system

Abstract

We provide the system. [Solution] A means of analyzing communication content using natural language processing technology, A means for detecting errors and generating correction suggestions based on these analysis results, A means of displaying the aforementioned proposed revisions in a way that the sender can verify, In cases where the analyzed communication content is a commercial expression, a means for evaluating the efficiency of that expression and reflecting it in the proposed revisions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When sending electronic messages in enterprises, there are problems such as a decrease in reliability and human errors due to typos, grammar errors, and even inappropriate content. Especially in modern times when communication means such as emails play an important role in business, these problems may lead to a decrease in business efficiency and a loss of trust. Therefore, there is a demand for a system that can efficiently and accurately review the message content and present necessary corrections.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system for analyzing the content of electronic messages using natural language processing technology. Specifically, the system includes means for detecting errors based on the analysis results and generating correction suggestions, and further includes means for displaying these correction suggestions in a form that the sender can confirm, thereby enabling the sender to efficiently improve the quality of their messages. This system effectively identifies spelling mistakes, grammatical errors, and contextual errors, and the user can easily ensure the accuracy of the content through interactive correction suggestions.

[0006] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate human language.

[0007] An "electronic message" is a message sent or received through electronic means such as email or text message.

[0008] "Analysis results" refer to data and evaluation information obtained as a result of analyzing electronic messages using natural language processing technology.

[0009] An "error" refers to a spelling, grammatical, or contextually inappropriate expression contained in an electronic message.

[0010] A "correction suggestion" is a recommendation that indicates appropriate wording or corrections for errors that have been detected.

[0011] The "sender" is an individual or organization that intends to create and send an electronic message.

[0012] "Means of display" refers to an interface or method for presenting revision suggestions so that the user can visually see and confirm them. [Brief explanation of the drawing]

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

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

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

[0019] In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

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

[0021] [First Embodiment]

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

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

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

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

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

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0034] The following system is conceivable as an embodiment of the present invention. This system aims to automatically analyze the content of electronic messages using natural language processing technology, detect errors, and generate correction suggestions.

[0035] First, the user creates an electronic message using a standard email client on their device. The created message is sent from the device to the server before being sent. The server receives this message and passes it to a generative AI model for analysis. The generative AI model analyzes the content of the electronic message and identifies spelling mistakes, grammatical errors, and contextually inappropriate parts.

[0036] Based on the analysis results, the server automatically generates suggestions to correct errors. This includes, for example, suggesting the correct "Plan" if "Plants" is misspelled as "Plan." The server returns these correction suggestions to the user's terminal, allowing the user to review the suggestions and adjust the message.

[0037] As a concrete example, consider a scenario where a sales representative sends an email to a customer about a new product. This email includes the product name, specifications, and pricing information, but may contain typos or ambiguous expressions. The system would identify errors, such as writing "Project A" instead of "Product A," and suggest the correct name. Furthermore, if pricing information is ambiguous, the system would recommend using more specific figures.

[0038] Thus, the system according to the present invention helps users send accurate and clear information by efficiently scrutinizing the content of electronic messages. It also reduces the verification work required by personnel and promotes operational efficiency.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user composes an electronic message on their device. The user enters the message content and prepares it for sending. At this point, the message includes a subject, body, and recipient.

[0042] Step 2:

[0043] The terminal sends the created electronic message to the server. The transmitted data is temporarily stored on the server for analysis.

[0044] Step 3:

[0045] The server receives the electronic message and passes the data to the natural language processing engine. This engine first checks whether the message is properly formatted.

[0046] Step 4:

[0047] A generation AI model on the server analyzes the electronic message. This analysis identifies typos, grammatical errors, and contextually inappropriate expressions. Simultaneously, the content is scrutinized based on established evaluation criteria.

[0048] Step 5:

[0049] The generative AI model generates correction suggestions based on the analysis results. For example, it may suggest correcting incorrect words or changing to more effective expressions.

[0050] Step 6:

[0051] The server compiles the suggested corrections and sends them back to the user's terminal. The user reviews them and modifies the message content as needed.

[0052] Step 7:

[0053] The user reviews the electronic message on their device based on the suggestions, makes any necessary corrections, and then sends it. Once the corrections are complete, the electronic message is sent to the specified recipient.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern electronic communications, accuracy and clarity of transmitted messages are crucial. However, spelling errors, syntactic errors, and contextual inaccuracies can lead to misunderstandings, posing a significant problem, especially in business communications. There is a need to address this issue and help users efficiently create error-free and clear communications.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for analyzing the content of a communication message using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, and means for sending the generated correction suggestions back to the transmitting device. This enables the user to create accurate and clear communication messages free from errors, and to quickly review and correct them.

[0059] "Natural language processing technology" refers to the technology used to convert human language into a format that can be handled by computers, and then to perform analysis and generation.

[0060] "Communication message" refers to information in message format that is sent and received via electronic means.

[0061] "Errors" refer to information containing misspellings, grammatical errors, or inappropriate expressions in context within a communication.

[0062] A "revision proposal" is a suggestion for correcting a communication message in which an error has been detected, in order to improve its accuracy and clarity.

[0063] A "transmitting device" refers to a device that can generate communication messages and transmit information to other devices via a network.

[0064] An "information processing device" refers to a device that receives data via a computer network, performs analysis and generates correction suggestions, and returns the results.

[0065] This invention provides a system that automatically detects errors in communication text and provides correction suggestions using natural language processing technology. This system mainly consists of a terminal, a server, and a generative AI model.

[0066] A terminal is a device for generating electronic communications and is equipped with email client software (e.g., a mail application). Users use this terminal to create communications such as business emails. The terminal then sends the created communications to the server via the network.

[0067] The server plays a central role in executing natural language processing techniques. The server sends received messages to a generative AI model. This generative AI model (e.g., an AI service providing a natural language processing API) analyzes the message content, identifying spelling errors, syntax errors, and contextually inappropriate parts. Furthermore, it generates correction suggestions for these errors and sends them back to the terminal via the server.

[0068] As a concrete example, consider a scenario where a user creates an email to inform customers about a new product feature. This email may contain incorrect product names or pricing information. For example, if "Project X" is misspelled, the system would suggest the correct name, "Product X." Furthermore, it would suggest correcting numerical expressions from "approximately $100" to "$99.99." An example of a prompt to input into the generative AI model would be, "Analyze this email, identify errors, and suggest corrections."

[0069] This system allows users to easily and efficiently create accurate communications and send clear, unambiguous information to recipients. This improves the accuracy and efficiency of work and enhances the quality of business communication.

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

[0071] Step 1:

[0072] The user launches an email client on the terminal and composes a new message. Once the user has entered the information and the message is ready to send, it is temporarily stored on the terminal. The input is the message created by the user, and the output is the message data ready to send.

[0073] Step 2:

[0074] The terminal sends a message to the server when the user initiates a send operation. The server receives this message over the network. The input is the message sent from the terminal, and the output is the message data received by the server.

[0075] Step 3:

[0076] The server sends the received communication message to the generative AI model for analysis. At this time, it provides the model with a data package that includes the prompt message. The input is the communication message and prompt message received by the server, and the output is a state of waiting for the results of the analysis by the generative AI model.

[0077] Step 4:

[0078] The generative AI model begins analyzing the communication text. It detects spelling mistakes, syntax errors, and inappropriate context, and generates correction suggestions. Data processing involves error detection and correction suggestion generation using natural language processing. The input is the communication text to be analyzed, and the output is a pair list of error identifications and suggested corrections.

[0079] Step 5:

[0080] The server sends the correction suggestions received from the generating AI model back to the terminal. The input is the correction suggestions from the generating AI model, and the output is message data with the correction suggestions sent to the user's terminal.

[0081] Step 6:

[0082] The user's device displays the received revision suggestions. The user reviews them and adjusts the message content as needed. Specifically, the user has the option to accept the revision suggestions or re-edit them. The input is the revision suggestions sent from the server, and the output is the final, confirmed communication.

[0083] Step 7:

[0084] The revised message is ready to be sent again, and the user performs the final send operation. This final transmission sends the message to its intended recipient. The input is the message reviewed and modified by the user, and the output is the final transmitted message.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In commercial communication, errors and inefficiencies can negatively impact brand image and business results. This is especially true when creating advertising copy, where not only typographical errors and grammatical mistakes, but also inaccuracies and ambiguities in expression can be serious problems. Therefore, there is a need for systems that can detect errors in advance and efficiently correct them to enhance commercial effectiveness.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for analyzing communication content using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, means for displaying the correction suggestions in a form that can be confirmed by the sender, and means for evaluating the efficiency of the expression and reflecting it in the correction suggestions when the analyzed communication content is a commercial expression. This enables automatic error detection and efficiency improvement in commercial expressions.

[0090] "Natural language processing technology" is the technology that enables computers to understand, analyze, and generate human language.

[0091] "Communication content" refers to the entirety of text and words used for information transmission between people, such as emails and messages.

[0092] An "error" is an expression that may be unintentionally inaccurate or misleading, such as a spelling mistake, a grammatical error, or an inappropriate context.

[0093] A "correction suggestion" is a recommended, accurate, and appropriate expression for an error that has been detected.

[0094] A "sender" is a person or organization that sends out a message or information for the purpose of communication.

[0095] "Commercial expression" refers to text or content created to advertise a product or service.

[0096] "Efficiency" is a measure of how effectively a particular expression functions to achieve its purpose.

[0097] This invention is implemented using a system with a server and a user's terminal to analyze commercial communication content and detect and correct errors. The server is equipped with advanced natural language processing technology and utilizes generative AI models. When a user creates a commercial message from their terminal and sends it to the server, the server receives the message.

[0098] Here, the server first uses natural language processing models such as BERT and GPT to analyze the message and detect errors, including spelling mistakes, grammatical errors, and inappropriate context. Then, it evaluates the efficiency required, especially for commercial communication, and generates correction suggestions. These suggestions include not only corrections of typographical errors but also suggestions for improving the wording to enhance commercial effectiveness.

[0099] This analysis and suggested revisions are sent back to the user's device, where they can review it and modify the message as needed. For example, if a brand's social media team creates a statement like, "This new product has proven to be convenient," the analysis suggests improvements using specific facts and figures, such as, "89% of consumers found it convenient."

[0100] An example of a prompt message for the generation AI would be: "Analyze the following ad copy, identify errors, and suggest corrections. Ad copy: 'This sample is a perfect choice.'" This system allows users to efficiently create more accurate and commercially effective messages.

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

[0102] Step 1:

[0103] The user composes a commercial message on their device. The input is the user's text message, and the output is the text sent directly to the server. Here, the user completes the message draft and provides the information ready for communication.

[0104] Step 2:

[0105] The server receives messages sent by users. The input is the user's text message, and the output is text data ready for analysis, which is then passed to an AI model. Here, the server converts the message into a format that can be understood as a data structure, preparing the initial data for processing.

[0106] Step 3:

[0107] The server uses a generative AI model to analyze the message. The input is text data passed to the server, and the output is the result of error detection. The message is tokenized, and spelling and grammatical errors are identified using natural language processing techniques. For example, the BERT model is used, and the data is evaluated in a high-dimensional vector space.

