered and completes the final review process. It generates a review result and notifies the user of the final result. For example, it generates a result such as "Taro Tanaka's review has been approved." Input: All necessary information (JSON format). Output: Final review result (text).

[0555] Step 10: The server notifies the user of the final result

[0556] The server notifies the user of the generated final review result. For example, it may notify the user of the result, "Taro Tanaka's review has been approved." Input: Final review result (text). Output: Notification of the final result to the user (text).

[0557] (Application example 1)

[0558] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0559] Traditional visitor authentication systems require manual verification, which is inefficient and has accuracy issues. While there is a need to automate the process of instantly reviewing and authenticating visitor identities, no effective system exists to achieve this. This has prevented improvements in security and operational efficiency from being achieved.

[0560] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0561] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for completing the examination process and notifying the user of the final examination result, means for photographing the visitor's face with a camera and obtaining the visitor's name by voice input to authenticate the visitor's identity, and means for comparing the photographed face information and the voice-input name with information in a database to examine the visitor's identity. This makes it possible to automate the visitor authentication process and perform examination in real time.

[0562] The "user interface" refers to an input device and a screen through which a user inputs an examination request.

[0563] A "review request" is a review requirement or inquiry submitted by a user to the system.

[0564] "Natural language processing" is a technology that allows a computer to understand text entered by a user and is a means of analyzing and generating responses.

[0565] A "review process" is a series of analysis, judgment, and review actions that the system performs based on a received review request.

[0566] "Progress" means the current status or progress at each stage of the review process.

[0567] "Reporting means" refers to the means by which the system notifies the user of the progress and results of the review.

[0568] "Additional Information" means information not provided in the initial input that is necessary to continue or complete the review process.

[0569] The "notification means" is a means of communication for informing the user of the final review result.

[0570] "Authentication Method" means the method used to verify and authenticate a visitor's identity.

[0571] A "camera" is an image capturing device for capturing images of visitors' faces.

[0572] The "voice input means" is a means for inputting the name of the visitor by voice.

[0573] The "verification means" is a means for comparing the acquired face information and the voice-input name with existing information in a database.

[0574] "Database" means a digital recording device for storing and managing information necessary for visitor screening.

[0575] This invention relates to a visitor authentication system, which provides a series of means for receiving an examination request entered by a user, analyzing it using natural language processing, and reporting the progress and final results of the examination process to the user. Furthermore, the invention aims to efficiently authenticate visitors by verifying their identity using a visitor's facial image and voice input and comparing it with information in a database.

[0576] System configuration

[0577] User Interface

[0578] Provide an input device and screen for users to input review requests, such as a smartphone or smart glasses.

[0579] server

[0580] The server receives the review request sent by the user and analyzes it using natural language processing (NLP). Based on the analysis results, it retrieves the necessary data from the database and starts the review process. The server reports the review progress and final results to the user, and requests additional information if necessary.

[0581] Database

[0582] It stores visitor information (such as facial images, names, and past screening history) and provides the necessary information in response to data matching requests from the server.

[0583] Reporting method

[0584] Smartphones and notification systems are used to notify users of the progress and results of the review.

[0585] Hardware and software used

[0586] camera

[0587] It is used as an image capture device to capture the visitor's face, for example, a built-in camera in a smartphone or smart glasses.

[0588] microphone

[0589] Used as a device for voice input of the visitor's name, such as the built-in microphone of a smartphone or smart glasses.

[0590] Natural Language Processing Engine

[0591] The software used is a natural language processing engine such as SpaCy.

[0592] Facial Recognition Software

[0593] Facial images are captured using image processing libraries such as OpenCV and matched with a database.

[0594] Notification System

[0595] The notification function of smartphones is used as a means of notifying users of the review results and progress in real time.

[0596] Specific examples of processing

[0597] Example 1: Visitor authentication

[0598] 1. The user takes a photo of the visitor's face with their smartphone camera and then voice-inputs the visitor's name following the prompt, "Hello, please enter the visitor's name by voice."

[0599] 2. The server applies natural language processing to the facial image and voice-input name to retrieve relevant information from a database and verify the visitor's identity.

[0600] 3. Within a few seconds, a notification will appear on your smartphone saying, "Visitor authentication completed. The review result is approved."

[0601] Prompt Sentence Examples

[0602] "Hello, please speak your visitor's name."

[0603] The system automates the visitor authentication process and enables real-time screening, improving security and operational efficiency.

[0604] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0605] Step 1:

[0606] The user takes a photo of the visitor's face with a camera and inputs the visitor's name by voice into the interface. The input at this time is the face image acquired from the camera and the voice data acquired from the microphone. These data are sent to the terminal, which temporarily stores them.

[0607] Step 2:

[0608] The facial image and audio data stored on the device are sent to the server. The server first processes the facial image using OpenCV to extract facial features. The input is the facial image and the output is facial feature data. At the same time, the server converts the audio data into text using the SpeechRecognition library. The input is audio data and the output is audio text.

[0609] Step 3:

[0610] The server analyzes the acquired text data using a natural language processing (NLP) engine (e.g., SpaCy). The input is speech text, and the output is analyzed name data. The server uses the analyzed name data and facial feature data to match it with registration information in a database. The input is facial feature data and name data, and the output is the visitor's identity information.

[0611] Step 4:

[0612] The server starts the screening process based on the visitor's identity information obtained from the database. Initial data acquisition and analysis is performed, which includes acquiring and analyzing necessary data. The input is facial feature data and name data, and the output is the screening result.

[0613] Step 5:

[0614] The server tracks the progress of the verification process and reports the results to the user's device. For example, it sends a notification saying, "Visitor authentication has been completed. The verification result is approved." The input is the verification result, and the output is a notification to the user's device.

[0615] Step 6:

[0616] If additional information is required during the review process, the server will request that information from the user. The user inputs the requested additional information and sends it from their terminal to the server. The input is the requested additional information, and the output is the receipt of the additional information.

[0617] Step 7:

[0618] The server receives the additional information and starts the review process again, reacquiring and analyzing the necessary data. The input is the additional information, and the output is the updated review result.

[0619] Step 8:

[0620] Once the final review result is obtained, the server notifies the user of the result. For example, it sends a notification to the user's terminal saying, "Visitor authentication was denied." The input is the final review result, and the output is the final notification to the user's terminal.

[0621] This series of steps makes the visitor authentication process automated, secure, and efficient.

[0622] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0623] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[0624] System configuration

[0625] The system includes the following main components:

[0626] 1. User Interface

[0627] A screen or application where users can enter review requests.

[0628] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[0629] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[0630] 2. Server

[0631] It receives user input and passes the input data to the natural language processing engine and emotion engine to begin analysis. Analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[0632] Initiate the review process and report progress to the user accordingly.

[0633] If additional information is required, it will be requested from the user at the appropriate time.

[0634] Once the review process is complete, the user will be notified of the final outcome.

[0635] Example: If the server receives a request saying "Starting the review of new customer, Taro Tanaka," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[0636] 3. Database

[0637] Stores initial data and information required for review.

[0638] The server retrieves the necessary data based on the user's input.

[0639] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[0640] 4. Reporting and Notification System

[0641] Notify users of the progress and final outcome of the review process.

[0642] If any additional information is needed, it will be requested from the user.

[0643] Make appropriate reports or requests based on the results of sentiment analysis.

[0644] Example: If a request such as "Please tell me Taro Tanaka's place of employment" is required during the screening process, display a message according to the user's emotions.

[0645] Specific examples

[0646] Receiving and analyzing review requests

[0647] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[0648] The server receives the request and begins analyzing it with the natural language processing engine and emotion engine. The natural language processing engine interprets the intent of the request as "starting a new customer screening," and the emotion engine identifies the user as "nervous."

[0649] Obtaining initial data and beginning the review process

[0650] Based on the analysis results, the server retrieves initial data about "Taro Tanaka" (e.g., basic information, credit score) from the database and prepares to begin the screening process.

[0651] Progress Report

[0652] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." It also sends a follow-up message based on the results of the emotion engine, saying, "Don't worry, we will let you know the results soon."

[0653] The terminal receives progress messages from the server and displays them to the user, allowing the user to check the progress of the screening and reduce anxiety.

[0654] Requesting and Receiving Additional Information

[0655] During the review process, if the server needs to request more information about "Taro Tanaka's place of employment," it will send a request for additional information to the terminal. Based on the results of the emotion engine, it will display a polite message such as, "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?"

[0656] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[0657] Completion of the review process and notification of the final result

[0658] The server receives any additional information sent by the user and updates / continues the review process accordingly. Once all necessary information is available, the server completes the review.

[0659] The server generates the final review result and sends it to the device. For example, along with the result "Taro Tanaka's review has been approved," the emotion engine sends a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[0660] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[0661] The processing flow will be explained below.

[0662] Step 1:

[0663] A user logs in to a dedicated user interface and enters a new review request. For example, they might enter, "I'd like to begin reviewing a new customer." At this time, the emotion engine analyzes the user's input, facial expressions, tone of voice, etc. to identify the user's emotions.

[0664] Step 2:

[0665] The device receives the user's input and the emotion analysis results, and sends them to the server, packaging the request data and emotion data in an appropriate format.

[0666] Step 3:

[0667] The server receives a user request and passes the input data to a natural language processing engine to begin analysis. The analysis identifies the intent of the request and the initial data required. Sentiment analysis results are also taken into account.

[0668] Step 4:

[0669] Based on the analysis results and sentiment analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[0670] Step 5:

[0671] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." Based on the results of the emotion engine, it also sends a reassuring message such as, "Don't worry, we will let you know the results soon."

[0672] Step 6:

[0673] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the screening and feel reassured and understood by the emotionally appropriate messages.

[0674] Step 7:

[0675] As the server progresses through the review process, if additional information becomes necessary, it will request it from the user. For example, a request such as "Please tell me Taro Tanaka's place of employment information" is sent to the terminal. Based on the results of the emotion engine, a polite message such as "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?" is displayed.

[0676] Step 8:

[0677] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[0678] Step 9:

[0679] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[0680] Step 10:

[0681] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[0682] Step 11:

[0683] The server obtains all necessary information and completes the review process. It generates a final review result (e.g., "Taro Tanaka's review has been approved") and sends it to the device. It also sends a follow-up message based on the emotion engine, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[0684] Step 12:

[0685] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[0686] Example 2

[0687] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0688] In the conventional review process, the user's emotions are not taken into consideration when analyzing the request entered by the user, which often leads to anxiety and dissatisfaction. Furthermore, there are cases where progress reports and requests for additional information are not properly made, which reduces the efficiency of the entire review process. Furthermore, there is a need for a method to improve the user experience by communicating with the user in a way that takes emotions into consideration during the review process.

[0689] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0690] In this invention, the server includes means for receiving a review request input by a user to an interface, means for analyzing the received review request using a natural language processing engine and starting a review process based on the analysis result and the result of the sentiment analysis engine, means for reporting the progress of the review process to the user, means for requesting additional information from the user if necessary during the review process, means for sending an appropriate follow-up message to the user based on the sentiment analysis result during the review process, and means for completing the review process and notifying the user of the final review result, thereby enabling an efficient and effective review process while taking user sentiment into consideration.

[0691] An "interface" is a screen or application through which a user inputs a review request.

[0692] A "server" is a central processing unit that receives user input data and performs the analysis and review process.

[0693] A "natural language processing engine" is a software system that analyzes text data entered by a user and interprets their intent.

[0694] An "emotion analysis engine" is a software system that identifies emotions from user input and responses.

[0695] "Analysis results" are information regarding the intent of a review request obtained by a natural language processing engine.

[0696] "Emotion analysis results" are information about the user's emotions obtained by the emotion analysis engine.

[0697] A "review process" is a series of evaluation and judgment steps based on the content of a review request.

[0698] "Progress" is information about the progress of the review process.

[0699] "Additional Information" means any additional data or details required to continue or complete the review process.

[0700] A "follow-up message" is a message of comfort or reassurance that is generated based on the user's emotional state.

[0701] "Final Review Result" means the final evaluation and judgment conclusion reached after the review process is completed.

[0702] "Data storage" means a database or other storage device for storing information necessary for the screening process.

[0703] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[0704] System configuration

[0705] The system includes the following main components:

[0706] 1. User Interface

[0707] Use a screen or application that allows users to enter review requests.

[0708] When a user enters a review request, an emotion engine (e.g., IBM Watson Tone Analyzer) is used to recognize the user's emotion.

