system

Abstract

Provide a system. 【Solution means】 Means for receiving communication data and collecting the data; Means for analyzing the received data and calculating an emotion score; Means for determining signs of abnormal behavior based on the emotion score; Means for automatically generating warning and feedback information; Means for notifying the user terminal of the generated information; Means for interacting with the user and promoting improvement of behavior; Means for cooperating with multiple applications and analyzing data in real time; Means for providing customized advice regarding communication approaches; A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 a conventional workplace, even when a harassment act occurs, often neither the perpetrator nor the victim is aware of it, and there is a problem that countermeasures are not taken until the problem becomes serious. In addition, since the recognition of harassment varies from person to person, there may be a lack of common understanding of what constitutes a harassment act. As a result, the working environment deteriorates, leading to an increase in the psychological stress of employees and a decrease in productivity. There is a need for technical means to overcome such problems and provide a workplace environment in which all employees can work with peace of mind.

Means for Solving the Problems

[0005] This invention solves the above problem by providing a system that collects communication data in real time and analyzes that data using natural language processing technology. The analyzed data is quantified as an emotion score, and signs of harassment are determined based on this emotion score. Furthermore, warning and feedback messages are automatically generated and notified to the user's terminal, providing educational support to help employees reflect on their own words and actions. As a result, employees can review their own words and actions and work to prevent and improve workplace harassment.

[0006] "Communication data" refers to messages and information sent and received via computer networks, and specifically includes text data in the form of chat and email.

[0007] An "emotion score" is a numerical representation of the emotional characteristics of words and expressions within text data, and is an index used to identify emotional states such as positive, negative, and neutral.

[0008] "Signs of harassment" refer to a state in which certain words or expressions may cause discomfort or intimidation to others, and are indicators that can be used to detect the occurrence of such harassment.

[0009] A "feedback message" is an automatically generated notification that indicates an evaluation of the user's communication content and areas for improvement, serving as a guide for users to reflect on their own words and actions.

[0010] "Natural language processing technology" refers to the techniques used by computers to analyze and understand human language and extract meaning, and is applied to things like sentiment analysis of text data.

[0011] A "user terminal" refers to a device that a user can directly operate and use to receive information, and can take various forms such as computers, smartphones, and tablets. [Brief explanation of the drawing]

[0012] [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] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a 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.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system aimed at the early detection and prevention of harassment in the workplace. The system mainly consists of three components: a server, terminals, and users. The specific roles of each component and the operation of the overall system are described below.

[0034] Server Role

[0035] The server monitors communication data flowing through the corporate network in real time and collects text data such as chats and emails. The server analyzes the received data using natural language processing technology and calculates a sentiment score for each message. This sentiment score serves as a criterion for determining whether the expressions in the message are positive, negative, or neutral. The server uses the sentiment score to determine if there are signs of harassment and generates warning and feedback messages as needed.

[0036] Terminal role

[0037] The device receives notifications sent from the server and presents information to the user. Specifically, it notifies the user of feedback messages generated by the server, displaying warnings and advice. In addition, the device provides a dialogue interface with the user, offering educational support to help the user reflect on their own words and actions and make improvements. This dialogue is primarily conducted by a conversational AI, which automatically provides appropriate advice.

[0038] User roles

[0039] Users reflect on their words and actions based on feedback received from their devices. If signs of harassment are identified, users can improve their communication style by referring to the displayed advice. Furthermore, users can learn better ways of expressing themselves and communication approaches through interaction with the conversational AI. In this way, users can proactively contribute to improving the workplace environment.

[0040] As described above, this system utilizes real-time data analysis and automated feedback functions to reduce workplace harassment and provide a safe working environment. This invention is particularly characterized by its emotion scoring process and the feedback generation process based on it.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server receives communication data in real time from chat and email on the corporate network. The server stores the received data in a database for analysis.

[0044] Step 2:

[0045] The server provides unanalyzed data from the database to the sentiment analysis module. The server uses natural language processing techniques to analyze the text data and calculate a sentiment score for each message. This sentiment score indicates whether the message is positive, negative, or neutral.

[0046] Step 3:

[0047] The server determines whether a message may be harassment based on the calculated sentiment score. The server sets a threshold and selects messages with negative scores exceeding that threshold as targets.

[0048] Step 4:

[0049] When the server determines a message may constitute harassment, it automatically generates a warning and feedback message using a feedback generation module. This message includes specific areas for improvement and behaviors that should be reconsidered.

[0050] Step 5:

[0051] The device receives feedback messages sent from the server and displays them on the user screen. The device visually provides the user with warnings and specific advice.

[0052] Step 6:

[0053] Users can review the feedback displayed on their device and reflect on their own words and actions. Based on the advice displayed, users can re-evaluate their communication methods and learn ways to improve.

[0054] Step 7:

[0055] The device provides conversational support to the user through an interactive AI. Through these conversations, the user can receive specific improvement examples and further advice. The device suggests appropriate solutions to the problems the user is facing.

[0056] (Example 1)

[0057] 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."

[0058] In today's workplace environment, there is a need for proactive prevention and rapid response to inappropriate behavior. However, traditional methods make it difficult to easily detect such behavior, potentially leading to the escalation of problems. Furthermore, there is a lack of appropriate feedback on the situation and individualized support, which hinders the improvement of user behavior.

[0059] 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.

[0060] In this invention, the server includes means for receiving and collecting communication information, means for analyzing the received information and calculating an emotional score, and means for determining signs of inappropriate behavior based on the emotional score. This makes it possible to detect inappropriate behavior in real time, issue warnings as needed, and provide appropriate advice to the user.

[0061] "Communication information" refers to all digital messages and data sent and received over a network.

[0062] "Received information" refers to data and messages acquired by the system from external sources.

[0063] "Sentiment metric" is a numerical indicator that represents the emotional characteristics of text data and is used to evaluate the intensity of emotions such as positive, negative, and neutral.

[0064] "Inappropriate conduct" refers to actions or words that violate the ethics and rules required within the workplace or organization.

[0065] A "warning" refers to a message or notification intended to draw attention to specific actions or behaviors.

[0066] "User terminal" refers to a computing device used to receive information from a server.

[0067] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0068] "Dialogue support" refers to support aimed at promoting behavioral improvement through two-way communication with users.

[0069] This invention is a system for detecting and preventing inappropriate behavior in the workplace. This system primarily consists of three components: a server, a terminal, and a user. The specific operations of each component and an embodiment of the overall system are described below.

[0070] The server initiates the process by receiving and collecting communication information from the network. The server then analyzes the collected data using natural language processing technologies such as Google® Cloud Natural Language API and IBM Watson®. Based on the analysis, a sentiment score is calculated for each message, which measures whether the message is positive, negative, or neutral. For example, the message "The proposals at yesterday's meeting were excellent" would be assigned a positive sentiment score.

[0071] The server uses these emotion scores to determine the likelihood of inappropriate behavior in the workplace. If the negative emotion score exceeds a certain threshold, the server automatically generates a warning and feedback message. In this process, OpenAI's (registered trademark) generative AI model is used to construct the feedback, which includes appropriate advice and guidelines for behavioral improvement. Specific feedback might include suggestions such as, "Please try to adopt the following communication style."

[0072] The device receives feedback messages sent from the server and provides information to the user. Notifications are provided in the form of pop-ups or emails, allowing users to receive feedback immediately.

[0073] In addition, the device provides a conversational interface with the user and offers educational advice through dialogue support from a generative AI model. For example, if a user asks, "How can I reduce negative language in the workplace?", the conversational AI will offer advice such as, "It would be good to incorporate expressions that show appreciation for the other person's opinion."

[0074] Users can reflect on their own behavior through feedback from their devices and improve their individual communication styles as needed. A practical example might be setting a goal such as "strive to provide constructive feedback on suggestions."

[0075] As an example of a prompt, you can input something like, "Please give specific examples of the language and approaches that are recommended to improve communication in the workplace," into the AI ​​generation model.

[0076] In this way, servers, terminals, and users work together to create a system that prevents inappropriate behavior in the workplace and supports the improvement of user behavior.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server receives and collects communication information flowing through the company's network in real time. Input is text data such as chat messages and emails, and the acquired data is stored as output. This step also includes data encryption for security purposes. For example, data is collected from an email server using the SMTP protocol.

[0080] Step 2:

[0081] The server analyzes the collected text data using natural language processing techniques. The input is the previously collected text data, and the output is a sentiment score for each message. Specifically, it uses the Google Cloud Natural Language API to extract keywords and quantify the intensity of sentiment.

[0082] Step 3:

[0083] The server determines signs of inappropriate behavior based on calculated sentiment scores. The input is a sentiment score, and the output is a flag indicating the possibility of inappropriate behavior. The server evaluates the sentiment score using a threshold, and if the negative score exceeds the threshold, it determines that inappropriate behavior has occurred.

[0084] Step 4:

[0085] The server automatically generates warning and feedback messages when signs of inappropriate behavior are detected. The input is a flag indicating inappropriate behavior, and the output is a feedback message. In this generation process, a generative AI model is used to construct messages that include specific and constructive advice.

[0086] Step 5:

[0087] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is the notification received by the user. In this case, the terminal displays the notification in the form of a pop-up or email.

[0088] Step 6:

[0089] The device provides a conversational interface with the user and offers educational support. Input consists of questions and responses from the user, and output is advice from a conversational AI. Specifically, in response to a user's question, it uses a generative AI model to provide a response such as, "It would be good to incorporate expressions that show gratitude for the other person's opinion."

[0090] Step 7:

[0091] Users reflect on their own behavior and make improvements based on feedback from their devices. The input consists of feedback messages and advice from the AI, while the output is the improved behavior. Users set self-improvement goals, such as "strive to provide constructive feedback on suggestions."

[0092] (Application Example 1)

[0093] 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."

[0094] In modern workplaces and communities, inappropriate remarks and harassment in communication are serious problems. However, effective systems for early detection and appropriate response to these behaviors are still insufficient. As a result, there are many situations where problems have to be dealt with only after they have escalated. This invention aims to prevent these problems and provide a safe communication environment by analyzing data in real time across various communication tools and detecting abnormal behavior.

[0095] 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.

[0096] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of abnormal behavior based on the emotional score. This enables real-time early detection of abnormal behavior and appropriate feedback in the workplace and community.

[0097] "Communication data" refers to a portion of the information sent and received over a network, and includes data such as text messages and emails.

[0098] An "emotion score" is an index that analyzes received text data using natural language processing and numerically indicates whether the content is positive, negative, or neutral.

[0099] "Signs of abnormal behavior" refer to patterns, based on the results of emotional score analysis, that suggest communication or harassment behaviors that may worsen interpersonal relationships.

[0100] "Warning and feedback information" refers to text generated for the purpose of notifying users, and includes advice and warnings to encourage the improvement of abnormal behavior.

[0101] A "user terminal" is a user's operating device that can receive warnings and feedback information, and includes smartphones, computers, and other devices.

[0102] "Natural language processing technology" is a technique that uses computers to analyze text data written in human language and understand its content.

[0103] A "messaging service" is an information and communication service that enables the sending and receiving of text messages in real time.

[0104] The embodiments for carrying out this invention will now be described. This system mainly consists of three components: a server, a terminal, and a user.

[0105] Server Role

[0106] The server monitors and receives communication data flowing through the network in real time. The server uses natural language processing (NLP) techniques to analyze the collected text data. Existing NLP services such as Google Cloud Natural Language API and Azure® Text Analytics are used for this analysis. The server calculates a sentiment score and uses this score to determine signs of abnormal behavior. If an anomaly is detected, the server generates warning and feedback information and sends it to the terminal.

[0107] Terminal role

[0108] The device receives feedback information sent from the server. Based on this information, it displays alerts to the user to encourage improvements in their communication style. Furthermore, the device uses conversational AI to provide customized advice to the user. OpenAI's GPT model is one example of a conversational AI that can be used. For example, if the user asks prompts such as, "Looking back on recent conversations, how did you feel? What kind of conversations can you think of to improve your communication style?", the device will generate appropriate advice.

[0109] User roles

[0110] Users can receive feedback and advice from conversational AI provided through their devices, allowing them to reflect on their own actions and statements. This enables users to proactively improve their communication and contribute to building healthy relationships in the workplace and community.

[0111] For example, if a user's emotional score decreases through a series of messages exchanged in a chat, the system immediately sends an alert to the device and provides advice to the user via conversational AI, such as, "Your recent conversations seem a little tense. How can we try to communicate in a more relaxed manner in our next conversation?" This series of actions makes it possible to create a safe and positive communication environment.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server receives communication data in real time over the network. The input consists of text data such as chat messages and emails. The server collects the data and stores it for analysis. During this process, it uses communication protocols to ensure data integrity.

