system

Abstract

We provide the system. [Solution] Means for acquiring user communication data, A means for analyzing the aforementioned data using a natural language processing algorithm, Based on the analysis results, a means of scoring the likelihood of harassment, A means for generating a warning message when the score exceeds a predetermined threshold, A means for notifying the user terminal of the aforementioned warning message, A system that includes means for storing user behavior history in a database.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Recently, harassment in the workplace and society still spreads, and one of the reasons is that the perpetrator has no awareness. Such harassment behavior imposes a great psychological burden on the victim and deteriorates the workplace environment and human relationships. Therefore, it is important to prompt the perpetrator to be aware of his or her words and deeds, but there has been no effective system for obtaining this awareness. Therefore, it is an issue to prevent harassment behavior and realize an environment where one can work with peace of mind.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system that acquires user communication data and analyzes it using a natural language processing algorithm. Based on the analysis results, this system scores the likelihood of harassment, and if the score exceeds a predetermined threshold, it generates a warning message and notifies the user's terminal, thereby prompting the perpetrator to become aware of their actions. Furthermore, by accumulating the user's behavior history in a database and aggregating the analysis results to generate a report that visualizes trends in harassment behavior, the system supports long-term behavioral improvement. In addition, by including means for converting voice data into text data, the system also aims to prevent harassment in voice communication.

[0006] A "user" is an individual or group that uses the system to send and receive communication data.

[0007] "Communication data" refers to data that represents the exchange of information between users, expressed in text or audio format.

[0008] A "natural language processing algorithm" is a computer program that analyzes text data and interprets its syntax and meaning.

[0009] "Analysis" is the process of classifying and interpreting data in detail to clarify its content and characteristics.

[0010] "Scoring" is a method of expressing an evaluation as a numerical value based on a set standard, using the results of an analysis.

[0011] A "threshold" is a reference value set as a condition for determining a specific action.

[0012] A "warning message" is a message generated to draw the user's attention.

[0013] A "user terminal" is a device that a user uses to access and utilize systems and services.

[0014] The "action history" refers to the records of a user's past actions and transactions.

[0015] A "database" is a system that organizes, stores, manages information, and enables easy retrieval.

[0016] A "report" is a document that organizes and visually presents collected data and information.

[0017] "Voice data" refers to data obtained by recording voice as digital information.

[0018] "Text data" refers to data that stores character information in digital form.

Brief Description of Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. **Mode for Carrying Out the Invention**

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0022] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0023] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

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

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0034] 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.

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

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention relates to a computer system aimed at preventing harassment, which is integrated into communication tools used by users on a daily basis. This system acquires user communication data in real time, analyzes it to assess the possibility of harassment, and issues a warning to the user. The following describes a specific embodiment of the system.

[0041] The server receives user communication data and analyzes it using a natural language processing algorithm. Based on the analysis, it scores whether the words and actions in the data may constitute harassment. If this score exceeds a pre-set threshold, the server generates a warning message and sends it to the user's terminal.

[0042] For example, if a user uses their device to send an aggressive message to a colleague at work, such as "Submit the report immediately," the server will analyze the message. The analysis will score the message as being aggressive, and if it is determined to exceed a threshold, the server will notify the user with a warning such as, "This expression may be perceived as aggressive. Please consider rephrasing it."

[0043] Furthermore, to prevent harassment in voice communication, the server can convert received voice data into text data and analyze it similarly using the aforementioned process. This enables real-time harassment detection even in voice conferencing applications such as Zoom.

[0044] Furthermore, the server stores all analysis results and user behavior history in a database, and compiles daily and monthly trends. This allows users to regularly receive reports that enable them to reflect on and improve their own behavior. This promotes long-term behavioral improvement and safe and effective communication.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The device captures text and voice data entered by the user into communication tools in real time. This is done automatically using the application's API functionality.

[0048] Step 2:

[0049] The device transmits the acquired data, along with identification information (user ID, conversation ID, timestamp, etc.), to the server via a secure communication protocol. During this process, the data is encrypted to protect privacy.

[0050] Step 3:

[0051] The server analyzes the received data using natural language processing algorithms. Text data undergoes tokenization, syntactic analysis, and keyword extraction, while speech data is converted to text using speech recognition technology.

[0052] Step 4:

[0053] The server scores each behavior based on the analyzed data to determine if it constitutes harassment. The scoring is performed using a machine learning model trained on a dataset.

[0054] Step 5:

[0055] The server generates warning messages and advice for improvement if the score exceeds a set threshold. The generated messages are selected from different templates depending on the case.

[0056] Step 6:

[0057] The server sends the generated warning message to the user's terminal, providing immediate notification. The notification appears as a pop-up or alert message to prompt the user to take notice.

[0058] Step 7:

[0059] The server stores analysis results and user communication history in a database, enabling analysis of long-term behavioral patterns. It also accumulates data for generating daily and monthly reports.

[0060] Step 8:

[0061] Users can review the provided warning messages and periodic reports, reflect on their communication methods, and make improvements as needed. This allows users to proactively learn desirable behaviors.

[0062] (Example 1)

[0063] 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."

[0064] In modern workplaces and online environments, unintentional harassment can occur. A key challenge is the lack of opportunities for individual users to become aware of their own communication styles and improve their behavior. Preventing harassment is particularly difficult in voice communication, where real-time responses are required. Therefore, there is a need for a system that can detect expressions that cause harassment in real time and provide immediate feedback to users.

[0065] 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.

[0066] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for quantifying the likelihood of harassment based on the analysis results. This makes it possible to detect potential harassment in voice and text communication in real time and provide rapid feedback to the user.

[0067] "User communication information" refers to all information that users exchange with others through email, chat messages, voice calls, etc.

[0068] A "natural language processing algorithm" refers to computational methods that enable computers to understand and analyze text written or spoken by humans.

[0069] "Quantifying the potential for harassment" refers to quantitatively evaluating, based on specific criteria or algorithms, the extent to which a user's expressions or behavior are considered harassment, and expressing this numerically.

[0070] "Prescribed threshold values" refer to the thresholds set as criteria for determining whether an act constitutes harassment, and a warning is issued if this value is exceeded.

[0071] "Warning information" refers to messages or notifications provided to users to inform them of a potential harassment issue when their behavior is deemed to be such.

[0072] "User equipment" refers to terminals or devices used by users for communication.

[0073] "Storing behavioral history in a memory device" refers to recording a user's past communication content and analysis results, and saving them so that they can be analyzed and referenced later.

[0074] "Converting audio information to text information" refers to using speech recognition technology to convert spoken content into text.

[0075] "Preventing harassment in voice communication" refers to methods or means of preventing harassment from occurring by analyzing the content of speech in real time during the process of communication via voice.

[0076] This invention is a communication analysis system aimed at preventing harassment. The following describes in detail how this system can be implemented.

[0077] The server receives communication information sent from the user's terminal. This information includes text messages and voice communications. For voice information, the server converts it into text using speech recognition software. A common speech recognition API is used for this conversion process.

[0078] Next, the server analyzes the text information using a natural language processing algorithm. This analysis utilizes a natural language processing library that runs in Python. The purpose of the analysis is to evaluate whether the user's statements constitute harassment.

[0079] If the analysis determines that a statement may constitute harassment, the server quantifies it and compares it to a predetermined threshold value. If the threshold value is exceeded, the server generates a warning message and notifies the terminal. The warning message encourages the user to reconsider their statement.

[0080] Furthermore, the server stores past analysis results and behavioral history in storage and periodically evaluates the user's behavioral history. This allows users to receive feedback to improve their communication style. This process is particularly applicable to communication using voice conferencing software.

[0081] As a concrete example, suppose a user sends a message to a colleague at work using a strong tone. In this case, the server analyzes the message and detects that it uses high-pressure language such as "Submit the report immediately." If the score exceeds a certain threshold, the server sends a prompt message to the user saying, "This language may be perceived as high-pressure. Please consider using a different expression." This allows the user to reflect on their message and correct it if necessary.

[0082] This system will allow users to communicate more smoothly online and receive support to maintain a safe environment.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users use communication tools through their devices to send text messages and voice data. This data is immediately transferred to a server for the purpose of recording the user's words and actions. The input is messages and voices generated by the user, and the output is communication data sent to the server.

[0086] Step 2:

[0087] The server converts the received audio data into text information using a speech recognition API. This conversion creates the foundation for handling audio as text and outputs the string data necessary for analysis. During this process, the user's speech is stored as text data on the server.

[0088] Step 3:

[0089] The server acquires text information and performs analysis using natural language processing algorithms. This analysis utilizes a generative AI model to analyze the structure and sentiment of the text and assess the potential for harassment. The input is the transformed text data, and the output is a score indicating the likelihood that the behavior is considered harassment.

[0090] Step 4:

[0091] The server determines whether the score exceeds a predetermined threshold. If it does, it generates a warning message and prepares a prompt to provide feedback to the user. The input is the harassment score, and the output is the warning message sent to the user.

[0092] Step 5:

[0093] The server sends a generated warning message to the terminal. The terminal provides immediate feedback to the user by displaying this message in its user interface. The input is the warning message sent from the server, and the output is the notification displayed on the user's screen.

[0094] Step 6:

[0095] The server stores all analysis results and user behavior history in storage. This data is later used as the basis for generating reports to help users improve their communication style. The input is analysis results and behavior history, and the output is storage in the database.

[0096] (Application Example 1)

[0097] 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."

[0098] In traditional in-store communication, harassment was often overlooked, which negatively impacted the work environment and customer experience. Furthermore, employees lacked opportunities to objectively evaluate and improve the quality of their own communication.

[0099] 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.

[0100] In this invention, the server includes means for analyzing user communication data using a natural language processing algorithm and evaluating the emotions associated with that data, means for acquiring voice data and converting it into text data, and means for notifying the user terminal of a warning message in real time based on the analysis. This promotes healthy communication within the store and enables employees to reflect on and improve their own behavior.

[0101] "User communication data" refers to the content of all forms of dialogue and messages that users engage in, and this data includes both text and audio formats.

[0102] A "natural language processing algorithm" is a technology that enables computers to understand and analyze human language, analyzing text and audio data to evaluate emotions and intentions.

[0103] "Scoring the potential for harassment" is a process of quantifying the degree of harassment risk associated with analyzed communication data based on its content.

[0104] A "warning message" is a notification issued to alert users to potential harassment behavior and encourage them to improve their actions.

[0105] A "user terminal" refers to a device on which warning messages or analysis results are displayed, and includes smartphones, tablets, computers, and other similar devices.

[0106] "Storing user behavior history in a database" is the process of recording users' past communication data and analysis results as history, making it available for later evaluation and improvement.

[0107] "Converting audio data to text data" is a technology that converts audio information, such as conversations, into text information, making it easier to analyze.

[0108] A "report that visualizes trends in harassment behavior" is a report that uses accumulated data to visually show trends in harassment that have occurred, in order to help users understand them.

[0109] "Promoting healthy communication" means taking steps to reduce the risk of harassment and create a better and more positive communication environment.

[0110] A system that implements an application of this invention analyzes in-store communication in real time and issues warning messages aimed at preventing harassment. The server acquires user communication data and analyzes it using natural language processing algorithms. Specifically, programming languages ​​such as Python, libraries such as SpeechRecognition and TextBlob, and the Google® Speech Recognition API are used to monitor text and voice exchanges conducted by users via smartphones and other devices.

[0111] The hardware used includes a smartphone and microphone to acquire audio data. This audio data is converted into text data, which is then analyzed to score the likelihood of harassment. If this score exceeds a predetermined threshold, the server generates a warning message and notifies the user's device. This gives the user an opportunity to improve the quality of their communication.

[0112] Warning messages are displayed in real time on user terminals to encourage the maintenance of a healthy work environment. Furthermore, all analysis results and user behavior history are stored in a database and provided as reports that visualize trends in harassment behavior, generated periodically. These reports serve as a guide for users to reflect on their own behavior and make long-term improvements.

[0113] For example, if a store staff member uses aggressive language in a conversation with a customer or chat with a colleague, the system will immediately issue a warning such as, "This language may be perceived as aggressive. Please consider using a different expression," prompting improvement. This kind of real-time feedback allows staff members to strive for healthy communication.

[0114] Examples of prompts include phrases that instruct the user to analyze text data, such as, "Evaluate the sentiment score of the following conversation text and determine whether it is aggressive or not."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server acquires user communication data. It collects voice and text data in real time from smartphones and microphones, with voice data received via a voice recording device. This data is then transmitted from the user's device to the server via the internet. Input is voice and text data, and output is digital data in an analyzable format.

[0118] Step 2:

[0119] The server converts audio data into text data. It uses the Google Speech Recognition API to transcribe the audio data into text. The input is audio data, and the output is a string (text format). This converts the data into a format that can be parsed by natural language processing algorithms.