[0709] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[0710] 2. Server

[0711] It receives user input and passes the input data to a natural language processing engine (e.g., Google's Natural Language API) and an emotion engine to begin parsing it. The analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from data storage (e.g., MySQL, PostgreSQL).

[0712] Initiate the review process and report progress to the user accordingly.

[0713] If additional information is required, it will be requested from the user at the appropriate time.

[0714] Once the review process is complete, the user will be notified of the final outcome.

[0715] Example: If the server receives a request saying "We will begin reviewing a new customer, Mr. A," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[0716] 3. Data Storage

[0717] Stores initial data and information required for review.

[0718] The server retrieves the necessary data based on the user's input.

[0719] Example: Accumulate credit scores and past transaction history for "Mr. A" and provide them as needed.

[0720] 4. Reporting and Notification System

[0721] Notify users of the progress and final outcome of the review process.

[0722] If any additional information is needed, it will be requested from the user.

[0723] Make appropriate reports or requests based on the results of sentiment analysis.

[0724] Example: If a request such as "Please tell me Mr. A's place of employment information" is required during the screening process, a message appropriate to the user's emotions will be displayed.

[0725] Specific examples

[0726] Receiving and analyzing review requests

[0727] The terminal receives an examination request entered by the user and sends it to the server. For example, a request may be sent saying, "Please start an examination of a new customer." The server receives the request and begins analyzing it using a natural language processing engine and an emotion engine. Natural language processing interprets the intent of the request as "start an examination of a new customer," and the emotion engine identifies the user as "nervous."

[0728] Review process and reporting

[0729] Based on the analysis results, the server retrieves initial data about "Mr. A" (e.g., basic information, credit score) from data storage and prepares to start the screening process. It then reports the start of the screening process to the user. For example, it sends a message to the device saying, "We have retrieved Mr. A's initial data. The screening is in progress." Based on the results of the emotion engine, it sends a follow-up message saying, "Don't worry, we will report the results to you soon." The device receives progress messages from the server and displays them to the user.

[0730] Requesting and Receiving Additional Information

[0731] During the review process, if the server needs to request more information about "Mr. A's place of employment," it sends a request for the additional information to the terminal. Based on the results of the emotion engine, it generates and displays a polite message, such as "Sorry to trouble you, but could you please tell me Mr. A's place of employment information?" The user enters the required additional information, which is then received by the terminal and sent to the server. For example, the user might enter "Mr. A works for X Co., Ltd."

[0732] Completion of the review process and notification of the final result

[0733] The server receives additional information sent by the user and updates and continues the review process based on that information. Once all necessary information is collected, the server completes the review. The server generates the final review result and sends it to the device. For example, along with the result "Mr. A's review has been approved," the emotion engine may send a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The device receives the final review result from the server and notifies the user. The user can check the final result and take further action based on the results.

[0734] Real-life examples and prompts

[0735] Examples:

[0736] Imagine a process that involves receiving a new customer review request, analyzing and reporting progress, requesting additional information as needed, and reporting the final results.

[0737] Example prompt sentence:

[0738] "Start screening new clients and report back on progress and final results."

[0739] "Please politely request any additional information you need during the review process and communicate the final review result sensitively."

[0740] Based on this explanation, it becomes clear how the system works, and the roles of the user, server, and terminal can be concretely understood. In addition, by specifying the specific names of the hardware and software used, the specific steps for implementing the invention become clear.

[0741] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0742] Step 1:

[0743] The user inputs a review request into the interface. For example, they input "I would like to start reviewing a new customer." This input is sent to the terminal. The terminal then sends the input request to the server. The input data also includes the user's emotional information (e.g., facial expression analysis and voice analysis data).

[0744] Step 2:

[0745] The server analyzes the review request and emotional information received from the device. A natural language processing engine (e.g., Google's Natural Language API) and an emotional analysis engine (e.g., IBM Watson Tone Analyzer) are used for the analysis. The input data (review request) is passed to the natural language processing engine, which extracts the intent of "starting a new customer review." At the same time, the emotional information is passed to the emotional analysis engine, which identifies the user as "nervous." The output is the analysis results and the emotional analysis results.

[0746] Step 3:

[0747] The server retrieves initial data from data storage based on the analysis results. In this process, a query is sent to a database (e.g., MySQL, PostgreSQL) to obtain basic information and a credit score for the new customer being screened. The input is the analysis results and a query to the data storage, and the output is the retrieved initial data.

[0748] Step 4:

[0749] The server prepares to start the review process. Based on the acquired data, review items and procedures are automatically assigned. Data indicating that the review process is ready to start is generated. The input is the initial data, and the output is data indicating that the review process is ready to start.

[0750] Step 5:

[0751] The server reports the progress of the review process to the user. In this process, the server uses a notification API to send a message to the device, such as "We have obtained the initial data of the new customer. The review is in progress." Based on the sentiment analysis results, the server also generates and sends a follow-up message, such as "Don't worry, we will let you know the review results soon." The inputs are the progress data and the sentiment analysis results, and the outputs are the progress message and the follow-up message.

[0752] Step 6:

[0753] The server requests the user for any additional information they require. For example, if it is necessary to request "employment information" during the screening process, it sends a polite message to the terminal saying, "Sorry to trouble you, but please tell us your employment information." The input is the need for the additional information request and the result of sentiment analysis, and the output is the additional information request message.

[0754] Step 7:

[0755] The user uses the terminal to enter additional information, for example, "I work for Company X." The terminal receives this information and sends it to the server. The input is the user's additional information, and the output is the transmission of data from the terminal to the server.

[0756] Step 8:

[0757] The server receives the additional information sent by the user and updates and continues the review process. It analyzes the additional information entered and reflects it in the review process. The input is the user's additional information, and the output is the updated review process data.

[0758] Step 9:

[0759] The server generates the final review result. It evaluates all the information in the review process and makes a final decision. For example, it generates a result such as "the review is approved." The input is all the review process data, and the output is the final review result data.

[0760] Step 10:

[0761] The server notifies the user of the final review result. In this process, a notification API is used to send a message to the device, such as "The review has been approved." Based on the sentiment analysis results, a follow-up message is also sent, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The input is the final review result data and the sentiment analysis results, and the output is the final review result notification and the follow-up message.

[0762] In this way, an efficient and emotionally sensitive screening process is realized through each processing step at the server, terminal, and user.

[0763] (Application example 2)

[0764] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0765] In conventional screening processes, the process proceeds without taking the user's emotions into consideration, which can make the user feel anxious or tense. This leads to a decrease in user satisfaction and a decrease in trust in the screening process. Furthermore, when requesting additional information, the burden felt by the user and unclear communication can cause delays in the process. To solve these issues, a system that analyzes the user's emotions and responds appropriately is needed.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a review request entered by a user into an interface; means for analyzing the received review request using natural language processing and starting a review process based on the analysis results and sentiment analysis results; means for reporting the progress of the review process to the user and providing a message appropriate to the user's situation based on the sentiment analysis results; means for making polite requests to the user based on the sentiment analysis results when additional information is needed during the review process; and means for completing the review process and notifying the user of the final review result together with an appropriate follow-up message based on the sentiment analysis results. This makes it possible to proceed with the review process while taking the user's emotions into consideration, reduce the anxiety and tension felt by the user, and improve the efficiency and reliability of the entire review process.

[0767] A "user" is a person who enters a review request into the interface and sends and receives information related to the review process.

[0768] An "interface" is an input or display device that allows a user to input a review request.

[0769] An "audit request" is a request sent by a user to a server through an interface to initiate an audit.

[0770] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[0771] "Emotion analysis" is a technology that analyzes and identifies a user's emotional state from their input.

[0772] The "review process" refers to a series of processes from when the server receives a review request to when it notifies the user of the final result.

[0773] "Status" refers to the current status at each stage of the review process.

[0774] "Message" refers to text or information used to notify users of progress in the review process, requests for additional information, etc.

[0775] "Additional Information" is any additional information the server requests from the user to continue the review process.

[0776] "Results" or "Review Results" means information that informs the user of the final judgment or conclusion of the review process.

[0777] "Database" means a digital storage system for storing the initial data and history required for the review process.

[0778] A "follow-up message" is a message that provides comfort and reassurance to a user as the review process progresses or is completed, taking into account the user's emotional state.

[0779] The present invention is a system for automating a review process that takes into account the emotions of users. This system receives review requests entered by users through an interface and advances the review process while performing natural language processing and emotion analysis. Details are described below.

[0780] System configuration

[0781] The system includes the following main components:

[0782] 1. User Interface

[0783] A screen or application for users to enter review requests.

[0784] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[0785] 2. Server

[0786] The server receives the user's input and passes it to a natural language processing engine (e.g., Google Cloud NLP) and an emotion engine (e.g., IBM Watson Tone Analyzer) to begin analysis. The analysis identifies the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[0787] Initiate the review process and keep you informed of progress. Request any additional information you require from the user in a timely manner.

[0788] After the review process is complete, the final result will be communicated to the user along with a follow-up message based on the sentiment analysis results.

[0789] 3. Database

[0790] The database stores initial data and information required for review.

[0791] The server retrieves the necessary data based on the user's input.

[0792] 4. Reporting and Notification System

[0793] Inform users about the progress and final outcome of the review process, and politely request any additional information required based on sentiment analysis results.

[0794] Example of operation

[0795] An example of the system operation is shown below.

[0796] The worker (user) speaks to the maintenance assistant robot, saying, "This machine needs maintenance."

[0797] The server analyzes the request, obtains initial data such as the machine's condition and past maintenance history, and uses sentiment analysis to identify that the user is nervous.

[0798] Maintenance begins, and the server reports progress by saying, "Maintenance is underway. Please rest assured."

[0799] If additional machine information is needed, the server will politely ask, "Excuse me for asking, but could you please provide more information about your machine?"

[0800] Once the maintenance is complete, the server will report the final result, saying, "Maintenance has been completed successfully. Please rest assured."

[0801] Prompt Sentence Examples

[0802] "The maintenance assistant robot should collect information on machines that need maintenance and report the progress appropriately based on the worker's emotions."

[0803] As described above, the present invention provides a system that can efficiently advance the screening process while taking into consideration the user's feelings. This system reduces the user's anxiety and tension and improves the efficiency and reliability of the entire screening process.

[0804] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0805] Step 1:

[0806] The user inputs an audit request into the interface. The user inputs "This machine needs maintenance" and the terminal receives it. Input: User's audit request. Output: Request data sent by the terminal to the server.

[0807] Step 2:

[0808] The server passes the received review request to the natural language processing engine to begin analysis. Input: Received request data. Output: Request intent and identified emotional state. The server uses the natural language processing engine to interpret the intent of the request and the emotion analysis engine to identify the user's emotion.

[0809] Step 3:

[0810] The server retrieves initial data from the database based on the analysis results. Input: Analysis results (intention and sentiment of the request). Output: Initial data (machine status and past maintenance history). The server queries the database and retrieves the necessary initial data.

[0811] Step 4:

[0812] The server starts the review process and reports progress to the user. Input: Initial data and analysis results. Output: Progress message. The server starts the review process and notifies the user of the progress, saying "Maintenance is in progress. Please rest assured."

[0813] Step 5:

[0814] If the server needs additional information during the review process, it requests it from the user. Input: Additional information required during the review process. Output: Request message for additional information. The server sends a polite message based on the results of sentiment analysis, saying, "Excuse me for bothering you, but could you please tell me more information about your machine?"

[0815] Step 6:

[0816] The user enters additional information, which the device sends to the server. Input: Additional information provided by the user. Output: Additional information data sent to the server. The server continues the review process based on this additional information.

[0817] Step 7:

[0818] The server completes the review process and notifies the user of the final result. Input: All required information and the final result of the review process. Output: A notification message of the final result. Once the review is complete, the server notifies the user of the final result, saying "The maintenance has been completed successfully. Please rest assured," along with a follow-up message based on the sentiment analysis results.

[0819] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0820] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0821] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0822] [Third embodiment]

[0823] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0824] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0825] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0826] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0827] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0828] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0829] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0830] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0831] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0832] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0833] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0834] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0835] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[0836] System configuration

[0837] The system includes the following main components:

[0838] 1. User Interface

[0839] A screen or application where users can enter review requests.

[0840] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[0841] 2. Server

[0842] Receives user input and parses it using a natural language processing engine.

[0843] Based on the analysis results, the necessary initial data is retrieved from the database to begin the review process.

[0844] Updates and reports progress to the user.