[0115] Step 2:

[0116] The server uses natural language processing technologies such as Google Cloud Natural Language API and Azure Text Analytics to analyze incoming text data. The input is text data, and the output is a sentiment score. The analyzed sentiment score is expressed numerically and classified as positive, negative, or neutral. The data is analyzed immediately, and the results proceed to the next step.

[0117] Step 3:

[0118] The server determines signs of abnormal behavior based on a sentiment score. The input is the sentiment score, and the output is a warning flag for abnormal behavior. When the sentiment score exceeds a certain threshold, a warning flag is set, and the process moves to the next step. If abnormal behavior is detected, preparations are made to respond immediately.

[0119] Step 4:

[0120] The server generates warning and feedback information as needed. Input is a warning flag for abnormal behavior, and output is a feedback message to be sent to the user. Specifically, it selects the appropriate message from a pre-prepared message template and creates a message containing warnings and advice for the user.

[0121] Step 5:

[0122] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is displayed through the terminal's user interface. The terminal uses an intuitive GUI to enable the user to easily understand the feedback and take action.

[0123] Step 6:

[0124] The user refers to the feedback displayed on the device and receives advice on improving communication through conversational AI. Input is the user's prompts and responses to feedback, and output is specific advice generated by the AI. Based on this feedback, the user uses the conversational AI to formulate prompts to input to the AI ​​model. A concrete example of a prompt used is, "What are some ways to ease tension in recent conversations?"

[0125] 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.

[0126] This invention provides more effective feedback and interactive support by combining a system that detects and responds to signs of workplace harassment early with an emotion engine that recognizes the user's emotions. This invention mainly consists of three components: a server, a terminal, and a user.

[0127] Server Role

[0128] The server receives chat and email data flowing through the corporate network in real time and collects it in a database. The received data is then analyzed using natural language processing technology to calculate an emotional score. During this process, an integrated emotional engine analyzes the user's emotional state in detail. The emotional engine extracts emotional characteristics from the raw data, evaluates positive, negative, and neutral emotional states, and uses this to assess the risk of harassment.

[0129] Terminal role

[0130] The device notifies the user based on analysis results sent from the server and evaluations from the emotion engine. Specifically, if signs of harassment are detected, a feedback message reflecting the emotion engine's analysis results is displayed on the user's screen. This feedback includes specific areas for improvement and personalized advice tailored to the user's emotional state. Furthermore, the device provides a dialogue interface with the user and supports the improvement of the user's behavior through responses that take emotional changes into account.

[0131] User roles

[0132] Users can improve themselves through feedback messages and interactive support displayed on their devices. For example, if a user exhibits negative emotions during workplace communication, the system will consider the cause and suggest specific improvement measures and positive communication techniques. As a result, users can reduce the risk of unconsciously engaging in harassment.

[0133] This system uses an emotion engine to provide feedback that takes user emotions into account, resulting in more personalized support than ever before. It represents an important technological tool for promoting a healthy communication environment in the workplace.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The server receives chat and email communication data flowing from the corporate network in real time. The server stores the received data in an internal database for analysis.

[0137] Step 2:

[0138] The server analyzes unanalyzed data in the database using natural language processing techniques. Specifically, the server extracts keywords from the text and calculates a sentiment score based on them. A built-in sentiment engine is used for this calculation.

[0139] Step 3:

[0140] The server utilizes an emotion engine to identify the user's emotional state from the analyzed data. This emotional state includes positive, negative, and neutral states, and changes in the emotional state are also tracked.

[0141] Step 4:

[0142] The server determines signs of harassment based on the emotional score and the user's emotional state. This includes detecting when a negative emotional score exceeds a certain threshold.

[0143] Step 5:

[0144] If the server detects signs of harassment, it activates a feedback generation module to automatically generate warning and feedback messages for the user. This feedback includes personalized advice tailored to the user's feelings.

[0145] Step 6:

[0146] The device receives feedback messages sent from the server and notifies the user of the message. The device provides feedback using visual and audio notifications.

[0147] Step 7:

[0148] Users review the feedback displayed on their device and reflect on their own words and actions. Based on the specific improvement suggestions provided, users modify their communication style. They can also request detailed interactive advice via their device if needed.

[0149] Step 8:

[0150] The device responds to the user's additional questions and provides further advice and explanations through its conversational AI function. During the conversation, it can re-evaluate the user's emotional changes and reflect them in its responses.

[0151] (Example 2)

[0152] 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".

[0153] There is a need for early detection of harassment in the workplace and the provision of effective countermeasures. Traditional systems relied on simple rule-based approaches, which hindered the proper assessment of emotions and context. This resulted in potential delays in necessary feedback and the risk of inappropriate dialogue.

[0154] 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.

[0155] In this invention, the server includes means for receiving and storing communication information, means for processing the received information and calculating an emotional evaluation, and means for identifying risk indicators based on the emotional evaluation. This makes it possible to detect the risk of harassment early by utilizing emotional analysis technology and to improve user behavior through prompt and appropriate feedback and dialogue.

[0156] "Communication information" is a general term for data and messages that are sent and received via a digital network.

[0157] "Storage" refers to the process of saving received data in a specific format so that it can be used later.

[0158] "Processing" refers to a series of operations that analyze raw data and extract or transform useful information.

[0159] "Sentimental evaluation" is a way of expressing the emotional elements contained in the information in question using numerical values ​​or categories.

[0160] "Risk indicators" refer to identifying signs that suggest the possibility of potential problems or disruptions occurring.

[0161] "Automatically generated information" refers to data or messages generated by a system, rather than manually, based on specific algorithms or rules.

[0162] "User terminal" is a general term for electronic devices used to display information and enable users to operate it.

[0163] "Adjusting behavior" is the process of helping users improve or optimize their behavior and work patterns.

[0164] "Emotion analysis technology" is a technology that analyzes text and audio data to identify and evaluate emotions.

[0165] "Real-time" refers to the simultaneous or near-simultaneous generation and processing of data.

[0166] This invention is a system for early detection of signs of harassment in the workplace and providing interactive support, and consists of three components: a server, a terminal, and a user.

[0167] The server receives communication information, such as chats and emails, flowing over the network in real time and stores it in a database. The server processes the received communication information using natural language processing (NLP) technology and performs sentiment evaluation using sentiment analysis technology. Specifically, it extracts emotional elements from the text, performs sentiment evaluation by classifying them into categories such as positive, negative, and neutral, and identifies risk indicators based on this evaluation.

[0168] The device receives sentiment ratings and risk indicators transmitted from the server and provides automatically generated feedback information to the user based on this data. This feedback includes specific actions and measures to mitigate risks, and the device presents this information through interaction with the user. Furthermore, the device features an interactive dialogue interface that suggests appropriate improvement suggestions based on the user's information.

[0169] Users can adjust their behavior based on the feedback information displayed on their device. For example, if a user expresses negative emotions, the system considers the cause and suggests positive communication methods to reduce the risk of harassment.

[0170] As a concrete example, the prompt text used would be, "Assess your emotional state based on your recent communication history at work and suggest positive areas for improvement." This prompt makes it easier for users to understand how to improve their behavior according to the system's guidelines.

[0171] In this way, the system utilizes emotion analysis and natural language processing technologies to provide users with personalized feedback, thereby maintaining and promoting a healthy communication environment in the workplace.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The server receives communication information from the network, specifically chat messages and emails. This raw text data is immediately stored in a database. The stored data is then organized and saved for later analysis.

[0175] Step 2:

[0176] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, it breaks down the elements that make up a sentence through morphological analysis and extracts keywords. The input to this process is the stored raw data, and the output is processed data ready for sentiment analysis.

[0177] Step 3:

[0178] The server uses sentiment analysis technology to perform an emotional assessment based on the processed data. The emotion engine receives this data and calculates an emotional score, such as positive, negative, or neutral. This assessment is output in specific numerical or categorical form, which serves as a basis for deciding the next step.

[0179] Step 4:

[0180] The server identifies risk indicators based on the calculated sentiment assessment results. Here, if the sentiment score exceeds a certain threshold, it is determined that there is a high risk of harassment. In this step, the sentiment score is used as input, and risk assessment data is generated as output.

[0181] Step 5:

[0182] The device generates feedback information based on sentiment assessments and risk indicators obtained from the server. For example, if a negative emotion is detected, the device creates feedback that includes suggested positive actions. In this process, risk assessment data is used as input, and a feedback message is generated as output.

[0183] Step 6:

[0184] The device displays the generated feedback message on the user screen. Specifically, this involves a process of notifying the user so they can immediately see the generated message. This notification information serves as a foundation for the user to understand the situation and begin adjusting their actions.

[0185] Step 7:

[0186] Users review feedback messages received from their devices and reflect them in their actions. They improve their interactions in the workplace by trying out suggested positive communication techniques. The input in this process is the feedback messages, and the output is the user's improved behavior.

[0187] (Application Example 2)

[0188] 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".

[0189] In today's workplace, it is crucial to detect early signs of harassment in communication and maintain a healthy work environment. However, monitoring emotional changes in real time and responding appropriately is not easy. Furthermore, there is a lack of means to specifically assess high-risk situations and provide appropriate corrective measures. Therefore, a more effective system is needed to prevent harassment from occurring and improve the workplace environment.

[0190] 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.

[0191] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of harassment based on the emotional score. This makes it possible to evaluate the health of workplace communication in real time, identify high-risk situations specifically, and provide necessary feedback at the appropriate time.

[0192] "Communication data" refers to information transmitted and received in digital format, including voice, text, and images transmitted over a network.

[0193] An "emotion score" is a numerical indicator of a user's emotional state, extracted from text analyzed using natural language processing, and shows emotional tendencies such as positive, negative, or neutral.

[0194] "Signs of harassment" are indicators that suggest the possibility of aggressive or inappropriate behavior towards others in workplace communication or conduct.

[0195] A "feedback message" is a message that provides users with specific suggestions for improvement or advice based on the results of the analysis of received data.

[0196] A "user terminal" refers to a device used to receive information transmitted from a system, and includes personal computers, smartphones, and other similar devices.

[0197] "Workplace environment risks" refer to factors that can affect the health of an organization, such as harassment and interpersonal problems that may arise in the course of work.

[0198] "Means of proposing improvement measures" refers to a process for providing specific actions and methods to address problems identified based on the analysis results.

[0199] "Natural language processing technology" is a technology that processes human language using computers, enabling the analysis of text data and the understanding of intentions and emotions based on that analysis.

[0200] This invention is a system for analyzing signs of harassment in the workplace, with a server, terminals, and users each playing specific roles. The server receives communication data via the network and collects data. Natural language processing techniques are used to analyze the text of the received data and calculate an emotion score. The Hugging Face transformers library is suitable for this process.

[0201] The server analyzes the data to identify signs of harassment and automatically generates warning and feedback messages. These messages are sent to the user's device, which can be a personal computer or smartphone. The user receives the feedback message displayed on the device. The device also displays the results of a workplace risk assessment and suggests specific improvement measures.

[0202] Based on this information, users can deepen their understanding of emotions and improve their behavior based on feedback. This improves the quality of communication in the workplace and reduces the risk of harassment.

[0203] For example, if an employee sends a message through the company system stating, "I'm frustrated because my opinions aren't being heard in recent meetings," the server analyzes this in real time and determines that negative emotions are on the rise. Based on this, a message recommending "considering opportunities to learn how to give constructive feedback" is generated and sent to the user's terminal. This encourages the user to take actions that will lead to improved communication in the workplace.

[0204] An example of a prompt message might be: "Analyze the following workplace communication data and calculate the sentiment score. Classify it as positive, negative, or neutral, and determine whether there is a risk of harassment."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server receives communication data through the corporate network. Specifically, this includes text data such as chat messages and emails. The input is raw communication data obtained over the network, and the output is in a digital data format that can be processed within the server. In this step, data formatting and cleaning are performed.

[0208] Step 2:

[0209] The server performs natural language processing on the received data and analyzes the text. Specifically, the text is tokenized, and keywords related to important emotions are extracted. Based on this, an emotion score is calculated. In this process, the Hugging Face transformers library is used to derive positive, negative, and neutral scores through analysis. The input is the data formatted in the previous step, and the output is the emotion score.

[0210] Step 3:

[0211] The server determines signs of harassment based on the calculated emotional score. The analysis results are compared to a threshold, and if negative emotions exceed a certain value, it is determined that there is a risk of harassment. In this step, the input is the emotional score, and the output is the determination result indicating whether or not there is a risk of harassment.

[0212] Step 4:

[0213] The server automatically generates warning and feedback messages based on the assessment results. These messages include particularly necessary corrective actions and countermeasures for risks. The output is a feedback message containing specific warnings and advice. The input is the harassment risk assessment result.