[0120] Step 3:

[0121] The server analyzes text data using the TextBlob library and evaluates its sentiment. It passes the acquired text data to TextBlob to calculate sentiment polarity. The output is a numerical value indicating the degree of positive or negative sentiment contained in the text. This value allows for scoring in the next step.

[0122] Step 4:

[0123] The server scores the likelihood of harassment based on the analysis results. Based on the acquired sentiment score, it evaluates whether the message content is aggressive towards others and determines whether it exceeds the threshold. The input is the numerical score from the sentiment evaluation, and the output is a flag indicating whether that score exceeds the threshold.

[0124] Step 5:

[0125] The server generates a warning message and notifies the user's terminal if the score exceeds a predetermined threshold. The warning message may include content such as, "This expression may be perceived as aggressive. Please consider using alternative wording." The input is the score evaluation result, and the output is the warning message displayed on the user's screen.

[0126] Step 6:

[0127] The server stores all analysis results and user behavior history in a database. It organizes the data collected in real time and records it as history. The input is the score evaluation results and the data behind them, and the output is a database of behavior history organized for each user. This serves as the basis for reports generated later.

[0128] 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.

[0129] This invention provides a system that offers more accurate judgments and feedback by combining an emotion engine that analyzes the user's emotional state with an evaluation of harassment in user communication. This system consists of multiple processing modules and is integrated into the user's communication tools.

[0130] The terminal is responsible for acquiring the user's communication data (text and voice) as part of the session. The data is encrypted and sent to the server along with identification information.

[0131] The server analyzes the received data using natural language processing algorithms to analyze context and tone. During this process, the emotion engine extracts emotional information from the data. The emotion engine identifies the emotions expressed by the user (e.g., anger, joy, sadness) through keyword detection and speech tone analysis.

[0132] The server comprehensively considers the results of analysis using natural language processing and emotional information from the emotion engine to score the likelihood of harassment. This scoring evaluates how the user's emotions influence the communication and adjusts the weighting of the score as needed.

[0133] If the score exceeds a set threshold, the server generates a warning message and improvement advice, adding emotionally-based customization to it. Specifically, if a state of heightened emotion is detected, advice will be provided that takes this into account.

[0134] For example, if a user exhibits behavior that indicates anxiety or frustration during a meeting, the server will take that emotional state into consideration and send a customized message such as, "You appear to be emotionally agitated. Let's try to maintain a calm demeanor."

[0135] The terminal notifies the user of the generated warning message and prompts them to review its contents. Furthermore, the server stores all analysis results and emotional states in a database and creates reports based on the results to support long-term behavioral improvement.

[0136] Users can utilize the information provided through their devices to help manage their communication style and emotions. This system aims to flexibly respond to emotional changes, fostering seamless communication and a safe work environment.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The device captures text input and voice speech performed by the user in real time using communication tools. This is achieved through a mechanism that automatically acquires data using communication APIs.

[0140] Step 2:

[0141] The device sends the acquired data to the server in an encrypted state for privacy protection, along with information identifiers (such as user ID, session ID, and timestamp).

[0142] Step 3:

[0143] The server analyzes the received data using natural language processing algorithms to extract communication context, tone, content keywords, and other relevant information. This process includes tokenization, morphological analysis, and sentiment scoring.

[0144] Step 4:

[0145] The server uses an emotion engine to analyze the user's emotions from the received data. It identifies the user's emotional state by evaluating emotional keywords in text and tone and pitch in audio.

[0146] Step 5:

[0147] The server integrates natural language processing results with emotional information to score the likelihood of harassment. Since emotional information is used as a weight in the scoring, cases where the emotional state is clear will receive a higher score.

[0148] Step 6:

[0149] The server generates a warning message and advice for improvement if the score exceeds a set threshold. Based on sentiment information, the message content is customized and includes specific sentiment management suggestions.

[0150] Step 7:

[0151] The server sends the generated message to the user's device. The device then displays this to the user via a pop-up alert or notification bar to draw their attention.

[0152] Step 8:

[0153] The server stores the analysis results and sentiment analysis results in a database. The stored data is aggregated on a daily and monthly basis and used to understand users' long-term behavioral trends and to create reports to support improvements.

[0154] Step 9:

[0155] Users can review feedback received through their devices and use it to manage their communication and emotions. This allows users to improve their behavior and enhance their communication skills.

[0156] (Example 2)

[0157] 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".

[0158] In the current communication environment, evaluating harassment is subjective, making accurate judgment difficult. Furthermore, while emotional analysis and feedback provision require appropriate responses that consider the user's emotional state, conventional technologies fall short in this regard. Additionally, there is a lack of mechanisms to systematically store user behavior history and utilize it for future behavioral improvement.

[0159] 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.

[0160] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for evaluating the likelihood of harassment based on the analysis results and emotional information. This enables accurate evaluation of harassment that takes into account the user's emotional state and the provision of appropriate feedback. Furthermore, it becomes possible to accumulate behavioral history in an information storage device and create reports that contribute to long-term behavioral improvement.

[0161] A "user" refers to an entity that inputs information and communicates through a system.

[0162] "Communication information" refers to digital communication data such as text messages and voice data obtained from users.

[0163] A "natural language processing algorithm" refers to a technical means of analyzing communication information to understand its context and emotions.

[0164] "Evaluation scoring" refers to the process of numerically evaluating and quantifying whether the content of the analyzed data is related to harassment.

[0165] "Warning information" refers to messages that include cautions and improvement suggestions, which are sent to the user based on the analysis results.

[0166] "Emotional information" refers to data that indicates the user's emotional state, extracted from communication information.

[0167] "User terminal" refers to an electronic device used by a user to input and receive information.

[0168] An "information storage device" refers to a database system that stores communication information and analysis results for later analysis and report creation.

[0169] A "report" refers to a document that visualizes the analysis results and trends in harassment behavior and explains them to the user.

[0170] "Textual information" refers to data that is represented by converting audio information into text.

[0171] This invention is a system for evaluating harassment in user communication and analyzing the user's emotional state to provide more accurate judgments and feedback. Specifically, it is implemented using a system that combines a server and terminals.

[0172] The device collects user communication information, including text messages and voice data. The device encrypts this information and transfers it to the server using a secure communication protocol (e.g., HTTPS). Common computer systems and smartphones are used as devices.

[0173] The server processes the received information using a natural language processing algorithm. This algorithm performs text analysis and speech tone analysis to understand the context of the user's speech, and the emotion engine extracts emotional information. This allows the server to understand the user's emotional state.

[0174] The server evaluates and scores the potential for harassment based on analysis results and emotional information. This evaluation helps determine the impact of excessive emotions on communication. When the score exceeds a set threshold, a warning message is generated. The warning message is customized according to the user's emotional state and includes appropriate advice for improvement.

[0175] For example, if a user shows heightened emotions during a video conference, the server will recognize this and provide specific feedback such as, "You appear to be emotionally agitated at the moment. Let's discuss this calmly."

[0176] Users review the warning information notified through their devices and use it to improve their communication style. Furthermore, the server stores all information and emotional states in a database and generates reports to support long-term behavioral improvement. These reports are used as reference material in users' decision-making.

[0177] An example of a prompt message could be something like, "Please tell me how to analyze the user's current emotional state and determine what kind of feedback to provide." This could be input into a generative AI model.

[0178] This system aims to provide appropriate communication and a safe environment, and is designed to respond flexibly to changes in emotions.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The terminal acquires user communication information. Data is entered by capturing text messages and voice information entered by the user in real time. This input information serves as the basic data for system processing.

[0182] Step 2:

[0183] The terminal encrypts the acquired communication information. Before sending the data, it encrypts the information using a security protocol and prepares it for transfer to the server. The output is an encrypted data packet.

[0184] Step 3:

[0185] The server receives encrypted data sent from the terminal and decrypts it. The decrypted data is input into a natural language processing algorithm, and the analysis of text and speech tone begins. At this stage, the context of the data and the initial emotional state are output.

[0186] Step 4:

[0187] The server uses an emotion engine to extract emotional information from the analyzed data. Specifically, it analyzes keyword and voice tone patterns to identify the user's emotions (e.g., anger, joy). An emotion statement is then generated as output.

[0188] Step 5:

[0189] The server integrates the obtained natural language processing results and sentiment information to score the likelihood of harassment. This score quantifies the harassment risk based on a set scale. The output is an evaluation score.

[0190] Step 6:

[0191] The server generates a warning message when the evaluation score exceeds a set threshold. This includes customized advice tailored to specific emotional states. The output is a customized warning message.

[0192] Step 7:

[0193] The terminal notifies the user of the generated warning information. It displays the message on the user interface for immediate confirmation. The output is the notification information displayed on the user's screen.

[0194] Step 8:

[0195] The server stores all analysis results and sentiment information in an information storage device. This data forms the basis for long-term analysis used for future reference and behavioral improvement. In actual operation, the data is stored in a database.

[0196] Step 9:

[0197] Users strive to improve their communication style through the provided warnings and reports. Based on the output data, introspection takes place to manage emotions and accept feedback on behavior.

[0198] (Application Example 2)

[0199] 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".

[0200] There is a need to detect harassment in communication and understand emotional states in real time to improve safety and productivity in the workplace and public spaces. Furthermore, it is necessary to provide feedback that appropriately reflects individual emotional changes to improve the work environment and facilitate smoother communication. This necessitates a system that reduces user stress and supports long-term behavioral improvement.

[0201] 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.

[0202] In this invention, the server includes means for acquiring user information data, means for analyzing the data using a natural language processing algorithm, and means for scoring the likelihood of harassment using the analysis results and an emotion engine. This makes it possible to detect harassment in the user's communication and provide feedback that reflects changes in their emotions.

[0203] "User information data" refers to data related to communication obtained from users, including voice and text.

[0204] A "natural language processing algorithm" is a set of computational techniques used to analyze context and sentiment from text data.

[0205] An "emotion engine" is a software component that identifies and extracts emotional information from user communication data.

[0206] "Scoring the potential for harassment" means numerically evaluating the risk of problematic behaviors based on users' communication.

[0207] "Generating warning information" means creating information to notify the user when potential harassment is detected.

[0208] An "information terminal" is an electronic device used by a user to receive warning information.

[0209] "Behavioral information" refers to a history of actions and emotions extracted from a user's past communication data.

[0210] A "storage device" is a data storage device used to accumulate user behavior information.

[0211] A "report" is a document that summarizes analysis results and trends, and is information provided to the user.

[0212] "Text data" refers to data in text format that has been converted from audio data.

[0213] The system implementing this invention is primarily composed of an information terminal and a server. First, the user's information terminal is responsible for encrypting communication data acquired in voice or text format and transmitting it to the server. The information terminal is equipped with voice input and text conversion functions, making it possible to acquire the user's speech and text as digital data in real time.

[0214] The server is the central component that processes the received data. First, to implement natural language processing algorithms, it uses the Python spaCy library to analyze the text context and evaluate the content of the communication. Furthermore, it uses TextBlob as an emotion engine to extract emotional information from the data. This makes visible the emotional state hidden in each word spoken by the user.

[0215] Based on the analysis results, the server scores the likelihood of harassment. This scoring system takes into account the user's emotions and context, and generates a warning if the threshold is exceeded. The generated warning information is sent to the information terminal using the Pusher API. The information terminal displays the warning message to the user and provides immediate feedback as needed.

[0216] For example, if an employee expresses frustration during a meeting, the server will send a message via the employee's terminal saying, "You seem to be getting emotional. Please consider taking a short break." This approach helps to prevent inappropriate communication from occurring in advance.

[0217] The system uses a MySQL® database to store data, accumulating behavioral information and analysis results over long periods. This makes it possible to create reports for users to improve their behavior based on past trends.

[0218] An example of a prompt for a generative AI model is: "Analyze recent conversation data to determine if emotions such as anger or frustration are heightened. Also, score whether these emotions may be causing inappropriate communication." Using this kind of sentence can prompt the model to perform appropriate data analysis and generate feedback.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] The device receives user voice or text as input data. If voice data is used, the device converts it into text data using its speech recognition function. The converted text data is encrypted using the SSL protocol to ensure information security. The device then generates encrypted text data as output.

[0222] Step 2:

[0223] The server receives encrypted text data sent from the terminal and decrypts it. The decrypted data is then input into a natural language processing algorithm to analyze the context. During this process, spaCy is used for grammatical analysis and keyword extraction. The output is structured data as a result of the analysis.

[0224] Step 3:

[0225] The server takes the analysis results as input and extracts emotional information using TextBlob, an emotion engine. This process calculates emotional scores such as positive, negative, and neutral, and outputs these scores.

[0226] Step 4:

[0227] The server integrates the results of natural language processing with sentiment information to score the likelihood of harassment. In this step, a generative AI model is used to score based on the prompt text and evaluate the score value. It determines whether a threshold is exceeded, and if so, generates warning information. The output is the generated score and warning information.