[0108] Step 4:

[0109] The server generates correction suggestions based on the analysis results. The input is the error detection result, and the output is the correction suggestions presented to the user. The generating AI model proposes new sentences that are grammatically correct and commercially effective based on the detected problems. In specific operations, it generates suggestions for commercially effective expression improvements, such as specific sentences using numerical data.

[0110] Step 5:

[0111] The server sends the suggested corrections to the user's terminal. The input is the generated suggested corrections, and the output is the suggested corrections presented to the user's terminal. Here, the server reformats the message and prepares it to be presented in a way that is easily understandable to the user.

[0112] Step 6:

[0113] The user reviews the suggested revisions on their device and approves or makes revisions as needed. The input is the suggested revisions sent from the server, and the output forms the final message. The user reviews the suggestions and makes final adjustments to improve the quality of the message.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] To implement this invention, a system incorporating an emotion engine is necessary. This system analyzes the content of electronic messages based on natural language processing technology and recognizes the user's emotional state using the emotion engine, thereby providing more accurate correction suggestions.

[0116] The user first creates an electronic message on their device. The created message is sent to the server. The server then passes the received message to a natural language processing engine for analysis. This process not only checks for typos and grammar, but also uses an emotion engine to determine the emotion the user is trying to express.

[0117] The sentiment engine analyzes the tone, word choice, and context within electronic messages to assess whether the user is trying to convey a positive, negative, or neutral emotion. The results of this sentiment assessment are reflected in the generated revision suggestions. For example, if a negative context is detected, it suggests using more positive language to effectively adjust the intended message.

[0118] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. If the email does not adequately express the user's intended sincere apology, the emotion engine will detect this flaw and recommend changes to a more formal and sincere expression.

[0119] The generated revision suggestions are returned to the user's device, where the user reviews them and makes corrections as needed. In this way, users can send error-free messages while accurately conveying their emotions. This system is a powerful aid in facilitating effective and smooth communication for users.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] Users compose electronic messages on their devices. These messages include a body, subject, and recipient. In business contexts, messages often convey emotions, and users want to express them appropriately.

[0123] Step 2:

[0124] The terminal sends the created electronic message to the server. Since the transmission is in real time, the user can proceed to the next step immediately after completing the input.

[0125] Step 3:

[0126] The server receives the electronic message and passes the data to both the natural language processing engine and the emotion engine. This allows the analysis of the message content and the evaluation of the emotional state to begin simultaneously.

[0127] Step 4:

[0128] The natural language processing engine analyzes the linguistic structure of electronic messages, detecting spelling mistakes, grammatical errors, and contextual inaccuracies. This process involves analysis based on various dictionaries and grammatical rules.

[0129] Step 5:

[0130] The emotion engine analyzes the text within the message to recognize the user's emotional state. Based on word choice, sentence structure, tone of expression, etc., the emotion engine determines whether the emotion is positive, negative, or neutral.

[0131] Step 6:

[0132] The server integrates the analysis results from the natural language processing engine and the sentiment engine to generate correction suggestions. These suggestions include error corrections, improved ways of expressing sentiment, and adjustments to the overall message tone.

[0133] Step 7:

[0134] The server sends the proposed corrections it has generated back to the user's terminal. The terminal then displays these to the user, allowing them to review the proposed changes.

[0135] Step 8:

[0136] The user reviews the suggested revisions and modifies the electronic message as needed. Once the content is satisfactory, the message is sent as the final version.

[0137] This process enables users to express their emotions appropriately and effectively while sending accurate and error-free messages.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0140] When creating electronic messages, misunderstandings can arise not only from simple typos and grammatical errors, but also from the emotional tone of the message. Therefore, to achieve more effective communication, skills are needed that go beyond merely correcting typos and grammatical errors, to appropriately express the emotions the message intends to convey and to revise them as needed.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for analyzing the content of an electronic message using natural language processing technology, means for detecting errors and generating correction suggestions, and means for determining the sentiment of a message using a sentiment engine. This enables users to send error-free messages while accurately conveying their emotions.

[0143] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language.

[0144] An "electronic message" is a text message that is sent and received in digital format.

[0145] "Analysis" is the process of analyzing data or information to reveal its elements and structure.

[0146] "Means for detecting errors" refers to methods or devices for identifying typographical errors, grammatical errors, and contextual errors within a message.

[0147] A "correction suggestion" is a proposal for changes presented to the user based on detected errors and areas for improvement.

[0148] An "emotion engine" is a mechanism that analyzes words and context within text to identify and evaluate emotions.

[0149] "Sentiment analysis" is the process of identifying, classifying, and evaluating the emotions contained in a text.

[0150] "Evaluation criteria" refer to the indicators or standards used to evaluate the content and emotional impact of a message.

[0151] To implement this invention, a system incorporating an emotion engine is required. The user first creates an electronic message on a terminal and sends that message to the server. The server uses a natural language processing engine to analyze the received message. Specifically, the server passes the text data to the natural language processing engine, which first detects typographical errors and grammatical mistakes.

[0152] Furthermore, the server uses an emotion engine to determine the emotions the user is trying to express in the message. The emotion engine analyzes the tone, word choice, and context of the message using a variety of language models and algorithms. As a result, it evaluates whether the user's emotions are positive, negative, or neutral. The results of this emotion evaluation are then used with a generative AI model to generate revision suggestions. These revision suggestions are returned from the server to the user's terminal, allowing the user to review and revise the message as needed.

[0153] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. In this case, if the message does not adequately express the user's intended sincere apology, the sentiment engine will detect this flaw and recommend a more appropriate expression. This recommendation will be presented to the user in text format.

[0154] Examples of prompt messages include: "I have drafted an apology email to the customer, but I feel that it does not fully convey the sincerity I intended. Please suggest ways to revise this email to be more positive and sincere."

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The user composes an electronic message on the terminal and presses the send button. This action sends the message to the server as text data. The input is the raw text message entered by the user, and the output is the text data transferred to the server.

[0158] Step 2:

[0159] The server passes the received text data to the natural language processing engine. At this stage, the server receives the message text as input, and the natural language processing engine analyzes it, detecting typographical errors and grammatical errors. The output is the analysis result, including typographical and grammatical errors. Morphological analysis is performed during this process, and analysis is carried out at the word level.

[0160] Step 3:

[0161] The server uses an emotion engine to further analyze the text data and evaluate the emotions the message is trying to convey. The input is the analyzed text data obtained in step 2, where the emotion engine calculates an emotion score. The output is the result of the emotion evaluation, such as positive, negative, or neutral.

[0162] Step 4:

[0163] The server uses a generative AI model to create correction suggestions. The input consists of error information and sentiment evaluation results obtained in steps 2 and 3. Based on this, the generative AI model generates sentiment-consistent correction suggestions. The output is the revised message draft.

[0164] Step 5:

[0165] The server sends the generated correction suggestions back to the user's terminal. The input is the data generated as correction suggestions, and the output is the corrected text displayed on the user's terminal. The user reviews the correction suggestions through their terminal and considers their contents.

[0166] Step 6:

[0167] The user edits and modifies the electronic message as needed based on the suggested revisions. This action completes the final message. In this step, the user makes final confirmations and revisions based on the suggestions and completes the sending process.

[0168] (Application Example 2)

[0169] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0170] In modern communication technology, accurately conveying a user's intended emotions without misunderstanding is difficult, and typographical errors and grammatical mistakes can also hinder communication. Therefore, a system is needed to identify and appropriately correct the emotions and errors contained in the electronic communications that users are sending.

[0171] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0172] In this invention, the server includes means for analyzing the content of electronic communications using natural language processing technology, means for detecting errors and generating correction suggestions, and means for recognizing the emotional state of electronic communications and generating emotional correction suggestions. This enables electronic communications in which the user can accurately convey their intended emotions without errors.

[0173] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate human language.

[0174] "Electronic communication" refers to messages and data transmitted using electronic means.

[0175] An "error" is a spelling mistake, a grammatical error, or an element that could lead to a misunderstanding of the context.

[0176] "Correction proposal" refers to the proposed solutions for correcting the detected errors.

[0177] "Emotional state" refers to the emotions of the sender inferred from the content of electronic communications.

[0178] "Emotional modification suggestions" refer to suggestions for improving expressions that are provided to appropriately convey a particular emotion.

[0179] "Sender" refers to the person who creates and attempts to transmit electronic communications.

[0180] "Display means" refers to the means by which revision suggestions are presented in a way that users can review.

[0181] To implement this invention, the server first plays the role of receiving electronic communications created by the user on their terminal. The server utilizes natural language processing technology to process these electronic communications. For example, Google® Cloud Natural Language API can be used for this technology. Natural language processing technology detects spelling mistakes, grammatical errors, and potential contextual misunderstandings in the electronic communications.

[0182] Furthermore, the server uses emotion engines such as IBM Watson® Tone Analyzer to analyze the emotions contained within electronic communications. As a result, it identifies the positive, negative, or neutral emotions intended by the sender and generates appropriate emotional modification suggestions as needed. This allows the server to provide senders with specific expression improvements to prevent misunderstandings of emotions.

[0183] Users review the corrections analyzed and suggested by the server on their device and edit their electronic communications as needed. Ultimately, users are able to send error-free messages that accurately express their intended emotions.

[0184] For example, if a user makes a negative social media post such as "I hate my job lately," the server might offer a positive suggestion to revise it, such as "It might be good to talk about something you found particularly rewarding about your job."

[0185] An example of a prompt to input into the generative AI model is as follows: "Evaluate the emotion inferred from the user's message '○○' and suggest a way to revise it to a more positive tone." Thus, this invention is a system that minimizes misunderstandings of user intent and enables more effective and smoother communication.

[0186] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0187] Step 1:

[0188] The user creates electronic communications on their terminal. These created electronic communications become the system's input.

[0189] Step 2:

[0190] The terminal sends the generated electronic communication to the server. The content of the electronic communication is then entered into the server, and it is ready for analysis.

[0191] Step 3:

[0192] The server passes the received electronic communications to a natural language processing engine, which analyzes for potential spelling errors, grammatical errors, and contextual misunderstandings. For example, it uses the Google Cloud Natural Language API to process the data and detect errors. This process outputs the identified errors.

[0193] Step 4:

[0194] Simultaneously, the server sends electronic communications to the emotion engine to analyze the user's emotional state. Here, IBM Watson Tone Analyzer is used to analyze the word choice and tone of the electronic communications and output an emotion evaluation.

[0195] Step 5:

[0196] The server integrates the results of natural language processing and sentiment engine analysis to generate correction suggestions. Based on the detected errors and sentiment evaluations, specific corrections to optimize electronic communications are output as suggestions.

[0197] Step 6:

[0198] The server sends the generated correction suggestions to the terminal for the user to review. Based on these suggestions, the user reviews the content of the electronic communication and makes corrections as necessary.