[0214] Step 5:

[0215] The server notifies the user terminal of the generated feedback message. The terminal displays the notification to the user, making the content viewable. The input from the server is the feedback message, and the output is the content displayed on the terminal. This step allows the user to take quick action as needed.

[0216] Step 6:

[0217] Based on the displayed feedback, users assess risks in the workplace environment and implement suggested improvements. They utilize the information displayed on their devices to plan and implement actions aimed at improving workplace communication. Input is feedback messages, and output is changes in user behavior and efforts toward improvement. Voluntary user participation is essential in this process.

[0218] 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.

[0219] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention is a system aimed at the early detection and prevention of harassment in the workplace. The system mainly consists of three components: a server, terminals, and users. The specific roles of each component and the operation of the overall system are described below.

[0235] Server Role

[0236] The server monitors communication data flowing through the corporate network in real time and collects text data such as chats and emails. The server analyzes the received data using natural language processing technology and calculates a sentiment score for each message. This sentiment score serves as a criterion for determining whether the expressions in the message are positive, negative, or neutral. The server uses the sentiment score to determine if there are signs of harassment and generates warning and feedback messages as needed.

[0237] Terminal role

[0238] The device receives notifications sent from the server and presents information to the user. Specifically, it notifies the user of feedback messages generated by the server, displaying warnings and advice. In addition, the device provides a dialogue interface with the user, offering educational support to help the user reflect on their own words and actions and make improvements. This dialogue is primarily conducted by a conversational AI, which automatically provides appropriate advice.

[0239] User roles

[0240] Users reflect on their words and actions based on feedback received from their devices. If signs of harassment are identified, users can improve their communication style by referring to the displayed advice. Furthermore, users can learn better ways of expressing themselves and communication approaches through interaction with the conversational AI. In this way, users can proactively contribute to improving the workplace environment.

[0241] As described above, this system utilizes real-time data analysis and automated feedback functions to reduce workplace harassment and provide a safe working environment. This invention is particularly characterized by its emotion scoring process and the feedback generation process based on it.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server receives communication data in real time from chat and email on the corporate network. The server stores the received data in a database for analysis.

[0245] Step 2:

[0246] The server provides unanalyzed data from the database to the sentiment analysis module. The server uses natural language processing techniques to analyze the text data and calculate a sentiment score for each message. This sentiment score indicates whether the message is positive, negative, or neutral.

[0247] Step 3:

[0248] The server determines whether a message may be harassment based on the calculated sentiment score. The server sets a threshold and selects messages with negative scores exceeding that threshold as targets.

[0249] Step 4:

[0250] When the server determines a message may constitute harassment, it automatically generates a warning and feedback message using a feedback generation module. This message includes specific areas for improvement and behaviors that should be reconsidered.

[0251] Step 5:

[0252] The device receives feedback messages sent from the server and displays them on the user screen. The device visually provides the user with warnings and specific advice.

[0253] Step 6:

[0254] Users can review the feedback displayed on their device and reflect on their own words and actions. Based on the advice displayed, users can re-evaluate their communication methods and learn ways to improve.

[0255] Step 7:

[0256] The device provides conversational support to the user through an interactive AI. Through these conversations, the user can receive specific improvement examples and further advice. The device suggests appropriate solutions to the problems the user is facing.

[0257] (Example 1)

[0258] 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".

[0259] In today's workplace environment, there is a need for proactive prevention and rapid response to inappropriate behavior. However, traditional methods make it difficult to easily detect such behavior, potentially leading to the escalation of problems. Furthermore, there is a lack of appropriate feedback on the situation and individualized support, which hinders the improvement of user behavior.

[0260] 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.

[0261] In this invention, the server includes means for receiving and collecting communication information, means for analyzing the received information and calculating an emotional score, and means for determining signs of inappropriate behavior based on the emotional score. This makes it possible to detect inappropriate behavior in real time, issue warnings as needed, and provide appropriate advice to the user.

[0262] "Communication information" refers to all digital messages and data sent and received over a network.

[0263] "Received information" refers to data and messages acquired by the system from external sources.

[0264] "Sentiment metric" is a numerical indicator that represents the emotional characteristics of text data and is used to evaluate the intensity of emotions such as positive, negative, and neutral.

[0265] "Inappropriate conduct" refers to actions or words that violate the ethics and rules required within the workplace or organization.

[0266] A "warning" refers to a message or notification intended to draw attention to specific actions or behaviors.

[0267] "User terminal" refers to a computing device used to receive information from a server.

[0268] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0269] "Dialogue support" refers to support aimed at promoting behavioral improvement through two-way communication with users.

[0270] This invention is a system for detecting and preventing inappropriate behavior in the workplace. This system primarily consists of three components: a server, a terminal, and a user. The specific operations of each component and an embodiment of the overall system are described below.

[0271] The system starts from a server and receives and collects communication information from the network. The server then analyzes the collected data using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson. Based on the analysis, it calculates a sentiment score for each message, which measures whether the message is positive, negative, or neutral. For example, the message "The proposals at yesterday's meeting were great" would be assigned a positive sentiment score.

[0272] The server uses these emotion scores to determine the likelihood of inappropriate behavior in the workplace. If the negative emotion score exceeds a certain threshold, the server automatically generates a warning and feedback message. In this process, OpenAI's generative AI model is used to construct the feedback, which includes appropriate advice and guidelines for behavioral improvement. Specific feedback might include suggestions such as, "Please try to adopt the following communication style."

[0273] The device receives feedback messages sent from the server and provides information to the user. Notifications are provided in the form of pop-ups or emails, allowing users to receive feedback immediately.

[0274] In addition, the device provides a conversational interface with the user and offers educational advice through dialogue support from a generative AI model. For example, if a user asks, "How can I reduce negative language in the workplace?", the conversational AI will offer advice such as, "It would be good to incorporate expressions that show appreciation for the other person's opinion."

[0275] Users can reflect on their own behavior through feedback from their devices and improve their individual communication styles as needed. A practical example might be setting a goal such as "strive to provide constructive feedback on suggestions."

[0276] As an example of a prompt, you can input something like, "Please give specific examples of the language and approaches that are recommended to improve communication in the workplace," into the AI ​​generation model.

[0277] In this way, servers, terminals, and users work together to create a system that prevents inappropriate behavior in the workplace and supports the improvement of user behavior.

[0278] The flow of the specific process in Example 1 will be described using FIG. 11.

[0279] Step 1:

[0280] The server receives and collects communication information flowing through the enterprise network in real time. The input is text data such as chat and email, and the acquired data is stored as output. In this step, considering security, the data is also encrypted. For example, data is collected from the mail server using the SMTP protocol.

[0281] Step 2:

[0282] The server analyzes the collected text data using natural language processing technology. The input is the text data collected previously, and the output is the sentiment numerical value for each message. As a specific operation, the Google Cloud Natural Language API is used to perform processing such as keyword extraction and quantification of sentiment intensity.

[0283] Step 3:

[0284] The server determines signs of inappropriate behavior based on the calculated sentiment numerical value. The input is the sentiment numerical value, and the output is a flag indicating the possibility of inappropriate behavior. The server evaluates the sentiment numerical value using a threshold, and if the negative score exceeds the standard, it determines that there is inappropriate behavior.

[0285] Step 4:

[0286] When signs of inappropriate behavior are confirmed, the server automatically generates a warning and a feedback message. The input is the flag of inappropriate behavior, and the output is the feedback message. In this generation process, a generation AI model is utilized to compose a message including specific and constructive advice.

[0287] Step 5:

[0288] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is the notification received by the user. In this case, the terminal displays the notification in the form of a pop-up or email.

[0289] Step 6:

[0290] The device provides a conversational interface with the user and offers educational support. Input consists of questions and responses from the user, and output is advice from a conversational AI. Specifically, in response to a user's question, it uses a generative AI model to provide a response such as, "It would be good to incorporate expressions that show gratitude for the other person's opinion."

[0291] Step 7:

[0292] Users reflect on their own behavior and make improvements based on feedback from their devices. The input consists of feedback messages and advice from the AI, while the output is the improved behavior. Users set self-improvement goals, such as "strive to provide constructive feedback on suggestions."

[0293] (Application Example 1)

[0294] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0295] In modern workplaces and communities, inappropriate remarks and harassment in communication are serious problems. However, effective systems for early detection and appropriate response to these behaviors are still insufficient. As a result, there are many situations where problems have to be dealt with only after they have escalated. This invention aims to prevent these problems and provide a safe communication environment by analyzing data in real time across various communication tools and detecting abnormal behavior.

[0296] 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.

[0297] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of abnormal behavior based on the emotional score. This enables real-time early detection of abnormal behavior and appropriate feedback in the workplace and community.

[0298] "Communication data" refers to a portion of the information sent and received over a network, and includes data such as text messages and emails.

[0299] An "emotion score" is an index that analyzes received text data using natural language processing and numerically indicates whether the content is positive, negative, or neutral.

[0300] "Signs of abnormal behavior" refer to patterns, based on the results of emotional score analysis, that suggest communication or harassment behaviors that may worsen interpersonal relationships.

[0301] "Warning and feedback information" refers to text generated for the purpose of notifying users, and includes advice and warnings to encourage the improvement of abnormal behavior.

[0302] A "user terminal" is a user's operating device that can receive warnings and feedback information, and includes smartphones, computers, and other devices.

[0303] "Natural language processing technology" is a technique that uses computers to analyze text data written in human language and understand its content.

[0304] A "messaging service" is an information and communication service that enables the real-time sending and receiving of text messages.

[0305] The embodiments for implementing this invention will be described. This system mainly consists of three components: a server, a terminal, and a user.

[0306] Role of the Server

[0307] The server monitors and receives communication data flowing through the network in real time. The server uses natural language processing technology to analyze the collected text data. Existing natural language processing services such as Google Cloud Natural Language API and Azure Text Analytics are used for this analysis. The server calculates an emotion score and determines signs of abnormal behavior based on that score. If an abnormality is detected, the server generates warning and feedback information and sends it to the terminal.

[0308] Role of the Terminal

[0309] The terminal receives the feedback information sent from the server. By displaying an alert to the user based on that information, it promotes the improvement of the communication style. Furthermore, the terminal uses an interactive AI to provide customized advice to the user. As the interactive AI, models such as OpenAI's GPT model can be used. For example, when the user asks a prompt like "How did you feel when looking back on recent conversations? What kind of conversations can be considered to improve the communication style?", the terminal generates appropriate advice.

[0310] Role of the User

[0311] Users can receive feedback and advice from conversational AI provided through their devices, allowing them to reflect on their own actions and statements. This enables users to proactively improve their communication and contribute to building healthy relationships in the workplace and community.

[0312] For example, if a user's emotional score decreases through a series of messages exchanged in a chat, the system immediately sends an alert to the device and provides advice to the user via conversational AI, such as, "Your recent conversations seem a little tense. How can we try to communicate in a more relaxed manner in our next conversation?" This series of actions makes it possible to create a safe and positive communication environment.

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server receives communication data in real time over the network. The input consists of text data such as chat messages and emails. The server collects the data and stores it for analysis. During this process, it uses communication protocols to ensure data integrity.

[0316] Step 2:

[0317] The server uses natural language processing technologies such as Google Cloud Natural Language API and Azure Text Analytics to analyze incoming text data. The input is text data, and the output is a sentiment score. The analyzed sentiment score is expressed numerically and classified as positive, negative, or neutral. The data is analyzed immediately, and the results proceed to the next step.

[0318] Step 3:

[0319] The server determines signs of abnormal behavior based on a sentiment score. The input is the sentiment score, and the output is a warning flag for abnormal behavior. When the sentiment score exceeds a certain threshold, a warning flag is set, and the process moves to the next step. If abnormal behavior is detected, preparations are made to respond immediately.

[0320] Step 4:

[0321] The server generates warning and feedback information as needed. Input is a warning flag for abnormal behavior, and output is a feedback message to be sent to the user. Specifically, it selects the appropriate message from a pre-prepared message template and creates a message containing warnings and advice for the user.

[0322] Step 5:

[0323] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is displayed through the terminal's user interface. The terminal uses an intuitive GUI to enable the user to easily understand the feedback and take action.

[0324] Step 6:

[0325] The user refers to the feedback displayed on the device and receives advice on improving communication through conversational AI. Input is the user's prompts and responses to feedback, and output is specific advice generated by the AI. Based on this feedback, the user uses the conversational AI to formulate prompts to input to the AI ​​model. A concrete example of a prompt used is, "What are some ways to ease tension in recent conversations?"

[0326] 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.

