System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving input from a user; means for analyzing context using a generative AI that analyzes context; means for comparing a result of the analysis with a preset condition; means for generating a warning when the condition is satisfied; and means for notifying the user of the generated warning.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] Humans are prone to making careless mistakes on a daily basis, such as typos and forgetting to attach files. However, conventional rule-based systems have difficulty effectively detecting context-dependent mistakes, making it difficult to efficiently prevent such mistakes. The objective of this invention is to provide a new method for reducing user work errors and improving work efficiency. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving data entered by a user, a context analysis means for analyzing the context using a generation AI, a means for comparing the analysis result with preset conditions, a means for generating a warning when the condition is met, and a means for notifying the user of the generated warning. In particular, by providing a function for detecting whether an email has an attachment or if an important address is missing from the CC field, it can effectively prevent careless user mistakes.

[0006] A "user" is an individual or entity that is said to represent an individual who operates the system.

[0007] "Input data" refers to information such as text or files sent by a user via a terminal.

[0008] "Means for receiving" refers to a function or device for acquiring and recording input data.

[0009] "Generative AI" is a type of artificial intelligence that has the capabilities of natural language processing and contextual understanding and can analyze the meaning of text data.

[0010] "Context analysis means" refers to a function or device that uses generative AI to analyze the context surrounding input data.

[0011] The "analysis results" are data relating to the meaning and intent of the text obtained by the context analysis means.

[0012] "Pre-defined conditions" are rules or standards that are set as criteria for the system to detect mistakes.

[0013] "Means for comparing conditions" refers to a function or device that compares the analysis results with pre-set conditions and checks whether they match.

[0014] The "means for generating a warning" is a function or device that generates a message to alert the user when a condition is met.

[0015] The "notification means" refers to a function or device for delivering the generated warning to the user in real time.

[0016] "Email" means a digital message sent or received over the Internet.

[0017] An "attachment" is a related file intended to be added to an email.

[0018] The "CC field" is a field in an email that allows sharing of the contents of the message in addition to the sender and specific recipients.

[0019] A "warning" is a message that notifies the user of a mistake in advance and urges them to correct it. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The system of the present invention analyzes data entered by the user in real time and issues appropriate warnings based on the context, thereby preventing careless mistakes such as typos, forgetting to attach files, and missing CCs. The program and processing of the system that embodies the present invention will be described in detail below.

[0042] System Configuration

[0043] The system consists of three main components:

[0044] 1. Terminal

[0045] 2. Server

[0046] 3. Users

[0047] Device role and operation

[0048] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[0049] Server Roles and Operations

[0050] The server analyzes the received text data using the generative AI. It works in the following steps:

[0051] 1. Contextual analysis:

[0052] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[0053] 2. Compare conditions:

[0054] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[0055] 3. Warning generation:

[0056] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[0057] User notification and response

[0058] The terminal notifies the user of the warning received from the server, which is presented as a pop-up message or a dialog box, and the user can confirm it and make any necessary corrections.

[0059] Specific examples

[0060] Example 1: Forgotten attachment

[0061] A user enters an email like this:

[0062] Subject: Next week's meeting materials

[0063] Body of the message: I have attached the meeting materials, so please check them.

[0064] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0065] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0066] This warning is notified to the user via the terminal.

[0067] Example 2: CC leak detection

[0068] A user enters an email like this:

[0069] Subject: Progress Report

[0070] Body: I'll report on my progress. Please add important people to CC too.

[0071] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[0072] Warning: Important person not included in CC. Please check your CC field.

[0073] This warning is notified to the user via the terminal.

[0074] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[0078] Step 2:

[0079] The device captures the text data entered (e.g., the body of the email), along with any related data, such as whether there are any attachments or addresses in the CC field.

[0080] Step 3:

[0081] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's transmission actions.

[0082] Step 4:

[0083] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[0084] Step 5:

[0085] The server compares the results analyzed by the generated AI with pre-set conditions (e.g., when there are no attachments or when the CC field does not contain important addresses).

[0086] Step 6:

[0087] If the server finds that the comparison matches the condition, it will generate a warning message. For example, if the phrase "attached" is found and there is no attachment, it will generate a warning saying "There are no attachments. Please add an attachment."

[0088] Step 7:

[0089] The server sends the generated warning message to the terminal.

[0090] Step 8:

[0091] The device will notify the user of the received warning message, typically as a pop-up message or alert dialog.

[0092] Step 9:

[0093] The user reviews the warning message and makes the necessary corrections, such as adding attachments again or adding important addresses to the CC field.

[0094] These are the specific processing steps of the "Careless Checker" system, which is designed to prevent users from making careless mistakes. At each step, data is collected and analyzed in a timely manner, and ultimately feedback is provided to the user in real time.

[0095] Example 1

[0096] 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."

[0097] When entering emails or text, careless mistakes such as forgetting to attach files or omitting to CC often occur, which often results in the incomplete transmission of important information. A system that can prevent such mistakes before they occur is needed, but existing systems have difficulty analyzing context in real time or generating appropriate warnings, which reduces user work efficiency.

[0098] 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.

[0099] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning if the condition is met, and a means for notifying the user of the generated warning. This makes it possible to detect careless mistakes when entering emails or other text in real time and notify the user of an appropriate warning.

[0100] "User" means a person or end user who utilizes the system to input data and receive results.

[0101] "Data" is information that a user enters into the system, such as email or other text information.

[0102] A "means" is a device or method designed to perform a particular function or role.

[0103] A "server" is a computer system that processes, stores, and manages data over a network.

[0104] "Generative AI" is a system that generates and analyzes text using artificial intelligence techniques, including natural language processing.

[0105] "Contextual analysis" is the process of understanding the context of input text and analyzing its meaning.

[0106] A "warning" is a message or notification that alerts the user.

[0107] The "receiving means" is a device or method for obtaining data input by a user and transmitting it to a server.

[0108] "Context analysis means" means a device or method for analyzing the context of input text using generative AI.

[0109] The "comparison means" refers to a device or method for comparing the analysis results with pre-set conditions.

[0110] A "means for generating an alert" is a device or method for generating an alert message when a condition is met.

[0111] A "means for notifying" is a device or method for communicating the generated warning message to a user.

[0112] MODE FOR CARRYING OUT THE INVENTION

[0113] The system of the present invention analyzes data entered by the user in real time and issues appropriate context-based warnings to prevent careless mistakes. To explain the operation of the system, specific hardware and software examples will be described in detail.

[0114] System Configuration

[0115] The system consists of three main components:

[0116] 1. Terminal

[0117] 2. Server

[0118] 3. Users

[0119] Device role and operation

[0120] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[0121] Server Roles and Operations

[0122] The server uses generative AI (e.g., a natural language processing model such as GPT-4®) to analyze the received text data. The specific operation of the server is as follows:

[0123] Contextual analysis

[0124] The server uses generative AI to analyze the context of the text, recognizing important phrases like "I've attached it" or "Please add me to CC." For example, if a user types "I've attached a document," the server recognizes the importance of this phrase.

[0125] Condition Comparison

[0126] The server checks whether the analysis results match pre-defined conditions (for example, "there is no attachment" or "important addresses are omitted from CC"). For example, if the context "attached" is detected and there is no attachment, this condition is met.

[0127] Warning generation

[0128] If the condition is met, the server generates a specific warning message, for example "No attachments, please add attachments."

[0129] User notification and response

[0130] The terminal notifies the user of warning messages received from the server. These notifications are presented as pop-up messages or dialog boxes, and the user can check them and make corrections if necessary.

[0131] Specific examples

[0132] Example 1: Forgotten attachment

[0133] A user enters an email like this:

[0134] text

[0135] Subject: Next week's meeting materials

[0136] Body of the message: I have attached the meeting materials, so please check them.

[0137] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0138] text

[0139] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0140] This warning is notified to the user via the terminal.

[0141] Example 2: CC leak detection

[0142] A user enters an email like this:

[0143] text

[0144] Subject: Progress Report

[0145] Body: I'll report on my progress. Please add important people to CC too.

[0146] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning message like this:

[0147] text

[0148] Warning: Important person not included in CC. Please check your CC field.

[0149] This warning is notified to the user via the terminal.

[0150] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The terminal captures the data entered by the user in real time and transmits it to the server.

[0154] How it works: When a user types an email or text, the device immediately captures the data, which is then securely sent to the server using the HTTPS protocol.

[0155] Input: Text data entered by the user.

[0156] Output: The text data sent to the server.

[0157] Step 2:

[0158] The server uses the generative AI model to analyze the data received from the device.

[0159] How it works: The server inputs the received text data into a generative AI model (e.g., GPT-4), which analyzes the context of the text to recognize important phrases such as "I've attached it" or "Please add me to CC."

[0160] Input: Text data received from the terminal.

[0161] Output: Context analysis results from the generative AI model.

[0162] Step 3:

[0163] The server compares the analysis results with pre-set conditions.

[0164] Specific operation: The server checks whether the analysis results obtained from the generated AI model match the pre-set conditions (for example, "if there is no attachment").

[0165] Input: Context analysis results from a generative AI model.

[0166] Output: The result of whether the condition is met.

[0167] Step 4:

[0168] The server generates a warning message if the condition is met.

[0169] Specific action: If the condition is met, the server will generate a specific warning message such as "Attachment is missing. Please add an attachment."

[0170] Input: The result of the condition match.

[0171] Output: The warning message generated.

[0172] Step 5:

[0173] The server sends the generated warning message to the terminal.

[0174] Specific operation: The server sends the generated warning message to the terminal and prepares for notification.

[0175] Input: The generated warning message.

[0176] Output: The warning message sent to the terminal.

[0177] Step 6:

[0178] The terminal notifies the user of a warning message.

[0179] Specific behavior: The device will notify the user by displaying a pop-up message or notification bar to inform the user of the received warning message, and the user can check it and make necessary corrections.

[0180] Input: The warning message received from the server.

[0181] Output: The warning message that is displayed to the user.

[0182] (Application example 1)

[0183] 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."

[0184] In factory work, labor shortages and skill variations can easily lead to mistakes, resulting in wasted time and resources. In particular, overlooking work instructions and procedures, and missing necessary parts and attachments, can reduce productivity. Missing or missing important information can also be a serious problem in quality control. The purpose of this invention is to provide a system that prevents these mistakes and improves work efficiency and quality.

[0185] 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.

[0186] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning when the conditions are met, a means for notifying the user of the generated warning, and a means for detecting omissions in work instructions and procedures within the factory and providing appropriate advice in real time, thereby reducing work errors within the factory and improving work efficiency and quality.

[0187] A "user" is an entity that uses the system to input data and receive the results.

[0188] "Input data" refers to textual, numerical, or other data formats that a user provides to a system.

[0189] "Contextual analysis means" is a function that uses generative AI to analyze the context and meaning of text data and extract important phrases and information.

[0190] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to understand and analyze the context of input data.

[0191] The "means for comparing conditions" is a function for checking the results obtained from the context analysis means against pre-set conditions to see if they match.

[0192] The "means for generating a warning" is a function for generating an appropriate warning message for the user when the analysis result meets the conditions.

[0193] The "means for notifying a warning" refers to an interface or function for immediately notifying the user of a generated warning message.

[0194] "Means to detect omissions in work instructions and procedures within the factory" refers to a function that monitors in real time whether work is being performed in accordance with instructions and procedures in the factory work environment, and detects omissions and mistakes.

[0195] A "work instruction document" is a document that describes instructions for performing work in a factory.

[0196] "Means for checking whether or not there are attached files or necessary parts" is a function for checking whether or not the attached files or parts required for the work instructions or procedure manuals are properly present.

[0197] The "means for detecting the absence of important information" is a function that detects when important instructions or information are not included in the input data.

[0198] The present invention is a system aimed at improving the efficiency of work in factories and preventing mistakes. The system is equipped with a means for analyzing data entered by users in real time and providing appropriate advice and warnings.

[0199] System Configuration

[0200] The system consists of the following main components:

[0201] 1. Terminal: A device that receives data entered by a user.

[0202] 2. Server: The central processing unit that analyzes data and generates alerts

[0203] 3. User: A person or machine (e.g., robot) that operates the system and inputs data.

[0204] Device role and operation

[0205] A terminal is a device that receives data entered by a user. This could be a PC or tablet in a factory, or a robot's control panel. The terminal transmits the text and voice data entered by the user to a server in real time. At this time, the user enters data such as work instructions, procedure manuals, and progress reports.

[0206] Server Roles and Operations

[0207] The server analyzes the received text data using a generative AI model, which has the following functions:

[0208] 1. Contextual analysis: Generative AI (e.g., the BERT model for Hugging Face, a natural language processing model) is used to analyze the context of the text data. This analysis allows for the recognition and extraction of important phrases such as "I've attached it," "Please add me to CC," and "Important parts."

[0209] 2. Condition comparison method: Based on the analysis results, it is checked whether they match pre-set conditions (for example, "if there are no attachments," "if there are important addresses outside of CC," "if necessary parts are missing").

[0210] 3. Warning Generation Method: If the condition is met, the server will generate a specific warning message, such as "Attachment is missing, please add it" or "Important parts are missing, please check them."

[0211] User notification and response

[0212] The terminal notifies the user of the warning received from the server, which is presented to the user through a pop-up message or a dialog box, allowing the user to confirm and take any necessary corrective action.

[0213] Specific examples

[0214] Example 1: Forgotten attachment

[0215] A user enters a work instruction document such as:

[0216] Instructions: Assemble the parts according to the following steps. Assembly instructions are attached.

[0217] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0218] Warning: The work order says "attached" but there are no attachments. Please add attachments.

[0219] This warning is notified to the user via the terminal.

[0220] Example 2: Missing critical parts

[0221] A user enters instructions such as:

[0222] Assembly Instructions: This job requires essential part X. Please check the parts list.

[0223] The server parses and recognizes the phrases "important parts" and "check parts list". If part X is not in the list, it generates a warning like this:

[0224] WARNING: Important part X is not included in the list. Check your parts list.

[0225] This warning is notified to the user via the terminal.

[0226] As described above, the system of the present invention can prevent mistakes in factory work and improve work efficiency and quality.

[0227] Prompt Sentence Examples

[0228] "Assemble using the following parts. Please also add important part X to the list."

[0229]

[0230] "Work Instructions: Assemble the parts according to these instructions. Assembly instructions are attached."

[0231] This allows users to receive appropriate feedback in real time on the data they enter and make any necessary corrections.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] A user inputs a work instruction document in the factory.

[0235] Input: The user enters the text "Assembly instructions attached" into the terminal.

[0236] How it works: The device captures this text data in real time and sends it to the server.

[0237] Step 2:

[0238] The server analyzes the received text data using a generative AI model.

[0239] Input: Text data sent from the terminal.

[0240] Data processing and computation: The server uses a generative AI model (such as Hugging Face's BERT model) to analyze the context of the text.

[0241] Output: As a result of the analysis, important phrases and keywords (e.g., "attached") are extracted.

[0242] Step 3:

[0243] The server compares the analysis results with pre-set conditions.

[0244] Input: The results of the context analysis and a list of predefined conditions (e.g., "if there are no attachments").

[0245] Data processing and calculation: Check whether important phrases contained in the analysis results match the set conditions.

[0246] Output: If a phrase that meets the condition exists, record that fact.

[0247] Step 4:

[0248] If the condition is met, the server generates a warning message.

[0249] Input: Condition match detection information.

[0250] Data processing and calculation: The server generates an appropriate warning message, for example, "There are no attachments. Please add an attachment."

[0251] Output: The warning message generated.

[0252] Step 5:

[0253] The server sends the generated warning message to the terminal.

[0254] Input: The generated warning message.

[0255] Action: The server sends this message to the terminal.

[0256] Output: A warning message is sent to the terminal.

[0257] Step 6:

[0258] The terminal notifies the user of a warning message.

[0259] Input: The warning message received from the server.

[0260] Behavior: The terminal will present a warning to the user via a pop-up message or dialog box.

[0261] Output: A warning message is displayed to the user.

[0262] Step 7:

[0263] The user checks the warning message and makes the necessary corrections.

[0264] Input: The warning message displayed from the terminal.

[0265] Action: The user reviews the warning and can add attachments or make corrections as needed.

[0266] Output: A revised work instruction document.

[0267] At each step, specific processes such as context analysis and condition comparison are performed using generative AI models, and appropriate feedback is provided to the user, improving work efficiency in the factory and reducing errors.

[0268] 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.

[0269] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages, thereby preventing careless mistakes. The following describes in detail the mode for carrying out the present invention.

[0270] System Configuration

[0271] The system mainly consists of the following components:

[0272] 1. Terminal

[0273] 2. Server

[0274] 3. Users

[0275] 4. Emotion Engine

[0276] Device role and operation

[0277] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[0278] Server Roles and Operations

[0279] The server analyzes the received text data using the generative AI. It works in the following steps:

[0280] 1. Contextual analysis:

[0281] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[0282] 2. Compare conditions:

[0283] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[0284] 3. Warning generation:

[0285] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[0286] Role and operation of the emotion engine

[0287] The emotion engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[0288] 1. Emotion detection:

[0289] The emotion engine analyzes user input data and interaction data to estimate the user's emotions. For example, if the user uses shorter sentences than usual or more emotional words, the emotion engine will determine that the user is in a "high stress" state.

[0290] 2. Tailor your message:

[0291] The emotion engine adjusts the tone and content of the warning message based on the user's emotions. For example, if it detects that the user is stressed, it will change the warning message to a more gentle one, such as "No attachments" to "You may have forgotten the attachment, please check."

[0292] User notification and response

[0293] The device notifies the user of warnings adjusted by the emotion engine received from the server, which are presented as pop-up messages or alert dialogs. The user can confirm the warnings and make any necessary corrections.

[0294] Specific examples

[0295] Example 1: Forgotten attachment

[0296] A user enters an email like this:

[0297] Subject: Next week's meeting materials

[0298] Body of the message: I have attached the meeting materials, so please check them.