[0199] Step 7:

[0200] The user ultimately sends the revised electronic message. As a result of incorporating the suggestions, the user is able to send a message that accurately expresses the intended emotions.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0217] The following system is conceivable as an embodiment of the present invention. This system aims to automatically analyze the content of electronic messages using natural language processing technology, detect errors, and generate correction suggestions.

[0218] First, the user creates an electronic message using a standard email client on their device. The created message is sent from the device to the server before being sent. The server receives this message and passes it to a generative AI model for analysis. The generative AI model analyzes the content of the electronic message and identifies spelling mistakes, grammatical errors, and contextually inappropriate parts.

[0219] Based on the analysis results, the server automatically generates suggestions to correct errors. This includes, for example, suggesting the correct "Plan" if "Plants" is misspelled as "Plan." The server returns these correction suggestions to the user's terminal, allowing the user to review the suggestions and adjust the message.

[0220] As a concrete example, consider a scenario where a sales representative sends an email to a customer about a new product. This email includes the product name, specifications, and pricing information, but may contain typos or ambiguous expressions. The system would identify errors, such as writing "Project A" instead of "Product A," and suggest the correct name. Furthermore, if pricing information is ambiguous, the system would recommend using more specific figures.

[0221] Thus, the system according to the present invention helps users send accurate and clear information by efficiently scrutinizing the content of electronic messages. It also reduces the verification work required by personnel and promotes operational efficiency.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user composes an electronic message on their device. The user enters the message content and prepares it for sending. At this point, the message includes a subject, body, and recipient.

[0225] Step 2:

[0226] The terminal sends the created electronic message to the server. The transmitted data is temporarily stored on the server for analysis.

[0227] Step 3:

[0228] The server receives the electronic message and passes the data to the natural language processing engine. This engine first checks whether the message is properly formatted.

[0229] Step 4:

[0230] A generation AI model on the server analyzes the electronic message. This analysis identifies typos, grammatical errors, and contextually inappropriate expressions. Simultaneously, the content is scrutinized based on established evaluation criteria.

[0231] Step 5:

[0232] The generative AI model generates correction suggestions based on the analysis results. For example, it may suggest correcting incorrect words or changing to more effective expressions.

[0233] Step 6:

[0234] The server compiles the suggested corrections and sends them back to the user's terminal. The user reviews them and modifies the message content as needed.

[0235] Step 7:

[0236] The user reviews the electronic message on their device based on the suggestions, makes any necessary corrections, and then sends it. Once the corrections are complete, the electronic message is sent to the specified recipient.

[0237] (Example 1)

[0238] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0239] In modern electronic communications, accuracy and clarity of transmitted messages are crucial. However, spelling errors, syntactic errors, and contextual inaccuracies can lead to misunderstandings, posing a significant problem, especially in business communications. There is a need to address this issue and help users efficiently create error-free and clear communications.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes means for analyzing the content of a communication message using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, and means for sending the generated correction suggestions back to the transmitting device. This enables the user to create accurate and clear communication messages free from errors, and to quickly review and correct them.

[0242] "Natural language processing technology" refers to the technology used to convert human language into a format that can be handled by computers, and then to perform analysis and generation.

[0243] "Communication message" refers to information in message format that is sent and received via electronic means.

[0244] "Errors" refer to information containing misspellings, grammatical errors, or inappropriate expressions in context within a communication.

[0245] A "revision proposal" is a suggestion for correcting a communication message in which an error has been detected, in order to improve its accuracy and clarity.

[0246] A "transmitting device" refers to a device that can generate communication messages and transmit information to other devices via a network.

[0247] An "information processing device" refers to a device that receives data via a computer network, performs analysis and generates correction suggestions, and returns the results.

[0248] This invention provides a system that automatically detects errors in communication text and provides correction suggestions using natural language processing technology. This system mainly consists of a terminal, a server, and a generative AI model.

[0249] A terminal is a device for generating electronic communications and is equipped with email client software (e.g., a mail application). Users use this terminal to create communications such as business emails. The terminal then sends the created communications to the server via the network.

[0250] The server plays a central role in executing natural language processing techniques. The server sends received messages to a generative AI model. This generative AI model (e.g., an AI service providing a natural language processing API) analyzes the message content, identifying spelling errors, syntax errors, and contextually inappropriate parts. Furthermore, it generates correction suggestions for these errors and sends them back to the terminal via the server.

[0251] As a concrete example, consider a scenario where a user creates an email to inform customers about a new product feature. This email may contain incorrect product names or pricing information. For example, if "Project X" is misspelled, the system would suggest the correct name, "Product X." Furthermore, it would suggest correcting numerical expressions from "approximately $100" to "$99.99." An example of a prompt to input into the generative AI model would be, "Analyze this email, identify errors, and suggest corrections."

[0252] This system allows users to easily and efficiently create accurate communications and send clear, unambiguous information to recipients. This improves the accuracy and efficiency of work and enhances the quality of business communication.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The user launches an email client on the terminal and composes a new message. Once the user has entered the information and the message is ready to send, it is temporarily stored on the terminal. The input is the message created by the user, and the output is the message data ready to send.

[0256] Step 2:

[0257] The terminal sends a message to the server when the user initiates a send operation. The server receives this message over the network. The input is the message sent from the terminal, and the output is the message data received by the server.

[0258] Step 3:

[0259] The server sends the received communication message to the generative AI model for analysis. At this time, it provides the model with a data package that includes the prompt message. The input is the communication message and prompt message received by the server, and the output is a state of waiting for the results of the analysis by the generative AI model.

[0260] Step 4:

[0261] The generative AI model begins analyzing the communication text. It detects spelling mistakes, syntax errors, and inappropriate context, and generates correction suggestions. Data processing involves error detection and correction suggestion generation using natural language processing. The input is the communication text to be analyzed, and the output is a pair list of error identifications and suggested corrections.

[0262] Step 5:

[0263] The server sends the correction suggestions received from the generating AI model back to the terminal. The input is the correction suggestions from the generating AI model, and the output is message data with the correction suggestions sent to the user's terminal.

[0264] Step 6:

[0265] The user's device displays the received revision suggestions. The user reviews them and adjusts the message content as needed. Specifically, the user has the option to accept the revision suggestions or re-edit them. The input is the revision suggestions sent from the server, and the output is the final, confirmed communication.

[0266] Step 7:

[0267] The revised message is ready to be sent again, and the user performs the final send operation. This final transmission sends the message to its intended recipient. The input is the message reviewed and modified by the user, and the output is the final transmitted message.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] In commercial communication, errors and inefficiencies can negatively impact brand image and business results. This is especially true when creating advertising copy, where not only typographical errors and grammatical mistakes, but also inaccuracies and ambiguities in expression can be serious problems. Therefore, there is a need for systems that can detect errors in advance and efficiently correct them to enhance commercial effectiveness.

[0271] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0272] In this invention, the server includes means for analyzing communication content using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, means for displaying the correction suggestions in a form that can be confirmed by the sender, and means for evaluating the efficiency of the expression and reflecting it in the correction suggestions when the analyzed communication content is a commercial expression. This enables automatic error detection and efficiency improvement in commercial expressions.

[0273] "Natural language processing technology" is the technology that enables computers to understand, analyze, and generate human language.

[0274] "Communication content" refers to the entirety of text and words used for information transmission between people, such as emails and messages.

[0275] An "error" is an expression that may be unintentionally inaccurate or misleading, such as a spelling mistake, a grammatical error, or an inappropriate context.

[0276] A "correction suggestion" is a recommended, accurate, and appropriate expression for an error that has been detected.

[0277] A "sender" is a person or organization that sends out a message or information for the purpose of communication.

[0278] "Commercial expression" refers to text or content created to advertise a product or service.

[0279] "Efficiency" is a measure of how effectively a particular expression functions to achieve its purpose.

[0280] This invention is implemented using a system with a server and a user's terminal to analyze commercial communication content and detect and correct errors. The server is equipped with advanced natural language processing technology and utilizes generative AI models. When a user creates a commercial message from their terminal and sends it to the server, the server receives the message.

[0281] Here, the server first utilizes natural language processing models such as BERT or GPT to analyze the message, detecting errors including spelling mistakes, grammar errors, and inappropriate contexts. Then, it evaluates the efficiency particularly required in commercial communication and generates correction proposals. The correction proposals include, in addition to correcting misrecords, suggestions for improving expressions to enhance commercial effects.

[0282] This analysis and correction proposal are sent back to the user's terminal, and the user checks it and modifies the message if necessary. For example, when a brand's social media team creates an expression like "This new product has been proven to be convenient", it proposes an improvement in expression such as "89% of consumers answered that it is convenient" using specific facts and numerical values.

[0283] As an example of the prompt text for the generative AI, it is input to the server in the form of "Analyze the following advertisement text, point out the errors, and provide correction proposals. Advertisement text: 'This sample is the perfect choice.'" With this system, users can efficiently create more accurate and commercially effective messages.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The user creates a message for commercial purposes on the terminal. The input is the user's text message, and the text is directly sent to the server as the output. Here, the user completes the draft of the message and provides information that the communication is ready.

[0287] Step 2:

[0288] The server receives messages sent by users. The input is the user's text message, and the output is text data ready for analysis, which is then passed to an AI model. Here, the server converts the message into a format that can be understood as a data structure, preparing the initial data for processing.

[0289] Step 3:

[0290] The server uses a generative AI model to analyze the message. The input is text data passed to the server, and the output is the result of error detection. The message is tokenized, and spelling and grammatical errors are identified using natural language processing techniques. For example, the BERT model is used, and the data is evaluated in a high-dimensional vector space.

[0291] Step 4:

[0292] The server generates correction suggestions based on the analysis results. The input is the error detection result, and the output is the correction suggestions presented to the user. The generating AI model proposes new sentences that are grammatically correct and commercially effective based on the detected problems. In specific operations, it generates suggestions for commercially effective expression improvements, such as specific sentences using numerical data.

[0293] Step 5:

[0294] The server sends the suggested corrections to the user's terminal. The input is the generated suggested corrections, and the output is the suggested corrections presented to the user's terminal. Here, the server reformats the message and prepares it to be presented in a way that is easily understandable to the user.

[0295] Step 6:

[0296] The user reviews the suggested revisions on their device and approves or makes revisions as needed. The input is the suggested revisions sent from the server, and the output forms the final message. The user reviews the suggestions and makes final adjustments to improve the quality of the message.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] To implement this invention, a system incorporating an emotion engine is necessary. This system analyzes the content of electronic messages based on natural language processing technology and recognizes the user's emotional state using the emotion engine, thereby providing more accurate correction suggestions.

[0299] The user first creates an electronic message on their device. The created message is sent to the server. The server then passes the received message to a natural language processing engine for analysis. This process not only checks for typos and grammar, but also uses an emotion engine to determine the emotion the user is trying to express.

[0300] The sentiment engine analyzes the tone, word choice, and context within electronic messages to assess whether the user is trying to convey a positive, negative, or neutral emotion. The results of this sentiment assessment are reflected in the generated revision suggestions. For example, if a negative context is detected, it suggests using more positive language to effectively adjust the intended message.