[0327] This invention provides more effective feedback and interactive support by combining a system that detects and responds to signs of workplace harassment early with an emotion engine that recognizes the user's emotions. This invention mainly consists of three components: a server, a terminal, and a user.

[0328] Server Role

[0329] The server receives chat and email data flowing through the corporate network in real time and collects it in a database. The received data is then analyzed using natural language processing technology to calculate an emotional score. During this process, an integrated emotional engine analyzes the user's emotional state in detail. The emotional engine extracts emotional characteristics from the raw data, evaluates positive, negative, and neutral emotional states, and uses this to assess the risk of harassment.

[0330] Terminal role

[0331] The device notifies the user based on analysis results sent from the server and evaluations from the emotion engine. Specifically, if signs of harassment are detected, a feedback message reflecting the emotion engine's analysis results is displayed on the user's screen. This feedback includes specific areas for improvement and personalized advice tailored to the user's emotional state. Furthermore, the device provides a dialogue interface with the user and supports the improvement of the user's behavior through responses that take emotional changes into account.

[0332] User roles

[0333] Users can improve themselves through feedback messages and interactive support displayed on their devices. For example, if a user exhibits negative emotions during workplace communication, the system will consider the cause and suggest specific improvement measures and positive communication techniques. As a result, users can reduce the risk of unconsciously engaging in harassment.

[0334] This system uses an emotion engine to provide feedback that takes user emotions into account, resulting in more personalized support than ever before. It represents an important technological tool for promoting a healthy communication environment in the workplace.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The server receives chat and email communication data flowing from the corporate network in real time. The server stores the received data in an internal database for analysis.

[0338] Step 2:

[0339] The server analyzes unanalyzed data in the database using natural language processing techniques. Specifically, the server extracts keywords from the text and calculates a sentiment score based on them. A built-in sentiment engine is used for this calculation.

[0340] Step 3:

[0341] The server utilizes an emotion engine to identify the user's emotional state from the analyzed data. This emotional state includes positive, negative, and neutral states, and changes in the emotional state are also tracked.

[0342] Step 4:

[0343] The server determines signs of harassment based on the emotional score and the user's emotional state. This includes detecting when a negative emotional score exceeds a certain threshold.

[0344] Step 5:

[0345] If the server detects signs of harassment, it activates a feedback generation module to automatically generate warning and feedback messages for the user. This feedback includes personalized advice tailored to the user's feelings.

[0346] Step 6:

[0347] The device receives feedback messages sent from the server and notifies the user of the message. The device provides feedback using visual and audio notifications.

[0348] Step 7:

[0349] Users review the feedback displayed on their device and reflect on their own words and actions. Based on the specific improvement suggestions provided, users modify their communication style. They can also request detailed interactive advice via their device if needed.

[0350] Step 8:

[0351] The device responds to the user's additional questions and provides further advice and explanations through its conversational AI function. During the conversation, it can re-evaluate the user's emotional changes and reflect them in its responses.

[0352] (Example 2)

[0353] 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".

[0354] There is a need for early detection of harassment in the workplace and the provision of effective countermeasures. Traditional systems relied on simple rule-based approaches, which hindered the proper assessment of emotions and context. This resulted in potential delays in necessary feedback and the risk of inappropriate dialogue.

[0355] 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.

[0356] In this invention, the server includes means for receiving and storing communication information, means for processing the received information and calculating an emotional evaluation, and means for identifying risk indicators based on the emotional evaluation. This makes it possible to detect the risk of harassment early by utilizing emotional analysis technology and to improve user behavior through prompt and appropriate feedback and dialogue.

[0357] "Communication information" is a general term for data and messages that are sent and received via a digital network.

[0358] "Storage" refers to the process of saving received data in a specific format so that it can be used later.

[0359] "Processing" refers to a series of operations that analyze raw data and extract or transform useful information.

[0360] "Sentimental evaluation" is a way of expressing the emotional elements contained in the information in question using numerical values ​​or categories.

[0361] "Risk indicators" refer to identifying signs that suggest the possibility of potential problems or disruptions occurring.

[0362] "Automatically generated information" refers to data or messages generated by a system, rather than manually, based on specific algorithms or rules.

[0363] "User terminal" is a general term for electronic devices used to display information and enable users to operate it.

[0364] "Adjusting behavior" is the process of helping users improve or optimize their behavior and work patterns.

[0365] "Emotion analysis technology" is a technology that analyzes text and audio data to identify and evaluate emotions.

[0366] "Real-time" refers to the simultaneous or near-simultaneous generation and processing of data.

[0367] This invention is a system for early detection of signs of harassment in the workplace and providing interactive support, and consists of three components: a server, a terminal, and a user.

[0368] The server receives communication information, such as chats and emails, flowing over the network in real time and stores it in a database. The server processes the received communication information using natural language processing (NLP) technology and performs sentiment evaluation using sentiment analysis technology. Specifically, it extracts emotional elements from the text, performs sentiment evaluation by classifying them into categories such as positive, negative, and neutral, and identifies risk indicators based on this evaluation.

[0369] The device receives sentiment ratings and risk indicators transmitted from the server and provides automatically generated feedback information to the user based on this data. This feedback includes specific actions and measures to mitigate risks, and the device presents this information through interaction with the user. Furthermore, the device features an interactive dialogue interface that suggests appropriate improvement suggestions based on the user's information.

[0370] Users can adjust their behavior based on the feedback information displayed on their device. For example, if a user expresses negative emotions, the system considers the cause and suggests positive communication methods to reduce the risk of harassment.

[0371] As a concrete example, the prompt text used would be, "Assess your emotional state based on your recent communication history at work and suggest positive areas for improvement." This prompt makes it easier for users to understand how to improve their behavior according to the system's guidelines.

[0372] In this way, the system utilizes emotion analysis and natural language processing technologies to provide users with personalized feedback, thereby maintaining and promoting a healthy communication environment in the workplace.

[0373] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0374] Step 1:

[0375] The server receives communication information from the network, specifically chat messages and emails. This raw text data is immediately stored in a database. The stored data is then organized and saved for later analysis.

[0376] Step 2:

[0377] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, it breaks down the elements that make up a sentence through morphological analysis and extracts keywords. The input to this process is the stored raw data, and the output is processed data ready for sentiment analysis.

[0378] Step 3:

[0379] The server uses sentiment analysis technology to perform an emotional assessment based on the processed data. The emotion engine receives this data and calculates an emotional score, such as positive, negative, or neutral. This assessment is output in specific numerical or categorical form, which serves as a basis for deciding the next step.

[0380] Step 4:

[0381] The server identifies risk indicators based on the calculated sentiment assessment results. Here, if the sentiment score exceeds a certain threshold, it is determined that there is a high risk of harassment. In this step, the sentiment score is used as input, and risk assessment data is generated as output.

[0382] Step 5:

[0383] The device generates feedback information based on sentiment assessments and risk indicators obtained from the server. For example, if a negative emotion is detected, the device creates feedback that includes suggested positive actions. In this process, risk assessment data is used as input, and a feedback message is generated as output.

[0384] Step 6:

[0385] The device displays the generated feedback message on the user screen. Specifically, this involves a process of notifying the user so they can immediately see the generated message. This notification information serves as a foundation for the user to understand the situation and begin adjusting their actions.

[0386] Step 7:

[0387] Users review feedback messages received from their devices and reflect them in their actions. They improve their interactions in the workplace by trying out suggested positive communication techniques. The input in this process is the feedback messages, and the output is the user's improved behavior.

[0388] (Application Example 2)

[0389] 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."

[0390] In today's workplace, it is crucial to detect early signs of harassment in communication and maintain a healthy work environment. However, monitoring emotional changes in real time and responding appropriately is not easy. Furthermore, there is a lack of means to specifically assess high-risk situations and provide appropriate corrective measures. Therefore, a more effective system is needed to prevent harassment from occurring and improve the workplace environment.

[0391] 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.

[0392] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of harassment based on the emotional score. This makes it possible to evaluate the health of workplace communication in real time, identify high-risk situations specifically, and provide necessary feedback at the appropriate time.

[0393] "Communication data" refers to information transmitted and received in digital format, including voice, text, and images transmitted over a network.

[0394] An "emotion score" is a numerical indicator of a user's emotional state, extracted from text analyzed using natural language processing, and shows emotional tendencies such as positive, negative, or neutral.

[0395] "Signs of harassment" are indicators that suggest the possibility of aggressive or inappropriate behavior towards others in workplace communication or conduct.

[0396] A "feedback message" is a message that provides users with specific suggestions for improvement or advice based on the results of the analysis of received data.

[0397] A "user terminal" refers to a device used to receive information transmitted from a system, and includes personal computers, smartphones, and other similar devices.

[0398] "Workplace environment risks" refer to factors that can affect the health of an organization, such as harassment and interpersonal problems that may arise in the course of work.

[0399] "Means of proposing improvement measures" refers to a process for providing specific actions and methods to address problems identified based on the analysis results.

[0400] "Natural language processing technology" is a technology that processes human language using computers, enabling the analysis of text data and the understanding of intentions and emotions based on that analysis.

[0401] This invention is a system for analyzing signs of harassment in the workplace, with a server, terminals, and users each playing specific roles. The server receives communication data via the network and collects data. Natural language processing techniques are used to analyze the text of the received data and calculate an emotion score. The Hugging Face transformers library is suitable for this process.

[0402] The server analyzes the data to identify signs of harassment and automatically generates warning and feedback messages. These messages are sent to the user's device, which can be a personal computer or smartphone. The user receives the feedback message displayed on the device. The device also displays the results of a workplace risk assessment and suggests specific improvement measures.

[0403] Based on this information, users can deepen their understanding of emotions and improve their behavior based on feedback. This improves the quality of communication in the workplace and reduces the risk of harassment.

[0404] For example, if an employee sends a message through the company system stating, "I'm frustrated because my opinions aren't being heard in recent meetings," the server analyzes this in real time and determines that negative emotions are on the rise. Based on this, a message recommending "considering opportunities to learn how to give constructive feedback" is generated and sent to the user's terminal. This encourages the user to take actions that will lead to improved communication in the workplace.

[0405] An example of a prompt message might be: "Analyze the following workplace communication data and calculate the sentiment score. Classify it as positive, negative, or neutral, and determine whether there is a risk of harassment."

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The server receives communication data through the corporate network. Specifically, this includes text data such as chat messages and emails. The input is raw communication data obtained over the network, and the output is in a digital data format that can be processed within the server. In this step, data formatting and cleaning are performed.

[0409] Step 2:

[0410] The server performs natural language processing on the received data and analyzes the text. Specifically, the text is tokenized, and keywords related to important emotions are extracted. Based on this, an emotion score is calculated. In this process, the Hugging Face transformers library is used to derive positive, negative, and neutral scores through analysis. The input is the data formatted in the previous step, and the output is the emotion score.

[0411] Step 3:

[0412] The server determines signs of harassment based on the calculated emotional score. The analysis results are compared to a threshold, and if negative emotions exceed a certain value, it is determined that there is a risk of harassment. In this step, the input is the emotional score, and the output is the determination result indicating whether or not there is a risk of harassment.

[0413] Step 4:

[0414] The server automatically generates warning and feedback messages based on the assessment results. These messages include particularly necessary corrective actions and countermeasures for risks. The output is a feedback message containing specific warnings and advice. The input is the harassment risk assessment result.

[0415] Step 5:

[0416] The server notifies the user terminal of the generated feedback message. The terminal displays the notification to the user, making the content viewable. The input from the server is the feedback message, and the output is the content displayed on the terminal. This step allows the user to take quick action as needed.

[0417] Step 6:

[0418] Based on the displayed feedback, users assess risks in the workplace environment and implement suggested improvements. They utilize the information displayed on their devices to plan and implement actions aimed at improving workplace communication. Input is feedback messages, and output is changes in user behavior and efforts toward improvement. Voluntary user participation is essential in this process.

[0419] 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.

[0420] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention is a system aimed at the early detection and prevention of harassment in the workplace. The system mainly consists of three components: a server, terminals, and users. The specific roles of each component and the operation of the overall system are described below.

[0436] Server Role

[0437] The server monitors communication data flowing through the corporate network in real time and collects text data such as chats and emails. The server analyzes the received data using natural language processing technology and calculates a sentiment score for each message. This sentiment score serves as a criterion for determining whether the expressions in the message are positive, negative, or neutral. The server uses the sentiment score to determine if there are signs of harassment and generates warning and feedback messages as needed.

[0438] Terminal role

[0439] The device receives notifications sent from the server and presents information to the user. Specifically, it notifies the user of feedback messages generated by the server, displaying warnings and advice. In addition, the device provides a dialogue interface with the user, offering educational support to help the user reflect on their own words and actions and make improvements. This dialogue is primarily conducted by a conversational AI, which automatically provides appropriate advice.

