system

Abstract

We provide the system. [Solution] A means of collecting communication data entered by the user, A means for analyzing the aforementioned communication data and detecting discrepancies in understanding between users, Means for generating a conversion message to mitigate the aforementioned misunderstanding, A means of providing the generated conversion message to the user, 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the communication between superiors and subordinates within an organization, there is a problem that recognition discrepancies in instructions and questions are likely to occur due to differences in experience and knowledge. This may lead to a decrease in work efficiency and have an adverse impact on the productivity of the entire organization. Therefore, means for preventing such discrepancies and realizing smooth communication are required.

Means for Solving the Problems

[0005] This invention provides a means for detecting misunderstandings by collecting and analyzing communication data input by users, analyzing intent using a generation AI model, and generating a converted message that mitigates the misunderstanding. This enables easy communication between users with different backgrounds, facilitating smooth communication. Furthermore, by collecting user feedback and improving the accuracy of generated messages, the effectiveness of the system is continuously improved.

[0006] A "user" is an individual or organization that uses a system to input or output information.

[0007] "Communication data" refers to digital information that users input into a system to send messages or retrieve information.

[0008] "Analysis" is the process of investigating the content of communication data to identify its intent, background, or any misunderstandings.

[0009] "Misunderstanding" refers to a situation where the intent or content of a message is interpreted differently by different users, leading to misunderstandings.

[0010] A "converted message" is a modified message generated based on analysis, intended to reduce misunderstandings between users and clearly convey intent.

[0011] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to analyze communication data and provide appropriate output.

[0012] "Feedback" refers to information submitted by users regarding their opinions and impressions about the effectiveness and accuracy of the messages provided by the system. [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 the 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 the emotion engine is combined.

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

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

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

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

[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] This invention is a system for improving the efficiency of communication between superiors and subordinates, or between different departments, within an organization. The system consists of a server, terminals, and users, and is designed to allow users to understand instructions more clearly and obtain answers to their questions.

[0035] Users input messages for communication via their devices. These messages are sent to a server. The server uses generative AI models to analyze the messages in detail, identifying any parts that may be misunderstood or misinterpreted. After analysis, the server generates a transformed message, modifying it so that users can understand it without misunderstanding. This prevents misunderstandings due to differences in experience and knowledge, enabling smooth communication.

[0036] For example, if a user asks via their terminal, "What documents are needed for the next meeting?", the server analyzes past meeting materials and related project information to generate a converted message containing specific instructions, including the appropriate document names and items to prepare. This converted message is displayed to the user on their terminal, allowing them to clearly understand what they need to prepare.

[0037] Furthermore, users can provide feedback on the messages they receive, and this feedback information is sent to the server. Based on this feedback information, the server updates the generated AI model as needed, improving the accuracy of the analysis and enabling it to provide more effective communication support. In this way, the system continuously learns and improves, growing through interaction with users.

[0038] In this way, the present invention provides a practical means for reducing communication gaps within an organization and improving work efficiency.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user accesses their device and launches an email or chat application. The user then types a question for their supervisor or instructions for a subordinate.

[0042] Step 2:

[0043] The terminal converts the message entered by the user into text format and establishes a network connection to send it to the server.

[0044] Step 3:

[0045] The server receives messages sent from the terminal and passes that text data to the AI ​​model that generates the data.

[0046] Step 4:

[0047] The generative AI model uses natural language processing techniques to analyze the intent and context of the message. This allows it to assess the likelihood of misinterpretations.

[0048] Step 5:

[0049] Based on the analysis results, the server generates a conversion message to mitigate misunderstandings between users. Supplementary information and specific instructions are added as needed.

[0050] Step 6:

[0051] The generated conversion message is formatted and prepared as data to be sent to the terminal.

[0052] Step 7:

[0053] The terminal displays the converted message received from the server to the user, who then uses that message to perform instructions or obtain answers to questions.

[0054] Step 8:

[0055] Users can provide feedback on messages provided by the system. This feedback information is sent from the terminal to the server.

[0056] Step 9:

[0057] The server uses feedback information to update the generated AI model, improving the accuracy of the analysis. This allows for continuous improvement of the system's effectiveness.

[0058] (Example 1)

[0059] 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."

[0060] In organizational communication, information sharing and instruction transmission among users can become unclear, leading to discrepancies and misunderstandings. This can reduce work efficiency and negatively impact project progress. To solve this problem, there is a need to achieve clearer and more accurate information transmission.

[0061] 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.

[0062] In this invention, the server includes means for collecting communication information entered by the user, means for analyzing the communication information and detecting information discrepancies between users, and means for generating adjustment information to mitigate information discrepancies using a generation AI. As a result, users can receive clear information without misunderstanding, improving the efficiency and effectiveness of communication.

[0063] "Communication information" refers to data that a user sends or receives via a digital system, and which takes the form of messages, instructions, questions, etc.

[0064] "Information discrepancy" refers to a situation where the same information is interpreted differently by users, leading to misunderstandings and inconsistent actions.

[0065] "Generative AI" refers to a technology that uses artificial intelligence to automatically generate new information and solutions based on data.

[0066] "Adjustment information" refers to adjusted instructions or messages generated to mitigate or resolve information discrepancies.

[0067] "User feedback" refers to opinions, evaluations, or responses provided by users, and the data used to improve and adjust the system.

[0068] "Natural language processing" refers to the technology that enables computers to understand and analyze human language, and is used for analyzing intent and generating information.

[0069] A "user interface" refers to the means by which a user interacts with a system, and includes elements that present information to the user through screen displays and input devices.

[0070] This invention is a communication support system consisting of a user, a terminal, and a server, aimed at clarifying information sharing and instruction transmission within an organization. The user first inputs messages or questions through the terminal's interface. This terminal includes a general-purpose input device and display.

[0071] The entered message is sent to the server via the terminal. The server receives it and analyzes it as communication information. In doing so, the server uses a generative AI model. Specific generative AI models include OpenAI's GPT series. The server utilizes the AI ​​model and natural language processing techniques to analyze the intent of the message in detail.

[0072] Based on the analysis, the server detects information discrepancies between users and generates adjustment information to mitigate them. This adjustment information is created by referencing relevant past databases and information repositories as needed, so that users can understand the content without misunderstanding. The generated adjustment information is sent back to the terminal and presented to the user visually.

[0073] For example, if a user asks, "What documents are needed for the next meeting?", the server analyzes past meeting history and project documents and generates specific coordination information such as, "The latest documents for Project X are needed for the meeting." This coordination information is clearly displayed to the user through their terminal, allowing them to clearly understand the necessary preparations.

[0074] Furthermore, users can provide feedback on the presented adjustment information. This feedback is sent to the server via the terminal, and the server uses it to adjust the performance of the generated AI model, continuously improving the system's analysis accuracy.

[0075] A concrete example of a prompt message would be something like, "Please tell me what materials are needed to prepare for the meeting." This allows the system to function smoothly and streamlines communication within the organization.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users input messages and questions on the device. It is important that they clearly describe the information or instructions they intend to convey. The input is then checked for formal consistency on the device and converted into the appropriate data format.

[0079] Step 2:

[0080] The terminal sends formatted communication information to the server. This communication information includes user input and is ready for analysis on the server.

[0081] Step 3:

[0082] The server analyzes the received communication information. It then activates a generative AI model, using natural language processing techniques to analyze the intent and purpose of the input in detail. This analysis includes checking for any misunderstandings between users. The input data consists of raw messages from users, and the output provides the analysis results along with potential points of misunderstanding.

[0083] Step 4:

[0084] The server generates adjustment information based on the analysis results. The generating AI model also refers to past databases and related information, combining the most appropriate content to construct a specific message. The input here is the analysis results from step 3, and the output is the adjustment information.

[0085] Step 5:

[0086] The generated adjustment information is sent from the server to the terminal. The server also verifies the integrity of the communication to ensure that accurate information is transmitted reliably.

[0087] Step 6:

[0088] The terminal displays the received adjustment information to the user. The information is presented on the screen in a visually organized manner, and the format and layout are appropriately adjusted so that the user can understand it immediately.

[0089] Step 7:

[0090] Users review the presented adjustment information and take action based on it. Furthermore, they provide feedback on the appropriateness and usefulness of the information. This feedback includes user ratings and opinions.

[0091] Step 8:

[0092] The device sends user feedback to the server. The server receives this feedback and incorporates it as training data for the generated AI model, using it as foundational information to improve the model's accuracy. This allows the system to continuously improve.

[0093] (Application Example 1)

[0094] 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."

[0095] In current factory operations involving automated machinery, misunderstandings and inconsistencies in information can occur between the human giving instructions and the machine. This leads to decreased production efficiency and a lack of guaranteed accuracy. More specifically, when instructions are given in natural language, the machine may misinterpret the meaning of the instructions, resulting in the intended task not being performed properly.

[0096] 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.

[0097] In this invention, the server includes means for collecting communication information entered by a user, means for analyzing the communication information and detecting misunderstandings between users, means for generating conversion information to mitigate the misunderstandings, and means for the automated machine to execute specific work commands based on the generated conversion information. This minimizes misunderstandings between the person giving the instructions and the automated machine, while ensuring that instructions in natural language are accurately interpreted and enabling efficient and precise work execution.

[0098] "Communication information" refers to all types of messages, including text and audio, that users input.

[0099] "Misunderstanding" refers to a situation in information transmission where the content is not fully shared between the sender and receiver, leading to different interpretations.

[0100] "Converted information" refers to a message that has been modified and processed based on the original communication information in order to be understood without misunderstanding.

[0101] "User" refers to any person who directly inputs or receives information through an interface with the system.

[0102] "Automated equipment" refers to machines and robots that perform actions based on instructions with minimal human intervention in factories and workplaces.

[0103] The system implementing this invention is based on the premise that the user operates using a terminal managed by the user. The terminal is equipped with an input device for collecting communication information entered by the user in natural language. The terminal transmits the collected communication information to a server.

[0104] The server uses a generative AI model to analyze this communication information in detail and detect misunderstandings. Natural language processing techniques are used to accurately grasp the context and intent of the communication information. Based on the analysis results, the server generates corrective information to prevent misunderstandings.

[0105] The generated conversion information is sent back to the terminal and provided to the user. Based on this conversion information, automated equipment installed in the factory or work site then executes specific work commands. This allows the user to adjust the operation of the automated equipment and proceed with efficient work.

[0106] For example, if a user gives the instruction "Please send the next product sample for inspection," the server analyzes this message and translates it into a specific instruction such as "Lift the specified product sample with the robot's arm and move it to the inspection station." This allows the automated equipment (in this case, the robot) to perform the operation precisely as instructed.

[0107] An example of a prompt using a generative AI model is: "Analyze the user message and convert it into instructions that the robot can easily understand. Message: Send the following product sample for inspection." The server uses such prompts to accurately analyze the user's intent and generate the most appropriate work instructions.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user uses a terminal to input communication information in natural language. The user inputs instructions and questions in text format, and the terminal stores this communication information as digital data. The input data is then sent directly to the server.

[0111] Step 2:

[0112] The server receives communication information sent from the terminal. Based on the received data, it analyzes the content using a generative AI model. Here, it understands the context and intent of the communication information and detects discrepancies in understanding between users. In this analysis process, the AI ​​uses prompt sentences to analyze the information in detail. The input is the message from the user, and the output is a digital report of the analysis results.