[0301] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. If the email does not adequately express the user's intended sincere apology, the emotion engine will detect this flaw and recommend changes to a more formal and sincere expression.

[0302] The generated revised proposal is returned to the user's terminal, and the user checks it and makes corrections if necessary. In this way, the user can send a message without errors while accurately conveying their emotions. This system serves as a powerful aid for effectively and smoothly communicating the user's intentions.

[0303] The processing flow will be described below.

[0304] Step 1:

[0305] The user creates an electronic message on their terminal. The message includes the body, subject, and recipient. When it is related to business, the message often contains emotions, and the user wants to express them appropriately.

[0306] Step 2:

[0307] The terminal sends the created electronic message to the server. Since the transmission is done in real-time, the user can proceed to the next process immediately after completing the input.

[0308] Step 3:

[0309] The server receives the electronic message and passes the data to both the natural language processing engine and the emotion engine. As a result, the analysis of the message content and the evaluation of the emotional state start simultaneously.

[0310] Step 4:

[0311] The natural language processing engine analyzes the language structure of the electronic message and detects spelling mistakes, grammar errors, and contextual errors. In this process, analysis based on various dictionaries and grammar rules is performed.

[0312] Step 5:

[0313] The emotion engine analyzes the text within the message to recognize the user's emotional state. Based on word choice, sentence structure, tone of expression, etc., the emotion engine determines whether the emotion is positive, negative, or neutral.

[0314] Step 6:

[0315] The server integrates the analysis results from the natural language processing engine and the sentiment engine to generate correction suggestions. These suggestions include error corrections, improved ways of expressing sentiment, and adjustments to the overall message tone.

[0316] Step 7:

[0317] The server sends the proposed corrections it has generated back to the user's terminal. The terminal then displays these to the user, allowing them to review the proposed changes.

[0318] Step 8:

[0319] The user reviews the suggested revisions and modifies the electronic message as needed. Once the content is satisfactory, the message is sent as the final version.

[0320] This process enables users to express their emotions appropriately and effectively while sending accurate and error-free messages.

[0321] (Example 2)

[0322] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0323] When creating electronic messages, misunderstandings can arise not only from simple typos and grammatical errors, but also from the emotional tone of the message. Therefore, to achieve more effective communication, skills are needed that go beyond merely correcting typos and grammatical errors, to appropriately express the emotions the message intends to convey and to revise them as needed.

[0324] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0325] In this invention, the server includes means for analyzing the content of an electronic message using natural language processing technology, means for detecting errors and generating correction suggestions, and means for determining the sentiment of a message using a sentiment engine. This enables users to send error-free messages while accurately conveying their emotions.

[0326] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language.

[0327] An "electronic message" is a text message that is sent and received in digital format.

[0328] "Analysis" is the process of analyzing data or information to reveal its elements and structure.

[0329] "Means for detecting errors" refers to methods or devices for identifying typographical errors, grammatical errors, and contextual errors within a message.

[0330] A "correction suggestion" is a proposal for changes presented to the user based on detected errors and areas for improvement.

[0331] An "emotion engine" is a mechanism that analyzes words and context within text to identify and evaluate emotions.

[0332] "Sentiment analysis" is the process of identifying, classifying, and evaluating the emotions contained in a text.

[0333] "Evaluation criteria" refer to the indicators or standards used to evaluate the content and emotional impact of a message.

[0334] To implement this invention, a system incorporating an emotion engine is required. The user first creates an electronic message on a terminal and sends that message to the server. The server uses a natural language processing engine to analyze the received message. Specifically, the server passes the text data to the natural language processing engine, which first detects typographical errors and grammatical mistakes.

[0335] Furthermore, the server uses an emotion engine to determine the emotions the user is trying to express in the message. The emotion engine analyzes the tone, word choice, and context of the message using a variety of language models and algorithms. As a result, it evaluates whether the user's emotions are positive, negative, or neutral. The results of this emotion evaluation are then used with a generative AI model to generate revision suggestions. These revision suggestions are returned from the server to the user's terminal, allowing the user to review and revise the message as needed.

[0336] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. In this case, if the message does not adequately express the user's intended sincere apology, the sentiment engine will detect this flaw and recommend a more appropriate expression. This recommendation will be presented to the user in text format.

[0337] Examples of prompt messages include: "I have drafted an apology email to the customer, but I feel that it does not fully convey the sincerity I intended. Please suggest ways to revise this email to be more positive and sincere."

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user composes an electronic message on the terminal and presses the send button. This action sends the message to the server as text data. The input is the raw text message entered by the user, and the output is the text data transferred to the server.

[0341] Step 2:

[0342] The server passes the received text data to the natural language processing engine. At this stage, the server receives the message text as input, and the natural language processing engine analyzes it, detecting typographical errors and grammatical errors. The output is the analysis result, including typographical and grammatical errors. Morphological analysis is performed during this process, and analysis is carried out at the word level.

[0343] Step 3:

[0344] The server uses an emotion engine to further analyze the text data and evaluate the emotions the message is trying to convey. The input is the analyzed text data obtained in step 2, where the emotion engine calculates an emotion score. The output is the result of the emotion evaluation, such as positive, negative, or neutral.

[0345] Step 4:

[0346] The server uses a generative AI model to create correction suggestions. The input consists of error information and sentiment evaluation results obtained in steps 2 and 3. Based on this, the generative AI model generates sentiment-consistent correction suggestions. The output is the revised message draft.

[0347] Step 5:

[0348] The server sends the generated correction suggestions back to the user's terminal. The input is the data generated as correction suggestions, and the output is the corrected text displayed on the user's terminal. The user reviews the correction suggestions through their terminal and considers their contents.

[0349] Step 6:

[0350] The user edits and modifies the electronic message as needed based on the suggested revisions. This action completes the final message. In this step, the user makes final confirmations and revisions based on the suggestions and completes the sending process.

[0351] (Application Example 2)

[0352] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0353] In modern communication technology, accurately conveying a user's intended emotions without misunderstanding is difficult, and typographical errors and grammatical mistakes can also hinder communication. Therefore, a system is needed to identify and appropriately correct the emotions and errors contained in the electronic communications that users are sending.

[0354] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0355] In this invention, the server includes means for analyzing the content of electronic communications using natural language processing technology, means for detecting errors and generating correction suggestions, and means for recognizing the emotional state of electronic communications and generating emotional correction suggestions. This enables electronic communications in which the user can accurately convey their intended emotions without errors.

[0356] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate human language.

[0357] "Electronic communication" refers to messages and data transmitted using electronic means.

[0358] An "error" is a spelling mistake, a grammatical error, or an element that could lead to a misunderstanding of the context.

[0359] "Correction proposal" refers to the proposed solutions for correcting the detected errors.

[0360] "Emotional state" refers to the emotions of the sender inferred from the content of electronic communications.

[0361] "Emotional modification suggestions" refer to suggestions for improving expressions that are provided to appropriately convey a particular emotion.

[0362] "Sender" refers to the person who creates and attempts to transmit electronic communications.

[0363] "Display means" refers to the means by which revision suggestions are presented in a way that users can review.

[0364] To implement this invention, the server first plays the role of receiving electronic communications created by the user on their terminal. The server utilizes natural language processing technology to process these electronic communications. For example, the Google Cloud Natural Language API can be used for this technology. Natural language processing technology detects spelling mistakes, grammatical errors, and potential contextual misunderstandings in the electronic communications.

[0365] Furthermore, the server uses emotion engines such as IBM Watson Tone Analyzer to analyze the emotions contained within electronic communications. As a result, it identifies the positive, negative, or neutral emotions intended by the sender and generates appropriate emotional modification suggestions as needed. This allows the server to provide senders with specific expression improvements to prevent misunderstandings of emotions.

[0366] Users review the corrections analyzed and suggested by the server on their device and edit their electronic communications as needed. Ultimately, users are able to send error-free messages that accurately express their intended emotions.

[0367] For example, if a user makes a negative social media post such as "I hate my job lately," the server might offer a positive suggestion to revise it, such as "It might be good to talk about something you found particularly rewarding about your job."

[0368] An example of a prompt to input into the generative AI model is as follows: "Evaluate the emotion inferred from the user's message '○○' and suggest a way to revise it to a more positive tone." Thus, this invention is a system that minimizes misunderstandings of user intent and enables more effective and smoother communication.

[0369] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0370] Step 1:

[0371] The user creates electronic communications on their terminal. These created electronic communications become the system's input.

[0372] Step 2:

[0373] The terminal sends the generated electronic communication to the server. The content of the electronic communication is then entered into the server, and it is ready for analysis.

[0374] Step 3:

[0375] The server passes the received electronic communications to a natural language processing engine, which analyzes for potential spelling errors, grammatical errors, and contextual misunderstandings. For example, it uses the Google Cloud Natural Language API to process the data and detect errors. This process outputs the identified errors.

[0376] Step 4:

[0377] Simultaneously, the server sends electronic communications to the emotion engine to analyze the user's emotional state. Here, IBM Watson Tone Analyzer is used to analyze the word choice and tone of the electronic communications and output an emotion evaluation.

[0378] Step 5:

[0379] The server integrates the results of natural language processing and sentiment engine analysis to generate correction suggestions. Based on the detected errors and sentiment evaluations, specific corrections to optimize electronic communications are output as suggestions.

[0380] Step 6:

[0381] The server sends the generated correction suggestions to the terminal for the user to review. Based on these suggestions, the user reviews the content of the electronic communication and makes corrections as necessary.

[0382] Step 7:

[0383] The user ultimately sends the revised electronic message. As a result of incorporating the suggestions, the user is able to send a message that accurately expresses the intended emotions.

[0384] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] The following system is conceivable as an embodiment of the present invention. This system aims to automatically analyze the content of electronic messages using natural language processing technology, detect errors, and generate correction suggestions.

[0401] First, the user creates an electronic message using a standard email client on their device. The created message is sent from the device to the server before being sent. The server receives this message and passes it to a generative AI model for analysis. The generative AI model analyzes the content of the electronic message and identifies spelling mistakes, grammatical errors, and contextually inappropriate parts.

[0402] Based on the analysis results, the server automatically generates suggestions to correct errors. This includes, for example, suggesting the correct "Plan" if "Plants" is misspelled as "Plan." The server returns these correction suggestions to the user's terminal, allowing the user to review the suggestions and adjust the message.

[0403] As a concrete example, consider a scenario where a sales representative sends an email to a customer about a new product. This email includes the product name, specifications, and pricing information, but may contain typos or ambiguous expressions. The system would identify errors, such as writing "Project A" instead of "Product A," and suggest the correct name. Furthermore, if pricing information is ambiguous, the system would recommend using more specific figures.

[0404] Thus, the system according to the present invention helps users send accurate and clear information by efficiently scrutinizing the content of electronic messages. It also reduces the verification work required by personnel and promotes operational efficiency.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The user composes an electronic message on their device. The user enters the message content and prepares it for sending. At this point, the message includes a subject, body, and recipient.

