system

Abstract

We provide the system. [Solution] A means of collecting communication information in the workplace environment in real time, A means for analyzing collected communication information using speech recognition technology and natural language processing technology, A means of evaluating risks related to violations of labor laws and harassment based on the analysis results, A means of issuing alerts to users based on risk assessment, A means of monitoring risks during project progress in conjunction with project management tools, and providing suggestions to support effective project management. Based on risk assessment, a means of realizing risk notification using smart devices, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Communication in the workplace can often become a breeding ground for labor standard violations and power harassment. Such problems often occur where they are not noticed by managers, and have the risk of causing deterioration of employees' mental health and an increase in the number of resignations. Therefore, it is required to detect these problems in real time and respond quickly. Also, means for predicting communication risks and schedule management problems during project progress and realizing effective project management are needed.

Means for Solving the Problems

[0005] This invention provides a system that collects communication data in the workplace environment in real time and performs analysis using speech recognition and natural language processing technologies. Based on the collected and analyzed data, this system evaluates risks related to labor law violations and harassment, and issues alerts to the user based on the risk assessment. It can also be linked with project management tools to monitor risks during project progress and provide suggestions to support effective project management. Furthermore, when a risk is detected, it has the function to provide suggestions for educational programs and mental health support, and to propose reconfiguration of project members and rescheduling according to the detected risk.

[0006] "Work environment" refers to the overall environment, including physical, social, and psychological conditions and interpersonal relationships, in and around the place where employees perform their daily work.

[0007] "Communication data" refers to information recorded through conversations, messages, emails, and other forms of information exchange, and includes both audio and text data.

[0008] "Real-time data collection" means capturing data with virtually no delay from the moment it is generated, and making it ready for immediate processing.

[0009] "Speech recognition technology" refers to technology that analyzes speech and converts it into text, enabling it to identify the content of speech and treat it as digital information.

[0010] "Natural language processing technology" is a technology that enables computers to understand and process human language, making it possible to mechanically interpret the meaning and context of text.

[0011] "Analysis results" refer to information that includes conclusions and insights derived through the analysis of data.

[0012] "Violation of the Labor Standards Act" refers to a situation that does not meet the standards stipulated in the Labor Standards Act, or an act that goes against them.

[0013] "Harassment" refers to disruptive or coercive behaviors carried out through words, actions, or attitudes that cause discomfort to an individual, and is considered particularly unethical in the workplace.

[0014] "Risk assessment" is the process of identifying potential hazards, considering their impact and probability of occurrence, and developing countermeasures in advance.

[0015] "Issuing an alert" means issuing a warning or notification to quickly convey important or urgent information to relevant parties.

[0016] A "project management tool" is software or a system used to plan, execute, monitor, control, and successfully complete a project.

[0017] An "educational program" is a set of educational tools and content systematically designed to help individuals acquire specific skills and knowledge.

[0018] "Mental health support" refers to the assistance and interventions provided to maintain mental health, and includes counseling and support services. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5]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 multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 according to the accompanying drawings.

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

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as 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] The system of this invention effectively monitors communication data in the workplace environment and detects and responds to risks related to labor law violations and harassment in real time. This system consists of three main components: terminals, servers, and users.

[0041] First, the terminal continuously collects voice and text communication data through applications installed on devices within the workplace. This data is transmitted to a server via the company network, allowing for analysis without delay.

[0042] The server uses speech recognition technology to convert the received data into text. Then, natural language processing technology is used to analyze the converted text data and check for the presence of specific keywords or phrases. Through this analysis, the server assesses the risk of labor law violations and harassment.

[0043] If a risk is detected, the server will send an alert to quickly notify the relevant users. This alert will include details about the problem and specific countermeasures, designed to enable users to take appropriate action.

[0044] Furthermore, the server is integrated with project management tools to monitor the quality of communication and schedule management during project progress. This allows the server to suggest effective project management strategies to the user when project risks increase.

[0045] For example, if a statement suggesting "reducing break times" is made during a meeting, it will be immediately detected as a risk by the server. The server will then alert the relevant project leader and HR department users to support swift action on the issue. Based on the detected risks and their frequency, the server will also propose educational programs and provide mental health support to improve the work environment in the long term.