[0228] Step 5:

[0229] The server uses the Pusher API to notify the terminal of the generated warning information. The terminal displays the received warning information to the user as a pop-up message. This message includes specific feedback based on the user's emotional state. The output is a real-time warning display to the user.

[0230] Step 6:

[0231] The server stores all processing results, analysis results, and behavioral information in a database. MySQL is used for this purpose, accumulating data for later report generation. The output consists of past behavioral information stored in the database.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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".

[0248] This invention relates to a computer system aimed at preventing harassment, which is integrated into communication tools used by users on a daily basis. This system acquires user communication data in real time, analyzes it to assess the possibility of harassment, and issues a warning to the user. The following describes a specific embodiment of the system.

[0249] The server receives user communication data and analyzes it using a natural language processing algorithm. Based on the analysis, it scores whether the words and actions in the data may constitute harassment. If this score exceeds a pre-set threshold, the server generates a warning message and sends it to the user's terminal.

[0250] For example, if a user uses their device to send an aggressive message to a colleague at work, such as "Submit the report immediately," the server will analyze the message. The analysis will score the message as being aggressive, and if it is determined to exceed a threshold, the server will notify the user with a warning such as, "This expression may be perceived as aggressive. Please consider rephrasing it."

[0251] Furthermore, to prevent harassment in voice communication, the server can convert received voice data into text data and analyze it similarly using the aforementioned process. This enables real-time harassment detection even in voice conferencing applications such as Zoom.

[0252] Furthermore, the server stores all analysis results and user behavior history in a database, and compiles daily and monthly trends. This allows users to regularly receive reports that enable them to reflect on and improve their own behavior. This promotes long-term behavioral improvement and safe and effective communication.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The device captures text and voice data entered by the user into communication tools in real time. This is done automatically using the application's API functionality.

[0256] Step 2:

[0257] The device transmits the acquired data, along with identification information (user ID, conversation ID, timestamp, etc.), to the server via a secure communication protocol. During this process, the data is encrypted to protect privacy.

[0258] Step 3:

[0259] The server analyzes the received data using natural language processing algorithms. Text data undergoes tokenization, syntactic analysis, and keyword extraction, while speech data is converted to text using speech recognition technology.

[0260] Step 4:

[0261] The server scores each behavior based on the analyzed data to determine if it constitutes harassment. The scoring is performed using a machine learning model trained on a dataset.

[0262] Step 5:

[0263] The server generates warning messages and advice for improvement if the score exceeds a set threshold. The generated messages are selected from different templates depending on the case.

[0264] Step 6:

[0265] The server sends the generated warning message to the user's terminal, providing immediate notification. The notification appears as a pop-up or alert message to prompt the user to take notice.

[0266] Step 7:

[0267] The server stores analysis results and user communication history in a database, enabling analysis of long-term behavioral patterns. It also accumulates data for generating daily and monthly reports.

[0268] Step 8:

[0269] Users can review the provided warning messages and periodic reports, reflect on their communication methods, and make improvements as needed. This allows users to proactively learn desirable behaviors.

[0270] (Example 1)

[0271] 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."

[0272] In modern workplaces and online environments, unintentional harassment can occur. A key challenge is the lack of opportunities for individual users to become aware of their own communication styles and improve their behavior. Preventing harassment is particularly difficult in voice communication, where real-time responses are required. Therefore, there is a need for a system that can detect expressions that cause harassment in real time and provide immediate feedback to users.

[0273] 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.

[0274] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for quantifying the likelihood of harassment based on the analysis results. This makes it possible to detect potential harassment in voice and text communication in real time and provide rapid feedback to the user.

[0275] "User communication information" refers to all information that users exchange with others through email, chat messages, voice calls, etc.

[0276] The "natural language processing algorithm" refers to a computational method for a computer to understand and analyze text written or spoken by humans.

[0277] "Quantifying the possibility of harassment" means quantitatively evaluating and expressing numerically to what extent a user's expressions and behaviors are regarded as harassment based on specific criteria or algorithms.

[0278] The "predetermined reference value" refers to a threshold value set as a criterion for determining a harassment act, and a warning is issued when this value is exceeded.

[0279] The "warning information" refers to messages or notifications provided to inform the user of the fact when it is determined that the user's behavior may be harassment.

[0280] The "user device" refers to a terminal or device used by a user to communicate.

[0281] "Storing the behavior history in a storage device" means recording the user's past communication content and analysis results and saving them so that they can be analyzed and referenced later.

[0282] "Converting voice information into text information" means using speech recognition technology to convert the content spoken in voice into text.

[0283] "Preventing harassment in voice communication" refers to methods or means for analyzing the speech content in real time during the process of communication via voice and preventing the occurrence of harassment.

[0284] The present invention is a communication analysis system for the purpose of preventing harassment. Hereinafter, the embodiments of this system will be specifically described.

[0285] The server receives communication information sent from the user's terminal. This information includes text messages and voice communications. For voice information, the server converts it into text information using voice recognition software. A general voice recognition API is used for this conversion process.

[0286] Next, the server analyzes the text information using natural language processing algorithms. A natural language processing library executed in Python is utilized for this analysis. The purpose of the analysis is to evaluate whether the user's utterance corresponds to harassment.

[0287] If, as a result of the analysis, it is determined that the utterance has the potential of being harassment, the server quantifies it and compares it with a predetermined reference value. If it exceeds the reference value, the server generates warning information and notifies the terminal. The warning information is content that prompts the user to review their utterance.

[0288] Furthermore, the server stores past analysis results and behavioral histories in a storage device and periodically evaluates the user's behavioral history. As a result, the user can receive feedback for improving their communication style. This process is also applicable particularly to communications using voice conferencing software.

[0289] As a specific example, assume that the user sends a message in a strong tone to a colleague at work. In this case, the server analyzes the message and detects that a high-pressure expression such as "Submit the report immediately" is used. If the score exceeds the reference value, the server sends a prompt message to the user saying, "This expression may be received as high-pressure. Please consider other ways of saying it." As a result, the user can review their utterance and make corrections if necessary.

[0290] With this system, the user can have smoother online communication and receive support for maintaining a safe environment.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] Users use communication tools through their devices to send text messages and voice data. This data is immediately transferred to a server for the purpose of recording the user's words and actions. The input is messages and voices generated by the user, and the output is communication data sent to the server.

[0294] Step 2:

[0295] The server converts the received audio data into text information using a speech recognition API. This conversion creates the foundation for handling audio as text and outputs the string data necessary for analysis. During this process, the user's speech is stored as text data on the server.

[0296] Step 3:

[0297] The server acquires text information and performs analysis using natural language processing algorithms. This analysis utilizes a generative AI model to analyze the structure and sentiment of the text and assess the potential for harassment. The input is the transformed text data, and the output is a score indicating the likelihood that the behavior is considered harassment.

[0298] Step 4:

[0299] The server determines whether the score exceeds a predetermined threshold. If it does, it generates a warning message and prepares a prompt to provide feedback to the user. The input is the harassment score, and the output is the warning message sent to the user.

[0300] Step 5:

[0301] The server sends the warning message generated to the terminal. The terminal provides immediate feedback to the user by displaying this message on the user interface. The input is the warning message sent from the server, and the output is the notification displayed on the user's screen.

[0302] Step 6:

[0303] The server stores all the analysis results and the user's behavior history in the storage device. This data is later used as the basis for generating reports for the user to improve their communication style. The input is the analysis results and the behavior history, and the output is the accumulation in the database.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0306] In conventional in-store communication, harassment behaviors are often overlooked, which has a problem of adversely affecting the workplace environment and the customer experience. Also, it has been difficult for employees themselves to objectively evaluate and improve the quality of their own communication.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] In this invention, the server includes means for analyzing the user's communication data with a natural language processing algorithm and evaluating its sentiment, means for acquiring voice data and converting it into text data, and means for notifying the user terminal of a warning message in real time based on the analysis. Thereby, it promotes sound communication in the store and enables employees to review and improve their own actions.

[0309] "User communication data" refers to the content of all forms of dialogue and messages that users engage in, and this data includes both text and audio formats.

[0310] A "natural language processing algorithm" is a technology that enables computers to understand and analyze human language, analyzing text and audio data to evaluate emotions and intentions.

[0311] "Scoring the potential for harassment" is a process of quantifying the degree of harassment risk associated with analyzed communication data based on its content.

[0312] A "warning message" is a notification issued to alert users to potential harassment behavior and encourage them to improve their actions.

[0313] A "user terminal" refers to a device on which warning messages or analysis results are displayed, and includes smartphones, tablets, computers, and other similar devices.

[0314] "Storing user behavior history in a database" is the process of recording users' past communication data and analysis results as history, making it available for later evaluation and improvement.

[0315] "Converting audio data to text data" is a technology that converts audio information, such as conversations, into text information, making it easier to analyze.

[0316] A "report that visualizes trends in harassment behavior" is a report that uses accumulated data to visually show trends in harassment that have occurred, in order to help users understand them.

[0317] "Promoting healthy communication" means taking steps to reduce the risk of harassment and create a better and more positive communication environment.

[0318] A system that implements an application of this invention analyzes in-store communication in real time and issues warning messages aimed at preventing harassment. The server acquires user communication data and analyzes it using natural language processing algorithms. Specifically, programming languages ​​such as Python, libraries such as SpeechRecognition and TextBlob, and the Google Speech Recognition API are used to monitor text and voice exchanges conducted by users via smartphones and other devices.

[0319] The hardware used includes a smartphone and microphone to acquire audio data. This audio data is converted into text data, which is then analyzed to score the likelihood of harassment. If this score exceeds a predetermined threshold, the server generates a warning message and notifies the user's device. This gives the user an opportunity to improve the quality of their communication.

[0320] Warning messages are displayed in real time on user terminals to encourage the maintenance of a healthy work environment. Furthermore, all analysis results and user behavior history are stored in a database and provided as reports that visualize trends in harassment behavior, generated periodically. These reports serve as a guide for users to reflect on their own behavior and make long-term improvements.

[0321] For example, if a store staff member uses aggressive language in a conversation with a customer or chat with a colleague, the system will immediately issue a warning such as, "This language may be perceived as aggressive. Please consider using a different expression," prompting improvement. This kind of real-time feedback allows staff members to strive for healthy communication.

[0322] Examples of prompts include phrases that instruct the user to analyze text data, such as, "Evaluate the sentiment score of the following conversation text and determine whether it is aggressive or not."

[0323] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0324] Step 1:

[0325] The server acquires user communication data. It collects voice and text data in real time from smartphones and microphones, with voice data received via a voice recording device. This data is then transmitted from the user's device to the server via the internet. Input is voice and text data, and output is digital data in an analyzable format.

[0326] Step 2:

[0327] The server converts audio data into text data. It uses the Google Speech Recognition API to transcribe the audio data into text. The input is audio data, and the output is a string (text format). This converts the data into a format that can be parsed by natural language processing algorithms.

[0328] Step 3:

[0329] The server analyzes text data using the TextBlob library and evaluates its sentiment. It passes the acquired text data to TextBlob to calculate sentiment polarity. The output is a numerical value indicating the degree of positive or negative sentiment contained in the text. This value allows for scoring in the next step.

[0330] Step 4:

[0331] The server scores the likelihood of harassment based on the analysis results. Based on the acquired sentiment score, it evaluates whether the message content is aggressive towards others and determines whether it exceeds the threshold. The input is the numerical score from the sentiment evaluation, and the output is a flag indicating whether that score exceeds the threshold.

[0332] Step 5:

[0333] The server generates a warning message and notifies the user's terminal if the score exceeds a predetermined threshold. The warning message may include content such as, "This expression may be perceived as aggressive. Please consider using alternative wording." The input is the score evaluation result, and the output is the warning message displayed on the user's screen.

[0334] Step 6:

[0335] The server stores all analysis results and user behavior history in a database. It organizes the data collected in real time and records it as history. The input is the score evaluation results and the data behind them, and the output is a database of behavior history organized for each user. This serves as the basis for reports generated later.

[0336] 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.

[0337] This invention provides a system that offers more accurate judgments and feedback by combining an emotion engine that analyzes the user's emotional state with an evaluation of harassment in user communication. This system consists of multiple processing modules and is integrated into the user's communication tools.

[0338] The terminal is responsible for acquiring the user's communication data (text and voice) as part of the session. The data is encrypted and sent to the server along with identification information.

[0339] The server analyzes the received data using natural language processing algorithms to analyze context and tone. During this process, the emotion engine extracts emotional information from the data. The emotion engine identifies the emotions expressed by the user (e.g., anger, joy, sadness) through keyword detection and speech tone analysis.

[0340] The server comprehensively considers the results of analysis using natural language processing and emotional information from the emotion engine to score the likelihood of harassment. This scoring evaluates how the user's emotions influence the communication and adjusts the weighting of the score as needed.

[0341] If the score exceeds a set threshold, the server generates a warning message and improvement advice, adding emotionally-based customization to it. Specifically, if a state of heightened emotion is detected, advice will be provided that takes this into account.