[0408] Step 2:

[0409] The terminal sends the created electronic message to the server. The transmitted data is temporarily stored on the server for analysis.

[0410] Step 3:

[0411] The server receives the electronic message and passes the data to the natural language processing engine. This engine first checks whether the message is properly formatted.

[0412] Step 4:

[0413] A generation AI model on the server analyzes the electronic message. This analysis identifies typos, grammatical errors, and contextually inappropriate expressions. Simultaneously, the content is scrutinized based on established evaluation criteria.

[0414] Step 5:

[0415] The generative AI model generates correction suggestions based on the analysis results. For example, it may suggest correcting incorrect words or changing to more effective expressions.

[0416] Step 6:

[0417] The server compiles the suggested corrections and sends them back to the user's terminal. The user reviews them and modifies the message content as needed.

[0418] Step 7:

[0419] The user reviews the electronic message on their device based on the suggestions, makes any necessary corrections, and then sends it. Once the corrections are complete, the electronic message is sent to the specified recipient.

[0420] (Example 1)

[0421] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0422] In modern electronic communications, accuracy and clarity of transmitted messages are crucial. However, spelling errors, syntactic errors, and contextual inaccuracies can lead to misunderstandings, posing a significant problem, especially in business communications. There is a need to address this issue and help users efficiently create error-free and clear communications.

[0423] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0424] In this invention, the server includes means for analyzing the content of a communication message using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, and means for sending the generated correction suggestions back to the transmitting device. This enables the user to create accurate and clear communication messages free from errors, and to quickly review and correct them.

[0425] "Natural language processing technology" refers to the technology used to convert human language into a format that can be handled by computers, and then to perform analysis and generation.

[0426] "Communication message" refers to information in message format that is sent and received via electronic means.

[0427] "Errors" refer to information containing misspellings, grammatical errors, or inappropriate expressions in context within a communication.

[0428] A "revision proposal" is a suggestion for correcting a communication message in which an error has been detected, in order to improve its accuracy and clarity.

[0429] A "transmitting device" refers to a device that can generate communication messages and transmit information to other devices via a network.

[0430] An "information processing device" refers to a device that receives data via a computer network, performs analysis and generates correction suggestions, and returns the results.

[0431] This invention provides a system that automatically detects errors in communication text and provides correction suggestions using natural language processing technology. This system mainly consists of a terminal, a server, and a generative AI model.

[0432] A terminal is a device for generating electronic communications and is equipped with email client software (e.g., a mail application). Users use this terminal to create communications such as business emails. The terminal then sends the created communications to the server via the network.

[0433] The server plays a central role in executing natural language processing techniques. The server sends received messages to a generative AI model. This generative AI model (e.g., an AI service providing a natural language processing API) analyzes the message content, identifying spelling errors, syntax errors, and contextually inappropriate parts. Furthermore, it generates correction suggestions for these errors and sends them back to the terminal via the server.

[0434] As a concrete example, consider a scenario where a user creates an email to inform customers about a new product feature. This email may contain incorrect product names or pricing information. For example, if "Project X" is misspelled, the system would suggest the correct name, "Product X." Furthermore, it would suggest correcting numerical expressions from "approximately $100" to "$99.99." An example of a prompt to input into the generative AI model would be, "Analyze this email, identify errors, and suggest corrections."

[0435] This system allows users to easily and efficiently create accurate communications and send clear, unambiguous information to recipients. This improves the accuracy and efficiency of work and enhances the quality of business communication.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] The user launches an email client on the terminal and composes a new message. Once the user has entered the information and the message is ready to send, it is temporarily stored on the terminal. The input is the message created by the user, and the output is the message data ready to send.

[0439] Step 2:

[0440] The terminal sends a message to the server when the user initiates a send operation. The server receives this message over the network. The input is the message sent from the terminal, and the output is the message data received by the server.

[0441] Step 3:

[0442] The server sends the received communication message to the generative AI model for analysis. At this time, it provides the model with a data package that includes the prompt message. The input is the communication message and prompt message received by the server, and the output is a state of waiting for the results of the analysis by the generative AI model.

[0443] Step 4:

[0444] The generative AI model begins analyzing the communication text. It detects spelling mistakes, syntax errors, and inappropriate context, and generates correction suggestions. Data processing involves error detection and correction suggestion generation using natural language processing. The input is the communication text to be analyzed, and the output is a pair list of error identifications and suggested corrections.

[0445] Step 5:

[0446] The server sends the correction suggestions received from the generating AI model back to the terminal. The input is the correction suggestions from the generating AI model, and the output is message data with the correction suggestions sent to the user's terminal.

[0447] Step 6:

[0448] The user's device displays the received revision suggestions. The user reviews them and adjusts the message content as needed. Specifically, the user has the option to accept the revision suggestions or re-edit them. The input is the revision suggestions sent from the server, and the output is the final, confirmed communication.

[0449] Step 7:

[0450] The revised message is ready to be sent again, and the user performs the final send operation. This final transmission sends the message to its intended recipient. The input is the message reviewed and modified by the user, and the output is the final transmitted message.

[0451] (Application Example 1)

[0452] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0453] In commercial communication, errors and inefficiencies can negatively impact brand image and business results. This is especially true when creating advertising copy, where not only typographical errors and grammatical mistakes, but also inaccuracies and ambiguities in expression can be serious problems. Therefore, there is a need for systems that can detect errors in advance and efficiently correct them to enhance commercial effectiveness.

[0454] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0455] In this invention, the server includes means for analyzing communication content using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, means for displaying the correction suggestions in a form that can be confirmed by the sender, and means for evaluating the efficiency of the expression and reflecting it in the correction suggestions when the analyzed communication content is a commercial expression. This enables automatic error detection and efficiency improvement in commercial expressions.

[0456] "Natural language processing technology" is the technology that enables computers to understand, analyze, and generate human language.

[0457] "Communication content" refers to the entirety of text and words used for information transmission between people, such as emails and messages.

[0458] An "error" is an expression that may be unintentionally inaccurate or misleading, such as a spelling mistake, a grammatical error, or an inappropriate context.

[0459] A "correction suggestion" is a recommended, accurate, and appropriate expression for an error that has been detected.

[0460] A "sender" is a person or organization that sends out a message or information for the purpose of communication.

[0461] "Commercial expression" refers to text or content created to advertise a product or service.

[0462] "Efficiency" is a measure of how effectively a particular expression functions to achieve its purpose.

[0463] This invention is implemented using a system with a server and a user's terminal to analyze commercial communication content and detect and correct errors. The server is equipped with advanced natural language processing technology and utilizes generative AI models. When a user creates a commercial message from their terminal and sends it to the server, the server receives the message.

[0464] Here, the server first uses natural language processing models such as BERT and GPT to analyze the message and detect errors, including spelling mistakes, grammatical errors, and inappropriate context. Then, it evaluates the efficiency required, especially for commercial communication, and generates correction suggestions. These suggestions include not only corrections of typographical errors but also suggestions for improving the wording to enhance commercial effectiveness.

[0465] This analysis and suggested revisions are sent back to the user's device, where they can review it and modify the message as needed. For example, if a brand's social media team creates a statement like, "This new product has proven to be convenient," the analysis suggests improvements using specific facts and figures, such as, "89% of consumers found it convenient."

[0466] An example of a prompt message for the generation AI would be: "Analyze the following ad copy, identify errors, and suggest corrections. Ad copy: 'This sample is a perfect choice.'" This system allows users to efficiently create more accurate and commercially effective messages.

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The user composes a commercial message on their device. The input is the user's text message, and the output is the text sent directly to the server. Here, the user completes the message draft and provides the information ready for communication.

[0470] Step 2:

[0471] The server receives messages sent by users. The input is the user's text message, and the output is text data ready for analysis, which is then passed to an AI model. Here, the server converts the message into a format that can be understood as a data structure, preparing the initial data for processing.

[0472] Step 3:

[0473] The server uses a generative AI model to analyze the message. The input is text data passed to the server, and the output is the result of error detection. The message is tokenized, and spelling and grammatical errors are identified using natural language processing techniques. For example, the BERT model is used, and the data is evaluated in a high-dimensional vector space.

[0474] Step 4:

[0475] The server generates correction suggestions based on the analysis results. The input is the error detection result, and the output is the correction suggestions presented to the user. The generating AI model proposes new sentences that are grammatically correct and commercially effective based on the detected problems. In specific operations, it generates suggestions for commercially effective expression improvements, such as specific sentences using numerical data.

[0476] Step 5:

[0477] The server sends the suggested corrections to the user's terminal. The input is the generated suggested corrections, and the output is the suggested corrections presented to the user's terminal. Here, the server reformats the message and prepares it to be presented in a way that is easily understandable to the user.

[0478] Step 6:

[0479] The user reviews the suggested revisions on their device and approves or makes revisions as needed. The input is the suggested revisions sent from the server, and the output forms the final message. The user reviews the suggestions and makes final adjustments to improve the quality of the message.

[0480] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0481] To implement this invention, a system incorporating an emotion engine is necessary. This system analyzes the content of electronic messages based on natural language processing technology and recognizes the user's emotional state using the emotion engine, thereby providing more accurate correction suggestions.

[0482] The user first creates an electronic message on their device. The created message is sent to the server. The server then passes the received message to a natural language processing engine for analysis. This process not only checks for typos and grammar, but also uses an emotion engine to determine the emotion the user is trying to express.

[0483] The sentiment engine analyzes the tone, word choice, and context within electronic messages to assess whether the user is trying to convey a positive, negative, or neutral emotion. The results of this sentiment assessment are reflected in the generated revision suggestions. For example, if a negative context is detected, it suggests using more positive language to effectively adjust the intended message.

[0484] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. If the email does not adequately express the user's intended sincere apology, the emotion engine will detect this flaw and recommend changes to a more formal and sincere expression.

[0485] The generated revision suggestions are returned to the user's device, where the user reviews them and makes corrections as needed. In this way, users can send error-free messages while accurately conveying their emotions. This system is a powerful aid in facilitating effective and smooth communication for users.

[0486] The following describes the processing flow.

[0487] Step 1:

[0488] Users compose electronic messages on their devices. These messages include a body, subject, and recipient. In business contexts, messages often convey emotions, and users want to express them appropriately.

[0489] Step 2:

[0490] The terminal sends the created electronic message to the server. Since the transmission is in real time, the user can proceed to the next step immediately after completing the input.

[0491] Step 3:

[0492] The server receives the electronic message and passes the data to both the natural language processing engine and the emotion engine. This allows the analysis of the message content and the evaluation of the emotional state to begin simultaneously.

[0493] Step 4:

[0494] The natural language processing engine analyzes the linguistic structure of electronic messages, detecting spelling mistakes, grammatical errors, and contextual inaccuracies. This process involves analysis based on various dictionaries and grammatical rules.

[0495] Step 5:

[0496] The emotion engine analyzes the text within the message to recognize the user's emotional state. Based on word choice, sentence structure, tone of expression, etc., the emotion engine determines whether the emotion is positive, negative, or neutral.