[0299] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0300] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0301] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[0302] Please check: It says "attached", but it seems you forgot the attachment.

[0303] This warning is notified to the user via the terminal.

[0304] Example 2: CC leak detection

[0305] A user enters an email like this:

[0306] Subject: Progress Report

[0307] Body: I'll report on my progress. Please add important people to CC too.

[0308] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[0309] Warning: Important person not included in CC. Please check your CC field.

[0310] If the emotion engine analyzes the user's emotions and determines that the user is tired, the warning message will be adjusted as follows:

[0311] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[0312] This warning is notified to the user via the terminal.

[0313] As described above, the system of the present invention performs real-time contextual analysis of data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[0314] The processing flow will be explained below.

[0315] Step 1:

[0316] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[0317] Step 2:

[0318] The device captures the text data entered (e.g., the body of the email), as well as any associated data such as whether there are any attachments and addresses in the CC field.

[0319] Step 3:

[0320] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's sending actions.

[0321] Step 4:

[0322] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[0323] Step 5:

[0324] The server compares the results of the analysis by the AI ​​with pre-defined conditions. For example, if the phrase "attached" is detected, it checks whether or not there is an attachment.

[0325] Step 6:

[0326] If the comparison results in a match, the server generates an initial warning message, such as "No attachments" or "No attachments. Please add an attachment."

[0327] Step 7:

[0328] The server passes the initial warning message and related data to the emotion engine for emotion analysis, which analyzes the user's past and current input data to estimate the user's emotional state.

[0329] Step 8:

[0330] The emotion engine adjusts the tone and content of the warning message based on the results of emotion analysis. For example, if it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "Attachment missing" to "You may have forgotten the attachment, please check."

[0331] Step 9:

[0332] The server sends the final warning message adjusted by the emotion engine to the terminal.

[0333] Step 10:

[0334] The device will notify the user of the final warning message, which may be displayed as a pop-up message or an alert dialog, providing appropriate feedback to the user.

[0335] Step 11:

[0336] The user reviews the warning message and makes any necessary corrections, such as adding back attachments or adding important addresses to the CC field.

[0337] These are the specific processing steps of the "Careless Checker" system, which combines an emotion engine. Data is collected and analyzed at each step, and real-time feedback is ultimately provided to the user, helping to prevent careless mistakes such as typos, forgotten attachments, and omissions of important information.

[0338] Example 2

[0339] 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."

[0340] When creating emails or documents, many users often forget to attach files or add important addresses to CC, which can lead to business mistakes. In addition, there is a problem that the user experience is not improved because warning messages are not provided that take into account the user's emotional state.

[0341] 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 a means for receiving data input by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with a preset condition, a means for generating a warning if the condition is met, a means for adjusting the generated warning based on emotion analysis, and a means for notifying the user of the adjusted warning. This makes it possible to analyze the data input by the user and generate a warning in real time, and to provide an appropriate warning message that takes into account the emotional state of the user.

[0342] "User" refers to an individual or legal entity that inputs data such as an email or document.

[0343] "Input data" refers to text information entered by a user using a terminal and transmitted to a server.

[0344] "Terminal" refers to a hardware device through which a user inputs data and transmits that data to a server.

[0345] "Server" refers to a computer system that analyzes data sent from users and performs the necessary processing.

[0346] "Contextual analysis" refers to the process of using generative AI to understand the context of input data and recognize key phrases.

[0347] "Generative AI" refers to an artificial intelligence model that uses natural language processing techniques to analyze text data.

[0348] "Conditions" are pre-established rules or standards that are used to compare analysis results.

[0349] A "warning message" refers to a notification message that alerts the user based on input data.

[0350] "Emotion engine" refers to technology that estimates a user's emotional state from input data and interaction data and adjusts warning messages.

[0351] "Tuning" refers to the process of changing the tone and content of a warning message based on the results of the emotion engine's analysis.

[0352] "Notification" refers to the act of presenting a warning message to the user via the terminal.

[0353] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages to prevent careless mistakes. Specific embodiments for carrying out the invention are described in detail below.

[0354] System Configuration

[0355] The system mainly consists of the following components:

[0356] 1. Terminal

[0357] 2. Server

[0358] 3. Users

[0359] 4. Emotion Engine

[0360] Device role and operation

[0361] A terminal is a device through which a user inputs data, typically a PC or smartphone. When a user inputs emails or other documents, the terminal captures the data in real time and sends it to a server. This transmission is performed using communication methods such as HTTP requests or WebSockets.

[0362] Server Roles and Operations

[0363] The server analyzes the input data received from the device and performs the necessary processing. Specifically, the process goes through the following steps:

[0364] 1. Contextual analysis:

[0365] The server uses a generative AI model (e.g., a model that leverages natural language processing techniques) to analyze the context of the incoming data, recognizing important phrases such as "I've attached it" or "Please add me to CC."

[0366] 2. Compare conditions:

[0367] The server compares the analysis results with pre-defined conditions, such as whether there are no attachments or whether there are important addresses outside of the CC field.

[0368] 3. Generate a warning message:

[0369] If the condition is met, the server generates a warning message.

[0370] 4. Sentiment analysis and message tailoring:

[0371] The server uses an emotion engine to analyze the user's emotional state and tailor the alert message to an appropriate tone and content.

[0372] Role and operation of the emotion engine

[0373] The emotion engine analyzes user input and interaction data to infer user emotions. It works in the following steps:

[0374] 1. Emotion detection:

[0375] The emotion engine infers the user's emotional state from the user's data. For example, if the user writes shorter sentences than usual or uses a lot of emotional words, the emotion engine will determine that the state is "high stress."

[0376] 2. Tailor your message:

[0377] The emotion engine adjusts the tone and content of the warning message based on the user's emotional state. For example, if the user is feeling stressed, the warning message will be softer.

[0378] User notification and response

[0379] The server sends a tailored warning message to the terminal, which then notifies the user of the message. The notification method is a pop-up message or an alert dialog.

[0380] Specific examples

[0381] Example 1: Forgotten attachment

[0382] The user enters an email containing:

[0383] Subject: Next week's meeting materials

[0384] Body of the message: I have attached the meeting materials, so please check them.

[0385] When the user enters this information, the device sends the data to the server, which parses it and recognizes the phrase "attached." It then checks for the existence of the attachment and generates a warning message like this if the attachment is not present:

[0386] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0387] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[0388] Please check: It says "attached", but it seems you forgot the attachment.

[0389] The terminal notifies the user of this warning message.

[0390] Example 2: CC leak detection

[0391] The user enters an email containing:

[0392] Subject: Progress Report

[0393] Body: I'll report on my progress. Please add important people to CC too.

[0394] The server parses this input and recognizes the phrase "please add me to CC." If it determines that an important address is not included in the CC field, it generates a warning like this:

[0395] Warning: Important person not included in CC. Please check your CC field.

[0396] The emotion engine analyzes the user's emotions and if it determines that the user is tired, the warning message will be adjusted as follows:

[0397] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[0398] The terminal notifies the user of this warning message.

[0399] Prompt Sentence Examples

[0400] An example of a prompt that the server might use to generate an AI model is:

[0401] Prompt text: "The user has entered the following text: 'Conference materials have been attached. Please review them.' Please generate the required warning message based on this text."

[0402] According to the above procedure, the system of the present invention performs real-time context analysis on the data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[0403] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0404] Step 1: Getting user-entered data

[0405] Specific operation:

[0406] A user uses a terminal to input an e-mail or other document. For example, the user inputs "I have attached the meeting materials, please check them."

[0407] input:

[0408] Text data entered by the user into the terminal.

[0409] output:

[0410] Text data to be sent from the terminal to the server.

[0411] ---

[0412] Step 2: Submitting input data

[0413] Specific operation:

[0414] The device sends the acquired input data to the server at regular intervals, using HTTP requests or WebSockets.

[0415] input:

[0416] User input data stored on the device.

[0417] output:

[0418] The text data sent to the server.

[0419] ---

[0420] Step 3: Receiving and storing data

[0421] Specific operation:

[0422] The server receives the text data sent from the terminal and stores it in a specified memory area or database.

[0423] input:

[0424] Text data sent from the device.

[0425] output:

[0426] Input data stored in the server's storage.

[0427] ---

[0428] Step 4: Contextual analysis

[0429] Specific operation:

[0430] The server uses a generative AI model (e.g., GPT-3®) to analyze the context of the input text data and recognize important phrases such as "I've attached it" or "Please add me to CC."

[0431] input:

[0432] Text data stored on the server.

[0433] Data processing / calculation:

[0434] Perform contextual analysis using generative AI models.

[0435] output:

[0436] Analyzed text data and key phrases.

[0437] ---

[0438] Step 5: Compare conditions

[0439] Specific operation:

[0440] The server compares the results of the contextual analysis with pre-defined conditions (for example, "if there are no attachments").

[0441] input:

[0442] Data obtained through contextual analysis and set conditions.

[0443] Data processing / calculation:

[0444] Checking for condition matches.

[0445] output:

[0446] Flag to indicate whether the condition is met.

[0447] ---

[0448] Step 6: Generate a warning message

[0449] Specific operation:

[0450] If the conditions are met, the server generates a warning message, using a message template and filling in the necessary data.

[0451] input:

[0452] Condition match flags and parsed data.

[0453] Data processing / calculation:

[0454] Generate a warning message.

[0455] output:

[0456] The warning message that was generated.

[0457] ---

[0458] Step 7: Sentiment Analysis

[0459] Specific operation:

[0460] The server uses an emotion engine to infer emotions from user input and interaction data and checks whether the warning message needs to be adjusted.

[0461] input:

[0462] User input data and past interaction data.

[0463] Data processing / calculation:

[0464] Emotional state analysis and warning message adjustment.

[0465] output:

[0466] Adjusted warning messages (where appropriate).

[0467] ---

[0468] Step 8: Sending a warning message

[0469] Specific operation:

[0470] The server sends the adjusted alert message to the device using HTTP requests or WebSockets.

[0471] input:

[0472] Adjusted warning messages.

[0473] output:

[0474] The warning message sent to the terminal.

[0475] ---

[0476] Step 9: User Notification

[0477] Specific operation:

[0478] The terminal notifies the user of the received warning message using a pop-up message or an alert dialog.

[0479] input:

[0480] The warning message sent by the server.

[0481] output:

[0482] The warning message to be displayed to the user.

[0483] ---

[0484] Step 10: User Action

[0485] Specific operation:

[0486] The user checks the warning message and makes any necessary corrections (for example, adding an attachment or correcting the CC field).

[0487] input:

[0488] The warning message displayed to the user.

[0489] output:

[0490] Modified input data, e.g. emails with attachments added.

[0491] (Application example 2)

[0492] 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."

[0493] In conventional food delivery systems, users often make mistakes in ordering and delivery instructions, which reduces customer satisfaction. Furthermore, since appropriate warning messages are not provided based on the user's emotional state, stress increases, resulting in a tendency for ordering errors to increase. There is a need to solve these problems.

[0494] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data input by a user, context analysis means using a generation AI that analyzes the context, means for comparing the analysis result with preset conditions, means for generating a warning if the conditions are met, means for notifying the user of the generated warning, means for detecting the user's emotions using an emotion analysis engine, means for adjusting the content and tone of the warning message according to the detected emotion, and means for analyzing data in real time and generating a warning to prevent errors in food delivery order details and delivery instructions. This makes it possible to detect errors that may occur when a user places an order in real time and provide a warning message that takes the user's emotions into consideration.

[0495] The "means for receiving data input by a user" refers to a device or software for receiving data such as order details and delivery instructions input by a user through a food delivery application in real time.

[0496] "Contextual analysis means using generative AI" is a function that uses a generative AI model to analyze the context before and after text data entered by the user and understand the appropriate meaning.

[0497] "Means for comparing analysis results with pre-set conditions" refers to a system that compares the results of contextual analysis with pre-set rules and conditions to determine data consistency and errors.

[0498] The "means for generating a warning" is a function that automatically generates a message to warn or caution the user when a condition is met.

[0499] The "means for notifying the user of the generated warning" is a function or process for notifying the user's terminal of the automatically generated warning message.

[0500] The "means for detecting user emotions using an emotion analysis engine" is an engine for analyzing data entered by the user and the state of interaction to determine the user's emotional state.

[0501] "Means for adjusting the content and tone of the warning message according to the detected emotion" is a function that flexibly changes the content and tone of the warning message based on the user's emotion detected by the emotion analysis engine.

[0502] "Means for analyzing data in real time and generating warnings to prevent errors in food delivery order details and delivery instructions" refers to a function that analyzes the order details and delivery instructions entered by the user in real time, checks for errors, and issues warnings as necessary.

[0503] This invention is a system that analyzes food delivery order details entered by users in real time and generates and notifies warning messages that take the user's emotions into consideration. This system mainly consists of three main components: a user terminal, a server, and an emotion analysis engine.

[0504] Roles and operations of user terminals

[0505] The user terminal is a device that receives order details and delivery instructions entered by the user through the food delivery application. When the user enters the order details, the details are captured in real time and sent to the server. For example, when a user adds a dish to a cart and enters delivery instructions, the details are sent immediately to the server.

[0506] Server Roles and Operations

[0507] The server is the central device that analyzes the data it receives. It works in the following steps:

[0508] 1. Contextual analysis:

[0509] The server uses generative AI (e.g., models that leverage natural language processing) to analyze the context of the order, recognizing key phrases like "nut allergy" or "dairy-free."

[0510] 2. Compare conditions:

[0511] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if allergens are included" or "if there are duplicate orders").

[0512] 3. Warning generation:

[0513] If the condition is met, the server will generate a specific warning message. For example, if "I have a nut allergy" is entered but a dish containing nuts is ordered, the server will generate a warning saying "You may have a nut allergy. Please check your order."

[0514] Role and operation of the sentiment analysis engine

[0515] The sentiment analysis engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[0516] 1. Emotion detection:

[0517] The emotion analysis engine analyzes user input data and interaction data to estimate the user's emotions. For example, if a user inputs "I'm in a hurry," the emotion analysis engine will determine that state as "high stress."

[0518] 2. Tailor your message:

[0519] The sentiment analysis engine adjusts the tone and content of the warning message based on the user's emotions. If it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "You may have a nut allergy" to "We apologize for the inconvenience, but this product may contain nuts. Please check."

[0520] User notification and response

[0521] The user terminal notifies the user of the warning adjusted by the sentiment analysis engine received from the server. This is presented to the user as a pop-up message or an alert dialog. The user confirms this and makes any necessary corrections.

[0522] Specific examples

[0523] Example 1: Detection of allergens

[0524] A user enters an order like this:

[0525] I have a nut allergy and would like to order this dish. Is it included?

[0526] This input is sent to the server, which recognizes the phrase "nut allergy" and checks if the order contains nuts. If so, it generates a warning message like this:

[0527] WARNING: Possible nut allergy, please check your order.

[0528] The sentiment analysis engine analyzes the user's emotions and if it determines that the user is in a hurry, the warning message will be adjusted as follows:

[0529] We apologize for the rush, but please be aware that this product may contain nuts.

[0530] This warning is notified to the user via the user terminal.

[0531] Example prompt sentence:

[0532] A user says, "I want to order this dish." Check if this dish contains any ingredients and generate an allergy warning. If the sentiment is negative, modify the message to use a softer tone.

[0533] This system allows users to enjoy safe and stress-free food delivery and significantly reduces the risk of ordering the wrong items.

[0534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0535] Step 1:

[0536] A user uses a food delivery application to input order details. The input order data includes the name of the dish, delivery instructions, allergy information, etc. The input data is immediately sent from the user's device to the server. The input here is the order text entered by the user, and the output is the text data sent from the user's device.

[0537] Step 2:

[0538] The server uses generative AI to perform contextual analysis on the received order data. The contextual analysis model analyzes the text data and recognizes important phrases such as "nut allergy" and "dairy-free." The input here is the text data sent by the user, and the output is the analysis results.

[0539] Step 3:

[0540] The server compares the results of the context analysis with pre-set conditions (whether the product contains allergens, whether there are duplicate orders, etc.). It processes the data to see if it matches the conditions, and if it does, generates the necessary warning. The input here is the result of the context analysis, and the output is the result of checking the conditions for generating the warning.

[0541] Step 4:

[0542] If the condition is met, the server generates an appropriate warning message. If an allergic ingredient is included, the server generates a warning saying "You may have a nut allergy. Please check your order." The input here is the result of checking the warning generation condition, and the output is the generated warning message.

[0543] Step 5:

[0544] The server uses an emotion analysis engine to analyze the user's emotions. It estimates the user's emotional state from the input text data and interaction data. It runs the emotion analysis engine and determines the emotional state as "high stress" or "hurry," etc. The input here is the user's text data and interaction data, and the output is the emotion analysis results.

[0545] Step 6:

[0546] The server adjusts the content and tone of the generated warning message based on the detected emotional state. For example, if the server determines that the user is in a hurry, it changes the warning message to "Sorry for the inconvenience, but this may contain nuts. Please check." The input here is the emotion analysis result and the generated warning message, and the output is the adjusted warning message.

[0547] Step 7:

[0548] The server finally sends the adjusted warning message to the user terminal. The user terminal displays this warning message as a pop-up or alert dialog. The user checks it and corrects the order contents as necessary. The input here is the adjusted warning message, and the output is the notification and display on the user terminal.

[0549] 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.

[0550] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] 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.

[0552] [Second embodiment]

[0553] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0554] 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.

[0555] 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).

[0556] 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.

[0557] 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.

[0558] 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).

[0559] 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. 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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."

[0565] The system of the present invention analyzes data entered by the user in real time and issues appropriate warnings based on the context, thereby preventing careless mistakes such as typos, forgetting to attach files, and missing CCs. The program and processing of the system that embodies the present invention will be described in detail below.