[0113] Step 3:

[0114] Based on the analysis, the server generates conversion information if there is a misunderstanding. The generating AI model then uses the prompt again to generate a message that accurately reflects the user's intent. In this step, the input is the analysis result, and the output is the conversion information.

[0115] Step 4:

[0116] The generated conversion information is sent from the server to the terminal. The terminal receives this information and presents it to the user in an easy-to-understand format. Specifically, the conversion information is displayed on the terminal's screen so that the user can confirm the content without misunderstanding. Here, the input is the conversion information, and the output is the content displayed to the user.

[0117] Step 5:

[0118] The user reviews the provided conversion information and sends specific instructions to the automated equipment based on that information. The automated equipment then performs physical actions according to those instructions. For example, a robotic arm might move a specified object, which is then reflected in on-site work. The input is the instructions based on the user's conversion information, and the output is the operation of the automated equipment.

[0119] 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.

[0120] This invention provides a system that enables more nuanced communication by reducing misunderstandings in user-to-user communication and providing responses that take into account the user's emotions. This system is realized through the collaboration of an emotion engine and a generative AI model, with the server, terminal, and user each fulfilling their respective roles.

[0121] Users input communication content, such as questions for their superiors or instructions for their subordinates, using their devices. These messages are sent to a server, which uses an emotion engine to analyze the emotional elements of the messages. This analysis identifies the emotions and tone the user is experiencing.

[0122] The results of the emotion analysis are reflected in the detection of recognition discrepancies and the generation of corrected messages by the generative AI model. The server adjusts the content and expression of the message according to the user's emotional state. For example, if the user is feeling anxious or stressed, it can choose expressions that provide a greater sense of reassurance.

[0123] For example, if a user enters "I'm worried about the progress of this project" into their terminal, the server's emotion engine detects the user's anxiety. Based on this, the generative AI model generates a reassuring message, such as "The current progress is fine, but we will provide additional support if needed," and sends it to the user. In this way, intentions can be conveyed more effectively, and communication can be more empathetic to the other person's feelings.

[0124] Furthermore, user feedback is collected by the server and used to improve the performance of the emotion engine and generative AI models. By analyzing the feedback information and continuously improving the accuracy of emotion analysis and message generation, the system's effectiveness increases, and the user experience is further enhanced.

[0125] This invention makes it possible to improve the efficiency and quality of communication and increase work productivity.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user uses the device and launches a messaging application. The user then types specific instructions or questions as text.

[0129] Step 2:

[0130] The terminal receives the message entered by the user and prepares to send that data to the server in text format.

[0131] Step 3:

[0132] The server receives text data sent from the terminal and first uses an emotion engine to analyze the user's emotions from the message. The analyzed emotion data provides information about the tone and emotional aspects of the message.

[0133] Step 4:

[0134] The server passes the sentiment analysis results and communication data to the generating AI model, which performs analysis to detect discrepancies in perception between users. The analysis takes into account the intent and background of the words, as well as sentiment data, to identify the intent behind the message.

[0135] Step 5:

[0136] The server's AI model generates conversion messages based on the analysis results to mitigate misunderstandings. In doing so, it adjusts the messages according to the user's emotions, aiming to provide reassurance and appropriate feedback.

[0137] Step 6:

[0138] The server formats the generated conversion message and prepares it as data for transmission.

[0139] Step 7:

[0140] The terminal displays the converted message received from the server to the user, who then reviews it and uses it as a guide for action or to answer questions.

[0141] Step 8:

[0142] If a user wants to send feedback about a message provided by the system, that feedback is sent from the terminal to the server.

[0143] Step 9:

[0144] The server collects user feedback and uses it to update and improve the emotion engine and generative AI models. This improves the overall accuracy of the system and the user experience.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] With the widespread adoption of information technology, misunderstandings and discrepancies in communication between users are becoming more common, often leading to misinterpretations and conflicts. Furthermore, while it's necessary to consider the emotions of each individual user, individual responses are time-consuming and inefficient. Therefore, it's essential to analyze emotional states and provide appropriate alternative information to achieve smoother and more efficient communication.

[0148] 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.

[0149] In this invention, the server includes means for collecting information input by the user using an output device, means for analyzing the information and detecting discrepancies in perception between users, and means for generating alternative information whose content is adjusted according to the emotional state based on the discrepancies in perception. This makes it possible to provide appropriate information while taking into account the user's emotions and mitigating discrepancies in perception.

[0150] "Output device" refers to a device used by users to input or receive information.

[0151] "Information" refers to messages and data transmitted by users through output devices.

[0152] "Discrepancy in understanding" refers to misunderstandings or disagreements that occur in communication between users.

[0153] "Alternative information" refers to messages with adjusted content that are generated for the purpose of mitigating misunderstandings.

[0154] "Emotional state" refers to the tone and nuances of emotions that a user conveys in a message.

[0155] "Natural language processing technology" refers to the technology that enables computers to understand and process human language appropriately.

[0156] A "learning model" refers to an algorithm used to learn patterns from data and make judgments or predictions.

[0157] This system aims to detect discrepancies in perception between users and provide appropriate alternative information based on their emotional state. The system is realized through the respective roles of the server, terminal, and users.

[0158] Users input communication information using a terminal. This terminal refers to electronic devices such as general computers and smartphones, equipped with an interface for information input. This information is transmitted to a server via the network.

[0159] The server uses an emotion engine and a generative AI model to analyze information received from the user. The emotion engine uses natural language processing techniques to analyze the emotional state of the message. In doing so, it extracts the user's emotional tone and nuances to understand the emotions behind the message.

[0160] Based on the analysis results, the server applies a generative AI model. This model is an advanced algorithm that generates alternative information to mitigate recognition discrepancies. The generative AI model utilizes natural language generation technology to generate flexible messages that correspond to the analyzed emotional state.

[0161] As a concrete example, consider a scenario where a user enters "I'm worried about whether the project will meet the deadline" into their terminal. The server detects the user's anxiety through its emotion engine. Then, using a generative AI model, it generates alternative information such as "The project is on schedule, but please let us know if you have any concerns," and sends it back to the user.

[0162] This system's feedback function allows users to provide satisfaction levels with the messages they receive, which contributes to the system's continuous improvement. The server uses this feedback data to improve the accuracy of its emotion engine and generative AI models.

[0163] An example of a prompt might be an instruction to the generative AI model such as, "Based on the sentiment analysis results from the sentiment engine, generate the most appropriate response message for the user." This prompt clarifies the guidelines for the generative AI model to specifically generate the desired response.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user enters a message using the terminal. This input consists of the user typing the message they want to convey as text on the terminal. For example, if the user types "I'm worried about this weekend's project deadline," that message will be output as text data on the terminal.

[0167] Step 2:

[0168] The terminal processes the input message and sends it to the server. Here, a communication protocol is used to send the input data to the server as a data packet. As a result, the server receives the text data.

[0169] Step 3:

[0170] The server inputs the received message into the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional elements of the text and outputs emotion tags such as anxiety, joy, and anger. For example, because the message contains "I'm worried," it outputs the emotion tag "anxiety."

[0171] Step 4:

[0172] The server inputs the results of the sentiment analysis into a generative AI model. This model processes the data to generate an appropriate alternative message based on the prompt text. Specifically, the generative AI model uses the input sentiment tags to generate text such as "The project is progressing smoothly. We will address any issues immediately."

[0173] Step 5:

[0174] The server processes the generated alternative message and sends it to the user's terminal. Here, the generated message is sent as output data via the communication protocol. As a result of this step, the user receives a reassuring message on their terminal.

[0175] Step 6:

[0176] Users review the messages they receive and provide feedback as needed. This feedback is entered into the device as an evaluation of the message content and suggestions for improvement, and then sent to the server. This data is then used to improve the accuracy of subsequent analyses and generation.

[0177] (Application Example 2)

[0178] 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".

[0179] In situations where misunderstandings are likely to occur during communication, or when users are experiencing anxiety or stress, effective communication is difficult. Furthermore, in situations like electronic payments, it is necessary to alleviate user anxiety and provide an environment where users can use the service with peace of mind.

[0180] 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.

[0181] In this invention, the server includes means for analyzing the user's emotional state in real time, means for generating messages that provide reassurance as needed based on the emotional state, and means for generating conversion messages to reduce misunderstandings between users. This makes it possible to provide effective communication that is attentive to the user's emotions and an electronic payment environment that can be used with peace of mind.

[0182] "Communication data" refers to digital data that includes the content of information exchange and conversations between users.

[0183] "Discrepancy in understanding" refers to a difference in interpretation of intent or content that occurs in communication between users.

[0184] A "conversion message" is a message with appropriate content that is generated to reduce misunderstandings and facilitate smooth communication.

[0185] "Emotional state" refers to the user's psychological state, specifically emotional states such as anxiety and stress.

[0186] A "reassuring message" is a message generated to provide reassurance to users when they feel worried or anxious, enabling them to use the service with peace of mind.

[0187] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and manipulate human language.

[0188] "Emotional analysis technology" is a technology that identifies a user's emotional state from text data and analyzes its content.

[0189] A "generative AI model" is an artificial intelligence model that can generate content for a specific task.

[0190] "Feedback" refers to information about users' experiences and opinions.

[0191] "Accuracy" refers to the degree to which a system is able to deliver exactly the intended results or outputs.

[0192] The system for implementing this invention consists of a server, a terminal, and a user. The server collects communication data entered by the user via the terminal and analyzes the emotional state in real time based on that data. Specifically, it uses emotion analysis technology to determine the user's emotional state. Based on this analysis, the generative AI model generates conversion messages to reduce misunderstandings with the user and messages to provide a sense of security.

[0193] The server utilizes natural language processing technology to deliver messages to users that effectively alleviate their anxiety and stress. This process employs programming languages ​​like Python, sentiment analysis tools, and generative AI tools. As a result, users can use electronic payments and other communication services with peace of mind.

[0194] As a concrete example, consider a scenario where a user uses an electronic payment system for the first time and enters a message expressing concern: "Is this service safe?" The server analyzes this as an emotional state of "anxiety" and uses a generative AI model to generate a translated message such as, "Please rest assured. This service is trusted by many users," and provides it to the user.

[0195] An example of a prompt is, "Analyze this user's sentiment and generate an appropriate response message." Based on this prompt, the system performs the necessary analysis and message generation.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user inputs communication data into the server via their terminal. This communication data includes the user's anxieties, questions, or requests for information. The input data is sent to the server in digital format and analyzed in the next processing step.

[0199] Step 2:

[0200] The server uses emotion analysis technology to analyze the emotional state of the incoming communication data. Using emotion analysis tools, it identifies emotions such as "anxiety" and "stress" from the data. The output of this step is the identified emotional state, which is used for message generation in the next step.

[0201] Step 3:

[0202] The server uses a generative AI model to generate a translated message based on the emotional state obtained in step 2. Here, a "prompt" is used to instruct the AI ​​model to perform a specified generation task. For example, if an emotion indicating anxiety is identified, the prompt "Generate a message that provides the best possible sense of reassurance for this situation" is used. The output of this step is the generated translated message.

[0203] Step 4:

[0204] The server sends the generated translation message to the user's terminal. This action allows the user to receive a message that appropriately responds to their communication data. This reduces anxiety and allows the user to use the service with peace of mind. This step includes the message provided to the user as output.