[0497] Step 6:

[0498] The server integrates the analysis results from the natural language processing engine and the sentiment engine to generate correction suggestions. These suggestions include error corrections, improved ways of expressing sentiment, and adjustments to the overall message tone.

[0499] Step 7:

[0500] The server sends the proposed corrections it has generated back to the user's terminal. The terminal then displays these to the user, allowing them to review the proposed changes.

[0501] Step 8:

[0502] The user reviews the suggested revisions and modifies the electronic message as needed. Once the content is satisfactory, the message is sent as the final version.

[0503] This process enables users to express their emotions appropriately and effectively while sending accurate and error-free messages.

[0504] (Example 2)

[0505] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] When creating electronic messages, misunderstandings can arise not only from simple typos and grammatical errors, but also from the emotional tone of the message. Therefore, to achieve more effective communication, skills are needed that go beyond merely correcting typos and grammatical errors, to appropriately express the emotions the message intends to convey and to revise them as needed.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0508] In this invention, the server includes means for analyzing the content of an electronic message using natural language processing technology, means for detecting errors and generating correction suggestions, and means for determining the sentiment of a message using a sentiment engine. This enables users to send error-free messages while accurately conveying their emotions.

[0509] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language.

[0510] An "electronic message" is a text message that is sent and received in digital format.

[0511] "Analysis" is the process of analyzing data or information to reveal its elements and structure.

[0512] "Means for detecting errors" refers to methods or devices for identifying typographical errors, grammatical errors, and contextual errors within a message.

[0513] A "correction suggestion" is a proposal for changes presented to the user based on detected errors and areas for improvement.

[0514] An "emotion engine" is a mechanism that analyzes words and context within text to identify and evaluate emotions.

[0515] "Sentiment analysis" is the process of identifying, classifying, and evaluating the emotions contained in a text.

[0516] "Evaluation criteria" refer to the indicators or standards used to evaluate the content and emotional impact of a message.

[0517] To implement this invention, a system incorporating an emotion engine is required. The user first creates an electronic message on a terminal and sends that message to the server. The server uses a natural language processing engine to analyze the received message. Specifically, the server passes the text data to the natural language processing engine, which first detects typographical errors and grammatical mistakes.

[0518] Furthermore, the server uses an emotion engine to determine the emotions the user is trying to express in the message. The emotion engine analyzes the tone, word choice, and context of the message using a variety of language models and algorithms. As a result, it evaluates whether the user's emotions are positive, negative, or neutral. The results of this emotion evaluation are then used with a generative AI model to generate revision suggestions. These revision suggestions are returned from the server to the user's terminal, allowing the user to review and revise the message as needed.

[0519] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. In this case, if the message does not adequately express the user's intended sincere apology, the sentiment engine will detect this flaw and recommend a more appropriate expression. This recommendation will be presented to the user in text format.

[0520] Examples of prompt messages include: "I have drafted an apology email to the customer, but I feel that it does not fully convey the sincerity I intended. Please suggest ways to revise this email to be more positive and sincere."

[0521] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0522] Step 1:

[0523] The user composes an electronic message on the terminal and presses the send button. This action sends the message to the server as text data. The input is the raw text message entered by the user, and the output is the text data transferred to the server.

[0524] Step 2:

[0525] The server passes the received text data to the natural language processing engine. At this stage, the server receives the message text as input, and the natural language processing engine analyzes it, detecting typographical errors and grammatical errors. The output is the analysis result, including typographical and grammatical errors. Morphological analysis is performed during this process, and analysis is carried out at the word level.

[0526] Step 3:

[0527] The server uses an emotion engine to further analyze the text data and evaluate the emotions the message is trying to convey. The input is the analyzed text data obtained in step 2, where the emotion engine calculates an emotion score. The output is the result of the emotion evaluation, such as positive, negative, or neutral.

[0528] Step 4:

[0529] The server uses a generative AI model to create correction suggestions. The input consists of error information and sentiment evaluation results obtained in steps 2 and 3. Based on this, the generative AI model generates sentiment-consistent correction suggestions. The output is the revised message draft.

[0530] Step 5:

[0531] The server sends the generated correction suggestions back to the user's terminal. The input is the data generated as correction suggestions, and the output is the corrected text displayed on the user's terminal. The user reviews the correction suggestions through their terminal and considers their contents.

[0532] Step 6:

[0533] The user edits and modifies the electronic message as needed based on the suggested revisions. This action completes the final message. In this step, the user makes final confirmations and revisions based on the suggestions and completes the sending process.

[0534] (Application Example 2)

[0535] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0536] In modern communication technology, accurately conveying a user's intended emotions without misunderstanding is difficult, and typographical errors and grammatical mistakes can also hinder communication. Therefore, a system is needed to identify and appropriately correct the emotions and errors contained in the electronic communications that users are sending.

[0537] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0538] In this invention, the server includes means for analyzing the content of electronic communications using natural language processing technology, means for detecting errors and generating correction suggestions, and means for recognizing the emotional state of electronic communications and generating emotional correction suggestions. This enables electronic communications in which the user can accurately convey their intended emotions without errors.

[0539] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate human language.

[0540] "Electronic communication" refers to messages and data transmitted using electronic means.

[0541] An "error" is a spelling mistake, a grammatical error, or an element that could lead to a misunderstanding of the context.

[0542] "Correction proposal" refers to the proposed solutions for correcting the detected errors.

[0543] "Emotional state" refers to the emotions of the sender inferred from the content of electronic communications.

[0544] "Emotional modification suggestions" refer to suggestions for improving expressions that are provided to appropriately convey a particular emotion.

[0545] "Sender" refers to the person who creates and attempts to transmit electronic communications.

[0546] "Display means" refers to the means by which revision suggestions are presented in a way that users can review.

[0547] To implement this invention, the server first plays the role of receiving electronic communications created by the user on their terminal. The server utilizes natural language processing technology to process these electronic communications. For example, the Google Cloud Natural Language API can be used for this technology. Natural language processing technology detects spelling mistakes, grammatical errors, and potential contextual misunderstandings in the electronic communications.

[0548] Furthermore, the server uses emotion engines such as IBM Watson Tone Analyzer to analyze the emotions contained within electronic communications. As a result, it identifies the positive, negative, or neutral emotions intended by the sender and generates appropriate emotional modification suggestions as needed. This allows the server to provide senders with specific expression improvements to prevent misunderstandings of emotions.

[0549] Users review the corrections analyzed and suggested by the server on their device and edit their electronic communications as needed. Ultimately, users are able to send error-free messages that accurately express their intended emotions.

[0550] For example, if a user makes a negative social media post such as "I hate my job lately," the server might offer a positive suggestion to revise it, such as "It might be good to talk about something you found particularly rewarding about your job."

[0551] An example of a prompt to input into the generative AI model is as follows: "Evaluate the emotion inferred from the user's message '○○' and suggest a way to revise it to a more positive tone." Thus, this invention is a system that minimizes misunderstandings of user intent and enables more effective and smoother communication.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The user creates electronic communications on their terminal. These created electronic communications become the system's input.

[0555] Step 2:

[0556] The terminal sends the generated electronic communication to the server. The content of the electronic communication is then entered into the server, and it is ready for analysis.

[0557] Step 3:

[0558] The server passes the received electronic communications to a natural language processing engine, which analyzes for potential spelling errors, grammatical errors, and contextual misunderstandings. For example, it uses the Google Cloud Natural Language API to process the data and detect errors. This process outputs the identified errors.

[0559] Step 4:

[0560] Simultaneously, the server sends electronic communications to the emotion engine to analyze the user's emotional state. Here, IBM Watson Tone Analyzer is used to analyze the word choice and tone of the electronic communications and output an emotion evaluation.

[0561] Step 5:

[0562] The server integrates the results of natural language processing and sentiment engine analysis to generate correction suggestions. Based on the detected errors and sentiment evaluations, specific corrections to optimize electronic communications are output as suggestions.

[0563] Step 6:

[0564] The server sends the generated correction suggestions to the terminal for the user to review. Based on these suggestions, the user reviews the content of the electronic communication and makes corrections as necessary.

[0565] Step 7:

[0566] The user ultimately sends the revised electronic message. As a result of incorporating the suggestions, the user is able to send a message that accurately expresses the intended emotions.

[0567] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0569] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0573] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0574] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0575] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0576] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0577] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0578] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0579] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0580] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0583] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] The following system is conceivable as an embodiment of the present invention. This system aims to automatically analyze the content of electronic messages using natural language processing technology, detect errors, and generate correction suggestions.

[0585] First, the user creates an electronic message using a standard email client on their device. The created message is sent from the device to the server before being sent. The server receives this message and passes it to a generative AI model for analysis. The generative AI model analyzes the content of the electronic message and identifies spelling mistakes, grammatical errors, and contextually inappropriate parts.

[0586] Based on the analysis results, the server automatically generates suggestions to correct errors. This includes, for example, suggesting the correct "Plan" if "Plants" is misspelled as "Plan." The server returns these correction suggestions to the user's terminal, allowing the user to review the suggestions and adjust the message.

[0587] As a concrete example, consider a scenario where a sales representative sends an email to a customer about a new product. This email includes the product name, specifications, and pricing information, but may contain typos or ambiguous expressions. The system would identify errors, such as writing "Project A" instead of "Product A," and suggest the correct name. Furthermore, if pricing information is ambiguous, the system would recommend using more specific figures.

[0588] Thus, the system according to the present invention helps users send accurate and clear information by efficiently scrutinizing the content of electronic messages. It also reduces the verification work required by personnel and promotes operational efficiency.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The user composes an electronic message on their device. The user enters the message content and prepares it for sending. At this point, the message includes a subject, body, and recipient.

[0592] Step 2:

[0593] The terminal sends the created electronic message to the server. The transmitted data is temporarily stored on the server for analysis.

[0594] Step 3:

[0595] The server receives the electronic message and passes the data to the natural language processing engine. This engine first checks whether the message is properly formatted.

[0596] Step 4:

[0597] A generation AI model on the server analyzes the electronic message. This analysis identifies typos, grammatical errors, and contextually inappropriate expressions. Simultaneously, the content is scrutinized based on established evaluation criteria.

[0598] Step 5:

[0599] The generative AI model generates correction suggestions based on the analysis results. For example, it may suggest correcting incorrect words or changing to more effective expressions.

[0600] Step 6:

[0601] The server compiles the suggested corrections and sends them back to the user's terminal. The user reviews them and modifies the message content as needed.

[0602] Step 7:

[0603] The user reviews the electronic message on their device based on the suggestions, makes any necessary corrections, and then sends it. Once the corrections are complete, the electronic message is sent to the specified recipient.

[0604] (Example 1)

[0605] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] In modern electronic communications, accuracy and clarity of transmitted messages are crucial. However, spelling errors, syntactic errors, and contextual inaccuracies can lead to misunderstandings, posing a significant problem, especially in business communications. There is a need to address this issue and help users efficiently create error-free and clear communications.

[0607] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0608] In this invention, the server includes means for analyzing the content of a communication message using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, and means for sending the generated correction suggestions back to the transmitting device. This enables the user to create accurate and clear communication messages free from errors, and to quickly review and correct them.