[0845] Example: If the server receives a request saying "Start reviewing new customer, Taro Tanaka," it uses natural language processing to identify "Taro Tanaka" and retrieves his information from the database.

[0846] 3. Database

[0847] Stores initial data and information required for review.

[0848] The server retrieves the necessary data based on the user's input.

[0849] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[0850] 4. Reporting and Notification System

[0851] Notify users of the progress and final outcome of the review process.

[0852] If any additional information is needed, it will be requested from the user.

[0853] Example: During the screening process, you may request additional information from the user, such as "Please tell me Taro Tanaka's place of employment."

[0854] Processing flow

[0855] Receiving a review request

[0856] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[0857] Request analysis and initiation of review process

[0858] The server analyzes the received request using a natural language processing engine and retrieves the initial data required to start the review process based on the analysis results from the database, allowing the review process to proceed automatically.

[0859] Review process and reporting

[0860] The server tracks the progress of the review process and reports the status to the user as needed, for example, "Taro Tanaka's review data has been obtained. The review is in progress."

[0861] Request for additional information

[0862] If additional information is required during the review process, the server sends a request to the user, and the terminal displays it to the user. The additional information entered by the user is then sent back to the server, and the review process continues.

[0863] Completion of the review process and notification of the final result

[0864] Once all necessary information is available, the server completes the review process, generates a final review result, and notifies the user, for example, "Taro Tanaka's review has been approved."

[0865] This system will streamline the review process, allowing it to proceed quickly and accurately, minimizing human error and providing highly reliable review results.

[0866] The processing flow will be explained below.

[0867] Step 1:

[0868] A user logs in to a dedicated user interface and enters a new review request, for example, "Start a review for a new customer."

[0869] Step 2:

[0870] The terminal receives the user's input and sends it to the server, packaging the request data in an appropriate format.

[0871] Step 3:

[0872] The server receives the user's request and passes the input data to a natural language processing engine to begin parsing it, which determines the intent of the request and any initial data required.

[0873] Step 4:

[0874] Based on the analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[0875] Step 5:

[0876] The server notifies the user that the review process has started. For example, it sends a message to the terminal saying, "Taro Tanaka's initial data has been acquired. Review is in progress."

[0877] Step 6:

[0878] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the review.

[0879] Step 7:

[0880] If the server needs additional information as it goes through the screening process, it will request it from the user. For example, it may send a request such as "Please tell me Taro Tanaka's place of employment information" to the terminal.

[0881] Step 8:

[0882] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[0883] Step 9:

[0884] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[0885] Step 10:

[0886] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[0887] Step 11:

[0888] The server obtains all necessary information, completes the review process, generates a final review result (e.g., "Taro Tanaka's review has been approved"), and sends it to the device.

[0889] Step 12:

[0890] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[0891] Example 1

[0892] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0893] Traditional review processes are often manual, which not only takes time but also makes them prone to human error. Furthermore, it is difficult to report review progress in a timely manner, making it difficult for users to grasp the review progress. Furthermore, the need to obtain additional information and the generation and notification of final review results are not always smooth, which can lead to a decline in overall efficiency.

[0894] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0895] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for generating a final examination result and notifying the user, and means for conducting the examination using an automated algorithm and constantly monitoring the progress. This makes it possible to efficiently automate the examination process and quickly grasp the progress and obtain necessary additional information.

[0896] "User" means a person or entity that uses the System to enter review requests and view the progress and results of the review process.

[0897] An "interface" is a point of contact with a user, including a screen or application through which the user inputs an examination request.

[0898] A "review request" is a request or inquiry entered by a user to initiate a review.

[0899] "Server" means the central processing unit that receives and analyzes review requests and manages and executes the review process.

[0900] "Natural language processing" is a technology that analyzes language data entered by the user and understands and processes its meaning.

[0901] "Analysis results" refers to data and information obtained as a result of the natural language processing engine analyzing the review request.

[0902] The "review process" is a series of evaluations and confirmations carried out based on the analysis results.

[0903] "Progress" refers to the progress made at each stage of the review process.

[0904] "Additional Information" is supplemental information required for further evaluation during the review process.

[0905] "Final review outcome" means the conclusion or decision reached after the entire review process.

[0906] An "automated algorithm" is a program that automatically conducts the review process based on specific rules and calculation procedures.

[0907] "Monitoring" refers to the act of constantly observing and checking the progress of the review process.

[0908] The "database" is a system that stores and manages necessary initial data and information related to the review.

[0909] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[0910] First, the user inputs a review request using a device. For example, the user might input "Start reviewing a new customer" on a dedicated screen or application. This input is sent from the device to the server. The server then sends the received request to a natural language processing engine (e.g., Google Cloud Natural Language API) for analysis. The natural language processing engine analyzes the request and extracts keywords such as "new customer" and "review."

[0911] The server receives the analysis results and retrieves the necessary initial data from a database (e.g., MySQL or PostgreSQL). This initial data includes, for example, the customer's credit score and past transaction history. The server then starts the screening process based on the retrieved initial data and constantly monitors its progress. Specifically, the screening process progresses using an automated algorithm, and reports are sent to the user as the process progresses. For example, the server may notify the user, "Taro Tanaka's screening data has been retrieved. The screening is currently underway."

[0912] If the server needs to request additional information during the review process, the request is sent to the user and displayed on the terminal. The user enters the additional information and sends it back to the server from the terminal, allowing the server to continue the review process. Once all the necessary information is gathered, the server completes the review and generates a final review result, which is notified to the user. For example, the server may inform the user of the result, "Taro Tanaka's review has been approved."

[0913] In this way, the review process is automated efficiently and accurately, enabling faster reviews while minimizing human error.

[0914] Specific examples

[0915] For example, if a sales representative wants to begin reviewing a new customer, Taro Tanaka, the process would proceed through the following specific steps:

[0916] 1. User (sales representative): Enters "Start reviewing a new customer" into the terminal.

[0917] 2. Terminal: Sends input to the server.

[0918] 3. Server: Sends a request to the natural language processing engine for analysis.

[0919] 4. Natural language processing engine: Analyzes the request and extracts keywords such as "new customer" and "review."

[0920] 5. Server: Based on the analysis results, obtain initial data about Taro Tanaka from the database.

[0921] 6. Server: Initiates the review process based on the acquired data, monitors the progress and reports back to the user.

[0922] 7. Server: If additional information is needed, the server requests the user, "Please tell me Taro Tanaka's place of employment information."

[0923] 8. Terminal: The server's request is displayed to the user, who enters additional information and sends it back to the server.

[0924] 9. Server: Once all necessary information is gathered, the review is completed and the final result, "Taro Tanaka's review has been approved," is generated and notified to the user.

[0925] Prompt Sentence Examples

[0926] "We'll begin screening new clients."

[0927] "Please tell me Taro Tanaka's place of employment information."

[0928] "Taro Tanaka's review has been approved."

[0929] The system utilizes generative AI models to improve the accuracy of natural language processing and process prompts smoothly, making the entire screening process more efficient and enabling more reliable screening.

[0930] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0931] Step 1: User enters review request using terminal

[0932] The user enters "Start review of a new customer" into the terminal interface. This input data is sent to the server as a review request. Input: User review request (text). Output: Review request data (e.g., JSON format) from the terminal to the server.

[0933] Step 2: The device sends a request to the server

[0934] The terminal receives the user's input, converts it into an appropriate data format, and sends it to the server. For example, it packages the data in JSON format and sends it to the server using an HTTP request. Input: User's review request (text). Output: Review request received by the server (JSON format).

[0935] Step 3: The server analyzes the received request using a natural language processing engine

[0936] The server passes the received request data to a natural language processing engine for analysis. A generative AI model (e.g., Google Cloud Natural Language API) is used to extract keywords and elements of the review request. Input: Review request data received by the server (JSON format). Output: Parsed request keywords and elements (JSON format).

[0937] Step 4: The server uses the analysis results to retrieve the initial data from the database.

[0938] The server retrieves the necessary initial data from the database based on the analysis results obtained from the natural language processing engine. For example, it queries the database for the credit score and past transaction history of a customer identified as a "new customer." Input: Keywords and elements of the analysis results (JSON format). Output: Initial data (JSON format) retrieved from the database.

[0939] Step 5: The server starts the review process based on the initial data it has obtained.

[0940] The server uses an automated algorithm to start the screening process based on the initial data acquired. For example, it evaluates the credit score and analyzes past transaction history to proceed with the first stage of screening. Input: Initial data (JSON format). Output: Progress of the screening process (JSON format).

[0941] Step 6: The server constantly monitors the progress of the review and reports it to the user.

[0942] The server monitors the progress of the review process in real time and notifies the user of the progress. For example, it sends a message to the user saying, "Taro Tanaka's review data has been obtained. The review is in progress." Input: Progress of the review process (JSON format). Output: Progress notification to the user (text).

[0943] Step 7: Server requests additional information

[0944] If additional information is required during the review, the server sends a request to the user and displays it on the terminal. For example, it sends a message saying "Please tell me Taro Tanaka's place of employment." Input: Progress of the review process (JSON format). Output: Request for additional information from the user (text).

[0945] Step 8: User enters additional information and submits it to the server

[0946] The user checks the request from the server on the device and enters additional information. For example, they enter "Taro Tanaka's workplace information" and send it from the device to the server. Input: User's additional information (text). Output: Additional information sent to the server (JSON format).

[0947] Step 9: The server verifies all necessary information and completes the verification

[0948] The server confirms that all necessary information is gathered and completes the final review process. It generates a review result and notifies the user of the final result. For example, it generates a result such as "Taro Tanaka's review has been approved." Input: All necessary information (JSON format). Output: Final review result (text).

[0949] Step 10: The server notifies the user of the final result

[0950] The server notifies the user of the generated final review result. For example, it may notify the user of the result, "Taro Tanaka's review has been approved." Input: Final review result (text). Output: Notification of the final result to the user (text).

[0951] (Application example 1)

[0952] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0953] Traditional visitor authentication systems require manual verification, which is inefficient and has accuracy issues. While there is a need to automate the process of instantly reviewing and authenticating visitor identities, no effective system exists to achieve this. This has prevented improvements in security and operational efficiency from being achieved.

[0954] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0955] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for completing the examination process and notifying the user of the final examination result, means for photographing the visitor's face with a camera and obtaining the visitor's name by voice input to authenticate the visitor's identity, and means for comparing the photographed face information and the voice-input name with information in a database to examine the visitor's identity. This makes it possible to automate the visitor authentication process and perform examination in real time.

[0956] The "user interface" refers to an input device and a screen through which a user inputs an examination request.

[0957] A "review request" is a review requirement or inquiry submitted by a user to the system.

[0958] "Natural language processing" is a technology that allows a computer to understand text entered by a user and is a means of analyzing and generating responses.

[0959] A "review process" is a series of analysis, judgment, and review actions that the system performs based on a received review request.

[0960] "Progress" means the current status or progress at each stage of the review process.

[0961] "Reporting means" refers to the means by which the system notifies the user of the progress and results of the review.

[0962] "Additional Information" means information not provided in the initial input that is necessary to continue or complete the review process.

[0963] The "notification means" is a means of communication for informing the user of the final review result.

[0964] "Authentication Method" means the method used to verify and authenticate a visitor's identity.

[0965] A "camera" is an image capturing device for capturing images of visitors' faces.

[0966] The "voice input means" is a means for inputting the name of the visitor by voice.

[0967] The "verification means" is a means for comparing the acquired face information and the voice-input name with existing information in a database.

[0968] "Database" means a digital recording device for storing and managing information necessary for visitor screening.

[0969] This invention relates to a visitor authentication system, which provides a series of means for receiving an examination request entered by a user, analyzing it using natural language processing, and reporting the progress and final results of the examination process to the user. Furthermore, the invention aims to efficiently authenticate visitors by verifying their identity using a visitor's facial image and voice input and comparing it with information in a database.

[0970] System configuration

[0971] User Interface

[0972] Provide an input device and screen for users to input review requests, such as a smartphone or smart glasses.

[0973] server

[0974] The server receives the review request sent by the user and analyzes it using natural language processing (NLP). Based on the analysis results, it retrieves the necessary data from the database and starts the review process. The server reports the review progress and final results to the user, and requests additional information if necessary.

[0975] Database

[0976] It stores visitor information (such as facial images, names, and past screening history) and provides the necessary information in response to data matching requests from the server.

[0977] Reporting method

[0978] Smartphones and notification systems are used to notify users of the progress and results of the review.

[0979] Hardware and software used

[0980] camera

[0981] It is used as an image capture device to capture the visitor's face, for example, a built-in camera in a smartphone or smart glasses.