[0046] Thus, the present invention provides an effective solution for continuously monitoring workplace communication and supporting legal compliance and the realization of a safe working environment.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The terminals use applications installed on each device within the workplace to collect voice and text communication data in real time. This data is securely transmitted to a server via the corporate network.

[0050] Step 2:

[0051] The server performs initial processing on the received data, standardizing the format and removing noise. This preprocessing improves the quality of the data.

[0052] Step 3:

[0053] The server uses speech recognition technology to convert the audio data into text. This process is a preparatory step for a detailed analysis of the audio content.

[0054] Step 4:

[0055] The server uses natural language processing techniques to analyze text data and detect pre-configured keywords and phrases. This analysis determines whether specific language patterns are associated with risk.

[0056] Step 5:

[0057] The server performs a risk assessment based on the analysis results. Here, it checks whether the detected language patterns constitute violations of labor laws or harassment, and determines the risk level.

[0058] Step 6:

[0059] The server will issue an alert to the relevant users if it detects high-risk statements or situations. The alert will include specific details about the risks and countermeasures.

[0060] Step 7:

[0061] The server works in conjunction with project management tools to monitor project progress and detect signs of communication risks and schedule delays.

[0062] Step 8:

[0063] Based on the project's risk assessment, the server will propose to the user, as needed, new project members and revised schedules.

[0064] Step 9:

[0065] Users take appropriate action based on the alerts and suggestions they receive. The results of user actions and feedback are recorded on the server and used to improve the accuracy of risk assessments and countermeasures.

[0066] (Example 1)

[0067] 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."

[0068] In modern workplaces, the risk of legal violations and harassment in employee communication is increasing. Furthermore, there are challenges in the early detection and response to risks during project progress. In addition, there are concerns about the deterioration of the work environment due to a lack of appropriate training and support. Addressing these challenges and achieving legal compliance while ensuring a safe and healthy work environment is essential.

[0069] 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.

[0070] In this invention, the server includes means for collecting information in the workplace environment in real time, means for analyzing the collected information using speech processing technology and language processing technology, and means for evaluating risks related to compliance and mental burden based on the analysis results. This makes it possible to detect risks of legal violations and harassment at an early stage and take effective countermeasures.

[0071] "Work environment" refers to the physical or virtual space in which employees perform their work, and the activities within this space are directly related to communication and the progress of work.

[0072] "Information" refers to data expressed in audio, text, and other formats, including conversations and interactions within the workplace.

[0073] "Means of collecting data in real time" refers to mechanisms and technologies that can instantly capture and analyze information without prior storage.

[0074] "Speech processing technology" refers to the technology used to convert speech data into text data, and includes speech recognition software and algorithms.

[0075] "Language processing technology" refers to the technology used to semantically analyze text data and transform it into a form that humans can understand, and includes natural language processing technology.

[0076] "Compliance with the law" refers to a state of being in accordance with the Labor Standards Act and other laws and regulations, and aims to prevent and ensure compliance with violations.

[0077] "Mental burden" refers to the psychological pressure and stress that workplace communication and work environment impose on employees.

[0078] "Means of risk assessment" refers to methods and technologies for detecting potential legal violations and harassment based on analyzed data, and for evaluating the probability of these occurring.

[0079] This invention is a system that supports legal compliance and a safe work environment by effectively collecting and analyzing communication data in the workplace and assessing risks. This system is mainly implemented with three components: terminals, servers, and users.

[0080] The terminal, acting as a workplace device, continuously collects voice and text data through a dedicated application. This application can, for example, capture text data in real time from voice input devices and chat applications. The terminal then transmits the acquired data to a server via the company network.

[0081] The server plays a crucial role in analyzing the received data. First, it converts audio data to text using speech processing technologies such as Google Cloud Speech-to-Text API. Next, it applies natural language processing, utilizing Python's NLTK library and SpaCy, to the converted text data to attempt to detect specific keywords and phrases. Based on these results, the server accurately assesses risks related to labor law violations and harassment.

[0082] If a specific risk is detected, project managers and HR personnel (the users) receive an immediate alert from the server. This alert is delivered via email or a dedicated application, clearly outlining the specific risk and recommended countermeasures. This enables a swift and appropriate response.

