System

Abstract

To provide a system for avoiding a risk that an offensive expression or a non - assertive expression is included in text-based communication.SOLUTION: Selecting, by a user, an assertive mode on a setting screen of a communication tool, transmitting, by the user, an assertive message to a server in real time, analyzing, by the server, the input message to detect an offensive expression or a non-input expression, generating, by the server, a assertive proposed modification based on the detected expression, transmitting, by the server, the generated proposed modification to the user, displaying, by the server, the proposed modification to the user, confirming and adopting, by the user, a feedback to the server, and updating, by the server, a learning model based on the feedback; Means for reflecting in future revision suggestion generation.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] In modern business, text-based communication is on the rise, but it's often difficult to convey the nuances behind the text. As a result, there's a risk of aggressive or non-assertive language being included, which can lead to miscommunication and friction between employees, resulting in decreased productivity and labor issues. Technological solutions are needed to solve this problem and ensure smooth communication. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a means for a user to select assertive mode in a communication tool and a means for a terminal to transmit input messages to a server in real time. Furthermore, the server includes a means for analyzing received messages and detecting aggressive or non-assertive expressions, and a means for generating assertive revision suggestions based on the detected expressions. The system also includes a means for transmitting the generated revision suggestions to the terminal, which then displays the revision suggestions to the user. The user can confirm and adopt the revision suggestions and send feedback to the server. The server then updates the learning model based on the feedback and reflects this in future revision suggestions, thereby supporting appropriate communication. The system also provides a means for the server to analyze the user's past communication logs and learn the user's unique expressions, phrases, and sentence logic, enabling assertive suggestions tailored to the user. To ensure security, the system also provides a means for the terminal to apply encryption technology when transmitting the user's input messages to the server in real time.

[0006] The following are definitions of important terms contained in the claims.

[0007] The "assertive mode" is a mode in which aggressive or non-assertive expressions are converted into assertive expressions in order to facilitate smooth communication between users.

[0008] A "terminal" is a device used by a user, such as a computer or smartphone, that communicates with a server.

[0009] A "server" is a computer system that provides the computational resources to analyze user-entered messages and generate suggested revisions.

[0010] "Real-time" means that the processing is almost instantaneous after the user inputs a message.

[0011] A "message" is text-based information that a user sends through a communication tool.

[0012] "Offensive language" refers to words or phrases that may provoke hostile, critical or negative feelings towards another person.

[0013] "Non-assertive expression" refers to expressions that do not adequately convey one's opinions or feelings, and are overly modest and lack assertiveness.

[0014] "Repair" refers to a server-generated suggestion that transforms aggressive or non-assertive language into assertive language.

[0015] "Feedback" is information that the user sends to the server about the results of applying suggested corrections, in order to improve the system's learning model.

[0016] A "learning model" is a combination of algorithms and data that the server uses to analyze future messages and suggest corrections based on user communication logs and feedback.

[0017] "Encryption technology" is a technology that converts message data to be sent into a format that cannot be deciphered by third parties, and is a means of ensuring the security of communications.

[0018] A "communication log" is a record of messages sent and received by a user in the past, and is data used by the server for analysis and learning. [Brief explanation of the drawings]

[0019] [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 illustrating 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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention is a system that allows users to use assertive expressions in communication tools to promote smooth communication. The main elements that make up the system are the user's terminal, a server, and the communication that takes place between them.

[0041] Basic system configuration

[0042] 1. User Settings

[0043] User: Log in to the communication tool they use and select "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[0044] 2. Enter and send a message

[0045] User: Enter text in the message input field.

[0046] Terminal: Messages entered by the user are sent to the server in real time. Data security is ensured during transmission using encryption technology.

[0047] 3. Message Analysis

[0048] Server: Analyzes received messages and detects aggressive or non-assertive expressions. Natural language processing technology is used for the analysis. For example, it analyzes a message such as "Please correct this report immediately. There are too many mistakes."

[0049] 4. Generate correction suggestions

[0050] Server: Generate assertive revision suggestions based on the detected expressions. In the above example, the server generates a revision suggestion such as "Could you please revise this report? I found some mistakes, so I would like you to check them."

[0051] 5. Submitting and Viewing Revisions

[0052] Server: Sends the generated correction proposal to the device.

[0053] Terminal: A suggested fix will pop up above the message field.

[0054] 6. User Verification and Adoption

[0055] User: Review the suggested fixes and adopt them as needed, or make minor adjustments to the suggested fixes.

[0056] 7. Feedback and Learning

[0057] Server: Records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis.

[0058] Specific examples

[0059] Example 1: Requesting a report revision

[0060] User: Type "Please fix this report immediately. There are too many mistakes."

[0061] Terminal: Sends the entered message to the server.

[0062] Server: Detects offensive language and generates a suggested correction, such as: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0063] Terminal: Display suggested fixes to the user.

[0064] User: Review and adopt the proposed fix.

[0065] Example 2: Checking project progress

[0066] User: Type "No progress report yet. Please let me know soon."

[0067] Terminal: Sends the entered message to the server.

[0068] Server: Detects offensive language and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[0069] Terminal: Display suggested fixes to the user.

[0070] User: Review and adopt the proposed fix.

[0071] In this way, by implementing the system according to the embodiment of the present invention, users can communicate smoothly.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[0075] Step 2:

[0076] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[0077] Step 3:

[0078] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[0079] Step 4:

[0080] User: Enter text into the message input field in the communications tool.

[0081] For example: "Please fix this report immediately. There are too many mistakes."

[0082] Step 5:

[0083] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[0084] Step 6:

[0085] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[0086] Step 7:

[0087] Server: Generates correction suggestions to convert the detected expressions into assertive expressions.

[0088] For example: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0089] Step 8:

[0090] Server: Sends the generated correction proposal to the device.

[0091] Step 9:

[0092] Terminal: A suggested fix will pop up over the message input field.

[0093] Step 10:

[0094] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[0095] Step 11:

[0096] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[0097] Step 12:

[0098] Server: Updates the learning model based on feedback and learns the user's unique expressions, phrases, and sentence logic on a daily basis.

[0099] The above are the specific processing steps in this system.

[0100] Example 1

[0101] 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."

[0102] In modern communication tools, messages sent by users often cause misunderstandings and conflicts. In particular, when aggressive or non-assertive expressions are included, smooth communication becomes difficult. There is a need for a method to solve this problem and enable users to communicate more constructively and amicably.

[0103] 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.

[0104] In this invention, the server includes means for analyzing received communications and detecting aggressive or non-assertive expressions, means for using a generative AI model to generate assertive revision suggestions based on the detected expressions, and means for updating the machine learning model based on feedback and reflecting the feedback in generating future revision suggestions, thereby enabling users to have more constructive and friendly communications.

[0105] 1. A "communication tool" is a system that includes application software and hardware that allows users to exchange messages with other users.

[0106] 2. "Assertive Mode" refers to a setting that allows users to communicate in a non-aggressive and clear manner.

[0107] 3. "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to input or view messages.

[0108] 4. "Communications" refers to text messages and other digital information sent by a User via a Device.

[0109] 5. "Server" means a central (or cloud-based) computing system for receiving, analyzing, and processing communications.

[0110] 6. "Generative AI model" refers to a machine learning model that uses natural language processing technology to analyze strings of characters and generate appropriate correction suggestions.

[0111] 7. "Offensive language" refers to words or phrases that may be offensive to the recipient.

[0112] 8. "Non-assertive language" refers to words or phrases that convey an ambiguous message or unclear intent to the receiver.

[0113] 9. "Revision" refers to an alternative message that improves on an original message that contains offensive or non-assertive language.

[0114] 10. "Machine Learning Model" refers to algorithms and systems that automatically improve their performance based on feedback data.

[0115] This invention is a system that allows users to use assertive expressions to promote smooth communication through communication tools. The system mainly consists of a flow of user settings, message input and sending, analysis and generation of correction suggestions by the server, presentation of the correction suggestions, feedback and learning.

[0116] User Settings

[0117] The user logs in to the settings screen of the communication tool and selects "assertive mode." This switches the communication tool to a mode that prioritizes assertive language. This setting converts messages sent by the user into more constructive and friendly language. The setting information is saved on the device and also sent to the server.

[0118] Enter and send a message

[0119] The user enters a message into the input field of the communication tool. For example, "Please correct this report immediately. There are too many mistakes." The device encrypts the message in real time and sends it to the server. This encryption uses SSL / TLS technology to ensure data security.

[0120] Message Parsing

[0121] The server records the received message in a database and analyzes it using natural language processing techniques (e.g., BERT and GPT). This detects aggressive and non-assertive expressions. For example, aggressive expressions such as "Please fix it immediately" and "There are too many mistakes" are detected.

[0122] Generate correction suggestions

[0123] The server generates assertive correction suggestions using a generative AI model (e.g., GPT-3) based on the detected inappropriate expressions. The generated correction suggestion might be, for example, "Could you please correct this report? I found some mistakes, so I would like you to check them."

[0124] Submitting and Viewing Proposed Revisions

[0125] The server encodes the proposed changes and sends them to the device after encryption. The device decodes the changes and displays them as a pop-up in the message field. The user can review them and either accept them or make further adjustments. After final confirmation, the revised message is sent back to the server.

[0126] Feedback and Learning

[0127] The server records the revision suggestions adopted by the user as feedback data. This data is used the next time the analysis is performed. The server's machine learning model is updated daily based on this feedback, learning the user's unique expressions, phrasing, and sentence logic. This allows the generation of more natural and appropriate revision suggestions.

[0128] Examples and prompts

[0129] Specific examples

[0130] User: Type "Please fix this report immediately. There are too many mistakes."

[0131] Terminal: Sends the entered message to the server.

[0132] Server: Detect offensive language and generate suggested corrections: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0133] Terminal: Display suggested fixes to the user.

[0134] User: Review and adopt the proposed fix.

[0135] Prompt Sentence Examples

[0136] Prompt 1: "Please assertively correct the user-entered aggressive message 'Please fix this report immediately. There are too many mistakes.'"

[0137] Prompt 2: "Please revise the message 'You haven't provided a progress report yet. Please provide one soon.' to be more polite and assertive."

[0138] This system allows users to communicate naturally and, in particular, to avoid conflicts and misunderstandings.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] User Settings

[0142] User: Log in to the settings screen of the communication tool and select "Assertive Mode." This action changes the current user settings.

[0143] Input: The configuration option selected by the user

[0144] Output: Configuration changes are logged and propagated to the device and server.

[0145] What it does: The user turns on the "Assertive Mode" switch in the settings screen and saves the changes. The device saves this setting in local storage and also sends it to the server.

[0146] Step 2:

[0147] Enter and send a message

[0148] User: Enters communication in the message input field. For example, "Please fix this report immediately. There are too many mistakes."

[0149] Terminal: The input message is sent to the server in real time using SSL / TLS encryption technology.

[0150] Input: The communication entered by the user

[0151] Output: The encrypted message is sent to the server.

[0152] Specific operation: The user enters a message in the input field and clicks the "Send" button. The device encrypts the message and sends it to the server.

[0153] Step 3:

[0154] Message Parsing

[0155] Server: Records received messages in a database and analyzes them using natural language processing techniques (e.g., BERT and GPT).

[0156] Input: Message decrypted and logged to the database

[0157] Output: Detection results for aggressive and non-assertive expressions

[0158] What it does: The server stores the received message in a database and starts the parsing process, using natural language processing models such as BERT and GPT to detect specific expressions.

[0159] Step 4:

[0160] Generate correction suggestions

[0161] Server: Based on the detected offensive expressions, a generative AI model (e.g., GPT-3) is used to generate assertive correction suggestions.

[0162] Input: Detected offensive or non-assertive language

[0163] Output: Correction suggestions generated by the generative AI model

[0164] Specific operation: The server takes the detected inappropriate expressions as input and sends a prompt to the generative AI model. The generated correction suggestions are received as output. Example: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[0165] Step 5:

[0166] Submitting and Viewing Proposed Revisions

[0167] Server: The generated correction proposal is encoded, encrypted, and then sent to the device.

[0168] Terminal: Decodes received correction suggestions and displays them in a popup in the message field.

[0169] Input: Generated correction proposal

[0170] Output: The suggested fixes shown to the user

[0171] What it does: The server encodes the proposed correction, encrypts it, and sends it to the device, which decodes it and pops up the proposed correction in the message field.

[0172] Step 6:

[0173] User Verification and Adoption

[0174] User: Review the suggested fixes and adopt or tweak them as needed.

[0175] Terminal: Sends the user's final message to the server.

[0176] Enter: The suggested fix that pops up

[0177] Output: Final message confirmed and accepted by the user

[0178] What happens: The user reviews the suggested changes, clicks the "Accept" button, makes any necessary adjustments, makes corrections in the edit fields, and then submits the changes.

[0179] Step 7:

[0180] Feedback and Learning

[0181] Server: Records the proposed corrections adopted by the user and saves them as feedback data. This data is used for future analysis and revision generation.

[0182] Input: The suggested fix that the user adopted

[0183] Output: An updated machine learning model

[0184] How it works: The server uses the feedback data to update the machine learning model and learn the user's unique expressions and phrasing, leading to improved analysis accuracy and quality of correction suggestions in the future.

[0185] Through the above processing steps, this system enables users to achieve smoother and more assertive communication.

[0186] (Application example 1)

[0187] 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."

[0188] In interactions between store clerks and customers in physical stores, store clerks sometimes unconsciously use aggressive or non-assertive language, resulting in a decrease in customer satisfaction. These unconscious expressions also contribute to the stress and fatigue of the store clerks themselves, lowering the overall quality of service. Therefore, there is a need for a method to support smooth communication in real time and improve interactions with customers.

[0189] 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.

[0190] In this invention, the server includes: means for a user to select assertive mode on a setting screen of a communication tool; means for a terminal to transmit an input message to the server in real time; means for the server to analyze the received message and detect aggressive or non-assertive expressions; means for the server to generate assertive correction suggestions based on the detected expressions; and means for supporting communication in direct dialogue by displaying the correction suggestions on a display device in real time. This enables store clerks to smoothly conduct dialogue with customers and improve customer satisfaction.

[0191] "User setting" refers to the act of the user selecting assertive mode on the setting screen of the communication tool.

[0192] A "terminal" is a device that transmits messages entered by a user to a server in real time and displays suggested revisions sent from the server.

[0193] The "server" is a computer system that analyzes received messages, detects offensive or non-assertive language, and generates assertive revision suggestions.

[0194] "Message analysis" is the process of analyzing the content of received messages and detecting aggressive or non-assertive expressions.

[0195] "Generating a correction" is the process of creating assertive expressions to replace the detected aggressive or non-assertive expressions.

[0196] "Feedback" refers to the evaluation and usage history data that users send to the server after reviewing and adopting proposed revisions.

[0197] The "learning model" is an algorithm within the server that is updated based on user feedback to improve the accuracy of generating revision suggestions.

[0198] A "display device" is a device that displays suggested revisions in real time, such as smart glasses.

[0199] "Assertive mode" is a special mode that allows the user to modify aggressive or non-assertive statements into assertive statements.

[0200] "Real time" means that processing is performed in real time and results are obtained without delay.

[0201] "Encryption technology" is a method for ensuring the security of data when it is sent from a terminal to a server.

[0202] The program of the system for carrying out the present invention promotes smooth communication by mutually coordinating users, terminals, and servers.

[0203] Program Overview

[0204] The user selects "assertive mode" on the settings screen of the communication tool. This setting switches the entire system to a mode that uses assertive expressions. The user's device (e.g., smart glasses) sends the input message to the server in real time.

[0205] Server Processing

[0206] The server analyzes the received message to detect aggressive or non-assertive language. It uses natural language processing technologies such as Spacy and Transformers for the analysis. Based on the analysis results, the server generates assertive correction suggestions. The suggestions are created using a generative AI model, and include specific examples of prompts, such as:

[0207] Prompt: "Provide assertive language in customer interactions."

[0208] Example: "First input: 'This product is completely useless!'"

[0209] Result: "Could you please let me know if you have any problems with this product?"

[0210] Terminal handling

[0211] The proposed revisions are then sent back to the terminal, which then notifies the user. Using a real-time display system such as smart glasses, the salesperson can view the revisions while interacting with the customer.

[0212] Feedback and Learning

[0213] Finally, the user reviews the suggested revisions and either adopts or fine-tunes them, sending feedback to the server. The server uses this feedback to update the learning model and reflect it in future message analysis and revision generation. This feedback data allows the system to more precisely learn the user's unique expressions, phrasing, and sentence logic over time.

[0214] Specific examples

[0215] For example, if a store clerk uses an aggressive expression such as "This product is completely unusable!", the device will send this message to the server in real time. The server will detect this expression, generate an assertive correction suggestion such as "Could you please tell us if there are any problems with this product?", and send this to the device. The store clerk can then view this correction suggestion through the smart glasses and respond to the customer in an assertive manner.

[0216] Hardware and software used

[0217] Hardware: Real-time display devices such as smart glasses (e.g., Google Glass)

[0218] Software: Spacy, Transformers (BERT model)

[0219] This facilitates smooth communication between store staff and customers, and improves customer satisfaction.

[0220] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0221] Step 1:

[0222] User Settings

[0223] The user selects "assertive mode" on the settings screen of the communication tool. This switches the system into a mode that encourages assertive expression. This operation is performed by the user, and the setting change is reflected on the device.

[0224] Input: User selection of assertive mode

[0225] Output: Change system mode

[0226] Step 2:

[0227] Enter and send a message

[0228] The user enters text into the message field. The terminal sends this entered message to the server in real time. During transmission, encryption technology (e.g., SSL / TLS) is used to ensure data security.

[0229] Input: User enters a message

[0230] Output: Encrypted message sent to server

[0231] Step 3:

[0232] Message Parsing

[0233] The server analyzes the received message, using natural language processing techniques (e.g., SpaCy, Transformers) to detect aggressive or non-assertive language. Specifically, it uses a generative AI model to tokenize and classify the text.

[0234] Input: The message received by the server

[0235] Output: Detecting aggressive or non-assertive language

[0236] Step 4:

[0237] Generate correction suggestions

[0238] The server generates assertive correction suggestions for offensive or non-assertive expressions using a generative AI model and prompts such as the following:

[0239] Prompt: "Provide assertive language in customer interactions."

[0240] Example: "First input: 'This product is completely useless!'"

[0241] Result: "Could you please let me know if you have any problems with this product?"

[0242] Input: Detected offensive or non-assertive language

[0243] Output: Assertive fix

[0244] Step 5:

[0245] Submitting a proposed revision

[0246] The server then sends the generated revision proposal to the device, where it is securely transmitted using encryption technology.