[0566] System Configuration

[0567] The system consists of three main components:

[0568] 1. Terminal

[0569] 2. Server

[0570] 3. Users

[0571] Device role and operation

[0572] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[0573] Server Roles and Operations

[0574] The server analyzes the received text data using the generative AI. It works in the following steps:

[0575] 1. Contextual analysis:

[0576] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[0577] 2. Compare conditions:

[0578] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[0579] 3. Warning generation:

[0580] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[0581] User notification and response

[0582] The terminal notifies the user of the warning received from the server, which is presented as a pop-up message or a dialog box, and the user can confirm it and make any necessary corrections.

[0583] Specific examples

[0584] Example 1: Forgotten attachment

[0585] A user enters an email like this:

[0586] Subject: Next week's meeting materials

[0587] Body of the message: I have attached the meeting materials, so please check them.

[0588] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0589] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0590] This warning is notified to the user via the terminal.

[0591] Example 2: CC leak detection

[0592] A user enters an email like this:

[0593] Subject: Progress Report

[0594] Body: I'll report on my progress. Please add important people to CC too.

[0595] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[0596] Warning: Important person not included in CC. Please check your CC field.

[0597] This warning is notified to the user via the terminal.

[0598] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[0599] The processing flow will be explained below.

[0600] Step 1:

[0601] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[0602] Step 2:

[0603] The device captures the text data entered (e.g., the body of the email), along with any related data, such as whether there are any attachments or addresses in the CC field.

[0604] Step 3:

[0605] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's transmission actions.

[0606] Step 4:

[0607] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[0608] Step 5:

[0609] The server compares the results analyzed by the generated AI with pre-set conditions (e.g., when there are no attachments or when the CC field does not contain important addresses).

[0610] Step 6:

[0611] If the server finds that the comparison matches the condition, it will generate a warning message. For example, if the phrase "attached" is found and there is no attachment, it will generate a warning saying "There are no attachments. Please add an attachment."

[0612] Step 7:

[0613] The server sends the generated warning message to the terminal.

[0614] Step 8:

[0615] The device will notify the user of the received warning message, typically as a pop-up message or alert dialog.

[0616] Step 9:

[0617] The user reviews the warning message and makes the necessary corrections, such as adding attachments again or adding important addresses to the CC field.

[0618] These are the specific processing steps of the "Careless Checker" system, which is designed to prevent users from making careless mistakes. At each step, data is collected and analyzed in a timely manner, and ultimately feedback is provided to the user in real time.

[0619] Example 1

[0620] 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."

[0621] When entering emails or text, careless mistakes such as forgetting to attach files or omitting to CC often occur, which often results in the incomplete transmission of important information. A system that can prevent such mistakes before they occur is needed, but existing systems have difficulty analyzing context in real time or generating appropriate warnings, which reduces user work efficiency.

[0622] 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.

[0623] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning if the condition is met, and a means for notifying the user of the generated warning. This makes it possible to detect careless mistakes when entering emails or other text in real time and notify the user of an appropriate warning.

[0624] "User" means a person or end user who utilizes the system to input data and receive results.

[0625] "Data" is information that a user enters into the system, such as email or other text information.

[0626] A "means" is a device or method designed to perform a particular function or role.

[0627] A "server" is a computer system that processes, stores, and manages data over a network.

[0628] "Generative AI" is a system that generates and analyzes text using artificial intelligence techniques, including natural language processing.

[0629] "Contextual analysis" is the process of understanding the context of input text and analyzing its meaning.

[0630] A "warning" is a message or notification that alerts the user.

[0631] The "receiving means" is a device or method for obtaining data input by a user and transmitting it to a server.

[0632] "Context analysis means" means a device or method for analyzing the context of input text using generative AI.

[0633] The "comparison means" refers to a device or method for comparing the analysis results with pre-set conditions.

[0634] A "means for generating an alert" is a device or method for generating an alert message when a condition is met.

[0635] A "means for notifying" is a device or method for communicating the generated warning message to a user.

[0636] MODE FOR CARRYING OUT THE INVENTION

[0637] The system of the present invention analyzes data entered by the user in real time and issues appropriate context-based warnings to prevent careless mistakes. To explain the operation of the system, specific hardware and software examples will be described in detail.

[0638] System Configuration

[0639] The system consists of three main components:

[0640] 1. Terminal

[0641] 2. Server

[0642] 3. Users

[0643] Device role and operation

[0644] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[0645] Server Roles and Operations

[0646] The server uses generative AI (e.g., a natural language processing model such as GPT-4) to analyze the received text data. The specific operation of the server is as follows:

[0647] Contextual analysis

[0648] The server uses generative AI to analyze the context of the text, recognizing important phrases like "I've attached it" or "Please add me to CC." For example, if a user types "I've attached a document," the server recognizes the importance of this phrase.

[0649] Condition Comparison

[0650] The server checks whether the analysis results match pre-defined conditions (for example, "there is no attachment" or "important addresses are omitted from CC"). For example, if the context "attached" is detected and there is no attachment, this condition is met.

[0651] Warning generation

[0652] If the condition is met, the server generates a specific warning message, for example "No attachments, please add attachments."

[0653] User notification and response

[0654] The terminal notifies the user of warning messages received from the server. These notifications are presented as pop-up messages or dialog boxes, and the user can check them and make corrections if necessary.

[0655] Specific examples

[0656] Example 1: Forgotten attachment

[0657] A user enters an email like this:

[0658] text

[0659] Subject: Next week's meeting materials

[0660] Body of the message: I have attached the meeting materials, so please check them.

[0661] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0662] text

[0663] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0664] This warning is notified to the user via the terminal.

[0665] Example 2: CC leak detection

[0666] A user enters an email like this:

[0667] text

[0668] Subject: Progress Report

[0669] Body: I'll report on my progress. Please add important people to CC too.

[0670] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning message like this:

[0671] text

[0672] Warning: Important person not included in CC. Please check your CC field.

[0673] This warning is notified to the user via the terminal.

[0674] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[0675] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0676] Step 1:

[0677] The terminal captures the data entered by the user in real time and transmits it to the server.

[0678] How it works: When a user types an email or text, the device immediately captures the data, which is then securely sent to the server using the HTTPS protocol.

[0679] Input: Text data entered by the user.

[0680] Output: The text data sent to the server.

[0681] Step 2:

[0682] The server uses the generative AI model to analyze the data received from the device.

[0683] How it works: The server inputs the received text data into a generative AI model (e.g., GPT-4), which analyzes the context of the text to recognize important phrases such as "I've attached it" or "Please add me to CC."

[0684] Input: Text data received from the terminal.

[0685] Output: Context analysis results from the generative AI model.

[0686] Step 3:

[0687] The server compares the analysis results with pre-set conditions.

[0688] Specific operation: The server checks whether the analysis results obtained from the generated AI model match the pre-set conditions (for example, "if there is no attachment").

[0689] Input: Context analysis results from a generative AI model.

[0690] Output: The result of whether the condition is met.

[0691] Step 4:

[0692] The server generates a warning message if the condition is met.

[0693] Specific action: If the condition is met, the server will generate a specific warning message such as "Attachment is missing. Please add an attachment."

[0694] Input: The result of the condition match.

[0695] Output: The warning message generated.

[0696] Step 5:

[0697] The server sends the generated warning message to the terminal.

[0698] Specific operation: The server sends the generated warning message to the terminal and prepares for notification.

[0699] Input: The generated warning message.

[0700] Output: The warning message sent to the terminal.

[0701] Step 6:

[0702] The terminal notifies the user of a warning message.

[0703] Specific behavior: The device will notify the user by displaying a pop-up message or notification bar to inform the user of the received warning message, and the user can check it and make necessary corrections.

[0704] Input: The warning message received from the server.

[0705] Output: The warning message that is displayed to the user.

[0706] (Application example 1)

[0707] 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."

[0708] In factory work, labor shortages and skill variations can easily lead to mistakes, resulting in wasted time and resources. In particular, overlooking work instructions and procedures, and missing necessary parts and attachments, can reduce productivity. Missing or missing important information can also be a serious problem in quality control. The purpose of this invention is to provide a system that prevents these mistakes and improves work efficiency and quality.

[0709] 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.

[0710] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning when the conditions are met, a means for notifying the user of the generated warning, and a means for detecting omissions in work instructions and procedures within the factory and providing appropriate advice in real time, thereby reducing work errors within the factory and improving work efficiency and quality.

[0711] A "user" is an entity that uses the system to input data and receive the results.

[0712] "Input data" refers to textual, numerical, or other data formats that a user provides to a system.

[0713] "Contextual analysis means" is a function that uses generative AI to analyze the context and meaning of text data and extract important phrases and information.

[0714] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to understand and analyze the context of input data.

[0715] The "means for comparing conditions" is a function for checking the results obtained from the context analysis means against pre-set conditions to see if they match.

[0716] The "means for generating a warning" is a function for generating an appropriate warning message for the user when the analysis result meets the conditions.

[0717] The "means for notifying a warning" refers to an interface or function for immediately notifying the user of a generated warning message.

[0718] "Means to detect omissions in work instructions and procedures within the factory" refers to a function that monitors in real time whether work is being performed in accordance with instructions and procedures in the factory work environment, and detects omissions and mistakes.

[0719] A "work instruction document" is a document that describes instructions for performing work in a factory.

[0720] "Means for checking whether or not there are attached files or necessary parts" is a function for checking whether or not the attached files or parts required for the work instructions or procedure manuals are properly present.

[0721] The "means for detecting the absence of important information" is a function that detects when important instructions or information are not included in the input data.

[0722] The present invention is a system aimed at improving the efficiency of work in factories and preventing mistakes. The system is equipped with a means for analyzing data entered by users in real time and providing appropriate advice and warnings.

[0723] System Configuration

[0724] The system consists of the following main components:

[0725] 1. Terminal: A device that receives data entered by a user.

[0726] 2. Server: The central processing unit that analyzes data and generates alerts

[0727] 3. User: A person or machine (e.g., robot) that operates the system and inputs data.

[0728] Device role and operation

[0729] A terminal is a device that receives data entered by a user. This could be a PC or tablet in a factory, or a robot's control panel. The terminal transmits the text and voice data entered by the user to a server in real time. At this time, the user enters data such as work instructions, procedure manuals, and progress reports.

[0730] Server Roles and Operations

[0731] The server analyzes the received text data using a generative AI model, which has the following functions:

[0732] 1. Contextual analysis: Generative AI (e.g., the BERT model for Hugging Face, a natural language processing model) is used to analyze the context of the text data. This analysis allows for the recognition and extraction of important phrases such as "I've attached it," "Please add me to CC," and "Important parts."

[0733] 2. Condition comparison method: Based on the analysis results, it is checked whether they match pre-set conditions (for example, "if there are no attachments," "if there are important addresses outside of CC," "if necessary parts are missing").

[0734] 3. Warning Generation Method: If the condition is met, the server will generate a specific warning message, such as "Attachment is missing, please add it" or "Important parts are missing, please check them."

[0735] User notification and response

[0736] The terminal notifies the user of the warning received from the server, which is presented to the user through a pop-up message or a dialog box, allowing the user to confirm and take any necessary corrective action.

[0737] Specific examples

[0738] Example 1: Forgotten attachment

[0739] A user enters a work instruction document such as:

[0740] Instructions: Assemble the parts according to the following steps. Assembly instructions are attached.

[0741] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0742] Warning: The work order says "attached" but there are no attachments. Please add attachments.

[0743] This warning is notified to the user via the terminal.

[0744] Example 2: Missing critical parts

[0745] A user enters instructions such as:

[0746] Assembly Instructions: This job requires essential part X. Please check the parts list.

[0747] The server parses and recognizes the phrases "important parts" and "check parts list". If part X is not in the list, it generates a warning like this:

[0748] WARNING: Important part X is not included in the list. Check your parts list.

[0749] This warning is notified to the user via the terminal.

[0750] As described above, the system of the present invention can prevent mistakes in factory work and improve work efficiency and quality.

[0751] Prompt Sentence Examples

[0752] "Assemble using the following parts. Please also add important part X to the list."

[0753]

[0754] "Work Instructions: Assemble the parts according to these instructions. Assembly instructions are attached."

[0755] This allows users to receive appropriate feedback in real time on the data they enter and make any necessary corrections.

[0756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0757] Step 1:

[0758] A user inputs a work instruction document in the factory.

[0759] Input: The user enters the text "Assembly instructions attached" into the terminal.

[0760] How it works: The device captures this text data in real time and sends it to the server.

[0761] Step 2:

[0762] The server analyzes the received text data using a generative AI model.

[0763] Input: Text data sent from the terminal.

[0764] Data processing and computation: The server uses a generative AI model (such as Hugging Face's BERT model) to analyze the context of the text.

[0765] Output: As a result of the analysis, important phrases and keywords (e.g., "attached") are extracted.

[0766] Step 3:

[0767] The server compares the analysis results with pre-set conditions.

[0768] Input: The results of the context analysis and a list of predefined conditions (e.g., "if there are no attachments").

[0769] Data processing and calculation: Check whether important phrases contained in the analysis results match the set conditions.

[0770] Output: If a phrase that meets the condition exists, record that fact.

[0771] Step 4:

[0772] If the condition is met, the server generates a warning message.

[0773] Input: Condition match detection information.

[0774] Data processing and calculation: The server generates an appropriate warning message, for example, "There are no attachments. Please add an attachment."

[0775] Output: The warning message generated.

[0776] Step 5:

[0777] The server sends the generated warning message to the terminal.

[0778] Input: The generated warning message.

[0779] Action: The server sends this message to the terminal.

[0780] Output: A warning message is sent to the terminal.

[0781] Step 6:

[0782] The terminal notifies the user of a warning message.

[0783] Input: The warning message received from the server.

[0784] Behavior: The terminal will present a warning to the user via a pop-up message or dialog box.

[0785] Output: A warning message is displayed to the user.

[0786] Step 7:

[0787] The user checks the warning message and makes the necessary corrections.

[0788] Input: The warning message displayed from the terminal.

[0789] Action: The user reviews the warning and can add attachments or make corrections as needed.

[0790] Output: A revised work instruction document.

[0791] At each step, specific processes such as context analysis and condition comparison are performed using generative AI models, and appropriate feedback is provided to the user, improving work efficiency in the factory and reducing errors.

[0792] 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.

[0793] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages, thereby preventing careless mistakes. The following describes in detail the mode for carrying out the present invention.

[0794] System Configuration

[0795] The system mainly consists of the following components:

[0796] 1. Terminal

[0797] 2. Server

[0798] 3. Users

[0799] 4. Emotion Engine

[0800] Device role and operation

[0801] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[0802] Server Roles and Operations

[0803] The server analyzes the received text data using the generative AI. It works in the following steps:

[0804] 1. Contextual analysis:

[0805] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[0806] 2. Compare conditions:

[0807] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[0808] 3. Warning generation:

[0809] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[0810] Role and operation of the emotion engine

[0811] The emotion engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[0812] 1. Emotion detection:

[0813] The emotion engine analyzes user input data and interaction data to estimate the user's emotions. For example, if the user uses shorter sentences than usual or more emotional words, the emotion engine will determine that the user is in a "high stress" state.

[0814] 2. Tailor your message:

[0815] The emotion engine adjusts the tone and content of the warning message based on the user's emotions. For example, if it detects that the user is stressed, it will change the warning message to a more gentle one, such as "No attachments" to "You may have forgotten the attachment, please check."

[0816] User notification and response

[0817] The device notifies the user of warnings adjusted by the emotion engine received from the server, which are presented as pop-up messages or alert dialogs. The user can confirm the warnings and make any necessary corrections.

[0818] Specific examples

[0819] Example 1: Forgotten attachment

[0820] A user enters an email like this:

[0821] Subject: Next week's meeting materials

[0822] Body of the message: I have attached the meeting materials, so please check them.

[0823] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[0824] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0825] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[0826] Please check: It says "attached", but it seems you forgot the attachment.

[0827] This warning is notified to the user via the terminal.

[0828] Example 2: CC leak detection

[0829] A user enters an email like this:

[0830] Subject: Progress Report

[0831] Body: I'll report on my progress. Please add important people to CC too.

[0832] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[0833] Warning: Important person not included in CC. Please check your CC field.

[0834] If the emotion engine analyzes the user's emotions and determines that the user is tired, the warning message will be adjusted as follows:

[0835] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[0836] This warning is notified to the user via the terminal.

[0837] As described above, the system of the present invention performs real-time contextual analysis of data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[0838] The processing flow will be explained below.

[0839] Step 1:

[0840] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[0841] Step 2:

[0842] The device captures the text data entered (e.g., the body of the email), as well as any associated data such as whether there are any attachments and addresses in the CC field.

[0843] Step 3:

[0844] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's sending actions.

[0845] Step 4:

[0846] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[0847] Step 5:

[0848] The server compares the results of the analysis by the AI ​​with pre-defined conditions. For example, if the phrase "attached" is detected, it checks whether or not there is an attachment.

[0849] Step 6:

[0850] If the comparison results in a match, the server generates an initial warning message, such as "No attachments" or "No attachments. Please add an attachment."

[0851] Step 7:

[0852] The server passes the initial warning message and related data to the emotion engine for emotion analysis, which analyzes the user's past and current input data to estimate the user's emotional state.

[0853] Step 8:

[0854] The emotion engine adjusts the tone and content of the warning message based on the results of emotion analysis. For example, if it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "Attachment missing" to "You may have forgotten the attachment, please check."

[0855] Step 9:

[0856] The server sends the final warning message adjusted by the emotion engine to the terminal.

[0857] Step 10:

[0858] The device will notify the user of the final warning message, which may be displayed as a pop-up message or an alert dialog, providing appropriate feedback to the user.

[0859] Step 11:

[0860] The user reviews the warning message and makes any necessary corrections, such as adding back attachments or adding important addresses to the CC field.