[0205] Step 5:

[0206] The user sends feedback to the server regarding the message received as a response. The server analyzes this feedback and uses it to improve the accuracy of the generative AI model and sentiment analysis technology. The input for this step is the user's feedback, and the output is data used to improve the model.

[0207] 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.

[0208] 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 those described above. 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 shown 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention is a system for improving the efficiency of communication between superiors and subordinates, or between different departments, within an organization. The system consists of a server, terminals, and users, and is designed to allow users to understand instructions more clearly and obtain answers to their questions.

[0224] Users input messages for communication via their devices. These messages are sent to a server. The server uses generative AI models to analyze the messages in detail, identifying any parts that may be misunderstood or misinterpreted. After analysis, the server generates a transformed message, modifying it so that users can understand it without misunderstanding. This prevents misunderstandings due to differences in experience and knowledge, enabling smooth communication.

[0225] For example, if a user asks via their terminal, "What documents are needed for the next meeting?", the server analyzes past meeting materials and related project information to generate a converted message containing specific instructions, including the appropriate document names and items to prepare. This converted message is displayed to the user on their terminal, allowing them to clearly understand what they need to prepare.

[0226] Furthermore, users can provide feedback on the messages they receive, and this feedback information is sent to the server. Based on this feedback information, the server updates the generated AI model as needed, improving the accuracy of the analysis and enabling it to provide more effective communication support. In this way, the system continuously learns and improves, growing through interaction with users.

[0227] In this way, the present invention provides a practical means for reducing communication gaps within an organization and improving work efficiency.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user accesses their device and launches an email or chat application. The user then types a question for their supervisor or instructions for a subordinate.

[0231] Step 2:

[0232] The terminal converts the message entered by the user into text format and establishes a network connection to send it to the server.

[0233] Step 3:

[0234] The server receives messages sent from the terminal and passes that text data to the AI ​​model that generates the data.

[0235] Step 4:

[0236] The generative AI model uses natural language processing techniques to analyze the intent and context of the message. This allows it to assess the likelihood of misinterpretations.

[0237] Step 5:

[0238] Based on the analysis results, the server generates a conversion message to mitigate misunderstandings between users. Supplementary information and specific instructions are added as needed.

[0239] Step 6:

[0240] The generated conversion message is formatted and prepared as data to be sent to the terminal.

[0241] Step 7:

[0242] The terminal displays the converted message received from the server to the user, who then uses that message to perform instructions or obtain answers to questions.

[0243] Step 8:

[0244] Users can provide feedback on messages provided by the system. This feedback information is sent from the terminal to the server.

[0245] Step 9:

[0246] The server uses feedback information to update the generated AI model, improving the accuracy of the analysis. This allows for continuous improvement of the system's effectiveness.

[0247] (Example 1)

[0248] 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."

[0249] In organizational communication, information sharing and instruction transmission among users can become unclear, leading to discrepancies and misunderstandings. This can reduce work efficiency and negatively impact project progress. To solve this problem, there is a need to achieve clearer and more accurate information transmission.

[0250] 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.

[0251] In this invention, the server includes means for collecting communication information entered by the user, means for analyzing the communication information and detecting information discrepancies between users, and means for generating adjustment information to mitigate information discrepancies using a generation AI. As a result, users can receive clear information without misunderstanding, improving the efficiency and effectiveness of communication.

[0252] "Communication information" refers to data that a user sends or receives via a digital system, and which takes the form of messages, instructions, questions, etc.

[0253] "Information discrepancy" refers to a situation where the same information is interpreted differently by users, leading to misunderstandings and inconsistent actions.

[0254] "Generative AI" refers to a technology that uses artificial intelligence to automatically generate new information and solutions based on data.

[0255] "Adjustment information" refers to adjusted instructions or messages generated to mitigate or resolve information discrepancies.

[0256] "User feedback" refers to opinions, evaluations, or responses provided by users, and the data used to improve and adjust the system.

[0257] "Natural language processing" refers to the technology that enables computers to understand and analyze human language, and is used for analyzing intent and generating information.

[0258] A "user interface" refers to the means by which a user interacts with a system, and includes elements that present information to the user through screen displays and input devices.

[0259] This invention is a communication support system consisting of a user, a terminal, and a server, aimed at clarifying information sharing and instruction transmission within an organization. The user first inputs messages or questions through the terminal's interface. This terminal includes a general-purpose input device and display.

[0260] The entered message is sent to the server via the terminal. The server receives it and analyzes it as communication information. In doing so, the server uses a generative AI model. Specific generative AI models include OpenAI's GPT series. The server utilizes the AI ​​model and natural language processing techniques to analyze the intent of the message in detail.

[0261] Based on the analysis, the server detects information discrepancies between users and generates adjustment information to mitigate them. This adjustment information is created by referencing relevant past databases and information repositories as needed, so that users can understand the content without misunderstanding. The generated adjustment information is sent back to the terminal and presented to the user visually.

[0262] For example, if a user asks, "What documents are needed for the next meeting?", the server analyzes past meeting history and project documents and generates specific coordination information such as, "The latest documents for Project X are needed for the meeting." This coordination information is clearly displayed to the user through their terminal, allowing them to clearly understand the necessary preparations.

[0263] Furthermore, users can provide feedback on the presented adjustment information. This feedback is sent to the server via the terminal, and the server uses it to adjust the performance of the generated AI model, continuously improving the system's analysis accuracy.

[0264] A concrete example of a prompt message would be something like, "Please tell me what materials are needed to prepare for the meeting." This allows the system to function smoothly and streamlines communication within the organization.

[0265] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0266] Step 1:

[0267] Users input messages and questions on the device. It is important that they clearly describe the information or instructions they intend to convey. The input is then checked for formal consistency on the device and converted into the appropriate data format.

[0268] Step 2:

[0269] The terminal sends formatted communication information to the server. This communication information includes user input and is ready for analysis on the server.

[0270] Step 3:

[0271] The server analyzes the received communication information. It then activates a generative AI model, using natural language processing techniques to analyze the intent and purpose of the input in detail. This analysis includes checking for any misunderstandings between users. The input data consists of raw messages from users, and the output provides the analysis results along with potential points of misunderstanding.

[0272] Step 4:

[0273] The server generates adjustment information based on the analysis results. The generating AI model also refers to past databases and related information, combining the most appropriate content to construct a specific message. The input here is the analysis results from step 3, and the output is the adjustment information.

[0274] Step 5:

[0275] The generated adjustment information is sent from the server to the terminal. The server also verifies the integrity of the communication to ensure that accurate information is transmitted reliably.

[0276] Step 6:

[0277] The terminal displays the received adjustment information to the user. The information is presented on the screen in a visually organized manner, and the format and layout are appropriately adjusted so that the user can understand it immediately.

[0278] Step 7:

[0279] Users review the presented adjustment information and take action based on it. Furthermore, they provide feedback on the appropriateness and usefulness of the information. This feedback includes user ratings and opinions.

[0280] Step 8:

[0281] The terminal sends feedback from the user to the server. The server receives this, incorporates it as training data for the generative AI model, and uses it as basic information to improve the accuracy of the model. As a result, the system is continuously improved.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] In the current collaborative work with automated equipment in factories, misunderstandings or inconsistencies in information may occur between the person giving instructions and the equipment. Therefore, a problem is that production efficiency decreases and the accuracy of work is not guaranteed. More specifically, when giving instructions in natural language, a problem is that the equipment misinterprets the meaning of the instructions and the intended work is not properly performed.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for collecting communication information input by the user, means for analyzing the communication information to detect understanding inconsistencies between users, means for generating conversion information for reducing the understanding inconsistencies, and means for the automated equipment to execute specific work instructions based on the generated conversion information. As a result, while minimizing misunderstandings between the person giving instructions and the automated equipment, instructions in natural language are accurately interpreted, enabling efficient and accurate execution of work.

[0287] "Communication information" refers to all various forms of messages including text and voice input by the user.

[0288] "Understanding inconsistency" refers to the situation where the content is not completely shared between the sender and the receiver in information transmission and different interpretations are made.

[0289] "Converted information" refers to a message that has been modified and processed based on the original communication information in order to be understood without misunderstanding.

[0290] "User" refers to any person who directly inputs or receives information through an interface with the system.

[0291] "Automated equipment" refers to machines and robots that perform actions based on instructions with minimal human intervention in factories and workplaces.

[0292] The system implementing this invention is based on the premise that the user operates using a terminal managed by the user. The terminal is equipped with an input device for collecting communication information entered by the user in natural language. The terminal transmits the collected communication information to a server.

[0293] The server uses a generative AI model to analyze this communication information in detail and detect misunderstandings. Natural language processing techniques are used to accurately grasp the context and intent of the communication information. Based on the analysis results, the server generates corrective information to prevent misunderstandings.

[0294] The generated conversion information is sent back to the terminal and provided to the user. Based on this conversion information, automated equipment installed in the factory or work site then executes specific work commands. This allows the user to adjust the operation of the automated equipment and proceed with efficient work.

[0295] For example, if a user gives the instruction "Please send the next product sample for inspection," the server analyzes this message and translates it into a specific instruction such as "Lift the specified product sample with the robot's arm and move it to the inspection station." This allows the automated equipment (in this case, the robot) to perform the operation precisely as instructed.

[0296] An example of a prompt using a generative AI model is: "Analyze the user message and convert it into instructions that the robot can easily understand. Message: Send the following product sample for inspection." The server uses such prompts to accurately analyze the user's intent and generate the most appropriate work instructions.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The user uses a terminal to input communication information in natural language. The user inputs instructions and questions in text format, and the terminal stores this communication information as digital data. The input data is then sent directly to the server.

[0300] Step 2:

[0301] The server receives communication information sent from the terminal. Based on the received data, it analyzes the content using a generative AI model. Here, it understands the context and intent of the communication information and detects discrepancies in understanding between users. In this analysis process, the AI ​​uses prompt sentences to analyze the information in detail. The input is the message from the user, and the output is a digital report of the analysis results.

[0302] Step 3:

[0303] Based on the analysis, the server generates conversion information if there is a misunderstanding. The generating AI model then uses the prompt again to generate a message that accurately reflects the user's intent. In this step, the input is the analysis result, and the output is the conversion information.

[0304] Step 4:

[0305] The generated conversion information is sent from the server to the terminal. The terminal receives this information and presents it in a user-friendly format. Specifically, by displaying the conversion information on the terminal's display, the user can confirm the content without misunderstanding. Here, the input is the conversion information, and the output is the content displayed to the user.

[0306] Step 5:

[0307] The user checks the presented conversion information and sends specific instructions based on that information to the automation device. The automation device performs physical operations according to those instructions. For example, it is reflected in on-site work such as a robotic arm moving a specified object. The input is the instruction based on the user's conversion information, and the output is the operation of the automation device.

[0308] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0309] The present invention is a system that enables more detailed communication by reducing recognition discrepancies and providing responses that take into account the user's emotions in communication between users. This system is realized by the server, the terminal, and the user each playing their respective roles and the cooperation of the emotion engine and the generation AI model.

[0310] The user inputs communication content such as questions to superiors or instructions to subordinates using the terminal. This message is sent to the server, and the server analyzes the emotional elements of the message using the emotion engine. Through this analysis, the emotion and tone held by the user are identified.

[0311] The results of the emotion analysis are reflected in the detection of recognition discrepancies and the generation of corrected messages by the generative AI model. The server adjusts the content and expression of the message according to the user's emotional state. For example, if the user is feeling anxious or stressed, it can choose expressions that provide a greater sense of reassurance.