[0247] Input: Server-generated correction suggestions

[0248] Output: Send encrypted revision to device

[0249] Step 6:

[0250] View suggested fixes

[0251] The terminal displays the proposed revisions to the user in real time, and the user can view the revisions through a display device such as smart glasses.

[0252] Input: Suggested fix sent to device

[0253] Output: The suggested fixes shown to the user

[0254] Step 7:

[0255] User Review and Feedback

[0256] The user reviews the suggested modifications and accepts or fine-tunes them as necessary. The user's selections are sent to the server as feedback, which is used for further analysis and modification generation.

[0257] Input: User reviews and accepts proposed changes

[0258] Output: Feedback data sent to server

[0259] Step 8:

[0260] Update the learning model

[0261] The server updates the learning model based on the feedback data, learning the user's unique expressions, phrasing, and sentence logic, which are then reflected in future revision suggestions.

[0262] Input: Feedback data

[0263] Output: Updated training model

[0264] This will allow for continuous improvement of the entire system and facilitate smoother communication between store staff and customers.

[0265] 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.

[0266] The present invention provides a system for promoting smooth communication by allowing users to use assertive expressions in a communication tool. The system components include a user terminal, a server, communication between them, and an emotion engine.

[0267] Basic system configuration

[0268] 1. User Settings

[0269] User: Log in to the communication tool they use and turn on "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[0270] 2. Enter and send a message

[0271] User: Enter text in the message input field.

[0272] Terminal: Messages entered by the user are sent to the server in real time, with encryption technology applied to ensure data security during transmission.

[0273] 3. Message Analysis

[0274] Server: Analyzes received messages to detect aggressive or non-assertive expressions using natural language processing (NLP) technology, and recognizes the user's emotions using an emotion engine.

[0275] 4. Generate correction suggestions

[0276] Server: Generates assertive correction suggestions based on the detected expressions. The emotion engine optimizes replacement expressions by taking into account the user's emotions.

[0277] Example: User message: "Please correct this report immediately. There are too many mistakes." → Server suggestion: "Could you please correct this report? I found some mistakes, so I'd like you to take a look at them."

[0278] 5. Submitting and Viewing Revisions

[0279] Server: Sends the generated correction proposal to the device.

[0280] Terminal: A suggested fix will pop up over the message input field.

[0281] 6. User Verification and Adoption

[0282] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[0283] 7. Feedback and Learning

[0284] Server: Records the correction suggestions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis. It also updates the emotion engine, reflecting this in the generation of correction suggestions that take emotions into account, thereby providing more appropriate responses.

[0285] Specific examples

[0286] Example 1: Requesting a report revision

[0287] User: Type "Please fix this report immediately. There are too many mistakes."

[0288] Terminal: Sends the entered message to the server.

[0289] Server: Detects offensive language and the user's feelings of frustration and generates a correction suggestion like this: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0290] Terminal: Display suggested fixes to the user.

[0291] User: Review and adopt the proposed fix.

[0292] Example 2: Checking project progress

[0293] User: Type "No progress report yet. Please let me know soon."

[0294] Terminal: Sends the entered message to the server.

[0295] Server: Detects aggressive language and the user's sense of impatience and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[0296] Terminal: Display suggested fixes to the user.

[0297] User: Review and adopt the proposed fix.

[0298] In this way, by implementing the system according to the embodiment of the invention, users can communicate smoothly while taking their emotions into consideration.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[0302] Step 2:

[0303] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[0304] Step 3:

[0305] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[0306] Step 4:

[0307] User: Enter text into the message input field in the communications tool.

[0308] For example: "Please fix this report immediately. There are too many mistakes."

[0309] Step 5:

[0310] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[0311] Step 6:

[0312] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[0313] Step 7:

[0314] Server: Recognizes the user's emotions in real time using the emotion engine. In this process, it identifies the emotion behind the message.

[0315] Step 8:

[0316] Server: Generates assertive correction suggestions based on the detected expressions and recognized emotions. The emotion engine optimizes replacement expressions by taking into account the user's emotions recognized by the emotion engine.

[0317] For example: If the perceived emotion is irritation, change it to a more gentle expression: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0318] Step 9:

[0319] Server: Sends the generated correction proposal to the device.

[0320] Step 10:

[0321] Terminal: A suggested fix will pop up over the message input field.

[0322] Step 11:

[0323] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[0324] Step 12:

[0325] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[0326] Step 13:

[0327] Server: Based on the feedback, the learning model and emotion engine are updated to generate correction suggestions that take into account the user's unique expressions, phrasing, sentence logic, and emotions.

[0328] The above are the specific processing steps in this system.

[0329] Example 2

[0330] 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."

[0331] With conventional communication tools, users often used aggressive or non-assertive language, which hindered smooth communication. This also posed the risk of causing interpersonal problems and reducing work efficiency. Furthermore, because users' messages were sent as is, it was time-consuming to correct and optimize the language, which caused stress for users. There was a need to solve these issues.

[0332] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing received data and detecting specific expressions and non-specific expressions, means for generating specific correction suggestions based on the detected expressions, and means for updating the learning model based on feedback and reflecting the feedback in generating future correction suggestions. This allows the user to avoid aggressive and non-assertive expressions and enable more assertive communication.

[0333] A "User" is an individual or entity that uses a communication tool to send or receive messages.

[0334] A "communication tool" is software or hardware that a user uses to send and receive messages.

[0335] The "specific mode" is one of the settings of the communication tool used by the user, and is a mode for using specific phrases and expressions.

[0336] A "terminal" is a device that a user uses to operate a communication tool.

[0337] "Data" means any message or information that a User sends or receives through a Communication Tool.

[0338] "Server" means a device or system that receives and processes data sent from a terminal and transmits the data to other terminals.

[0339] "Analysis" refers to the analytical work performed by the server on the data received, including the detection of specific and non-specific expressions.

[0340] "Specific language" refers to offensive or non-assertive language used by users in their messages.

[0341] "Non-specific expressions" refer to general phrases that users use in their messages.

[0342] A "revision suggestion" is a more assertive expression that the server generates based on the detected expression.

[0343] "Feedback" refers to the evaluation information that a user sends to the server after reviewing and adopting a proposed revision.

[0344] A "learning model" is a collection of algorithms and data that improves the predictions the server makes based on feedback.

[0345] "Data protection technology" is a technology for ensuring the security of data when it is transmitted from a terminal to a server.

[0346] This invention is a system that allows users to use assertive expressions to promote smooth communication in communication tools. The system components include the terminal used by the user, a server that analyzes and processes data, the communication between these, as well as a natural language processing engine and an emotion engine.

[0347] System Components

[0348] 1. User's device

[0349] A device used to operate communication tools. It allows users to input messages and send them to a server. Typically, this type of device is a PC, smartphone, or tablet.

[0350] 2. Server

[0351] This system analyzes received data and detects aggressive or non-assertive expressions. It then generates assertive correction suggestions based on the detected expressions and sends them to the user. It is written in programming languages ​​such as Python and JavaScript and runs on cloud or on-premise servers.

[0352] 3. Data Protection Technology

[0353] This technology ensures the security of data sent from the user's device to the server. Specifically, it uses TLS / SSL encryption technology.

[0354] 4. Natural Language Processing Engine (NLP)

[0355] An engine for analyzing received messages. Examples include Python's NLTK library and Google's BERT.

[0356] 5. Emotion Engine

[0357] This engine recognizes emotions from the user's message and generates revision suggestions based on those emotions, thereby proposing the most appropriate expression that takes the user's emotions into account.

[0358] Example of operation

[0359] Example 1: Requesting a report revision

[0360] 1. User: Type, "Please fix this report immediately. There are too many mistakes."

[0361] 2. Terminal: Sends this message to the server in real time.

[0362] 3. Server: Analyzes the message and detects offensive language and user annoyance.

[0363] 4. Server: Generate a correction suggestion: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[0364] 5. Terminal: Display suggested fixes to the user.

[0365] 6. User: Review and adopt the proposed fix.

[0366] Example 2: Checking project progress

[0367] 1. User: Type "No progress report yet. Please report soon."

[0368] 2. Terminal: Send this message to the server.

[0369] 3. Server: Detects aggressive language and the user's impatient emotions and generates a correction suggestion like this: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[0370] 4. Terminal: Display suggested fixes to the user.

[0371] 5. User: Review and adopt the proposed fix.

[0372] Examples of prompt statements

[0373] An example prompt sentence might have the following natural language form:

[0374] Please modify the user-entered message to be more assertive:

[0375] "There is no progress report yet. Please report it soon."

[0376] Transform the following aggressive messages into assertive ones:

[0377] "Please correct this report immediately. There are too many mistakes."

[0378] With the above components and operations, the present invention provides a system that facilitates communication and helps users to communicate more assertively.

[0379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0380] Step 1: User Setup

[0381] The user turns on "assertive mode" in the settings screen of the communication tool.

[0382] Input: The user selects "Assertive Mode" on the settings screen

[0383] Output: The communication tool is changed to assertive mode.

[0384] Specific behavior: The user opens the settings menu of the communication tool and toggles on "Assertive Mode." This action updates the communication tool's settings.

[0385] Step 2: Write and send your message

[0386] The user enters text into the message input field.

[0387] Input: The user types a message into the message input field of the communication tool.

[0388] Output: The input message

[0389] Specific operation: The user enters "There is no progress report yet. Please report soon." and clicks the send button.

[0390] The terminal encrypts the input message and sends it to the server in real time.

[0391] Input: The message entered by the user

[0392] Output: Encrypted message

[0393] Specific operation: The terminal encrypts the input message using TLS / SSL encryption technology and sends it to the server.

[0394] Step 3: Message Analysis

[0395] The server analyzes the messages it receives and detects aggressive or non-assertive language.

[0396] Input: The encrypted message sent from the device

[0397] Output: Analysis results (aggressive expressions, non-assertive expressions, etc.)

[0398] What it does: The server decodes the message and uses a natural language processing engine (e.g., NLTK, BERT, etc.) to detect offensive or non-assertive language.

[0399] Step 4: Generate correction suggestions

[0400] The server generates assertive correction suggestions based on the detected expressions.

[0401] Input: Analysis results and user emotions (recognition by emotion engine)

[0402] Output: Assertive fix

[0403] How it works: The emotion engine recognizes emotions such as irritation or impatience from the user's message, and based on this, the server uses a generative AI model to generate assertive correction suggestions.

[0404] For example, in response to the message "There is no progress report yet. Please report it soon," a correction suggestion is generated that reads, "Could you please provide a progress report? I apologize for bothering you when you're busy, but it would be helpful if you could let me know the current situation."

[0405] Step 5: Submit and view proposed revisions

[0406] The server sends the generated revision proposal to the terminal.

[0407] Input: Generated assertive revision suggestions

[0408] Output: Data sent to the terminal

[0409] Specific operation: The server prepares the generated revision proposal as data to be sent to the terminal.

[0410] The device will pop up suggested fixes over the message entry field.

[0411] Input: Correction proposal sent by server

[0412] Output: Suggested fixes displayed on the user's device

[0413] Specific behavior: The device displays the received correction suggestions as a pop-up above the message input field.

[0414] Step 6: User Verification and Adoption

[0415] The user can check the suggested revisions and, if necessary, adopt them. They can also make minor adjustments to the suggested revisions.

[0416] Input: The suggested fix pops up

[0417] Output: Adopted or fine-tuned revisions

[0418] Specific behavior: The user reviews the proposed fix and either accepts the suggestion as is, saying, "Could you please give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on." or makes some minor changes and sends it.

[0419] Step 7: Feedback and learning

[0420] The server records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions.

[0421] Input: Adopted amendments and feedback data

[0422] Output: Updated training model

[0423] How it works: The server records the correction suggestions adopted by the user as feedback data and updates the machine learning model. This update allows for more accurate message analysis and correction suggestions from the next time onwards.

[0424] The above processing steps enable users to communicate smoothly.

[0425] (Application example 2)

[0426] 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."

[0427] In factories and other workplaces, there is a problem of communication between workers and machines not being carried out smoothly using conventional methods. In particular, work instructions are often given in aggressive or non-assertive language, which can have a negative impact on the work environment and work efficiency. As a result, there are an increase in work mistakes and problems, which leads to problems such as reduced productivity and increased stress.

[0428] 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: a means for a user to select assertive mode on a setting screen of a communication tool; a means for a terminal to send an input message to the server in real time; a means for the server to analyze the received message and detect aggressive or non-assertive expressions; a means for the server to generate an assertive revision proposal based on the detected expression; a means for the server to send the generated revision proposal to the terminal; a means for the terminal to display the revision proposal to the user; a means for the user to confirm and adopt the revision proposal and send feedback to the server; a means for the server to update the learning model based on the feedback and reflect this in future revision proposal generation; a means for correcting voice or text work instructions to assertive expressions; and a means for displaying or presenting the corrected work instructions on industrial machines. This enables smoother communication and improved work efficiency at construction sites and production lines.

[0429] A "user" is a person or organization that uses the system to input messages and communicate.

[0430] "Communication tools" refers to applications and software that users use to exchange information with each other.

[0431] The "assertive mode" is a mode in which the user uses expressions that are not aggressive but assertive and respectful of the other person.

[0432] "Terminal" means a device used by a user to input messages and receive and display instructions from a server.

[0433] "Server" is a central computer system that analyzes messages sent from user terminals and generates and sends appropriate corrections.

[0434] A "message" is text or voice data that is input by a user and sent to a server via a terminal.

[0435] "Real-time" refers to data being sent and received almost instantly.

[0436] "Offensive language" means language that contains words or sentences intended to offend or be offensive to the target.

[0437] "Non-assertive expression" refers to language that does not make one's own position clear or that is non-assertive toward others.

[0438] An "assertive amendment" is a proposal that transforms aggressive or non-assertive language into language that clarifies one's own position while showing respect for the other party.

[0439] "Voice or text work instructions" are voice or text messages used to instruct work content or procedures in a factory or on-site.

[0440] "Industrial machinery" refers to various machines and robots used in factories and production sites.

[0441] "Displayed or audibly presented" means that the corrected instruction content is displayed on a display or played audibly.

[0442] The present invention is a system that supports users in using assertive expressions in communication tools, and is particularly applied to improving work instructions in factories and production sites. Specific embodiments for carrying out the present invention will be described below.

[0443] System Configuration

[0444] Hardware

[0445] User device: A PC, tablet, or smartphone used by a factory leader or worker.

[0446] Server: A central computer system located in the cloud that analyzes messages and generates correction suggestions.

[0447] Industrial machines: Robots and other industrial equipment that operate according to instructions.

[0448] software

[0449] Natural Language Processing (NLP) library: Spacy is used.

[0450] Generative AI model: Uses OpenAI's API.

[0451] Web server framework: Use Flask or Django.

[0452] Operating principle

[0453] 1. User Settings

[0454] When a user selects "assertive mode" on the settings screen of a communication tool, the system switches to that mode.

[0455] 2. Enter and send a message

[0456] Users input factory work instructions by voice or text, such as "Assemble this part quickly. We're running late!"

[0457] The entered message is sent to the server in real time, and the device uses encryption technology to ensure the security of the data.

[0458] 3. Message Analysis

[0459] The server analyzes the received messages and detects aggressive or non-assertive expressions using natural language processing (NLP) technology and an emotion engine.

[0460] For example, aggressive expressions such as "fast" and "late" are detected.

[0461] 4. Generate correction suggestions

[0462] The server generates assertive revision suggestions based on the detected expressions and the user's emotions. Using a generative AI model, a revision suggestion such as, "Could you please assemble this part? Progress is behind schedule, so please check it."

[0463] 5. Submitting and Viewing Revisions

[0464] The proposed corrections are sent to a terminal where the user can review them, and the terminal displays the corrections as text or audio, and posts them on the industrial machine as needed.

[0465] 6. User Verification and Adoption

[0466] The user reviews the proposed modifications and adopts or fine-tunes them as necessary.

[0467] 7. Feedback and Learning

[0468] The revision suggestions adopted by the user are sent to the server and recorded as feedback data, which the server uses to update the learning model and reflect in future revision suggestions.

[0469] Specific examples

[0470] Example 1: Part assembly instructions

[0471] Original message: "Hurry up and assemble this part. You're late!"

[0472] Suggested fix after analysis: "Can you please assemble this part? We're running behind schedule, so we'd really appreciate your help."

[0473] Prompt Sentence Examples

[0474] Modify the following message to be more assertive: "Hurry up and put this part together. We're running late!" Emotion: Annoyance

[0475] Using this system is expected to greatly facilitate communication on-site, leading to improved work efficiency and working conditions.

[0476] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0477] Step 1:

[0478] A user logs into a communication tool and turns on "assertive mode" on the settings screen. The user performs this operation on their own device (PC, tablet, or smartphone), and this setting is sent to the server. The input is the user's setting information, and the output is the setting data with assertive mode turned on. This allows the server to recognize that a specific user is using assertive mode.

[0479] Step 2:

[0480] The user inputs factory work instructions via text or voice. For example, "Assemble this part quickly. We're running late!" The input is the work instruction message, and the output is the message data. The input message is securely transmitted to the server in real time by the terminal. Data encryption is used during this process to ensure the security of the message.

[0481] Step 3:

[0482] The server analyzes the received message. First, it uses a natural language processing (NLP) library (Spacy) to tokenize the message and analyze its grammatical structure. Next, the emotion engine analyzes the user's emotions and detects aggressive or non-assertive expressions. The input is the received message data, and the output is the analysis results (detection of aggressive expressions and emotions).

[0483] Step 4:

[0484] The server generates assertive revision suggestions based on the analysis results. It uses a generative AI model (OpenAI API) to convert detected aggressive or non-assertive expressions into assertive expressions. An example prompt used here is in the following format: "Please revise the following message to an assertive expression: "Assemble this part quickly. We're late!" Emotion: Annoyed." The input is the analysis results and the prompt from the generative AI model, and the output is an assertive revision suggestion.

[0485] Step 5:

[0486] The generated revision suggestions are sent from the server to the terminal for the user to review. The terminal displays the revision suggestions for the user to review. The user can either accept the revision suggestions as they are or make fine adjustments as needed. The input is the revision suggestion data, and the output is the revised instructions after the user has reviewed them.

[0487] Step 6:

[0488] The revision suggestions that the user confirms or fine-tunes are sent to the server as feedback. The server updates the learning model based on this feedback data and reflects it in future revision suggestions. The input is the user's feedback data, and the output is the updated learning model.

[0489] Step 7:

[0490] The modified work instructions are displayed or played audibly on the industrial machine, which then performs the work based on the new assertive instructions. The input is the modified work instructions, and the output is the start of machine operation.