[0982] microphone

[0983] Used as a device for voice input of the visitor's name, such as the built-in microphone of a smartphone or smart glasses.

[0984] Natural Language Processing Engine

[0985] The software used is a natural language processing engine such as SpaCy.

[0986] Facial Recognition Software

[0987] Facial images are captured using image processing libraries such as OpenCV and matched with a database.

[0988] Notification System

[0989] The notification function of smartphones is used as a means of notifying users of the review results and progress in real time.

[0990] Specific examples of processing

[0991] Example 1: Visitor authentication

[0992] 1. The user takes a photo of the visitor's face with their smartphone camera and then voice-inputs the visitor's name following the prompt, "Hello, please enter the visitor's name by voice."

[0993] 2. The server applies natural language processing to the facial image and voice-input name to retrieve relevant information from a database and verify the visitor's identity.

[0994] 3. Within a few seconds, a notification will appear on your smartphone saying, "Visitor authentication completed. The review result is approved."

[0995] Prompt Sentence Examples

[0996] "Hello, please speak your visitor's name."

[0997] The system automates the visitor authentication process and enables real-time screening, improving security and operational efficiency.

[0998] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0999] Step 1:

[1000] The user takes a photo of the visitor's face with a camera and inputs the visitor's name by voice into the interface. The input at this time is the face image acquired from the camera and the voice data acquired from the microphone. These data are sent to the terminal, which temporarily stores them.

[1001] Step 2:

[1002] The facial image and audio data stored on the device are sent to the server. The server first processes the facial image using OpenCV to extract facial features. The input is the facial image and the output is facial feature data. At the same time, the server converts the audio data into text using the SpeechRecognition library. The input is audio data and the output is audio text.

[1003] Step 3:

[1004] The server analyzes the acquired text data using a natural language processing (NLP) engine (e.g., SpaCy). The input is speech text, and the output is analyzed name data. The server uses the analyzed name data and facial feature data to match it with registration information in a database. The input is facial feature data and name data, and the output is the visitor's identity information.

[1005] Step 4:

[1006] The server starts the screening process based on the visitor's identity information obtained from the database. Initial data acquisition and analysis is performed, which includes acquiring and analyzing necessary data. The input is facial feature data and name data, and the output is the screening result.

[1007] Step 5:

[1008] The server tracks the progress of the verification process and reports the results to the user's device. For example, it sends a notification saying, "Visitor authentication has been completed. The verification result is approved." The input is the verification result, and the output is a notification to the user's device.

[1009] Step 6:

[1010] If additional information is required during the review process, the server will request that information from the user. The user inputs the requested additional information and sends it from their terminal to the server. The input is the requested additional information, and the output is the receipt of the additional information.

[1011] Step 7:

[1012] The server receives the additional information and starts the review process again, reacquiring and analyzing the necessary data. The input is the additional information, and the output is the updated review result.

[1013] Step 8:

[1014] Once the final review result is obtained, the server notifies the user of the result. For example, it sends a notification to the user's terminal saying, "Visitor authentication was denied." The input is the final review result, and the output is the final notification to the user's terminal.

[1015] This series of steps makes the visitor authentication process automated, secure, and efficient.

[1016] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1017] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[1018] System configuration

[1019] The system includes the following main components:

[1020] 1. User Interface

[1021] A screen or application where users can enter review requests.

[1022] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[1023] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[1024] 2. Server

[1025] It receives user input and passes the input data to the natural language processing engine and emotion engine to begin analysis. Analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[1026] Initiate the review process and report progress to the user accordingly.

[1027] If additional information is required, it will be requested from the user at the appropriate time.

[1028] Once the review process is complete, the user will be notified of the final outcome.

[1029] Example: If the server receives a request saying "Starting the review of new customer, Taro Tanaka," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[1030] 3. Database

[1031] Stores initial data and information required for review.

[1032] The server retrieves the necessary data based on the user's input.

[1033] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[1034] 4. Reporting and Notification System

[1035] Notify users of the progress and final outcome of the review process.

[1036] If any additional information is needed, it will be requested from the user.

[1037] Make appropriate reports or requests based on the results of sentiment analysis.

[1038] Example: If a request such as "Please tell me Taro Tanaka's place of employment" is required during the screening process, display a message according to the user's emotions.

[1039] Specific examples

[1040] Receiving and analyzing review requests

[1041] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[1042] The server receives the request and begins analyzing it with the natural language processing engine and emotion engine. The natural language processing engine interprets the intent of the request as "starting a new customer screening," and the emotion engine identifies the user as "nervous."

[1043] Obtaining initial data and beginning the review process

[1044] Based on the analysis results, the server retrieves initial data about "Taro Tanaka" (e.g., basic information, credit score) from the database and prepares to begin the screening process.

[1045] Progress Report

[1046] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." It also sends a follow-up message based on the results of the emotion engine, saying, "Don't worry, we will let you know the results soon."

[1047] The terminal receives progress messages from the server and displays them to the user, allowing the user to check the progress of the screening and reduce anxiety.

[1048] Requesting and Receiving Additional Information

[1049] During the review process, if the server needs to request more information about "Taro Tanaka's place of employment," it will send a request for additional information to the terminal. Based on the results of the emotion engine, it will display a polite message such as, "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?"

[1050] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[1051] Completion of the review process and notification of the final result

[1052] The server receives any additional information sent by the user and updates / continues the review process accordingly. Once all necessary information is available, the server completes the review.

[1053] The server generates the final review result and sends it to the device. For example, along with the result "Taro Tanaka's review has been approved," the emotion engine sends a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[1054] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[1055] The processing flow will be explained below.

[1056] Step 1:

[1057] A user logs in to a dedicated user interface and enters a new review request. For example, they might enter, "I'd like to begin reviewing a new customer." At this time, the emotion engine analyzes the user's input, facial expressions, tone of voice, etc. to identify the user's emotions.

[1058] Step 2:

[1059] The device receives the user's input and the emotion analysis results, and sends them to the server, packaging the request data and emotion data in an appropriate format.

[1060] Step 3:

[1061] The server receives a user request and passes the input data to a natural language processing engine to begin analysis. The analysis identifies the intent of the request and the initial data required. Sentiment analysis results are also taken into account.

[1062] Step 4:

[1063] Based on the analysis results and sentiment analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[1064] Step 5:

[1065] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." Based on the results of the emotion engine, it also sends a reassuring message such as, "Don't worry, we will let you know the results soon."

[1066] Step 6:

[1067] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the screening and feel reassured and understood by the emotionally appropriate messages.

[1068] Step 7:

[1069] As the server progresses through the review process, if additional information becomes necessary, it will request it from the user. For example, a request such as "Please tell me Taro Tanaka's place of employment information" is sent to the terminal. Based on the results of the emotion engine, a polite message such as "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?" is displayed.

[1070] Step 8:

[1071] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[1072] Step 9:

[1073] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[1074] Step 10:

[1075] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[1076] Step 11:

[1077] The server obtains all necessary information and completes the review process. It generates a final review result (e.g., "Taro Tanaka's review has been approved") and sends it to the device. It also sends a follow-up message based on the emotion engine, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[1078] Step 12:

[1079] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[1080] Example 2

[1081] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1082] In the conventional review process, the user's emotions are not taken into consideration when analyzing the request entered by the user, which often leads to anxiety and dissatisfaction. Furthermore, there are cases where progress reports and requests for additional information are not properly made, which reduces the efficiency of the entire review process. Furthermore, there is a need for a method to improve the user experience by communicating with the user in a way that takes emotions into consideration during the review process.

[1083] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1084] In this invention, the server includes means for receiving a review request input by a user to an interface, means for analyzing the received review request using a natural language processing engine and starting a review process based on the analysis result and the result of the sentiment analysis engine, means for reporting the progress of the review process to the user, means for requesting additional information from the user if necessary during the review process, means for sending an appropriate follow-up message to the user based on the sentiment analysis result during the review process, and means for completing the review process and notifying the user of the final review result, thereby enabling an efficient and effective review process while taking user sentiment into consideration.

[1085] An "interface" is a screen or application through which a user inputs a review request.

[1086] A "server" is a central processing unit that receives user input data and performs the analysis and review process.

[1087] A "natural language processing engine" is a software system that analyzes text data entered by a user and interprets their intent.

[1088] An "emotion analysis engine" is a software system that identifies emotions from user input and responses.

[1089] "Analysis results" are information regarding the intent of a review request obtained by a natural language processing engine.

[1090] "Emotion analysis results" are information about the user's emotions obtained by the emotion analysis engine.

[1091] A "review process" is a series of evaluation and judgment steps based on the content of a review request.

[1092] "Progress" is information about the progress of the review process.

[1093] "Additional Information" means any additional data or details required to continue or complete the review process.

[1094] A "follow-up message" is a message of comfort or reassurance that is generated based on the user's emotional state.

[1095] "Final Review Result" means the final evaluation and judgment conclusion reached after the review process is completed.

[1096] "Data storage" means a database or other storage device for storing information necessary for the screening process.

[1097] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[1098] System configuration

[1099] The system includes the following main components:

[1100] 1. User Interface

[1101] Use a screen or application that allows users to enter review requests.

[1102] When a user enters a review request, an emotion engine (e.g., IBM Watson Tone Analyzer) is used to recognize the user's emotion.

[1103] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[1104] 2. Server

[1105] It receives user input and passes the input data to a natural language processing engine (e.g., Google's Natural Language API) and an emotion engine to begin parsing it. The analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from data storage (e.g., MySQL, PostgreSQL).

[1106] Initiate the review process and report progress to the user accordingly.

[1107] If additional information is required, it will be requested from the user at the appropriate time.

[1108] Once the review process is complete, the user will be notified of the final outcome.

[1109] Example: If the server receives a request saying "We will begin reviewing a new customer, Mr. A," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[1110] 3. Data Storage

[1111] Stores initial data and information required for review.

[1112] The server retrieves the necessary data based on the user's input.

[1113] Example: Accumulate credit scores and past transaction history for "Mr. A" and provide them as needed.

[1114] 4. Reporting and Notification System

[1115] Notify users of the progress and final outcome of the review process.

[1116] If any additional information is needed, it will be requested from the user.

[1117] Make appropriate reports or requests based on the results of sentiment analysis.

[1118] Example: If a request such as "Please tell me Mr. A's place of employment information" is required during the screening process, a message appropriate to the user's emotions will be displayed.

[1119] Specific examples

[1120] Receiving and analyzing review requests

[1121] The terminal receives an examination request entered by the user and sends it to the server. For example, a request may be sent saying, "Please start an examination of a new customer." The server receives the request and begins analyzing it using a natural language processing engine and an emotion engine. Natural language processing interprets the intent of the request as "start an examination of a new customer," and the emotion engine identifies the user as "nervous."

[1122] Review process and reporting

[1123] Based on the analysis results, the server retrieves initial data about "Mr. A" (e.g., basic information, credit score) from data storage and prepares to start the screening process. It then reports the start of the screening process to the user. For example, it sends a message to the device saying, "We have retrieved Mr. A's initial data. The screening is in progress." Based on the results of the emotion engine, it sends a follow-up message saying, "Don't worry, we will report the results to you soon." The device receives progress messages from the server and displays them to the user.

[1124] Requesting and Receiving Additional Information

[1125] During the review process, if the server needs to request more information about "Mr. A's place of employment," it sends a request for the additional information to the terminal. Based on the results of the emotion engine, it generates and displays a polite message, such as "Sorry to trouble you, but could you please tell me Mr. A's place of employment information?" The user enters the required additional information, which is then received by the terminal and sent to the server. For example, the user might enter "Mr. A works for X Co., Ltd."

[1126] Completion of the review process and notification of the final result

[1127] The server receives additional information sent by the user and updates and continues the review process based on that information. Once all necessary information is collected, the server completes the review. The server generates the final review result and sends it to the device. For example, along with the result "Mr. A's review has been approved," the emotion engine may send a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The device receives the final review result from the server and notifies the user. The user can check the final result and take further action based on the results.

[1128] Real-life examples and prompts

[1129] Examples:

[1130] Imagine a process that involves receiving a new customer review request, analyzing and reporting progress, requesting additional information as needed, and reporting the final results.

[1131] Example prompt sentence:

[1132] "Start screening new clients and report back on progress and final results."

[1133] "Please politely request any additional information you need during the review process and communicate the final review result sensitively."