[0861] These are the specific processing steps of the "Careless Checker" system, which combines an emotion engine. Data is collected and analyzed at each step, and real-time feedback is ultimately provided to the user, helping to prevent careless mistakes such as typos, forgotten attachments, and omissions of important information.

[0862] Example 2

[0863] 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."

[0864] When creating emails or documents, many users often forget to attach files or add important addresses to CC, which can lead to business mistakes. In addition, there is a problem that the user experience is not improved because warning messages are not provided that take into account the user's emotional state.

[0865] 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 a means for receiving data input by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with a preset condition, a means for generating a warning if the condition is met, a means for adjusting the generated warning based on emotion analysis, and a means for notifying the user of the adjusted warning. This makes it possible to analyze the data input by the user and generate a warning in real time, and to provide an appropriate warning message that takes into account the emotional state of the user.

[0866] "User" refers to an individual or legal entity that inputs data such as an email or document.

[0867] "Input data" refers to text information entered by a user using a terminal and transmitted to a server.

[0868] "Terminal" refers to a hardware device through which a user inputs data and transmits that data to a server.

[0869] "Server" refers to a computer system that analyzes data sent from users and performs the necessary processing.

[0870] "Contextual analysis" refers to the process of using generative AI to understand the context of input data and recognize key phrases.

[0871] "Generative AI" refers to an artificial intelligence model that uses natural language processing techniques to analyze text data.

[0872] "Conditions" are pre-established rules or standards that are used to compare analysis results.

[0873] A "warning message" refers to a notification message that alerts the user based on input data.

[0874] "Emotion engine" refers to technology that estimates a user's emotional state from input data and interaction data and adjusts warning messages.

[0875] "Tuning" refers to the process of changing the tone and content of a warning message based on the results of the emotion engine's analysis.

[0876] "Notification" refers to the act of presenting a warning message to the user via the terminal.

[0877] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages to prevent careless mistakes. Specific embodiments for carrying out the invention are described in detail below.

[0878] System Configuration

[0879] The system mainly consists of the following components:

[0880] 1. Terminal

[0881] 2. Server

[0882] 3. Users

[0883] 4. Emotion Engine

[0884] Device role and operation

[0885] A terminal is a device through which a user inputs data, typically a PC or smartphone. When a user inputs emails or other documents, the terminal captures the data in real time and sends it to a server. This transmission is performed using communication methods such as HTTP requests or WebSockets.

[0886] Server Roles and Operations

[0887] The server analyzes the input data received from the device and performs the necessary processing. Specifically, the process goes through the following steps:

[0888] 1. Contextual analysis:

[0889] The server uses a generative AI model (e.g., a model that leverages natural language processing techniques) to analyze the context of the incoming data, recognizing important phrases such as "I've attached it" or "Please add me to CC."

[0890] 2. Compare conditions:

[0891] The server compares the analysis results with pre-defined conditions, such as whether there are no attachments or whether there are important addresses outside of the CC field.

[0892] 3. Generate a warning message:

[0893] If the condition is met, the server generates a warning message.

[0894] 4. Sentiment analysis and message tailoring:

[0895] The server uses an emotion engine to analyze the user's emotional state and tailor the alert message to an appropriate tone and content.

[0896] Role and operation of the emotion engine

[0897] The emotion engine analyzes user input and interaction data to infer user emotions. It works in the following steps:

[0898] 1. Emotion detection:

[0899] The emotion engine infers the user's emotional state from the user's data. For example, if the user writes shorter sentences than usual or uses a lot of emotional words, the emotion engine will determine that the state is "high stress."

[0900] 2. Tailor your message:

[0901] The emotion engine adjusts the tone and content of the warning message based on the user's emotional state. For example, if the user is feeling stressed, the warning message will be softer.

[0902] User notification and response

[0903] The server sends a tailored warning message to the terminal, which then notifies the user of the message. The notification method is a pop-up message or an alert dialog.

[0904] Specific examples

[0905] Example 1: Forgotten attachment

[0906] The user enters an email containing:

[0907] Subject: Next week's meeting materials

[0908] Body of the message: I have attached the meeting materials, so please check them.

[0909] When the user enters this information, the device sends the data to the server, which parses it and recognizes the phrase "attached." It then checks for the existence of the attachment and generates a warning message like this if the attachment is not present:

[0910] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[0911] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[0912] Please check: It says "attached", but it seems you forgot the attachment.

[0913] The terminal notifies the user of this warning message.

[0914] Example 2: CC leak detection

[0915] The user enters an email containing:

[0916] Subject: Progress Report

[0917] Body: I'll report on my progress. Please add important people to CC too.

[0918] The server parses this input and recognizes the phrase "please add me to CC." If it determines that an important address is not included in the CC field, it generates a warning like this:

[0919] Warning: Important person not included in CC. Please check your CC field.

[0920] The emotion engine analyzes the user's emotions and if it determines that the user is tired, the warning message will be adjusted as follows:

[0921] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[0922] The terminal notifies the user of this warning message.

[0923] Prompt Sentence Examples

[0924] An example of a prompt that the server might use to generate an AI model is:

[0925] Prompt text: "The user has entered the following text: 'Conference materials have been attached. Please review them.' Please generate the required warning message based on this text."

[0926] According to the above procedure, the system of the present invention performs real-time context analysis on the data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[0927] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0928] Step 1: Getting user-entered data

[0929] Specific operation:

[0930] A user uses a terminal to input an e-mail or other document. For example, the user inputs "I have attached the meeting materials, please check them."

[0931] input:

[0932] Text data entered by the user into the terminal.

[0933] output:

[0934] Text data to be sent from the terminal to the server.

[0935] ---

[0936] Step 2: Submitting input data

[0937] Specific operation:

[0938] The device sends the acquired input data to the server at regular intervals, using HTTP requests or WebSockets.

[0939] input:

[0940] User input data stored on the device.

[0941] output:

[0942] The text data sent to the server.

[0943] ---

[0944] Step 3: Receiving and storing data

[0945] Specific operation:

[0946] The server receives the text data sent from the terminal and stores it in a specified memory area or database.

[0947] input:

[0948] Text data sent from the device.

[0949] output:

[0950] Input data stored in the server's storage.

[0951] ---

[0952] Step 4: Contextual analysis

[0953] Specific operation:

[0954] The server uses a generative AI model (e.g., GPT-3) to analyze the context of the input text data and recognize important phrases such as "I've attached it" or "Please add me to CC."

[0955] input:

[0956] Text data stored on the server.

[0957] Data processing / calculation:

[0958] Perform contextual analysis using generative AI models.

[0959] output:

[0960] Analyzed text data and key phrases.

[0961] ---

[0962] Step 5: Compare conditions

[0963] Specific operation:

[0964] The server compares the results of the contextual analysis with pre-defined conditions (for example, "if there are no attachments").

[0965] input:

[0966] Data obtained through contextual analysis and set conditions.

[0967] Data processing / calculation:

[0968] Checking for condition matches.

[0969] output:

[0970] Flag to indicate whether the condition is met.

[0971] ---

[0972] Step 6: Generate a warning message

[0973] Specific operation:

[0974] If the conditions are met, the server generates a warning message, using a message template and filling in the necessary data.

[0975] input:

[0976] Condition match flags and parsed data.

[0977] Data processing / calculation:

[0978] Generate a warning message.

[0979] output:

[0980] The warning message that was generated.

[0981] ---

[0982] Step 7: Sentiment Analysis

[0983] Specific operation:

[0984] The server uses an emotion engine to infer emotions from user input and interaction data and checks whether the warning message needs to be adjusted.

[0985] input:

[0986] User input data and past interaction data.

[0987] Data processing / calculation:

[0988] Emotional state analysis and warning message adjustment.

[0989] output:

[0990] Adjusted warning messages (where appropriate).

[0991] ---

[0992] Step 8: Sending a warning message

[0993] Specific operation:

[0994] The server sends the adjusted alert message to the device using HTTP requests or WebSockets.

[0995] input:

[0996] Adjusted warning messages.

[0997] output:

[0998] The warning message sent to the terminal.

[0999] ---

[1000] Step 9: User Notification

[1001] Specific operation:

[1002] The terminal notifies the user of the received warning message using a pop-up message or an alert dialog.

[1003] input:

[1004] The warning message sent by the server.

[1005] output:

[1006] The warning message to be displayed to the user.

[1007] ---

[1008] Step 10: User Action

[1009] Specific operation:

[1010] The user checks the warning message and makes any necessary corrections (for example, adding an attachment or correcting the CC field).

[1011] input:

[1012] The warning message displayed to the user.

[1013] output:

[1014] Modified input data, e.g. emails with attachments added.

[1015] (Application example 2)

[1016] 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."

[1017] In conventional food delivery systems, users often make mistakes in ordering and delivery instructions, which reduces customer satisfaction. Furthermore, since appropriate warning messages are not provided based on the user's emotional state, stress increases, resulting in a tendency for ordering errors to increase. There is a need to solve these problems.

[1018] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data input by a user, context analysis means using a generation AI that analyzes the context, means for comparing the analysis result with preset conditions, means for generating a warning if the conditions are met, means for notifying the user of the generated warning, means for detecting the user's emotions using an emotion analysis engine, means for adjusting the content and tone of the warning message according to the detected emotion, and means for analyzing data in real time and generating a warning to prevent errors in food delivery order details and delivery instructions. This makes it possible to detect errors that may occur when a user places an order in real time and provide a warning message that takes the user's emotions into consideration.

[1019] The "means for receiving data input by a user" refers to a device or software for receiving data such as order details and delivery instructions input by a user through a food delivery application in real time.

[1020] "Contextual analysis means using generative AI" is a function that uses a generative AI model to analyze the context before and after text data entered by the user and understand the appropriate meaning.

[1021] "Means for comparing analysis results with pre-set conditions" refers to a system that compares the results of contextual analysis with pre-set rules and conditions to determine data consistency and errors.

[1022] The "means for generating a warning" is a function that automatically generates a message to warn or caution the user when a condition is met.

[1023] The "means for notifying the user of the generated warning" is a function or process for notifying the user's terminal of the automatically generated warning message.

[1024] The "means for detecting user emotions using an emotion analysis engine" is an engine for analyzing data entered by the user and the state of interaction to determine the user's emotional state.

[1025] "Means for adjusting the content and tone of the warning message according to the detected emotion" is a function that flexibly changes the content and tone of the warning message based on the user's emotion detected by the emotion analysis engine.

[1026] "Means for analyzing data in real time and generating warnings to prevent errors in food delivery order details and delivery instructions" refers to a function that analyzes the order details and delivery instructions entered by the user in real time, checks for errors, and issues warnings as necessary.

[1027] This invention is a system that analyzes food delivery order details entered by users in real time and generates and notifies warning messages that take the user's emotions into consideration. This system mainly consists of three main components: a user terminal, a server, and an emotion analysis engine.

[1028] Roles and operations of user terminals

[1029] The user terminal is a device that receives order details and delivery instructions entered by the user through the food delivery application. When the user enters the order details, the details are captured in real time and sent to the server. For example, when a user adds a dish to a cart and enters delivery instructions, the details are sent immediately to the server.

[1030] Server Roles and Operations

[1031] The server is the central device that analyzes the data it receives. It works in the following steps:

[1032] 1. Contextual analysis:

[1033] The server uses generative AI (e.g., models that leverage natural language processing) to analyze the context of the order, recognizing key phrases like "nut allergy" or "dairy-free."

[1034] 2. Compare conditions:

[1035] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if allergens are included" or "if there are duplicate orders").

[1036] 3. Warning generation:

[1037] If the condition is met, the server will generate a specific warning message. For example, if "I have a nut allergy" is entered but a dish containing nuts is ordered, the server will generate a warning saying "You may have a nut allergy. Please check your order."

[1038] Role and operation of the sentiment analysis engine

[1039] The sentiment analysis engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[1040] 1. Emotion detection:

[1041] The emotion analysis engine analyzes user input data and interaction data to estimate the user's emotions. For example, if a user inputs "I'm in a hurry," the emotion analysis engine will determine that state as "high stress."

[1042] 2. Tailor your message:

[1043] The sentiment analysis engine adjusts the tone and content of the warning message based on the user's emotions. If it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "You may have a nut allergy" to "We apologize for the inconvenience, but this product may contain nuts. Please check."

[1044] User notification and response

[1045] The user terminal notifies the user of the warning adjusted by the sentiment analysis engine received from the server. This is presented to the user as a pop-up message or an alert dialog. The user confirms this and makes any necessary corrections.

[1046] Specific examples

[1047] Example 1: Detection of allergens

[1048] A user enters an order like this:

[1049] I have a nut allergy and would like to order this dish. Is it included?

[1050] This input is sent to the server, which recognizes the phrase "nut allergy" and checks if the order contains nuts. If so, it generates a warning message like this:

[1051] WARNING: Possible nut allergy, please check your order.

[1052] The sentiment analysis engine analyzes the user's emotions and if it determines that the user is in a hurry, the warning message will be adjusted as follows:

[1053] We apologize for the rush, but please be aware that this product may contain nuts.

[1054] This warning is notified to the user via the user terminal.

[1055] Example prompt sentence:

[1056] A user says, "I want to order this dish." Check if this dish contains any ingredients and generate an allergy warning. If the sentiment is negative, modify the message to use a softer tone.

[1057] This system allows users to enjoy safe and stress-free food delivery and significantly reduces the risk of ordering the wrong items.

[1058] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1059] Step 1:

[1060] A user uses a food delivery application to input order details. The input order data includes the name of the dish, delivery instructions, allergy information, etc. The input data is immediately sent from the user's device to the server. The input here is the order text entered by the user, and the output is the text data sent from the user's device.

[1061] Step 2:

[1062] The server uses generative AI to perform contextual analysis on the received order data. The contextual analysis model analyzes the text data and recognizes important phrases such as "nut allergy" and "dairy-free." The input here is the text data sent by the user, and the output is the analysis results.

[1063] Step 3:

[1064] The server compares the results of the context analysis with pre-set conditions (whether the product contains allergens, whether there are duplicate orders, etc.). It processes the data to see if it matches the conditions, and if it does, generates the necessary warning. The input here is the result of the context analysis, and the output is the result of checking the conditions for generating the warning.

[1065] Step 4:

[1066] If the condition is met, the server generates an appropriate warning message. If an allergic ingredient is included, the server generates a warning saying "You may have a nut allergy. Please check your order." The input here is the result of checking the warning generation condition, and the output is the generated warning message.

[1067] Step 5:

[1068] The server uses an emotion analysis engine to analyze the user's emotions. It estimates the user's emotional state from the input text data and interaction data. It runs the emotion analysis engine and determines the emotional state as "high stress" or "hurry," etc. The input here is the user's text data and interaction data, and the output is the emotion analysis results.

[1069] Step 6:

[1070] The server adjusts the content and tone of the generated warning message based on the detected emotional state. For example, if the server determines that the user is in a hurry, it changes the warning message to "Sorry for the inconvenience, but this may contain nuts. Please check." The input here is the emotion analysis result and the generated warning message, and the output is the adjusted warning message.

[1071] Step 7:

[1072] The server finally sends the adjusted warning message to the user terminal. The user terminal displays this warning message as a pop-up or alert dialog. The user checks it and corrects the order contents as necessary. The input here is the adjusted warning message, and the output is the notification and display on the user terminal.

[1073] 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.

[1074] 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.

[1075] 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.

[1076] [Third embodiment]

[1077] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1078] 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.

[1079] 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).

[1080] 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.

[1081] 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.

[1082] 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).

[1083] 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. 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.

[1084] 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.

[1085] 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.

[1086] 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.

[1087] 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.

[1088] 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."

[1089] The system of the present invention analyzes data entered by the user in real time and issues appropriate warnings based on the context, thereby preventing careless mistakes such as typos, forgetting to attach files, and missing CCs. The program and processing of the system that embodies the present invention will be described in detail below.

[1090] System Configuration

[1091] The system consists of three main components:

[1092] 1. Terminal

[1093] 2. Server

[1094] 3. Users

[1095] Device role and operation

[1096] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[1097] Server Roles and Operations

[1098] The server analyzes the received text data using the generative AI. It works in the following steps:

[1099] 1. Contextual analysis:

[1100] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[1101] 2. Compare conditions:

[1102] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[1103] 3. Warning generation:

[1104] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[1105] User notification and response

[1106] The terminal notifies the user of the warning received from the server, which is presented as a pop-up message or a dialog box, and the user can confirm it and make any necessary corrections.

[1107] Specific examples

[1108] Example 1: Forgotten attachment

[1109] A user enters an email like this:

[1110] Subject: Next week's meeting materials

[1111] Body of the message: I have attached the meeting materials, so please check them.

[1112] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1113] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1114] This warning is notified to the user via the terminal.

[1115] Example 2: CC leak detection

[1116] A user enters an email like this:

[1117] Subject: Progress Report

[1118] Body: I'll report on my progress. Please add important people to CC too.

[1119] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[1120] Warning: Important person not included in CC. Please check your CC field.

[1121] This warning is notified to the user via the terminal.

[1122] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[1123] The processing flow will be explained below.

[1124] Step 1:

[1125] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[1126] Step 2:

[1127] The device captures the text data entered (e.g., the body of the email), along with any related data, such as whether there are any attachments or addresses in the CC field.

[1128] Step 3:

[1129] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's transmission actions.

[1130] Step 4:

[1131] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[1132] Step 5:

[1133] The server compares the results analyzed by the generated AI with pre-set conditions (e.g., when there are no attachments or when the CC field does not contain important addresses).

[1134] Step 6:

[1135] If the server finds that the comparison matches the condition, it will generate a warning message. For example, if the phrase "attached" is found and there is no attachment, it will generate a warning saying "There are no attachments. Please add an attachment."

[1136] Step 7:

[1137] The server sends the generated warning message to the terminal.