[0491] Through these steps, users can give work instructions using assertive language rather than aggressive language, which will facilitate smooth communication and improve work efficiency in factories and production sites.

[0492] 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.

[0493] 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.

[0494] 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.

[0495] [Second embodiment]

[0496] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0497] 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.

[0498] 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).

[0499] 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.

[0500] 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.

[0501] 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).

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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.

[0506] 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.

[0507] 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."

[0508] This invention is a system that allows users to use assertive expressions in communication tools to promote smooth communication. The main elements that make up the system are the user's terminal, a server, and the communication that takes place between them.

[0509] Basic system configuration

[0510] 1. User Settings

[0511] User: Log in to the communication tool they use and select "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[0512] 2. Enter and send a message

[0513] User: Enter text in the message input field.

[0514] Terminal: Messages entered by the user are sent to the server in real time. Data security is ensured during transmission using encryption technology.

[0515] 3. Message Analysis

[0516] Server: Analyzes received messages and detects aggressive or non-assertive expressions. Natural language processing technology is used for the analysis. For example, it analyzes a message such as "Please correct this report immediately. There are too many mistakes."

[0517] 4. Generate correction suggestions

[0518] Server: Generate assertive revision suggestions based on the detected expressions. In the above example, the server generates a revision suggestion such as "Could you please revise this report? I found some mistakes, so I would like you to check them."

[0519] 5. Submitting and Viewing Revisions

[0520] Server: Sends the generated correction proposal to the device.

[0521] Terminal: A suggested fix will pop up above the message field.

[0522] 6. User Verification and Adoption

[0523] User: Review the suggested fixes and adopt them as needed, or make minor adjustments to the suggested fixes.

[0524] 7. Feedback and Learning

[0525] Server: Records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis.

[0526] Specific examples

[0527] Example 1: Requesting a report revision

[0528] User: Type "Please fix this report immediately. There are too many mistakes."

[0529] Terminal: Sends the entered message to the server.

[0530] Server: Detects offensive language and generates a suggested correction, such as: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0531] Terminal: Display suggested fixes to the user.

[0532] User: Review and adopt the proposed fix.

[0533] Example 2: Checking project progress

[0534] User: Type "No progress report yet. Please let me know soon."

[0535] Terminal: Sends the entered message to the server.

[0536] Server: Detects offensive language and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[0537] Terminal: Display suggested fixes to the user.

[0538] User: Review and adopt the proposed fix.

[0539] In this way, by implementing the system according to the embodiment of the present invention, users can communicate smoothly.

[0540] The processing flow will be explained below.

[0541] Step 1:

[0542] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[0543] Step 2:

[0544] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[0545] Step 3:

[0546] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[0547] Step 4:

[0548] User: Enter text into the message input field in the communications tool.

[0549] For example: "Please fix this report immediately. There are too many mistakes."

[0550] Step 5:

[0551] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[0552] Step 6:

[0553] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[0554] Step 7:

[0555] Server: Generates correction suggestions to convert the detected expressions into assertive expressions.

[0556] For example: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0557] Step 8:

[0558] Server: Sends the generated correction proposal to the device.

[0559] Step 9:

[0560] Terminal: A suggested fix will pop up over the message input field.

[0561] Step 10:

[0562] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[0563] Step 11:

[0564] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[0565] Step 12:

[0566] Server: Updates the learning model based on feedback and learns the user's unique expressions, phrases, and sentence logic on a daily basis.

[0567] The above are the specific processing steps in this system.

[0568] Example 1

[0569] 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."

[0570] In modern communication tools, messages sent by users often cause misunderstandings and conflicts. In particular, when aggressive or non-assertive expressions are included, smooth communication becomes difficult. There is a need for a method to solve this problem and enable users to communicate more constructively and amicably.

[0571] 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.

[0572] In this invention, the server includes means for analyzing received communications and detecting aggressive or non-assertive expressions, means for using a generative AI model to generate assertive revision suggestions based on the detected expressions, and means for updating the machine learning model based on feedback and reflecting the feedback in generating future revision suggestions, thereby enabling users to have more constructive and friendly communications.

[0573] 1. A "communication tool" is a system that includes application software and hardware that allows users to exchange messages with other users.

[0574] 2. "Assertive Mode" refers to a setting that allows users to communicate in a non-aggressive and clear manner.

[0575] 3. "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to input or view messages.

[0576] 4. "Communications" refers to text messages and other digital information sent by a User via a Device.

[0577] 5. "Server" means a central (or cloud-based) computing system for receiving, analyzing, and processing communications.

[0578] 6. "Generative AI model" refers to a machine learning model that uses natural language processing technology to analyze strings of characters and generate appropriate correction suggestions.

[0579] 7. "Offensive language" refers to words or phrases that may be offensive to the recipient.

[0580] 8. "Non-assertive language" refers to words or phrases that convey an ambiguous message or unclear intent to the receiver.

[0581] 9. "Revision" refers to an alternative message that improves on an original message that contains offensive or non-assertive language.

[0582] 10. "Machine Learning Model" refers to algorithms and systems that automatically improve their performance based on feedback data.

[0583] This invention is a system that allows users to use assertive expressions to promote smooth communication through communication tools. The system mainly consists of a flow of user settings, message input and sending, analysis and generation of correction suggestions by the server, presentation of the correction suggestions, feedback and learning.

[0584] User Settings

[0585] The user logs in to the settings screen of the communication tool and selects "assertive mode." This switches the communication tool to a mode that prioritizes assertive language. This setting converts messages sent by the user into more constructive and friendly language. The setting information is saved on the device and also sent to the server.

[0586] Enter and send a message

[0587] The user enters a message into the input field of the communication tool. For example, "Please correct this report immediately. There are too many mistakes." The device encrypts the message in real time and sends it to the server. This encryption uses SSL / TLS technology to ensure data security.

[0588] Message Parsing

[0589] The server records the received message in a database and analyzes it using natural language processing techniques (e.g., BERT and GPT). This detects aggressive and non-assertive expressions. For example, aggressive expressions such as "Please fix it immediately" and "There are too many mistakes" are detected.

[0590] Generate correction suggestions

[0591] The server generates assertive correction suggestions using a generative AI model (e.g., GPT-3) based on the detected inappropriate expressions. The generated correction suggestion might be, for example, "Could you please correct this report? I found some mistakes, so I would like you to check them."

[0592] Submitting and Viewing Proposed Revisions

[0593] The server encodes the proposed changes and sends them to the device after encryption. The device decodes the changes and displays them as a pop-up in the message field. The user can review them and either accept them or make further adjustments. After final confirmation, the revised message is sent back to the server.

[0594] Feedback and Learning

[0595] The server records the revision suggestions adopted by the user as feedback data. This data is used the next time the analysis is performed. The server's machine learning model is updated daily based on this feedback, learning the user's unique expressions, phrasing, and sentence logic. This allows the generation of more natural and appropriate revision suggestions.

[0596] Examples and prompts

[0597] Specific examples

[0598] User: Type "Please fix this report immediately. There are too many mistakes."

[0599] Terminal: Sends the entered message to the server.

[0600] Server: Detect offensive language and generate suggested corrections: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0601] Terminal: Display suggested fixes to the user.

[0602] User: Review and adopt the proposed fix.

[0603] Prompt Sentence Examples

[0604] Prompt 1: "Please assertively correct the user-entered aggressive message 'Please fix this report immediately. There are too many mistakes.'"

[0605] Prompt 2: "Please revise the message 'You haven't provided a progress report yet. Please provide one soon.' to be more polite and assertive."

[0606] This system allows users to communicate naturally and, in particular, to avoid conflicts and misunderstandings.

[0607] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] User Settings

[0610] User: Log in to the settings screen of the communication tool and select "Assertive Mode." This action changes the current user settings.

[0611] Input: The configuration option selected by the user

[0612] Output: Configuration changes are logged and propagated to the device and server.

[0613] What it does: The user turns on the "Assertive Mode" switch in the settings screen and saves the changes. The device saves this setting in local storage and also sends it to the server.

[0614] Step 2:

[0615] Enter and send a message

[0616] User: Enters communication in the message input field. For example, "Please fix this report immediately. There are too many mistakes."

[0617] Terminal: The input message is sent to the server in real time using SSL / TLS encryption technology.

[0618] Input: The communication entered by the user

[0619] Output: The encrypted message is sent to the server.

[0620] Specific operation: The user enters a message in the input field and clicks the "Send" button. The device encrypts the message and sends it to the server.

[0621] Step 3:

[0622] Message Parsing

[0623] Server: Records received messages in a database and analyzes them using natural language processing techniques (e.g., BERT and GPT).

[0624] Input: Message decrypted and logged to the database

[0625] Output: Detection results for aggressive and non-assertive expressions

[0626] What it does: The server stores the received message in a database and starts the parsing process, using natural language processing models such as BERT and GPT to detect specific expressions.

[0627] Step 4:

[0628] Generate correction suggestions

[0629] Server: Based on the detected offensive expressions, a generative AI model (e.g., GPT-3) is used to generate assertive correction suggestions.

[0630] Input: Detected offensive or non-assertive language

[0631] Output: Correction suggestions generated by the generative AI model

[0632] Specific operation: The server takes the detected inappropriate expressions as input and sends a prompt to the generative AI model. The generated correction suggestions are received as output. Example: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[0633] Step 5:

[0634] Submitting and Viewing Proposed Revisions

[0635] Server: The generated correction proposal is encoded, encrypted, and then sent to the device.

[0636] Terminal: Decodes received correction suggestions and displays them in a popup in the message field.

[0637] Input: Generated correction proposal

[0638] Output: The suggested fixes shown to the user

[0639] What it does: The server encodes the proposed correction, encrypts it, and sends it to the device, which decodes it and pops up the proposed correction in the message field.

[0640] Step 6:

[0641] User Verification and Adoption

[0642] User: Review the suggested fixes and adopt or tweak them as needed.

[0643] Terminal: Sends the user's final message to the server.

[0644] Enter: The suggested fix that pops up

[0645] Output: Final message confirmed and accepted by the user

[0646] What happens: The user reviews the suggested changes, clicks the "Accept" button, makes any necessary adjustments, makes corrections in the edit fields, and then submits the changes.

[0647] Step 7:

[0648] Feedback and Learning

[0649] Server: Records the proposed corrections adopted by the user and saves them as feedback data. This data is used for future analysis and revision generation.

[0650] Input: The suggested fix that the user adopted

[0651] Output: An updated machine learning model

[0652] How it works: The server uses the feedback data to update the machine learning model and learn the user's unique expressions and phrasing, leading to improved analysis accuracy and quality of correction suggestions in the future.

[0653] Through the above processing steps, this system enables users to achieve smoother and more assertive communication.

[0654] (Application example 1)

[0655] 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."

[0656] In interactions between store clerks and customers in physical stores, store clerks sometimes unconsciously use aggressive or non-assertive language, resulting in a decrease in customer satisfaction. These unconscious expressions also contribute to the stress and fatigue of the store clerks themselves, lowering the overall quality of service. Therefore, there is a need for a method to support smooth communication in real time and improve interactions with customers.

[0657] 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.

[0658] In this invention, the server includes: means for a user to select assertive mode on a setting screen of a communication tool; means for a terminal to transmit an input message to the server in real time; means for the server to analyze the received message and detect aggressive or non-assertive expressions; means for the server to generate assertive correction suggestions based on the detected expressions; and means for supporting communication in direct dialogue by displaying the correction suggestions on a display device in real time. This enables store clerks to smoothly conduct dialogue with customers and improve customer satisfaction.

[0659] "User setting" refers to the act of the user selecting assertive mode on the setting screen of the communication tool.

[0660] A "terminal" is a device that transmits messages entered by a user to a server in real time and displays suggested revisions sent from the server.

[0661] The "server" is a computer system that analyzes received messages, detects offensive or non-assertive language, and generates assertive revision suggestions.

[0662] "Message analysis" is the process of analyzing the content of received messages and detecting aggressive or non-assertive expressions.

[0663] "Generating a correction" is the process of creating assertive expressions to replace the detected aggressive or non-assertive expressions.

[0664] "Feedback" refers to the evaluation and usage history data that users send to the server after reviewing and adopting proposed revisions.

[0665] The "learning model" is an algorithm within the server that is updated based on user feedback to improve the accuracy of generating revision suggestions.

[0666] A "display device" is a device that displays suggested revisions in real time, such as smart glasses.

[0667] "Assertive mode" is a special mode that allows the user to modify aggressive or non-assertive statements into assertive statements.

[0668] "Real time" means that processing is performed in real time and results are obtained without delay.

[0669] "Encryption technology" is a method for ensuring the security of data when it is sent from a terminal to a server.

[0670] The program of the system for carrying out the present invention promotes smooth communication by mutually coordinating users, terminals, and servers.

[0671] Program Overview

[0672] The user selects "assertive mode" on the settings screen of the communication tool. This setting switches the entire system to a mode that uses assertive expressions. The user's device (e.g., smart glasses) sends the input message to the server in real time.

[0673] Server Processing

[0674] The server analyzes the received message to detect aggressive or non-assertive language. It uses natural language processing technologies such as Spacy and Transformers for the analysis. Based on the analysis results, the server generates assertive correction suggestions. The suggestions are created using a generative AI model, and include specific examples of prompts, such as:

[0675] Prompt: "Provide assertive language in customer interactions."

[0676] Example: "First input: 'This product is completely useless!'"

[0677] Result: "Could you please let me know if you have any problems with this product?"

[0678] Terminal handling

[0679] The proposed revisions are then sent back to the terminal, which then notifies the user. Using a real-time display system such as smart glasses, the salesperson can view the revisions while interacting with the customer.

[0680] Feedback and Learning

[0681] Finally, the user reviews the suggested revisions and either adopts or fine-tunes them, sending feedback to the server. The server uses this feedback to update the learning model and reflect it in future message analysis and revision generation. This feedback data allows the system to more precisely learn the user's unique expressions, phrasing, and sentence logic over time.

[0682] Specific examples

[0683] For example, if a store clerk uses an aggressive expression such as "This product is completely unusable!", the device will send this message to the server in real time. The server will detect this expression, generate an assertive correction suggestion such as "Could you please tell us if there are any problems with this product?", and send this to the device. The store clerk can then view this correction suggestion through the smart glasses and respond to the customer in an assertive manner.

[0684] Hardware and software used

[0685] Hardware: Real-time display devices such as smart glasses (e.g., Google Glass)

[0686] Software: Spacy, Transformers (BERT model)

[0687] This facilitates smooth communication between store staff and customers, and improves customer satisfaction.

[0688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0689] Step 1:

[0690] User Settings

[0691] The user selects "assertive mode" on the settings screen of the communication tool. This switches the system into a mode that encourages assertive expression. This operation is performed by the user, and the setting change is reflected on the device.

[0692] Input: User selection of assertive mode

[0693] Output: Change system mode

[0694] Step 2:

[0695] Enter and send a message

[0696] The user enters text into the message field. The terminal sends this entered message to the server in real time. During transmission, encryption technology (e.g., SSL / TLS) is used to ensure data security.

[0697] Input: User enters a message

[0698] Output: Encrypted message sent to server

[0699] Step 3:

[0700] Message Parsing

[0701] The server analyzes the received message, using natural language processing techniques (e.g., SpaCy, Transformers) to detect aggressive or non-assertive language. Specifically, it uses a generative AI model to tokenize and classify the text.

[0702] Input: The message received by the server

[0703] Output: Detecting aggressive or non-assertive language

[0704] Step 4:

[0705] Generate correction suggestions

[0706] The server generates assertive correction suggestions for offensive or non-assertive expressions using a generative AI model and prompts such as the following:

[0707] Prompt: "Provide assertive language in customer interactions."

[0708] Example: "First input: 'This product is completely useless!'"

[0709] Result: "Could you please let me know if you have any problems with this product?"

[0710] Input: Detected offensive or non-assertive language

[0711] Output: Assertive fix

[0712] Step 5:

[0713] Submitting a proposed revision

[0714] The server then sends the generated revision proposal to the device, where it is securely transmitted using encryption technology.

[0715] Input: Server-generated correction suggestions

[0716] Output: Send encrypted revision to device

[0717] Step 6:

[0718] View suggested fixes

[0719] The terminal displays the proposed revisions to the user in real time, and the user can view the revisions through a display device such as smart glasses.

[0720] Input: Suggested fix sent to device

[0721] Output: The suggested fixes shown to the user

[0722] Step 7:

[0723] User Review and Feedback

[0724] The user reviews the suggested modifications and accepts or fine-tunes them as necessary. The user's selections are sent to the server as feedback, which is used for further analysis and modification generation.

[0725] Input: User reviews and accepts proposed changes

[0726] Output: Feedback data sent to server

[0727] Step 8:

[0728] Update the learning model

[0729] The server updates the learning model based on the feedback data, learning the user's unique expressions, phrasing, and sentence logic, which are then reflected in future revision suggestions.

[0730] Input: Feedback data

[0731] Output: Updated training model

[0732] This will allow for continuous improvement of the entire system and facilitate smoother communication between store staff and customers.

[0733] 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.

[0734] The present invention provides a system for promoting smooth communication by allowing users to use assertive expressions in a communication tool. The system components include a user terminal, a server, communication between them, and an emotion engine.

[0735] Basic system configuration

[0736] 1. User Settings

[0737] User: Log in to the communication tool they use and turn on "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[0738] 2. Enter and send a message

[0739] User: Enter text in the message input field.

[0740] Terminal: Messages entered by the user are sent to the server in real time, with encryption technology applied to ensure data security during transmission.

[0741] 3. Message Analysis

[0742] Server: Analyzes received messages to detect aggressive or non-assertive expressions using natural language processing (NLP) technology, and recognizes the user's emotions using an emotion engine.

[0743] 4. Generate correction suggestions

[0744] Server: Generates assertive correction suggestions based on the detected expressions. The emotion engine optimizes replacement expressions by taking into account the user's emotions.

[0745] Example: User message: "Please correct this report immediately. There are too many mistakes." → Server suggestion: "Could you please correct this report? I found some mistakes, so I'd like you to take a look at them."

[0746] 5. Submitting and Viewing Revisions

[0747] Server: Sends the generated correction proposal to the device.

[0748] Terminal: A suggested fix will pop up over the message input field.

[0749] 6. User Verification and Adoption

[0750] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[0751] 7. Feedback and Learning

[0752] Server: Records the correction suggestions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis. It also updates the emotion engine, reflecting this in the generation of correction suggestions that take emotions into account, thereby providing more appropriate responses.

[0753] Specific examples

[0754] Example 1: Requesting a report revision

[0755] User: Type "Please fix this report immediately. There are too many mistakes."