[0342] For example, if a user exhibits behavior that indicates anxiety or frustration during a meeting, the server will take that emotional state into consideration and send a customized message such as, "You appear to be emotionally agitated. Let's try to maintain a calm demeanor."

[0343] The terminal notifies the user of the generated warning message and prompts them to review its contents. Furthermore, the server stores all analysis results and emotional states in a database and creates reports based on the results to support long-term behavioral improvement.

[0344] Users can utilize the information provided through their devices to help manage their communication style and emotions. This system aims to flexibly respond to emotional changes, fostering seamless communication and a safe work environment.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] The device captures text input and voice speech performed by the user in real time using communication tools. This is achieved through a mechanism that automatically acquires data using communication APIs.

[0348] Step 2:

[0349] The device sends the acquired data to the server in an encrypted state for privacy protection, along with information identifiers (such as user ID, session ID, and timestamp).

[0350] Step 3:

[0351] The server analyzes the received data using natural language processing algorithms to extract communication context, tone, content keywords, and other relevant information. This process includes tokenization, morphological analysis, and sentiment scoring.

[0352] Step 4:

[0353] The server uses an emotion engine to analyze the user's emotions from the received data. It identifies the user's emotional state by evaluating emotional keywords in text and tone and pitch in audio.

[0354] Step 5:

[0355] The server integrates natural language processing results with emotional information to score the likelihood of harassment. Since emotional information is used as a weight in the scoring, cases where the emotional state is clear will receive a higher score.

[0356] Step 6:

[0357] The server generates a warning message and advice for improvement if the score exceeds a set threshold. Based on sentiment information, the message content is customized and includes specific sentiment management suggestions.

[0358] Step 7:

[0359] The server sends the generated message to the user's device. The device then displays this to the user via a pop-up alert or notification bar to draw their attention.

[0360] Step 8:

[0361] The server stores the analysis results and sentiment analysis results in a database. The stored data is aggregated on a daily and monthly basis and used to understand users' long-term behavioral trends and to create reports to support improvements.

[0362] Step 9:

[0363] Users can review feedback received through their devices and use it to manage their communication and emotions. This allows users to improve their behavior and enhance their communication skills.

[0364] (Example 2)

[0365] 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".

[0366] In the current communication environment, evaluating harassment is subjective, making accurate judgment difficult. Furthermore, while emotional analysis and feedback provision require appropriate responses that consider the user's emotional state, conventional technologies fall short in this regard. Additionally, there is a lack of mechanisms to systematically store user behavior history and utilize it for future behavioral improvement.

[0367] 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.

[0368] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for evaluating the likelihood of harassment based on the analysis results and emotional information. This enables accurate evaluation of harassment that takes into account the user's emotional state and the provision of appropriate feedback. Furthermore, it becomes possible to accumulate behavioral history in an information storage device and create reports that contribute to long-term behavioral improvement.

[0369] A "user" refers to an entity that inputs information and communicates through a system.

[0370] "Communication information" refers to digital communication data such as text messages and voice data obtained from users.

[0371] A "natural language processing algorithm" refers to a technical means of analyzing communication information to understand its context and emotions.

[0372] "Evaluation scoring" refers to the process of numerically evaluating and quantifying whether the content of the analyzed data is related to harassment.

[0373] "Warning information" refers to messages that include cautions and improvement suggestions, which are sent to the user based on the analysis results.

[0374] "Emotional information" refers to data that indicates the user's emotional state, extracted from communication information.

[0375] "User terminal" refers to an electronic device used by a user to input and receive information.

[0376] An "information storage device" refers to a database system that stores communication information and analysis results for later analysis and report creation.

[0377] A "report" refers to a document that visualizes the analysis results and trends in harassment behavior and explains them to the user.

[0378] "Textual information" refers to data that is represented by converting audio information into text.

[0379] This invention is a system for evaluating harassment in user communication and analyzing the user's emotional state to provide more accurate judgments and feedback. Specifically, it is implemented using a system that combines a server and terminals.

[0380] The device collects user communication information, including text messages and voice data. The device encrypts this information and transfers it to the server using a secure communication protocol (e.g., HTTPS). Common computer systems and smartphones are used as devices.

[0381] The server processes the received information using a natural language processing algorithm. This algorithm performs text analysis and speech tone analysis to understand the context of the user's speech, and the emotion engine extracts emotional information. This allows the server to understand the user's emotional state.

[0382] The server evaluates and scores the potential for harassment based on analysis results and emotional information. This evaluation helps determine the impact of excessive emotions on communication. When the score exceeds a set threshold, a warning message is generated. The warning message is customized according to the user's emotional state and includes appropriate advice for improvement.

[0383] For example, if a user shows heightened emotions during a video conference, the server will recognize this and provide specific feedback such as, "You appear to be emotionally agitated at the moment. Let's discuss this calmly."

[0384] Users review the warning information notified through their devices and use it to improve their communication style. Furthermore, the server stores all information and emotional states in a database and generates reports to support long-term behavioral improvement. These reports are used as reference material in users' decision-making.

[0385] An example of a prompt message could be something like, "Please tell me how to analyze the user's current emotional state and determine what kind of feedback to provide." This could be input into a generative AI model.

[0386] This system aims to provide appropriate communication and a safe environment, and is designed to respond flexibly to changes in emotions.

[0387] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0388] Step 1:

[0389] The terminal acquires user communication information. Data is entered by capturing text messages and voice information entered by the user in real time. This input information serves as the basic data for system processing.

[0390] Step 2:

[0391] The terminal encrypts the acquired communication information. Before sending the data, it encrypts the information using a security protocol and prepares it for transfer to the server. The output is an encrypted data packet.

[0392] Step 3:

[0393] The server receives encrypted data sent from the terminal and decrypts it. The decrypted data is input into a natural language processing algorithm, and the analysis of text and speech tone begins. At this stage, the context of the data and the initial emotional state are output.

[0394] Step 4:

[0395] The server uses an emotion engine to extract emotional information from the analyzed data. Specifically, it analyzes keyword and voice tone patterns to identify the user's emotions (e.g., anger, joy). An emotion statement is then generated as output.

[0396] Step 5:

[0397] The server integrates the obtained natural language processing results and sentiment information to score the likelihood of harassment. This score quantifies the harassment risk based on a set scale. The output is an evaluation score.

[0398] Step 6:

[0399] The server generates a warning message when the evaluation score exceeds a set threshold. This includes customized advice tailored to specific emotional states. The output is a customized warning message.

[0400] Step 7:

[0401] The terminal notifies the user of the generated warning information. It displays the message on the user interface for immediate confirmation. The output is the notification information displayed on the user's screen.

[0402] Step 8:

[0403] The server stores all analysis results and sentiment information in an information storage device. This data forms the basis for long-term analysis used for future reference and behavioral improvement. In actual operation, the data is stored in a database.

[0404] Step 9:

[0405] Users strive to improve their communication style through the provided warnings and reports. Based on the output data, introspection takes place to manage emotions and accept feedback on behavior.

[0406] (Application Example 2)

[0407] 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."

[0408] There is a need to detect harassment in communication and understand emotional states in real time to improve safety and productivity in the workplace and public spaces. Furthermore, it is necessary to provide feedback that appropriately reflects individual emotional changes to improve the work environment and facilitate smoother communication. This necessitates a system that reduces user stress and supports long-term behavioral improvement.

[0409] 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.

[0410] In this invention, the server includes means for acquiring user information data, means for analyzing the data using a natural language processing algorithm, and means for scoring the likelihood of harassment using the analysis results and an emotion engine. This makes it possible to detect harassment in the user's communication and provide feedback that reflects changes in their emotions.

[0411] "User information data" refers to data related to communication obtained from users, including voice and text.

[0412] A "natural language processing algorithm" is a set of computational techniques used to analyze context and sentiment from text data.

[0413] An "emotion engine" is a software component that identifies and extracts emotional information from user communication data.

[0414] "Scoring the potential for harassment" means numerically evaluating the risk of problematic behaviors based on users' communication.

[0415] "Generating warning information" means creating information to notify the user when potential harassment is detected.

[0416] An "information terminal" is an electronic device used by a user to receive warning information.

[0417] "Behavioral information" refers to a history of actions and emotions extracted from a user's past communication data.

[0418] A "storage device" is a data storage device used to accumulate user behavior information.

[0419] A "report" is a document that summarizes analysis results and trends, and is information provided to the user.

[0420] "Text data" refers to data in text format that has been converted from audio data.

[0421] The system implementing this invention is primarily composed of an information terminal and a server. First, the user's information terminal is responsible for encrypting communication data acquired in voice or text format and transmitting it to the server. The information terminal is equipped with voice input and text conversion functions, making it possible to acquire the user's speech and text as digital data in real time.

[0422] The server is the central component that processes the received data. First, to implement natural language processing algorithms, it uses the Python spaCy library to analyze the text context and evaluate the content of the communication. Furthermore, it uses TextBlob as an emotion engine to extract emotional information from the data. This makes visible the emotional state hidden in each word spoken by the user.

[0423] Based on the analysis results, the server scores the likelihood of harassment. This scoring system takes into account the user's emotions and context, and generates a warning if the threshold is exceeded. The generated warning information is sent to the information terminal using the Pusher API. The information terminal displays the warning message to the user and provides immediate feedback as needed.

[0424] For example, if an employee expresses frustration during a meeting, the server will send a message via the employee's terminal saying, "You seem to be getting emotional. Please consider taking a short break." This approach helps to prevent inappropriate communication from occurring in advance.

[0425] A MySQL database is used to store the acquired behavioral information and analysis results over a long period. This makes it possible to create reports for users to improve their behavior based on past trends.

[0426] An example of a prompt for a generative AI model is: "Analyze recent conversation data to determine if emotions such as anger or frustration are heightened. Also, score whether these emotions may be causing inappropriate communication." Using this kind of sentence can prompt the model to perform appropriate data analysis and generate feedback.

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] The device receives user voice or text as input data. If voice data is used, the device converts it into text data using its speech recognition function. The converted text data is encrypted using the SSL protocol to ensure information security. The device then generates encrypted text data as output.

[0430] Step 2:

[0431] The server receives encrypted text data sent from the terminal and decrypts it. The decrypted data is then input into a natural language processing algorithm to analyze the context. During this process, spaCy is used for grammatical analysis and keyword extraction. The output is structured data as a result of the analysis.

[0432] Step 3:

[0433] The server takes the analysis results as input and extracts emotional information using TextBlob, an emotion engine. This process calculates emotional scores such as positive, negative, and neutral, and outputs these scores.

[0434] Step 4:

[0435] The server integrates the results of natural language processing with sentiment information to score the likelihood of harassment. In this step, a generative AI model is used to score based on the prompt text and evaluate the score value. It determines whether a threshold is exceeded, and if so, generates warning information. The output is the generated score and warning information.

[0436] Step 5:

[0437] The server uses the Pusher API to notify the terminal of the generated warning information. The terminal displays the received warning information to the user as a pop-up message. This message includes specific feedback based on the user's emotional state. The output is a real-time warning display to the user.

[0438] Step 6:

[0439] The server stores all processing results, analysis results, and behavioral information in a database. MySQL is used for this purpose, accumulating data for later report generation. The output consists of past behavioral information stored in the database.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] [Third Embodiment]

[0444] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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).

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] 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".

[0456] This invention relates to a computer system aimed at preventing harassment, which is integrated into communication tools used by users on a daily basis. This system acquires user communication data in real time, analyzes it to assess the possibility of harassment, and issues a warning to the user. The following describes a specific embodiment of the system.

[0457] The server receives user communication data and analyzes it using a natural language processing algorithm. Based on the analysis, it scores whether the words and actions in the data may constitute harassment. If this score exceeds a pre-set threshold, the server generates a warning message and sends it to the user's terminal.

[0458] For example, if a user uses their device to send an aggressive message to a colleague at work, such as "Submit the report immediately," the server will analyze the message. The analysis will score the message as being aggressive, and if it is determined to exceed a threshold, the server will notify the user with a warning such as, "This expression may be perceived as aggressive. Please consider rephrasing it."

[0459] Furthermore, to prevent harassment in voice communication, the server can convert received voice data into text data and analyze it similarly using the aforementioned process. This enables real-time harassment detection even in voice conferencing applications such as Zoom.

[0460] Furthermore, the server stores all analysis results and user behavior history in a database, and compiles daily and monthly trends. This allows users to regularly receive reports that enable them to reflect on and improve their own behavior. This promotes long-term behavioral improvement and safe and effective communication.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The device captures text and voice data entered by the user into communication tools in real time. This is done automatically using the application's API functionality.

[0464] Step 2:

[0465] The device transmits the acquired data, along with identification information (user ID, conversation ID, timestamp, etc.), to the server via a secure communication protocol. During this process, the data is encrypted to protect privacy.