[1134] Based on this explanation, it becomes clear how the system works, and the roles of the user, server, and terminal can be concretely understood. In addition, by specifying the specific names of the hardware and software used, the specific steps for implementing the invention become clear.

[1135] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1136] Step 1:

[1137] The user inputs a review request into the interface. For example, they input "I would like to start reviewing a new customer." This input is sent to the terminal. The terminal then sends the input request to the server. The input data also includes the user's emotional information (e.g., facial expression analysis and voice analysis data).

[1138] Step 2:

[1139] The server analyzes the review request and emotional information received from the device. A natural language processing engine (e.g., Google's Natural Language API) and an emotional analysis engine (e.g., IBM Watson Tone Analyzer) are used for the analysis. The input data (review request) is passed to the natural language processing engine, which extracts the intent of "starting a new customer review." At the same time, the emotional information is passed to the emotional analysis engine, which identifies the user as "nervous." The output is the analysis results and the emotional analysis results.

[1140] Step 3:

[1141] The server retrieves initial data from data storage based on the analysis results. In this process, a query is sent to a database (e.g., MySQL, PostgreSQL) to obtain basic information and a credit score for the new customer being screened. The input is the analysis results and a query to the data storage, and the output is the retrieved initial data.

[1142] Step 4:

[1143] The server prepares to start the review process. Based on the acquired data, review items and procedures are automatically assigned. Data indicating that the review process is ready to start is generated. The input is the initial data, and the output is data indicating that the review process is ready to start.

[1144] Step 5:

[1145] The server reports the progress of the review process to the user. In this process, the server uses a notification API to send a message to the device, such as "We have obtained the initial data of the new customer. The review is in progress." Based on the sentiment analysis results, the server also generates and sends a follow-up message, such as "Don't worry, we will let you know the review results soon." The inputs are the progress data and the sentiment analysis results, and the outputs are the progress message and the follow-up message.

[1146] Step 6:

[1147] The server requests the user for any additional information they require. For example, if it is necessary to request "employment information" during the screening process, it sends a polite message to the terminal saying, "Sorry to trouble you, but please tell us your employment information." The input is the need for the additional information request and the result of sentiment analysis, and the output is the additional information request message.

[1148] Step 7:

[1149] The user uses the terminal to enter additional information, for example, "I work for Company X." The terminal receives this information and sends it to the server. The input is the user's additional information, and the output is the transmission of data from the terminal to the server.

[1150] Step 8:

[1151] The server receives the additional information sent by the user and updates and continues the review process. It analyzes the additional information entered and reflects it in the review process. The input is the user's additional information, and the output is the updated review process data.

[1152] Step 9:

[1153] The server generates the final review result. It evaluates all the information in the review process and makes a final decision. For example, it generates a result such as "the review is approved." The input is all the review process data, and the output is the final review result data.

[1154] Step 10:

[1155] The server notifies the user of the final review result. In this process, a notification API is used to send a message to the device, such as "The review has been approved." Based on the sentiment analysis results, a follow-up message is also sent, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The input is the final review result data and the sentiment analysis results, and the output is the final review result notification and the follow-up message.

[1156] In this way, an efficient and emotionally sensitive screening process is realized through each processing step at the server, terminal, and user.

[1157] (Application example 2)

[1158] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1159] In conventional screening processes, the process proceeds without taking the user's emotions into consideration, which can make the user feel anxious or tense. This leads to a decrease in user satisfaction and a decrease in trust in the screening process. Furthermore, when requesting additional information, the burden felt by the user and unclear communication can cause delays in the process. To solve these issues, a system that analyzes the user's emotions and responds appropriately is needed.

[1160] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a review request entered by a user into an interface; means for analyzing the received review request using natural language processing and starting a review process based on the analysis results and sentiment analysis results; means for reporting the progress of the review process to the user and providing a message appropriate to the user's situation based on the sentiment analysis results; means for making polite requests to the user based on the sentiment analysis results when additional information is needed during the review process; and means for completing the review process and notifying the user of the final review result together with an appropriate follow-up message based on the sentiment analysis results. This makes it possible to proceed with the review process while taking the user's emotions into consideration, reduce the anxiety and tension felt by the user, and improve the efficiency and reliability of the entire review process.

[1161] A "user" is a person who enters a review request into the interface and sends and receives information related to the review process.

[1162] An "interface" is an input or display device that allows a user to input a review request.

[1163] An "audit request" is a request sent by a user to a server through an interface to initiate an audit.

[1164] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[1165] "Emotion analysis" is a technology that analyzes and identifies a user's emotional state from their input.

[1166] The "review process" refers to a series of processes from when the server receives a review request to when it notifies the user of the final result.

[1167] "Status" refers to the current status at each stage of the review process.

[1168] "Message" refers to text or information used to notify users of progress in the review process, requests for additional information, etc.

[1169] "Additional Information" is any additional information the server requests from the user to continue the review process.

[1170] "Results" or "Review Results" means information that informs the user of the final judgment or conclusion of the review process.

[1171] "Database" means a digital storage system for storing the initial data and history required for the review process.

[1172] A "follow-up message" is a message that provides comfort and reassurance to a user as the review process progresses or is completed, taking into account the user's emotional state.

[1173] The present invention is a system for automating a review process that takes into account the emotions of users. This system receives review requests entered by users through an interface and advances the review process while performing natural language processing and emotion analysis. Details are described below.

[1174] System configuration

[1175] The system includes the following main components:

[1176] 1. User Interface

[1177] A screen or application for users to enter review requests.

[1178] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[1179] 2. Server

[1180] The server receives the user's input and passes it to a natural language processing engine (e.g., Google Cloud NLP) and an emotion engine (e.g., IBM Watson Tone Analyzer) to begin analysis. The analysis identifies the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[1181] Initiate the review process and keep you informed of progress. Request any additional information you require from the user in a timely manner.

[1182] After the review process is complete, the final result will be communicated to the user along with a follow-up message based on the sentiment analysis results.

[1183] 3. Database

[1184] The database stores initial data and information required for review.

[1185] The server retrieves the necessary data based on the user's input.

[1186] 4. Reporting and Notification System

[1187] Inform users about the progress and final outcome of the review process, and politely request any additional information required based on sentiment analysis results.

[1188] Example of operation

[1189] An example of the system operation is shown below.

[1190] The worker (user) speaks to the maintenance assistant robot, saying, "This machine needs maintenance."

[1191] The server analyzes the request, obtains initial data such as the machine's condition and past maintenance history, and uses sentiment analysis to identify that the user is nervous.

[1192] Maintenance begins, and the server reports progress by saying, "Maintenance is underway. Please rest assured."

[1193] If additional machine information is needed, the server will politely ask, "Excuse me for asking, but could you please provide more information about your machine?"

[1194] Once the maintenance is complete, the server will report the final result, saying, "Maintenance has been completed successfully. Please rest assured."

[1195] Prompt Sentence Examples

[1196] "The maintenance assistant robot should collect information on machines that need maintenance and report the progress appropriately based on the worker's emotions."

[1197] As described above, the present invention provides a system that can efficiently advance the screening process while taking into consideration the user's feelings. This system reduces the user's anxiety and tension and improves the efficiency and reliability of the entire screening process.

[1198] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1199] Step 1:

[1200] The user inputs an audit request into the interface. The user inputs "This machine needs maintenance" and the terminal receives it. Input: User's audit request. Output: Request data sent by the terminal to the server.

[1201] Step 2:

[1202] The server passes the received review request to the natural language processing engine to begin analysis. Input: Received request data. Output: Request intent and identified emotional state. The server uses the natural language processing engine to interpret the intent of the request and the emotion analysis engine to identify the user's emotion.

[1203] Step 3:

[1204] The server retrieves initial data from the database based on the analysis results. Input: Analysis results (intention and sentiment of the request). Output: Initial data (machine status and past maintenance history). The server queries the database and retrieves the necessary initial data.

[1205] Step 4:

[1206] The server starts the review process and reports progress to the user. Input: Initial data and analysis results. Output: Progress message. The server starts the review process and notifies the user of the progress, saying "Maintenance is in progress. Please rest assured."

[1207] Step 5:

[1208] If the server needs additional information during the review process, it requests it from the user. Input: Additional information required during the review process. Output: Request message for additional information. The server sends a polite message based on the results of sentiment analysis, saying, "Excuse me for bothering you, but could you please tell me more information about your machine?"

[1209] Step 6:

[1210] The user enters additional information, which the device sends to the server. Input: Additional information provided by the user. Output: Additional information data sent to the server. The server continues the review process based on this additional information.

[1211] Step 7:

[1212] The server completes the review process and notifies the user of the final result. Input: All required information and the final result of the review process. Output: A notification message of the final result. Once the review is complete, the server notifies the user of the final result, saying "The maintenance has been completed successfully. Please rest assured," along with a follow-up message based on the sentiment analysis results.

[1213] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1214] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1215] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1216] [Fourth embodiment]

[1217] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1218] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1219] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1220] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1221] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1223] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1224] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1225] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1226] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1227] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1228] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1229] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1230] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[1231] System configuration

[1232] The system includes the following main components:

[1233] 1. User Interface

[1234] A screen or application where users can enter review requests.

[1235] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[1236] 2. Server

[1237] Receives user input and parses it using a natural language processing engine.

[1238] Based on the analysis results, the necessary initial data is retrieved from the database to begin the review process.

[1239] Updates and reports progress to the user.

[1240] Example: If the server receives a request saying "Start reviewing new customer, Taro Tanaka," it uses natural language processing to identify "Taro Tanaka" and retrieves his information from the database.

[1241] 3. Database

[1242] Stores initial data and information required for review.

[1243] The server retrieves the necessary data based on the user's input.

[1244] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[1245] 4. Reporting and Notification System

[1246] Notify users of the progress and final outcome of the review process.

[1247] If any additional information is needed, it will be requested from the user.

[1248] Example: During the screening process, you may request additional information from the user, such as "Please tell me Taro Tanaka's place of employment."

[1249] Processing flow

[1250] Receiving a review request

[1251] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[1252] Request analysis and initiation of review process

[1253] The server analyzes the received request using a natural language processing engine and retrieves the initial data required to start the review process based on the analysis results from the database, allowing the review process to proceed automatically.

[1254] Review process and reporting

[1255] The server tracks the progress of the review process and reports the status to the user as needed, for example, "Taro Tanaka's review data has been obtained. The review is in progress."

[1256] Request for additional information

[1257] If additional information is required during the review process, the server sends a request to the user, and the terminal displays it to the user. The additional information entered by the user is then sent back to the server, and the review process continues.

[1258] Completion of the review process and notification of the final result

[1259] Once all necessary information is available, the server completes the review process, generates a final review result, and notifies the user, for example, "Taro Tanaka's review has been approved."

[1260] This system will streamline the review process, allowing it to proceed quickly and accurately, minimizing human error and providing highly reliable review results.

[1261] The processing flow will be explained below.

[1262] Step 1:

[1263] A user logs in to a dedicated user interface and enters a new review request, for example, "Start a review for a new customer."

[1264] Step 2:

[1265] The terminal receives the user's input and sends it to the server, packaging the request data in an appropriate format.

[1266] Step 3:

[1267] The server receives the user's request and passes the input data to a natural language processing engine to begin parsing it, which determines the intent of the request and any initial data required.

[1268] Step 4:

[1269] Based on the analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[1270] Step 5:

[1271] The server notifies the user that the review process has started. For example, it sends a message to the terminal saying, "Taro Tanaka's initial data has been acquired. Review is in progress."

[1272] Step 6:

[1273] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the review.

[1274] Step 7:

[1275] If the server needs additional information as it goes through the screening process, it will request it from the user. For example, it may send a request such as "Please tell me Taro Tanaka's place of employment information" to the terminal.

[1276] Step 8:

[1277] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[1278] Step 9:

[1279] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[1280] Step 10:

[1281] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[1282] Step 11:

[1283] The server obtains all necessary information, completes the review process, generates a final review result (e.g., "Taro Tanaka's review has been approved"), and sends it to the device.

[1284] Step 12:

[1285] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[1286] Example 1

[1287] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1288] Traditional review processes are often manual, which not only takes time but also makes them prone to human error. Furthermore, it is difficult to report review progress in a timely manner, making it difficult for users to grasp the review progress. Furthermore, the need to obtain additional information and the generation and notification of final review results are not always smooth, which can lead to a decline in overall efficiency.

[1289] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1290] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for generating a final examination result and notifying the user, and means for conducting the examination using an automated algorithm and constantly monitoring the progress. This makes it possible to efficiently automate the examination process and quickly grasp the progress and obtain necessary additional information.