[1138] Step 8:

[1139] The device will notify the user of the received warning message, typically as a pop-up message or alert dialog.

[1140] Step 9:

[1141] The user reviews the warning message and makes the necessary corrections, such as adding attachments again or adding important addresses to the CC field.

[1142] These are the specific processing steps of the "Careless Checker" system, which is designed to prevent users from making careless mistakes. At each step, data is collected and analyzed in a timely manner, and ultimately feedback is provided to the user in real time.

[1143] Example 1

[1144] 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."

[1145] When entering emails or text, careless mistakes such as forgetting to attach files or omitting to CC often occur, which often results in the incomplete transmission of important information. A system that can prevent such mistakes before they occur is needed, but existing systems have difficulty analyzing context in real time or generating appropriate warnings, which reduces user work efficiency.

[1146] 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.

[1147] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning if the condition is met, and a means for notifying the user of the generated warning. This makes it possible to detect careless mistakes when entering emails or other text in real time and notify the user of an appropriate warning.

[1148] "User" means a person or end user who utilizes the system to input data and receive results.

[1149] "Data" is information that a user enters into the system, such as email or other text information.

[1150] A "means" is a device or method designed to perform a particular function or role.

[1151] A "server" is a computer system that processes, stores, and manages data over a network.

[1152] "Generative AI" is a system that generates and analyzes text using artificial intelligence techniques, including natural language processing.

[1153] "Contextual analysis" is the process of understanding the context of input text and analyzing its meaning.

[1154] A "warning" is a message or notification that alerts the user.

[1155] The "receiving means" is a device or method for obtaining data input by a user and transmitting it to a server.

[1156] "Context analysis means" means a device or method for analyzing the context of input text using generative AI.

[1157] The "comparison means" refers to a device or method for comparing the analysis results with pre-set conditions.

[1158] A "means for generating an alert" is a device or method for generating an alert message when a condition is met.

[1159] A "means for notifying" is a device or method for communicating the generated warning message to a user.

[1160] MODE FOR CARRYING OUT THE INVENTION

[1161] The system of the present invention analyzes data entered by the user in real time and issues appropriate context-based warnings to prevent careless mistakes. To explain the operation of the system, specific hardware and software examples will be described in detail.

[1162] System Configuration

[1163] The system consists of three main components:

[1164] 1. Terminal

[1165] 2. Server

[1166] 3. Users

[1167] Device role and operation

[1168] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[1169] Server Roles and Operations

[1170] The server uses generative AI (e.g., a natural language processing model such as GPT-4) to analyze the received text data. The specific operation of the server is as follows:

[1171] Contextual analysis

[1172] The server uses generative AI to analyze the context of the text, recognizing important phrases like "I've attached it" or "Please add me to CC." For example, if a user types "I've attached a document," the server recognizes the importance of this phrase.

[1173] Condition Comparison

[1174] The server checks whether the analysis results match pre-defined conditions (for example, "there is no attachment" or "important addresses are omitted from CC"). For example, if the context "attached" is detected and there is no attachment, this condition is met.

[1175] Warning generation

[1176] If the condition is met, the server generates a specific warning message, for example "No attachments, please add attachments."

[1177] User notification and response

[1178] The terminal notifies the user of warning messages received from the server. These notifications are presented as pop-up messages or dialog boxes, and the user can check them and make corrections if necessary.

[1179] Specific examples

[1180] Example 1: Forgotten attachment

[1181] A user enters an email like this:

[1182] text

[1183] Subject: Next week's meeting materials

[1184] Body of the message: I have attached the meeting materials, so please check them.

[1185] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1186] text

[1187] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1188] This warning is notified to the user via the terminal.

[1189] Example 2: CC leak detection

[1190] A user enters an email like this:

[1191] text

[1192] Subject: Progress Report

[1193] Body: I'll report on my progress. Please add important people to CC too.

[1194] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning message like this:

[1195] text

[1196] Warning: Important person not included in CC. Please check your CC field.

[1197] This warning is notified to the user via the terminal.

[1198] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[1199] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1200] Step 1:

[1201] The terminal captures the data entered by the user in real time and transmits it to the server.

[1202] How it works: When a user types an email or text, the device immediately captures the data, which is then securely sent to the server using the HTTPS protocol.

[1203] Input: Text data entered by the user.

[1204] Output: The text data sent to the server.

[1205] Step 2:

[1206] The server uses the generative AI model to analyze the data received from the device.

[1207] How it works: The server inputs the received text data into a generative AI model (e.g., GPT-4), which analyzes the context of the text to recognize important phrases such as "I've attached it" or "Please add me to CC."

[1208] Input: Text data received from the terminal.

[1209] Output: Context analysis results from the generative AI model.

[1210] Step 3:

[1211] The server compares the analysis results with pre-set conditions.

[1212] Specific operation: The server checks whether the analysis results obtained from the generated AI model match the pre-set conditions (for example, "if there is no attachment").

[1213] Input: Context analysis results from a generative AI model.

[1214] Output: The result of whether the condition is met.

[1215] Step 4:

[1216] The server generates a warning message if the condition is met.

[1217] Specific action: If the condition is met, the server will generate a specific warning message such as "Attachment is missing. Please add an attachment."

[1218] Input: The result of the condition match.

[1219] Output: The warning message generated.

[1220] Step 5:

[1221] The server sends the generated warning message to the terminal.

[1222] Specific operation: The server sends the generated warning message to the terminal and prepares for notification.

[1223] Input: The generated warning message.

[1224] Output: The warning message sent to the terminal.

[1225] Step 6:

[1226] The terminal notifies the user of a warning message.

[1227] Specific behavior: The device will notify the user by displaying a pop-up message or notification bar to inform the user of the received warning message, and the user can check it and make necessary corrections.

[1228] Input: The warning message received from the server.

[1229] Output: The warning message that is displayed to the user.

[1230] (Application example 1)

[1231] 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."

[1232] In factory work, labor shortages and skill variations can easily lead to mistakes, resulting in wasted time and resources. In particular, overlooking work instructions and procedures, and missing necessary parts and attachments, can reduce productivity. Missing or missing important information can also be a serious problem in quality control. The purpose of this invention is to provide a system that prevents these mistakes and improves work efficiency and quality.

[1233] 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.

[1234] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning when the conditions are met, a means for notifying the user of the generated warning, and a means for detecting omissions in work instructions and procedures within the factory and providing appropriate advice in real time, thereby reducing work errors within the factory and improving work efficiency and quality.

[1235] A "user" is an entity that uses the system to input data and receive the results.

[1236] "Input data" refers to textual, numerical, or other data formats that a user provides to a system.

[1237] "Contextual analysis means" is a function that uses generative AI to analyze the context and meaning of text data and extract important phrases and information.

[1238] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to understand and analyze the context of input data.

[1239] The "means for comparing conditions" is a function for checking the results obtained from the context analysis means against pre-set conditions to see if they match.

[1240] The "means for generating a warning" is a function for generating an appropriate warning message for the user when the analysis result meets the conditions.

[1241] The "means for notifying a warning" refers to an interface or function for immediately notifying the user of a generated warning message.

[1242] "Means to detect omissions in work instructions and procedures within the factory" refers to a function that monitors in real time whether work is being performed in accordance with instructions and procedures in the factory work environment, and detects omissions and mistakes.

[1243] A "work instruction document" is a document that describes instructions for performing work in a factory.

[1244] "Means for checking whether or not there are attached files or necessary parts" is a function for checking whether or not the attached files or parts required for the work instructions or procedure manuals are properly present.

[1245] The "means for detecting the absence of important information" is a function that detects when important instructions or information are not included in the input data.

[1246] The present invention is a system aimed at improving the efficiency of work in factories and preventing mistakes. The system is equipped with a means for analyzing data entered by users in real time and providing appropriate advice and warnings.

[1247] System Configuration

[1248] The system consists of the following main components:

[1249] 1. Terminal: A device that receives data entered by a user.

[1250] 2. Server: The central processing unit that analyzes data and generates alerts

[1251] 3. User: A person or machine (e.g., robot) that operates the system and inputs data.

[1252] Device role and operation

[1253] A terminal is a device that receives data entered by a user. This could be a PC or tablet in a factory, or a robot's control panel. The terminal transmits the text and voice data entered by the user to a server in real time. At this time, the user enters data such as work instructions, procedure manuals, and progress reports.

[1254] Server Roles and Operations

[1255] The server analyzes the received text data using a generative AI model, which has the following functions:

[1256] 1. Contextual analysis: Generative AI (e.g., the BERT model for Hugging Face, a natural language processing model) is used to analyze the context of the text data. This analysis allows for the recognition and extraction of important phrases such as "I've attached it," "Please add me to CC," and "Important parts."

[1257] 2. Condition comparison method: Based on the analysis results, it is checked whether they match pre-set conditions (for example, "if there are no attachments," "if there are important addresses outside of CC," "if necessary parts are missing").

[1258] 3. Warning Generation Method: If the condition is met, the server will generate a specific warning message, such as "Attachment is missing, please add it" or "Important parts are missing, please check them."

[1259] User notification and response

[1260] The terminal notifies the user of the warning received from the server, which is presented to the user through a pop-up message or a dialog box, allowing the user to confirm and take any necessary corrective action.

[1261] Specific examples

[1262] Example 1: Forgotten attachment

[1263] A user enters a work instruction document such as:

[1264] Instructions: Assemble the parts according to the following steps. Assembly instructions are attached.

[1265] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1266] Warning: The work order says "attached" but there are no attachments. Please add attachments.

[1267] This warning is notified to the user via the terminal.

[1268] Example 2: Missing critical parts

[1269] A user enters instructions such as:

[1270] Assembly Instructions: This job requires essential part X. Please check the parts list.

[1271] The server parses and recognizes the phrases "important parts" and "check parts list". If part X is not in the list, it generates a warning like this:

[1272] WARNING: Important part X is not included in the list. Check your parts list.

[1273] This warning is notified to the user via the terminal.

[1274] As described above, the system of the present invention can prevent mistakes in factory work and improve work efficiency and quality.

[1275] Prompt Sentence Examples

[1276] "Assemble using the following parts. Please also add important part X to the list."

[1277]

[1278] "Work Instructions: Assemble the parts according to these instructions. Assembly instructions are attached."

[1279] This allows users to receive appropriate feedback in real time on the data they enter and make any necessary corrections.

[1280] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1281] Step 1:

[1282] A user inputs a work instruction document in the factory.

[1283] Input: The user enters the text "Assembly instructions attached" into the terminal.

[1284] How it works: The device captures this text data in real time and sends it to the server.

[1285] Step 2:

[1286] The server analyzes the received text data using a generative AI model.

[1287] Input: Text data sent from the terminal.

[1288] Data processing and computation: The server uses a generative AI model (such as Hugging Face's BERT model) to analyze the context of the text.

[1289] Output: As a result of the analysis, important phrases and keywords (e.g., "attached") are extracted.

[1290] Step 3:

[1291] The server compares the analysis results with pre-set conditions.

[1292] Input: The results of the context analysis and a list of predefined conditions (e.g., "if there are no attachments").

[1293] Data processing and calculation: Check whether important phrases contained in the analysis results match the set conditions.

[1294] Output: If a phrase that meets the condition exists, record that fact.

[1295] Step 4:

[1296] If the condition is met, the server generates a warning message.

[1297] Input: Condition match detection information.

[1298] Data processing and calculation: The server generates an appropriate warning message, for example, "There are no attachments. Please add an attachment."

[1299] Output: The warning message generated.

[1300] Step 5:

[1301] The server sends the generated warning message to the terminal.

[1302] Input: The generated warning message.

[1303] Action: The server sends this message to the terminal.

[1304] Output: A warning message is sent to the terminal.

[1305] Step 6:

[1306] The terminal notifies the user of a warning message.

[1307] Input: The warning message received from the server.

[1308] Behavior: The terminal will present a warning to the user via a pop-up message or dialog box.

[1309] Output: A warning message is displayed to the user.

[1310] Step 7:

[1311] The user checks the warning message and makes the necessary corrections.

[1312] Input: The warning message displayed from the terminal.

[1313] Action: The user reviews the warning and can add attachments or make corrections as needed.

[1314] Output: A revised work instruction document.

[1315] At each step, specific processes such as context analysis and condition comparison are performed using generative AI models, and appropriate feedback is provided to the user, improving work efficiency in the factory and reducing errors.

[1316] 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.

[1317] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages, thereby preventing careless mistakes. The following describes in detail the mode for carrying out the present invention.

[1318] System Configuration

[1319] The system mainly consists of the following components:

[1320] 1. Terminal

[1321] 2. Server

[1322] 3. Users

[1323] 4. Emotion Engine

[1324] Device role and operation

[1325] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[1326] Server Roles and Operations

[1327] The server analyzes the received text data using the generative AI. It works in the following steps:

[1328] 1. Contextual analysis:

[1329] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[1330] 2. Compare conditions:

[1331] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[1332] 3. Warning generation:

[1333] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[1334] Role and operation of the emotion engine

[1335] The emotion engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[1336] 1. Emotion detection:

[1337] The emotion engine analyzes user input data and interaction data to estimate the user's emotions. For example, if the user uses shorter sentences than usual or more emotional words, the emotion engine will determine that the user is in a "high stress" state.

[1338] 2. Tailor your message:

[1339] The emotion engine adjusts the tone and content of the warning message based on the user's emotions. For example, if it detects that the user is stressed, it will change the warning message to a more gentle one, such as "No attachments" to "You may have forgotten the attachment, please check."

[1340] User notification and response

[1341] The device notifies the user of warnings adjusted by the emotion engine received from the server, which are presented as pop-up messages or alert dialogs. The user can confirm the warnings and make any necessary corrections.

[1342] Specific examples

[1343] Example 1: Forgotten attachment

[1344] A user enters an email like this:

[1345] Subject: Next week's meeting materials

[1346] Body of the message: I have attached the meeting materials, so please check them.

[1347] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1348] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1349] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[1350] Please check: It says "attached", but it seems you forgot the attachment.

[1351] This warning is notified to the user via the terminal.

[1352] Example 2: CC leak detection

[1353] A user enters an email like this:

[1354] Subject: Progress Report

[1355] Body: I'll report on my progress. Please add important people to CC too.

[1356] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[1357] Warning: Important person not included in CC. Please check your CC field.

[1358] If the emotion engine analyzes the user's emotions and determines that the user is tired, the warning message will be adjusted as follows:

[1359] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[1360] This warning is notified to the user via the terminal.

[1361] As described above, the system of the present invention performs real-time contextual analysis of data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[1362] The processing flow will be explained below.

[1363] Step 1:

[1364] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[1365] Step 2:

[1366] The device captures the text data entered (e.g., the body of the email), as well as any associated data such as whether there are any attachments and addresses in the CC field.

[1367] Step 3:

[1368] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's sending actions.

[1369] Step 4:

[1370] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[1371] Step 5:

[1372] The server compares the results of the analysis by the AI ​​with pre-defined conditions. For example, if the phrase "attached" is detected, it checks whether or not there is an attachment.

[1373] Step 6:

[1374] If the comparison results in a match, the server generates an initial warning message, such as "No attachments" or "No attachments. Please add an attachment."

[1375] Step 7:

[1376] The server passes the initial warning message and related data to the emotion engine for emotion analysis, which analyzes the user's past and current input data to estimate the user's emotional state.

[1377] Step 8:

[1378] The emotion engine adjusts the tone and content of the warning message based on the results of emotion analysis. For example, if it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "Attachment missing" to "You may have forgotten the attachment, please check."

[1379] Step 9:

[1380] The server sends the final warning message adjusted by the emotion engine to the terminal.

[1381] Step 10:

[1382] The device will notify the user of the final warning message, which may be displayed as a pop-up message or an alert dialog, providing appropriate feedback to the user.

[1383] Step 11:

[1384] The user reviews the warning message and makes any necessary corrections, such as adding back attachments or adding important addresses to the CC field.

[1385] These are the specific processing steps of the "Careless Checker" system, which combines an emotion engine. Data is collected and analyzed at each step, and real-time feedback is ultimately provided to the user, helping to prevent careless mistakes such as typos, forgotten attachments, and omissions of important information.

[1386] Example 2

[1387] 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."

[1388] When creating emails or documents, many users often forget to attach files or add important addresses to CC, which can lead to business mistakes. In addition, there is a problem that the user experience is not improved because warning messages are not provided that take into account the user's emotional state.

[1389] 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 a means for receiving data input by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with a preset condition, a means for generating a warning if the condition is met, a means for adjusting the generated warning based on emotion analysis, and a means for notifying the user of the adjusted warning. This makes it possible to analyze the data input by the user and generate a warning in real time, and to provide an appropriate warning message that takes into account the emotional state of the user.

[1390] "User" refers to an individual or legal entity that inputs data such as an email or document.

[1391] "Input data" refers to text information entered by a user using a terminal and transmitted to a server.

[1392] "Terminal" refers to a hardware device through which a user inputs data and transmits that data to a server.

[1393] "Server" refers to a computer system that analyzes data sent from users and performs the necessary processing.

[1394] "Contextual analysis" refers to the process of using generative AI to understand the context of input data and recognize key phrases.

[1395] "Generative AI" refers to an artificial intelligence model that uses natural language processing techniques to analyze text data.

[1396] "Conditions" are pre-established rules or standards that are used to compare analysis results.

[1397] A "warning message" refers to a notification message that alerts the user based on input data.

[1398] "Emotion engine" refers to technology that estimates a user's emotional state from input data and interaction data and adjusts warning messages.

[1399] "Tuning" refers to the process of changing the tone and content of a warning message based on the results of the emotion engine's analysis.

[1400] "Notification" refers to the act of presenting a warning message to the user via the terminal.

[1401] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages to prevent careless mistakes. Specific embodiments for carrying out the invention are described in detail below.