[0312] For example, if a user enters "I'm worried about the progress of this project" into their terminal, the server's emotion engine detects the user's anxiety. Based on this, the generative AI model generates a reassuring message, such as "The current progress is fine, but we will provide additional support if needed," and sends it to the user. In this way, intentions can be conveyed more effectively, and communication can be more empathetic to the other person's feelings.

[0313] Furthermore, user feedback is collected by the server and used to improve the performance of the emotion engine and generative AI models. By analyzing the feedback information and continuously improving the accuracy of emotion analysis and message generation, the system's effectiveness increases, and the user experience is further enhanced.

[0314] This invention makes it possible to improve the efficiency and quality of communication and increase work productivity.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The user uses the device and launches a messaging application. The user then types specific instructions or questions as text.

[0318] Step 2:

[0319] The terminal receives the message entered by the user and prepares to send that data to the server in text format.

[0320] Step 3:

[0321] The server receives text data sent from the terminal and first uses an emotion engine to analyze the user's emotions from the message. The analyzed emotion data provides information about the tone and emotional aspects of the message.

[0322] Step 4:

[0323] The server passes the sentiment analysis results and communication data to the generating AI model, which performs analysis to detect discrepancies in perception between users. The analysis takes into account the intent and background of the words, as well as sentiment data, to identify the intent behind the message.

[0324] Step 5:

[0325] The server's AI model generates conversion messages based on the analysis results to mitigate misunderstandings. In doing so, it adjusts the messages according to the user's emotions, aiming to provide reassurance and appropriate feedback.

[0326] Step 6:

[0327] The server formats the generated conversion message and prepares it as data for transmission.

[0328] Step 7:

[0329] The terminal displays the converted message received from the server to the user, who then reviews it and uses it as a guide for action or to answer questions.

[0330] Step 8:

[0331] If a user wants to send feedback about a message provided by the system, that feedback is sent from the terminal to the server.

[0332] Step 9:

[0333] The server collects user feedback and uses it to update and improve the emotion engine and generative AI models. This improves the overall accuracy of the system and the user experience.

[0334] (Example 2)

[0335] 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".

[0336] With the widespread adoption of information technology, misunderstandings and discrepancies in communication between users are becoming more common, often leading to misinterpretations and conflicts. Furthermore, while it's necessary to consider the emotions of each individual user, individual responses are time-consuming and inefficient. Therefore, it's essential to analyze emotional states and provide appropriate alternative information to achieve smoother and more efficient communication.

[0337] 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.

[0338] In this invention, the server includes means for collecting information input by the user using an output device, means for analyzing the information and detecting discrepancies in perception between users, and means for generating alternative information whose content is adjusted according to the emotional state based on the discrepancies in perception. This makes it possible to provide appropriate information while taking into account the user's emotions and mitigating discrepancies in perception.

[0339] "Output device" refers to a device used by users to input or receive information.

[0340] "Information" refers to messages and data transmitted by users through output devices.

[0341] "Discrepancy in understanding" refers to misunderstandings or disagreements that occur in communication between users.

[0342] "Alternative information" refers to messages with adjusted content that are generated for the purpose of mitigating misunderstandings.

[0343] "Emotional state" refers to the tone and nuances of emotions that a user conveys in a message.

[0344] "Natural language processing technology" refers to the technology that enables computers to understand and process human language appropriately.

[0345] A "learning model" refers to an algorithm used to learn patterns from data and make judgments or predictions.

[0346] This system aims to detect discrepancies in perception between users and provide appropriate alternative information based on their emotional state. The system is realized through the respective roles of the server, terminal, and users.

[0347] Users input communication information using a terminal. This terminal refers to electronic devices such as general computers and smartphones, equipped with an interface for information input. This information is transmitted to a server via the network.

[0348] The server uses an emotion engine and a generative AI model to analyze information received from the user. The emotion engine uses natural language processing techniques to analyze the emotional state of the message. In doing so, it extracts the user's emotional tone and nuances to understand the emotions behind the message.

[0349] Based on the analysis results, the server applies a generative AI model. This model is an advanced algorithm that generates alternative information to mitigate recognition discrepancies. The generative AI model utilizes natural language generation technology to generate flexible messages that correspond to the analyzed emotional state.

[0350] As a concrete example, consider a scenario where a user enters "I'm worried about whether the project will meet the deadline" into their terminal. The server detects the user's anxiety through its emotion engine. Then, using a generative AI model, it generates alternative information such as "The project is on schedule, but please let us know if you have any concerns," and sends it back to the user.

[0351] This system's feedback function allows users to provide satisfaction levels with the messages they receive, which contributes to the system's continuous improvement. The server uses this feedback data to improve the accuracy of its emotion engine and generative AI models.

[0352] An example of a prompt might be an instruction to the generative AI model such as, "Based on the sentiment analysis results from the sentiment engine, generate the most appropriate response message for the user." This prompt clarifies the guidelines for the generative AI model to specifically generate the desired response.

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The user enters a message using the terminal. This input consists of the user typing the message they want to convey as text on the terminal. For example, if the user types "I'm worried about this weekend's project deadline," that message will be output as text data on the terminal.

[0356] Step 2:

[0357] The terminal processes the input message and sends it to the server. Here, a communication protocol is used to send the input data to the server as a data packet. As a result, the server receives the text data.

[0358] Step 3:

[0359] The server inputs the received message into the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional elements of the text and outputs emotion tags such as anxiety, joy, and anger. For example, because the message contains "I'm worried," it outputs the emotion tag "anxiety."

[0360] Step 4:

[0361] The server inputs the results of the sentiment analysis into a generative AI model. This model processes the data to generate an appropriate alternative message based on the prompt text. Specifically, the generative AI model uses the input sentiment tags to generate text such as "The project is progressing smoothly. We will address any issues immediately."

[0362] Step 5:

[0363] The server processes the generated alternative message and sends it to the user's terminal. Here, the generated message is sent as output data via the communication protocol. As a result of this step, the user receives a reassuring message on their terminal.

[0364] Step 6:

[0365] Users review the messages they receive and provide feedback as needed. This feedback is entered into the device as an evaluation of the message content and suggestions for improvement, and then sent to the server. This data is then used to improve the accuracy of subsequent analyses and generation.

[0366] (Application Example 2)

[0367] 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 will be referred to as the "terminal."

[0368] In situations where misunderstandings are likely to occur during communication, or when users are experiencing anxiety or stress, effective communication is difficult. Furthermore, in situations like electronic payments, it is necessary to alleviate user anxiety and provide an environment where users can use the service with peace of mind.

[0369] 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.

[0370] In this invention, the server includes means for analyzing the user's emotional state in real time, means for generating messages that provide reassurance as needed based on the emotional state, and means for generating conversion messages to reduce misunderstandings between users. This makes it possible to provide effective communication that is attentive to the user's emotions and an electronic payment environment that can be used with peace of mind.

[0371] "Communication data" refers to digital data that includes the content of information exchange and conversations between users.

[0372] "Discrepancy in understanding" refers to a difference in interpretation of intent or content that occurs in communication between users.

[0373] A "conversion message" is a message with appropriate content that is generated to reduce misunderstandings and facilitate smooth communication.

[0374] "Emotional state" refers to the user's psychological state, specifically emotional states such as anxiety and stress.

[0375] A "reassuring message" is a message generated to provide reassurance to users when they feel worried or anxious, enabling them to use the service with peace of mind.

[0376] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and manipulate human language.

[0377] "Emotional analysis technology" is a technology that identifies a user's emotional state from text data and analyzes its content.

[0378] A "generative AI model" is an artificial intelligence model that can generate content for a specific task.

[0379] "Feedback" refers to information about users' experiences and opinions.

[0380] "Accuracy" refers to the degree to which a system is able to deliver exactly the intended results or outputs.

[0381] The system for implementing this invention consists of a server, a terminal, and a user. The server collects communication data entered by the user via the terminal and analyzes the emotional state in real time based on that data. Specifically, it uses emotion analysis technology to determine the user's emotional state. Based on this analysis, the generative AI model generates conversion messages to reduce misunderstandings with the user and messages to provide a sense of security.

[0382] The server utilizes natural language processing technology to deliver messages to users that effectively alleviate their anxiety and stress. This process employs programming languages ​​like Python, sentiment analysis tools, and generative AI tools. As a result, users can use electronic payments and other communication services with peace of mind.

[0383] As a concrete example, consider a scenario where a user uses an electronic payment system for the first time and enters a message expressing concern: "Is this service safe?" The server analyzes this as an emotional state of "anxiety" and uses a generative AI model to generate a translated message such as, "Please rest assured. This service is trusted by many users," and provides it to the user.

[0384] An example of a prompt is, "Analyze this user's sentiment and generate an appropriate response message." Based on this prompt, the system performs the necessary analysis and message generation.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user inputs communication data into the server via their terminal. This communication data includes the user's anxieties, questions, or requests for information. The input data is sent to the server in digital format and analyzed in the next processing step.

[0388] Step 2:

[0389] The server uses emotion analysis technology to analyze the emotional state of the incoming communication data. Using emotion analysis tools, it identifies emotions such as "anxiety" and "stress" from the data. The output of this step is the identified emotional state, which is used for message generation in the next step.

[0390] Step 3:

[0391] The server uses a generative AI model to generate a translated message based on the emotional state obtained in step 2. Here, a "prompt" is used to instruct the AI ​​model to perform a specified generation task. For example, if an emotion indicating anxiety is identified, the prompt "Generate a message that provides the best possible sense of reassurance for this situation" is used. The output of this step is the generated translated message.

[0392] Step 4:

[0393] The server sends the generated translation message to the user's terminal. This action allows the user to receive a message that appropriately responds to their communication data. This reduces anxiety and allows the user to use the service with peace of mind. This step includes the message provided to the user as output.

[0394] Step 5:

[0395] The user sends feedback to the server regarding the message received as a response. The server analyzes this feedback and uses it to improve the accuracy of the generative AI model and sentiment analysis technology. The input for this step is the user's feedback, and the output is data used to improve the model.

[0396] 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.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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 those described above. 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 shown 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.

[0398] 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.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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".

[0412] This invention is a system for improving the efficiency of communication between superiors and subordinates, or between different departments, within an organization. The system consists of a server, terminals, and users, and is designed to allow users to understand instructions more clearly and obtain answers to their questions.

[0413] Users input messages for communication via their devices. These messages are sent to a server. The server uses generative AI models to analyze the messages in detail, identifying any parts that may be misunderstood or misinterpreted. After analysis, the server generates a transformed message, modifying it so that users can understand it without misunderstanding. This prevents misunderstandings due to differences in experience and knowledge, enabling smooth communication.

[0414] For example, if a user asks via their terminal, "What documents are needed for the next meeting?", the server analyzes past meeting materials and related project information to generate a converted message containing specific instructions, including the appropriate document names and items to prepare. This converted message is displayed to the user on their terminal, allowing them to clearly understand what they need to prepare.

[0415] Furthermore, users can provide feedback on the messages they receive, and this feedback information is sent to the server. Based on this feedback information, the server updates the generated AI model as needed, improving the accuracy of the analysis and enabling it to provide more effective communication support. In this way, the system continuously learns and improves, growing through interaction with users.

[0416] In this way, the present invention provides a practical means for reducing communication gaps within an organization and improving work efficiency.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The user accesses their device and launches an email or chat application. The user then types a question for their supervisor or instructions for a subordinate.