[0466] Step 3:

[0467] The server analyzes the received data using natural language processing algorithms. Text data undergoes tokenization, syntactic analysis, and keyword extraction, while speech data is converted to text using speech recognition technology.

[0468] Step 4:

[0469] The server scores each behavior based on the analyzed data to determine if it constitutes harassment. The scoring is performed using a machine learning model trained on a dataset.

[0470] Step 5:

[0471] The server generates warning messages and advice for improvement if the score exceeds a set threshold. The generated messages are selected from different templates depending on the case.

[0472] Step 6:

[0473] The server sends the generated warning message to the user's terminal, providing immediate notification. The notification appears as a pop-up or alert message to prompt the user to take notice.

[0474] Step 7:

[0475] The server stores analysis results and user communication history in a database, enabling analysis of long-term behavioral patterns. It also accumulates data for generating daily and monthly reports.

[0476] Step 8:

[0477] Users can review the provided warning messages and periodic reports, reflect on their communication methods, and make improvements as needed. This allows users to proactively learn desirable behaviors.

[0478] (Example 1)

[0479] 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."

[0480] In modern workplaces and online environments, unintentional harassment can occur. A key challenge is the lack of opportunities for individual users to become aware of their own communication styles and improve their behavior. Preventing harassment is particularly difficult in voice communication, where real-time responses are required. Therefore, there is a need for a system that can detect expressions that cause harassment in real time and provide immediate feedback to users.

[0481] 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.

[0482] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for quantifying the likelihood of harassment based on the analysis results. This makes it possible to detect potential harassment in voice and text communication in real time and provide rapid feedback to the user.

[0483] "User communication information" refers to all information that users exchange with others through email, chat messages, voice calls, etc.

[0484] A "natural language processing algorithm" refers to computational methods that enable computers to understand and analyze text written or spoken by humans.

[0485] "Quantifying the potential for harassment" refers to quantitatively evaluating, based on specific criteria or algorithms, the extent to which a user's expressions or behavior are considered harassment, and expressing this numerically.

[0486] "Prescribed threshold values" refer to the thresholds set as criteria for determining whether an act constitutes harassment, and a warning is issued if this value is exceeded.

[0487] "Warning information" refers to messages or notifications provided to users to inform them of a potential harassment issue when their behavior is deemed to be such.

[0488] "User equipment" refers to terminals or devices used by users for communication.

[0489] "Storing behavioral history in a memory device" refers to recording a user's past communication content and analysis results, and saving them so that they can be analyzed and referenced later.

[0490] "Converting audio information to text information" refers to using speech recognition technology to convert spoken content into text.

[0491] "Preventing harassment in voice communication" refers to methods or means of preventing harassment from occurring by analyzing the content of speech in real time during the process of communication via voice.

[0492] This invention is a communication analysis system aimed at preventing harassment. The following describes in detail how this system can be implemented.

[0493] The server receives communication information sent from the user's terminal. This information includes text messages and voice communications. For voice information, the server converts it into text using speech recognition software. A common speech recognition API is used for this conversion process.

[0494] Next, the server analyzes the text information using a natural language processing algorithm. This analysis utilizes a natural language processing library that runs in Python. The purpose of the analysis is to evaluate whether the user's statements constitute harassment.

[0495] If the analysis determines that a statement may constitute harassment, the server quantifies it and compares it to a predetermined threshold value. If the threshold value is exceeded, the server generates a warning message and notifies the terminal. The warning message encourages the user to reconsider their statement.

[0496] Furthermore, the server stores past analysis results and behavioral history in storage and periodically evaluates the user's behavioral history. This allows users to receive feedback to improve their communication style. This process is particularly applicable to communication using voice conferencing software.

[0497] As a concrete example, suppose a user sends a message to a colleague at work using a strong tone. In this case, the server analyzes the message and detects that it uses high-pressure language such as "Submit the report immediately." If the score exceeds a certain threshold, the server sends a prompt message to the user saying, "This language may be perceived as high-pressure. Please consider using a different expression." This allows the user to reflect on their message and correct it if necessary.

[0498] This system will allow users to communicate more smoothly online and receive support to maintain a safe environment.

[0499] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0500] Step 1:

[0501] Users use communication tools through their devices to send text messages and voice data. This data is immediately transferred to a server for the purpose of recording the user's words and actions. The input is messages and voices generated by the user, and the output is communication data sent to the server.

[0502] Step 2:

[0503] The server converts the received audio data into text information using a speech recognition API. This conversion creates the foundation for handling audio as text and outputs the string data necessary for analysis. During this process, the user's speech is stored as text data on the server.

[0504] Step 3:

[0505] The server acquires text information and performs analysis using natural language processing algorithms. This analysis utilizes a generative AI model to analyze the structure and sentiment of the text and assess the potential for harassment. The input is the transformed text data, and the output is a score indicating the likelihood that the behavior is considered harassment.

[0506] Step 4:

[0507] The server determines whether the score exceeds a predetermined threshold. If it does, it generates a warning message and prepares a prompt to provide feedback to the user. The input is the harassment score, and the output is the warning message sent to the user.

[0508] Step 5:

[0509] The server sends a generated warning message to the terminal. The terminal provides immediate feedback to the user by displaying this message in its user interface. The input is the warning message sent from the server, and the output is the notification displayed on the user's screen.

[0510] Step 6:

[0511] The server stores all analysis results and user behavior history in storage. This data is later used as the basis for generating reports to help users improve their communication style. The input is analysis results and behavior history, and the output is storage in the database.

[0512] (Application Example 1)

[0513] 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."

[0514] In traditional in-store communication, harassment was often overlooked, which negatively impacted the work environment and customer experience. Furthermore, employees lacked opportunities to objectively evaluate and improve the quality of their own communication.

[0515] 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.

[0516] In this invention, the server includes means for analyzing user communication data using a natural language processing algorithm and evaluating the emotions associated with that data, means for acquiring voice data and converting it into text data, and means for notifying the user terminal of a warning message in real time based on the analysis. This promotes healthy communication within the store and enables employees to reflect on and improve their own behavior.

[0517] "User communication data" refers to the content of all forms of dialogue and messages that users engage in, and this data includes both text and audio formats.

[0518] A "natural language processing algorithm" is a technology that enables computers to understand and analyze human language, analyzing text and audio data to evaluate emotions and intentions.

[0519] "Scoring the potential for harassment" is a process of quantifying the degree of harassment risk associated with analyzed communication data based on its content.

[0520] A "warning message" is a notification issued to alert users to potential harassment behavior and encourage them to improve their actions.

[0521] A "user terminal" refers to a device on which warning messages or analysis results are displayed, and includes smartphones, tablets, computers, and other similar devices.

[0522] "Storing user behavior history in a database" is the process of recording users' past communication data and analysis results as history, making it available for later evaluation and improvement.

[0523] "Converting audio data to text data" is a technology that converts audio information, such as conversations, into text information, making it easier to analyze.

[0524] A "report that visualizes trends in harassment behavior" is a report that uses accumulated data to visually show trends in harassment that have occurred, in order to help users understand them.

[0525] "Promoting healthy communication" means taking steps to reduce the risk of harassment and create a better and more positive communication environment.

[0526] A system that implements an application of this invention analyzes in-store communication in real time and issues warning messages aimed at preventing harassment. The server acquires user communication data and analyzes it using natural language processing algorithms. Specifically, programming languages ​​such as Python, libraries such as SpeechRecognition and TextBlob, and the Google Speech Recognition API are used to monitor text and voice exchanges conducted by users via smartphones and other devices.

[0527] The hardware used includes a smartphone and microphone to acquire audio data. This audio data is converted into text data, which is then analyzed to score the likelihood of harassment. If this score exceeds a predetermined threshold, the server generates a warning message and notifies the user's device. This gives the user an opportunity to improve the quality of their communication.

[0528] Warning messages are displayed in real time on user terminals to encourage the maintenance of a healthy work environment. Furthermore, all analysis results and user behavior history are stored in a database and provided as reports that visualize trends in harassment behavior, generated periodically. These reports serve as a guide for users to reflect on their own behavior and make long-term improvements.

[0529] For example, if a store staff member uses aggressive language in a conversation with a customer or chat with a colleague, the system will immediately issue a warning such as, "This language may be perceived as aggressive. Please consider using a different expression," prompting improvement. This kind of real-time feedback allows staff members to strive for healthy communication.

[0530] Examples of prompts include phrases that instruct the user to analyze text data, such as, "Evaluate the sentiment score of the following conversation text and determine whether it is aggressive or not."

[0531] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0532] Step 1:

[0533] The server acquires user communication data. It collects voice and text data in real time from smartphones and microphones, with voice data received via a voice recording device. This data is then transmitted from the user's device to the server via the internet. Input is voice and text data, and output is digital data in an analyzable format.

[0534] Step 2:

[0535] The server converts audio data into text data. It uses the Google Speech Recognition API to transcribe the audio data into text. The input is audio data, and the output is a string (text format). This converts the data into a format that can be parsed by natural language processing algorithms.

[0536] Step 3:

[0537] The server analyzes text data using the TextBlob library and evaluates its sentiment. It passes the acquired text data to TextBlob to calculate sentiment polarity. The output is a numerical value indicating the degree of positive or negative sentiment contained in the text. This value allows for scoring in the next step.

[0538] Step 4:

[0539] The server scores the likelihood of harassment based on the analysis results. Based on the acquired sentiment score, it evaluates whether the message content is aggressive towards others and determines whether it exceeds the threshold. The input is the numerical score from the sentiment evaluation, and the output is a flag indicating whether that score exceeds the threshold.

[0540] Step 5:

[0541] The server generates a warning message and notifies the user's terminal if the score exceeds a predetermined threshold. The warning message may include content such as, "This expression may be perceived as aggressive. Please consider using alternative wording." The input is the score evaluation result, and the output is the warning message displayed on the user's screen.

[0542] Step 6:

[0543] The server stores all analysis results and user behavior history in a database. It organizes the data collected in real time and records it as history. The input is the score evaluation results and the data behind them, and the output is a database of behavior history organized for each user. This serves as the basis for reports generated later.

[0544] 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.

[0545] This invention provides a system that offers more accurate judgments and feedback by combining an emotion engine that analyzes the user's emotional state with an evaluation of harassment in user communication. This system consists of multiple processing modules and is integrated into the user's communication tools.

[0546] The terminal is responsible for acquiring the user's communication data (text and voice) as part of the session. The data is encrypted and sent to the server along with identification information.

[0547] The server analyzes the received data using natural language processing algorithms to analyze context and tone. During this process, the emotion engine extracts emotional information from the data. The emotion engine identifies the emotions expressed by the user (e.g., anger, joy, sadness) through keyword detection and speech tone analysis.

[0548] The server comprehensively considers the results of analysis using natural language processing and emotional information from the emotion engine to score the likelihood of harassment. This scoring evaluates how the user's emotions influence the communication and adjusts the weighting of the score as needed.

[0549] If the score exceeds a set threshold, the server generates a warning message and improvement advice, adding emotionally-based customization to it. Specifically, if a state of heightened emotion is detected, advice will be provided that takes this into account.

[0550] For example, if a user exhibits behavior that indicates anxiety or frustration during a meeting, the server will take that emotional state into consideration and send a customized message such as, "You appear to be emotionally agitated. Let's try to maintain a calm demeanor."

[0551] The terminal notifies the user of the generated warning message and prompts them to review its contents. Furthermore, the server stores all analysis results and emotional states in a database and creates reports based on the results to support long-term behavioral improvement.

[0552] Users can utilize the information provided through their devices to help manage their communication style and emotions. This system aims to flexibly respond to emotional changes, fostering seamless communication and a safe work environment.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The device captures text input and voice speech performed by the user in real time using communication tools. This is achieved through a mechanism that automatically acquires data using communication APIs.

[0556] Step 2:

[0557] The device sends the acquired data to the server in an encrypted state for privacy protection, along with information identifiers (such as user ID, session ID, and timestamp).

[0558] Step 3:

[0559] The server analyzes the received data using natural language processing algorithms to extract communication context, tone, content keywords, and other relevant information. This process includes tokenization, morphological analysis, and sentiment scoring.

[0560] Step 4:

[0561] The server uses an emotion engine to analyze the user's emotions from the received data. It identifies the user's emotional state by evaluating emotional keywords in text and tone and pitch in audio.

[0562] Step 5:

[0563] The server integrates natural language processing results with emotional information to score the likelihood of harassment. Since emotional information is used as a weight in the scoring, cases where the emotional state is clear will receive a higher score.

[0564] Step 6:

[0565] The server generates a warning message and advice for improvement if the score exceeds a set threshold. Based on sentiment information, the message content is customized and includes specific sentiment management suggestions.

[0566] Step 7:

[0567] The server sends the generated message to the user's device. The device then displays this to the user via a pop-up alert or notification bar to draw their attention.