[0756] Terminal: Sends the entered message to the server.

[0757] Server: Detects offensive language and the user's feelings of frustration and generates a correction suggestion like this: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0758] Terminal: Display suggested fixes to the user.

[0759] User: Review and adopt the proposed fix.

[0760] Example 2: Checking project progress

[0761] User: Type "No progress report yet. Please let me know soon."

[0762] Terminal: Sends the entered message to the server.

[0763] Server: Detects aggressive language and the user's sense of impatience and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[0764] Terminal: Display suggested fixes to the user.

[0765] User: Review and adopt the proposed fix.

[0766] In this way, by implementing the system according to the embodiment of the invention, users can communicate smoothly while taking their emotions into consideration.

[0767] The processing flow will be explained below.

[0768] Step 1:

[0769] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[0770] Step 2:

[0771] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[0772] Step 3:

[0773] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[0774] Step 4:

[0775] User: Enter text into the message input field in the communications tool.

[0776] For example: "Please fix this report immediately. There are too many mistakes."

[0777] Step 5:

[0778] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[0779] Step 6:

[0780] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[0781] Step 7:

[0782] Server: Recognizes the user's emotions in real time using the emotion engine. In this process, it identifies the emotion behind the message.

[0783] Step 8:

[0784] Server: Generates assertive correction suggestions based on the detected expressions and recognized emotions. The emotion engine optimizes replacement expressions by taking into account the user's emotions recognized by the emotion engine.

[0785] For example: If the perceived emotion is irritation, change it to a more gentle expression: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0786] Step 9:

[0787] Server: Sends the generated correction proposal to the device.

[0788] Step 10:

[0789] Terminal: A suggested fix will pop up over the message input field.

[0790] Step 11:

[0791] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[0792] Step 12:

[0793] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[0794] Step 13:

[0795] Server: Based on the feedback, the learning model and emotion engine are updated to generate correction suggestions that take into account the user's unique expressions, phrasing, sentence logic, and emotions.

[0796] The above are the specific processing steps in this system.

[0797] Example 2

[0798] 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."

[0799] With conventional communication tools, users often used aggressive or non-assertive language, which hindered smooth communication. This also posed the risk of causing interpersonal problems and reducing work efficiency. Furthermore, because users' messages were sent as is, it was time-consuming to correct and optimize the language, which caused stress for users. There was a need to solve these issues.

[0800] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing received data and detecting specific expressions and non-specific expressions, means for generating specific correction suggestions based on the detected expressions, and means for updating the learning model based on feedback and reflecting the feedback in generating future correction suggestions. This allows the user to avoid aggressive and non-assertive expressions and enable more assertive communication.

[0801] A "User" is an individual or entity that uses a communication tool to send or receive messages.

[0802] A "communication tool" is software or hardware that a user uses to send and receive messages.

[0803] The "specific mode" is one of the settings of the communication tool used by the user, and is a mode for using specific phrases and expressions.

[0804] A "terminal" is a device that a user uses to operate a communication tool.

[0805] "Data" means any message or information that a User sends or receives through a Communication Tool.

[0806] "Server" means a device or system that receives and processes data sent from a terminal and transmits the data to other terminals.

[0807] "Analysis" refers to the analytical work performed by the server on the data received, including the detection of specific and non-specific expressions.

[0808] "Specific language" refers to offensive or non-assertive language used by users in their messages.

[0809] "Non-specific expressions" refer to general phrases that users use in their messages.

[0810] A "revision suggestion" is a more assertive expression that the server generates based on the detected expression.

[0811] "Feedback" refers to the evaluation information that a user sends to the server after reviewing and adopting a proposed revision.

[0812] A "learning model" is a collection of algorithms and data that improves the predictions the server makes based on feedback.

[0813] "Data protection technology" is a technology for ensuring the security of data when it is transmitted from a terminal to a server.

[0814] This invention is a system that allows users to use assertive expressions to promote smooth communication in communication tools. The system components include the terminal used by the user, a server that analyzes and processes data, the communication between these, as well as a natural language processing engine and an emotion engine.

[0815] System Components

[0816] 1. User's device

[0817] A device used to operate communication tools. It allows users to input messages and send them to a server. Typically, this type of device is a PC, smartphone, or tablet.

[0818] 2. Server

[0819] This system analyzes received data and detects aggressive or non-assertive expressions. It then generates assertive correction suggestions based on the detected expressions and sends them to the user. It is written in programming languages ​​such as Python and JavaScript and runs on cloud or on-premise servers.

[0820] 3. Data Protection Technology

[0821] This technology ensures the security of data sent from the user's device to the server. Specifically, it uses TLS / SSL encryption technology.

[0822] 4. Natural Language Processing Engine (NLP)

[0823] An engine for analyzing received messages. Examples include Python's NLTK library and Google's BERT.

[0824] 5. Emotion Engine

[0825] This engine recognizes emotions from the user's message and generates revision suggestions based on those emotions, thereby proposing the most appropriate expression that takes the user's emotions into account.

[0826] Example of operation

[0827] Example 1: Requesting a report revision

[0828] 1. User: Type, "Please fix this report immediately. There are too many mistakes."

[0829] 2. Terminal: Sends this message to the server in real time.

[0830] 3. Server: Analyzes the message and detects offensive language and user annoyance.

[0831] 4. Server: Generate a correction suggestion: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[0832] 5. Terminal: Display suggested fixes to the user.

[0833] 6. User: Review and adopt the proposed fix.

[0834] Example 2: Checking project progress

[0835] 1. User: Type "No progress report yet. Please report soon."

[0836] 2. Terminal: Send this message to the server.

[0837] 3. Server: Detects aggressive language and the user's impatient emotions and generates a correction suggestion like this: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[0838] 4. Terminal: Display suggested fixes to the user.

[0839] 5. User: Review and adopt the proposed fix.

[0840] Examples of prompt statements

[0841] An example prompt sentence might have the following natural language form:

[0842] Please modify the user-entered message to be more assertive:

[0843] "There is no progress report yet. Please report it soon."

[0844] Transform the following aggressive messages into assertive ones:

[0845] "Please correct this report immediately. There are too many mistakes."

[0846] With the above components and operations, the present invention provides a system that facilitates communication and helps users to communicate more assertively.

[0847] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0848] Step 1: User Setup

[0849] The user turns on "assertive mode" in the settings screen of the communication tool.

[0850] Input: The user selects "Assertive Mode" on the settings screen

[0851] Output: The communication tool is changed to assertive mode.

[0852] Specific behavior: The user opens the settings menu of the communication tool and toggles on "Assertive Mode." This action updates the communication tool's settings.

[0853] Step 2: Write and send your message

[0854] The user enters text into the message input field.

[0855] Input: The user types a message into the message input field of the communication tool.

[0856] Output: The input message

[0857] Specific operation: The user enters "There is no progress report yet. Please report soon." and clicks the send button.

[0858] The terminal encrypts the input message and sends it to the server in real time.

[0859] Input: The message entered by the user

[0860] Output: Encrypted message

[0861] Specific operation: The terminal encrypts the input message using TLS / SSL encryption technology and sends it to the server.

[0862] Step 3: Message Analysis

[0863] The server analyzes the messages it receives and detects aggressive or non-assertive language.

[0864] Input: The encrypted message sent from the device

[0865] Output: Analysis results (aggressive expressions, non-assertive expressions, etc.)

[0866] What it does: The server decodes the message and uses a natural language processing engine (e.g., NLTK, BERT, etc.) to detect offensive or non-assertive language.

[0867] Step 4: Generate correction suggestions

[0868] The server generates assertive correction suggestions based on the detected expressions.

[0869] Input: Analysis results and user emotions (recognition by emotion engine)

[0870] Output: Assertive fix

[0871] How it works: The emotion engine recognizes emotions such as irritation or impatience from the user's message, and based on this, the server uses a generative AI model to generate assertive correction suggestions.

[0872] For example, in response to the message "There is no progress report yet. Please report it soon," a correction suggestion is generated that reads, "Could you please provide a progress report? I apologize for bothering you when you're busy, but it would be helpful if you could let me know the current situation."

[0873] Step 5: Submit and view proposed revisions

[0874] The server sends the generated revision proposal to the terminal.

[0875] Input: Generated assertive revision suggestions

[0876] Output: Data sent to the terminal

[0877] Specific operation: The server prepares the generated revision proposal as data to be sent to the terminal.

[0878] The device will pop up suggested fixes over the message entry field.

[0879] Input: Correction proposal sent by server

[0880] Output: Suggested fixes displayed on the user's device

[0881] Specific behavior: The device displays the received correction suggestions as a pop-up above the message input field.

[0882] Step 6: User Verification and Adoption

[0883] The user can check the suggested revisions and, if necessary, adopt them. They can also make minor adjustments to the suggested revisions.

[0884] Input: The suggested fix pops up

[0885] Output: Adopted or fine-tuned revisions

[0886] Specific behavior: The user reviews the proposed fix and either accepts the suggestion as is, saying, "Could you please give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on." or makes some minor changes and sends it.

[0887] Step 7: Feedback and learning

[0888] The server records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions.

[0889] Input: Adopted amendments and feedback data

[0890] Output: Updated training model

[0891] How it works: The server records the correction suggestions adopted by the user as feedback data and updates the machine learning model. This update allows for more accurate message analysis and correction suggestions from the next time onwards.

[0892] The above processing steps enable users to communicate smoothly.

[0893] (Application example 2)

[0894] 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."

[0895] In factories and other workplaces, there is a problem of communication between workers and machines not being carried out smoothly using conventional methods. In particular, work instructions are often given in aggressive or non-assertive language, which can have a negative impact on the work environment and work efficiency. As a result, there are an increase in work mistakes and problems, which leads to problems such as reduced productivity and increased stress.

[0896] 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: a means for a user to select assertive mode on a setting screen of a communication tool; a means for a terminal to send an input message to the server in real time; a means for the server to analyze the received message and detect aggressive or non-assertive expressions; a means for the server to generate an assertive revision proposal based on the detected expression; a means for the server to send the generated revision proposal to the terminal; a means for the terminal to display the revision proposal to the user; a means for the user to confirm and adopt the revision proposal and send feedback to the server; a means for the server to update the learning model based on the feedback and reflect this in future revision proposal generation; a means for correcting voice or text work instructions to assertive expressions; and a means for displaying or presenting the corrected work instructions on industrial machines. This enables smoother communication and improved work efficiency at construction sites and production lines.

[0897] A "user" is a person or organization that uses the system to input messages and communicate.

[0898] "Communication tools" refers to applications and software that users use to exchange information with each other.

[0899] The "assertive mode" is a mode in which the user uses expressions that are not aggressive but assertive and respectful of the other person.

[0900] "Terminal" means a device used by a user to input messages and receive and display instructions from a server.

[0901] "Server" is a central computer system that analyzes messages sent from user terminals and generates and sends appropriate corrections.

[0902] A "message" is text or voice data that is input by a user and sent to a server via a terminal.

[0903] "Real-time" refers to data being sent and received almost instantly.

[0904] "Offensive language" means language that contains words or sentences intended to offend or be offensive to the target.

[0905] "Non-assertive expression" refers to language that does not make one's own position clear or that is non-assertive toward others.

[0906] An "assertive amendment" is a proposal that transforms aggressive or non-assertive language into language that clarifies one's own position while showing respect for the other party.

[0907] "Voice or text work instructions" are voice or text messages used to instruct work content or procedures in a factory or on-site.

[0908] "Industrial machinery" refers to various machines and robots used in factories and production sites.

[0909] "Displayed or audibly presented" means that the corrected instruction content is displayed on a display or played audibly.

[0910] The present invention is a system that supports users in using assertive expressions in communication tools, and is particularly applied to improving work instructions in factories and production sites. Specific embodiments for carrying out the present invention will be described below.

[0911] System Configuration

[0912] Hardware

[0913] User device: A PC, tablet, or smartphone used by a factory leader or worker.

[0914] Server: A central computer system located in the cloud that analyzes messages and generates correction suggestions.

[0915] Industrial machines: Robots and other industrial equipment that operate according to instructions.

[0916] software

[0917] Natural Language Processing (NLP) library: Spacy is used.

[0918] Generative AI model: Uses OpenAI's API.

[0919] Web server framework: Use Flask or Django.

[0920] Operating principle

[0921] 1. User Settings

[0922] When a user selects "assertive mode" on the settings screen of a communication tool, the system switches to that mode.

[0923] 2. Enter and send a message

[0924] Users input factory work instructions by voice or text, such as "Assemble this part quickly. We're running late!"

[0925] The entered message is sent to the server in real time, and the device uses encryption technology to ensure the security of the data.

[0926] 3. Message Analysis

[0927] The server analyzes the received messages and detects aggressive or non-assertive expressions using natural language processing (NLP) technology and an emotion engine.

[0928] For example, aggressive expressions such as "fast" and "late" are detected.

[0929] 4. Generate correction suggestions

[0930] The server generates assertive revision suggestions based on the detected expressions and the user's emotions. Using a generative AI model, a revision suggestion such as, "Could you please assemble this part? Progress is behind schedule, so please check it."

[0931] 5. Submitting and Viewing Revisions

[0932] The proposed corrections are sent to a terminal where the user can review them, and the terminal displays the corrections as text or audio, and posts them on the industrial machine as needed.

[0933] 6. User Verification and Adoption

[0934] The user reviews the proposed modifications and adopts or fine-tunes them as necessary.

[0935] 7. Feedback and Learning

[0936] The revision suggestions adopted by the user are sent to the server and recorded as feedback data, which the server uses to update the learning model and reflect in future revision suggestions.

[0937] Specific examples

[0938] Example 1: Part assembly instructions

[0939] Original message: "Hurry up and assemble this part. You're late!"

[0940] Suggested fix after analysis: "Can you please assemble this part? We're running behind schedule, so we'd really appreciate your help."

[0941] Prompt Sentence Examples

[0942] Modify the following message to be more assertive: "Hurry up and put this part together. We're running late!" Emotion: Annoyance

[0943] Using this system is expected to greatly facilitate communication on-site, leading to improved work efficiency and working conditions.

[0944] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0945] Step 1:

[0946] A user logs into a communication tool and turns on "assertive mode" on the settings screen. The user performs this operation on their own device (PC, tablet, or smartphone), and this setting is sent to the server. The input is the user's setting information, and the output is the setting data with assertive mode turned on. This allows the server to recognize that a specific user is using assertive mode.

[0947] Step 2:

[0948] The user inputs factory work instructions via text or voice. For example, "Assemble this part quickly. We're running late!" The input is the work instruction message, and the output is the message data. The input message is securely transmitted to the server in real time by the terminal. Data encryption is used during this process to ensure the security of the message.

[0949] Step 3:

[0950] The server analyzes the received message. First, it uses a natural language processing (NLP) library (Spacy) to tokenize the message and analyze its grammatical structure. Next, the emotion engine analyzes the user's emotions and detects aggressive or non-assertive expressions. The input is the received message data, and the output is the analysis results (detection of aggressive expressions and emotions).

[0951] Step 4:

[0952] The server generates assertive revision suggestions based on the analysis results. It uses a generative AI model (OpenAI API) to convert detected aggressive or non-assertive expressions into assertive expressions. An example prompt used here is in the following format: "Please revise the following message to an assertive expression: "Assemble this part quickly. We're late!" Emotion: Annoyed." The input is the analysis results and the prompt from the generative AI model, and the output is an assertive revision suggestion.

[0953] Step 5:

[0954] The generated revision suggestions are sent from the server to the terminal for the user to review. The terminal displays the revision suggestions for the user to review. The user can either accept the revision suggestions as they are or make fine adjustments as needed. The input is the revision suggestion data, and the output is the revised instructions after the user has reviewed them.

[0955] Step 6:

[0956] The revision suggestions that the user confirms or fine-tunes are sent to the server as feedback. The server updates the learning model based on this feedback data and reflects it in future revision suggestions. The input is the user's feedback data, and the output is the updated learning model.

[0957] Step 7:

[0958] The modified work instructions are displayed or played audibly on the industrial machine, which then performs the work based on the new assertive instructions. The input is the modified work instructions, and the output is the start of machine operation.

[0959] Through these steps, users can give work instructions using assertive language rather than aggressive language, which will facilitate smooth communication and improve work efficiency in factories and production sites.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] [Third embodiment]

[0964] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0965] 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.

[0966] 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).

[0967] 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.

[0968] 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.

[0969] 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).

[0970] 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.

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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."

[0976] This invention is a system that allows users to use assertive expressions in communication tools to promote smooth communication. The main elements that make up the system are the user's terminal, a server, and the communication that takes place between them.

[0977] Basic system configuration

[0978] 1. User Settings

[0979] User: Log in to the communication tool they use and select "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[0980] 2. Enter and send a message

[0981] User: Enter text in the message input field.

[0982] Terminal: Messages entered by the user are sent to the server in real time. Data security is ensured during transmission using encryption technology.

[0983] 3. Message Analysis

[0984] Server: Analyzes received messages and detects aggressive or non-assertive expressions. Natural language processing technology is used for the analysis. For example, it analyzes a message such as "Please correct this report immediately. There are too many mistakes."

[0985] 4. Generate correction suggestions

[0986] Server: Generate assertive revision suggestions based on the detected expressions. In the above example, the server generates a revision suggestion such as "Could you please revise this report? I found some mistakes, so I would like you to check them."

[0987] 5. Submitting and Viewing Revisions

[0988] Server: Sends the generated correction proposal to the device.

[0989] Terminal: A suggested fix will pop up above the message field.

[0990] 6. User Verification and Adoption

[0991] User: Review the suggested fixes and adopt them as needed, or make minor adjustments to the suggested fixes.

[0992] 7. Feedback and Learning

[0993] Server: Records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis.

[0994] Specific examples

[0995] Example 1: Requesting a report revision

[0996] User: Type "Please fix this report immediately. There are too many mistakes."

[0997] Terminal: Sends the entered message to the server.

[0998] Server: Detects offensive language and generates a suggested correction, such as: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[0999] Terminal: Display suggested fixes to the user.

[1000] User: Review and adopt the proposed fix.

[1001] Example 2: Checking project progress

[1002] User: Type "No progress report yet. Please let me know soon."

[1003] Terminal: Sends the entered message to the server.

[1004] Server: Detects offensive language and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[1005] Terminal: Display suggested fixes to the user.

[1006] User: Review and adopt the proposed fix.

[1007] In this way, by implementing the system according to the embodiment of the present invention, users can communicate smoothly.

[1008] The processing flow will be explained below.

[1009] Step 1:

[1010] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[1011] Step 2:

[1012] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[1013] Step 3:

[1014] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[1015] Step 4:

[1016] User: Enter text into the message input field in the communications tool.