[0420] Step 2:

[0421] The terminal converts the message entered by the user into text format and establishes a network connection to send it to the server.

[0422] Step 3:

[0423] The server receives messages sent from the terminal and passes that text data to the AI ​​model that generates the data.

[0424] Step 4:

[0425] The generative AI model uses natural language processing techniques to analyze the intent and context of the message. This allows it to assess the likelihood of misinterpretations.

[0426] Step 5:

[0427] Based on the analysis results, the server generates a conversion message to mitigate misunderstandings between users. Supplementary information and specific instructions are added as needed.

[0428] Step 6:

[0429] The generated conversion message is formatted and prepared as data to be sent to the terminal.

[0430] Step 7:

[0431] The terminal displays the converted message received from the server to the user, who then uses that message to perform instructions or obtain answers to questions.

[0432] Step 8:

[0433] Users can provide feedback on messages provided by the system. This feedback information is sent from the terminal to the server.

[0434] Step 9:

[0435] The server uses feedback information to update the generated AI model, improving the accuracy of the analysis. This allows for continuous improvement of the system's effectiveness.

[0436] (Example 1)

[0437] 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."

[0438] In organizational communication, information sharing and instruction transmission among users can become unclear, leading to discrepancies and misunderstandings. This can reduce work efficiency and negatively impact project progress. To solve this problem, there is a need to achieve clearer and more accurate information transmission.

[0439] 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.

[0440] In this invention, the server includes means for collecting communication information entered by the user, means for analyzing the communication information and detecting information discrepancies between users, and means for generating adjustment information to mitigate information discrepancies using a generation AI. As a result, users can receive clear information without misunderstanding, improving the efficiency and effectiveness of communication.

[0441] "Communication information" refers to data that a user sends or receives via a digital system, and which takes the form of messages, instructions, questions, etc.

[0442] "Information discrepancy" refers to a situation where the same information is interpreted differently by users, leading to misunderstandings and inconsistent actions.

[0443] "Generative AI" refers to a technology that uses artificial intelligence to automatically generate new information and solutions based on data.

[0444] "Adjustment information" refers to adjusted instructions or messages generated to mitigate or resolve information discrepancies.

[0445] "User feedback" refers to opinions, evaluations, or responses provided by users, and the data used to improve and adjust the system.

[0446] "Natural language processing" refers to the technology that enables computers to understand and analyze human language, and is used for analyzing intent and generating information.

[0447] A "user interface" refers to the means by which a user interacts with a system, and includes elements that present information to the user through screen displays and input devices.

[0448] This invention is a communication support system consisting of a user, a terminal, and a server, aimed at clarifying information sharing and instruction transmission within an organization. The user first inputs messages or questions through the terminal's interface. This terminal includes a general-purpose input device and display.

[0449] The entered message is sent to the server via the terminal. The server receives it and analyzes it as communication information. In doing so, the server uses a generative AI model. Specific generative AI models include OpenAI's GPT series. The server utilizes the AI ​​model and natural language processing techniques to analyze the intent of the message in detail.

[0450] Based on the analysis, the server detects information discrepancies between users and generates adjustment information to mitigate them. This adjustment information is created by referencing relevant past databases and information repositories as needed, so that users can understand the content without misunderstanding. The generated adjustment information is sent back to the terminal and presented to the user visually.

[0451] For example, if a user asks, "What documents are needed for the next meeting?", the server analyzes past meeting history and project documents and generates specific coordination information such as, "The latest documents for Project X are needed for the meeting." This coordination information is clearly displayed to the user through their terminal, allowing them to clearly understand the necessary preparations.

[0452] Furthermore, users can provide feedback on the presented adjustment information. This feedback is sent to the server via the terminal, and the server uses it to adjust the performance of the generated AI model, continuously improving the system's analysis accuracy.

[0453] A concrete example of a prompt message would be something like, "Please tell me what materials are needed to prepare for the meeting." This allows the system to function smoothly and streamlines communication within the organization.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] Users input messages and questions on the device. It is important that they clearly describe the information or instructions they intend to convey. The input is then checked for formal consistency on the device and converted into the appropriate data format.

[0457] Step 2:

[0458] The terminal sends formatted communication information to the server. This communication information includes user input and is ready for analysis on the server.

[0459] Step 3:

[0460] The server analyzes the received communication information. It then activates a generative AI model, using natural language processing techniques to analyze the intent and purpose of the input in detail. This analysis includes checking for any misunderstandings between users. The input data consists of raw messages from users, and the output provides the analysis results along with potential points of misunderstanding.

[0461] Step 4:

[0462] The server generates adjustment information based on the analysis results. The generating AI model also refers to past databases and related information, combining the most appropriate content to construct a specific message. The input here is the analysis results from step 3, and the output is the adjustment information.

[0463] Step 5:

[0464] The generated adjustment information is sent from the server to the terminal. The server also verifies the integrity of the communication to ensure that accurate information is transmitted reliably.

[0465] Step 6:

[0466] The terminal displays the received adjustment information to the user. The information is presented on the screen in a visually organized manner, and the format and layout are appropriately adjusted so that the user can understand it immediately.

[0467] Step 7:

[0468] Users review the presented adjustment information and take action based on it. Furthermore, they provide feedback on the appropriateness and usefulness of the information. This feedback includes user ratings and opinions.

[0469] Step 8:

[0470] The device sends user feedback to the server. The server receives this feedback and incorporates it as training data for the generated AI model, using it as foundational information to improve the model's accuracy. This allows the system to continuously improve.

[0471] (Application Example 1)

[0472] 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."

[0473] In current factory operations involving automated machinery, misunderstandings and inconsistencies in information can occur between the human giving instructions and the machine. This leads to decreased production efficiency and a lack of guaranteed accuracy. More specifically, when instructions are given in natural language, the machine may misinterpret the meaning of the instructions, resulting in the intended task not being performed properly.

[0474] 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.

[0475] In this invention, the server includes means for collecting communication information entered by a user, means for analyzing the communication information and detecting misunderstandings between users, means for generating conversion information to mitigate the misunderstandings, and means for the automated machine to execute specific work commands based on the generated conversion information. This minimizes misunderstandings between the person giving the instructions and the automated machine, while ensuring that instructions in natural language are accurately interpreted and enabling efficient and precise work execution.

[0476] "Communication information" refers to all types of messages, including text and audio, that users input.

[0477] "Misunderstanding" refers to a situation in information transmission where the content is not fully shared between the sender and receiver, leading to different interpretations.

[0478] "Converted information" refers to a message that has been modified and processed based on the original communication information in order to be understood without misunderstanding.

[0479] "User" refers to any person who directly inputs or receives information through an interface with the system.

[0480] "Automated equipment" refers to machines and robots that perform actions based on instructions with minimal human intervention in factories and workplaces.

[0481] The system implementing this invention is based on the premise that the user operates using a terminal managed by the user. The terminal is equipped with an input device for collecting communication information entered by the user in natural language. The terminal transmits the collected communication information to a server.

[0482] The server uses a generative AI model to analyze this communication information in detail and detect misunderstandings. Natural language processing techniques are used to accurately grasp the context and intent of the communication information. Based on the analysis results, the server generates corrective information to prevent misunderstandings.

[0483] The generated conversion information is sent back to the terminal and provided to the user. Based on this conversion information, automated equipment installed in the factory or work site then executes specific work commands. This allows the user to adjust the operation of the automated equipment and proceed with efficient work.

[0484] For example, if a user gives the instruction "Please send the next product sample for inspection," the server analyzes this message and translates it into a specific instruction such as "Lift the specified product sample with the robot's arm and move it to the inspection station." This allows the automated equipment (in this case, the robot) to perform the operation precisely as instructed.

[0485] An example of a prompt using a generative AI model is: "Analyze the user message and convert it into instructions that the robot can easily understand. Message: Send the following product sample for inspection." The server uses such prompts to accurately analyze the user's intent and generate the most appropriate work instructions.

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The user uses a terminal to input communication information in natural language. The user inputs instructions and questions in text format, and the terminal stores this communication information as digital data. The input data is then sent directly to the server.

[0489] Step 2:

[0490] The server receives communication information sent from the terminal. Based on the received data, it analyzes the content using a generative AI model. Here, it understands the context and intent of the communication information and detects discrepancies in understanding between users. In this analysis process, the AI ​​uses prompt sentences to analyze the information in detail. The input is the message from the user, and the output is a digital report of the analysis results.

[0491] Step 3:

[0492] Based on the analysis, the server generates conversion information if there is a misunderstanding. The generating AI model then uses the prompt again to generate a message that accurately reflects the user's intent. In this step, the input is the analysis result, and the output is the conversion information.

[0493] Step 4:

[0494] The generated conversion information is sent from the server to the terminal. The terminal receives this information and presents it to the user in an easy-to-understand format. Specifically, the conversion information is displayed on the terminal's screen so that the user can confirm the content without misunderstanding. Here, the input is the conversion information, and the output is the content displayed to the user.

[0495] Step 5:

[0496] The user reviews the provided conversion information and sends specific instructions to the automated equipment based on that information. The automated equipment then performs physical actions according to those instructions. For example, a robotic arm might move a specified object, which is then reflected in on-site work. The input is the instructions based on the user's conversion information, and the output is the operation of the automated equipment.

[0497] 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.

[0498] This invention provides a system that enables more nuanced communication by reducing misunderstandings in user-to-user communication and providing responses that take into account the user's emotions. This system is realized through the collaboration of an emotion engine and a generative AI model, with the server, terminal, and user each fulfilling their respective roles.

[0499] Users input communication content, such as questions for their superiors or instructions for their subordinates, using their devices. These messages are sent to a server, which uses an emotion engine to analyze the emotional elements of the messages. This analysis identifies the emotions and tone the user is experiencing.

[0500] The results of the emotion analysis are reflected in the detection of recognition discrepancies and the generation of corrected messages by the generative AI model. The server adjusts the content and expression of the message according to the user's emotional state. For example, if the user is feeling anxious or stressed, it can choose expressions that provide a greater sense of reassurance.

[0501] For example, if a user enters "I'm worried about the progress of this project" into their terminal, the server's emotion engine detects the user's anxiety. Based on this, the generative AI model generates a reassuring message, such as "The current progress is fine, but we will provide additional support if needed," and sends it to the user. In this way, intentions can be conveyed more effectively, and communication can be more empathetic to the other person's feelings.

[0502] Furthermore, user feedback is collected by the server and used to improve the performance of the emotion engine and generative AI models. By analyzing the feedback information and continuously improving the accuracy of emotion analysis and message generation, the system's effectiveness increases, and the user experience is further enhanced.

[0503] This invention makes it possible to improve the efficiency and quality of communication and increase work productivity.

[0504] The following describes the processing flow.

[0505] Step 1:

[0506] The user uses the device and launches a messaging application. The user then types specific instructions or questions as text.

[0507] Step 2:

[0508] The terminal receives the message entered by the user and prepares to send that data to the server in text format.

[0509] Step 3:

[0510] The server receives text data sent from the terminal and first uses an emotion engine to analyze the user's emotions from the message. The analyzed emotion data provides information about the tone and emotional aspects of the message.

[0511] Step 4:

[0512] The server passes the sentiment analysis results and communication data to the generating AI model, which performs analysis to detect discrepancies in perception between users. The analysis takes into account the intent and background of the words, as well as sentiment data, to identify the intent behind the message.