[0083] Furthermore, the server also provides support for project progress. It integrates with the business management system to monitor project progress and risk trends, and provides appropriate management suggestions. This process aims to improve overall work efficiency and reduce risks throughout the workplace.

[0084] As a concrete example, suppose a statement suggesting a reduction in break times is made during a meeting. In this case, the server immediately detects this information as a risk and issues a warning to users such as the human resources department. Furthermore, the server proposes appropriate educational programs and psychological support to promote long-term workplace improvement.

[0085] An example of a prompt for a generative AI model is: "Design a system that analyzes audio data within the workplace in real time and checks for the presence of specific keywords. Also, suggest the speech recognition technology and natural language processing tools to use." This prompt allows the system to provide the optimal technical configuration to achieve its objective.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] As a workplace device, the terminal collects communication data in real time from voice input devices, keyboards, and other sources. This input includes meeting contributions and chat messages. The terminal formats this data appropriately and transmits it to the server via the company network.

[0089] Step 2:

[0090] The server receives data sent from the terminal. In the case of audio data, it is first converted to text using speech processing technology. For example, the Google Cloud Speech-to-Text API is used for this process, with an audio file as input and the corresponding text as output.

[0091] Step 3:

[0092] The server applies natural language processing to the text data. It uses Python's NLTK library and SpaCy to analyze keywords and important phrases. Input is either text converted from speech or directly entered text, and output is the analysis result including the detected keywords and phrases.

[0093] Step 4:

[0094] Based on the analysis results, the server performs a risk assessment. If specific keywords are determined to be related to harassment or violations of labor laws, a risk is recognized. The input is the analysis results, and the output is evaluation data indicating the presence and degree of risk.

[0095] Step 5:

[0096] If a risk is detected, the server immediately notifies the relevant users. Notifications are sent via email or a dedicated application, with risk assessment data as input and an alert message as output.

[0097] Step 6:

[0098] The server integrates with project management tools to monitor project progress data and communication trends. Inputs include progress data obtained from the project management tools, and outputs include reports containing improvement suggestions.

[0099] Step 7:

[0100] In response to detected risks, the server proposes educational programs and mental health support from a long-term perspective. This allows users to take concrete actions to improve their work environment. Input is historical risk data and trends, and output is provided as a proposal document.

[0101] (Application Example 1)

[0102] 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."

[0103] Workplace communication often involves issues related to labor law violations and harassment, and it is crucial to detect these early and take appropriate action. However, traditional methods make it difficult to monitor these risks in real time and respond quickly in large workplace environments. Furthermore, it is important to provide flexible response methods using smart devices for detected risks.

[0104] 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.

[0105] In this invention, the server includes means for collecting communication information in the workplace environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for realizing risk notifications using smart devices based on risk assessment. This makes it possible to accurately detect potential labor law violations and harassment-related problems that may occur in the workplace and to provide a rapid and efficient solution for taking immediate countermeasures.

[0106] "Work environment" refers to the physical or virtual environment in which work is performed, and the place where staff members communicate with each other.

[0107] "Communication information" refers to audio or text data related to information exchange within the workplace, such as conversations, messages, and emails.

[0108] "Methods for collecting information in real time" refers to a system that acquires communication information instantly, enabling processing of information without delay.

[0109] "Speech recognition technology" is a technology that converts speech data into text data, and it is a technology that automatically interprets the content of speech data and turns it into text.

[0110] "Natural language processing technology" is a technology that interprets text data, performs semantic analysis and information extraction, and processes content written in natural language in a way that machines can understand.

[0111] "Risk assessment" refers to the process of determining the possibility of labor law violations or harassment in the workplace environment based on collected communication information.

[0112] "Risk notification using smart devices" refers to a method of instantly informing users of risk information via digital devices such as smartphones and tablets.

[0113] "Project management tools" refer to systems for monitoring the progress of a project and effectively managing its plan and progress.

[0114] The system of this invention is designed to monitor communication information in the workplace environment in real time, quickly detect risks related to violations of labor laws and harassment, and provide necessary countermeasures. The system mainly consists of three main elements: terminals, servers, and users.