[1291] "User" means a person or entity that uses the System to enter review requests and view the progress and results of the review process.

[1292] An "interface" is a point of contact with a user, including a screen or application through which the user inputs an examination request.

[1293] A "review request" is a request or inquiry entered by a user to initiate a review.

[1294] "Server" means the central processing unit that receives and analyzes review requests and manages and executes the review process.

[1295] "Natural language processing" is a technology that analyzes language data entered by the user and understands and processes its meaning.

[1296] "Analysis results" refers to data and information obtained as a result of the natural language processing engine analyzing the review request.

[1297] The "review process" is a series of evaluations and confirmations carried out based on the analysis results.

[1298] "Progress" refers to the progress made at each stage of the review process.

[1299] "Additional Information" is supplemental information required for further evaluation during the review process.

[1300] "Final review outcome" means the conclusion or decision reached after the entire review process.

[1301] An "automated algorithm" is a program that automatically conducts the review process based on specific rules and calculation procedures.

[1302] "Monitoring" refers to the act of constantly observing and checking the progress of the review process.

[1303] The "database" is a system that stores and manages necessary initial data and information related to the review.

[1304] The present invention provides a system for efficiently automating the review process. This system receives a review request entered by a user, analyzes it using natural language processing, starts the review process based on the analysis results, and reports the progress and final results to the user.

[1305] First, the user inputs a review request using a device. For example, the user might input "Start reviewing a new customer" on a dedicated screen or application. This input is sent from the device to the server. The server then sends the received request to a natural language processing engine (e.g., Google Cloud Natural Language API) for analysis. The natural language processing engine analyzes the request and extracts keywords such as "new customer" and "review."

[1306] The server receives the analysis results and retrieves the necessary initial data from a database (e.g., MySQL or PostgreSQL). This initial data includes, for example, the customer's credit score and past transaction history. The server then starts the screening process based on the retrieved initial data and constantly monitors its progress. Specifically, the screening process progresses using an automated algorithm, and reports are sent to the user as the process progresses. For example, the server may notify the user, "Taro Tanaka's screening data has been retrieved. The screening is currently underway."

[1307] If the server needs to request additional information during the review process, the request is sent to the user and displayed on the terminal. The user enters the additional information and sends it back to the server from the terminal, allowing the server to continue the review process. Once all the necessary information is gathered, the server completes the review and generates a final review result, which is notified to the user. For example, the server may inform the user of the result, "Taro Tanaka's review has been approved."

[1308] In this way, the review process is automated efficiently and accurately, enabling faster reviews while minimizing human error.

[1309] Specific examples

[1310] For example, if a sales representative wants to begin reviewing a new customer, Taro Tanaka, the process would proceed through the following specific steps:

[1311] 1. User (sales representative): Enters "Start reviewing a new customer" into the terminal.

[1312] 2. Terminal: Sends input to the server.

[1313] 3. Server: Sends a request to the natural language processing engine for analysis.

[1314] 4. Natural language processing engine: Analyzes the request and extracts keywords such as "new customer" and "review."

[1315] 5. Server: Based on the analysis results, obtain initial data about Taro Tanaka from the database.

[1316] 6. Server: Initiates the review process based on the acquired data, monitors the progress and reports back to the user.

[1317] 7. Server: If additional information is needed, the server requests the user, "Please tell me Taro Tanaka's place of employment information."

[1318] 8. Terminal: The server's request is displayed to the user, who enters additional information and sends it back to the server.

[1319] 9. Server: Once all necessary information is gathered, the review is completed and the final result, "Taro Tanaka's review has been approved," is generated and notified to the user.

[1320] Prompt Sentence Examples

[1321] "We'll begin screening new clients."

[1322] "Please tell me Taro Tanaka's place of employment information."

[1323] "Taro Tanaka's review has been approved."

[1324] The system utilizes generative AI models to improve the accuracy of natural language processing and process prompts smoothly, making the entire screening process more efficient and enabling more reliable screening.

[1325] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1326] Step 1: User enters review request using terminal

[1327] The user enters "Start review of a new customer" into the terminal interface. This input data is sent to the server as a review request. Input: User review request (text). Output: Review request data (e.g., JSON format) from the terminal to the server.

[1328] Step 2: The device sends a request to the server

[1329] The terminal receives the user's input, converts it into an appropriate data format, and sends it to the server. For example, it packages the data in JSON format and sends it to the server using an HTTP request. Input: User's review request (text). Output: Review request received by the server (JSON format).

[1330] Step 3: The server analyzes the received request using a natural language processing engine

[1331] The server passes the received request data to a natural language processing engine for analysis. A generative AI model (e.g., Google Cloud Natural Language API) is used to extract keywords and elements of the review request. Input: Review request data received by the server (JSON format). Output: Parsed request keywords and elements (JSON format).

[1332] Step 4: The server uses the analysis results to retrieve the initial data from the database.

[1333] The server retrieves the necessary initial data from the database based on the analysis results obtained from the natural language processing engine. For example, it queries the database for the credit score and past transaction history of a customer identified as a "new customer." Input: Keywords and elements of the analysis results (JSON format). Output: Initial data (JSON format) retrieved from the database.

[1334] Step 5: The server starts the review process based on the initial data it has obtained.

[1335] The server uses an automated algorithm to start the screening process based on the initial data acquired. For example, it evaluates the credit score and analyzes past transaction history to proceed with the first stage of screening. Input: Initial data (JSON format). Output: Progress of the screening process (JSON format).

[1336] Step 6: The server constantly monitors the progress of the review and reports it to the user.

[1337] The server monitors the progress of the review process in real time and notifies the user of the progress. For example, it sends a message to the user saying, "Taro Tanaka's review data has been obtained. The review is in progress." Input: Progress of the review process (JSON format). Output: Progress notification to the user (text).

[1338] Step 7: Server requests additional information

[1339] If additional information is required during the review, the server sends a request to the user and displays it on the terminal. For example, it sends a message saying "Please tell me Taro Tanaka's place of employment." Input: Progress of the review process (JSON format). Output: Request for additional information from the user (text).

[1340] Step 8: User enters additional information and submits it to the server

[1341] The user checks the request from the server on the device and enters additional information. For example, they enter "Taro Tanaka's workplace information" and send it from the device to the server. Input: User's additional information (text). Output: Additional information sent to the server (JSON format).

[1342] Step 9: The server verifies all necessary information and completes the verification

[1343] The server confirms that all necessary information is gathered and completes the final review process. It generates a review result and notifies the user of the final result. For example, it generates a result such as "Taro Tanaka's review has been approved." Input: All necessary information (JSON format). Output: Final review result (text).

[1344] Step 10: The server notifies the user of the final result

[1345] The server notifies the user of the generated final review result. For example, it may notify the user of the result, "Taro Tanaka's review has been approved." Input: Final review result (text). Output: Notification of the final result to the user (text).

[1346] (Application example 1)

[1347] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1348] Traditional visitor authentication systems require manual verification, which is inefficient and has accuracy issues. While there is a need to automate the process of instantly reviewing and authenticating visitor identities, no effective system exists to achieve this. This has prevented improvements in security and operational efficiency from being achieved.

[1349] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1350] In this invention, the server includes means for receiving an examination request entered by a user into an interface, means for analyzing the received examination request using natural language processing and starting the examination process based on the analysis result, means for reporting the progress of the examination process to the user, means for requesting additional information from the user if necessary during the examination process, means for completing the examination process and notifying the user of the final examination result, means for photographing the visitor's face with a camera and obtaining the visitor's name by voice input to authenticate the visitor's identity, and means for comparing the photographed face information and the voice-input name with information in a database to examine the visitor's identity. This makes it possible to automate the visitor authentication process and perform examination in real time.

[1351] The "user interface" refers to an input device and a screen through which a user inputs an examination request.

[1352] A "review request" is a review requirement or inquiry submitted by a user to the system.

[1353] "Natural language processing" is a technology that allows a computer to understand text entered by a user and is a means of analyzing and generating responses.

[1354] A "review process" is a series of analysis, judgment, and review actions that the system performs based on a received review request.

[1355] "Progress" means the current status or progress at each stage of the review process.

[1356] "Reporting means" refers to the means by which the system notifies the user of the progress and results of the review.

[1357] "Additional Information" means information not provided in the initial input that is necessary to continue or complete the review process.

[1358] The "notification means" is a means of communication for informing the user of the final review result.

[1359] "Authentication Method" means the method used to verify and authenticate a visitor's identity.

[1360] A "camera" is an image capturing device for capturing images of visitors' faces.

[1361] The "voice input means" is a means for inputting the name of the visitor by voice.

[1362] The "verification means" is a means for comparing the acquired face information and the voice-input name with existing information in a database.

[1363] "Database" means a digital recording device for storing and managing information necessary for visitor screening.

[1364] This invention relates to a visitor authentication system, which provides a series of means for receiving an examination request entered by a user, analyzing it using natural language processing, and reporting the progress and final results of the examination process to the user. Furthermore, the invention aims to efficiently authenticate visitors by verifying their identity using a visitor's facial image and voice input and comparing it with information in a database.

[1365] System configuration

[1366] User Interface

[1367] Provide an input device and screen for users to input review requests, such as a smartphone or smart glasses.

[1368] server

[1369] The server receives the review request sent by the user and analyzes it using natural language processing (NLP). Based on the analysis results, it retrieves the necessary data from the database and starts the review process. The server reports the review progress and final results to the user, and requests additional information if necessary.

[1370] Database

[1371] It stores visitor information (such as facial images, names, and past screening history) and provides the necessary information in response to data matching requests from the server.

[1372] Reporting method

[1373] Smartphones and notification systems are used to notify users of the progress and results of the review.

[1374] Hardware and software used

[1375] camera

[1376] It is used as an image capture device to capture the visitor's face, for example, a built-in camera in a smartphone or smart glasses.

[1377] microphone

[1378] Used as a device for voice input of the visitor's name, such as the built-in microphone of a smartphone or smart glasses.

[1379] Natural Language Processing Engine

[1380] The software used is a natural language processing engine such as SpaCy.

[1381] Facial Recognition Software

[1382] Facial images are captured using image processing libraries such as OpenCV and matched with a database.

[1383] Notification System

[1384] The notification function of smartphones is used as a means of notifying users of the review results and progress in real time.

[1385] Specific examples of processing

[1386] Example 1: Visitor authentication

[1387] 1. The user takes a photo of the visitor's face with their smartphone camera and then voice-inputs the visitor's name following the prompt, "Hello, please enter the visitor's name by voice."

[1388] 2. The server applies natural language processing to the facial image and voice-input name to retrieve relevant information from a database and verify the visitor's identity.

[1389] 3. Within a few seconds, a notification will appear on your smartphone saying, "Visitor authentication completed. The review result is approved."

[1390] Prompt Sentence Examples

[1391] "Hello, please speak your visitor's name."

[1392] The system automates the visitor authentication process and enables real-time screening, improving security and operational efficiency.

[1393] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1394] Step 1:

[1395] The user takes a photo of the visitor's face with a camera and inputs the visitor's name by voice into the interface. The input at this time is the face image acquired from the camera and the voice data acquired from the microphone. These data are sent to the terminal, which temporarily stores them.

[1396] Step 2:

[1397] The facial image and audio data stored on the device are sent to the server. The server first processes the facial image using OpenCV to extract facial features. The input is the facial image and the output is facial feature data. At the same time, the server converts the audio data into text using the SpeechRecognition library. The input is audio data and the output is audio text.

[1398] Step 3:

[1399] The server analyzes the acquired text data using a natural language processing (NLP) engine (e.g., SpaCy). The input is speech text, and the output is analyzed name data. The server uses the analyzed name data and facial feature data to match it with registration information in a database. The input is facial feature data and name data, and the output is the visitor's identity information.

[1400] Step 4:

[1401] The server starts the screening process based on the visitor's identity information obtained from the database. Initial data acquisition and analysis is performed, which includes acquiring and analyzing necessary data. The input is facial feature data and name data, and the output is the screening result.

[1402] Step 5:

[1403] The server tracks the progress of the verification process and reports the results to the user's device. For example, it sends a notification saying, "Visitor authentication has been completed. The verification result is approved." The input is the verification result, and the output is a notification to the user's device.

[1404] Step 6:

[1405] If additional information is required during the review process, the server will request that information from the user. The user inputs the requested additional information and sends it from their terminal to the server. The input is the requested additional information, and the output is the receipt of the additional information.