[0513] Step 5:

[0514] The server's AI model generates conversion messages based on the analysis results to mitigate misunderstandings. In doing so, it adjusts the messages according to the user's emotions, aiming to provide reassurance and appropriate feedback.

[0515] Step 6:

[0516] The server formats the generated conversion message and prepares it as data for transmission.

[0517] Step 7:

[0518] The terminal displays the converted message received from the server to the user, who then reviews it and uses it as a guide for action or to answer questions.

[0519] Step 8:

[0520] If a user wants to send feedback about a message provided by the system, that feedback is sent from the terminal to the server.

[0521] Step 9:

[0522] The server collects user feedback and uses it to update and improve the emotion engine and generative AI models. This improves the overall accuracy of the system and the user experience.

[0523] (Example 2)

[0524] 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."

[0525] With the widespread adoption of information technology, misunderstandings and discrepancies in communication between users are becoming more common, often leading to misinterpretations and conflicts. Furthermore, while it's necessary to consider the emotions of each individual user, individual responses are time-consuming and inefficient. Therefore, it's essential to analyze emotional states and provide appropriate alternative information to achieve smoother and more efficient communication.

[0526] 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.

[0527] In this invention, the server includes means for collecting information input by the user using an output device, means for analyzing the information and detecting discrepancies in perception between users, and means for generating alternative information whose content is adjusted according to the emotional state based on the discrepancies in perception. This makes it possible to provide appropriate information while taking into account the user's emotions and mitigating discrepancies in perception.

[0528] "Output device" refers to a device used by users to input or receive information.

[0529] "Information" refers to messages and data transmitted by users through output devices.

[0530] "Discrepancy in understanding" refers to misunderstandings or disagreements that occur in communication between users.

[0531] "Alternative information" refers to messages with adjusted content that are generated for the purpose of mitigating misunderstandings.

[0532] "Emotional state" refers to the tone and nuances of emotions that a user conveys in a message.

[0533] "Natural language processing technology" refers to the technology that enables computers to understand and process human language appropriately.

[0534] A "learning model" refers to an algorithm used to learn patterns from data and make judgments or predictions.

[0535] This system aims to detect discrepancies in perception between users and provide appropriate alternative information based on their emotional state. The system is realized through the respective roles of the server, terminal, and users.

[0536] Users input communication information using a terminal. This terminal refers to electronic devices such as general computers and smartphones, equipped with an interface for information input. This information is transmitted to a server via the network.

[0537] The server uses an emotion engine and a generative AI model to analyze information received from the user. The emotion engine uses natural language processing techniques to analyze the emotional state of the message. In doing so, it extracts the user's emotional tone and nuances to understand the emotions behind the message.

[0538] Based on the analysis results, the server applies a generative AI model. This model is an advanced algorithm that generates alternative information to mitigate recognition discrepancies. The generative AI model utilizes natural language generation technology to generate flexible messages that correspond to the analyzed emotional state.

[0539] As a concrete example, consider a scenario where a user enters "I'm worried about whether the project will meet the deadline" into their terminal. The server detects the user's anxiety through its emotion engine. Then, using a generative AI model, it generates alternative information such as "The project is on schedule, but please let us know if you have any concerns," and sends it back to the user.

[0540] This system's feedback function allows users to provide satisfaction levels with the messages they receive, which contributes to the system's continuous improvement. The server uses this feedback data to improve the accuracy of its emotion engine and generative AI models.

[0541] An example of a prompt might be an instruction to the generative AI model such as, "Based on the sentiment analysis results from the sentiment engine, generate the most appropriate response message for the user." This prompt clarifies the guidelines for the generative AI model to specifically generate the desired response.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The user enters a message using the terminal. This input consists of the user typing the message they want to convey as text on the terminal. For example, if the user types "I'm worried about this weekend's project deadline," that message will be output as text data on the terminal.

[0545] Step 2:

[0546] The terminal processes the input message and sends it to the server. Here, a communication protocol is used to send the input data to the server as a data packet. As a result, the server receives the text data.

[0547] Step 3:

[0548] The server inputs the received message into the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional elements of the text and outputs emotion tags such as anxiety, joy, and anger. For example, because the message contains "I'm worried," it outputs the emotion tag "anxiety."

[0549] Step 4:

[0550] The server inputs the results of the sentiment analysis into a generative AI model. This model processes the data to generate an appropriate alternative message based on the prompt text. Specifically, the generative AI model uses the input sentiment tags to generate text such as "The project is progressing smoothly. We will address any issues immediately."

[0551] Step 5:

[0552] The server processes the generated alternative message and sends it to the user's terminal. Here, the generated message is sent as output data via the communication protocol. As a result of this step, the user receives a reassuring message on their terminal.

[0553] Step 6:

[0554] Users review the messages they receive and provide feedback as needed. This feedback is entered into the device as an evaluation of the message content and suggestions for improvement, and then sent to the server. This data is then used to improve the accuracy of subsequent analyses and generation.

[0555] (Application Example 2)

[0556] 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."

[0557] In situations where misunderstandings are likely to occur during communication, or when users are experiencing anxiety or stress, effective communication is difficult. Furthermore, in situations like electronic payments, it is necessary to alleviate user anxiety and provide an environment where users can use the service with peace of mind.

[0558] 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.

[0559] In this invention, the server includes means for analyzing the user's emotional state in real time, means for generating messages that provide reassurance as needed based on the emotional state, and means for generating conversion messages to reduce misunderstandings between users. This makes it possible to provide effective communication that is attentive to the user's emotions and an electronic payment environment that can be used with peace of mind.

[0560] "Communication data" refers to digital data that includes the content of information exchange and conversations between users.

[0561] "Discrepancy in understanding" refers to a difference in interpretation of intent or content that occurs in communication between users.

[0562] A "conversion message" is a message with appropriate content that is generated to reduce misunderstandings and facilitate smooth communication.

[0563] "Emotional state" refers to the user's psychological state, specifically emotional states such as anxiety and stress.

[0564] A "reassuring message" is a message generated to provide reassurance to users when they feel worried or anxious, enabling them to use the service with peace of mind.

[0565] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and manipulate human language.

[0566] "Emotional analysis technology" is a technology that identifies a user's emotional state from text data and analyzes its content.

[0567] A "generative AI model" is an artificial intelligence model that can generate content for a specific task.

[0568] "Feedback" refers to information about users' experiences and opinions.

[0569] "Accuracy" refers to the degree to which a system is able to deliver exactly the intended results or outputs.

[0570] The system for implementing this invention consists of a server, a terminal, and a user. The server collects communication data entered by the user via the terminal and analyzes the emotional state in real time based on that data. Specifically, it uses emotion analysis technology to determine the user's emotional state. Based on this analysis, the generative AI model generates conversion messages to reduce misunderstandings with the user and messages to provide a sense of security.

[0571] The server utilizes natural language processing technology to deliver messages to users that effectively alleviate their anxiety and stress. This process employs programming languages ​​like Python, sentiment analysis tools, and generative AI tools. As a result, users can use electronic payments and other communication services with peace of mind.

[0572] As a concrete example, consider a scenario where a user uses an electronic payment system for the first time and enters a message expressing concern: "Is this service safe?" The server analyzes this as an emotional state of "anxiety" and uses a generative AI model to generate a translated message such as, "Please rest assured. This service is trusted by many users," and provides it to the user.

[0573] An example of a prompt is, "Analyze this user's sentiment and generate an appropriate response message." Based on this prompt, the system performs the necessary analysis and message generation.

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The user inputs communication data into the server via their terminal. This communication data includes the user's anxieties, questions, or requests for information. The input data is sent to the server in digital format and analyzed in the next processing step.

[0577] Step 2:

[0578] The server uses emotion analysis technology to analyze the emotional state of the incoming communication data. Using emotion analysis tools, it identifies emotions such as "anxiety" and "stress" from the data. The output of this step is the identified emotional state, which is used for message generation in the next step.

[0579] Step 3:

[0580] The server uses a generative AI model to generate a translated message based on the emotional state obtained in step 2. Here, a "prompt" is used to instruct the AI ​​model to perform a specified generation task. For example, if an emotion indicating anxiety is identified, the prompt "Generate a message that provides the best possible sense of reassurance for this situation" is used. The output of this step is the generated translated message.

[0581] Step 4:

[0582] The server sends the generated translation message to the user's terminal. This action allows the user to receive a message that appropriately responds to their communication data. This reduces anxiety and allows the user to use the service with peace of mind. This step includes the message provided to the user as output.

[0583] Step 5:

[0584] The user sends feedback to the server regarding the message received as a response. The server analyzes this feedback and uses it to improve the accuracy of the generative AI model and sentiment analysis technology. The input for this step is the user's feedback, and the output is data used to improve the model.

[0585] 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.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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 those described above. 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 shown 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.

[0587] 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.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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".

[0602] This invention is a system for improving the efficiency of communication between superiors and subordinates, or between different departments, within an organization. The system consists of a server, terminals, and users, and is designed to allow users to understand instructions more clearly and obtain answers to their questions.

[0603] Users input messages for communication via their devices. These messages are sent to a server. The server uses generative AI models to analyze the messages in detail, identifying any parts that may be misunderstood or misinterpreted. After analysis, the server generates a transformed message, modifying it so that users can understand it without misunderstanding. This prevents misunderstandings due to differences in experience and knowledge, enabling smooth communication.

[0604] For example, if a user asks via their terminal, "What documents are needed for the next meeting?", the server analyzes past meeting materials and related project information to generate a converted message containing specific instructions, including the appropriate document names and items to prepare. This converted message is displayed to the user on their terminal, allowing them to clearly understand what they need to prepare.

[0605] Furthermore, users can provide feedback on the messages they receive, and this feedback information is sent to the server. Based on this feedback information, the server updates the generated AI model as needed, improving the accuracy of the analysis and enabling it to provide more effective communication support. In this way, the system continuously learns and improves, growing through interaction with users.

[0606] In this way, the present invention provides a practical means for reducing communication gaps within an organization and improving work efficiency.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] The user accesses their device and launches an email or chat application. The user then types a question for their supervisor or instructions for a subordinate.

[0610] Step 2:

[0611] The terminal converts the message entered by the user into text format and establishes a network connection to send it to the server.

[0612] Step 3:

[0613] The server receives messages sent from the terminal and passes that text data to the AI ​​model that generates the data.

[0614] Step 4:

[0615] The generative AI model uses natural language processing techniques to analyze the intent and context of the message. This allows it to assess the likelihood of misinterpretations.

[0616] Step 5:

[0617] Based on the analysis results, the server generates a conversion message to mitigate misunderstandings between users. Supplementary information and specific instructions are added as needed.

[0618] Step 6:

[0619] The generated conversion message is formatted and prepared as data to be sent to the terminal.

[0620] Step 7:

[0621] The terminal displays the converted message received from the server to the user, who then uses that message to perform instructions or obtain answers to questions.

[0622] Step 8:

[0623] Users can provide feedback on messages provided by the system. This feedback information is sent from the terminal to the server.

[0624] Step 9:

[0625] The server uses feedback information to update the generated AI model, improving the accuracy of the analysis. This allows for continuous improvement of the system's effectiveness.

[0626] (Example 1)

[0627] 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".

[0628] In organizational communication, information sharing and instruction transmission among users can become unclear, leading to discrepancies and misunderstandings. This can reduce work efficiency and negatively impact project progress. To solve this problem, there is a need to achieve clearer and more accurate information transmission.