[0115] The terminals continuously collect voice and text communication information through applications installed on various devices within the workplace. This data is transmitted in real time to a server via the internet or the company network. The server converts the voice data into text using speech recognition software such as Google Cloud Speech-to-Text. The converted text data is then analyzed by natural language processing tools such as spaCy to check for risky keywords or phrases.

[0116] When a risk is detected, the server quickly generates a risk alert and notifies the user via smart devices such as smartphones and tablets. This allows users to take prompt action. The system is also integrated with workplace project management tools, continuously monitoring the risk status during project progress and generating suggestions to improve project management efficiency. For example, if a statement suggests "reducing break times," the server immediately detects this and sends an alert to the administrator.

[0117] An example of a prompt in a generated AI model is, "This statement may violate labor laws. Please investigate thoroughly and take appropriate action." This allows the user to recognize the problem and take necessary measures quickly.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The device collects voice and text communication information within the workplace. The collected input includes communication information such as conversations and messages. This data is sent directly to the server, so the device performs the operation of transferring data over the network.

[0121] Step 2:

[0122] The server converts the received audio data into text using a speech recognition API such as Google Cloud Speech-to-Text. The input for this step is communication data in audio format. Text data is generated as output. This conversion involves transforming audio data into machine-readable text information.

[0123] Step 3:

[0124] The server analyzes text data using natural language processing technology (e.g., spaCy). The input is text converted from audio data, and the output is the result of the analysis. This process identifies risky keywords and phrases and assigns meaning to the text data.

[0125] Step 4:

[0126] The server generates a risk alert when a risk is detected based on the analysis results. The alert input is the analysis result, and the output is a notification message. This operation determines what should be notified based on the identified risk.

[0127] Step 5:

[0128] The server notifies the user of the generated risk alerts via a smart device. The input for this step is the alert message, and the output is a notification to the user's device. The user receives and acknowledges these notifications and takes appropriate action regarding the risks.

[0129] Step 6:

[0130] The server interacts with the project management system to monitor project risks and generate suggestions to support effective management. Inputs are various project-related data, and outputs are suggestions for management improvement. This suggestion generation process involves comparative analysis and evaluation of the data.

[0131] 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.

[0132] This invention combines an existing system that monitors communication in the workplace and assesses risks with an emotion engine that recognizes the emotional state of users. This system allows for a more comprehensive analysis of information obtained from communication data, making it possible to understand not only the risks of labor law violations and harassment, but also the state of mental health.

[0133] The device collects voice and text communication data within the workplace and sends it to the server in real time. This data includes conversations, emails, and chats between employees. This data is processed on the server.

[0134] The server first uses speech recognition technology to convert audio data into text. Next, it uses natural language processing technology to analyze the text data and detect pre-configured keywords and phrases. This analysis allows for the assessment of risks such as violations of labor laws and harassment.

[0135] Furthermore, the server uses an emotion engine to analyze the user's emotional state within the communication data. This allows for a quantitative assessment of the user's stress level and psychological state. The results of the emotion analysis are used to assess mental health risks and, if necessary, to send appropriate alerts to the user.

[0136] For example, if emotional fluctuations are detected in a particular member's remarks during a project meeting, the server will use this information to send an alert to the project manager. The alert will include the nature of the emotional change and measures to minimize its impact. Furthermore, measures will be taken to reduce the emotional burden on employees through proposed educational programs and mental health support.

[0137] This system supports project management more effectively by using emotional data collected from users and the results of risk assessments. It monitors project progress and enables suggestions that take into account communication risks, stress indicators, and other factors. As a result, the workplace environment is expected to become safer and more constructive, leading to improved employee mental health.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The devices collect voice and text data within the workplace in real time through applications installed on each device. The collected data is transferred to a server via the corporate network.

[0141] Step 2:

[0142] The server converts the received audio data into text using speech recognition technology, and then analyzes the text data using natural language processing technology. The analysis checks whether specific keywords or phrases are included.

[0143] Step 3:

[0144] The server assesses the risk of labor law violations and harassment based on the results of analysis using natural language processing. This assessment process is based on historical data and existing legal regulations.

[0145] Step 4:

[0146] In parallel, the server utilizes an emotion engine to analyze the user's emotions from the analyzed text data. The emotion engine detects emotions such as stress and anxiety based on word choice and context.