[0609] "Natural language processing technology" refers to the technology used to convert human language into a format that can be handled by computers, and then to perform analysis and generation.

[0610] "Communication message" refers to information in message format that is sent and received via electronic means.

[0611] "Errors" refer to information containing misspellings, grammatical errors, or inappropriate expressions in context within a communication.

[0612] A "revision proposal" is a suggestion for correcting a communication message in which an error has been detected, in order to improve its accuracy and clarity.

[0613] A "transmitting device" refers to a device that can generate communication messages and transmit information to other devices via a network.

[0614] An "information processing device" refers to a device that receives data via a computer network, performs analysis and generates correction suggestions, and returns the results.

[0615] This invention provides a system that automatically detects errors in communication text and provides correction suggestions using natural language processing technology. This system mainly consists of a terminal, a server, and a generative AI model.

[0616] A terminal is a device for generating electronic communications and is equipped with email client software (e.g., a mail application). Users use this terminal to create communications such as business emails. The terminal then sends the created communications to the server via the network.

[0617] The server plays a central role in executing natural language processing techniques. The server sends received messages to a generative AI model. This generative AI model (e.g., an AI service providing a natural language processing API) analyzes the message content, identifying spelling errors, syntax errors, and contextually inappropriate parts. Furthermore, it generates correction suggestions for these errors and sends them back to the terminal via the server.

[0618] As a concrete example, consider a scenario where a user creates an email to inform customers about a new product feature. This email may contain incorrect product names or pricing information. For example, if "Project X" is misspelled, the system would suggest the correct name, "Product X." Furthermore, it would suggest correcting numerical expressions from "approximately $100" to "$99.99." An example of a prompt to input into the generative AI model would be, "Analyze this email, identify errors, and suggest corrections."

[0619] This system allows users to easily and efficiently create accurate communications and send clear, unambiguous information to recipients. This improves the accuracy and efficiency of work and enhances the quality of business communication.

[0620] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0621] Step 1:

[0622] The user launches an email client on the terminal and composes a new message. Once the user has entered the information and the message is ready to send, it is temporarily stored on the terminal. The input is the message created by the user, and the output is the message data ready to send.

[0623] Step 2:

[0624] The terminal sends a message to the server when the user initiates a send operation. The server receives this message over the network. The input is the message sent from the terminal, and the output is the message data received by the server.

[0625] Step 3:

[0626] The server sends the received communication message to the generative AI model for analysis. At this time, it provides the model with a data package that includes the prompt message. The input is the communication message and prompt message received by the server, and the output is a state of waiting for the results of the analysis by the generative AI model.

[0627] Step 4:

[0628] The generative AI model begins analyzing the communication text. It detects spelling mistakes, syntax errors, and inappropriate context, and generates correction suggestions. Data processing involves error detection and correction suggestion generation using natural language processing. The input is the communication text to be analyzed, and the output is a pair list of error identifications and suggested corrections.

[0629] Step 5:

[0630] The server sends the correction suggestions received from the generating AI model back to the terminal. The input is the correction suggestions from the generating AI model, and the output is message data with the correction suggestions sent to the user's terminal.

[0631] Step 6:

[0632] The user's device displays the received revision suggestions. The user reviews them and adjusts the message content as needed. Specifically, the user has the option to accept the revision suggestions or re-edit them. The input is the revision suggestions sent from the server, and the output is the final, confirmed communication.

[0633] Step 7:

[0634] The revised message is ready to be sent again, and the user performs the final send operation. This final transmission sends the message to its intended recipient. The input is the message reviewed and modified by the user, and the output is the final transmitted message.

[0635] (Application Example 1)

[0636] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0637] In commercial communication, errors and inefficiencies can negatively impact brand image and business results. This is especially true when creating advertising copy, where not only typographical errors and grammatical mistakes, but also inaccuracies and ambiguities in expression can be serious problems. Therefore, there is a need for systems that can detect errors in advance and efficiently correct them to enhance commercial effectiveness.

[0638] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0639] In this invention, the server includes means for analyzing communication content using natural language processing technology, means for detecting errors and generating correction suggestions based on the analysis results, means for displaying the correction suggestions in a form that can be confirmed by the sender, and means for evaluating the efficiency of the expression and reflecting it in the correction suggestions when the analyzed communication content is a commercial expression. This enables automatic error detection and efficiency improvement in commercial expressions.

[0640] "Natural language processing technology" is the technology that enables computers to understand, analyze, and generate human language.

[0641] "Communication content" refers to the entirety of text and words used for information transmission between people, such as emails and messages.

[0642] An "error" is an expression that may be unintentionally inaccurate or misleading, such as a spelling mistake, a grammatical error, or an inappropriate context.

[0643] A "correction suggestion" is a recommended, accurate, and appropriate expression for an error that has been detected.

[0644] A "sender" is a person or organization that sends out a message or information for the purpose of communication.

[0645] "Commercial expression" refers to text or content created to advertise a product or service.

[0646] "Efficiency" is a measure of how effectively a particular expression functions to achieve its purpose.

[0647] This invention is implemented using a system with a server and a user's terminal to analyze commercial communication content and detect and correct errors. The server is equipped with advanced natural language processing technology and utilizes generative AI models. When a user creates a commercial message from their terminal and sends it to the server, the server receives the message.

[0648] Here, the server first uses natural language processing models such as BERT and GPT to analyze the message and detect errors, including spelling mistakes, grammatical errors, and inappropriate context. Then, it evaluates the efficiency required, especially for commercial communication, and generates correction suggestions. These suggestions include not only corrections of typographical errors but also suggestions for improving the wording to enhance commercial effectiveness.

[0649] This analysis and suggested revisions are sent back to the user's device, where they can review it and modify the message as needed. For example, if a brand's social media team creates a statement like, "This new product has proven to be convenient," the analysis suggests improvements using specific facts and figures, such as, "89% of consumers found it convenient."

[0650] An example of a prompt message for the generation AI would be: "Analyze the following ad copy, identify errors, and suggest corrections. Ad copy: 'This sample is a perfect choice.'" This system allows users to efficiently create more accurate and commercially effective messages.

[0651] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0652] Step 1:

[0653] The user composes a commercial message on their device. The input is the user's text message, and the output is the text sent directly to the server. Here, the user completes the message draft and provides the information ready for communication.

[0654] Step 2:

[0655] The server receives messages sent by users. The input is the user's text message, and the output is text data ready for analysis, which is then passed to an AI model. Here, the server converts the message into a format that can be understood as a data structure, preparing the initial data for processing.

[0656] Step 3:

[0657] The server uses a generative AI model to analyze the message. The input is text data passed to the server, and the output is the result of error detection. The message is tokenized, and spelling and grammatical errors are identified using natural language processing techniques. For example, the BERT model is used, and the data is evaluated in a high-dimensional vector space.

[0658] Step 4:

[0659] The server generates correction suggestions based on the analysis results. The input is the error detection result, and the output is the correction suggestions presented to the user. The generating AI model proposes new sentences that are grammatically correct and commercially effective based on the detected problems. In specific operations, it generates suggestions for commercially effective expression improvements, such as specific sentences using numerical data.

[0660] Step 5:

[0661] The server sends the suggested corrections to the user's terminal. The input is the generated suggested corrections, and the output is the suggested corrections presented to the user's terminal. Here, the server reformats the message and prepares it to be presented in a way that is easily understandable to the user.

[0662] Step 6:

[0663] The user reviews the suggested revisions on their device and approves or makes revisions as needed. The input is the suggested revisions sent from the server, and the output forms the final message. The user reviews the suggestions and makes final adjustments to improve the quality of the message.

[0664] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0665] To implement this invention, a system incorporating an emotion engine is necessary. This system analyzes the content of electronic messages based on natural language processing technology and recognizes the user's emotional state using the emotion engine, thereby providing more accurate correction suggestions.

[0666] The user first creates an electronic message on their device. The created message is sent to the server. The server then passes the received message to a natural language processing engine for analysis. This process not only checks for typos and grammar, but also uses an emotion engine to determine the emotion the user is trying to express.

[0667] The sentiment engine analyzes the tone, word choice, and context within electronic messages to assess whether the user is trying to convey a positive, negative, or neutral emotion. The results of this sentiment assessment are reflected in the generated revision suggestions. For example, if a negative context is detected, it suggests using more positive language to effectively adjust the intended message.

[0668] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. If the email does not adequately express the user's intended sincere apology, the emotion engine will detect this flaw and recommend changes to a more formal and sincere expression.

[0669] The generated revision suggestions are returned to the user's device, where the user reviews them and makes corrections as needed. In this way, users can send error-free messages while accurately conveying their emotions. This system is a powerful aid in facilitating effective and smooth communication for users.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] Users compose electronic messages on their devices. These messages include a body, subject, and recipient. In business contexts, messages often convey emotions, and users want to express them appropriately.

[0673] Step 2:

[0674] The terminal sends the created electronic message to the server. Since the transmission is in real time, the user can proceed to the next step immediately after completing the input.

[0675] Step 3:

[0676] The server receives the electronic message and passes the data to both the natural language processing engine and the emotion engine. This allows the analysis of the message content and the evaluation of the emotional state to begin simultaneously.

[0677] Step 4:

[0678] The natural language processing engine analyzes the linguistic structure of electronic messages, detecting spelling mistakes, grammatical errors, and contextual inaccuracies. This process involves analysis based on various dictionaries and grammatical rules.

[0679] Step 5:

[0680] The emotion engine analyzes the text within the message to recognize the user's emotional state. Based on word choice, sentence structure, tone of expression, etc., the emotion engine determines whether the emotion is positive, negative, or neutral.

[0681] Step 6:

[0682] The server integrates the analysis results from the natural language processing engine and the sentiment engine to generate correction suggestions. These suggestions include error corrections, improved ways of expressing sentiment, and adjustments to the overall message tone.

[0683] Step 7:

[0684] The server sends the proposed corrections it has generated back to the user's terminal. The terminal then displays these to the user, allowing them to review the proposed changes.

[0685] Step 8:

[0686] The user reviews the suggested revisions and modifies the electronic message as needed. Once the content is satisfactory, the message is sent as the final version.

[0687] This process enables users to express their emotions appropriately and effectively while sending accurate and error-free messages.

[0688] (Example 2)

[0689] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] When creating electronic messages, misunderstandings can arise not only from simple typos and grammatical errors, but also from the emotional tone of the message. Therefore, to achieve more effective communication, skills are needed that go beyond merely correcting typos and grammatical errors, to appropriately express the emotions the message intends to convey and to revise them as needed.

[0691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0692] In this invention, the server includes means for analyzing the content of an electronic message using natural language processing technology, means for detecting errors and generating correction suggestions, and means for determining the sentiment of a message using a sentiment engine. This enables users to send error-free messages while accurately conveying their emotions.

[0693] "Natural language processing technology" refers to the technology used by computers to understand, interpret, and generate human language.

[0694] An "electronic message" is a text message that is sent and received in digital format.