[0629] 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.

[0630] In this invention, the server includes means for collecting communication information entered by the user, means for analyzing the communication information and detecting information discrepancies between users, and means for generating adjustment information to mitigate information discrepancies using a generation AI. As a result, users can receive clear information without misunderstanding, improving the efficiency and effectiveness of communication.

[0631] "Communication information" refers to data that a user sends or receives via a digital system, and which takes the form of messages, instructions, questions, etc.

[0632] "Information discrepancy" refers to a situation where the same information is interpreted differently by users, leading to misunderstandings and inconsistent actions.

[0633] "Generative AI" refers to a technology that uses artificial intelligence to automatically generate new information and solutions based on data.

[0634] "Adjustment information" refers to adjusted instructions or messages generated to mitigate or resolve information discrepancies.

[0635] "User feedback" refers to opinions, evaluations, or responses provided by users, and the data used to improve and adjust the system.

[0636] "Natural language processing" refers to the technology that enables computers to understand and analyze human language, and is used for analyzing intent and generating information.

[0637] A "user interface" refers to the means by which a user interacts with a system, and includes elements that present information to the user through screen displays and input devices.

[0638] This invention is a communication support system consisting of a user, a terminal, and a server, aimed at clarifying information sharing and instruction transmission within an organization. The user first inputs messages or questions through the terminal's interface. This terminal includes a general-purpose input device and display.

[0639] The entered message is sent to the server via the terminal. The server receives it and analyzes it as communication information. In doing so, the server uses a generative AI model. Specific generative AI models include OpenAI's GPT series. The server utilizes the AI ​​model and natural language processing techniques to analyze the intent of the message in detail.

[0640] Based on the analysis, the server detects information discrepancies between users and generates adjustment information to mitigate them. This adjustment information is created by referencing relevant past databases and information repositories as needed, so that users can understand the content without misunderstanding. The generated adjustment information is sent back to the terminal and presented to the user visually.

[0641] For example, if a user asks, "What documents are needed for the next meeting?", the server analyzes past meeting history and project documents and generates specific coordination information such as, "The latest documents for Project X are needed for the meeting." This coordination information is clearly displayed to the user through their terminal, allowing them to clearly understand the necessary preparations.

[0642] Furthermore, users can provide feedback on the presented adjustment information. This feedback is sent to the server via the terminal, and the server uses it to adjust the performance of the generated AI model, continuously improving the system's analysis accuracy.

[0643] A concrete example of a prompt message would be something like, "Please tell me what materials are needed to prepare for the meeting." This allows the system to function smoothly and streamlines communication within the organization.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] Users input messages and questions on the device. It is important that they clearly describe the information or instructions they intend to convey. The input is then checked for formal consistency on the device and converted into the appropriate data format.

[0647] Step 2:

[0648] The terminal sends formatted communication information to the server. This communication information includes user input and is ready for analysis on the server.

[0649] Step 3:

[0650] The server analyzes the received communication information. It then activates a generative AI model, using natural language processing techniques to analyze the intent and purpose of the input in detail. This analysis includes checking for any misunderstandings between users. The input data consists of raw messages from users, and the output provides the analysis results along with potential points of misunderstanding.

[0651] Step 4:

[0652] The server generates adjustment information based on the analysis results. The generating AI model also refers to past databases and related information, combining the most appropriate content to construct a specific message. The input here is the analysis results from step 3, and the output is the adjustment information.

[0653] Step 5:

[0654] The generated adjustment information is sent from the server to the terminal. The server also verifies the integrity of the communication to ensure that accurate information is transmitted reliably.

[0655] Step 6:

[0656] The terminal displays the received adjustment information to the user. The information is presented on the screen in a visually organized manner, and the format and layout are appropriately adjusted so that the user can understand it immediately.

[0657] Step 7:

[0658] Users review the presented adjustment information and take action based on it. Furthermore, they provide feedback on the appropriateness and usefulness of the information. This feedback includes user ratings and opinions.

[0659] Step 8:

[0660] The device sends user feedback to the server. The server receives this feedback and incorporates it as training data for the generated AI model, using it as foundational information to improve the model's accuracy. This allows the system to continuously improve.

[0661] (Application Example 1)

[0662] 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".

[0663] In current factory operations involving automated machinery, misunderstandings and inconsistencies in information can occur between the human giving instructions and the machine. This leads to decreased production efficiency and a lack of guaranteed accuracy. More specifically, when instructions are given in natural language, the machine may misinterpret the meaning of the instructions, resulting in the intended task not being performed properly.

[0664] 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.

[0665] In this invention, the server includes means for collecting communication information entered by a user, means for analyzing the communication information and detecting misunderstandings between users, means for generating conversion information to mitigate the misunderstandings, and means for the automated machine to execute specific work commands based on the generated conversion information. This minimizes misunderstandings between the person giving the instructions and the automated machine, while ensuring that instructions in natural language are accurately interpreted and enabling efficient and precise work execution.

[0666] "Communication information" refers to all types of messages, including text and audio, that users input.

[0667] "Misunderstanding" refers to a situation in information transmission where the content is not fully shared between the sender and receiver, leading to different interpretations.

[0668] "Converted information" refers to a message that has been modified and processed based on the original communication information in order to be understood without misunderstanding.

[0669] "User" refers to any person who directly inputs or receives information through an interface with the system.

[0670] "Automated equipment" refers to machines and robots that perform actions based on instructions with minimal human intervention in factories and workplaces.

[0671] The system implementing this invention is based on the premise that the user operates using a terminal managed by the user. The terminal is equipped with an input device for collecting communication information entered by the user in natural language. The terminal transmits the collected communication information to a server.

[0672] The server uses a generative AI model to analyze this communication information in detail and detect misunderstandings. Natural language processing techniques are used to accurately grasp the context and intent of the communication information. Based on the analysis results, the server generates corrective information to prevent misunderstandings.

[0673] The generated conversion information is sent back to the terminal and provided to the user. Based on this conversion information, automated equipment installed in the factory or work site then executes specific work commands. This allows the user to adjust the operation of the automated equipment and proceed with efficient work.

[0674] For example, if a user gives the instruction "Please send the next product sample for inspection," the server analyzes this message and translates it into a specific instruction such as "Lift the specified product sample with the robot's arm and move it to the inspection station." This allows the automated equipment (in this case, the robot) to perform the operation precisely as instructed.

[0675] An example of a prompt using a generative AI model is: "Analyze the user message and convert it into instructions that the robot can easily understand. Message: Send the following product sample for inspection." The server uses such prompts to accurately analyze the user's intent and generate the most appropriate work instructions.

[0676] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0677] Step 1:

[0678] The user uses a terminal to input communication information in natural language. The user inputs instructions and questions in text format, and the terminal stores this communication information as digital data. The input data is then sent directly to the server.

[0679] Step 2:

[0680] The server receives communication information sent from the terminal. Based on the received data, it analyzes the content using a generative AI model. Here, it understands the context and intent of the communication information and detects discrepancies in understanding between users. In this analysis process, the AI ​​uses prompt sentences to analyze the information in detail. The input is the message from the user, and the output is a digital report of the analysis results.

[0681] Step 3:

[0682] Based on the analysis, the server generates conversion information if there is a misunderstanding. The generating AI model then uses the prompt again to generate a message that accurately reflects the user's intent. In this step, the input is the analysis result, and the output is the conversion information.

[0683] Step 4:

[0684] The generated conversion information is sent from the server to the terminal. The terminal receives this information and presents it to the user in an easy-to-understand format. Specifically, the conversion information is displayed on the terminal's screen so that the user can confirm the content without misunderstanding. Here, the input is the conversion information, and the output is the content displayed to the user.

[0685] Step 5:

[0686] The user reviews the provided conversion information and sends specific instructions to the automated equipment based on that information. The automated equipment then performs physical actions according to those instructions. For example, a robotic arm might move a specified object, which is then reflected in on-site work. The input is the instructions based on the user's conversion information, and the output is the operation of the automated equipment.

[0687] 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.

[0688] This invention provides a system that enables more nuanced communication by reducing misunderstandings in user-to-user communication and providing responses that take into account the user's emotions. This system is realized through the collaboration of an emotion engine and a generative AI model, with the server, terminal, and user each fulfilling their respective roles.

[0689] Users input communication content, such as questions for their superiors or instructions for their subordinates, using their devices. These messages are sent to a server, which uses an emotion engine to analyze the emotional elements of the messages. This analysis identifies the emotions and tone the user is experiencing.

[0690] The results of the emotion analysis are reflected in the detection of recognition discrepancies and the generation of corrected messages by the generative AI model. The server adjusts the content and expression of the message according to the user's emotional state. For example, if the user is feeling anxious or stressed, it can choose expressions that provide a greater sense of reassurance.

[0691] For example, if a user enters "I'm worried about the progress of this project" into their terminal, the server's emotion engine detects the user's anxiety. Based on this, the generative AI model generates a reassuring message, such as "The current progress is fine, but we will provide additional support if needed," and sends it to the user. In this way, intentions can be conveyed more effectively, and communication can be more empathetic to the other person's feelings.

[0692] Furthermore, user feedback is collected by the server and used to improve the performance of the emotion engine and generative AI models. By analyzing the feedback information and continuously improving the accuracy of emotion analysis and message generation, the system's effectiveness increases, and the user experience is further enhanced.

[0693] This invention makes it possible to improve the efficiency and quality of communication and increase work productivity.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The user uses the device and launches a messaging application. The user then types specific instructions or questions as text.

[0697] Step 2:

[0698] The terminal receives the message entered by the user and prepares to send that data to the server in text format.

[0699] Step 3:

[0700] The server receives text data sent from the terminal and first uses an emotion engine to analyze the user's emotions from the message. The analyzed emotion data provides information about the tone and emotional aspects of the message.

[0701] Step 4:

[0702] The server passes the sentiment analysis results and communication data to the generating AI model, which performs analysis to detect discrepancies in perception between users. The analysis takes into account the intent and background of the words, as well as sentiment data, to identify the intent behind the message.

[0703] Step 5:

[0704] The server's AI model generates conversion messages based on the analysis results to mitigate misunderstandings. In doing so, it adjusts the messages according to the user's emotions, aiming to provide reassurance and appropriate feedback.

[0705] Step 6:

[0706] The server formats the generated conversion message and prepares it as data for transmission.

[0707] Step 7:

[0708] The terminal displays the converted message received from the server to the user, who then reviews it and uses it as a guide for action or to answer questions.

[0709] Step 8:

[0710] If a user wants to send feedback about a message provided by the system, that feedback is sent from the terminal to the server.

[0711] Step 9:

[0712] The server collects user feedback and uses it to update and improve the emotion engine and generative AI models. This improves the overall accuracy of the system and the user experience.

[0713] (Example 2)

[0714] 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".

[0715] With the widespread adoption of information technology, misunderstandings and discrepancies in communication between users are becoming more common, often leading to misinterpretations and conflicts. Furthermore, while it's necessary to consider the emotions of each individual user, individual responses are time-consuming and inefficient. Therefore, it's essential to analyze emotional states and provide appropriate alternative information to achieve smoother and more efficient communication.

[0716] 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.