[1017] For example: "Please fix this report immediately. There are too many mistakes."

[1018] Step 5:

[1019] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[1020] Step 6:

[1021] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[1022] Step 7:

[1023] Server: Generates correction suggestions to convert the detected expressions into assertive expressions.

[1024] For example: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1025] Step 8:

[1026] Server: Sends the generated correction proposal to the device.

[1027] Step 9:

[1028] Terminal: A suggested fix will pop up over the message input field.

[1029] Step 10:

[1030] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[1031] Step 11:

[1032] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[1033] Step 12:

[1034] Server: Updates the learning model based on feedback and learns the user's unique expressions, phrases, and sentence logic on a daily basis.

[1035] The above are the specific processing steps in this system.

[1036] Example 1

[1037] 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."

[1038] In modern communication tools, messages sent by users often cause misunderstandings and conflicts. In particular, when aggressive or non-assertive expressions are included, smooth communication becomes difficult. There is a need for a method to solve this problem and enable users to communicate more constructively and amicably.

[1039] 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.

[1040] In this invention, the server includes means for analyzing received communications and detecting aggressive or non-assertive expressions, means for using a generative AI model to generate assertive revision suggestions based on the detected expressions, and means for updating the machine learning model based on feedback and reflecting the feedback in generating future revision suggestions, thereby enabling users to have more constructive and friendly communications.

[1041] 1. A "communication tool" is a system that includes application software and hardware that allows users to exchange messages with other users.

[1042] 2. "Assertive Mode" refers to a setting that allows users to communicate in a non-aggressive and clear manner.

[1043] 3. "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to input or view messages.

[1044] 4. "Communications" refers to text messages and other digital information sent by a User via a Device.

[1045] 5. "Server" means a central (or cloud-based) computing system for receiving, analyzing, and processing communications.

[1046] 6. "Generative AI model" refers to a machine learning model that uses natural language processing technology to analyze strings of characters and generate appropriate correction suggestions.

[1047] 7. "Offensive language" refers to words or phrases that may be offensive to the recipient.

[1048] 8. "Non-assertive language" refers to words or phrases that convey an ambiguous message or unclear intent to the receiver.

[1049] 9. "Revision" refers to an alternative message that improves on an original message that contains offensive or non-assertive language.

[1050] 10. "Machine Learning Model" refers to algorithms and systems that automatically improve their performance based on feedback data.

[1051] This invention is a system that allows users to use assertive expressions to promote smooth communication through communication tools. The system mainly consists of a flow of user settings, message input and sending, analysis and generation of correction suggestions by the server, presentation of the correction suggestions, feedback and learning.

[1052] User Settings

[1053] The user logs in to the settings screen of the communication tool and selects "assertive mode." This switches the communication tool to a mode that prioritizes assertive language. This setting converts messages sent by the user into more constructive and friendly language. The setting information is saved on the device and also sent to the server.

[1054] Enter and send a message

[1055] The user enters a message into the input field of the communication tool. For example, "Please correct this report immediately. There are too many mistakes." The device encrypts the message in real time and sends it to the server. This encryption uses SSL / TLS technology to ensure data security.

[1056] Message Parsing

[1057] The server records the received message in a database and analyzes it using natural language processing techniques (e.g., BERT and GPT). This detects aggressive and non-assertive expressions. For example, aggressive expressions such as "Please fix it immediately" and "There are too many mistakes" are detected.

[1058] Generate correction suggestions

[1059] The server generates assertive correction suggestions using a generative AI model (e.g., GPT-3) based on the detected inappropriate expressions. The generated correction suggestion might be, for example, "Could you please correct this report? I found some mistakes, so I would like you to check them."

[1060] Submitting and Viewing Proposed Revisions

[1061] The server encodes the proposed changes and sends them to the device after encryption. The device decodes the changes and displays them as a pop-up in the message field. The user can review them and either accept them or make further adjustments. After final confirmation, the revised message is sent back to the server.

[1062] Feedback and Learning

[1063] The server records the revision suggestions adopted by the user as feedback data. This data is used the next time the analysis is performed. The server's machine learning model is updated daily based on this feedback, learning the user's unique expressions, phrasing, and sentence logic. This allows the generation of more natural and appropriate revision suggestions.

[1064] Examples and prompts

[1065] Specific examples

[1066] User: Type "Please fix this report immediately. There are too many mistakes."

[1067] Terminal: Sends the entered message to the server.

[1068] Server: Detect offensive language and generate suggested corrections: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1069] Terminal: Display suggested fixes to the user.

[1070] User: Review and adopt the proposed fix.

[1071] Prompt Sentence Examples

[1072] Prompt 1: "Please assertively correct the user-entered aggressive message 'Please fix this report immediately. There are too many mistakes.'"

[1073] Prompt 2: "Please revise the message 'You haven't provided a progress report yet. Please provide one soon.' to be more polite and assertive."

[1074] This system allows users to communicate naturally and, in particular, to avoid conflicts and misunderstandings.

[1075] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1076] Step 1:

[1077] User Settings

[1078] User: Log in to the settings screen of the communication tool and select "Assertive Mode." This action changes the current user settings.

[1079] Input: The configuration option selected by the user

[1080] Output: Configuration changes are logged and propagated to the device and server.

[1081] What it does: The user turns on the "Assertive Mode" switch in the settings screen and saves the changes. The device saves this setting in local storage and also sends it to the server.

[1082] Step 2:

[1083] Enter and send a message

[1084] User: Enters communication in the message input field. For example, "Please fix this report immediately. There are too many mistakes."

[1085] Terminal: The input message is sent to the server in real time using SSL / TLS encryption technology.

[1086] Input: The communication entered by the user

[1087] Output: The encrypted message is sent to the server.

[1088] Specific operation: The user enters a message in the input field and clicks the "Send" button. The device encrypts the message and sends it to the server.

[1089] Step 3:

[1090] Message Parsing

[1091] Server: Records received messages in a database and analyzes them using natural language processing techniques (e.g., BERT and GPT).

[1092] Input: Message decrypted and logged to the database

[1093] Output: Detection results for aggressive and non-assertive expressions

[1094] What it does: The server stores the received message in a database and starts the parsing process, using natural language processing models such as BERT and GPT to detect specific expressions.

[1095] Step 4:

[1096] Generate correction suggestions

[1097] Server: Based on the detected offensive expressions, a generative AI model (e.g., GPT-3) is used to generate assertive correction suggestions.

[1098] Input: Detected offensive or non-assertive language

[1099] Output: Correction suggestions generated by the generative AI model

[1100] Specific operation: The server takes the detected inappropriate expressions as input and sends a prompt to the generative AI model. The generated correction suggestions are received as output. Example: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[1101] Step 5:

[1102] Submitting and Viewing Proposed Revisions

[1103] Server: The generated correction proposal is encoded, encrypted, and then sent to the device.

[1104] Terminal: Decodes received correction suggestions and displays them in a popup in the message field.

[1105] Input: Generated correction proposal

[1106] Output: The suggested fixes shown to the user

[1107] What it does: The server encodes the proposed correction, encrypts it, and sends it to the device, which decodes it and pops up the proposed correction in the message field.

[1108] Step 6:

[1109] User Verification and Adoption

[1110] User: Review the suggested fixes and adopt or tweak them as needed.

[1111] Terminal: Sends the user's final message to the server.

[1112] Enter: The suggested fix that pops up

[1113] Output: Final message confirmed and accepted by the user

[1114] What happens: The user reviews the suggested changes, clicks the "Accept" button, makes any necessary adjustments, makes corrections in the edit fields, and then submits the changes.

[1115] Step 7:

[1116] Feedback and Learning

[1117] Server: Records the proposed corrections adopted by the user and saves them as feedback data. This data is used for future analysis and revision generation.

[1118] Input: The suggested fix that the user adopted

[1119] Output: An updated machine learning model

[1120] How it works: The server uses the feedback data to update the machine learning model and learn the user's unique expressions and phrasing, leading to improved analysis accuracy and quality of correction suggestions in the future.

[1121] Through the above processing steps, this system enables users to achieve smoother and more assertive communication.

[1122] (Application example 1)

[1123] 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."

[1124] In interactions between store clerks and customers in physical stores, store clerks sometimes unconsciously use aggressive or non-assertive language, resulting in a decrease in customer satisfaction. These unconscious expressions also contribute to the stress and fatigue of the store clerks themselves, lowering the overall quality of service. Therefore, there is a need for a method to support smooth communication in real time and improve interactions with customers.

[1125] 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.

[1126] In this invention, the server includes: means for a user to select assertive mode on a setting screen of a communication tool; means for a terminal to transmit an input message to the server in real time; means for the server to analyze the received message and detect aggressive or non-assertive expressions; means for the server to generate assertive correction suggestions based on the detected expressions; and means for supporting communication in direct dialogue by displaying the correction suggestions on a display device in real time. This enables store clerks to smoothly conduct dialogue with customers and improve customer satisfaction.

[1127] "User setting" refers to the act of the user selecting assertive mode on the setting screen of the communication tool.

[1128] A "terminal" is a device that transmits messages entered by a user to a server in real time and displays suggested revisions sent from the server.

[1129] The "server" is a computer system that analyzes received messages, detects offensive or non-assertive language, and generates assertive revision suggestions.

[1130] "Message analysis" is the process of analyzing the content of received messages and detecting aggressive or non-assertive expressions.

[1131] "Generating a correction" is the process of creating assertive expressions to replace the detected aggressive or non-assertive expressions.

[1132] "Feedback" refers to the evaluation and usage history data that users send to the server after reviewing and adopting proposed revisions.

[1133] The "learning model" is an algorithm within the server that is updated based on user feedback to improve the accuracy of generating revision suggestions.

[1134] A "display device" is a device that displays suggested revisions in real time, such as smart glasses.

[1135] "Assertive mode" is a special mode that allows the user to modify aggressive or non-assertive statements into assertive statements.

[1136] "Real time" means that processing is performed in real time and results are obtained without delay.

[1137] "Encryption technology" is a method for ensuring the security of data when it is sent from a terminal to a server.

[1138] The program of the system for carrying out the present invention promotes smooth communication by mutually coordinating users, terminals, and servers.

[1139] Program Overview

[1140] The user selects "assertive mode" on the settings screen of the communication tool. This setting switches the entire system to a mode that uses assertive expressions. The user's device (e.g., smart glasses) sends the input message to the server in real time.

[1141] Server Processing

[1142] The server analyzes the received message to detect aggressive or non-assertive language. It uses natural language processing technologies such as Spacy and Transformers for the analysis. Based on the analysis results, the server generates assertive correction suggestions. The suggestions are created using a generative AI model, and include specific examples of prompts, such as:

[1143] Prompt: "Provide assertive language in customer interactions."

[1144] Example: "First input: 'This product is completely useless!'"

[1145] Result: "Could you please let me know if you have any problems with this product?"

[1146] Terminal handling

[1147] The proposed revisions are then sent back to the terminal, which then notifies the user. Using a real-time display system such as smart glasses, the salesperson can view the revisions while interacting with the customer.

[1148] Feedback and Learning

[1149] Finally, the user reviews the suggested revisions and either adopts or fine-tunes them, sending feedback to the server. The server uses this feedback to update the learning model and reflect it in future message analysis and revision generation. This feedback data allows the system to more precisely learn the user's unique expressions, phrasing, and sentence logic over time.

[1150] Specific examples

[1151] For example, if a store clerk uses an aggressive expression such as "This product is completely unusable!", the device will send this message to the server in real time. The server will detect this expression, generate an assertive correction suggestion such as "Could you please tell us if there are any problems with this product?", and send this to the device. The store clerk can then view this correction suggestion through the smart glasses and respond to the customer in an assertive manner.

[1152] Hardware and software used

[1153] Hardware: Real-time display devices such as smart glasses (e.g., Google Glass)

[1154] Software: Spacy, Transformers (BERT model)

[1155] This facilitates smooth communication between store staff and customers, and improves customer satisfaction.

[1156] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1157] Step 1:

[1158] User Settings

[1159] The user selects "assertive mode" on the settings screen of the communication tool. This switches the system into a mode that encourages assertive expression. This operation is performed by the user, and the setting change is reflected on the device.

[1160] Input: User selection of assertive mode

[1161] Output: Change system mode

[1162] Step 2:

[1163] Enter and send a message

[1164] The user enters text into the message field. The terminal sends this entered message to the server in real time. During transmission, encryption technology (e.g., SSL / TLS) is used to ensure data security.

[1165] Input: User enters a message

[1166] Output: Encrypted message sent to server

[1167] Step 3:

[1168] Message Parsing

[1169] The server analyzes the received message, using natural language processing techniques (e.g., SpaCy, Transformers) to detect aggressive or non-assertive language. Specifically, it uses a generative AI model to tokenize and classify the text.

[1170] Input: The message received by the server

[1171] Output: Detecting aggressive or non-assertive language

[1172] Step 4:

[1173] Generate correction suggestions

[1174] The server generates assertive correction suggestions for offensive or non-assertive expressions using a generative AI model and prompts such as the following:

[1175] Prompt: "Provide assertive language in customer interactions."

[1176] Example: "First input: 'This product is completely useless!'"

[1177] Result: "Could you please let me know if you have any problems with this product?"

[1178] Input: Detected offensive or non-assertive language

[1179] Output: Assertive fix

[1180] Step 5:

[1181] Submitting a proposed revision

[1182] The server then sends the generated revision proposal to the device, where it is securely transmitted using encryption technology.

[1183] Input: Server-generated correction suggestions

[1184] Output: Send encrypted revision to device

[1185] Step 6:

[1186] View suggested fixes

[1187] The terminal displays the proposed revisions to the user in real time, and the user can view the revisions through a display device such as smart glasses.

[1188] Input: Suggested fix sent to device

[1189] Output: The suggested fixes shown to the user

[1190] Step 7:

[1191] User Review and Feedback

[1192] The user reviews the suggested modifications and accepts or fine-tunes them as necessary. The user's selections are sent to the server as feedback, which is used for further analysis and modification generation.

[1193] Input: User reviews and accepts proposed changes

[1194] Output: Feedback data sent to server

[1195] Step 8:

[1196] Update the learning model

[1197] The server updates the learning model based on the feedback data, learning the user's unique expressions, phrasing, and sentence logic, which are then reflected in future revision suggestions.

[1198] Input: Feedback data

[1199] Output: Updated training model

[1200] This will allow for continuous improvement of the entire system and facilitate smoother communication between store staff and customers.

[1201] 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.

[1202] The present invention provides a system for promoting smooth communication by allowing users to use assertive expressions in a communication tool. The system components include a user terminal, a server, communication between them, and an emotion engine.

[1203] Basic system configuration

[1204] 1. User Settings

[1205] User: Log in to the communication tool they use and turn on "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[1206] 2. Enter and send a message

[1207] User: Enter text in the message input field.

[1208] Terminal: Messages entered by the user are sent to the server in real time, with encryption technology applied to ensure data security during transmission.

[1209] 3. Message Analysis

[1210] Server: Analyzes received messages to detect aggressive or non-assertive expressions using natural language processing (NLP) technology, and recognizes the user's emotions using an emotion engine.

[1211] 4. Generate correction suggestions

[1212] Server: Generates assertive correction suggestions based on the detected expressions. The emotion engine optimizes replacement expressions by taking into account the user's emotions.

[1213] Example: User message: "Please correct this report immediately. There are too many mistakes." → Server suggestion: "Could you please correct this report? I found some mistakes, so I'd like you to take a look at them."

[1214] 5. Submitting and Viewing Revisions

[1215] Server: Sends the generated correction proposal to the device.

[1216] Terminal: A suggested fix will pop up over the message input field.

[1217] 6. User Verification and Adoption

[1218] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[1219] 7. Feedback and Learning

[1220] Server: Records the correction suggestions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis. It also updates the emotion engine, reflecting this in the generation of correction suggestions that take emotions into account, thereby providing more appropriate responses.

[1221] Specific examples

[1222] Example 1: Requesting a report revision

[1223] User: Type "Please fix this report immediately. There are too many mistakes."

[1224] Terminal: Sends the entered message to the server.

[1225] Server: Detects offensive language and the user's feelings of frustration and generates a correction suggestion like this: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1226] Terminal: Display suggested fixes to the user.

[1227] User: Review and adopt the proposed fix.

[1228] Example 2: Checking project progress

[1229] User: Type "No progress report yet. Please let me know soon."

[1230] Terminal: Sends the entered message to the server.

[1231] Server: Detects aggressive language and the user's sense of impatience and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[1232] Terminal: Display suggested fixes to the user.

[1233] User: Review and adopt the proposed fix.

[1234] In this way, by implementing the system according to the embodiment of the invention, users can communicate smoothly while taking their emotions into consideration.

[1235] The processing flow will be explained below.

[1236] Step 1:

[1237] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[1238] Step 2:

[1239] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[1240] Step 3:

[1241] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[1242] Step 4:

[1243] User: Enter text into the message input field in the communications tool.

[1244] For example: "Please fix this report immediately. There are too many mistakes."

[1245] Step 5:

[1246] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[1247] Step 6:

[1248] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[1249] Step 7:

[1250] Server: Recognizes the user's emotions in real time using the emotion engine. In this process, it identifies the emotion behind the message.

[1251] Step 8:

[1252] Server: Generates assertive correction suggestions based on the detected expressions and recognized emotions. The emotion engine optimizes replacement expressions by taking into account the user's emotions recognized by the emotion engine.

[1253] For example: If the perceived emotion is irritation, change it to a more gentle expression: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1254] Step 9:

[1255] Server: Sends the generated correction proposal to the device.

[1256] Step 10:

[1257] Terminal: A suggested fix will pop up over the message input field.

[1258] Step 11:

[1259] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[1260] Step 12:

[1261] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[1262] Step 13:

[1263] Server: Based on the feedback, the learning model and emotion engine are updated to generate correction suggestions that take into account the user's unique expressions, phrasing, sentence logic, and emotions.

[1264] The above are the specific processing steps in this system.

[1265] Example 2

[1266] 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."

[1267] With conventional communication tools, users often used aggressive or non-assertive language, which hindered smooth communication. This also posed the risk of causing interpersonal problems and reducing work efficiency. Furthermore, because users' messages were sent as is, it was time-consuming to correct and optimize the language, which caused stress for users. There was a need to solve these issues.

[1268] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing received data and detecting specific expressions and non-specific expressions, means for generating specific correction suggestions based on the detected expressions, and means for updating the learning model based on feedback and reflecting the feedback in generating future correction suggestions. This allows the user to avoid aggressive and non-assertive expressions and enable more assertive communication.

[1269] A "User" is an individual or entity that uses a communication tool to send or receive messages.