[1402] System Configuration

[1403] The system mainly consists of the following components:

[1404] 1. Terminal

[1405] 2. Server

[1406] 3. Users

[1407] 4. Emotion Engine

[1408] Device role and operation

[1409] A terminal is a device through which a user inputs data, typically a PC or smartphone. When a user inputs emails or other documents, the terminal captures the data in real time and sends it to a server. This transmission is performed using communication methods such as HTTP requests or WebSockets.

[1410] Server Roles and Operations

[1411] The server analyzes the input data received from the device and performs the necessary processing. Specifically, the process goes through the following steps:

[1412] 1. Contextual analysis:

[1413] The server uses a generative AI model (e.g., a model that leverages natural language processing techniques) to analyze the context of the incoming data, recognizing important phrases such as "I've attached it" or "Please add me to CC."

[1414] 2. Compare conditions:

[1415] The server compares the analysis results with pre-defined conditions, such as whether there are no attachments or whether there are important addresses outside of the CC field.

[1416] 3. Generate a warning message:

[1417] If the condition is met, the server generates a warning message.

[1418] 4. Sentiment analysis and message tailoring:

[1419] The server uses an emotion engine to analyze the user's emotional state and tailor the alert message to an appropriate tone and content.

[1420] Role and operation of the emotion engine

[1421] The emotion engine analyzes user input and interaction data to infer user emotions. It works in the following steps:

[1422] 1. Emotion detection:

[1423] The emotion engine infers the user's emotional state from the user's data. For example, if the user writes shorter sentences than usual or uses a lot of emotional words, the emotion engine will determine that the state is "high stress."

[1424] 2. Tailor your message:

[1425] The emotion engine adjusts the tone and content of the warning message based on the user's emotional state. For example, if the user is feeling stressed, the warning message will be softer.

[1426] User notification and response

[1427] The server sends a tailored warning message to the terminal, which then notifies the user of the message. The notification method is a pop-up message or an alert dialog.

[1428] Specific examples

[1429] Example 1: Forgotten attachment

[1430] The user enters an email containing:

[1431] Subject: Next week's meeting materials

[1432] Body of the message: I have attached the meeting materials, so please check them.

[1433] When the user enters this information, the device sends the data to the server, which parses it and recognizes the phrase "attached." It then checks for the existence of the attachment and generates a warning message like this if the attachment is not present:

[1434] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1435] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[1436] Please check: It says "attached", but it seems you forgot the attachment.

[1437] The terminal notifies the user of this warning message.

[1438] Example 2: CC leak detection

[1439] The user enters an email containing:

[1440] Subject: Progress Report

[1441] Body: I'll report on my progress. Please add important people to CC too.

[1442] The server parses this input and recognizes the phrase "please add me to CC." If it determines that an important address is not included in the CC field, it generates a warning like this:

[1443] Warning: Important person not included in CC. Please check your CC field.

[1444] The emotion engine analyzes the user's emotions and if it determines that the user is tired, the warning message will be adjusted as follows:

[1445] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[1446] The terminal notifies the user of this warning message.

[1447] Prompt Sentence Examples

[1448] An example of a prompt that the server might use to generate an AI model is:

[1449] Prompt text: "The user has entered the following text: 'Conference materials have been attached. Please review them.' Please generate the required warning message based on this text."

[1450] According to the above procedure, the system of the present invention performs real-time context analysis on the data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[1451] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1452] Step 1: Getting user-entered data

[1453] Specific operation:

[1454] A user uses a terminal to input an e-mail or other document. For example, the user inputs "I have attached the meeting materials, please check them."

[1455] input:

[1456] Text data entered by the user into the terminal.

[1457] output:

[1458] Text data to be sent from the terminal to the server.

[1459] ---

[1460] Step 2: Submitting input data

[1461] Specific operation:

[1462] The device sends the acquired input data to the server at regular intervals, using HTTP requests or WebSockets.

[1463] input:

[1464] User input data stored on the device.

[1465] output:

[1466] The text data sent to the server.

[1467] ---

[1468] Step 3: Receiving and storing data

[1469] Specific operation:

[1470] The server receives the text data sent from the terminal and stores it in a specified memory area or database.

[1471] input:

[1472] Text data sent from the device.

[1473] output:

[1474] Input data stored in the server's storage.

[1475] ---

[1476] Step 4: Contextual analysis

[1477] Specific operation:

[1478] The server uses a generative AI model (e.g., GPT-3) to analyze the context of the input text data and recognize important phrases such as "I've attached it" or "Please add me to CC."

[1479] input:

[1480] Text data stored on the server.

[1481] Data processing / calculation:

[1482] Perform contextual analysis using generative AI models.

[1483] output:

[1484] Analyzed text data and key phrases.

[1485] ---

[1486] Step 5: Compare conditions

[1487] Specific operation:

[1488] The server compares the results of the contextual analysis with pre-defined conditions (for example, "if there are no attachments").

[1489] input:

[1490] Data obtained through contextual analysis and set conditions.

[1491] Data processing / calculation:

[1492] Checking for condition matches.

[1493] output:

[1494] Flag to indicate whether the condition is met.

[1495] ---

[1496] Step 6: Generate a warning message

[1497] Specific operation:

[1498] If the conditions are met, the server generates a warning message, using a message template and filling in the necessary data.

[1499] input:

[1500] Condition match flags and parsed data.

[1501] Data processing / calculation:

[1502] Generate a warning message.

[1503] output:

[1504] The warning message that was generated.

[1505] ---

[1506] Step 7: Sentiment Analysis

[1507] Specific operation:

[1508] The server uses an emotion engine to infer emotions from user input and interaction data and checks whether the warning message needs to be adjusted.

[1509] input:

[1510] User input data and past interaction data.

[1511] Data processing / calculation:

[1512] Emotional state analysis and warning message adjustment.

[1513] output:

[1514] Adjusted warning messages (where appropriate).

[1515] ---

[1516] Step 8: Sending a warning message

[1517] Specific operation:

[1518] The server sends the adjusted alert message to the device using HTTP requests or WebSockets.

[1519] input:

[1520] Adjusted warning messages.

[1521] output:

[1522] The warning message sent to the terminal.

[1523] ---

[1524] Step 9: User Notification

[1525] Specific operation:

[1526] The terminal notifies the user of the received warning message using a pop-up message or an alert dialog.

[1527] input:

[1528] The warning message sent by the server.

[1529] output:

[1530] The warning message to be displayed to the user.

[1531] ---

[1532] Step 10: User Action

[1533] Specific operation:

[1534] The user checks the warning message and makes any necessary corrections (for example, adding an attachment or correcting the CC field).

[1535] input:

[1536] The warning message displayed to the user.

[1537] output:

[1538] Modified input data, e.g. emails with attachments added.

[1539] (Application example 2)

[1540] 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."

[1541] In conventional food delivery systems, users often make mistakes in ordering and delivery instructions, which reduces customer satisfaction. Furthermore, since appropriate warning messages are not provided based on the user's emotional state, stress increases, resulting in a tendency for ordering errors to increase. There is a need to solve these problems.

[1542] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data input by a user, context analysis means using a generation AI that analyzes the context, means for comparing the analysis result with preset conditions, means for generating a warning if the conditions are met, means for notifying the user of the generated warning, means for detecting the user's emotions using an emotion analysis engine, means for adjusting the content and tone of the warning message according to the detected emotion, and means for analyzing data in real time and generating a warning to prevent errors in food delivery order details and delivery instructions. This makes it possible to detect errors that may occur when a user places an order in real time and provide a warning message that takes the user's emotions into consideration.

[1543] The "means for receiving data input by a user" refers to a device or software for receiving data such as order details and delivery instructions input by a user through a food delivery application in real time.

[1544] "Contextual analysis means using generative AI" is a function that uses a generative AI model to analyze the context before and after text data entered by the user and understand the appropriate meaning.

[1545] "Means for comparing analysis results with pre-set conditions" refers to a system that compares the results of contextual analysis with pre-set rules and conditions to determine data consistency and errors.

[1546] The "means for generating a warning" is a function that automatically generates a message to warn or caution the user when a condition is met.

[1547] The "means for notifying the user of the generated warning" is a function or process for notifying the user's terminal of the automatically generated warning message.

[1548] The "means for detecting user emotions using an emotion analysis engine" is an engine for analyzing data entered by the user and the state of interaction to determine the user's emotional state.

[1549] "Means for adjusting the content and tone of the warning message according to the detected emotion" is a function that flexibly changes the content and tone of the warning message based on the user's emotion detected by the emotion analysis engine.

[1550] "Means for analyzing data in real time and generating warnings to prevent errors in food delivery order details and delivery instructions" refers to a function that analyzes the order details and delivery instructions entered by the user in real time, checks for errors, and issues warnings as necessary.

[1551] This invention is a system that analyzes food delivery order details entered by users in real time and generates and notifies warning messages that take the user's emotions into consideration. This system mainly consists of three main components: a user terminal, a server, and an emotion analysis engine.

[1552] Roles and operations of user terminals

[1553] The user terminal is a device that receives order details and delivery instructions entered by the user through the food delivery application. When the user enters the order details, the details are captured in real time and sent to the server. For example, when a user adds a dish to a cart and enters delivery instructions, the details are sent immediately to the server.

[1554] Server Roles and Operations

[1555] The server is the central device that analyzes the data it receives. It works in the following steps:

[1556] 1. Contextual analysis:

[1557] The server uses generative AI (e.g., models that leverage natural language processing) to analyze the context of the order, recognizing key phrases like "nut allergy" or "dairy-free."

[1558] 2. Compare conditions:

[1559] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if allergens are included" or "if there are duplicate orders").

[1560] 3. Warning generation:

[1561] If the condition is met, the server will generate a specific warning message. For example, if "I have a nut allergy" is entered but a dish containing nuts is ordered, the server will generate a warning saying "You may have a nut allergy. Please check your order."

[1562] Role and operation of the sentiment analysis engine

[1563] The sentiment analysis engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[1564] 1. Emotion detection:

[1565] The emotion analysis engine analyzes user input data and interaction data to estimate the user's emotions. For example, if a user inputs "I'm in a hurry," the emotion analysis engine will determine that state as "high stress."

[1566] 2. Tailor your message:

[1567] The sentiment analysis engine adjusts the tone and content of the warning message based on the user's emotions. If it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "You may have a nut allergy" to "We apologize for the inconvenience, but this product may contain nuts. Please check."

[1568] User notification and response

[1569] The user terminal notifies the user of the warning adjusted by the sentiment analysis engine received from the server. This is presented to the user as a pop-up message or an alert dialog. The user confirms this and makes any necessary corrections.

[1570] Specific examples

[1571] Example 1: Detection of allergens

[1572] A user enters an order like this:

[1573] I have a nut allergy and would like to order this dish. Is it included?

[1574] This input is sent to the server, which recognizes the phrase "nut allergy" and checks if the order contains nuts. If so, it generates a warning message like this:

[1575] WARNING: Possible nut allergy, please check your order.

[1576] The sentiment analysis engine analyzes the user's emotions and if it determines that the user is in a hurry, the warning message will be adjusted as follows:

[1577] We apologize for the rush, but please be aware that this product may contain nuts.

[1578] This warning is notified to the user via the user terminal.

[1579] Example prompt sentence:

[1580] A user says, "I want to order this dish." Check if this dish contains any ingredients and generate an allergy warning. If the sentiment is negative, modify the message to use a softer tone.

[1581] This system allows users to enjoy safe and stress-free food delivery and significantly reduces the risk of ordering the wrong items.

[1582] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1583] Step 1:

[1584] A user uses a food delivery application to input order details. The input order data includes the name of the dish, delivery instructions, allergy information, etc. The input data is immediately sent from the user's device to the server. The input here is the order text entered by the user, and the output is the text data sent from the user's device.

[1585] Step 2:

[1586] The server uses generative AI to perform contextual analysis on the received order data. The contextual analysis model analyzes the text data and recognizes important phrases such as "nut allergy" and "dairy-free." The input here is the text data sent by the user, and the output is the analysis results.

[1587] Step 3:

[1588] The server compares the results of the context analysis with pre-set conditions (whether the product contains allergens, whether there are duplicate orders, etc.). It processes the data to see if it matches the conditions, and if it does, generates the necessary warning. The input here is the result of the context analysis, and the output is the result of checking the conditions for generating the warning.

[1589] Step 4:

[1590] If the condition is met, the server generates an appropriate warning message. If an allergic ingredient is included, the server generates a warning saying "You may have a nut allergy. Please check your order." The input here is the result of checking the warning generation condition, and the output is the generated warning message.

[1591] Step 5:

[1592] The server uses an emotion analysis engine to analyze the user's emotions. It estimates the user's emotional state from the input text data and interaction data. It runs the emotion analysis engine and determines the emotional state as "high stress" or "hurry," etc. The input here is the user's text data and interaction data, and the output is the emotion analysis results.

[1593] Step 6:

[1594] The server adjusts the content and tone of the generated warning message based on the detected emotional state. For example, if the server determines that the user is in a hurry, it changes the warning message to "Sorry for the inconvenience, but this may contain nuts. Please check." The input here is the emotion analysis result and the generated warning message, and the output is the adjusted warning message.

[1595] Step 7:

[1596] The server finally sends the adjusted warning message to the user terminal. The user terminal displays this warning message as a pop-up or alert dialog. The user checks it and corrects the order contents as necessary. The input here is the adjusted warning message, and the output is the notification and display on the user terminal.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] [Fourth embodiment]

[1601] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1602] 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.

[1603] 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).

[1604] 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.

[1605] 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.

[1606] 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).

[1607] 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. 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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."

[1614] The system of the present invention analyzes data entered by the user in real time and issues appropriate warnings based on the context, thereby preventing careless mistakes such as typos, forgetting to attach files, and missing CCs. The program and processing of the system that embodies the present invention will be described in detail below.

[1615] System Configuration

[1616] The system consists of three main components:

[1617] 1. Terminal

[1618] 2. Server

[1619] 3. Users

[1620] Device role and operation

[1621] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[1622] Server Roles and Operations

[1623] The server analyzes the received text data using the generative AI. It works in the following steps:

[1624] 1. Contextual analysis:

[1625] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[1626] 2. Compare conditions:

[1627] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[1628] 3. Warning generation:

[1629] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[1630] User notification and response

[1631] The terminal notifies the user of the warning received from the server, which is presented as a pop-up message or a dialog box, and the user can confirm it and make any necessary corrections.

[1632] Specific examples

[1633] Example 1: Forgotten attachment

[1634] A user enters an email like this:

[1635] Subject: Next week's meeting materials

[1636] Body of the message: I have attached the meeting materials, so please check them.

[1637] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1638] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1639] This warning is notified to the user via the terminal.

[1640] Example 2: CC leak detection

[1641] A user enters an email like this:

[1642] Subject: Progress Report

[1643] Body: I'll report on my progress. Please add important people to CC too.

[1644] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[1645] Warning: Important person not included in CC. Please check your CC field.

[1646] This warning is notified to the user via the terminal.

[1647] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[1648] The processing flow will be explained below.

[1649] Step 1:

[1650] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[1651] Step 2:

[1652] The device captures the text data entered (e.g., the body of the email), along with any related data, such as whether there are any attachments or addresses in the CC field.

[1653] Step 3:

[1654] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's transmission actions.

[1655] Step 4:

[1656] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[1657] Step 5:

[1658] The server compares the results analyzed by the generated AI with pre-set conditions (e.g., when there are no attachments or when the CC field does not contain important addresses).

[1659] Step 6:

[1660] If the server finds that the comparison matches the condition, it will generate a warning message. For example, if the phrase "attached" is found and there is no attachment, it will generate a warning saying "There are no attachments. Please add an attachment."

[1661] Step 7:

[1662] The server sends the generated warning message to the terminal.

[1663] Step 8:

[1664] The device will notify the user of the received warning message, typically as a pop-up message or alert dialog.

[1665] Step 9:

[1666] The user reviews the warning message and makes the necessary corrections, such as adding attachments again or adding important addresses to the CC field.

[1667] These are the specific processing steps of the "Careless Checker" system, which is designed to prevent users from making careless mistakes. At each step, data is collected and analyzed in a timely manner, and ultimately feedback is provided to the user in real time.

[1668] Example 1

[1669] 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."

[1670] When entering emails or text, careless mistakes such as forgetting to attach files or omitting to CC often occur, which often results in the incomplete transmission of important information. A system that can prevent such mistakes before they occur is needed, but existing systems have difficulty analyzing context in real time or generating appropriate warnings, which reduces user work efficiency.

[1671] 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.

[1672] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning if the condition is met, and a means for notifying the user of the generated warning. This makes it possible to detect careless mistakes when entering emails or other text in real time and notify the user of an appropriate warning.

[1673] "User" means a person or end user who utilizes the system to input data and receive results.

[1674] "Data" is information that a user enters into the system, such as email or other text information.

[1675] A "means" is a device or method designed to perform a particular function or role.

[1676] A "server" is a computer system that processes, stores, and manages data over a network.

[1677] "Generative AI" is a system that generates and analyzes text using artificial intelligence techniques, including natural language processing.

[1678] "Contextual analysis" is the process of understanding the context of input text and analyzing its meaning.

[1679] A "warning" is a message or notification that alerts the user.

[1680] The "receiving means" is a device or method for obtaining data input by a user and transmitting it to a server.

[1681] "Context analysis means" means a device or method for analyzing the context of input text using generative AI.

[1682] The "comparison means" refers to a device or method for comparing the analysis results with pre-set conditions.

[1683] A "means for generating an alert" is a device or method for generating an alert message when a condition is met.

[1684] A "means for notifying" is a device or method for communicating the generated warning message to a user.

[1685] MODE FOR CARRYING OUT THE INVENTION

[1686] The system of the present invention analyzes data entered by the user in real time and issues appropriate context-based warnings to prevent careless mistakes. To explain the operation of the system, specific hardware and software examples will be described in detail.