[0568] Step 8:

[0569] The server stores the analysis results and sentiment analysis results in a database. The stored data is aggregated on a daily and monthly basis and used to understand users' long-term behavioral trends and to create reports to support improvements.

[0570] Step 9:

[0571] Users can review feedback received through their devices and use it to manage their communication and emotions. This allows users to improve their behavior and enhance their communication skills.

[0572] (Example 2)

[0573] 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."

[0574] In the current communication environment, evaluating harassment is subjective, making accurate judgment difficult. Furthermore, while emotional analysis and feedback provision require appropriate responses that consider the user's emotional state, conventional technologies fall short in this regard. Additionally, there is a lack of mechanisms to systematically store user behavior history and utilize it for future behavioral improvement.

[0575] 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.

[0576] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for evaluating the likelihood of harassment based on the analysis results and emotional information. This enables accurate evaluation of harassment that takes into account the user's emotional state and the provision of appropriate feedback. Furthermore, it becomes possible to accumulate behavioral history in an information storage device and create reports that contribute to long-term behavioral improvement.

[0577] A "user" refers to an entity that inputs information and communicates through a system.

[0578] "Communication information" refers to digital communication data such as text messages and voice data obtained from users.

[0579] A "natural language processing algorithm" refers to a technical means of analyzing communication information to understand its context and emotions.

[0580] "Evaluation scoring" refers to the process of numerically evaluating and quantifying whether the content of the analyzed data is related to harassment.

[0581] "Warning information" refers to messages that include cautions and improvement suggestions, which are sent to the user based on the analysis results.

[0582] "Emotional information" refers to data that indicates the user's emotional state, extracted from communication information.

[0583] "User terminal" refers to an electronic device used by a user to input and receive information.

[0584] An "information storage device" refers to a database system that stores communication information and analysis results for later analysis and report creation.

[0585] A "report" refers to a document that visualizes the analysis results and trends in harassment behavior and explains them to the user.

[0586] "Textual information" refers to data that is represented by converting audio information into text.

[0587] This invention is a system for evaluating harassment in user communication and analyzing the user's emotional state to provide more accurate judgments and feedback. Specifically, it is implemented using a system that combines a server and terminals.

[0588] The device collects user communication information, including text messages and voice data. The device encrypts this information and transfers it to the server using a secure communication protocol (e.g., HTTPS). Common computer systems and smartphones are used as devices.

[0589] The server processes the received information using a natural language processing algorithm. This algorithm performs text analysis and speech tone analysis to understand the context of the user's speech, and the emotion engine extracts emotional information. This allows the server to understand the user's emotional state.

[0590] The server evaluates and scores the potential for harassment based on analysis results and emotional information. This evaluation helps determine the impact of excessive emotions on communication. When the score exceeds a set threshold, a warning message is generated. The warning message is customized according to the user's emotional state and includes appropriate advice for improvement.

[0591] For example, if a user shows heightened emotions during a video conference, the server will recognize this and provide specific feedback such as, "You appear to be emotionally agitated at the moment. Let's discuss this calmly."

[0592] Users review the warning information notified through their devices and use it to improve their communication style. Furthermore, the server stores all information and emotional states in a database and generates reports to support long-term behavioral improvement. These reports are used as reference material in users' decision-making.

[0593] An example of a prompt message could be something like, "Please tell me how to analyze the user's current emotional state and determine what kind of feedback to provide." This could be input into a generative AI model.

[0594] This system aims to provide appropriate communication and a safe environment, and is designed to respond flexibly to changes in emotions.

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] The terminal acquires user communication information. Data is entered by capturing text messages and voice information entered by the user in real time. This input information serves as the basic data for system processing.

[0598] Step 2:

[0599] The terminal encrypts the acquired communication information. Before sending the data, it encrypts the information using a security protocol and prepares it for transfer to the server. The output is an encrypted data packet.

[0600] Step 3:

[0601] The server receives encrypted data sent from the terminal and decrypts it. The decrypted data is input into a natural language processing algorithm, and the analysis of text and speech tone begins. At this stage, the context of the data and the initial emotional state are output.

[0602] Step 4:

[0603] The server uses an emotion engine to extract emotional information from the analyzed data. Specifically, it analyzes keyword and voice tone patterns to identify the user's emotions (e.g., anger, joy). An emotion statement is then generated as output.

[0604] Step 5:

[0605] The server integrates the obtained natural language processing results and sentiment information to score the likelihood of harassment. This score quantifies the harassment risk based on a set scale. The output is an evaluation score.

[0606] Step 6:

[0607] The server generates a warning message when the evaluation score exceeds a set threshold. This includes customized advice tailored to specific emotional states. The output is a customized warning message.

[0608] Step 7:

[0609] The terminal notifies the user of the generated warning information. It displays the message on the user interface for immediate confirmation. The output is the notification information displayed on the user's screen.

[0610] Step 8:

[0611] The server stores all analysis results and sentiment information in an information storage device. This data forms the basis for long-term analysis used for future reference and behavioral improvement. In actual operation, the data is stored in a database.

[0612] Step 9:

[0613] Users strive to improve their communication style through the provided warnings and reports. Based on the output data, introspection takes place to manage emotions and accept feedback on behavior.

[0614] (Application Example 2)

[0615] 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."

[0616] There is a need to detect harassment in communication and understand emotional states in real time to improve safety and productivity in the workplace and public spaces. Furthermore, it is necessary to provide feedback that appropriately reflects individual emotional changes to improve the work environment and facilitate smoother communication. This necessitates a system that reduces user stress and supports long-term behavioral improvement.

[0617] 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.

[0618] In this invention, the server includes means for acquiring user information data, means for analyzing the data using a natural language processing algorithm, and means for scoring the likelihood of harassment using the analysis results and an emotion engine. This makes it possible to detect harassment in the user's communication and provide feedback that reflects changes in their emotions.

[0619] "User information data" refers to data related to communication obtained from users, including voice and text.

[0620] A "natural language processing algorithm" is a set of computational techniques used to analyze context and sentiment from text data.

[0621] An "emotion engine" is a software component that identifies and extracts emotional information from user communication data.

[0622] "Scoring the potential for harassment" means numerically evaluating the risk of problematic behaviors based on users' communication.

[0623] "Generating warning information" means creating information to notify the user when potential harassment is detected.

[0624] An "information terminal" is an electronic device used by a user to receive warning information.

[0625] "Behavioral information" refers to a history of actions and emotions extracted from a user's past communication data.

[0626] A "storage device" is a data storage device used to accumulate user behavior information.

[0627] A "report" is a document that summarizes analysis results and trends, and is information provided to the user.

[0628] "Text data" refers to data in text format that has been converted from audio data.

[0629] The system implementing this invention is primarily composed of an information terminal and a server. First, the user's information terminal is responsible for encrypting communication data acquired in voice or text format and transmitting it to the server. The information terminal is equipped with voice input and text conversion functions, making it possible to acquire the user's speech and text as digital data in real time.

[0630] The server is the central component that processes the received data. First, to implement natural language processing algorithms, it uses the Python spaCy library to analyze the text context and evaluate the content of the communication. Furthermore, it uses TextBlob as an emotion engine to extract emotional information from the data. This makes visible the emotional state hidden in each word spoken by the user.

[0631] Based on the analysis results, the server scores the likelihood of harassment. This scoring system takes into account the user's emotions and context, and generates a warning if the threshold is exceeded. The generated warning information is sent to the information terminal using the Pusher API. The information terminal displays the warning message to the user and provides immediate feedback as needed.

[0632] For example, if an employee expresses frustration during a meeting, the server will send a message via the employee's terminal saying, "You seem to be getting emotional. Please consider taking a short break." This approach helps to prevent inappropriate communication from occurring in advance.

[0633] A MySQL database is used to store the acquired behavioral information and analysis results over a long period. This makes it possible to create reports for users to improve their behavior based on past trends.

[0634] An example of a prompt for a generative AI model is: "Analyze recent conversation data to determine if emotions such as anger or frustration are heightened. Also, score whether these emotions may be causing inappropriate communication." Using this kind of sentence can prompt the model to perform appropriate data analysis and generate feedback.

[0635] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0636] Step 1:

[0637] The device receives user voice or text as input data. If voice data is used, the device converts it into text data using its speech recognition function. The converted text data is encrypted using the SSL protocol to ensure information security. The device then generates encrypted text data as output.

[0638] Step 2:

[0639] The server receives encrypted text data sent from the terminal and decrypts it. The decrypted data is then input into a natural language processing algorithm to analyze the context. During this process, spaCy is used for grammatical analysis and keyword extraction. The output is structured data as a result of the analysis.

[0640] Step 3:

[0641] The server takes the analysis results as input and extracts emotional information using TextBlob, an emotion engine. This process calculates emotional scores such as positive, negative, and neutral, and outputs these scores.

[0642] Step 4:

[0643] The server integrates the results of natural language processing with sentiment information to score the likelihood of harassment. In this step, a generative AI model is used to score based on the prompt text and evaluate the score value. It determines whether a threshold is exceeded, and if so, generates warning information. The output is the generated score and warning information.

[0644] Step 5:

[0645] The server uses the Pusher API to notify the terminal of the generated warning information. The terminal displays the received warning information to the user as a pop-up message. This message includes specific feedback based on the user's emotional state. The output is a real-time warning display to the user.

[0646] Step 6:

[0647] The server stores all processing results, analysis results, and behavioral information in a database. MySQL is used for this purpose, accumulating data for later report generation. The output consists of past behavioral information stored in the database.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] [Fourth Embodiment]

[0652] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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).

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] 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".

[0665] This invention relates to a computer system aimed at preventing harassment, which is integrated into communication tools used by users on a daily basis. This system acquires user communication data in real time, analyzes it to assess the possibility of harassment, and issues a warning to the user. The following describes a specific embodiment of the system.

[0666] The server receives user communication data and analyzes it using a natural language processing algorithm. Based on the analysis, it scores whether the words and actions in the data may constitute harassment. If this score exceeds a pre-set threshold, the server generates a warning message and sends it to the user's terminal.

[0667] For example, if a user uses their device to send an aggressive message to a colleague at work, such as "Submit the report immediately," the server will analyze the message. The analysis will score the message as being aggressive, and if it is determined to exceed a threshold, the server will notify the user with a warning such as, "This expression may be perceived as aggressive. Please consider rephrasing it."

[0668] Furthermore, to prevent harassment in voice communication, the server can convert received voice data into text data and analyze it similarly using the aforementioned process. This enables real-time harassment detection even in voice conferencing applications such as Zoom.

[0669] Furthermore, the server stores all analysis results and user behavior history in a database, and compiles daily and monthly trends. This allows users to regularly receive reports that enable them to reflect on and improve their own behavior. This promotes long-term behavioral improvement and safe and effective communication.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] The device captures text and voice data entered by the user into communication tools in real time. This is done automatically using the application's API functionality.

[0673] Step 2:

[0674] The device transmits the acquired data, along with identification information (user ID, conversation ID, timestamp, etc.), to the server via a secure communication protocol. During this process, the data is encrypted to protect privacy.

[0675] Step 3:

[0676] The server analyzes the received data using natural language processing algorithms. Text data undergoes tokenization, syntactic analysis, and keyword extraction, while speech data is converted to text using speech recognition technology.

[0677] Step 4:

[0678] The server scores each behavior based on the analyzed data to determine if it constitutes harassment. The scoring is performed using a machine learning model trained on a dataset.

[0679] Step 5:

[0680] The server generates warning messages and advice for improvement if the score exceeds a set threshold. The generated messages are selected from different templates depending on the case.

[0681] Step 6:

[0682] The server sends the generated warning message to the user's terminal, providing immediate notification. The notification appears as a pop-up or alert message to prompt the user to take notice.

[0683] Step 7:

[0684] The server stores analysis results and user communication history in a database, enabling analysis of long-term behavioral patterns. It also accumulates data for generating daily and monthly reports.

[0685] Step 8:

[0686] Users can review the provided warning messages and periodic reports, reflect on their communication methods, and make improvements as needed. This allows users to proactively learn desirable behaviors.

[0687] (Example 1)

[0688] 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".

[0689] In modern workplaces and online environments, unintentional harassment can occur. A key challenge is the lack of opportunities for individual users to become aware of their own communication styles and improve their behavior. Preventing harassment is particularly difficult in voice communication, where real-time responses are required. Therefore, there is a need for a system that can detect expressions that cause harassment in real time and provide immediate feedback to users.

[0690] 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.

[0691] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for quantifying the likelihood of harassment based on the analysis results. This makes it possible to detect potential harassment in voice and text communication in real time and provide rapid feedback to the user.

[0692] "User communication information" refers to all information that users exchange with others through email, chat messages, voice calls, etc.

[0693] A "natural language processing algorithm" refers to computational methods that enable computers to understand and analyze text written or spoken by humans.