[1406] Step 7:

[1407] The server receives the additional information and starts the review process again, reacquiring and analyzing the necessary data. The input is the additional information, and the output is the updated review result.

[1408] Step 8:

[1409] Once the final review result is obtained, the server notifies the user of the result. For example, it sends a notification to the user's terminal saying, "Visitor authentication was denied." The input is the final review result, and the output is the final notification to the user's terminal.

[1410] This series of steps makes the visitor authentication process automated, secure, and efficient.

[1411] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1412] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[1413] System configuration

[1414] The system includes the following main components:

[1415] 1. User Interface

[1416] A screen or application where users can enter review requests.

[1417] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[1418] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[1419] 2. Server

[1420] It receives user input and passes the input data to the natural language processing engine and emotion engine to begin analysis. Analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[1421] Initiate the review process and report progress to the user accordingly.

[1422] If additional information is required, it will be requested from the user at the appropriate time.

[1423] Once the review process is complete, the user will be notified of the final outcome.

[1424] Example: If the server receives a request saying "Starting the review of new customer, Taro Tanaka," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[1425] 3. Database

[1426] Stores initial data and information required for review.

[1427] The server retrieves the necessary data based on the user's input.

[1428] Example: Accumulate credit scores and past transaction history for "Taro Tanaka" and provide them as needed.

[1429] 4. Reporting and Notification System

[1430] Notify users of the progress and final outcome of the review process.

[1431] If any additional information is needed, it will be requested from the user.

[1432] Make appropriate reports or requests based on the results of sentiment analysis.

[1433] Example: If a request such as "Please tell me Taro Tanaka's place of employment" is required during the screening process, display a message according to the user's emotions.

[1434] Specific examples

[1435] Receiving and analyzing review requests

[1436] The terminal receives the review request input by the user and sends it to the server, for example, a request saying "Please start reviewing a new customer."

[1437] The server receives the request and begins analyzing it with the natural language processing engine and emotion engine. The natural language processing engine interprets the intent of the request as "starting a new customer screening," and the emotion engine identifies the user as "nervous."

[1438] Obtaining initial data and beginning the review process

[1439] Based on the analysis results, the server retrieves initial data about "Taro Tanaka" (e.g., basic information, credit score) from the database and prepares to begin the screening process.

[1440] Progress Report

[1441] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." It also sends a follow-up message based on the results of the emotion engine, saying, "Don't worry, we will let you know the results soon."

[1442] The terminal receives progress messages from the server and displays them to the user, allowing the user to check the progress of the screening and reduce anxiety.

[1443] Requesting and Receiving Additional Information

[1444] During the review process, if the server needs to request more information about "Taro Tanaka's place of employment," it will send a request for additional information to the terminal. Based on the results of the emotion engine, it will display a polite message such as, "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?"

[1445] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[1446] Completion of the review process and notification of the final result

[1447] The server receives any additional information sent by the user and updates / continues the review process accordingly. Once all necessary information is available, the server completes the review.

[1448] The server generates the final review result and sends it to the device. For example, along with the result "Taro Tanaka's review has been approved," the emotion engine sends a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[1449] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[1450] The processing flow will be explained below.

[1451] Step 1:

[1452] A user logs in to a dedicated user interface and enters a new review request. For example, they might enter, "I'd like to begin reviewing a new customer." At this time, the emotion engine analyzes the user's input, facial expressions, tone of voice, etc. to identify the user's emotions.

[1453] Step 2:

[1454] The device receives the user's input and the emotion analysis results, and sends them to the server, packaging the request data and emotion data in an appropriate format.

[1455] Step 3:

[1456] The server receives a user request and passes the input data to a natural language processing engine to begin analysis. The analysis identifies the intent of the request and the initial data required. Sentiment analysis results are also taken into account.

[1457] Step 4:

[1458] Based on the analysis results and sentiment analysis results, the server retrieves the necessary initial data (e.g., customer information, credit score) from the database and prepares to start the screening process.

[1459] Step 5:

[1460] The server notifies the user that the review process has begun. For example, it sends a message to the device saying, "We have acquired Taro Tanaka's initial data. The review is in progress." Based on the results of the emotion engine, it also sends a reassuring message such as, "Don't worry, we will let you know the results soon."

[1461] Step 6:

[1462] The terminal receives progress messages from the server and displays them to the user, who can check the progress of the screening and feel reassured and understood by the emotionally appropriate messages.

[1463] Step 7:

[1464] As the server progresses through the review process, if additional information becomes necessary, it will request it from the user. For example, a request such as "Please tell me Taro Tanaka's place of employment information" is sent to the terminal. Based on the results of the emotion engine, a polite message such as "Excuse me for bothering you, but could you please tell me Taro Tanaka's place of employment information?" is displayed.

[1465] Step 8:

[1466] The terminal displays the server's request for additional information to the user, and the user inputs the required information.

[1467] Step 9:

[1468] The user enters the necessary additional information, which is received by the terminal and sent to the server. For example, the user enters "Taro Tanaka works for ABC Co., Ltd."

[1469] Step 10:

[1470] The server receives any additional information sent by the user and updates and continues the review process accordingly.

[1471] Step 11:

[1472] The server obtains all necessary information and completes the review process. It generates a final review result (e.g., "Taro Tanaka's review has been approved") and sends it to the device. It also sends a follow-up message based on the emotion engine, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully."

[1473] Step 12:

[1474] The terminal receives the final result from the server and notifies the user, who can then check the result and take further action based on the result.

[1475] Example 2

[1476] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1477] In the conventional review process, the user's emotions are not taken into consideration when analyzing the request entered by the user, which often leads to anxiety and dissatisfaction. Furthermore, there are cases where progress reports and requests for additional information are not properly made, which reduces the efficiency of the entire review process. Furthermore, there is a need for a method to improve the user experience by communicating with the user in a way that takes emotions into consideration during the review process.

[1478] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1479] In this invention, the server includes means for receiving a review request input by a user to an interface, means for analyzing the received review request using a natural language processing engine and starting a review process based on the analysis result and the result of the sentiment analysis engine, means for reporting the progress of the review process to the user, means for requesting additional information from the user if necessary during the review process, means for sending an appropriate follow-up message to the user based on the sentiment analysis result during the review process, and means for completing the review process and notifying the user of the final review result, thereby enabling an efficient and effective review process while taking user sentiment into consideration.

[1480] An "interface" is a screen or application through which a user inputs a review request.

[1481] A "server" is a central processing unit that receives user input data and performs the analysis and review process.

[1482] A "natural language processing engine" is a software system that analyzes text data entered by a user and interprets their intent.

[1483] An "emotion analysis engine" is a software system that identifies emotions from user input and responses.

[1484] "Analysis results" are information regarding the intent of a review request obtained by a natural language processing engine.

[1485] "Emotion analysis results" are information about the user's emotions obtained by the emotion analysis engine.

[1486] A "review process" is a series of evaluation and judgment steps based on the content of a review request.

[1487] "Progress" is information about the progress of the review process.

[1488] "Additional Information" means any additional data or details required to continue or complete the review process.

[1489] A "follow-up message" is a message of comfort or reassurance that is generated based on the user's emotional state.

[1490] "Final Review Result" means the final evaluation and judgment conclusion reached after the review process is completed.

[1491] "Data storage" means a database or other storage device for storing information necessary for the screening process.

[1492] The present invention provides a system that efficiently automates the review process and takes user sentiment into consideration. This system receives a review request entered by a user, analyzes it using natural language processing, initiates the review process based on the analysis results and sentiment analysis results, and reports the progress and final results to the user.

[1493] System configuration

[1494] The system includes the following main components:

[1495] 1. User Interface

[1496] Use a screen or application that allows users to enter review requests.

[1497] When a user enters a review request, an emotion engine (e.g., IBM Watson Tone Analyzer) is used to recognize the user's emotion.

[1498] Example: A sales representative starts reviewing a new customer by typing "Start reviewing a new customer."

[1499] 2. Server

[1500] It receives user input and passes the input data to a natural language processing engine (e.g., Google's Natural Language API) and an emotion engine to begin parsing it. The analysis determines the intent of the request and the user's emotion, and retrieves the necessary initial data from data storage (e.g., MySQL, PostgreSQL).

[1501] Initiate the review process and report progress to the user accordingly.

[1502] If additional information is required, it will be requested from the user at the appropriate time.

[1503] Once the review process is complete, the user will be notified of the final outcome.

[1504] Example: If the server receives a request saying "We will begin reviewing a new customer, Mr. A," and the emotion engine identifies that the user is feeling anxious, it generates a reassuring message.

[1505] 3. Data Storage

[1506] Stores initial data and information required for review.

[1507] The server retrieves the necessary data based on the user's input.

[1508] Example: Accumulate credit scores and past transaction history for "Mr. A" and provide them as needed.

[1509] 4. Reporting and Notification System

[1510] Notify users of the progress and final outcome of the review process.

[1511] If any additional information is needed, it will be requested from the user.

[1512] Make appropriate reports or requests based on the results of sentiment analysis.

[1513] Example: If a request such as "Please tell me Mr. A's place of employment information" is required during the screening process, a message appropriate to the user's emotions will be displayed.

[1514] Specific examples

[1515] Receiving and analyzing review requests

[1516] The terminal receives an examination request entered by the user and sends it to the server. For example, a request may be sent saying, "Please start an examination of a new customer." The server receives the request and begins analyzing it using a natural language processing engine and an emotion engine. Natural language processing interprets the intent of the request as "start an examination of a new customer," and the emotion engine identifies the user as "nervous."

[1517] Review process and reporting

[1518] Based on the analysis results, the server retrieves initial data about "Mr. A" (e.g., basic information, credit score) from data storage and prepares to start the screening process. It then reports the start of the screening process to the user. For example, it sends a message to the device saying, "We have retrieved Mr. A's initial data. The screening is in progress." Based on the results of the emotion engine, it sends a follow-up message saying, "Don't worry, we will report the results to you soon." The device receives progress messages from the server and displays them to the user.

[1519] Requesting and Receiving Additional Information

[1520] During the review process, if the server needs to request more information about "Mr. A's place of employment," it sends a request for the additional information to the terminal. Based on the results of the emotion engine, it generates and displays a polite message, such as "Sorry to trouble you, but could you please tell me Mr. A's place of employment information?" The user enters the required additional information, which is then received by the terminal and sent to the server. For example, the user might enter "Mr. A works for X Co., Ltd."

[1521] Completion of the review process and notification of the final result

[1522] The server receives additional information sent by the user and updates and continues the review process based on that information. Once all necessary information is collected, the server completes the review. The server generates the final review result and sends it to the device. For example, along with the result "Mr. A's review has been approved," the emotion engine may send a follow-up message such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The device receives the final review result from the server and notifies the user. The user can check the final result and take further action based on the results.

[1523] Real-life examples and prompts

[1524] Examples:

[1525] Imagine a process that involves receiving a new customer review request, analyzing and reporting progress, requesting additional information as needed, and reporting the final results.

[1526] Example prompt sentence:

[1527] "Start screening new clients and report back on progress and final results."

[1528] "Please politely request any additional information you need during the review process and communicate the final review result sensitively."

[1529] Based on this explanation, it becomes clear how the system works, and the roles of the user, server, and terminal can be concretely understood. In addition, by specifying the specific names of the hardware and software used, the specific steps for implementing the invention become clear.

[1530] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1531] Step 1:

[1532] The user inputs a review request into the interface. For example, they input "I would like to start reviewing a new customer." This input is sent to the terminal. The terminal then sends the input request to the server. The input data also includes the user's emotional information (e.g., facial expression analysis and voice analysis data).

[1533] Step 2:

[1534] The server analyzes the review request and emotional information received from the device. A natural language processing engine (e.g., Google's Natural Language API) and an emotional analysis engine (e.g., IBM Watson Tone Analyzer) are used for the analysis. The input data (review request) is passed to the natural language processing engine, which extracts the intent of "starting a new customer review." At the same time, the emotional information is passed to the emotional analysis engine, which identifies the user as "nervous." The output is the analysis results and the emotional analysis results.

[1535] Step 3:

[1536] The server retrieves initial data from data storage based on the analysis results. In this process, a query is sent to a database (e.g., MySQL, PostgreSQL) to obtain basic information and a credit score for the new customer being screened. The input is the analysis results and a query to the data storage, and the output is the retrieved initial data.

[1537] Step 4:

[1538] The server prepares to start the review process. Based on the acquired data, review items and procedures are automatically assigned. Data indicating that the review process is ready to start is generated. The input is the initial data, and the output is data indicating that the review process is ready to start.