[0147] Step 5:

[0148] Based on the results of risk assessment and sentiment analysis, the server also assesses the user's mental health risks. If deemed necessary, it sends an alert to the user, which includes the detected risks and recommended actions.

[0149] Step 6:

[0150] The server integrates with project management tools to monitor project progress. This monitoring includes taking into account the quality of communication and the emotional state of team members, and notifies the project manager if any risks are detected.

[0151] Step 7:

[0152] Based on the collected sentiment data and risk assessment results, the server proposes new project member compositions and schedule adjustments as needed, thereby supporting the smooth progress of the project.

[0153] Step 8:

[0154] Users receive alerts and suggestions from the server and implement the suggested countermeasures. This feedback contributes to improving the system's accuracy and is used for future risk assessments and sentiment analysis.

[0155] (Example 2)

[0156] 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".

[0157] In traditional workplace environments, it has been difficult to timely detect and manage the risks of legal violations and harassment in communication. Furthermore, understanding psychological health risks based on employees' emotional states was insufficient, potentially leading to delays in appropriate responses. Therefore, there is a growing need for an integrated system to improve the work environment and achieve effective work management.

[0158] 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.

[0159] In this invention, the server includes means for collecting information in the work environment in real time, means for analyzing the collected information using acoustic recognition technology and natural language processing technology, and means for issuing warnings to users according to the assessed risk. This makes it possible to quickly identify risks in employee communication and take appropriate action. Furthermore, it is possible to assess psychological health risks through the analysis of emotional states and provide a safer and more constructive work environment.

[0160] "Work environment" refers to the physical and labor-related space where employees perform their duties, including offices and remote work locations.

[0161] "Information" is a general term for data exchanged in the workplace, including voice, text, email, and chat messages.

[0162] "Acoustic recognition technology" is a technology for converting audio data into text data, and includes the process of extracting linguistic information from audio.

[0163] "Natural language processing technology" refers to computer technologies used to analyze text data and perform semantic analysis and keyword detection.

[0164] "Risk" refers to elements or situations that may be related to legal violations or harassment, and includes potential problems in the work environment.

[0165] A "warning" is a notification issued when a risk is detected, and it includes information to prompt appropriate action.

[0166] "Emotional state" refers to information that indicates an employee's psychological state and stress level, and is obtained through sentiment analysis.

[0167] "Psychological health risks" refer to factors that may negatively impact employees' mental health and are related to stress and emotional fluctuations.

[0168] This system is designed to comprehensively monitor workplace communication data and assess risks. The terminals collect voice and text communications within the workplace in real time. This includes conversations, emails, and chat messages, and this data is transmitted from the terminals to the server using a secure protocol.

[0169] The server converts audio data into text data using acoustic recognition technology. This process utilizes speech recognition tools such as the Google Speech-to-Text API. The server then analyzes this text data using natural language processing techniques. Specifically, it leverages libraries such as the Natural Language Toolkit (NLTK) to detect pre-configured keywords and phrases and assess the risk of legal violations and harassment hidden within the communication.

[0170] Furthermore, the server incorporates an emotion engine that can analyze the user's emotional state. This analysis quantifies the user's stress level and psychological health risks based on specific phrases and contexts.

[0171] For example, if a particular member speaks in an unusual tone during a project meeting, the server will detect this emotional shift and send an alert to the project manager. This alert will include details about the emotional change and specific measures to mitigate its impact.

[0172] An example of a prompt sentence to input into the generating AI model is as follows: "Please tell me how to detect emotional fluctuations from business conversations and assess mental health risks."

[0173] This system enables the rapid identification of communication risks in the workplace and the proposal of specific measures to protect employees' mental health. This, in turn, makes it possible to create a safer and more constructive work environment.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] The device collects voice and text communication data within the workplace. Specifically, it monitors meeting speeches, emails, and chat messages in real time and records this data digitally. The input is voice and text data from within the company, and the output is digital communication data.

[0177] Step 2:

[0178] The terminal sends the collected data to the server using a secure communication protocol. Specifically, it encrypts the data using protocols such as SSL / TLS before transmission. The input is digital communication data, and the output is the encrypted data that arrives on the server.