[0695] "Analysis" is the process of analyzing data or information to reveal its elements and structure.

[0696] "Means for detecting errors" refers to methods or devices for identifying typographical errors, grammatical errors, and contextual errors within a message.

[0697] A "correction suggestion" is a proposal for changes presented to the user based on detected errors and areas for improvement.

[0698] An "emotion engine" is a mechanism that analyzes words and context within text to identify and evaluate emotions.

[0699] "Sentiment analysis" is the process of identifying, classifying, and evaluating the emotions contained in a text.

[0700] "Evaluation criteria" refer to the indicators or standards used to evaluate the content and emotional impact of a message.

[0701] To implement this invention, a system incorporating an emotion engine is required. The user first creates an electronic message on a terminal and sends that message to the server. The server uses a natural language processing engine to analyze the received message. Specifically, the server passes the text data to the natural language processing engine, which first detects typographical errors and grammatical mistakes.

[0702] Furthermore, the server uses an emotion engine to determine the emotions the user is trying to express in the message. The emotion engine analyzes the tone, word choice, and context of the message using a variety of language models and algorithms. As a result, it evaluates whether the user's emotions are positive, negative, or neutral. The results of this emotion evaluation are then used with a generative AI model to generate revision suggestions. These revision suggestions are returned from the server to the user's terminal, allowing the user to review and revise the message as needed.

[0703] As a concrete example, consider a scenario where a user is drafting an email to a customer to address a complaint. In this case, if the message does not adequately express the user's intended sincere apology, the sentiment engine will detect this flaw and recommend a more appropriate expression. This recommendation will be presented to the user in text format.

[0704] Examples of prompt messages include: "I have drafted an apology email to the customer, but I feel that it does not fully convey the sincerity I intended. Please suggest ways to revise this email to be more positive and sincere."

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] The user composes an electronic message on the terminal and presses the send button. This action sends the message to the server as text data. The input is the raw text message entered by the user, and the output is the text data transferred to the server.

[0708] Step 2:

[0709] The server passes the received text data to the natural language processing engine. At this stage, the server receives the message text as input, and the natural language processing engine analyzes it, detecting typographical errors and grammatical errors. The output is the analysis result, including typographical and grammatical errors. Morphological analysis is performed during this process, and analysis is carried out at the word level.

[0710] Step 3:

[0711] The server uses an emotion engine to further analyze the text data and evaluate the emotions the message is trying to convey. The input is the analyzed text data obtained in step 2, where the emotion engine calculates an emotion score. The output is the result of the emotion evaluation, such as positive, negative, or neutral.

[0712] Step 4:

[0713] The server uses a generative AI model to create correction suggestions. The input consists of error information and sentiment evaluation results obtained in steps 2 and 3. Based on this, the generative AI model generates sentiment-consistent correction suggestions. The output is the revised message draft.

[0714] Step 5:

[0715] The server sends the generated correction suggestions back to the user's terminal. The input is the data generated as correction suggestions, and the output is the corrected text displayed on the user's terminal. The user reviews the correction suggestions through their terminal and considers their contents.

[0716] Step 6:

[0717] The user edits and modifies the electronic message as needed based on the suggested revisions. This action completes the final message. In this step, the user makes final confirmations and revisions based on the suggestions and completes the sending process.

[0718] (Application Example 2)

[0719] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0720] In modern communication technology, accurately conveying a user's intended emotions without misunderstanding is difficult, and typographical errors and grammatical mistakes can also hinder communication. Therefore, a system is needed to identify and appropriately correct the emotions and errors contained in the electronic communications that users are sending.

[0721] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0722] In this invention, the server includes means for analyzing the content of electronic communications using natural language processing technology, means for detecting errors and generating correction suggestions, and means for recognizing the emotional state of electronic communications and generating emotional correction suggestions. This enables electronic communications in which the user can accurately convey their intended emotions without errors.

[0723] "Natural language processing technology" is a technology that uses computers to analyze, understand, and generate human language.

[0724] "Electronic communication" refers to messages and data transmitted using electronic means.

[0725] An "error" is a spelling mistake, a grammatical error, or an element that could lead to a misunderstanding of the context.

[0726] "Correction proposal" refers to the proposed solutions for correcting the detected errors.

[0727] "Emotional state" refers to the emotions of the sender inferred from the content of electronic communications.

[0728] "Emotional modification suggestions" refer to suggestions for improving expressions that are provided to appropriately convey a particular emotion.

[0729] "Sender" refers to the person who creates and attempts to transmit electronic communications.

[0730] "Display means" refers to the means by which revision suggestions are presented in a way that users can review.

[0731] To implement this invention, the server first plays the role of receiving electronic communications created by the user on their terminal. The server utilizes natural language processing technology to process these electronic communications. For example, the Google Cloud Natural Language API can be used for this technology. Natural language processing technology detects spelling mistakes, grammatical errors, and potential contextual misunderstandings in the electronic communications.

[0732] Furthermore, the server uses emotion engines such as IBM Watson Tone Analyzer to analyze the emotions contained within electronic communications. As a result, it identifies the positive, negative, or neutral emotions intended by the sender and generates appropriate emotional modification suggestions as needed. This allows the server to provide senders with specific expression improvements to prevent misunderstandings of emotions.

[0733] Users review the corrections analyzed and suggested by the server on their device and edit their electronic communications as needed. Ultimately, users are able to send error-free messages that accurately express their intended emotions.

[0734] For example, if a user makes a negative social media post such as "I hate my job lately," the server might offer a positive suggestion to revise it, such as "It might be good to talk about something you found particularly rewarding about your job."

[0735] An example of a prompt to input into the generative AI model is as follows: "Evaluate the emotion inferred from the user's message '○○' and suggest a way to revise it to a more positive tone." Thus, this invention is a system that minimizes misunderstandings of user intent and enables more effective and smoother communication.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The user creates electronic communications on their terminal. These created electronic communications become the system's input.

[0739] Step 2:

[0740] The terminal sends the generated electronic communication to the server. The content of the electronic communication is then entered into the server, and it is ready for analysis.

[0741] Step 3:

[0742] The server passes the received electronic communications to a natural language processing engine, which analyzes for potential spelling errors, grammatical errors, and contextual misunderstandings. For example, it uses the Google Cloud Natural Language API to process the data and detect errors. This process outputs the identified errors.

[0743] Step 4:

[0744] Simultaneously, the server sends electronic communications to the emotion engine to analyze the user's emotional state. Here, IBM Watson Tone Analyzer is used to analyze the word choice and tone of the electronic communications and output an emotion evaluation.

[0745] Step 5:

[0746] The server integrates the results of natural language processing and sentiment engine analysis to generate correction suggestions. Based on the detected errors and sentiment evaluations, specific corrections to optimize electronic communications are output as suggestions.

[0747] Step 6:

[0748] The server sends the generated correction suggestions to the terminal for the user to review. Based on these suggestions, the user reviews the content of the electronic communication and makes corrections as necessary.

[0749] Step 7:

[0750] The user ultimately sends the revised electronic message. As a result of incorporating the suggestions, the user is able to send a message that accurately expresses the intended emotions.

[0751] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0752] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0753] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0754] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0755] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0756] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0757] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0758] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0759] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0760] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0761] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0762] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0763] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0764] 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.

[0765] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0766] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0767] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0768] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0769] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0770] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0771] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] A means of analyzing the content of electronic messages using natural language processing technology,

[0775] A means for detecting errors and generating correction suggestions based on these analysis results,

[0776] A means of displaying the proposed revision in a form that can be confirmed by the sender,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, wherein the detection of the error includes at least one of a spelling mistake, a grammatical error, and a contextual error.

[0780] (Claim 3)

[0781] The system according to claim 1, wherein the proposed modifications relate to the content of an electronic message evaluated based on pre-set evaluation criteria.

[0782] "Example 1"

[0783] (Claim 1)

[0784] A means of analyzing the content of a communication using natural language processing technology,

[0785] A means for detecting errors and generating correction suggestions based on these analysis results,

[0786] Means for displaying the proposed revisions in a manner that can be confirmed by the transmitting device,

[0787] Means for transmitting the aforementioned communication message from a transmitting device to an information processing device,

[0788] The information processing device includes means for sending the generated correction suggestions back to the transmitting device,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the detection of the error includes at least one of a spelling error, a syntax error, and a contextual error.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the proposed modification relates to the content of a communication message evaluated based on pre-set evaluation criteria.

[0794] "Application Example 1"

[0795] (Claim 1)

[0796] A means of analyzing communication content using natural language processing technology,

[0797] A means for detecting errors and generating correction suggestions based on these analysis results,

[0798] A means of displaying the aforementioned proposed revisions in a way that the sender can verify,

[0799] In cases where the analyzed communication content is a commercial expression, a means for evaluating the efficiency of that expression and reflecting it in the proposed revisions,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, wherein the detection of the error includes at least one of a spelling mistake, a grammatical error, and a contextual error.

[0803] (Claim 3)

[0804] The system according to claim 1, wherein the proposed modifications relate to the content of an electronic message evaluated based on pre-set evaluation criteria.

[0805] "Example 2 of combining an emotion engine"

[0806] (Claim 1)

[0807] A means of analyzing the content of electronic messages using natural language processing technology,

[0808] A means for detecting errors and generating correction suggestions based on these analysis results,

[0809] A means of determining the emotion of a message using an emotion engine,

[0810] A means for generating suggested revisions to adjust the expression based on the emotions determined above,

[0811] A means of displaying the proposed revision in a form that can be confirmed by the sender,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the error detection includes at least one of a spelling mistake, a grammatical error, and a contextual error, and the system determines positive, negative, or neutral emotions by sentiment analysis.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the proposed modifications relate to the content of electronic messages and the results of sentiment analysis evaluated based on pre-set evaluation criteria.

[0817] "Application example 2 when combining with an emotional engine"

[0818] (Claim 1)

[0819] A means of analyzing the content of electronic communications using natural language processing technology,

[0820] A means for detecting errors and generating correction suggestions based on these analysis results,

[0821] A means for recognizing the emotional state of electronic communications and generating emotional modification suggestions based on those emotions,

[0822] A means of displaying the proposed revision in a form that can be confirmed by the sender,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the detection of the error includes at least one of a spelling mistake, a grammatical error, and a contextual error, and the system makes correction suggestions based on sentiment evaluation by a sentiment engine.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the proposed modifications relate to the content and emotions of electronic communications evaluated based on pre-set evaluation criteria. [Explanation of Symbols]

[0828] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing communication content using natural language processing technology, A means for detecting errors and generating correction suggestions based on these analysis results, A means of displaying the aforementioned proposed revisions in a way that the sender can verify, In cases where the analyzed communication content is a commercial expression, a means for evaluating the efficiency of that expression and reflecting it in the proposed revisions, A system that includes this.

2. The system according to claim 1, wherein the detection of the error includes at least one of a spelling mistake, a grammatical error, and a contextual error.

3. The system according to claim 1, wherein the proposed modification relates to the content of an electronic message evaluated based on pre-set evaluation criteria.

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