[0717] In this invention, the server includes means for collecting information input by the user using an output device, means for analyzing the information and detecting discrepancies in perception between users, and means for generating alternative information whose content is adjusted according to the emotional state based on the discrepancies in perception. This makes it possible to provide appropriate information while taking into account the user's emotions and mitigating discrepancies in perception.

[0718] "Output device" refers to a device used by users to input or receive information.

[0719] "Information" refers to messages and data transmitted by users through output devices.

[0720] "Discrepancy in understanding" refers to misunderstandings or disagreements that occur in communication between users.

[0721] "Alternative information" refers to messages with adjusted content that are generated for the purpose of mitigating misunderstandings.

[0722] "Emotional state" refers to the tone and nuances of emotions that a user conveys in a message.

[0723] "Natural language processing technology" refers to the technology that enables computers to understand and process human language appropriately.

[0724] A "learning model" refers to an algorithm used to learn patterns from data and make judgments or predictions.

[0725] This system aims to detect discrepancies in perception between users and provide appropriate alternative information based on their emotional state. The system is realized through the respective roles of the server, terminal, and users.

[0726] Users input communication information using a terminal. This terminal refers to electronic devices such as general computers and smartphones, equipped with an interface for information input. This information is transmitted to a server via the network.

[0727] The server uses an emotion engine and a generative AI model to analyze information received from the user. The emotion engine uses natural language processing techniques to analyze the emotional state of the message. In doing so, it extracts the user's emotional tone and nuances to understand the emotions behind the message.

[0728] Based on the analysis results, the server applies a generative AI model. This model is an advanced algorithm that generates alternative information to mitigate recognition discrepancies. The generative AI model utilizes natural language generation technology to generate flexible messages that correspond to the analyzed emotional state.

[0729] As a concrete example, consider a scenario where a user enters "I'm worried about whether the project will meet the deadline" into their terminal. The server detects the user's anxiety through its emotion engine. Then, using a generative AI model, it generates alternative information such as "The project is on schedule, but please let us know if you have any concerns," and sends it back to the user.

[0730] This system's feedback function allows users to provide satisfaction levels with the messages they receive, which contributes to the system's continuous improvement. The server uses this feedback data to improve the accuracy of its emotion engine and generative AI models.

[0731] An example of a prompt might be an instruction to the generative AI model such as, "Based on the sentiment analysis results from the sentiment engine, generate the most appropriate response message for the user." This prompt clarifies the guidelines for the generative AI model to specifically generate the desired response.

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The user enters a message using the terminal. This input consists of the user typing the message they want to convey as text on the terminal. For example, if the user types "I'm worried about this weekend's project deadline," that message will be output as text data on the terminal.

[0735] Step 2:

[0736] The terminal processes the input message and sends it to the server. Here, a communication protocol is used to send the input data to the server as a data packet. As a result, the server receives the text data.

[0737] Step 3:

[0738] The server inputs the received message into the emotion engine. The emotion engine uses natural language processing techniques to analyze the emotional elements of the text and outputs emotion tags such as anxiety, joy, and anger. For example, because the message contains "I'm worried," it outputs the emotion tag "anxiety."

[0739] Step 4:

[0740] The server inputs the results of the sentiment analysis into a generative AI model. This model processes the data to generate an appropriate alternative message based on the prompt text. Specifically, the generative AI model uses the input sentiment tags to generate text such as "The project is progressing smoothly. We will address any issues immediately."

[0741] Step 5:

[0742] The server processes the generated alternative message and sends it to the user's terminal. Here, the generated message is sent as output data via the communication protocol. As a result of this step, the user receives a reassuring message on their terminal.

[0743] Step 6:

[0744] Users review the messages they receive and provide feedback as needed. This feedback is entered into the device as an evaluation of the message content and suggestions for improvement, and then sent to the server. This data is then used to improve the accuracy of subsequent analyses and generation.

[0745] (Application Example 2)

[0746] 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".

[0747] In situations where misunderstandings are likely to occur during communication, or when users are experiencing anxiety or stress, effective communication is difficult. Furthermore, in situations like electronic payments, it is necessary to alleviate user anxiety and provide an environment where users can use the service with peace of mind.

[0748] 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.

[0749] In this invention, the server includes means for analyzing the user's emotional state in real time, means for generating messages that provide reassurance as needed based on the emotional state, and means for generating conversion messages to reduce misunderstandings between users. This makes it possible to provide effective communication that is attentive to the user's emotions and an electronic payment environment that can be used with peace of mind.

[0750] "Communication data" refers to digital data that includes the content of information exchange and conversations between users.

[0751] "Discrepancy in understanding" refers to a difference in interpretation of intent or content that occurs in communication between users.

[0752] A "conversion message" is a message with appropriate content that is generated to reduce misunderstandings and facilitate smooth communication.

[0753] "Emotional state" refers to the user's psychological state, specifically emotional states such as anxiety and stress.

[0754] A "reassuring message" is a message generated to provide reassurance to users when they feel worried or anxious, enabling them to use the service with peace of mind.

[0755] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and manipulate human language.

[0756] "Emotional analysis technology" is a technology that identifies a user's emotional state from text data and analyzes its content.

[0757] A "generative AI model" is an artificial intelligence model that can generate content for a specific task.

[0758] "Feedback" refers to information about users' experiences and opinions.

[0759] "Accuracy" refers to the degree to which a system is able to deliver exactly the intended results or outputs.

[0760] The system for implementing this invention consists of a server, a terminal, and a user. The server collects communication data entered by the user via the terminal and analyzes the emotional state in real time based on that data. Specifically, it uses emotion analysis technology to determine the user's emotional state. Based on this analysis, the generative AI model generates conversion messages to reduce misunderstandings with the user and messages to provide a sense of security.

[0761] The server utilizes natural language processing technology to deliver messages to users that effectively alleviate their anxiety and stress. This process employs programming languages ​​like Python, sentiment analysis tools, and generative AI tools. As a result, users can use electronic payments and other communication services with peace of mind.

[0762] As a concrete example, consider a scenario where a user uses an electronic payment system for the first time and enters a message expressing concern: "Is this service safe?" The server analyzes this as an emotional state of "anxiety" and uses a generative AI model to generate a translated message such as, "Please rest assured. This service is trusted by many users," and provides it to the user.

[0763] An example of a prompt is, "Analyze this user's sentiment and generate an appropriate response message." Based on this prompt, the system performs the necessary analysis and message generation.

[0764] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0765] Step 1:

[0766] The user inputs communication data into the server via their terminal. This communication data includes the user's anxieties, questions, or requests for information. The input data is sent to the server in digital format and analyzed in the next processing step.

[0767] Step 2:

[0768] The server uses emotion analysis technology to analyze the emotional state of the incoming communication data. Using emotion analysis tools, it identifies emotions such as "anxiety" and "stress" from the data. The output of this step is the identified emotional state, which is used for message generation in the next step.

[0769] Step 3:

[0770] The server uses a generative AI model to generate a translated message based on the emotional state obtained in step 2. Here, a "prompt" is used to instruct the AI ​​model to perform a specified generation task. For example, if an emotion indicating anxiety is identified, the prompt "Generate a message that provides the best possible sense of reassurance for this situation" is used. The output of this step is the generated translated message.

[0771] Step 4:

[0772] The server sends the generated translation message to the user's terminal. This action allows the user to receive a message that appropriately responds to their communication data. This reduces anxiety and allows the user to use the service with peace of mind. This step includes the message provided to the user as output.

[0773] Step 5:

[0774] The user sends feedback to the server regarding the message received as a response. The server analyzes this feedback and uses it to improve the accuracy of the generative AI model and sentiment analysis technology. The input for this step is the user's feedback, and the output is data used to improve the model.

[0775] 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.

[0776] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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 those described above. 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 shown 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.

[0777] 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 robot 414.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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."

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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 as being incorporated by reference.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] A means of collecting communication data entered by the user,

[0799] A means for analyzing the aforementioned communication data and detecting discrepancies in understanding between users,

[0800] Means for generating a conversion message to mitigate the aforementioned misunderstanding,

[0801] A means of providing the generated conversion message to the user,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, further comprising means for analyzing the intent of a message using natural language processing techniques when generating the aforementioned converted message.

[0805] (Claim 3)

[0806] The system according to claim 1, further comprising means for collecting user feedback and incorporating it into the training of a generating AI model in order to improve the accuracy of the generated messages.

[0807] "Example 1"

[0808] (Claim 1)

[0809] A means of collecting communication information entered by the user,

[0810] A means for analyzing the aforementioned communication information and detecting discrepancies in information between users,

[0811] A means for generating adjustment information to mitigate the aforementioned information discrepancies using a generation AI,

[0812] A means of providing the generated adjustment information to the user,

[0813] A means of collecting user feedback and incorporating it into learning to improve the accuracy of information presentation,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising means for referencing past databases and related information when analyzing the intent of communication information using natural language processing and generating adjustment information.

[0817] (Claim 3)

[0818] The system according to claim 1, comprising display technology that presents adjustment information in real time via a user interface, enabling the user to easily understand the information.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A means of collecting communication information entered by the user,

[0822] A means for analyzing the aforementioned communication information and detecting discrepancies in understanding between users,

[0823] Means for generating conversion information to mitigate the aforementioned misunderstanding,

[0824] A means of providing the generated conversion information to the user,

[0825] A means by which an automated device executes specific work instructions based on the generated conversion information,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, further comprising means for analyzing the intent of the information using natural language processing technology when generating the aforementioned converted information.

[0829] (Claim 3)

[0830] The system according to claim 1, further comprising means for collecting evaluation information from users and reflecting it in the learning of a generating AI model in order to improve the accuracy of the generated information.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A means of collecting information input by the user using an output device,

[0834] A means for analyzing the aforementioned information and detecting discrepancies in understanding among users,

[0835] Based on the aforementioned discrepancy in perception, means for generating alternative information whose content is adjusted according to the emotional state,

[0836] A means of sending the generated alternative information to the user,

[0837] Means for conducting emotion analysis,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for analyzing the intent of the alternative information using natural language processing technology and reflecting changes based on emotional states when generating the alternative information.

[0841] (Claim 3)

[0842] The system according to claim 1, further comprising means for collecting information from users and incorporating it into training a learning model in order to improve the accuracy of the generated information.

[0843] "Application example 2 when combining with an emotional engine"

[0844] (Claim 1)

[0845] A means of collecting communication data entered by the user,

[0846] A means for analyzing the aforementioned communication data and detecting discrepancies in understanding between users,

[0847] Means for generating a conversion message to mitigate the aforementioned misunderstanding,

[0848] A means of providing the generated conversion message to the user,

[0849] A means of analyzing the user's emotional state in real time,

[0850] A means of generating messages that provide reassurance as needed, based on emotional state,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising means for analyzing the intent of a message using natural language processing technology and sentiment analysis technology when generating the aforementioned converted message and the message that provides a sense of security.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising means for collecting user feedback and incorporating it into the training of a generative AI model in order to improve the accuracy of the generated messages and the accuracy of sentiment analysis. [Explanation of symbols]

[0856] 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 collecting communication data entered by the user, A means for analyzing the aforementioned communication data and detecting discrepancies in understanding between users, Means for generating a conversion message to mitigate the aforementioned misunderstanding, A means of providing the generated conversion message to the user, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing the intent of a message using natural language processing techniques when generating the aforementioned converted message.

3. The system according to claim 1, further comprising means for collecting user feedback and incorporating it into the training of a generating AI model in order to improve the accuracy of the generated messages.

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