[1687] System Configuration

[1688] The system consists of three main components:

[1689] 1. Terminal

[1690] 2. Server

[1691] 3. Users

[1692] Device role and operation

[1693] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[1694] Server Roles and Operations

[1695] The server uses generative AI (e.g., a natural language processing model such as GPT-4) to analyze the received text data. The specific operation of the server is as follows:

[1696] Contextual analysis

[1697] The server uses generative AI to analyze the context of the text, recognizing important phrases like "I've attached it" or "Please add me to CC." For example, if a user types "I've attached a document," the server recognizes the importance of this phrase.

[1698] Condition Comparison

[1699] The server checks whether the analysis results match pre-defined conditions (for example, "there is no attachment" or "important addresses are omitted from CC"). For example, if the context "attached" is detected and there is no attachment, this condition is met.

[1700] Warning generation

[1701] If the condition is met, the server generates a specific warning message, for example "No attachments, please add attachments."

[1702] User notification and response

[1703] The terminal notifies the user of warning messages received from the server. These notifications are presented as pop-up messages or dialog boxes, and the user can check them and make corrections if necessary.

[1704] Specific examples

[1705] Example 1: Forgotten attachment

[1706] A user enters an email like this:

[1707] text

[1708] Subject: Next week's meeting materials

[1709] Body of the message: I have attached the meeting materials, so please check them.

[1710] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1711] text

[1712] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1713] This warning is notified to the user via the terminal.

[1714] Example 2: CC leak detection

[1715] A user enters an email like this:

[1716] text

[1717] Subject: Progress Report

[1718] Body: I'll report on my progress. Please add important people to CC too.

[1719] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning message like this:

[1720] text

[1721] Warning: Important person not included in CC. Please check your CC field.

[1722] This warning is notified to the user via the terminal.

[1723] As described above, the system of the present invention performs real-time context analysis of data entered by the user and issues appropriate warnings, preventing typos, forgetting to attach files, and omitting important information. This is expected to improve user work efficiency and reduce errors.

[1724] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1725] Step 1:

[1726] The terminal captures the data entered by the user in real time and transmits it to the server.

[1727] How it works: When a user types an email or text, the device immediately captures the data, which is then securely sent to the server using the HTTPS protocol.

[1728] Input: Text data entered by the user.

[1729] Output: The text data sent to the server.

[1730] Step 2:

[1731] The server uses the generative AI model to analyze the data received from the device.

[1732] How it works: The server inputs the received text data into a generative AI model (e.g., GPT-4), which analyzes the context of the text to recognize important phrases such as "I've attached it" or "Please add me to CC."

[1733] Input: Text data received from the terminal.

[1734] Output: Context analysis results from the generative AI model.

[1735] Step 3:

[1736] The server compares the analysis results with pre-set conditions.

[1737] Specific operation: The server checks whether the analysis results obtained from the generated AI model match the pre-set conditions (for example, "if there is no attachment").

[1738] Input: Context analysis results from a generative AI model.

[1739] Output: The result of whether the condition is met.

[1740] Step 4:

[1741] The server generates a warning message if the condition is met.

[1742] Specific action: If the condition is met, the server will generate a specific warning message such as "Attachment is missing. Please add an attachment."

[1743] Input: The result of the condition match.

[1744] Output: The warning message generated.

[1745] Step 5:

[1746] The server sends the generated warning message to the terminal.

[1747] Specific operation: The server sends the generated warning message to the terminal and prepares for notification.

[1748] Input: The generated warning message.

[1749] Output: The warning message sent to the terminal.

[1750] Step 6:

[1751] The terminal notifies the user of a warning message.

[1752] Specific behavior: The device will notify the user by displaying a pop-up message or notification bar to inform the user of the received warning message, and the user can check it and make necessary corrections.

[1753] Input: The warning message received from the server.

[1754] Output: The warning message that is displayed to the user.

[1755] (Application example 1)

[1756] 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."

[1757] In factory work, labor shortages and skill variations can easily lead to mistakes, resulting in wasted time and resources. In particular, overlooking work instructions and procedures, and missing necessary parts and attachments, can reduce productivity. Missing or missing important information can also be a serious problem in quality control. The purpose of this invention is to provide a system that prevents these mistakes and improves work efficiency and quality.

[1758] 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.

[1759] In this invention, the server includes a means for receiving data entered by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with preset conditions, a means for generating a warning when the conditions are met, a means for notifying the user of the generated warning, and a means for detecting omissions in work instructions and procedures within the factory and providing appropriate advice in real time, thereby reducing work errors within the factory and improving work efficiency and quality.

[1760] A "user" is an entity that uses the system to input data and receive the results.

[1761] "Input data" refers to textual, numerical, or other data formats that a user provides to a system.

[1762] "Contextual analysis means" is a function that uses generative AI to analyze the context and meaning of text data and extract important phrases and information.

[1763] "Generative AI" refers to an artificial intelligence model that uses natural language processing technology to understand and analyze the context of input data.

[1764] The "means for comparing conditions" is a function for checking the results obtained from the context analysis means against pre-set conditions to see if they match.

[1765] The "means for generating a warning" is a function for generating an appropriate warning message for the user when the analysis result meets the conditions.

[1766] The "means for notifying a warning" refers to an interface or function for immediately notifying the user of a generated warning message.

[1767] "Means to detect omissions in work instructions and procedures within the factory" refers to a function that monitors in real time whether work is being performed in accordance with instructions and procedures in the factory work environment, and detects omissions and mistakes.

[1768] A "work instruction document" is a document that describes instructions for performing work in a factory.

[1769] "Means for checking whether or not there are attached files or necessary parts" is a function for checking whether or not the attached files or parts required for the work instructions or procedure manuals are properly present.

[1770] The "means for detecting the absence of important information" is a function that detects when important instructions or information are not included in the input data.

[1771] The present invention is a system aimed at improving the efficiency of work in factories and preventing mistakes. The system is equipped with a means for analyzing data entered by users in real time and providing appropriate advice and warnings.

[1772] System Configuration

[1773] The system consists of the following main components:

[1774] 1. Terminal: A device that receives data entered by a user.

[1775] 2. Server: The central processing unit that analyzes data and generates alerts

[1776] 3. User: A person or machine (e.g., robot) that operates the system and inputs data.

[1777] Device role and operation

[1778] A terminal is a device that receives data entered by a user. This could be a PC or tablet in a factory, or a robot's control panel. The terminal transmits the text and voice data entered by the user to a server in real time. At this time, the user enters data such as work instructions, procedure manuals, and progress reports.

[1779] Server Roles and Operations

[1780] The server analyzes the received text data using a generative AI model, which has the following functions:

[1781] 1. Contextual analysis: Generative AI (e.g., the BERT model for Hugging Face, a natural language processing model) is used to analyze the context of the text data. This analysis allows for the recognition and extraction of important phrases such as "I've attached it," "Please add me to CC," and "Important parts."

[1782] 2. Condition comparison method: Based on the analysis results, it is checked whether they match pre-set conditions (for example, "if there are no attachments," "if there are important addresses outside of CC," "if necessary parts are missing").

[1783] 3. Warning Generation Method: If the condition is met, the server will generate a specific warning message, such as "Attachment is missing, please add it" or "Important parts are missing, please check them."

[1784] User notification and response

[1785] The terminal notifies the user of the warning received from the server, which is presented to the user through a pop-up message or a dialog box, allowing the user to confirm and take any necessary corrective action.

[1786] Specific examples

[1787] Example 1: Forgotten attachment

[1788] A user enters a work instruction document such as:

[1789] Instructions: Assemble the parts according to the following steps. Assembly instructions are attached.

[1790] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1791] Warning: The work order says "attached" but there are no attachments. Please add attachments.

[1792] This warning is notified to the user via the terminal.

[1793] Example 2: Missing critical parts

[1794] A user enters instructions such as:

[1795] Assembly Instructions: This job requires essential part X. Please check the parts list.

[1796] The server parses and recognizes the phrases "important parts" and "check parts list". If part X is not in the list, it generates a warning like this:

[1797] WARNING: Important part X is not included in the list. Check your parts list.

[1798] This warning is notified to the user via the terminal.

[1799] As described above, the system of the present invention can prevent mistakes in factory work and improve work efficiency and quality.

[1800] Prompt Sentence Examples

[1801] "Assemble using the following parts. Please also add important part X to the list."

[1802]

[1803] "Work Instructions: Assemble the parts according to these instructions. Assembly instructions are attached."

[1804] This allows users to receive appropriate feedback in real time on the data they enter and make any necessary corrections.

[1805] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1806] Step 1:

[1807] A user inputs a work instruction document in the factory.

[1808] Input: The user enters the text "Assembly instructions attached" into the terminal.

[1809] How it works: The device captures this text data in real time and sends it to the server.

[1810] Step 2:

[1811] The server analyzes the received text data using a generative AI model.

[1812] Input: Text data sent from the terminal.

[1813] Data processing and computation: The server uses a generative AI model (such as Hugging Face's BERT model) to analyze the context of the text.

[1814] Output: As a result of the analysis, important phrases and keywords (e.g., "attached") are extracted.

[1815] Step 3:

[1816] The server compares the analysis results with pre-set conditions.

[1817] Input: The results of the context analysis and a list of predefined conditions (e.g., "if there are no attachments").

[1818] Data processing and calculation: Check whether important phrases contained in the analysis results match the set conditions.

[1819] Output: If a phrase that meets the condition exists, record that fact.

[1820] Step 4:

[1821] If the condition is met, the server generates a warning message.

[1822] Input: Condition match detection information.

[1823] Data processing and calculation: The server generates an appropriate warning message, for example, "There are no attachments. Please add an attachment."

[1824] Output: The warning message generated.

[1825] Step 5:

[1826] The server sends the generated warning message to the terminal.

[1827] Input: The generated warning message.

[1828] Action: The server sends this message to the terminal.

[1829] Output: A warning message is sent to the terminal.

[1830] Step 6:

[1831] The terminal notifies the user of a warning message.

[1832] Input: The warning message received from the server.

[1833] Behavior: The terminal will present a warning to the user via a pop-up message or dialog box.

[1834] Output: A warning message is displayed to the user.

[1835] Step 7:

[1836] The user checks the warning message and makes the necessary corrections.

[1837] Input: The warning message displayed from the terminal.

[1838] Action: The user reviews the warning and can add attachments or make corrections as needed.

[1839] Output: A revised work instruction document.

[1840] At each step, specific processes such as context analysis and condition comparison are performed using generative AI models, and appropriate feedback is provided to the user, improving work efficiency in the factory and reducing errors.

[1841] 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.

[1842] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages, thereby preventing careless mistakes. The following describes in detail the mode for carrying out the present invention.

[1843] System Configuration

[1844] The system mainly consists of the following components:

[1845] 1. Terminal

[1846] 2. Server

[1847] 3. Users

[1848] 4. Emotion Engine

[1849] Device role and operation

[1850] The terminal is a device that receives data entered by the user. When the user enters an email or other text, the terminal captures it in real time and sends it to the server. For example, if the user enters "attached" in the body of an email, the content is immediately sent to the server.

[1851] Server Roles and Operations

[1852] The server analyzes the received text data using the generative AI. It works in the following steps:

[1853] 1. Contextual analysis:

[1854] The server uses generative AI (e.g., a model that leverages natural language processing) to analyze the context of the text, recognizing important phrases such as "I've attached it" or "Please CC me."

[1855] 2. Compare conditions:

[1856] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if there are no attachments" or "if there are important addresses not in the CC field").

[1857] 3. Warning generation:

[1858] If the conditions are met, the server will generate a specific warning message, for example, if the phrase "attached" is present but there is no attachment, it will generate the warning "Attachment is missing. Please add an attachment."

[1859] Role and operation of the emotion engine

[1860] The emotion engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[1861] 1. Emotion detection:

[1862] The emotion engine analyzes user input data and interaction data to estimate the user's emotions. For example, if the user uses shorter sentences than usual or more emotional words, the emotion engine will determine that the user is in a "high stress" state.

[1863] 2. Tailor your message:

[1864] The emotion engine adjusts the tone and content of the warning message based on the user's emotions. For example, if it detects that the user is stressed, it will change the warning message to a more gentle one, such as "No attachments" to "You may have forgotten the attachment, please check."

[1865] User notification and response

[1866] The device notifies the user of warnings adjusted by the emotion engine received from the server, which are presented as pop-up messages or alert dialogs. The user can confirm the warnings and make any necessary corrections.

[1867] Specific examples

[1868] Example 1: Forgotten attachment

[1869] A user enters an email like this:

[1870] Subject: Next week's meeting materials

[1871] Body of the message: I have attached the meeting materials, so please check them.

[1872] This input is sent to the server, which recognizes the phrase "attached" and checks for the presence of an attachment. If there is no attachment, it generates a warning message like this:

[1873] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1874] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[1875] Please check: It says "attached", but it seems you forgot the attachment.

[1876] This warning is notified to the user via the terminal.

[1877] Example 2: CC leak detection

[1878] A user enters an email like this:

[1879] Subject: Progress Report

[1880] Body: I'll report on my progress. Please add important people to CC too.

[1881] The server parses and recognizes the phrase "please add me to CC." If an important address is not included in the CC field, it generates a warning like this:

[1882] Warning: Important person not included in CC. Please check your CC field.

[1883] If the emotion engine analyzes the user's emotions and determines that the user is tired, the warning message will be adjusted as follows:

[1884] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[1885] This warning is notified to the user via the terminal.

[1886] As described above, the system of the present invention performs real-time contextual analysis of data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[1887] The processing flow will be explained below.

[1888] Step 1:

[1889] The user inputs text such as the body of the email into the terminal. Once the input is confirmed, the user clicks the "Send" button.

[1890] Step 2:

[1891] The device captures the text data entered (e.g., the body of the email), as well as any associated data such as whether there are any attachments and addresses in the CC field.

[1892] Step 3:

[1893] The device transmits the captured data (text data and related data) to the server in real time, concurrently with the user's sending actions.

[1894] Step 4:

[1895] The server then passes the received data to the AI ​​generator, which analyzes the context of the text and detects important phrases such as "I've attached it" or "Please add me to CC."

[1896] Step 5:

[1897] The server compares the results of the analysis by the AI ​​with pre-defined conditions. For example, if the phrase "attached" is detected, it checks whether or not there is an attachment.

[1898] Step 6:

[1899] If the comparison results in a match, the server generates an initial warning message, such as "No attachments" or "No attachments. Please add an attachment."

[1900] Step 7:

[1901] The server passes the initial warning message and related data to the emotion engine for emotion analysis, which analyzes the user's past and current input data to estimate the user's emotional state.

[1902] Step 8:

[1903] The emotion engine adjusts the tone and content of the warning message based on the results of emotion analysis. For example, if it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "Attachment missing" to "You may have forgotten the attachment, please check."

[1904] Step 9:

[1905] The server sends the final warning message adjusted by the emotion engine to the terminal.

[1906] Step 10:

[1907] The device will notify the user of the final warning message, which may be displayed as a pop-up message or an alert dialog, providing appropriate feedback to the user.

[1908] Step 11:

[1909] The user reviews the warning message and makes any necessary corrections, such as adding back attachments or adding important addresses to the CC field.

[1910] These are the specific processing steps of the "Careless Checker" system, which combines an emotion engine. Data is collected and analyzed at each step, and real-time feedback is ultimately provided to the user, helping to prevent careless mistakes such as typos, forgotten attachments, and omissions of important information.

[1911] Example 2

[1912] 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."

[1913] When creating emails or documents, many users often forget to attach files or add important addresses to CC, which can lead to business mistakes. In addition, there is a problem that the user experience is not improved because warning messages are not provided that take into account the user's emotional state.

[1914] 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 a means for receiving data input by a user, a context analysis means using a generation AI that analyzes the context, a means for comparing the analysis result with a preset condition, a means for generating a warning if the condition is met, a means for adjusting the generated warning based on emotion analysis, and a means for notifying the user of the adjusted warning. This makes it possible to analyze the data input by the user and generate a warning in real time, and to provide an appropriate warning message that takes into account the emotional state of the user.

[1915] "User" refers to an individual or legal entity that inputs data such as an email or document.

[1916] "Input data" refers to text information entered by a user using a terminal and transmitted to a server.

[1917] "Terminal" refers to a hardware device through which a user inputs data and transmits that data to a server.

[1918] "Server" refers to a computer system that analyzes data sent from users and performs the necessary processing.

[1919] "Contextual analysis" refers to the process of using generative AI to understand the context of input data and recognize key phrases.

[1920] "Generative AI" refers to an artificial intelligence model that uses natural language processing techniques to analyze text data.

[1921] "Conditions" are pre-established rules or standards that are used to compare analysis results.

[1922] A "warning message" refers to a notification message that alerts the user based on input data.

[1923] "Emotion engine" refers to technology that estimates a user's emotional state from input data and interaction data and adjusts warning messages.

[1924] "Tuning" refers to the process of changing the tone and content of a warning message based on the results of the emotion engine's analysis.

[1925] "Notification" refers to the act of presenting a warning message to the user via the terminal.

[1926] The system of the present invention analyzes data entered by the user in real time, and combines it with an emotion engine to issue appropriate warning messages to prevent careless mistakes. Specific embodiments for carrying out the invention are described in detail below.

[1927] System Configuration

[1928] The system mainly consists of the following components:

[1929] 1. Terminal

[1930] 2. Server

[1931] 3. Users

[1932] 4. Emotion Engine

[1933] Device role and operation

[1934] A terminal is a device through which a user inputs data, typically a PC or smartphone. When a user inputs emails or other documents, the terminal captures the data in real time and sends it to a server. This transmission is performed using communication methods such as HTTP requests or WebSockets.

[1935] Server Roles and Operations

[1936] The server analyzes the input data received from the device and performs the necessary processing. Specifically, the process goes through the following steps:

[1937] 1. Contextual analysis:

[1938] The server uses a generative AI model (e.g., a model that leverages natural language processing techniques) to analyze the context of the incoming data, recognizing important phrases such as "I've attached it" or "Please add me to CC."