[1270] A "communication tool" is software or hardware that a user uses to send and receive messages.

[1271] The "specific mode" is one of the settings of the communication tool used by the user, and is a mode for using specific phrases and expressions.

[1272] A "terminal" is a device that a user uses to operate a communication tool.

[1273] "Data" means any message or information that a User sends or receives through a Communication Tool.

[1274] "Server" means a device or system that receives and processes data sent from a terminal and transmits the data to other terminals.

[1275] "Analysis" refers to the analytical work performed by the server on the data received, including the detection of specific and non-specific expressions.

[1276] "Specific language" refers to offensive or non-assertive language used by users in their messages.

[1277] "Non-specific expressions" refer to general phrases that users use in their messages.

[1278] A "revision suggestion" is a more assertive expression that the server generates based on the detected expression.

[1279] "Feedback" refers to the evaluation information that a user sends to the server after reviewing and adopting a proposed revision.

[1280] A "learning model" is a collection of algorithms and data that improves the predictions the server makes based on feedback.

[1281] "Data protection technology" is a technology for ensuring the security of data when it is transmitted from a terminal to a server.

[1282] This invention is a system that allows users to use assertive expressions to promote smooth communication in communication tools. The system components include the terminal used by the user, a server that analyzes and processes data, the communication between these, as well as a natural language processing engine and an emotion engine.

[1283] System Components

[1284] 1. User's device

[1285] A device used to operate communication tools. It allows users to input messages and send them to a server. Typically, this type of device is a PC, smartphone, or tablet.

[1286] 2. Server

[1287] This system analyzes received data and detects aggressive or non-assertive expressions. It then generates assertive correction suggestions based on the detected expressions and sends them to the user. It is written in programming languages ​​such as Python and JavaScript and runs on cloud or on-premise servers.

[1288] 3. Data Protection Technology

[1289] This technology ensures the security of data sent from the user's device to the server. Specifically, it uses TLS / SSL encryption technology.

[1290] 4. Natural Language Processing Engine (NLP)

[1291] An engine for analyzing received messages. Examples include Python's NLTK library and Google's BERT.

[1292] 5. Emotion Engine

[1293] This engine recognizes emotions from the user's message and generates revision suggestions based on those emotions, thereby proposing the most appropriate expression that takes the user's emotions into account.

[1294] Example of operation

[1295] Example 1: Requesting a report revision

[1296] 1. User: Type, "Please fix this report immediately. There are too many mistakes."

[1297] 2. Terminal: Sends this message to the server in real time.

[1298] 3. Server: Analyzes the message and detects offensive language and user annoyance.

[1299] 4. Server: Generate a correction suggestion: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[1300] 5. Terminal: Display suggested fixes to the user.

[1301] 6. User: Review and adopt the proposed fix.

[1302] Example 2: Checking project progress

[1303] 1. User: Type "No progress report yet. Please report soon."

[1304] 2. Terminal: Send this message to the server.

[1305] 3. Server: Detects aggressive language and the user's impatient emotions and generates a correction suggestion like this: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[1306] 4. Terminal: Display suggested fixes to the user.

[1307] 5. User: Review and adopt the proposed fix.

[1308] Examples of prompt statements

[1309] An example prompt sentence might have the following natural language form:

[1310] Please modify the user-entered message to be more assertive:

[1311] "There is no progress report yet. Please report it soon."

[1312] Transform the following aggressive messages into assertive ones:

[1313] "Please correct this report immediately. There are too many mistakes."

[1314] With the above components and operations, the present invention provides a system that facilitates communication and helps users to communicate more assertively.

[1315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1316] Step 1: User Setup

[1317] The user turns on "assertive mode" in the settings screen of the communication tool.

[1318] Input: The user selects "Assertive Mode" on the settings screen

[1319] Output: The communication tool is changed to assertive mode.

[1320] Specific behavior: The user opens the settings menu of the communication tool and toggles on "Assertive Mode." This action updates the communication tool's settings.

[1321] Step 2: Write and send your message

[1322] The user enters text into the message input field.

[1323] Input: The user types a message into the message input field of the communication tool.

[1324] Output: The input message

[1325] Specific operation: The user enters "There is no progress report yet. Please report soon." and clicks the send button.

[1326] The terminal encrypts the input message and sends it to the server in real time.

[1327] Input: The message entered by the user

[1328] Output: Encrypted message

[1329] Specific operation: The terminal encrypts the input message using TLS / SSL encryption technology and sends it to the server.

[1330] Step 3: Message Analysis

[1331] The server analyzes the messages it receives and detects aggressive or non-assertive language.

[1332] Input: The encrypted message sent from the device

[1333] Output: Analysis results (aggressive expressions, non-assertive expressions, etc.)

[1334] What it does: The server decodes the message and uses a natural language processing engine (e.g., NLTK, BERT, etc.) to detect offensive or non-assertive language.

[1335] Step 4: Generate correction suggestions

[1336] The server generates assertive correction suggestions based on the detected expressions.

[1337] Input: Analysis results and user emotions (recognition by emotion engine)

[1338] Output: Assertive fix

[1339] How it works: The emotion engine recognizes emotions such as irritation or impatience from the user's message, and based on this, the server uses a generative AI model to generate assertive correction suggestions.

[1340] For example, in response to the message "There is no progress report yet. Please report it soon," a correction suggestion is generated that reads, "Could you please provide a progress report? I apologize for bothering you when you're busy, but it would be helpful if you could let me know the current situation."

[1341] Step 5: Submit and view proposed revisions

[1342] The server sends the generated revision proposal to the terminal.

[1343] Input: Generated assertive revision suggestions

[1344] Output: Data sent to the terminal

[1345] Specific operation: The server prepares the generated revision proposal as data to be sent to the terminal.

[1346] The device will pop up suggested fixes over the message entry field.

[1347] Input: Correction proposal sent by server

[1348] Output: Suggested fixes displayed on the user's device

[1349] Specific behavior: The device displays the received correction suggestions as a pop-up above the message input field.

[1350] Step 6: User Verification and Adoption

[1351] The user can check the suggested revisions and, if necessary, adopt them. They can also make minor adjustments to the suggested revisions.

[1352] Input: The suggested fix pops up

[1353] Output: Adopted or fine-tuned revisions

[1354] Specific behavior: The user reviews the proposed fix and either accepts the suggestion as is, saying, "Could you please give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on." or makes some minor changes and sends it.

[1355] Step 7: Feedback and learning

[1356] The server records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions.

[1357] Input: Adopted amendments and feedback data

[1358] Output: Updated training model

[1359] How it works: The server records the correction suggestions adopted by the user as feedback data and updates the machine learning model. This update allows for more accurate message analysis and correction suggestions from the next time onwards.

[1360] The above processing steps enable users to communicate smoothly.

[1361] (Application example 2)

[1362] 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."

[1363] In factories and other workplaces, there is a problem of communication between workers and machines not being carried out smoothly using conventional methods. In particular, work instructions are often given in aggressive or non-assertive language, which can have a negative impact on the work environment and work efficiency. As a result, there are an increase in work mistakes and problems, which leads to problems such as reduced productivity and increased stress.

[1364] 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: a means for a user to select assertive mode on a setting screen of a communication tool; a means for a terminal to send an input message to the server in real time; a means for the server to analyze the received message and detect aggressive or non-assertive expressions; a means for the server to generate an assertive revision proposal based on the detected expression; a means for the server to send the generated revision proposal to the terminal; a means for the terminal to display the revision proposal to the user; a means for the user to confirm and adopt the revision proposal and send feedback to the server; a means for the server to update the learning model based on the feedback and reflect this in future revision proposal generation; a means for correcting voice or text work instructions to assertive expressions; and a means for displaying or presenting the corrected work instructions on industrial machines. This enables smoother communication and improved work efficiency at construction sites and production lines.

[1365] A "user" is a person or organization that uses the system to input messages and communicate.

[1366] "Communication tools" refers to applications and software that users use to exchange information with each other.

[1367] The "assertive mode" is a mode in which the user uses expressions that are not aggressive but assertive and respectful of the other person.

[1368] "Terminal" means a device used by a user to input messages and receive and display instructions from a server.

[1369] "Server" is a central computer system that analyzes messages sent from user terminals and generates and sends appropriate corrections.

[1370] A "message" is text or voice data that is input by a user and sent to a server via a terminal.

[1371] "Real-time" refers to data being sent and received almost instantly.

[1372] "Offensive language" means language that contains words or sentences intended to offend or be offensive to the target.

[1373] "Non-assertive expression" refers to language that does not make one's own position clear or that is non-assertive toward others.

[1374] An "assertive amendment" is a proposal that transforms aggressive or non-assertive language into language that clarifies one's own position while showing respect for the other party.

[1375] "Voice or text work instructions" are voice or text messages used to instruct work content or procedures in a factory or on-site.

[1376] "Industrial machinery" refers to various machines and robots used in factories and production sites.

[1377] "Displayed or audibly presented" means that the corrected instruction content is displayed on a display or played audibly.

[1378] The present invention is a system that supports users in using assertive expressions in communication tools, and is particularly applied to improving work instructions in factories and production sites. Specific embodiments for carrying out the present invention will be described below.

[1379] System Configuration

[1380] Hardware

[1381] User device: A PC, tablet, or smartphone used by a factory leader or worker.

[1382] Server: A central computer system located in the cloud that analyzes messages and generates correction suggestions.

[1383] Industrial machines: Robots and other industrial equipment that operate according to instructions.

[1384] software

[1385] Natural Language Processing (NLP) library: Spacy is used.

[1386] Generative AI model: Uses OpenAI's API.

[1387] Web server framework: Use Flask or Django.

[1388] Operating principle

[1389] 1. User Settings

[1390] When a user selects "assertive mode" on the settings screen of a communication tool, the system switches to that mode.

[1391] 2. Enter and send a message

[1392] Users input factory work instructions by voice or text, such as "Assemble this part quickly. We're running late!"

[1393] The entered message is sent to the server in real time, and the device uses encryption technology to ensure the security of the data.

[1394] 3. Message Analysis

[1395] The server analyzes the received messages and detects aggressive or non-assertive expressions using natural language processing (NLP) technology and an emotion engine.

[1396] For example, aggressive expressions such as "fast" and "late" are detected.

[1397] 4. Generate correction suggestions

[1398] The server generates assertive revision suggestions based on the detected expressions and the user's emotions. Using a generative AI model, a revision suggestion such as, "Could you please assemble this part? Progress is behind schedule, so please check it."

[1399] 5. Submitting and Viewing Revisions

[1400] The proposed corrections are sent to a terminal where the user can review them, and the terminal displays the corrections as text or audio, and posts them on the industrial machine as needed.

[1401] 6. User Verification and Adoption

[1402] The user reviews the proposed modifications and adopts or fine-tunes them as necessary.

[1403] 7. Feedback and Learning

[1404] The revision suggestions adopted by the user are sent to the server and recorded as feedback data, which the server uses to update the learning model and reflect in future revision suggestions.

[1405] Specific examples

[1406] Example 1: Part assembly instructions

[1407] Original message: "Hurry up and assemble this part. You're late!"

[1408] Suggested fix after analysis: "Can you please assemble this part? We're running behind schedule, so we'd really appreciate your help."

[1409] Prompt Sentence Examples

[1410] Modify the following message to be more assertive: "Hurry up and put this part together. We're running late!" Emotion: Annoyance

[1411] Using this system is expected to greatly facilitate communication on-site, leading to improved work efficiency and working conditions.

[1412] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1413] Step 1:

[1414] A user logs into a communication tool and turns on "assertive mode" on the settings screen. The user performs this operation on their own device (PC, tablet, or smartphone), and this setting is sent to the server. The input is the user's setting information, and the output is the setting data with assertive mode turned on. This allows the server to recognize that a specific user is using assertive mode.

[1415] Step 2:

[1416] The user inputs factory work instructions via text or voice. For example, "Assemble this part quickly. We're running late!" The input is the work instruction message, and the output is the message data. The input message is securely transmitted to the server in real time by the terminal. Data encryption is used during this process to ensure the security of the message.

[1417] Step 3:

[1418] The server analyzes the received message. First, it uses a natural language processing (NLP) library (Spacy) to tokenize the message and analyze its grammatical structure. Next, the emotion engine analyzes the user's emotions and detects aggressive or non-assertive expressions. The input is the received message data, and the output is the analysis results (detection of aggressive expressions and emotions).

[1419] Step 4:

[1420] The server generates assertive revision suggestions based on the analysis results. It uses a generative AI model (OpenAI API) to convert detected aggressive or non-assertive expressions into assertive expressions. An example prompt used here is in the following format: "Please revise the following message to an assertive expression: "Assemble this part quickly. We're late!" Emotion: Annoyed." The input is the analysis results and the prompt from the generative AI model, and the output is an assertive revision suggestion.

[1421] Step 5:

[1422] The generated revision suggestions are sent from the server to the terminal for the user to review. The terminal displays the revision suggestions for the user to review. The user can either accept the revision suggestions as they are or make fine adjustments as needed. The input is the revision suggestion data, and the output is the revised instructions after the user has reviewed them.

[1423] Step 6:

[1424] The revision suggestions that the user confirms or fine-tunes are sent to the server as feedback. The server updates the learning model based on this feedback data and reflects it in future revision suggestions. The input is the user's feedback data, and the output is the updated learning model.

[1425] Step 7:

[1426] The modified work instructions are displayed or played audibly on the industrial machine, which then performs the work based on the new assertive instructions. The input is the modified work instructions, and the output is the start of machine operation.

[1427] Through these steps, users can give work instructions using assertive language rather than aggressive language, which will facilitate smooth communication and improve work efficiency in factories and production sites.

[1428] 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.

[1429] 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.

[1430] 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.

[1431] [Fourth embodiment]

[1432] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1433] 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.

[1434] 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).

[1435] 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.

[1436] 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.

[1437] 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).

[1438] 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.

[1439] 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.

[1440] 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.

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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."

[1445] This invention is a system that allows users to use assertive expressions in communication tools to promote smooth communication. The main elements that make up the system are the user's terminal, a server, and the communication that takes place between them.

[1446] Basic system configuration

[1447] 1. User Settings

[1448] User: Log in to the communication tool they use and select "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[1449] 2. Enter and send a message

[1450] User: Enter text in the message input field.

[1451] Terminal: Messages entered by the user are sent to the server in real time. Data security is ensured during transmission using encryption technology.

[1452] 3. Message Analysis

[1453] Server: Analyzes received messages and detects aggressive or non-assertive expressions. Natural language processing technology is used for the analysis. For example, it analyzes a message such as "Please correct this report immediately. There are too many mistakes."

[1454] 4. Generate correction suggestions

[1455] Server: Generate assertive revision suggestions based on the detected expressions. In the above example, the server generates a revision suggestion such as "Could you please revise this report? I found some mistakes, so I would like you to check them."

[1456] 5. Submitting and Viewing Revisions

[1457] Server: Sends the generated correction proposal to the device.

[1458] Terminal: A suggested fix will pop up above the message field.

[1459] 6. User Verification and Adoption

[1460] User: Review the suggested fixes and adopt them as needed, or make minor adjustments to the suggested fixes.

[1461] 7. Feedback and Learning

[1462] Server: Records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis.

[1463] Specific examples

[1464] Example 1: Requesting a report revision

[1465] User: Type "Please fix this report immediately. There are too many mistakes."

[1466] Terminal: Sends the entered message to the server.

[1467] Server: Detects offensive language and generates a suggested correction, such as: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1468] Terminal: Display suggested fixes to the user.

[1469] User: Review and adopt the proposed fix.

[1470] Example 2: Checking project progress

[1471] User: Type "No progress report yet. Please let me know soon."

[1472] Terminal: Sends the entered message to the server.

[1473] Server: Detects offensive language and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[1474] Terminal: Display suggested fixes to the user.

[1475] User: Review and adopt the proposed fix.

[1476] In this way, by implementing the system according to the embodiment of the present invention, users can communicate smoothly.

[1477] The processing flow will be explained below.

[1478] Step 1:

[1479] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[1480] Step 2:

[1481] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[1482] Step 3:

[1483] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[1484] Step 4:

[1485] User: Enter text into the message input field in the communications tool.

[1486] For example: "Please fix this report immediately. There are too many mistakes."

[1487] Step 5:

[1488] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[1489] Step 6:

[1490] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[1491] Step 7:

[1492] Server: Generates correction suggestions to convert the detected expressions into assertive expressions.

[1493] For example: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1494] Step 8:

[1495] Server: Sends the generated correction proposal to the device.

[1496] Step 9:

[1497] Terminal: A suggested fix will pop up over the message input field.

[1498] Step 10:

[1499] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[1500] Step 11:

[1501] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[1502] Step 12:

[1503] Server: Updates the learning model based on feedback and learns the user's unique expressions, phrases, and sentence logic on a daily basis.

[1504] The above are the specific processing steps in this system.

[1505] Example 1

[1506] 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."

[1507] In modern communication tools, messages sent by users often cause misunderstandings and conflicts. In particular, when aggressive or non-assertive expressions are included, smooth communication becomes difficult. There is a need for a method to solve this problem and enable users to communicate more constructively and amicably.

[1508] 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.

[1509] In this invention, the server includes means for analyzing received communications and detecting aggressive or non-assertive expressions, means for using a generative AI model to generate assertive revision suggestions based on the detected expressions, and means for updating the machine learning model based on feedback and reflecting the feedback in generating future revision suggestions, thereby enabling users to have more constructive and friendly communications.

[1510] 1. A "communication tool" is a system that includes application software and hardware that allows users to exchange messages with other users.

[1511] 2. "Assertive Mode" refers to a setting that allows users to communicate in a non-aggressive and clear manner.

[1512] 3. "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to input or view messages.

[1513] 4. "Communications" refers to text messages and other digital information sent by a User via a Device.

[1514] 5. "Server" means a central (or cloud-based) computing system for receiving, analyzing, and processing communications.

[1515] 6. "Generative AI model" refers to a machine learning model that uses natural language processing technology to analyze strings of characters and generate appropriate correction suggestions.

[1516] 7. "Offensive language" refers to words or phrases that may be offensive to the recipient.

[1517] 8. "Non-assertive language" refers to words or phrases that convey an ambiguous message or unclear intent to the receiver.

[1518] 9. "Revision" refers to an alternative message that improves on an original message that contains offensive or non-assertive language.

[1519] 10. "Machine Learning Model" refers to algorithms and systems that automatically improve their performance based on feedback data.

[1520] This invention is a system that allows users to use assertive expressions to promote smooth communication through communication tools. The system mainly consists of a flow of user settings, message input and sending, analysis and generation of correction suggestions by the server, presentation of the correction suggestions, feedback and learning.