[0440] User roles

[0441] Users reflect on their words and actions based on feedback received from their devices. If signs of harassment are identified, users can improve their communication style by referring to the displayed advice. Furthermore, users can learn better ways of expressing themselves and communication approaches through interaction with the conversational AI. In this way, users can proactively contribute to improving the workplace environment.

[0442] As described above, this system utilizes real-time data analysis and automated feedback functions to reduce workplace harassment and provide a safe working environment. This invention is particularly characterized by its emotion scoring process and the feedback generation process based on it.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The server receives communication data in real time from chat and email on the corporate network. The server stores the received data in a database for analysis.

[0446] Step 2:

[0447] The server provides unanalyzed data from the database to the sentiment analysis module. The server uses natural language processing techniques to analyze the text data and calculate a sentiment score for each message. This sentiment score indicates whether the message is positive, negative, or neutral.

[0448] Step 3:

[0449] The server determines whether a message may be harassment based on the calculated sentiment score. The server sets a threshold and selects messages with negative scores exceeding that threshold as targets.

[0450] Step 4:

[0451] When the server determines a message may constitute harassment, it automatically generates a warning and feedback message using a feedback generation module. This message includes specific areas for improvement and behaviors that should be reconsidered.

[0452] Step 5:

[0453] The device receives feedback messages sent from the server and displays them on the user screen. The device visually provides the user with warnings and specific advice.

[0454] Step 6:

[0455] Users can review the feedback displayed on their device and reflect on their own words and actions. Based on the advice displayed, users can re-evaluate their communication methods and learn ways to improve.

[0456] Step 7:

[0457] The device provides conversational support to the user through an interactive AI. Through these conversations, the user can receive specific improvement examples and further advice. The device suggests appropriate solutions to the problems the user is facing.

[0458] (Example 1)

[0459] 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."

[0460] In today's workplace environment, there is a need for proactive prevention and rapid response to inappropriate behavior. However, traditional methods make it difficult to easily detect such behavior, potentially leading to the escalation of problems. Furthermore, there is a lack of appropriate feedback on the situation and individualized support, which hinders the improvement of user behavior.

[0461] 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.

[0462] In this invention, the server includes means for receiving and collecting communication information, means for analyzing the received information and calculating an emotional score, and means for determining signs of inappropriate behavior based on the emotional score. This makes it possible to detect inappropriate behavior in real time, issue warnings as needed, and provide appropriate advice to the user.

[0463] "Communication information" refers to all digital messages and data sent and received over a network.

[0464] "Received information" refers to data and messages acquired by the system from external sources.

[0465] "Sentiment metric" is a numerical indicator that represents the emotional characteristics of text data and is used to evaluate the intensity of emotions such as positive, negative, and neutral.

[0466] "Inappropriate conduct" refers to actions or words that violate the ethics and rules required within the workplace or organization.

[0467] A "warning" refers to a message or notification intended to draw attention to specific actions or behaviors.

[0468] "User terminal" refers to a computing device used to receive information from a server.

[0469] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0470] "Dialogue support" refers to support aimed at promoting behavioral improvement through two-way communication with users.

[0471] This invention is a system for detecting and preventing inappropriate behavior in the workplace. This system primarily consists of three components: a server, a terminal, and a user. The specific operations of each component and an embodiment of the overall system are described below.

[0472] The system starts from a server and receives and collects communication information from the network. The server then analyzes the collected data using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson. Based on the analysis, it calculates a sentiment score for each message, which measures whether the message is positive, negative, or neutral. For example, the message "The proposals at yesterday's meeting were great" would be assigned a positive sentiment score.

[0473] The server uses these emotion scores to determine the likelihood of inappropriate behavior in the workplace. If the negative emotion score exceeds a certain threshold, the server automatically generates a warning and feedback message. In this process, OpenAI's generative AI model is used to construct the feedback, which includes appropriate advice and guidelines for behavioral improvement. Specific feedback might include suggestions such as, "Please try to adopt the following communication style."

[0474] The device receives feedback messages sent from the server and provides information to the user. Notifications are provided in the form of pop-ups or emails, allowing users to receive feedback immediately.

[0475] In addition, the device provides a conversational interface with the user and offers educational advice through dialogue support from a generative AI model. For example, if a user asks, "How can I reduce negative language in the workplace?", the conversational AI will offer advice such as, "It would be good to incorporate expressions that show appreciation for the other person's opinion."

[0476] Users can reflect on their own behavior through feedback from their devices and improve their individual communication styles as needed. A practical example might be setting a goal such as "strive to provide constructive feedback on suggestions."

[0477] As an example of a prompt, you can input something like, "Please give specific examples of the language and approaches that are recommended to improve communication in the workplace," into the AI ​​generation model.

[0478] In this way, servers, terminals, and users work together to create a system that prevents inappropriate behavior in the workplace and supports the improvement of user behavior.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The server receives and collects communication information flowing through the company's network in real time. Input is text data such as chat messages and emails, and the acquired data is stored as output. This step also includes data encryption for security purposes. For example, data is collected from an email server using the SMTP protocol.

[0482] Step 2:

[0483] The server analyzes the collected text data using natural language processing techniques. The input is the previously collected text data, and the output is a sentiment score for each message. Specifically, it uses the Google Cloud Natural Language API to extract keywords and quantify the intensity of sentiment.

[0484] Step 3:

[0485] The server determines signs of inappropriate behavior based on calculated sentiment scores. The input is a sentiment score, and the output is a flag indicating the possibility of inappropriate behavior. The server evaluates the sentiment score using a threshold, and if the negative score exceeds the threshold, it determines that inappropriate behavior has occurred.

[0486] Step 4:

[0487] The server automatically generates warning and feedback messages when signs of inappropriate behavior are detected. The input is a flag indicating inappropriate behavior, and the output is a feedback message. In this generation process, a generative AI model is used to construct messages that include specific and constructive advice.

[0488] Step 5:

[0489] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is the notification received by the user. In this case, the terminal displays the notification in the form of a pop-up or email.

[0490] Step 6:

[0491] The device provides a conversational interface with the user and offers educational support. Input consists of questions and responses from the user, and output is advice from a conversational AI. Specifically, in response to a user's question, it uses a generative AI model to provide a response such as, "It would be good to incorporate expressions that show gratitude for the other person's opinion."

[0492] Step 7:

[0493] Users reflect on their own behavior and make improvements based on feedback from their devices. The input consists of feedback messages and advice from the AI, while the output is the improved behavior. Users set self-improvement goals, such as "strive to provide constructive feedback on suggestions."

[0494] (Application Example 1)

[0495] 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."

[0496] In modern workplaces and communities, inappropriate remarks and harassment in communication are serious problems. However, effective systems for early detection and appropriate response to these behaviors are still insufficient. As a result, there are many situations where problems have to be dealt with only after they have escalated. This invention aims to prevent these problems and provide a safe communication environment by analyzing data in real time across various communication tools and detecting abnormal behavior.

[0497] 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.

[0498] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of abnormal behavior based on the emotional score. This enables real-time early detection of abnormal behavior and appropriate feedback in the workplace and community.

[0499] "Communication data" refers to a portion of the information sent and received over a network, and includes data such as text messages and emails.

[0500] An "emotion score" is an index that analyzes received text data using natural language processing and numerically indicates whether the content is positive, negative, or neutral.

[0501] "Signs of abnormal behavior" refer to patterns, based on the results of emotional score analysis, that suggest communication or harassment behaviors that may worsen interpersonal relationships.

[0502] "Warning and feedback information" refers to text generated for the purpose of notifying users, and includes advice and warnings to encourage the improvement of abnormal behavior.

[0503] A "user terminal" is a user's operating device that can receive warnings and feedback information, and includes smartphones, computers, and other devices.

[0504] "Natural language processing technology" is a technique that uses computers to analyze text data written in human language and understand its content.

[0505] A "messaging service" is an information and communication service that enables the sending and receiving of text messages in real time.

[0506] The embodiments for carrying out this invention will now be described. This system mainly consists of three components: a server, a terminal, and a user.

[0507] Server Role

[0508] The server monitors and receives communication data flowing through the network in real time. The server uses natural language processing techniques to analyze the collected text data. Existing natural language processing services such as Google Cloud Natural Language API and Azure Text Analytics are used for this analysis. The server calculates a sentiment score and uses this score to determine signs of abnormal behavior. If an anomaly is detected, the server generates warning and feedback information and sends it to the terminal.

[0509] Terminal role

[0510] The device receives feedback information sent from the server. Based on this information, it displays alerts to the user to encourage improvements in their communication style. Furthermore, the device uses conversational AI to provide customized advice to the user. OpenAI's GPT model is one example of a conversational AI that can be used. For example, if the user asks prompts such as, "Looking back on recent conversations, how did you feel? What kind of conversations can you think of to improve your communication style?", the device will generate appropriate advice.

[0511] User roles

[0512] Users can receive feedback and advice from conversational AI provided through their devices, allowing them to reflect on their own actions and statements. This enables users to proactively improve their communication and contribute to building healthy relationships in the workplace and community.

[0513] For example, if a user's emotional score decreases through a series of messages exchanged in a chat, the system immediately sends an alert to the device and provides advice to the user via conversational AI, such as, "Your recent conversations seem a little tense. How can we try to communicate in a more relaxed manner in our next conversation?" This series of actions makes it possible to create a safe and positive communication environment.

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] The server receives communication data in real time over the network. The input consists of text data such as chat messages and emails. The server collects the data and stores it for analysis. During this process, it uses communication protocols to ensure data integrity.

[0517] Step 2:

[0518] The server uses natural language processing technologies such as Google Cloud Natural Language API and Azure Text Analytics to analyze incoming text data. The input is text data, and the output is a sentiment score. The analyzed sentiment score is expressed numerically and classified as positive, negative, or neutral. The data is analyzed immediately, and the results proceed to the next step.

[0519] Step 3:

[0520] The server determines signs of abnormal behavior based on a sentiment score. The input is the sentiment score, and the output is a warning flag for abnormal behavior. When the sentiment score exceeds a certain threshold, a warning flag is set, and the process moves to the next step. If abnormal behavior is detected, preparations are made to respond immediately.

[0521] Step 4:

[0522] The server generates warning and feedback information as needed. Input is a warning flag for abnormal behavior, and output is a feedback message to be sent to the user. Specifically, it selects the appropriate message from a pre-prepared message template and creates a message containing warnings and advice for the user.

[0523] Step 5:

[0524] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is displayed through the terminal's user interface. The terminal uses an intuitive GUI to enable the user to easily understand the feedback and take action.

[0525] Step 6:

[0526] The user refers to the feedback displayed on the device and receives advice on improving communication through conversational AI. Input is the user's prompts and responses to feedback, and output is specific advice generated by the AI. Based on this feedback, the user uses the conversational AI to formulate prompts to input to the AI ​​model. A concrete example of a prompt used is, "What are some ways to ease tension in recent conversations?"

[0527] 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.

[0528] This invention provides more effective feedback and interactive support by combining a system that detects and responds to signs of workplace harassment early with an emotion engine that recognizes the user's emotions. This invention mainly consists of three components: a server, a terminal, and a user.

[0529] Server Role

[0530] The server receives chat and email data flowing through the corporate network in real time and collects it in a database. The received data is then analyzed using natural language processing technology to calculate an emotional score. During this process, an integrated emotional engine analyzes the user's emotional state in detail. The emotional engine extracts emotional characteristics from the raw data, evaluates positive, negative, and neutral emotional states, and uses this to assess the risk of harassment.

[0531] Terminal role

[0532] The device notifies the user based on analysis results sent from the server and evaluations from the emotion engine. Specifically, if signs of harassment are detected, a feedback message reflecting the emotion engine's analysis results is displayed on the user's screen. This feedback includes specific areas for improvement and personalized advice tailored to the user's emotional state. Furthermore, the device provides a dialogue interface with the user and supports the improvement of the user's behavior through responses that take emotional changes into account.

[0533] User roles

[0534] Users can improve themselves through feedback messages and interactive support displayed on their devices. For example, if a user exhibits negative emotions during workplace communication, the system will consider the cause and suggest specific improvement measures and positive communication techniques. As a result, users can reduce the risk of unconsciously engaging in harassment.

[0535] This system uses an emotion engine to provide feedback that takes user emotions into account, resulting in more personalized support than ever before. It represents an important technological tool for promoting a healthy communication environment in the workplace.

[0536] The following describes the processing flow.

[0537] Step 1:

[0538] The server receives chat and email communication data flowing from the corporate network in real time. The server stores the received data in an internal database for analysis.