[0694] "Quantifying the potential for harassment" refers to quantitatively evaluating, based on specific criteria or algorithms, the extent to which a user's expressions or behavior are considered harassment, and expressing this numerically.

[0695] "Prescribed threshold values" refer to the thresholds set as criteria for determining whether an act constitutes harassment, and a warning is issued if this value is exceeded.

[0696] "Warning information" refers to messages or notifications provided to users to inform them of a potential harassment issue when their behavior is deemed to be such.

[0697] "User equipment" refers to terminals or devices used by users for communication.

[0698] "Storing behavioral history in a memory device" refers to recording a user's past communication content and analysis results, and saving them so that they can be analyzed and referenced later.

[0699] "Converting audio information to text information" refers to using speech recognition technology to convert spoken content into text.

[0700] "Preventing harassment in voice communication" refers to methods or means of preventing harassment from occurring by analyzing the content of speech in real time during the process of communication via voice.

[0701] This invention is a communication analysis system aimed at preventing harassment. The following describes in detail how this system can be implemented.

[0702] The server receives communication information sent from the user's terminal. This information includes text messages and voice communications. For voice information, the server converts it into text using speech recognition software. A common speech recognition API is used for this conversion process.

[0703] Next, the server analyzes the text information using a natural language processing algorithm. This analysis utilizes a natural language processing library that runs in Python. The purpose of the analysis is to evaluate whether the user's statements constitute harassment.

[0704] If the analysis determines that a statement may constitute harassment, the server quantifies it and compares it to a predetermined threshold value. If the threshold value is exceeded, the server generates a warning message and notifies the terminal. The warning message encourages the user to reconsider their statement.

[0705] Furthermore, the server stores past analysis results and behavioral history in storage and periodically evaluates the user's behavioral history. This allows users to receive feedback to improve their communication style. This process is particularly applicable to communication using voice conferencing software.

[0706] As a concrete example, suppose a user sends a message to a colleague at work using a strong tone. In this case, the server analyzes the message and detects that it uses high-pressure language such as "Submit the report immediately." If the score exceeds a certain threshold, the server sends a prompt message to the user saying, "This language may be perceived as high-pressure. Please consider using a different expression." This allows the user to reflect on their message and correct it if necessary.

[0707] This system will allow users to communicate more smoothly online and receive support to maintain a safe environment.

[0708] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0709] Step 1:

[0710] Users use communication tools through their devices to send text messages and voice data. This data is immediately transferred to a server for the purpose of recording the user's words and actions. The input is messages and voices generated by the user, and the output is communication data sent to the server.

[0711] Step 2:

[0712] The server converts the received audio data into text information using a speech recognition API. This conversion creates the foundation for handling audio as text and outputs the string data necessary for analysis. During this process, the user's speech is stored as text data on the server.

[0713] Step 3:

[0714] The server acquires text information and performs analysis using natural language processing algorithms. This analysis utilizes a generative AI model to analyze the structure and sentiment of the text and assess the potential for harassment. The input is the transformed text data, and the output is a score indicating the likelihood that the behavior is considered harassment.

[0715] Step 4:

[0716] The server determines whether the score exceeds a predetermined threshold. If it does, it generates a warning message and prepares a prompt to provide feedback to the user. The input is the harassment score, and the output is the warning message sent to the user.

[0717] Step 5:

[0718] The server sends a generated warning message to the terminal. The terminal provides immediate feedback to the user by displaying this message in its user interface. The input is the warning message sent from the server, and the output is the notification displayed on the user's screen.

[0719] Step 6:

[0720] The server stores all analysis results and user behavior history in storage. This data is later used as the basis for generating reports to help users improve their communication style. The input is analysis results and behavior history, and the output is storage in the database.

[0721] (Application Example 1)

[0722] 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".

[0723] In traditional in-store communication, harassment was often overlooked, which negatively impacted the work environment and customer experience. Furthermore, employees lacked opportunities to objectively evaluate and improve the quality of their own communication.

[0724] 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.

[0725] In this invention, the server includes means for analyzing user communication data using a natural language processing algorithm and evaluating the emotions associated with that data, means for acquiring voice data and converting it into text data, and means for notifying the user terminal of a warning message in real time based on the analysis. This promotes healthy communication within the store and enables employees to reflect on and improve their own behavior.

[0726] "User communication data" refers to the content of all forms of dialogue and messages that users engage in, and this data includes both text and audio formats.

[0727] A "natural language processing algorithm" is a technology that enables computers to understand and analyze human language, analyzing text and audio data to evaluate emotions and intentions.

[0728] "Scoring the potential for harassment" is a process of quantifying the degree of harassment risk associated with analyzed communication data based on its content.

[0729] A "warning message" is a notification issued to alert users to potential harassment behavior and encourage them to improve their actions.

[0730] A "user terminal" refers to a device on which warning messages or analysis results are displayed, and includes smartphones, tablets, computers, and other similar devices.

[0731] "Storing user behavior history in a database" is the process of recording users' past communication data and analysis results as history, making it available for later evaluation and improvement.

[0732] "Converting audio data to text data" is a technology that converts audio information, such as conversations, into text information, making it easier to analyze.

[0733] A "report that visualizes trends in harassment behavior" is a report that uses accumulated data to visually show trends in harassment that have occurred, in order to help users understand them.

[0734] "Promoting healthy communication" means taking steps to reduce the risk of harassment and create a better and more positive communication environment.

[0735] A system that implements an application of this invention analyzes in-store communication in real time and issues warning messages aimed at preventing harassment. The server acquires user communication data and analyzes it using natural language processing algorithms. Specifically, programming languages ​​such as Python, libraries such as SpeechRecognition and TextBlob, and the Google Speech Recognition API are used to monitor text and voice exchanges conducted by users via smartphones and other devices.

[0736] The hardware used includes a smartphone and microphone to acquire audio data. This audio data is converted into text data, which is then analyzed to score the likelihood of harassment. If this score exceeds a predetermined threshold, the server generates a warning message and notifies the user's device. This gives the user an opportunity to improve the quality of their communication.

[0737] Warning messages are displayed in real time on user terminals to encourage the maintenance of a healthy work environment. Furthermore, all analysis results and user behavior history are stored in a database and provided as reports that visualize trends in harassment behavior, generated periodically. These reports serve as a guide for users to reflect on their own behavior and make long-term improvements.

[0738] For example, if a store staff member uses aggressive language in a conversation with a customer or chat with a colleague, the system will immediately issue a warning such as, "This language may be perceived as aggressive. Please consider using a different expression," prompting improvement. This kind of real-time feedback allows staff members to strive for healthy communication.

[0739] Examples of prompts include phrases that instruct the user to analyze text data, such as, "Evaluate the sentiment score of the following conversation text and determine whether it is aggressive or not."

[0740] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0741] Step 1:

[0742] The server acquires user communication data. It collects voice and text data in real time from smartphones and microphones, with voice data received via a voice recording device. This data is then transmitted from the user's device to the server via the internet. Input is voice and text data, and output is digital data in an analyzable format.

[0743] Step 2:

[0744] The server converts audio data into text data. It uses the Google Speech Recognition API to transcribe the audio data into text. The input is audio data, and the output is a string (text format). This converts the data into a format that can be parsed by natural language processing algorithms.

[0745] Step 3:

[0746] The server analyzes text data using the TextBlob library and evaluates its sentiment. It passes the acquired text data to TextBlob to calculate sentiment polarity. The output is a numerical value indicating the degree of positive or negative sentiment contained in the text. This value allows for scoring in the next step.

[0747] Step 4:

[0748] The server scores the likelihood of harassment based on the analysis results. Based on the acquired sentiment score, it evaluates whether the message content is aggressive towards others and determines whether it exceeds the threshold. The input is the numerical score from the sentiment evaluation, and the output is a flag indicating whether that score exceeds the threshold.

[0749] Step 5:

[0750] The server generates a warning message and notifies the user's terminal if the score exceeds a predetermined threshold. The warning message may include content such as, "This expression may be perceived as aggressive. Please consider using alternative wording." The input is the score evaluation result, and the output is the warning message displayed on the user's screen.

[0751] Step 6:

[0752] The server stores all analysis results and user behavior history in a database. It organizes the data collected in real time and records it as history. The input is the score evaluation results and the data behind them, and the output is a database of behavior history organized for each user. This serves as the basis for reports generated later.

[0753] 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.

[0754] This invention provides a system that offers more accurate judgments and feedback by combining an emotion engine that analyzes the user's emotional state with an evaluation of harassment in user communication. This system consists of multiple processing modules and is integrated into the user's communication tools.

[0755] The terminal is responsible for acquiring the user's communication data (text and voice) as part of the session. The data is encrypted and sent to the server along with identification information.

[0756] The server analyzes the received data using natural language processing algorithms to analyze context and tone. During this process, the emotion engine extracts emotional information from the data. The emotion engine identifies the emotions expressed by the user (e.g., anger, joy, sadness) through keyword detection and speech tone analysis.

[0757] The server comprehensively considers the results of analysis using natural language processing and emotional information from the emotion engine to score the likelihood of harassment. This scoring evaluates how the user's emotions influence the communication and adjusts the weighting of the score as needed.

[0758] If the score exceeds a set threshold, the server generates a warning message and improvement advice, adding emotionally-based customization to it. Specifically, if a state of heightened emotion is detected, advice will be provided that takes this into account.

[0759] For example, if a user exhibits behavior that indicates anxiety or frustration during a meeting, the server will take that emotional state into consideration and send a customized message such as, "You appear to be emotionally agitated. Let's try to maintain a calm demeanor."

[0760] The terminal notifies the user of the generated warning message and prompts them to review its contents. Furthermore, the server stores all analysis results and emotional states in a database and creates reports based on the results to support long-term behavioral improvement.

[0761] Users can utilize the information provided through their devices to help manage their communication style and emotions. This system aims to flexibly respond to emotional changes, fostering seamless communication and a safe work environment.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The device captures text input and voice speech performed by the user in real time using communication tools. This is achieved through a mechanism that automatically acquires data using communication APIs.

[0765] Step 2:

[0766] The device sends the acquired data to the server in an encrypted state for privacy protection, along with information identifiers (such as user ID, session ID, and timestamp).

[0767] Step 3:

[0768] The server analyzes the received data using natural language processing algorithms to extract communication context, tone, content keywords, and other relevant information. This process includes tokenization, morphological analysis, and sentiment scoring.

[0769] Step 4:

[0770] The server uses an emotion engine to analyze the user's emotions from the received data. It identifies the user's emotional state by evaluating emotional keywords in text and tone and pitch in audio.

[0771] Step 5:

[0772] The server integrates natural language processing results with emotional information to score the likelihood of harassment. Since emotional information is used as a weight in the scoring, cases where the emotional state is clear will receive a higher score.

[0773] Step 6:

[0774] The server generates a warning message and advice for improvement if the score exceeds a set threshold. Based on sentiment information, the message content is customized and includes specific sentiment management suggestions.

[0775] Step 7:

[0776] The server sends the generated message to the user's device. The device then displays this to the user via a pop-up alert or notification bar to draw their attention.

[0777] Step 8:

[0778] The server stores the analysis results and sentiment analysis results in a database. The stored data is aggregated on a daily and monthly basis and used to understand users' long-term behavioral trends and to create reports to support improvements.

[0779] Step 9:

[0780] Users can review feedback received through their devices and use it to manage their communication and emotions. This allows users to improve their behavior and enhance their communication skills.

[0781] (Example 2)

[0782] 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".

[0783] In the current communication environment, evaluating harassment is subjective, making accurate judgment difficult. Furthermore, while emotional analysis and feedback provision require appropriate responses that consider the user's emotional state, conventional technologies fall short in this regard. Additionally, there is a lack of mechanisms to systematically store user behavior history and utilize it for future behavioral improvement.

[0784] 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.

[0785] In this invention, the server includes means for acquiring user communication information, means for analyzing the information using a natural language processing algorithm, and means for evaluating the likelihood of harassment based on the analysis results and emotional information. This enables accurate evaluation of harassment that takes into account the user's emotional state and the provision of appropriate feedback. Furthermore, it becomes possible to accumulate behavioral history in an information storage device and create reports that contribute to long-term behavioral improvement.

[0786] A "user" refers to an entity that inputs information and communicates through a system.

[0787] "Communication information" refers to digital communication data such as text messages and voice data obtained from users.

[0788] A "natural language processing algorithm" refers to a technical means of analyzing communication information to understand its context and emotions.

[0789] "Evaluation scoring" refers to the process of numerically evaluating and quantifying whether the content of the analyzed data is related to harassment.

[0790] "Warning information" refers to messages that include cautions and improvement suggestions, which are sent to the user based on the analysis results.

[0791] "Emotional information" refers to data that indicates the user's emotional state, extracted from communication information.

[0792] "User terminal" refers to an electronic device used by a user to input and receive information.

[0793] An "information storage device" refers to a database system that stores communication information and analysis results for later analysis and report creation.