[1521] User Settings

[1522] The user logs in to the settings screen of the communication tool and selects "assertive mode." This switches the communication tool to a mode that prioritizes assertive language. This setting converts messages sent by the user into more constructive and friendly language. The setting information is saved on the device and also sent to the server.

[1523] Enter and send a message

[1524] The user enters a message into the input field of the communication tool. For example, "Please correct this report immediately. There are too many mistakes." The device encrypts the message in real time and sends it to the server. This encryption uses SSL / TLS technology to ensure data security.

[1525] Message Parsing

[1526] The server records the received message in a database and analyzes it using natural language processing techniques (e.g., BERT and GPT). This detects aggressive and non-assertive expressions. For example, aggressive expressions such as "Please fix it immediately" and "There are too many mistakes" are detected.

[1527] Generate correction suggestions

[1528] The server generates assertive correction suggestions using a generative AI model (e.g., GPT-3) based on the detected inappropriate expressions. The generated correction suggestion might be, for example, "Could you please correct this report? I found some mistakes, so I would like you to check them."

[1529] Submitting and Viewing Proposed Revisions

[1530] The server encodes the proposed changes and sends them to the device after encryption. The device decodes the changes and displays them as a pop-up in the message field. The user can review them and either accept them or make further adjustments. After final confirmation, the revised message is sent back to the server.

[1531] Feedback and Learning

[1532] The server records the revision suggestions adopted by the user as feedback data. This data is used the next time the analysis is performed. The server's machine learning model is updated daily based on this feedback, learning the user's unique expressions, phrasing, and sentence logic. This allows the generation of more natural and appropriate revision suggestions.

[1533] Examples and prompts

[1534] Specific examples

[1535] User: Type "Please fix this report immediately. There are too many mistakes."

[1536] Terminal: Sends the entered message to the server.

[1537] Server: Detect offensive language and generate suggested corrections: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1538] Terminal: Display suggested fixes to the user.

[1539] User: Review and adopt the proposed fix.

[1540] Prompt Sentence Examples

[1541] Prompt 1: "Please assertively correct the user-entered aggressive message 'Please fix this report immediately. There are too many mistakes.'"

[1542] Prompt 2: "Please revise the message 'You haven't provided a progress report yet. Please provide one soon.' to be more polite and assertive."

[1543] This system allows users to communicate naturally and, in particular, to avoid conflicts and misunderstandings.

[1544] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1545] Step 1:

[1546] User Settings

[1547] User: Log in to the settings screen of the communication tool and select "Assertive Mode." This action changes the current user settings.

[1548] Input: The configuration option selected by the user

[1549] Output: Configuration changes are logged and propagated to the device and server.

[1550] What it does: The user turns on the "Assertive Mode" switch in the settings screen and saves the changes. The device saves this setting in local storage and also sends it to the server.

[1551] Step 2:

[1552] Enter and send a message

[1553] User: Enters communication in the message input field. For example, "Please fix this report immediately. There are too many mistakes."

[1554] Terminal: The input message is sent to the server in real time using SSL / TLS encryption technology.

[1555] Input: The communication entered by the user

[1556] Output: The encrypted message is sent to the server.

[1557] Specific operation: The user enters a message in the input field and clicks the "Send" button. The device encrypts the message and sends it to the server.

[1558] Step 3:

[1559] Message Parsing

[1560] Server: Records received messages in a database and analyzes them using natural language processing techniques (e.g., BERT and GPT).

[1561] Input: Message decrypted and logged to the database

[1562] Output: Detection results for aggressive and non-assertive expressions

[1563] What it does: The server stores the received message in a database and starts the parsing process, using natural language processing models such as BERT and GPT to detect specific expressions.

[1564] Step 4:

[1565] Generate correction suggestions

[1566] Server: Based on the detected offensive expressions, a generative AI model (e.g., GPT-3) is used to generate assertive correction suggestions.

[1567] Input: Detected offensive or non-assertive language

[1568] Output: Correction suggestions generated by the generative AI model

[1569] Specific operation: The server takes the detected inappropriate expressions as input and sends a prompt to the generative AI model. The generated correction suggestions are received as output. Example: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[1570] Step 5:

[1571] Submitting and Viewing Proposed Revisions

[1572] Server: The generated correction proposal is encoded, encrypted, and then sent to the device.

[1573] Terminal: Decodes received correction suggestions and displays them in a popup in the message field.

[1574] Input: Generated correction proposal

[1575] Output: The suggested fixes shown to the user

[1576] What it does: The server encodes the proposed correction, encrypts it, and sends it to the device, which decodes it and pops up the proposed correction in the message field.

[1577] Step 6:

[1578] User Verification and Adoption

[1579] User: Review the suggested fixes and adopt or tweak them as needed.

[1580] Terminal: Sends the user's final message to the server.

[1581] Enter: The suggested fix that pops up

[1582] Output: Final message confirmed and accepted by the user

[1583] What happens: The user reviews the suggested changes, clicks the "Accept" button, makes any necessary adjustments, makes corrections in the edit fields, and then submits the changes.

[1584] Step 7:

[1585] Feedback and Learning

[1586] Server: Records the proposed corrections adopted by the user and saves them as feedback data. This data is used for future analysis and revision generation.

[1587] Input: The suggested fix that the user adopted

[1588] Output: An updated machine learning model

[1589] How it works: The server uses the feedback data to update the machine learning model and learn the user's unique expressions and phrasing, leading to improved analysis accuracy and quality of correction suggestions in the future.

[1590] Through the above processing steps, this system enables users to achieve smoother and more assertive communication.

[1591] (Application example 1)

[1592] 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."

[1593] In interactions between store clerks and customers in physical stores, store clerks sometimes unconsciously use aggressive or non-assertive language, resulting in a decrease in customer satisfaction. These unconscious expressions also contribute to the stress and fatigue of the store clerks themselves, lowering the overall quality of service. Therefore, there is a need for a method to support smooth communication in real time and improve interactions with customers.

[1594] 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.

[1595] In this invention, the server includes: means for a user to select assertive mode on a setting screen of a communication tool; means for a terminal to transmit an input message to the server in real time; means for the server to analyze the received message and detect aggressive or non-assertive expressions; means for the server to generate assertive correction suggestions based on the detected expressions; and means for supporting communication in direct dialogue by displaying the correction suggestions on a display device in real time. This enables store clerks to smoothly conduct dialogue with customers and improve customer satisfaction.

[1596] "User setting" refers to the act of the user selecting assertive mode on the setting screen of the communication tool.

[1597] A "terminal" is a device that transmits messages entered by a user to a server in real time and displays suggested revisions sent from the server.

[1598] The "server" is a computer system that analyzes received messages, detects offensive or non-assertive language, and generates assertive revision suggestions.

[1599] "Message analysis" is the process of analyzing the content of received messages and detecting aggressive or non-assertive expressions.

[1600] "Generating a correction" is the process of creating assertive expressions to replace the detected aggressive or non-assertive expressions.

[1601] "Feedback" refers to the evaluation and usage history data that users send to the server after reviewing and adopting proposed revisions.

[1602] The "learning model" is an algorithm within the server that is updated based on user feedback to improve the accuracy of generating revision suggestions.

[1603] A "display device" is a device that displays suggested revisions in real time, such as smart glasses.

[1604] "Assertive mode" is a special mode that allows the user to modify aggressive or non-assertive statements into assertive statements.

[1605] "Real time" means that processing is performed in real time and results are obtained without delay.

[1606] "Encryption technology" is a method for ensuring the security of data when it is sent from a terminal to a server.

[1607] The program of the system for carrying out the present invention promotes smooth communication by mutually coordinating users, terminals, and servers.

[1608] Program Overview

[1609] The user selects "assertive mode" on the settings screen of the communication tool. This setting switches the entire system to a mode that uses assertive expressions. The user's device (e.g., smart glasses) sends the input message to the server in real time.

[1610] Server Processing

[1611] The server analyzes the received message to detect aggressive or non-assertive language. It uses natural language processing technologies such as Spacy and Transformers for the analysis. Based on the analysis results, the server generates assertive correction suggestions. The suggestions are created using a generative AI model, and include specific examples of prompts, such as:

[1612] Prompt: "Provide assertive language in customer interactions."

[1613] Example: "First input: 'This product is completely useless!'"

[1614] Result: "Could you please let me know if you have any problems with this product?"

[1615] Terminal handling

[1616] The proposed revisions are then sent back to the terminal, which then notifies the user. Using a real-time display system such as smart glasses, the salesperson can view the revisions while interacting with the customer.

[1617] Feedback and Learning

[1618] Finally, the user reviews the suggested revisions and either adopts or fine-tunes them, sending feedback to the server. The server uses this feedback to update the learning model and reflect it in future message analysis and revision generation. This feedback data allows the system to more precisely learn the user's unique expressions, phrasing, and sentence logic over time.

[1619] Specific examples

[1620] For example, if a store clerk uses an aggressive expression such as "This product is completely unusable!", the device will send this message to the server in real time. The server will detect this expression, generate an assertive correction suggestion such as "Could you please tell us if there are any problems with this product?", and send this to the device. The store clerk can then view this correction suggestion through the smart glasses and respond to the customer in an assertive manner.

[1621] Hardware and software used

[1622] Hardware: Real-time display devices such as smart glasses (e.g., Google Glass)

[1623] Software: Spacy, Transformers (BERT model)

[1624] This facilitates smooth communication between store staff and customers, and improves customer satisfaction.

[1625] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1626] Step 1:

[1627] User Settings

[1628] The user selects "assertive mode" on the settings screen of the communication tool. This switches the system into a mode that encourages assertive expression. This operation is performed by the user, and the setting change is reflected on the device.

[1629] Input: User selection of assertive mode

[1630] Output: Change system mode

[1631] Step 2:

[1632] Enter and send a message

[1633] The user enters text into the message field. The terminal sends this entered message to the server in real time. During transmission, encryption technology (e.g., SSL / TLS) is used to ensure data security.

[1634] Input: User enters a message

[1635] Output: Encrypted message sent to server

[1636] Step 3:

[1637] Message Parsing

[1638] The server analyzes the received message, using natural language processing techniques (e.g., SpaCy, Transformers) to detect aggressive or non-assertive language. Specifically, it uses a generative AI model to tokenize and classify the text.

[1639] Input: The message received by the server

[1640] Output: Detecting aggressive or non-assertive language

[1641] Step 4:

[1642] Generate correction suggestions

[1643] The server generates assertive correction suggestions for offensive or non-assertive expressions using a generative AI model and prompts such as the following:

[1644] Prompt: "Provide assertive language in customer interactions."

[1645] Example: "First input: 'This product is completely useless!'"

[1646] Result: "Could you please let me know if you have any problems with this product?"

[1647] Input: Detected offensive or non-assertive language

[1648] Output: Assertive fix

[1649] Step 5:

[1650] Submitting a proposed revision

[1651] The server then sends the generated revision proposal to the device, where it is securely transmitted using encryption technology.

[1652] Input: Server-generated correction suggestions

[1653] Output: Send encrypted revision to device

[1654] Step 6:

[1655] View suggested fixes

[1656] The terminal displays the proposed revisions to the user in real time, and the user can view the revisions through a display device such as smart glasses.

[1657] Input: Suggested fix sent to device

[1658] Output: The suggested fixes shown to the user

[1659] Step 7:

[1660] User Review and Feedback

[1661] The user reviews the suggested modifications and accepts or fine-tunes them as necessary. The user's selections are sent to the server as feedback, which is used for further analysis and modification generation.

[1662] Input: User reviews and accepts proposed changes

[1663] Output: Feedback data sent to server

[1664] Step 8:

[1665] Update the learning model

[1666] The server updates the learning model based on the feedback data, learning the user's unique expressions, phrasing, and sentence logic, which are then reflected in future revision suggestions.

[1667] Input: Feedback data

[1668] Output: Updated training model

[1669] This will allow for continuous improvement of the entire system and facilitate smoother communication between store staff and customers.

[1670] 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.

[1671] The present invention provides a system for promoting smooth communication by allowing users to use assertive expressions in a communication tool. The system components include a user terminal, a server, communication between them, and an emotion engine.

[1672] Basic system configuration

[1673] 1. User Settings

[1674] User: Log in to the communication tool they use and turn on "Assertive Mode" in the settings screen. This switches the tool into a mode that uses assertive language.

[1675] 2. Enter and send a message

[1676] User: Enter text in the message input field.

[1677] Terminal: Messages entered by the user are sent to the server in real time, with encryption technology applied to ensure data security during transmission.

[1678] 3. Message Analysis

[1679] Server: Analyzes received messages to detect aggressive or non-assertive expressions using natural language processing (NLP) technology, and recognizes the user's emotions using an emotion engine.

[1680] 4. Generate correction suggestions

[1681] Server: Generates assertive correction suggestions based on the detected expressions. The emotion engine optimizes replacement expressions by taking into account the user's emotions.

[1682] Example: User message: "Please correct this report immediately. There are too many mistakes." → Server suggestion: "Could you please correct this report? I found some mistakes, so I'd like you to take a look at them."

[1683] 5. Submitting and Viewing Revisions

[1684] Server: Sends the generated correction proposal to the device.

[1685] Terminal: A suggested fix will pop up over the message input field.

[1686] 6. User Verification and Adoption

[1687] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[1688] 7. Feedback and Learning

[1689] Server: Records the correction suggestions adopted by the user and uses them as feedback data for future analysis and suggestions. The server updates the learning model based on this data, learning the user's unique expressions, phrases, and sentence logic on a daily basis. It also updates the emotion engine, reflecting this in the generation of correction suggestions that take emotions into account, thereby providing more appropriate responses.

[1690] Specific examples

[1691] Example 1: Requesting a report revision

[1692] User: Type "Please fix this report immediately. There are too many mistakes."

[1693] Terminal: Sends the entered message to the server.

[1694] Server: Detects offensive language and the user's feelings of frustration and generates a correction suggestion like this: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1695] Terminal: Display suggested fixes to the user.

[1696] User: Review and adopt the proposed fix.

[1697] Example 2: Checking project progress

[1698] User: Type "No progress report yet. Please let me know soon."

[1699] Terminal: Sends the entered message to the server.

[1700] Server: Detects aggressive language and the user's sense of impatience and generates a suggested fix, such as: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[1701] Terminal: Display suggested fixes to the user.

[1702] User: Review and adopt the proposed fix.

[1703] In this way, by implementing the system according to the embodiment of the invention, users can communicate smoothly while taking their emotions into consideration.

[1704] The processing flow will be explained below.

[1705] Step 1:

[1706] User: Open the settings screen of your communication tool and turn on "Assertive Mode."

[1707] Step 2:

[1708] Server: Detects when the user turns on assertive mode and collects the user's past communication logs (chat history and email history).

[1709] Step 3:

[1710] Server: Analyzes collected communication logs and learns the user's unique expressions, phrases, and sentence logic.

[1711] Step 4:

[1712] User: Enter text into the message input field in the communications tool.

[1713] For example: "Please fix this report immediately. There are too many mistakes."

[1714] Step 5:

[1715] Terminal: The entered message is sent to the server in real time. During transmission, encryption technology is applied to ensure the security of the data.

[1716] Step 6:

[1717] Server: Analyzes received messages and detects offensive or non-assertive language using natural language processing (NLP) techniques.

[1718] Step 7:

[1719] Server: Recognizes the user's emotions in real time using the emotion engine. In this process, it identifies the emotion behind the message.

[1720] Step 8:

[1721] Server: Generates assertive correction suggestions based on the detected expressions and recognized emotions. The emotion engine optimizes replacement expressions by taking into account the user's emotions recognized by the emotion engine.

[1722] For example: If the perceived emotion is irritation, change it to a more gentle expression: "Could you please correct this report? I found some mistakes and would like you to take a look at them."

[1723] Step 9:

[1724] Server: Sends the generated correction proposal to the device.

[1725] Step 10:

[1726] Terminal: A suggested fix will pop up over the message input field.

[1727] Step 11:

[1728] User: Review the suggested changes and, if necessary, adopt them. They can also make minor adjustments to the suggested changes.

[1729] Step 12:

[1730] Server: Records the proposed modifications adopted by the user and uses them as feedback data for future analysis and proposals.

[1731] Step 13:

[1732] Server: Based on the feedback, the learning model and emotion engine are updated to generate correction suggestions that take into account the user's unique expressions, phrasing, sentence logic, and emotions.

[1733] The above are the specific processing steps in this system.

[1734] Example 2

[1735] 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."

[1736] With conventional communication tools, users often used aggressive or non-assertive language, which hindered smooth communication. This also posed the risk of causing interpersonal problems and reducing work efficiency. Furthermore, because users' messages were sent as is, it was time-consuming to correct and optimize the language, which caused stress for users. There was a need to solve these issues.

[1737] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for analyzing received data and detecting specific expressions and non-specific expressions, means for generating specific correction suggestions based on the detected expressions, and means for updating the learning model based on feedback and reflecting the feedback in generating future correction suggestions. This allows the user to avoid aggressive and non-assertive expressions and enable more assertive communication.

[1738] A "User" is an individual or entity that uses a communication tool to send or receive messages.

[1739] A "communication tool" is software or hardware that a user uses to send and receive messages.

[1740] The "specific mode" is one of the settings of the communication tool used by the user, and is a mode for using specific phrases and expressions.

[1741] A "terminal" is a device that a user uses to operate a communication tool.

[1742] "Data" means any message or information that a User sends or receives through a Communication Tool.

[1743] "Server" means a device or system that receives and processes data sent from a terminal and transmits the data to other terminals.

[1744] "Analysis" refers to the analytical work performed by the server on the data received, including the detection of specific and non-specific expressions.

[1745] "Specific language" refers to offensive or non-assertive language used by users in their messages.

[1746] "Non-specific expressions" refer to general phrases that users use in their messages.

[1747] A "revision suggestion" is a more assertive expression that the server generates based on the detected expression.

[1748] "Feedback" refers to the evaluation information that a user sends to the server after reviewing and adopting a proposed revision.

[1749] A "learning model" is a collection of algorithms and data that improves the predictions the server makes based on feedback.

[1750] "Data protection technology" is a technology for ensuring the security of data when it is transmitted from a terminal to a server.

[1751] This invention is a system that allows users to use assertive expressions to promote smooth communication in communication tools. The system components include the terminal used by the user, a server that analyzes and processes data, the communication between these, as well as a natural language processing engine and an emotion engine.

[1752] System Components

[1753] 1. User's device

[1754] A device used to operate communication tools. It allows users to input messages and send them to a server. Typically, this type of device is a PC, smartphone, or tablet.