[0539] Step 2:

[0540] The server analyzes unanalyzed data in the database using natural language processing techniques. Specifically, the server extracts keywords from the text and calculates a sentiment score based on them. A built-in sentiment engine is used for this calculation.

[0541] Step 3:

[0542] The server utilizes an emotion engine to identify the user's emotional state from the analyzed data. This emotional state includes positive, negative, and neutral states, and changes in the emotional state are also tracked.

[0543] Step 4:

[0544] The server determines signs of harassment based on the emotional score and the user's emotional state. This includes detecting when a negative emotional score exceeds a certain threshold.

[0545] Step 5:

[0546] If the server detects signs of harassment, it activates a feedback generation module to automatically generate warning and feedback messages for the user. This feedback includes personalized advice tailored to the user's feelings.

[0547] Step 6:

[0548] The device receives feedback messages sent from the server and notifies the user of the message. The device provides feedback using visual and audio notifications.

[0549] Step 7:

[0550] Users review the feedback displayed on their device and reflect on their own words and actions. Based on the specific improvement suggestions provided, users modify their communication style. They can also request detailed interactive advice via their device if needed.

[0551] Step 8:

[0552] The device responds to the user's additional questions and provides further advice and explanations through its conversational AI function. During the conversation, it can re-evaluate the user's emotional changes and reflect them in its responses.

[0553] (Example 2)

[0554] 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."

[0555] There is a need for early detection of harassment in the workplace and the provision of effective countermeasures. Traditional systems relied on simple rule-based approaches, which hindered the proper assessment of emotions and context. This resulted in potential delays in necessary feedback and the risk of inappropriate dialogue.

[0556] 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.

[0557] In this invention, the server includes means for receiving and storing communication information, means for processing the received information and calculating an emotional evaluation, and means for identifying risk indicators based on the emotional evaluation. This makes it possible to detect the risk of harassment early by utilizing emotional analysis technology and to improve user behavior through prompt and appropriate feedback and dialogue.

[0558] "Communication information" is a general term for data and messages that are sent and received via a digital network.

[0559] "Storage" refers to the process of saving received data in a specific format so that it can be used later.

[0560] "Processing" refers to a series of operations that analyze raw data and extract or transform useful information.

[0561] "Sentimental evaluation" is a way of expressing the emotional elements contained in the information in question using numerical values ​​or categories.

[0562] "Risk indicators" refer to identifying signs that suggest the possibility of potential problems or disruptions occurring.

[0563] "Automatically generated information" refers to data or messages generated by a system, rather than manually, based on specific algorithms or rules.

[0564] "User terminal" is a general term for electronic devices used to display information and enable users to operate it.

[0565] "Adjusting behavior" is the process of helping users improve or optimize their behavior and work patterns.

[0566] "Emotion analysis technology" is a technology that analyzes text and audio data to identify and evaluate emotions.

[0567] "Real-time" refers to the simultaneous or near-simultaneous generation and processing of data.

[0568] This invention is a system for early detection of signs of harassment in the workplace and providing interactive support, and consists of three components: a server, a terminal, and a user.

[0569] The server receives communication information, such as chats and emails, flowing over the network in real time and stores it in a database. The server processes the received communication information using natural language processing (NLP) technology and performs sentiment evaluation using sentiment analysis technology. Specifically, it extracts emotional elements from the text, performs sentiment evaluation by classifying them into categories such as positive, negative, and neutral, and identifies risk indicators based on this evaluation.

[0570] The device receives sentiment ratings and risk indicators transmitted from the server and provides automatically generated feedback information to the user based on this data. This feedback includes specific actions and measures to mitigate risks, and the device presents this information through interaction with the user. Furthermore, the device features an interactive dialogue interface that suggests appropriate improvement suggestions based on the user's information.

[0571] Users can adjust their behavior based on the feedback information displayed on their device. For example, if a user expresses negative emotions, the system considers the cause and suggests positive communication methods to reduce the risk of harassment.

[0572] As a concrete example, the prompt text used would be, "Assess your emotional state based on your recent communication history at work and suggest positive areas for improvement." This prompt makes it easier for users to understand how to improve their behavior according to the system's guidelines.

[0573] In this way, the system utilizes emotion analysis and natural language processing technologies to provide users with personalized feedback, thereby maintaining and promoting a healthy communication environment in the workplace.

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] The server receives communication information from the network, specifically chat messages and emails. This raw text data is immediately stored in a database. The stored data is then organized and saved for later analysis.

[0577] Step 2:

[0578] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, it breaks down the elements that make up a sentence through morphological analysis and extracts keywords. The input to this process is the stored raw data, and the output is processed data ready for sentiment analysis.

[0579] Step 3:

[0580] The server uses sentiment analysis technology to perform an emotional assessment based on the processed data. The emotion engine receives this data and calculates an emotional score, such as positive, negative, or neutral. This assessment is output in specific numerical or categorical form, which serves as a basis for deciding the next step.

[0581] Step 4:

[0582] The server identifies risk indicators based on the calculated sentiment assessment results. Here, if the sentiment score exceeds a certain threshold, it is determined that there is a high risk of harassment. In this step, the sentiment score is used as input, and risk assessment data is generated as output.

[0583] Step 5:

[0584] The device generates feedback information based on sentiment assessments and risk indicators obtained from the server. For example, if a negative emotion is detected, the device creates feedback that includes suggested positive actions. In this process, risk assessment data is used as input, and a feedback message is generated as output.

[0585] Step 6:

[0586] The device displays the generated feedback message on the user screen. Specifically, this involves a process of notifying the user so they can immediately see the generated message. This notification information serves as a foundation for the user to understand the situation and begin adjusting their actions.

[0587] Step 7:

[0588] Users review feedback messages received from their devices and reflect them in their actions. They improve their interactions in the workplace by trying out suggested positive communication techniques. The input in this process is the feedback messages, and the output is the user's improved behavior.

[0589] (Application Example 2)

[0590] 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."

[0591] In today's workplace, it is crucial to detect early signs of harassment in communication and maintain a healthy work environment. However, monitoring emotional changes in real time and responding appropriately is not easy. Furthermore, there is a lack of means to specifically assess high-risk situations and provide appropriate corrective measures. Therefore, a more effective system is needed to prevent harassment from occurring and improve the workplace environment.

[0592] 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.

[0593] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of harassment based on the emotional score. This makes it possible to evaluate the health of workplace communication in real time, identify high-risk situations specifically, and provide necessary feedback at the appropriate time.

[0594] "Communication data" refers to information transmitted and received in digital format, including voice, text, and images transmitted over a network.

[0595] An "emotion score" is a numerical indicator of a user's emotional state, extracted from text analyzed using natural language processing, and shows emotional tendencies such as positive, negative, or neutral.

[0596] "Signs of harassment" are indicators that suggest the possibility of aggressive or inappropriate behavior towards others in workplace communication or conduct.

[0597] A "feedback message" is a message that provides users with specific suggestions for improvement or advice based on the results of the analysis of received data.

[0598] A "user terminal" refers to a device used to receive information transmitted from a system, and includes personal computers, smartphones, and other similar devices.

[0599] "Workplace environment risks" refer to factors that can affect the health of an organization, such as harassment and interpersonal problems that may arise in the course of work.

[0600] "Means of proposing improvement measures" refers to a process for providing specific actions and methods to address problems identified based on the analysis results.

[0601] "Natural language processing technology" is a technology that processes human language using computers, enabling the analysis of text data and the understanding of intentions and emotions based on that analysis.

[0602] This invention is a system for analyzing signs of harassment in the workplace, with a server, terminals, and users each playing specific roles. The server receives communication data via the network and collects data. Natural language processing techniques are used to analyze the text of the received data and calculate an emotion score. The Hugging Face transformers library is suitable for this process.

[0603] The server analyzes the data to identify signs of harassment and automatically generates warning and feedback messages. These messages are sent to the user's device, which can be a personal computer or smartphone. The user receives the feedback message displayed on the device. The device also displays the results of a workplace risk assessment and suggests specific improvement measures.

[0604] Based on this information, users can deepen their understanding of emotions and improve their behavior based on feedback. This improves the quality of communication in the workplace and reduces the risk of harassment.

[0605] For example, if an employee sends a message through the company system stating, "I'm frustrated because my opinions aren't being heard in recent meetings," the server analyzes this in real time and determines that negative emotions are on the rise. Based on this, a message recommending "considering opportunities to learn how to give constructive feedback" is generated and sent to the user's terminal. This encourages the user to take actions that will lead to improved communication in the workplace.

[0606] An example of a prompt message might be: "Analyze the following workplace communication data and calculate the sentiment score. Classify it as positive, negative, or neutral, and determine whether there is a risk of harassment."

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] The server receives communication data through the corporate network. Specifically, this includes text data such as chat messages and emails. The input is raw communication data obtained over the network, and the output is in a digital data format that can be processed within the server. In this step, data formatting and cleaning are performed.

[0610] Step 2:

[0611] The server performs natural language processing on the received data and analyzes the text. Specifically, the text is tokenized, and keywords related to important emotions are extracted. Based on this, an emotion score is calculated. In this process, the Hugging Face transformers library is used to derive positive, negative, and neutral scores through analysis. The input is the data formatted in the previous step, and the output is the emotion score.

[0612] Step 3:

[0613] The server determines signs of harassment based on the calculated emotional score. The analysis results are compared to a threshold, and if negative emotions exceed a certain value, it is determined that there is a risk of harassment. In this step, the input is the emotional score, and the output is the determination result indicating whether or not there is a risk of harassment.

[0614] Step 4:

[0615] The server automatically generates warning and feedback messages based on the assessment results. These messages include particularly necessary corrective actions and countermeasures for risks. The output is a feedback message containing specific warnings and advice. The input is the harassment risk assessment result.

[0616] Step 5:

[0617] The server notifies the user terminal of the generated feedback message. The terminal displays the notification to the user, making the content viewable. The input from the server is the feedback message, and the output is the content displayed on the terminal. This step allows the user to take quick action as needed.

[0618] Step 6:

[0619] Based on the displayed feedback, users assess risks in the workplace environment and implement suggested improvements. They utilize the information displayed on their devices to plan and implement actions aimed at improving workplace communication. Input is feedback messages, and output is changes in user behavior and efforts toward improvement. Voluntary user participation is essential in this process.

[0620] 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.

[0621] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0622] 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.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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".

[0637] This invention is a system aimed at the early detection and prevention of harassment in the workplace. The system mainly consists of three components: a server, terminals, and users. The specific roles of each component and the operation of the overall system are described below.

[0638] Server Role

[0639] The server monitors communication data flowing through the corporate network in real time and collects text data such as chats and emails. The server analyzes the received data using natural language processing technology and calculates a sentiment score for each message. This sentiment score serves as a criterion for determining whether the expressions in the message are positive, negative, or neutral. The server uses the sentiment score to determine if there are signs of harassment and generates warning and feedback messages as needed.

[0640] Terminal role

[0641] The device receives notifications sent from the server and presents information to the user. Specifically, it notifies the user of feedback messages generated by the server, displaying warnings and advice. In addition, the device provides a dialogue interface with the user, offering educational support to help the user reflect on their own words and actions and make improvements. This dialogue is primarily conducted by a conversational AI, which automatically provides appropriate advice.

[0642] User roles

[0643] Users reflect on their words and actions based on feedback received from their devices. If signs of harassment are identified, users can improve their communication style by referring to the displayed advice. Furthermore, users can learn better ways of expressing themselves and communication approaches through interaction with the conversational AI. In this way, users can proactively contribute to improving the workplace environment.

[0644] As described above, this system utilizes real-time data analysis and automated feedback functions to reduce workplace harassment and provide a safe working environment. This invention is particularly characterized by its emotion scoring process and the feedback generation process based on it.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The server receives communication data in real time from chat and email on the corporate network. The server stores the received data in a database for analysis.

[0648] Step 2:

[0649] The server provides unanalyzed data from the database to the sentiment analysis module. The server uses natural language processing techniques to analyze the text data and calculate a sentiment score for each message. This sentiment score indicates whether the message is positive, negative, or neutral.

[0650] Step 3:

[0651] The server determines whether a message may be harassment based on the calculated sentiment score. The server sets a threshold and selects messages with negative scores exceeding that threshold as targets.

[0652] Step 4:

[0653] When the server determines a message may constitute harassment, it automatically generates a warning and feedback message using a feedback generation module. This message includes specific areas for improvement and behaviors that should be reconsidered.

[0654] Step 5:

[0655] The device receives feedback messages sent from the server and displays them on the user screen. The device visually provides the user with warnings and specific advice.

[0656] Step 6:

[0657] Users can review the feedback displayed on their device and reflect on their own words and actions. Based on the advice displayed, users can re-evaluate their communication methods and learn ways to improve.