[0794] A "report" refers to a document that visualizes the analysis results and trends in harassment behavior and explains them to the user.

[0795] "Textual information" refers to data that is represented by converting audio information into text.

[0796] This invention is a system for evaluating harassment in user communication and analyzing the user's emotional state to provide more accurate judgments and feedback. Specifically, it is implemented using a system that combines a server and terminals.

[0797] The device collects user communication information, including text messages and voice data. The device encrypts this information and transfers it to the server using a secure communication protocol (e.g., HTTPS). Common computer systems and smartphones are used as devices.

[0798] The server processes the received information using a natural language processing algorithm. This algorithm performs text analysis and speech tone analysis to understand the context of the user's speech, and the emotion engine extracts emotional information. This allows the server to understand the user's emotional state.

[0799] The server evaluates and scores the potential for harassment based on analysis results and emotional information. This evaluation helps determine the impact of excessive emotions on communication. When the score exceeds a set threshold, a warning message is generated. The warning message is customized according to the user's emotional state and includes appropriate advice for improvement.

[0800] For example, if a user shows heightened emotions during a video conference, the server will recognize this and provide specific feedback such as, "You appear to be emotionally agitated at the moment. Let's discuss this calmly."

[0801] Users review the warning information notified through their devices and use it to improve their communication style. Furthermore, the server stores all information and emotional states in a database and generates reports to support long-term behavioral improvement. These reports are used as reference material in users' decision-making.

[0802] An example of a prompt message could be something like, "Please tell me how to analyze the user's current emotional state and determine what kind of feedback to provide." This could be input into a generative AI model.

[0803] This system aims to provide appropriate communication and a safe environment, and is designed to respond flexibly to changes in emotions.

[0804] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0805] Step 1:

[0806] The terminal acquires user communication information. Data is entered by capturing text messages and voice information entered by the user in real time. This input information serves as the basic data for system processing.

[0807] Step 2:

[0808] The terminal encrypts the acquired communication information. Before sending the data, it encrypts the information using a security protocol and prepares it for transfer to the server. The output is an encrypted data packet.

[0809] Step 3:

[0810] The server receives encrypted data sent from the terminal and decrypts it. The decrypted data is input into a natural language processing algorithm, and the analysis of text and speech tone begins. At this stage, the context of the data and the initial emotional state are output.

[0811] Step 4:

[0812] The server uses an emotion engine to extract emotional information from the analyzed data. Specifically, it analyzes keyword and voice tone patterns to identify the user's emotions (e.g., anger, joy). An emotion statement is then generated as output.

[0813] Step 5:

[0814] The server integrates the obtained natural language processing results and sentiment information to score the likelihood of harassment. This score quantifies the harassment risk based on a set scale. The output is an evaluation score.

[0815] Step 6:

[0816] The server generates a warning message when the evaluation score exceeds a set threshold. This includes customized advice tailored to specific emotional states. The output is a customized warning message.

[0817] Step 7:

[0818] The terminal notifies the user of the generated warning information. It displays the message on the user interface for immediate confirmation. The output is the notification information displayed on the user's screen.

[0819] Step 8:

[0820] The server stores all analysis results and sentiment information in an information storage device. This data forms the basis for long-term analysis used for future reference and behavioral improvement. In actual operation, the data is stored in a database.

[0821] Step 9:

[0822] Users strive to improve their communication style through the provided warnings and reports. Based on the output data, introspection takes place to manage emotions and accept feedback on behavior.

[0823] (Application Example 2)

[0824] 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".

[0825] There is a need to detect harassment in communication and understand emotional states in real time to improve safety and productivity in the workplace and public spaces. Furthermore, it is necessary to provide feedback that appropriately reflects individual emotional changes to improve the work environment and facilitate smoother communication. This necessitates a system that reduces user stress and supports long-term behavioral improvement.

[0826] 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.

[0827] In this invention, the server includes means for acquiring user information data, means for analyzing the data using a natural language processing algorithm, and means for scoring the likelihood of harassment using the analysis results and an emotion engine. This makes it possible to detect harassment in the user's communication and provide feedback that reflects changes in their emotions.

[0828] "User information data" refers to data related to communication obtained from users, including voice and text.

[0829] A "natural language processing algorithm" is a set of computational techniques used to analyze context and sentiment from text data.

[0830] An "emotion engine" is a software component that identifies and extracts emotional information from user communication data.

[0831] "Scoring the potential for harassment" means numerically evaluating the risk of problematic behaviors based on users' communication.

[0832] "Generating warning information" means creating information to notify the user when potential harassment is detected.

[0833] An "information terminal" is an electronic device used by a user to receive warning information.

[0834] "Behavioral information" refers to a history of actions and emotions extracted from a user's past communication data.

[0835] A "storage device" is a data storage device used to accumulate user behavior information.

[0836] A "report" is a document that summarizes analysis results and trends, and is information provided to the user.

[0837] "Text data" refers to data in text format that has been converted from audio data.

[0838] The system implementing this invention is primarily composed of an information terminal and a server. First, the user's information terminal is responsible for encrypting communication data acquired in voice or text format and transmitting it to the server. The information terminal is equipped with voice input and text conversion functions, making it possible to acquire the user's speech and text as digital data in real time.

[0839] The server is the central component that processes the received data. First, to implement natural language processing algorithms, it uses the Python spaCy library to analyze the text context and evaluate the content of the communication. Furthermore, it uses TextBlob as an emotion engine to extract emotional information from the data. This makes visible the emotional state hidden in each word spoken by the user.

[0840] Based on the analysis results, the server scores the likelihood of harassment. This scoring system takes into account the user's emotions and context, and generates a warning if the threshold is exceeded. The generated warning information is sent to the information terminal using the Pusher API. The information terminal displays the warning message to the user and provides immediate feedback as needed.

[0841] For example, if an employee expresses frustration during a meeting, the server will send a message via the employee's terminal saying, "You seem to be getting emotional. Please consider taking a short break." This approach helps to prevent inappropriate communication from occurring in advance.

[0842] A MySQL database is used to store the acquired behavioral information and analysis results over a long period. This makes it possible to create reports for users to improve their behavior based on past trends.

[0843] An example of a prompt for a generative AI model is: "Analyze recent conversation data to determine if emotions such as anger or frustration are heightened. Also, score whether these emotions may be causing inappropriate communication." Using this kind of sentence can prompt the model to perform appropriate data analysis and generate feedback.

[0844] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0845] Step 1:

[0846] The device receives user voice or text as input data. If voice data is used, the device converts it into text data using its speech recognition function. The converted text data is encrypted using the SSL protocol to ensure information security. The device then generates encrypted text data as output.

[0847] Step 2:

[0848] The server receives encrypted text data sent from the terminal and decrypts it. The decrypted data is then input into a natural language processing algorithm to analyze the context. During this process, spaCy is used for grammatical analysis and keyword extraction. The output is structured data as a result of the analysis.

[0849] Step 3:

[0850] The server takes the analysis results as input and extracts emotional information using TextBlob, an emotion engine. This process calculates emotional scores such as positive, negative, and neutral, and outputs these scores.

[0851] Step 4:

[0852] The server integrates the results of natural language processing with sentiment information to score the likelihood of harassment. In this step, a generative AI model is used to score based on the prompt text and evaluate the score value. It determines whether a threshold is exceeded, and if so, generates warning information. The output is the generated score and warning information.

[0853] Step 5:

[0854] The server uses the Pusher API to notify the terminal of the generated warning information. The terminal displays the received warning information to the user as a pop-up message. This message includes specific feedback based on the user's emotional state. The output is a real-time warning display to the user.

[0855] Step 6:

[0856] The server stores all processing results, analysis results, and behavioral information in a database. MySQL is used for this purpose, accumulating data for later report generation. The output consists of past behavioral information stored in the database.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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."

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] The following is further disclosed regarding the embodiments described above.

[0879] (Claim 1)

[0880] Obtain user communication data

[0881] means and

[0882] The aforementioned data is analyzed using a natural language processing algorithm.

[0883] means and

[0884] Based on the analysis results, the likelihood of harassment is scored.

[0885] means and

[0886] A warning message is generated when the score exceeds a predetermined threshold.

[0887] means and

[0888] The aforementioned warning message is sent to the user terminal.

[0889] means and

[0890] Store user behavior history in a database.

[0891] A system that includes the means.

[0892] (Claim 2)

[0893] The analysis results are compiled to generate a report that visualizes trends in harassment behavior.

[0894] Includes means

[0895] The system according to claim 1.

[0896] (Claim 3)

[0897] Convert the aforementioned user voice data into text data.

[0898] Includes means

[0899] The system according to claim 1.

[0900] "Example 1"

[0901] (Claim 1)

[0902] Obtain user communication information

[0903] means and

[0904] The aforementioned information is analyzed using a natural language processing algorithm.

[0905] means and

[0906] Based on the analysis results, the likelihood of harassment is quantified.

[0907] means and

[0908] If the aforementioned value exceeds a predetermined threshold, warning information is generated.

[0909] means and

[0910] The aforementioned warning information is notified to the user device.

[0911] means and

[0912] The user's behavior history is stored in a memory device.

[0913] means and

[0914] Convert audio information into text information

[0915] A system that includes the means.

[0916] (Claim 2)

[0917] The analysis results are compiled to generate a report that visualizes trends in harassment behavior.

[0918] Includes means

[0919] The system according to claim 1.

[0920] (Claim 3)

[0921] To prevent harassment in voice communications, we analyze voice in real time.

[0922] Includes means

[0923] The system according to claim 1.

[0924] "Application Example 1"

[0925] (Claim 1)

[0926] Obtain user communication data

[0927] means and

[0928] The aforementioned data is analyzed using a natural language processing algorithm to evaluate its emotions.

[0929] means and

[0930] Based on the analysis results, the likelihood of harassment is scored.

[0931] means and

[0932] If the score exceeds a predetermined threshold, a warning message is generated and displayed.

[0933] means and

[0934] We store user behavior history in a database and use it to encourage improvements in future behavior.

[0935] means and

[0936] Acquire audio data and convert it to text data.

[0937] A system that includes the means.

[0938] (Claim 2)

[0939] The analysis results are compiled, a report visualizing trends in harassment behavior is generated, and this report is provided to users on a regular basis.

[0940] Includes means

[0941] The system according to claim 1.

[0942] (Claim 3)

[0943] Based on the analysis of the aforementioned communication data, warning messages are sent to user terminals in real time to promote healthy communication within the store.

[0944] Includes means

[0945] The system according to claim 1.

[0946] "Example 2 of combining an emotion engine"

[0947] (Claim 1)

[0948] Obtain user communication information.

[0949] means and

[0950] The aforementioned information is analyzed using a natural language processing algorithm.

[0951] means and

[0952] Based on the analysis results and emotional information, the likelihood of harassment is scored.

[0953] means and

[0954] If the aforementioned evaluation score exceeds a predetermined threshold, warning information is generated, and customization based on sentiment information is added.

[0955] means and

[0956] The aforementioned warning information is notified to the user's terminal.

[0957] means and

[0958] The user's behavior history is stored in an information storage device.

[0959] A system that includes the means.

[0960] (Claim 2)

[0961] The analysis results are compiled to generate a report that visualizes trends in harassment behavior.

[0962] Includes means

[0963] The system according to claim 1.

[0964] (Claim 3)

[0965] Convert the aforementioned user's voice information into text information.

[0966] Includes means

[0967] The system according to claim 1.

[0968] "Application example 2 when combining with an emotional engine"

[0969] (Claim 1)

[0970] Retrieve user information data

[0971] means and

[0972] The aforementioned data is analyzed using a natural language processing algorithm.

[0973] means and

[0974] Using the analysis results and the emotion engine, the likelihood of harassment is scored.

[0975] means and

[0976] If the score exceeds a predetermined threshold, warning information is generated.

[0977] means and

[0978] The aforementioned warning information is sent to the information terminal.

[0979] means and

[0980] User behavior information is stored in a memory device.

[0981] A system that includes the means.

[0982] (Claim 2)

[0983] The analysis results are compiled, and a report is generated that visualizes trends in information-related activities.

[0984] Includes means

[0985] The system according to claim 1.

[0986] (Claim 3)

[0987] Convert the aforementioned user's voice data into text data.

[0988] Includes means

[0989] The system according to claim 1. [Explanation of Symbols]

[0990] 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. Means for acquiring user communication data, A means for analyzing the aforementioned data using a natural language processing algorithm, Based on the analysis results, a means of scoring the likelihood of harassment, A means for generating a warning message when the score exceeds a predetermined threshold, A means for notifying the user terminal of the aforementioned warning message, A system that includes means for storing user behavior history in a database.

2. The system further includes means for aggregating the aforementioned analysis results and generating a report that visualizes trends in harassment behavior. The system according to claim 1.

3. The means for converting the user's voice data into text data is further included. The system according to claim 1.

Images