[1939] 2. Compare conditions:

[1940] The server compares the analysis results with pre-defined conditions, such as whether there are no attachments or whether there are important addresses outside of the CC field.

[1941] 3. Generate a warning message:

[1942] If the condition is met, the server generates a warning message.

[1943] 4. Sentiment analysis and message tailoring:

[1944] The server uses an emotion engine to analyze the user's emotional state and tailor the alert message to an appropriate tone and content.

[1945] Role and operation of the emotion engine

[1946] The emotion engine analyzes user input and interaction data to infer user emotions. It works in the following steps:

[1947] 1. Emotion detection:

[1948] The emotion engine infers the user's emotional state from the user's data. For example, if the user writes shorter sentences than usual or uses a lot of emotional words, the emotion engine will determine that the state is "high stress."

[1949] 2. Tailor your message:

[1950] The emotion engine adjusts the tone and content of the warning message based on the user's emotional state. For example, if the user is feeling stressed, the warning message will be softer.

[1951] User notification and response

[1952] The server sends a tailored warning message to the terminal, which then notifies the user of the message. The notification method is a pop-up message or an alert dialog.

[1953] Specific examples

[1954] Example 1: Forgotten attachment

[1955] The user enters an email containing:

[1956] Subject: Next week's meeting materials

[1957] Body of the message: I have attached the meeting materials, so please check them.

[1958] When the user enters this information, the device sends the data to the server, which parses it and recognizes the phrase "attached." It then checks for the existence of the attachment and generates a warning message like this if the attachment is not present:

[1959] Warning: The email says "attached" but there are no attachments. Please add an attachment.

[1960] The emotion engine analyzes the user's emotions, and if it determines that the user is stressed, the warning message will be adjusted as follows:

[1961] Please check: It says "attached", but it seems you forgot the attachment.

[1962] The terminal notifies the user of this warning message.

[1963] Example 2: CC leak detection

[1964] The user enters an email containing:

[1965] Subject: Progress Report

[1966] Body: I'll report on my progress. Please add important people to CC too.

[1967] The server parses this input and recognizes the phrase "please add me to CC." If it determines that an important address is not included in the CC field, it generates a warning like this:

[1968] Warning: Important person not included in CC. Please check your CC field.

[1969] The emotion engine analyzes the user's emotions and if it determines that the user is tired, the warning message will be adjusted as follows:

[1970] Thank you for your hard work: It seems that an important person is not included in the CC. Please check.

[1971] The terminal notifies the user of this warning message.

[1972] Prompt Sentence Examples

[1973] An example of a prompt that the server might use to generate an AI model is:

[1974] Prompt text: "The user has entered the following text: 'Conference materials have been attached. Please review them.' Please generate the required warning message based on this text."

[1975] According to the above procedure, the system of the present invention performs real-time context analysis on the data entered by the user and uses an emotion engine to provide appropriate warning messages, thereby effectively preventing user mistakes and improving the user experience.

[1976] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1977] Step 1: Getting user-entered data

[1978] Specific operation:

[1979] A user uses a terminal to input an e-mail or other document. For example, the user inputs "I have attached the meeting materials, please check them."

[1980] input:

[1981] Text data entered by the user into the terminal.

[1982] output:

[1983] Text data to be sent from the terminal to the server.

[1984] ---

[1985] Step 2: Submitting input data

[1986] Specific operation:

[1987] The device sends the acquired input data to the server at regular intervals, using HTTP requests or WebSockets.

[1988] input:

[1989] User input data stored on the device.

[1990] output:

[1991] The text data sent to the server.

[1992] ---

[1993] Step 3: Receiving and storing data

[1994] Specific operation:

[1995] The server receives the text data sent from the terminal and stores it in a specified memory area or database.

[1996] input:

[1997] Text data sent from the device.

[1998] output:

[1999] Input data stored in the server's storage.

[2000] ---

[2001] Step 4: Contextual analysis

[2002] Specific operation:

[2003] The server uses a generative AI model (e.g., GPT-3) to analyze the context of the input text data and recognize important phrases such as "I've attached it" or "Please add me to CC."

[2004] input:

[2005] Text data stored on the server.

[2006] Data processing / calculation:

[2007] Perform contextual analysis using generative AI models.

[2008] output:

[2009] Analyzed text data and key phrases.

[2010] ---

[2011] Step 5: Compare conditions

[2012] Specific operation:

[2013] The server compares the results of the contextual analysis with pre-defined conditions (for example, "if there are no attachments").

[2014] input:

[2015] Data obtained through contextual analysis and set conditions.

[2016] Data processing / calculation:

[2017] Checking for condition matches.

[2018] output:

[2019] Flag to indicate whether the condition is met.

[2020] ---

[2021] Step 6: Generate a warning message

[2022] Specific operation:

[2023] If the conditions are met, the server generates a warning message, using a message template and filling in the necessary data.

[2024] input:

[2025] Condition match flags and parsed data.

[2026] Data processing / calculation:

[2027] Generate a warning message.

[2028] output:

[2029] The warning message that was generated.

[2030] ---

[2031] Step 7: Sentiment Analysis

[2032] Specific operation:

[2033] The server uses an emotion engine to infer emotions from user input and interaction data and checks whether the warning message needs to be adjusted.

[2034] input:

[2035] User input data and past interaction data.

[2036] Data processing / calculation:

[2037] Emotional state analysis and warning message adjustment.

[2038] output:

[2039] Adjusted warning messages (where appropriate).

[2040] ---

[2041] Step 8: Sending a warning message

[2042] Specific operation:

[2043] The server sends the adjusted alert message to the device using HTTP requests or WebSockets.

[2044] input:

[2045] Adjusted warning messages.

[2046] output:

[2047] The warning message sent to the terminal.

[2048] ---

[2049] Step 9: User Notification

[2050] Specific operation:

[2051] The terminal notifies the user of the received warning message using a pop-up message or an alert dialog.

[2052] input:

[2053] The warning message sent by the server.

[2054] output:

[2055] The warning message to be displayed to the user.

[2056] ---

[2057] Step 10: User Action

[2058] Specific operation:

[2059] The user checks the warning message and makes any necessary corrections (for example, adding an attachment or correcting the CC field).

[2060] input:

[2061] The warning message displayed to the user.

[2062] output:

[2063] Modified input data, e.g. emails with attachments added.

[2064] (Application example 2)

[2065] 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."

[2066] In conventional food delivery systems, users often make mistakes in ordering and delivery instructions, which reduces customer satisfaction. Furthermore, since appropriate warning messages are not provided based on the user's emotional state, stress increases, resulting in a tendency for ordering errors to increase. There is a need to solve these problems.

[2067] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data input by a user, context analysis means using a generation AI that analyzes the context, means for comparing the analysis result with preset conditions, means for generating a warning if the conditions are met, means for notifying the user of the generated warning, means for detecting the user's emotions using an emotion analysis engine, means for adjusting the content and tone of the warning message according to the detected emotion, and means for analyzing data in real time and generating a warning to prevent errors in food delivery order details and delivery instructions. This makes it possible to detect errors that may occur when a user places an order in real time and provide a warning message that takes the user's emotions into consideration.

[2068] The "means for receiving data input by a user" refers to a device or software for receiving data such as order details and delivery instructions input by a user through a food delivery application in real time.

[2069] "Contextual analysis means using generative AI" is a function that uses a generative AI model to analyze the context before and after text data entered by the user and understand the appropriate meaning.

[2070] "Means for comparing analysis results with pre-set conditions" refers to a system that compares the results of contextual analysis with pre-set rules and conditions to determine data consistency and errors.

[2071] The "means for generating a warning" is a function that automatically generates a message to warn or caution the user when a condition is met.

[2072] The "means for notifying the user of the generated warning" is a function or process for notifying the user's terminal of the automatically generated warning message.

[2073] The "means for detecting user emotions using an emotion analysis engine" is an engine for analyzing data entered by the user and the state of interaction to determine the user's emotional state.

[2074] "Means for adjusting the content and tone of the warning message according to the detected emotion" is a function that flexibly changes the content and tone of the warning message based on the user's emotion detected by the emotion analysis engine.

[2075] "Means for analyzing data in real time and generating warnings to prevent errors in food delivery order details and delivery instructions" refers to a function that analyzes the order details and delivery instructions entered by the user in real time, checks for errors, and issues warnings as necessary.

[2076] This invention is a system that analyzes food delivery order details entered by users in real time and generates and notifies warning messages that take the user's emotions into consideration. This system mainly consists of three main components: a user terminal, a server, and an emotion analysis engine.

[2077] Roles and operations of user terminals

[2078] The user terminal is a device that receives order details and delivery instructions entered by the user through the food delivery application. When the user enters the order details, the details are captured in real time and sent to the server. For example, when a user adds a dish to a cart and enters delivery instructions, the details are sent immediately to the server.

[2079] Server Roles and Operations

[2080] The server is the central device that analyzes the data it receives. It works in the following steps:

[2081] 1. Contextual analysis:

[2082] The server uses generative AI (e.g., models that leverage natural language processing) to analyze the context of the order, recognizing key phrases like "nut allergy" or "dairy-free."

[2083] 2. Compare conditions:

[2084] Based on the analysis results, the server checks whether they match pre-set conditions (for example, "if allergens are included" or "if there are duplicate orders").

[2085] 3. Warning generation:

[2086] If the condition is met, the server will generate a specific warning message. For example, if "I have a nut allergy" is entered but a dish containing nuts is ordered, the server will generate a warning saying "You may have a nut allergy. Please check your order."

[2087] Role and operation of the sentiment analysis engine

[2088] The sentiment analysis engine analyzes the user's emotions and adjusts the content and tone of the generated warning messages. It works in the following steps:

[2089] 1. Emotion detection:

[2090] The emotion analysis engine analyzes user input data and interaction data to estimate the user's emotions. For example, if a user inputs "I'm in a hurry," the emotion analysis engine will determine that state as "high stress."

[2091] 2. Tailor your message:

[2092] The sentiment analysis engine adjusts the tone and content of the warning message based on the user's emotions. If it determines that the user is stressed, it will change the warning message to a more gentle one, such as changing "You may have a nut allergy" to "We apologize for the inconvenience, but this product may contain nuts. Please check."

[2093] User notification and response

[2094] The user terminal notifies the user of the warning adjusted by the sentiment analysis engine received from the server. This is presented to the user as a pop-up message or an alert dialog. The user confirms this and makes any necessary corrections.

[2095] Specific examples

[2096] Example 1: Detection of allergens

[2097] A user enters an order like this:

[2098] I have a nut allergy and would like to order this dish. Is it included?

[2099] This input is sent to the server, which recognizes the phrase "nut allergy" and checks if the order contains nuts. If so, it generates a warning message like this:

[2100] WARNING: Possible nut allergy, please check your order.

[2101] The sentiment analysis engine analyzes the user's emotions and if it determines that the user is in a hurry, the warning message will be adjusted as follows:

[2102] We apologize for the rush, but please be aware that this product may contain nuts.

[2103] This warning is notified to the user via the user terminal.

[2104] Example prompt sentence:

[2105] A user says, "I want to order this dish." Check if this dish contains any ingredients and generate an allergy warning. If the sentiment is negative, modify the message to use a softer tone.

[2106] This system allows users to enjoy safe and stress-free food delivery and significantly reduces the risk of ordering the wrong items.

[2107] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2108] Step 1:

[2109] A user uses a food delivery application to input order details. The input order data includes the name of the dish, delivery instructions, allergy information, etc. The input data is immediately sent from the user's device to the server. The input here is the order text entered by the user, and the output is the text data sent from the user's device.

[2110] Step 2:

[2111] The server uses generative AI to perform contextual analysis on the received order data. The contextual analysis model analyzes the text data and recognizes important phrases such as "nut allergy" and "dairy-free." The input here is the text data sent by the user, and the output is the analysis results.

[2112] Step 3:

[2113] The server compares the results of the context analysis with pre-set conditions (whether the product contains allergens, whether there are duplicate orders, etc.). It processes the data to see if it matches the conditions, and if it does, generates the necessary warning. The input here is the result of the context analysis, and the output is the result of checking the conditions for generating the warning.

[2114] Step 4:

[2115] If the condition is met, the server generates an appropriate warning message. If an allergic ingredient is included, the server generates a warning saying "You may have a nut allergy. Please check your order." The input here is the result of checking the warning generation condition, and the output is the generated warning message.

[2116] Step 5:

[2117] The server uses an emotion analysis engine to analyze the user's emotions. It estimates the user's emotional state from the input text data and interaction data. It runs the emotion analysis engine and determines the emotional state as "high stress" or "hurry," etc. The input here is the user's text data and interaction data, and the output is the emotion analysis results.

[2118] Step 6:

[2119] The server adjusts the content and tone of the generated warning message based on the detected emotional state. For example, if the server determines that the user is in a hurry, it changes the warning message to "Sorry for the inconvenience, but this may contain nuts. Please check." The input here is the emotion analysis result and the generated warning message, and the output is the adjusted warning message.

[2120] Step 7:

[2121] The server finally sends the adjusted warning message to the user terminal. The user terminal displays this warning message as a pop-up or alert dialog. The user checks it and corrects the order contents as necessary. The input here is the adjusted warning message, and the output is the notification and display on the user terminal.

[2122] 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.

[2123] 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.

[2124] 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.

[2125] 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.

[2126] 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.

[2127] 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.

[2128] 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).

[2129] 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.

[2130] 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."

[2131] 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.

[2132] 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).

[2133] 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.

[2134] 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.

[2135] 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.

[2136] 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.

[2137] 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.

[2138] 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.

[2139] 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.

[2140] 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.

[2141] 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.

[2142] 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.

[2143] The following is further disclosed regarding the above embodiment.

[2144] (Claim 1)

[2145] means for receiving data input from a user;

[2146] A context analysis method using generative AI that analyzes the context before and after the sentence,

[2147] A means for comparing the analysis results with pre-defined conditions;

[2148] means for generating an alert when the condition is met;

[2149] means for notifying a user of generated warnings;

[2150] A system including:

[2151] (Claim 2)

[2152] 2. The system according to claim 1, wherein the input data is an e-mail, and the context analysis means checks whether or not an attachment exists.

[2153] (Claim 3)

[2154] 10. The system of claim 1, further comprising: means for detecting that a significant address is missing from the CC field based on context.

[2155] "Example 1"

[2156] (Claim 1)

[2157] means for receiving data input from a user;

[2158] A context analysis method using generative AI that analyzes the context before and after the sentence,

[2159] A means for comparing the analysis results with pre-defined conditions;

[2160] means for generating an alert when the condition is met;

[2161] means for notifying a user of generated warnings;

[2162] A system including:

[2163] (Claim 2)

[2164] 2. The system according to claim 1, wherein the input data is an e-mail, and the context analysis means checks whether or not an attachment exists.

[2165] (Claim 3)

[2166] 10. The system of claim 1, further comprising: means for detecting that a significant address is missing from the CC field based on context.

[2167] "Application Example 1"

[2168] (Claim 1)

[2169] means for receiving data input from a user;

[2170] A context analysis method using generative AI that analyzes the context before and after the sentence,

[2171] A means for comparing the analysis results with pre-defined conditions;

[2172] means for generating an alert when the condition is met;

[2173] means for notifying a user of generated warnings;

[2174] A means to detect omissions in work instructions and procedures within the factory and provide appropriate advice in real time,

[2175] A system including:

[2176] (Claim 2)

[2177] 2. The system according to claim 1, wherein the input data is an email or a work instruction document, and the context analysis means checks whether or not there are any attachments or required parts.

[2178] (Claim 3)

[2179] 10. The system of claim 1, further comprising means for detecting that important information is missing based on context.

[2180] "Example 2: Combining Emotion Engines"

[2181] (Claim 1)

[2182] means for receiving data input from a user;

[2183] A context analysis method using generative AI that analyzes the context before and after the sentence,

[2184] A means for comparing the analysis results with pre-defined conditions;

[2185] means for generating an alert when the condition is met;

[2186] means for adjusting the generated alert based on sentiment analysis;

[2187] means for notifying a user of the adjusted warning;

[2188] A system including:

[2189] (Claim 2)

[2190] 2. The system according to claim 1, wherein the input data is an e-mail, and the context analysis means checks whether or not an attachment exists.

[2191] (Claim 3)

[2192] 10. The system of claim 1, further comprising: means for detecting that a significant address is missing from the CC field based on context.

[2193] "Application example 2 when combining emotion engines"

[2194] (Claim 1)

[2195] means for receiving data input from a user;

[2196] A context analysis method using generative AI that analyzes the context before and after the sentence,

[2197] A means for comparing the analysis results with pre-defined conditions;

[2198] means for generating an alert when the condition is met;

[2199] means for notifying a user of generated warnings;

[2200] means for detecting user emotions using an emotion analysis engine;

[2201] a means for adjusting the content and tone of the warning message in response to the detected emotion;

[2202] A means of analyzing data in real time and generating warnings to prevent errors in food delivery orders and delivery instructions;

[2203] A system including:

[2204] (Claim 2)

[2205] 2. The system according to claim 1, wherein the input data is an e-mail, and the context analysis means checks whether or not an attachment exists.

[2206] (Claim 3)

[2207] 10. The system of claim 1, further comprising: means for detecting that a significant address is missing from the CC field based on context. [Explanation of symbols]

[2208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving data input from a user; A context analysis method using generative AI that analyzes the context before and after the sentence, A means for comparing the analysis results with pre-defined conditions; means for generating an alert when the condition is met; means for notifying a user of generated warnings; A system including:

2. 2. The system according to claim 1, wherein the input data is an e-mail, and the context analysis means checks whether or not an attached file is present.

3. 2. The system of claim 1, further comprising means for detecting that a significant address is missing from the CC field based on context.

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