[1755] 2. Server

[1756] This system analyzes received data and detects aggressive or non-assertive expressions. It then generates assertive correction suggestions based on the detected expressions and sends them to the user. It is written in programming languages ​​such as Python and JavaScript and runs on cloud or on-premise servers.

[1757] 3. Data Protection Technology

[1758] This technology ensures the security of data sent from the user's device to the server. Specifically, it uses TLS / SSL encryption technology.

[1759] 4. Natural Language Processing Engine (NLP)

[1760] An engine for analyzing received messages. Examples include Python's NLTK library and Google's BERT.

[1761] 5. Emotion Engine

[1762] This engine recognizes emotions from the user's message and generates revision suggestions based on those emotions, thereby proposing the most appropriate expression that takes the user's emotions into account.

[1763] Example of operation

[1764] Example 1: Requesting a report revision

[1765] 1. User: Type, "Please fix this report immediately. There are too many mistakes."

[1766] 2. Terminal: Sends this message to the server in real time.

[1767] 3. Server: Analyzes the message and detects offensive language and user annoyance.

[1768] 4. Server: Generate a correction suggestion: "Could you please correct this report? I found some mistakes, so I would like you to check them."

[1769] 5. Terminal: Display suggested fixes to the user.

[1770] 6. User: Review and adopt the proposed fix.

[1771] Example 2: Checking project progress

[1772] 1. User: Type "No progress report yet. Please report soon."

[1773] 2. Terminal: Send this message to the server.

[1774] 3. Server: Detects aggressive language and the user's impatient emotions and generates a correction suggestion like this: "Can you give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on."

[1775] 4. Terminal: Display suggested fixes to the user.

[1776] 5. User: Review and adopt the proposed fix.

[1777] Examples of prompt statements

[1778] An example prompt sentence might have the following natural language form:

[1779] Please modify the user-entered message to be more assertive:

[1780] "There is no progress report yet. Please report it soon."

[1781] Transform the following aggressive messages into assertive ones:

[1782] "Please correct this report immediately. There are too many mistakes."

[1783] With the above components and operations, the present invention provides a system that facilitates communication and helps users to communicate more assertively.

[1784] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1785] Step 1: User Setup

[1786] The user turns on "assertive mode" in the settings screen of the communication tool.

[1787] Input: The user selects "Assertive Mode" on the settings screen

[1788] Output: The communication tool is changed to assertive mode.

[1789] Specific behavior: The user opens the settings menu of the communication tool and toggles on "Assertive Mode." This action updates the communication tool's settings.

[1790] Step 2: Write and send your message

[1791] The user enters text into the message input field.

[1792] Input: The user types a message into the message input field of the communication tool.

[1793] Output: The input message

[1794] Specific operation: The user enters "There is no progress report yet. Please report soon." and clicks the send button.

[1795] The terminal encrypts the input message and sends it to the server in real time.

[1796] Input: The message entered by the user

[1797] Output: Encrypted message

[1798] Specific operation: The terminal encrypts the input message using TLS / SSL encryption technology and sends it to the server.

[1799] Step 3: Message Analysis

[1800] The server analyzes the messages it receives and detects aggressive or non-assertive language.

[1801] Input: The encrypted message sent from the device

[1802] Output: Analysis results (aggressive expressions, non-assertive expressions, etc.)

[1803] What it does: The server decodes the message and uses a natural language processing engine (e.g., NLTK, BERT, etc.) to detect offensive or non-assertive language.

[1804] Step 4: Generate correction suggestions

[1805] The server generates assertive correction suggestions based on the detected expressions.

[1806] Input: Analysis results and user emotions (recognition by emotion engine)

[1807] Output: Assertive fix

[1808] How it works: The emotion engine recognizes emotions such as irritation or impatience from the user's message, and based on this, the server uses a generative AI model to generate assertive correction suggestions.

[1809] For example, in response to the message "There is no progress report yet. Please report it soon," a correction suggestion is generated that reads, "Could you please provide a progress report? I apologize for bothering you when you're busy, but it would be helpful if you could let me know the current situation."

[1810] Step 5: Submit and view proposed revisions

[1811] The server sends the generated revision proposal to the terminal.

[1812] Input: Generated assertive revision suggestions

[1813] Output: Data sent to the terminal

[1814] Specific operation: The server prepares the generated revision proposal as data to be sent to the terminal.

[1815] The device will pop up suggested fixes over the message entry field.

[1816] Input: Correction proposal sent by server

[1817] Output: Suggested fixes displayed on the user's device

[1818] Specific behavior: The device displays the received correction suggestions as a pop-up above the message input field.

[1819] Step 6: User Verification and Adoption

[1820] The user can check the suggested revisions and, if necessary, adopt them. They can also make minor adjustments to the suggested revisions.

[1821] Input: The suggested fix pops up

[1822] Output: Adopted or fine-tuned revisions

[1823] Specific behavior: The user reviews the proposed fix and either accepts the suggestion as is, saying, "Could you please give me a progress report? I know you're busy, but it would be helpful if you could let me know what's going on." or makes some minor changes and sends it.

[1824] Step 7: Feedback and learning

[1825] The server records the revisions adopted by the user and uses them as feedback data for future analysis and suggestions.

[1826] Input: Adopted amendments and feedback data

[1827] Output: Updated training model

[1828] How it works: The server records the correction suggestions adopted by the user as feedback data and updates the machine learning model. This update allows for more accurate message analysis and correction suggestions from the next time onwards.

[1829] The above processing steps enable users to communicate smoothly.

[1830] (Application example 2)

[1831] 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."

[1832] In factories and other workplaces, there is a problem of communication between workers and machines not being carried out smoothly using conventional methods. In particular, work instructions are often given in aggressive or non-assertive language, which can have a negative impact on the work environment and work efficiency. As a result, there are an increase in work mistakes and problems, which leads to problems such as reduced productivity and increased stress.

[1833] 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: a means for a user to select assertive mode on a setting screen of a communication tool; a means for a terminal to send an input message to the server in real time; a means for the server to analyze the received message and detect aggressive or non-assertive expressions; a means for the server to generate an assertive revision proposal based on the detected expression; a means for the server to send the generated revision proposal to the terminal; a means for the terminal to display the revision proposal to the user; a means for the user to confirm and adopt the revision proposal and send feedback to the server; a means for the server to update the learning model based on the feedback and reflect this in future revision proposal generation; a means for correcting voice or text work instructions to assertive expressions; and a means for displaying or presenting the corrected work instructions on industrial machines. This enables smoother communication and improved work efficiency at construction sites and production lines.

[1834] A "user" is a person or organization that uses the system to input messages and communicate.

[1835] "Communication tools" refers to applications and software that users use to exchange information with each other.

[1836] The "assertive mode" is a mode in which the user uses expressions that are not aggressive but assertive and respectful of the other person.

[1837] "Terminal" means a device used by a user to input messages and receive and display instructions from a server.

[1838] "Server" is a central computer system that analyzes messages sent from user terminals and generates and sends appropriate corrections.

[1839] A "message" is text or voice data that is input by a user and sent to a server via a terminal.

[1840] "Real-time" refers to data being sent and received almost instantly.

[1841] "Offensive language" means language that contains words or sentences intended to offend or be offensive to the target.

[1842] "Non-assertive expression" refers to language that does not make one's own position clear or that is non-assertive toward others.

[1843] An "assertive amendment" is a proposal that transforms aggressive or non-assertive language into language that clarifies one's own position while showing respect for the other party.

[1844] "Voice or text work instructions" are voice or text messages used to instruct work content or procedures in a factory or on-site.

[1845] "Industrial machinery" refers to various machines and robots used in factories and production sites.

[1846] "Displayed or audibly presented" means that the corrected instruction content is displayed on a display or played audibly.

[1847] The present invention is a system that supports users in using assertive expressions in communication tools, and is particularly applied to improving work instructions in factories and production sites. Specific embodiments for carrying out the present invention will be described below.

[1848] System Configuration

[1849] Hardware

[1850] User device: A PC, tablet, or smartphone used by a factory leader or worker.

[1851] Server: A central computer system located in the cloud that analyzes messages and generates correction suggestions.

[1852] Industrial machines: Robots and other industrial equipment that operate according to instructions.

[1853] software

[1854] Natural Language Processing (NLP) library: Spacy is used.

[1855] Generative AI model: Uses OpenAI's API.

[1856] Web server framework: Use Flask or Django.

[1857] Operating principle

[1858] 1. User Settings

[1859] When a user selects "assertive mode" on the settings screen of a communication tool, the system switches to that mode.

[1860] 2. Enter and send a message

[1861] Users input factory work instructions by voice or text, such as "Assemble this part quickly. We're running late!"

[1862] The entered message is sent to the server in real time, and the device uses encryption technology to ensure the security of the data.

[1863] 3. Message Analysis

[1864] The server analyzes the received messages and detects aggressive or non-assertive expressions using natural language processing (NLP) technology and an emotion engine.

[1865] For example, aggressive expressions such as "fast" and "late" are detected.

[1866] 4. Generate correction suggestions

[1867] The server generates assertive revision suggestions based on the detected expressions and the user's emotions. Using a generative AI model, a revision suggestion such as, "Could you please assemble this part? Progress is behind schedule, so please check it."

[1868] 5. Submitting and Viewing Revisions

[1869] The proposed corrections are sent to a terminal where the user can review them, and the terminal displays the corrections as text or audio, and posts them on the industrial machine as needed.

[1870] 6. User Verification and Adoption

[1871] The user reviews the proposed modifications and adopts or fine-tunes them as necessary.

[1872] 7. Feedback and Learning

[1873] The revision suggestions adopted by the user are sent to the server and recorded as feedback data, which the server uses to update the learning model and reflect in future revision suggestions.

[1874] Specific examples

[1875] Example 1: Part assembly instructions

[1876] Original message: "Hurry up and assemble this part. You're late!"

[1877] Suggested fix after analysis: "Can you please assemble this part? We're running behind schedule, so we'd really appreciate your help."

[1878] Prompt Sentence Examples

[1879] Modify the following message to be more assertive: "Hurry up and put this part together. We're running late!" Emotion: Annoyance

[1880] Using this system is expected to greatly facilitate communication on-site, leading to improved work efficiency and working conditions.

[1881] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1882] Step 1:

[1883] A user logs into a communication tool and turns on "assertive mode" on the settings screen. The user performs this operation on their own device (PC, tablet, or smartphone), and this setting is sent to the server. The input is the user's setting information, and the output is the setting data with assertive mode turned on. This allows the server to recognize that a specific user is using assertive mode.

[1884] Step 2:

[1885] The user inputs factory work instructions via text or voice. For example, "Assemble this part quickly. We're running late!" The input is the work instruction message, and the output is the message data. The input message is securely transmitted to the server in real time by the terminal. Data encryption is used during this process to ensure the security of the message.

[1886] Step 3:

[1887] The server analyzes the received message. First, it uses a natural language processing (NLP) library (Spacy) to tokenize the message and analyze its grammatical structure. Next, the emotion engine analyzes the user's emotions and detects aggressive or non-assertive expressions. The input is the received message data, and the output is the analysis results (detection of aggressive expressions and emotions).

[1888] Step 4:

[1889] The server generates assertive revision suggestions based on the analysis results. It uses a generative AI model (OpenAI API) to convert detected aggressive or non-assertive expressions into assertive expressions. An example prompt used here is in the following format: "Please revise the following message to an assertive expression: "Assemble this part quickly. We're late!" Emotion: Annoyed." The input is the analysis results and the prompt from the generative AI model, and the output is an assertive revision suggestion.

[1890] Step 5:

[1891] The generated revision suggestions are sent from the server to the terminal for the user to review. The terminal displays the revision suggestions for the user to review. The user can either accept the revision suggestions as they are or make fine adjustments as needed. The input is the revision suggestion data, and the output is the revised instructions after the user has reviewed them.

[1892] Step 6:

[1893] The revision suggestions that the user confirms or fine-tunes are sent to the server as feedback. The server updates the learning model based on this feedback data and reflects it in future revision suggestions. The input is the user's feedback data, and the output is the updated learning model.

[1894] Step 7:

[1895] The modified work instructions are displayed or played audibly on the industrial machine, which then performs the work based on the new assertive instructions. The input is the modified work instructions, and the output is the start of machine operation.

[1896] Through these steps, users can give work instructions using assertive language rather than aggressive language, which will facilitate smooth communication and improve work efficiency in factories and production sites.

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] 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.

[1902] 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.

[1903] 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).

[1904] 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.

[1905] 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."

[1906] 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.

[1907] 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).

[1908] 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.

[1909] 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.

[1910] 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.

[1911] 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.

[1912] 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.

[1913] 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.

[1914] 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.

[1915] 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.

[1916] 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.

[1917] 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.

[1918] The following is further disclosed regarding the above embodiment.

[1919] (Claim 1)

[1920] A means for a user to select assertive mode on a setting screen of a communication tool;

[1921] A means for transmitting input messages from the terminal to a server in real time;

[1922] A means for analyzing messages received by the server to detect offensive or non-assertive language;

[1923] a means for the server to generate assertive revision suggestions based on the detected expressions;

[1924] means for transmitting the generated revision proposals to the terminal;

[1925] means for the terminal to display the suggested revisions to the user;

[1926] A means for users to review and adopt proposed changes and send feedback to the server;

[1927] A means for the server to update the learning model based on the feedback and reflect it in future revision generation;

[1928] A system including:

[1929] (Claim 2)

[1930] 2. The system according to claim 1, further comprising means for the server to analyze the user's past communication log and learn the user's unique expressions, phrases, and sentence logic.

[1931] (Claim 3)

[1932] 10. The system of claim 1, further comprising means for applying encryption techniques when the terminal transmits user-entered messages to the server in real time.

[1933] "Example 1"

[1934] (Claim 1)

[1935] A means for a user to select assertive mode on a setting screen of a communication tool;

[1936] A means for transmitting communication content input by the terminal to a server in real time;

[1937] A means for analyzing the content of communications received by the server and detecting offensive or non-assertive language;

[1938] a means for the server to use the generative AI model to generate assertive revision suggestions based on the detected expressions;

[1939] means for transmitting the generated revision proposals to the terminal;

[1940] means for the terminal to display the suggested revisions to the user;

[1941] A means for users to review and adopt proposed modifications and send feedback to the server;

[1942] The server updates the machine learning model based on the feedback and reflects it in future revision generation.

[1943] A system including:

[1944] (Claim 2)

[1945] 2. The system of claim 1, further comprising means for the server to analyze the user's past communication history and learn the user's unique expressions, phrases, and sentence logic.

[1946] (Claim 3)

[1947] 10. The system of claim 1, further comprising means for applying encryption techniques when the terminal transmits user-entered communications to the server in real time.

[1948] "Application Example 1"

[1949] (Claim 1)

[1950] A means for a user to select assertive mode on a setting screen of a communication tool;

[1951] A means for transmitting input messages from the terminal to a server in real time;

[1952] A means for analyzing messages received by the server to detect offensive or non-assertive language;

[1953] a means for the server to generate assertive revision suggestions based on the detected expressions;

[1954] means for transmitting the generated revision proposals to the terminal;

[1955] means for the terminal to display the suggested revisions to the user;

[1956] A means for users to review and adopt proposed changes and send feedback to the server;

[1957] A means for the server to update the learning model based on the feedback and reflect it in future revision generation;

[1958] a means for supporting communication in face-to-face interactions by displaying suggested revisions on a display device in real time;

[1959] A system including:

[1960] (Claim 2)

[1961] 2. The system according to claim 1, further comprising means for the server to analyze the user's past communication log and learn the user's unique expressions, phrases, and sentence logic.

[1962] (Claim 3)

[1963] 10. The system of claim 1, further comprising means for applying encryption techniques when the terminal transmits user-entered messages to the server in real time.

[1964] "Example 2: Combining Emotion Engines"

[1965] (Claim 1)

[1966] A means for a user to select a particular mode on a setting screen of a communication tool;

[1967] A means for transmitting input data from the terminal to a server in real time;

[1968] a means for analyzing the data received by the server to detect specific expressions and non-specific expressions;

[1969] means for the server to generate specific revision suggestions based on the detected expressions;

[1970] means for transmitting the generated revision proposals to the terminal;

[1971] means for the terminal to display the suggested revisions to the user;

[1972] A means for users to review and adopt proposed changes and send feedback to the server;

[1973] A means for the server to update the learning model based on the feedback and reflect it in future revision generation;

[1974] A system including:

[1975] (Claim 2)

[1976] 2. The system according to claim 1, further comprising means for the server to analyze the user's past communication log and learn the user's unique expressions, phrases, and sentence structure.

[1977] (Claim 3)

[1978] 10. The system of claim 1, further comprising: means for applying a data protection technique when the terminal transmits user-input data to the server in real time.

[1979] "Application example 2 when combining emotion engines"

[1980] New Claims

[1981] (Claim 1)

[1982] A means for a user to select assertive mode on a setting screen of a communication tool;

[1983] A means for transmitting input messages from the terminal to a server in real time;

[1984] A means for analyzing messages received by the server to detect offensive or non-assertive language;

[1985] a means for the server to generate assertive revision suggestions based on the detected expressions;

[1986] means for transmitting the generated revision proposals to the terminal;

[1987] means for the terminal to display the suggested revisions to the user;

[1988] A means for users to review and adopt proposed changes and send feedback to the server;

[1989] A means for the server to update the learning model based on the feedback and reflect it in future revision generation;

[1990] A means for modifying voice or text work instructions to make them assertive;

[1991] a means for displaying or audibly presenting the modified work instructions on the industrial machine;

[1992] A system including:

[1993] (Claim 2)

[1994] 2. The system according to claim 1, further comprising means for the server to analyze the user's past communication log and learn the user's unique expressions, phrases, and sentence logic.

[1995] (Claim 3)

[1996] 10. The system of claim 1, further comprising means for applying encryption techniques when the terminal transmits user-entered messages to the server in real time. [Explanation of symbols]

[1997] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to select assertive mode on a setting screen of a communication tool; A means for transmitting input messages from the terminal to a server in real time; A means for analyzing messages received by the server to detect offensive or non-assertive language; a means for the server to generate assertive revision suggestions based on the detected expressions; means for transmitting the generated revision proposals to the terminal; means for the terminal to display the suggested revisions to the user; A means for users to review and adopt proposed changes and send feedback to the server; A means for the server to update the learning model based on the feedback and reflect it in future revision generation; A system including:

2. 2. The system according to claim 1, further comprising means for the server to analyze a past communication log of the user and learn expressions, phrases and sentence logic specific to the user.

3. 10. The system of claim 1, further comprising means for applying encryption techniques when the terminal transmits user-entered messages to the server in real time.

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