[1539] Step 5:

[1540] The server reports the progress of the review process to the user. In this process, the server uses a notification API to send a message to the device, such as "We have obtained the initial data of the new customer. The review is in progress." Based on the sentiment analysis results, the server also generates and sends a follow-up message, such as "Don't worry, we will let you know the review results soon." The inputs are the progress data and the sentiment analysis results, and the outputs are the progress message and the follow-up message.

[1541] Step 6:

[1542] The server requests the user for any additional information they require. For example, if it is necessary to request "employment information" during the screening process, it sends a polite message to the terminal saying, "Sorry to trouble you, but please tell us your employment information." The input is the need for the additional information request and the result of sentiment analysis, and the output is the additional information request message.

[1543] Step 7:

[1544] The user uses the terminal to enter additional information, for example, "I work for Company X." The terminal receives this information and sends it to the server. The input is the user's additional information, and the output is the transmission of data from the terminal to the server.

[1545] Step 8:

[1546] The server receives the additional information sent by the user and updates and continues the review process. It analyzes the additional information entered and reflects it in the review process. The input is the user's additional information, and the output is the updated review process data.

[1547] Step 9:

[1548] The server generates the final review result. It evaluates all the information in the review process and makes a final decision. For example, it generates a result such as "the review is approved." The input is all the review process data, and the output is the final review result data.

[1549] Step 10:

[1550] The server notifies the user of the final review result. In this process, a notification API is used to send a message to the device, such as "The review has been approved." Based on the sentiment analysis results, a follow-up message is also sent, such as "Sorry to keep you waiting. Please rest assured that the review has been completed successfully." The input is the final review result data and the sentiment analysis results, and the output is the final review result notification and the follow-up message.

[1551] In this way, an efficient and emotionally sensitive screening process is realized through each processing step at the server, terminal, and user.

[1552] (Application example 2)

[1553] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1554] In conventional screening processes, the process proceeds without taking the user's emotions into consideration, which can make the user feel anxious or tense. This leads to a decrease in user satisfaction and a decrease in trust in the screening process. Furthermore, when requesting additional information, the burden felt by the user and unclear communication can cause delays in the process. To solve these issues, a system that analyzes the user's emotions and responds appropriately is needed.

[1555] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving a review request entered by a user into an interface; means for analyzing the received review request using natural language processing and starting a review process based on the analysis results and sentiment analysis results; means for reporting the progress of the review process to the user and providing a message appropriate to the user's situation based on the sentiment analysis results; means for making polite requests to the user based on the sentiment analysis results when additional information is needed during the review process; and means for completing the review process and notifying the user of the final review result together with an appropriate follow-up message based on the sentiment analysis results. This makes it possible to proceed with the review process while taking the user's emotions into consideration, reduce the anxiety and tension felt by the user, and improve the efficiency and reliability of the entire review process.

[1556] A "user" is a person who enters a review request into the interface and sends and receives information related to the review process.

[1557] An "interface" is an input or display device that allows a user to input a review request.

[1558] An "audit request" is a request sent by a user to a server through an interface to initiate an audit.

[1559] "Natural language processing" is a technology that allows computers to understand and analyze the language that humans use on a daily basis.

[1560] "Emotion analysis" is a technology that analyzes and identifies a user's emotional state from their input.

[1561] The "review process" refers to a series of processes from when the server receives a review request to when it notifies the user of the final result.

[1562] "Status" refers to the current status at each stage of the review process.

[1563] "Message" refers to text or information used to notify users of progress in the review process, requests for additional information, etc.

[1564] "Additional Information" is any additional information the server requests from the user to continue the review process.

[1565] "Results" or "Review Results" means information that informs the user of the final judgment or conclusion of the review process.

[1566] "Database" means a digital storage system for storing the initial data and history required for the review process.

[1567] A "follow-up message" is a message that provides comfort and reassurance to a user as the review process progresses or is completed, taking into account the user's emotional state.

[1568] The present invention is a system for automating a review process that takes into account the emotions of users. This system receives review requests entered by users through an interface and advances the review process while performing natural language processing and emotion analysis. Details are described below.

[1569] System configuration

[1570] The system includes the following main components:

[1571] 1. User Interface

[1572] A screen or application for users to enter review requests.

[1573] When a user enters a review request, an emotion engine is used to recognize the user's emotion.

[1574] 2. Server

[1575] The server receives the user's input and passes it to a natural language processing engine (e.g., Google Cloud NLP) and an emotion engine (e.g., IBM Watson Tone Analyzer) to begin analysis. The analysis identifies the intent of the request and the user's emotion, and retrieves the necessary initial data from the database.

[1576] Initiate the review process and keep you informed of progress. Request any additional information you require from the user in a timely manner.

[1577] After the review process is complete, the final result will be communicated to the user along with a follow-up message based on the sentiment analysis results.

[1578] 3. Database

[1579] The database stores initial data and information required for review.

[1580] The server retrieves the necessary data based on the user's input.

[1581] 4. Reporting and Notification System

[1582] Inform users about the progress and final outcome of the review process, and politely request any additional information required based on sentiment analysis results.

[1583] Example of operation

[1584] An example of the system operation is shown below.

[1585] The worker (user) speaks to the maintenance assistant robot, saying, "This machine needs maintenance."

[1586] The server analyzes the request, obtains initial data such as the machine's condition and past maintenance history, and uses sentiment analysis to identify that the user is nervous.

[1587] Maintenance begins, and the server reports progress by saying, "Maintenance is underway. Please rest assured."

[1588] If additional machine information is needed, the server will politely ask, "Excuse me for asking, but could you please provide more information about your machine?"

[1589] Once the maintenance is complete, the server will report the final result, saying, "Maintenance has been completed successfully. Please rest assured."

[1590] Prompt Sentence Examples

[1591] "The maintenance assistant robot should collect information on machines that need maintenance and report the progress appropriately based on the worker's emotions."

[1592] As described above, the present invention provides a system that can efficiently advance the screening process while taking into consideration the user's feelings. This system reduces the user's anxiety and tension and improves the efficiency and reliability of the entire screening process.

[1593] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1594] Step 1:

[1595] The user inputs an audit request into the interface. The user inputs "This machine needs maintenance" and the terminal receives it. Input: User's audit request. Output: Request data sent by the terminal to the server.

[1596] Step 2:

[1597] The server passes the received review request to the natural language processing engine to begin analysis. Input: Received request data. Output: Request intent and identified emotional state. The server uses the natural language processing engine to interpret the intent of the request and the emotion analysis engine to identify the user's emotion.

[1598] Step 3:

[1599] The server retrieves initial data from the database based on the analysis results. Input: Analysis results (intention and sentiment of the request). Output: Initial data (machine status and past maintenance history). The server queries the database and retrieves the necessary initial data.

[1600] Step 4:

[1601] The server starts the review process and reports progress to the user. Input: Initial data and analysis results. Output: Progress message. The server starts the review process and notifies the user of the progress, saying "Maintenance is in progress. Please rest assured."

[1602] Step 5:

[1603] If the server needs additional information during the review process, it requests it from the user. Input: Additional information required during the review process. Output: Request message for additional information. The server sends a polite message based on the results of sentiment analysis, saying, "Excuse me for bothering you, but could you please tell me more information about your machine?"

[1604] Step 6:

[1605] The user enters additional information, which the device sends to the server. Input: Additional information provided by the user. Output: Additional information data sent to the server. The server continues the review process based on this additional information.

[1606] Step 7:

[1607] The server completes the review process and notifies the user of the final result. Input: All required information and the final result of the review process. Output: A notification message of the final result. Once the review is complete, the server notifies the user of the final result, saying "The maintenance has been completed successfully. Please rest assured," along with a follow-up message based on the sentiment analysis results.

[1608] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1609] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1610] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1611] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1612] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1613] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1614] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1615] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1616] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1617] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1618] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1619] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1620] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1621] 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.

[1622] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1623] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1624] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1625] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1626] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1627] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1628] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1629] The following is further disclosed regarding the above embodiment.

[1630] (Claim 1)

[1631] means for receiving a review request entered by a user into the interface;

[1632] A means for analyzing the received review request using natural language processing and starting a review process based on the analysis result;

[1633] a means of reporting the progress of the review process to the user;

[1634] a means to prompt the user for additional information during the review process if needed;

[1635] a means for completing the review process and notifying the user of the final review result;

[1636] A system including:

[1637] (Claim 2)

[1638] 10. The system of claim 1, further comprising means for obtaining initial data from the database based on the analysis results before the start of the review process.

[1639] (Claim 3)

[1640] 10. The system of claim 1, further comprising means for receiving additional information submitted by the user to continue the review process.

[1641] "Example 1"

[1642] (Claim 1)

[1643] means for receiving a review request entered by a user into the interface;

[1644] A means for analyzing the received review request using natural language processing and starting a review process based on the analysis result;

[1645] a means of reporting the progress of the review process to the user;

[1646] a means to prompt the user for additional information during the review process if needed;

[1647] A means for generating a final review result and notifying the user;

[1648] A means of constantly monitoring progress through automated algorithmic review;

[1649] A system including:

[1650] (Claim 2)

[1651] 10. The system of claim 1, further comprising means for obtaining initial data from the database based on the analysis results before the start of the review process.

[1652] (Claim 3)

[1653] 10. The system of claim 1, further comprising means for receiving additional information submitted by the user to continue the review process.

[1654] "Application Example 1"

[1655] (Claim 1)

[1656] means for receiving a review request entered by a user into the interface;

[1657] A means for analyzing the received review request using natural language processing and starting a review process based on the analysis result;

[1658] a means of reporting the progress of the review process to the user;

[1659] a means to prompt the user for additional information during the review process if needed;

[1660] a means for completing the review process and notifying the user of the final review result;

[1661] a means for capturing a face image with a camera to authenticate the visitor's identity and obtaining the visitor's name through voice input;

[1662] A means for verifying identity by comparing photographed facial information and voice-inputted name with information in a database;

[1663] A system including:

[1664] (Claim 2)

[1665] 10. The system of claim 1, further comprising means for obtaining initial data from the database based on the analysis results before the start of the review process.

[1666] (Claim 3)

[1667] 10. The system of claim 1, further comprising means for receiving additional information submitted by the user to continue the review process.

[1668] "Example 2: Combining Emotion Engines"

[1669] (Claim 1)

[1670] means for receiving a review request entered by a user into the interface;

[1671] A means for analyzing the received review request using a natural language processing engine and starting a review process based on the analysis result and the result of the sentiment analysis engine;

[1672] a means of reporting the progress of the review process to the user;

[1673] a means to prompt the user for additional information during the review process if needed;

[1674] a means for sending appropriate follow-up messages to users based on the results of sentiment analysis during the review process;

[1675] a means for completing the review process and notifying the user of the final review result;

[1676] A system including:

[1677] (Claim 2)

[1678] 10. The system of claim 1, further comprising means for retrieving initial data from the data storage based on the analysis results before the start of the review process.

[1679] (Claim 3)

[1680] 10. The system of claim 1, further comprising means for receiving additional information submitted by the user to continue and update the review process.

[1681] "Application example 2 when combining emotion engines"

[1682] (Claim 1)

[1683] means for receiving a review request entered by a user into the interface;

[1684] A means for analyzing the received review request using natural language processing and starting a review process based on the analysis result and the sentiment analysis result;

[1685] a means for reporting the progress of the review process to the user and providing a message appropriate to the user's situation based on the result of sentiment analysis;

[1686] a means of politely requesting additional information from the user during the review process based on the sentiment analysis results;

[1687] means for completing the review process and notifying the user of the final review result with an appropriate follow-up message based on the sentiment analysis results;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, further comprising: means for obtaining initial data from the database based on the analysis results before the start of the review process; and means for preparing to start the review process.

[1691] (Claim 3)

[1692] 10. The system of claim 1, further comprising means for receiving additional information submitted by the user to update and continue the review process. [Explanation of symbols]

[1693] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a review request entered by a user into the interface; A means for analyzing the received review request using natural language processing and starting a review process based on the analysis result; a means of reporting the progress of the review process to the user; a means to prompt the user for additional information during the review process if needed; a means for completing the review process and notifying the user of the final review result; A system including:

2. 10. The system of claim 1, further comprising means for obtaining initial data from the database based on the analysis results before the start of the screening process.

3. 10. The system of claim 1, further comprising means for receiving additional information submitted by the user to continue the review process.

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