[0658] Step 7:

[0659] The device provides conversational support to the user through an interactive AI. Through these conversations, the user can receive specific improvement examples and further advice. The device suggests appropriate solutions to the problems the user is facing.

[0660] (Example 1)

[0661] 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".

[0662] In today's workplace environment, there is a need for proactive prevention and rapid response to inappropriate behavior. However, traditional methods make it difficult to easily detect such behavior, potentially leading to the escalation of problems. Furthermore, there is a lack of appropriate feedback on the situation and individualized support, which hinders the improvement of user behavior.

[0663] 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.

[0664] In this invention, the server includes means for receiving and collecting communication information, means for analyzing the received information and calculating an emotional score, and means for determining signs of inappropriate behavior based on the emotional score. This makes it possible to detect inappropriate behavior in real time, issue warnings as needed, and provide appropriate advice to the user.

[0665] "Communication information" refers to all digital messages and data sent and received over a network.

[0666] "Received information" refers to data and messages acquired by the system from external sources.

[0667] "Sentiment metric" is a numerical indicator that represents the emotional characteristics of text data and is used to evaluate the intensity of emotions such as positive, negative, and neutral.

[0668] "Inappropriate conduct" refers to actions or words that violate the ethics and rules required within the workplace or organization.

[0669] A "warning" refers to a message or notification intended to draw attention to specific actions or behaviors.

[0670] "User terminal" refers to a computing device used to receive information from a server.

[0671] "Natural language processing technology" refers to the technology that enables computers to understand and analyze human language.

[0672] "Dialogue support" refers to support aimed at promoting behavioral improvement through two-way communication with users.

[0673] This invention is a system for detecting and preventing inappropriate behavior in the workplace. This system primarily consists of three components: a server, a terminal, and a user. The specific operations of each component and an embodiment of the overall system are described below.

[0674] The system starts from a server and receives and collects communication information from the network. The server then analyzes the collected data using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson. Based on the analysis, it calculates a sentiment score for each message, which measures whether the message is positive, negative, or neutral. For example, the message "The proposals at yesterday's meeting were great" would be assigned a positive sentiment score.

[0675] The server uses these emotion scores to determine the likelihood of inappropriate behavior in the workplace. If the negative emotion score exceeds a certain threshold, the server automatically generates a warning and feedback message. In this process, OpenAI's generative AI model is used to construct the feedback, which includes appropriate advice and guidelines for behavioral improvement. Specific feedback might include suggestions such as, "Please try to adopt the following communication style."

[0676] The device receives feedback messages sent from the server and provides information to the user. Notifications are provided in the form of pop-ups or emails, allowing users to receive feedback immediately.

[0677] In addition, the device provides a conversational interface with the user and offers educational advice through dialogue support from a generative AI model. For example, if a user asks, "How can I reduce negative language in the workplace?", the conversational AI will offer advice such as, "It would be good to incorporate expressions that show appreciation for the other person's opinion."

[0678] Users can reflect on their own behavior through feedback from their devices and improve their individual communication styles as needed. A practical example might be setting a goal such as "strive to provide constructive feedback on suggestions."

[0679] As an example of a prompt, you can input something like, "Please give specific examples of the language and approaches that are recommended to improve communication in the workplace," into the AI ​​generation model.

[0680] In this way, servers, terminals, and users work together to create a system that prevents inappropriate behavior in the workplace and supports the improvement of user behavior.

[0681] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0682] Step 1:

[0683] The server receives and collects communication information flowing through the company's network in real time. Input is text data such as chat messages and emails, and the acquired data is stored as output. This step also includes data encryption for security purposes. For example, data is collected from an email server using the SMTP protocol.

[0684] Step 2:

[0685] The server analyzes the collected text data using natural language processing techniques. The input is the previously collected text data, and the output is a sentiment score for each message. Specifically, it uses the Google Cloud Natural Language API to extract keywords and quantify the intensity of sentiment.

[0686] Step 3:

[0687] The server determines signs of inappropriate behavior based on calculated sentiment scores. The input is a sentiment score, and the output is a flag indicating the possibility of inappropriate behavior. The server evaluates the sentiment score using a threshold, and if the negative score exceeds the threshold, it determines that inappropriate behavior has occurred.

[0688] Step 4:

[0689] The server automatically generates warning and feedback messages when signs of inappropriate behavior are detected. The input is a flag indicating inappropriate behavior, and the output is a feedback message. In this generation process, a generative AI model is used to construct messages that include specific and constructive advice.

[0690] Step 5:

[0691] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is the notification received by the user. In this case, the terminal displays the notification in the form of a pop-up or email.

[0692] Step 6:

[0693] The device provides a conversational interface with the user and offers educational support. Input consists of questions and responses from the user, and output is advice from a conversational AI. Specifically, in response to a user's question, it uses a generative AI model to provide a response such as, "It would be good to incorporate expressions that show gratitude for the other person's opinion."

[0694] Step 7:

[0695] Users reflect on their own behavior and make improvements based on feedback from their devices. The input consists of feedback messages and advice from the AI, while the output is the improved behavior. Users set self-improvement goals, such as "strive to provide constructive feedback on suggestions."

[0696] (Application Example 1)

[0697] 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".

[0698] In modern workplaces and communities, inappropriate remarks and harassment in communication are serious problems. However, effective systems for early detection and appropriate response to these behaviors are still insufficient. As a result, there are many situations where problems have to be dealt with only after they have escalated. This invention aims to prevent these problems and provide a safe communication environment by analyzing data in real time across various communication tools and detecting abnormal behavior.

[0699] 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.

[0700] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of abnormal behavior based on the emotional score. This enables real-time early detection of abnormal behavior and appropriate feedback in the workplace and community.

[0701] "Communication data" refers to a portion of the information sent and received over a network, and includes data such as text messages and emails.

[0702] An "emotion score" is an index that analyzes received text data using natural language processing and numerically indicates whether the content is positive, negative, or neutral.

[0703] "Signs of abnormal behavior" refer to patterns, based on the results of emotional score analysis, that suggest communication or harassment behaviors that may worsen interpersonal relationships.

[0704] "Warning and feedback information" refers to text generated for the purpose of notifying users, and includes advice and warnings to encourage the improvement of abnormal behavior.

[0705] A "user terminal" is a user's operating device that can receive warnings and feedback information, and includes smartphones, computers, and other devices.

[0706] "Natural language processing technology" is a technique that uses computers to analyze text data written in human language and understand its content.

[0707] A "messaging service" is an information and communication service that enables the sending and receiving of text messages in real time.

[0708] The embodiments for carrying out this invention will now be described. This system mainly consists of three components: a server, a terminal, and a user.

[0709] Server Role

[0710] The server monitors and receives communication data flowing through the network in real time. The server uses natural language processing techniques to analyze the collected text data. Existing natural language processing services such as Google Cloud Natural Language API and Azure Text Analytics are used for this analysis. The server calculates a sentiment score and uses this score to determine signs of abnormal behavior. If an anomaly is detected, the server generates warning and feedback information and sends it to the terminal.

[0711] Terminal role

[0712] The device receives feedback information sent from the server. Based on this information, it displays alerts to the user to encourage improvements in their communication style. Furthermore, the device uses conversational AI to provide customized advice to the user. OpenAI's GPT model is one example of a conversational AI that can be used. For example, if the user asks prompts such as, "Looking back on recent conversations, how did you feel? What kind of conversations can you think of to improve your communication style?", the device will generate appropriate advice.

[0713] User roles

[0714] Users can receive feedback and advice from conversational AI provided through their devices, allowing them to reflect on their own actions and statements. This enables users to proactively improve their communication and contribute to building healthy relationships in the workplace and community.

[0715] For example, if a user's emotional score decreases through a series of messages exchanged in a chat, the system immediately sends an alert to the device and provides advice to the user via conversational AI, such as, "Your recent conversations seem a little tense. How can we try to communicate in a more relaxed manner in our next conversation?" This series of actions makes it possible to create a safe and positive communication environment.

[0716] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0717] Step 1:

[0718] The server receives communication data in real time over the network. The input consists of text data such as chat messages and emails. The server collects the data and stores it for analysis. During this process, it uses communication protocols to ensure data integrity.

[0719] Step 2:

[0720] The server uses natural language processing technologies such as Google Cloud Natural Language API and Azure Text Analytics to analyze incoming text data. The input is text data, and the output is a sentiment score. The analyzed sentiment score is expressed numerically and classified as positive, negative, or neutral. The data is analyzed immediately, and the results proceed to the next step.

[0721] Step 3:

[0722] The server determines signs of abnormal behavior based on a sentiment score. The input is the sentiment score, and the output is a warning flag for abnormal behavior. When the sentiment score exceeds a certain threshold, a warning flag is set, and the process moves to the next step. If abnormal behavior is detected, preparations are made to respond immediately.

[0723] Step 4:

[0724] The server generates warning and feedback information as needed. Input is a warning flag for abnormal behavior, and output is a feedback message to be sent to the user. Specifically, it selects the appropriate message from a pre-prepared message template and creates a message containing warnings and advice for the user.

[0725] Step 5:

[0726] The terminal receives feedback messages sent from the server and notifies the user. The input is the feedback message, and the output is displayed through the terminal's user interface. The terminal uses an intuitive GUI to enable the user to easily understand the feedback and take action.

[0727] Step 6:

[0728] The user refers to the feedback displayed on the device and receives advice on improving communication through conversational AI. Input is the user's prompts and responses to feedback, and output is specific advice generated by the AI. Based on this feedback, the user uses the conversational AI to formulate prompts to input to the AI ​​model. A concrete example of a prompt used is, "What are some ways to ease tension in recent conversations?"

[0729] 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.

[0730] This invention provides more effective feedback and interactive support by combining a system that detects and responds to signs of workplace harassment early with an emotion engine that recognizes the user's emotions. This invention mainly consists of three components: a server, a terminal, and a user.

[0731] Server Role

[0732] The server receives chat and email data flowing through the corporate network in real time and collects it in a database. The received data is then analyzed using natural language processing technology to calculate an emotional score. During this process, an integrated emotional engine analyzes the user's emotional state in detail. The emotional engine extracts emotional characteristics from the raw data, evaluates positive, negative, and neutral emotional states, and uses this to assess the risk of harassment.

[0733] Terminal role

[0734] The device notifies the user based on analysis results sent from the server and evaluations from the emotion engine. Specifically, if signs of harassment are detected, a feedback message reflecting the emotion engine's analysis results is displayed on the user's screen. This feedback includes specific areas for improvement and personalized advice tailored to the user's emotional state. Furthermore, the device provides a dialogue interface with the user and supports the improvement of the user's behavior through responses that take emotional changes into account.

[0735] User roles

[0736] Users can improve themselves through feedback messages and interactive support displayed on their devices. For example, if a user exhibits negative emotions during workplace communication, the system will consider the cause and suggest specific improvement measures and positive communication techniques. As a result, users can reduce the risk of unconsciously engaging in harassment.

[0737] This system uses an emotion engine to provide feedback that takes user emotions into account, resulting in more personalized support than ever before. It represents an important technological tool for promoting a healthy communication environment in the workplace.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] The server receives chat and email communication data flowing from the corporate network in real time. The server stores the received data in an internal database for analysis.

[0741] Step 2:

[0742] The server analyzes unanalyzed data in the database using natural language processing techniques. Specifically, the server extracts keywords from the text and calculates a sentiment score based on them. A built-in sentiment engine is used for this calculation.

[0743] Step 3:

[0744] The server utilizes an emotion engine to identify the user's emotional state from the analyzed data. This emotional state includes positive, negative, and neutral states, and changes in the emotional state are also tracked.

[0745] Step 4:

[0746] The server determines signs of harassment based on the emotional score and the user's emotional state. This includes detecting when a negative emotional score exceeds a certain threshold.

[0747] Step 5:

[0748] If the server detects signs of harassment, it activates a feedback generation module to automatically generate warning and feedback messages for the user. This feedback includes personalized advice tailored to the user's feelings.

[0749] Step 6:

[0750] The device receives feedback messages sent from the server and notifies the user of the message. The device provides feedback using visual and audio notifications.

[0751] Step 7:

[0752] Users review the feedback displayed on their device and reflect on their own words and actions. Based on the specific improvement suggestions provided, users modify their communication style. They can also request detailed interactive advice via their device if needed.

[0753] Step 8:

[0754] The device responds to the user's additional questions and provides further advice and explanations through its conversational AI function. During the conversation, it can re-evaluate the user's emotional changes and reflect them in its responses.

[0755] (Example 2)

[0756] 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".

[0757] There is a need for early detection of harassment in the workplace and the provision of effective countermeasures. Traditional systems relied on simple rule-based approaches, which hindered the proper assessment of emotions and context. This resulted in potential delays in necessary feedback and the risk of inappropriate dialogue.

[0758] 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.

[0759] In this invention, the server includes means for receiving and storing communication information, means for processing the received information and calculating an emotional evaluation, and means for identifying risk indicators based on the emotional evaluation. This makes it possible to detect the risk of harassment early by utilizing emotional analysis technology and to improve user behavior through prompt and appropriate feedback and dialogue.

[0760] "Communication information" is a general term for data and messages that are sent and received via a digital network.

[0761] "Storage" refers to the process of saving received data in a specific format so that it can be used later.

[0762] "Processing" refers to a series of operations that analyze raw data and extract or transform useful information.

[0763] "Sentimental evaluation" is a way of expressing the emotional elements contained in the information in question using numerical values ​​or categories.

[0764] "Risk indicators" refer to identifying signs that suggest the possibility of potential problems or disruptions occurring.

[0765] "Automatically generated information" refers to data or messages generated by a system, rather than manually, based on specific algorithms or rules.

[0766] "User terminal" is a general term for electronic devices used to display information and enable users to operate it.

[0767] "Adjusting behavior" is the process of helping users improve or optimize their behavior and work patterns.

[0768] "Emotion analysis technology" is a technology that analyzes text and audio data to identify and evaluate emotions.

[0769] "Real-time" refers to the simultaneous or near-simultaneous generation and processing of data.

[0770] This invention is a system for early detection of signs of harassment in the workplace and providing interactive support, and consists of three components: a server, a terminal, and a user.

[0771] The server receives communication information, such as chats and emails, flowing over the network in real time and stores it in a database. The server processes the received communication information using natural language processing (NLP) technology and performs sentiment evaluation using sentiment analysis technology. Specifically, it extracts emotional elements from the text, performs sentiment evaluation by classifying them into categories such as positive, negative, and neutral, and identifies risk indicators based on this evaluation.

[0772] The device receives sentiment ratings and risk indicators transmitted from the server and provides automatically generated feedback information to the user based on this data. This feedback includes specific actions and measures to mitigate risks, and the device presents this information through interaction with the user. Furthermore, the device features an interactive dialogue interface that suggests appropriate improvement suggestions based on the user's information.

[0773] Users can adjust their behavior based on the feedback information displayed on their device. For example, if a user expresses negative emotions, the system considers the cause and suggests positive communication methods to reduce the risk of harassment.

[0774] As a concrete example, the prompt text used would be, "Assess your emotional state based on your recent communication history at work and suggest positive areas for improvement." This prompt makes it easier for users to understand how to improve their behavior according to the system's guidelines.

[0775] In this way, the system utilizes emotion analysis and natural language processing technologies to provide users with personalized feedback, thereby maintaining and promoting a healthy communication environment in the workplace.

[0776] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0777] Step 1:

[0778] The server receives communication information from the network, specifically chat messages and emails. This raw text data is immediately stored in a database. The stored data is then organized and saved for later analysis.

[0779] Step 2:

[0780] The server analyzes the received text data using natural language processing (NLP) techniques. Specifically, it breaks down the elements that make up a sentence through morphological analysis and extracts keywords. The input to this process is the stored raw data, and the output is processed data ready for sentiment analysis.

[0781] Step 3:

[0782] The server uses sentiment analysis technology to perform an emotional assessment based on the processed data. The emotion engine receives this data and calculates an emotional score, such as positive, negative, or neutral. This assessment is output in specific numerical or categorical form, which serves as a basis for deciding the next step.

[0783] Step 4:

[0784] The server identifies risk indicators based on the calculated sentiment assessment results. Here, if the sentiment score exceeds a certain threshold, it is determined that there is a high risk of harassment. In this step, the sentiment score is used as input, and risk assessment data is generated as output.

[0785] Step 5:

[0786] The device generates feedback information based on sentiment assessments and risk indicators obtained from the server. For example, if a negative emotion is detected, the device creates feedback that includes suggested positive actions. In this process, risk assessment data is used as input, and a feedback message is generated as output.

[0787] Step 6:

[0788] The device displays the generated feedback message on the user screen. Specifically, this involves a process of notifying the user so they can immediately see the generated message. This notification information serves as a foundation for the user to understand the situation and begin adjusting their actions.

[0789] Step 7:

[0790] Users review feedback messages received from their devices and reflect them in their actions. They improve their interactions in the workplace by trying out suggested positive communication techniques. The input in this process is the feedback messages, and the output is the user's improved behavior.

[0791] (Application Example 2)

[0792] 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".

[0793] In today's workplace, it is crucial to detect early signs of harassment in communication and maintain a healthy work environment. However, monitoring emotional changes in real time and responding appropriately is not easy. Furthermore, there is a lack of means to specifically assess high-risk situations and provide appropriate corrective measures. Therefore, a more effective system is needed to prevent harassment from occurring and improve the workplace environment.

[0794] 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.

[0795] In this invention, the server includes means for receiving and collecting communication data, means for analyzing the received data and calculating an emotional score, and means for determining signs of harassment based on the emotional score. This makes it possible to evaluate the health of workplace communication in real time, identify high-risk situations specifically, and provide necessary feedback at the appropriate time.

[0796] "Communication data" refers to information transmitted and received in digital format, including voice, text, and images transmitted over a network.

[0797] An "emotion score" is a numerical indicator of a user's emotional state, extracted from text analyzed using natural language processing, and shows emotional tendencies such as positive, negative, or neutral.

[0798] "Signs of harassment" are indicators that suggest the possibility of aggressive or inappropriate behavior towards others in workplace communication or conduct.

[0799] A "feedback message" is a message that provides users with specific suggestions for improvement or advice based on the results of the analysis of received data.

[0800] A "user terminal" refers to a device used to receive information transmitted from a system, and includes personal computers, smartphones, and other similar devices.

[0801] "Workplace environment risks" refer to factors that can affect the health of an organization, such as harassment and interpersonal problems that may arise in the course of work.

[0802] "Means of proposing improvement measures" refers to a process for providing specific actions and methods to address problems identified based on the analysis results.

[0803] "Natural language processing technology" is a technology that processes human language using computers, enabling the analysis of text data and the understanding of intentions and emotions based on that analysis.

[0804] This invention is a system for analyzing signs of harassment in the workplace, with a server, terminals, and users each playing specific roles. The server receives communication data via the network and collects data. Natural language processing techniques are used to analyze the text of the received data and calculate an emotion score. The Hugging Face transformers library is suitable for this process.

[0805] The server analyzes the data to identify signs of harassment and automatically generates warning and feedback messages. These messages are sent to the user's device, which can be a personal computer or smartphone. The user receives the feedback message displayed on the device. The device also displays the results of a workplace risk assessment and suggests specific improvement measures.

[0806] Based on this information, users can deepen their understanding of emotions and improve their behavior based on feedback. This improves the quality of communication in the workplace and reduces the risk of harassment.

[0807] For example, if an employee sends a message through the company system stating, "I'm frustrated because my opinions aren't being heard in recent meetings," the server analyzes this in real time and determines that negative emotions are on the rise. Based on this, a message recommending "considering opportunities to learn how to give constructive feedback" is generated and sent to the user's terminal. This encourages the user to take actions that will lead to improved communication in the workplace.

[0808] An example of a prompt message might be: "Analyze the following workplace communication data and calculate the sentiment score. Classify it as positive, negative, or neutral, and determine whether there is a risk of harassment."

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] The server receives communication data through the corporate network. Specifically, this includes text data such as chat messages and emails. The input is raw communication data obtained over the network, and the output is in a digital data format that can be processed within the server. In this step, data formatting and cleaning are performed.

[0812] Step 2:

[0813] The server performs natural language processing on the received data and analyzes the text. Specifically, the text is tokenized, and keywords related to important emotions are extracted. Based on this, an emotion score is calculated. In this process, the Hugging Face transformers library is used to derive positive, negative, and neutral scores through analysis. The input is the data formatted in the previous step, and the output is the emotion score.

[0814] Step 3:

[0815] The server determines signs of harassment based on the calculated emotional score. The analysis results are compared to a threshold, and if negative emotions exceed a certain value, it is determined that there is a risk of harassment. In this step, the input is the emotional score, and the output is the determination result indicating whether or not there is a risk of harassment.

[0816] Step 4:

[0817] The server automatically generates warning and feedback messages based on the assessment results. These messages include particularly necessary corrective actions and countermeasures for risks. The output is a feedback message containing specific warnings and advice. The input is the harassment risk assessment result.

[0818] Step 5:

[0819] The server notifies the user terminal of the generated feedback message. The terminal displays the notification to the user, making the content viewable. The input from the server is the feedback message, and the output is the content displayed on the terminal. This step allows the user to take quick action as needed.

[0820] Step 6:

[0821] Based on the displayed feedback, users assess risks in the workplace environment and implement suggested improvements. They utilize the information displayed on their devices to plan and implement actions aimed at improving workplace communication. Input is feedback messages, and output is changes in user behavior and efforts toward improvement. Voluntary user participation is essential in this process.

[0822] 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.

[0823] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0824] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A means for receiving and collecting communication data,

[0846] A means of analyzing received data and calculating an emotional score,

[0847] A means of determining signs of harassment based on emotional scores,

[0848] A means for automatically generating warning and feedback messages,

[0849] A means of notifying the user terminal of the generated message,

[0850] A means of interacting with users and encouraging them to improve their behavior,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which analyzes the text of received data using natural language processing technology.

[0854] (Claim 3)

[0855] The system according to claim 1, comprising means for collecting communication data to monitor chats and emails in real time.

[0856] "Example 1"

[0857] (Claim 1)

[0858] Means for receiving and collecting communication information,

[0859] A means of analyzing received information and calculating emotion values,

[0860] A means of determining signs of inappropriate behavior based on emotional values,

[0861] A means for automatically generating warning and informational messages,

[0862] A means for sending the generated message to the user's terminal,

[0863] A means of engaging with users and helping to improve their expression,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which analyzes the character data of received information using natural language processing technology.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising means for collecting communication information to monitor electronic communications in real time.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A means for receiving and collecting communication data,

[0872] A means of analyzing received data and calculating an emotional score,

[0873] A means of determining signs of abnormal behavior based on emotional scores,

[0874] A means for automatically generating warning and feedback information,

[0875] A means of notifying the user terminal of the generated information,

[0876] A means of engaging with users and encouraging behavioral improvement,

[0877] A means of integrating with multiple applications and analyzing data in real time,

[0878] A means of providing customized advice on communication approaches,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, which analyzes the text of received data using natural language processing technology.

[0882] (Claim 3)

[0883] The system according to claim 1, comprising means for collecting communication data to monitor a messaging service in real time.

[0884] "Example 2 of combining an emotion engine"

[0885] (Claim 1)

[0886] A means for receiving communication information and storing said information,

[0887] A means for processing received information and calculating sentiment evaluation,

[0888] A means of identifying risk indicators based on emotional evaluation,

[0889] Means for outputting automatically generated warning and feedback information,

[0890] A means of notifying the user's terminal of the outputted information,

[0891] A means of interacting with users and adjusting their behavior,

[0892] A means of evaluating the content of received information in detail using emotion analysis technology,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, which processes the content of received information using text analysis technology.

[0896] (Claim 3)

[0897] The system according to claim 1, comprising information gathering means for monitoring communication information in real time.

[0898] "Application example 2 when combining with an emotional engine"

[0899] (Claim 1)

[0900] A means for receiving and collecting communication data,

[0901] A means of analyzing received data and calculating an emotional score,

[0902] A means of determining signs of harassment based on emotional scores,

[0903] A means for automatically generating warning and feedback messages,

[0904] A means of notifying the user terminal of the generated message,

[0905] A means of assessing workplace environment risks and proposing improvement measures,

[0906] A means of interacting with users and encouraging them to improve their behavior,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, which analyzes the text of received data using natural language processing technology.

[0910] (Claim 3)

[0911] The system according to claim 1, comprising means for collecting communication data to monitor chats and emails in real time. [Explanation of symbols]

[0912] 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 for receiving and collecting communication data, A means of analyzing received data and calculating an emotional score, A means of determining signs of abnormal behavior based on emotional scores, A means for automatically generating warning and feedback information, A means of notifying the user terminal of the generated information, A means of engaging with users and encouraging behavioral improvement, A means of integrating with multiple applications and analyzing data in real time, A means of providing customized advice on communication approaches, A system that includes this.

2. The system according to claim 1, which analyzes the text of received data using natural language processing technology.

3. The system according to claim 1, comprising means for collecting communication data to monitor a messaging service in real time.

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