[0179] Step 3:

[0180] The server converts audio data into text data using acoustic recognition technology. Specifically, it processes audio files using speech recognition software such as the Google Speech-to-Text API and outputs the spoken content as text. In this process, the input is audio data, and the output is text data.

[0181] Step 4:

[0182] The server analyzes text data using natural language processing techniques. Specifically, it extracts keywords and phrases from the text data and assesses risks related to labor laws and harassment. The input is text data, and the output is risk assessment data as a result of the analysis.

[0183] Step 5:

[0184] The server analyzes the user's emotional state using an emotion engine. Specifically, it applies an emotion analysis algorithm based on the text analysis results to quantify stress levels and psychological health risks. The input in this process is the text analysis results, and the output is emotion evaluation data.

[0185] Step 6:

[0186] The server issues alerts to users based on risk assessment data and sentiment assessment data. Specifically, it generates and sends alerts to responsible parties such as project managers, providing warnings as needed. The inputs for this process are risk assessment data and sentiment assessment data, and the output is an alert message.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0189] In modern workplaces and home environments, communication-related stress and misunderstandings are increasing, negatively impacting productivity and mental health. Furthermore, inadequate management of stress and emotional states makes it difficult to provide appropriate support promptly. This invention aims to solve these problems and provide a safer and more comfortable communication environment.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for collecting communication information in the workplace and daily environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for evaluating emotional states using the analysis results and emotion recognition technology, and quantifying stress and psychological states. This makes it possible to appropriately grasp the emotional state of users in a communication environment, reduce stress and risk, and achieve smooth communication.

[0192] "Work environment" refers to the place and conditions in which work is performed, and is the space in which employees engage in their daily activities.

[0193] The "everyday environment" refers to the place where a home or individual lives, and the space where daily communication takes place.

[0194] "Communication information" refers to the exchange of information in the form of conversations, chats, emails, etc.

[0195] "Methods of collecting information in real time" refers to methods of collecting information in a cumulative manner as soon as communication occurs.

[0196] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data.

[0197] "Natural language processing technology" refers to computer science techniques used to analyze and understand human language.

[0198] "Analysis results" refers to the results of analyzing data obtained using speech recognition or natural language processing.

[0199] "Emotion recognition technology" is a technology primarily aimed at enabling computers to analyze and understand human emotions.

[0200] "Methods for quantifying stress and psychological state" refers to methods that numerically evaluate a user's stress and psychological state based on emotional data.

[0201] "Users" refers to individuals or organizations that use this system.

[0202] "Means of providing emotional support" refers to methods of providing psychological support to users based on analyzed emotional data.

[0203] "Environmental management" refers to methods for controlling and improving factors related to communication and stress in the workplace and home.

[0204] To implement this invention, several elements constituting the system are necessary. First, a terminal is needed to collect voice and text communication information, and it is desirable that this terminal be equipped with a high-sensitivity microphone and network connectivity. This terminal transmits the collected information to a server. The server is responsible for converting the voice data into text using a speech recognition engine (e.g., Google Speech-to-Text API). This process makes it possible to analyze voice communication as text.

[0205] Next, the server analyzes the converted text data using natural language processing techniques to detect pre-configured important keywords and phrases. In this case, natural language processing engines such as spaCy or NLTK are used. Furthermore, as an emotion recognition technique, an analysis engine such as IBM Watson® Tone Analyzer is used to analyze the user's emotional state and stress level from the conversation content.

[0206] Based on this information, the server assesses potential risks and stressors and issues alerts to the user as needed. It also provides real-time suggestions and emotional support to maintain effective communication.

[0207] For example, when the system recognizes that a conversation within the family is tense, it might suggest, "Why don't you try to relax a little? I'll play some recommended music." This kind of support is effective in reducing stress in home and work environments.

[0208] Another example of a prompt for a generative AI model is, "Please suggest ideas on how a robot can use emotional data to improve communication within the home." Using such prompts allows the system to explore further suggestions and improvements.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The device collects voice and text communication information in real time. Specifically, it uses a microphone to acquire voice data and simultaneously transmits that data to a server via the network. As a result of collecting voice data as input, an audio file is sent to the server.

[0212] Step 2:

[0213] The server converts the received audio data into text data using a speech recognition engine. It analyzes the audio file using the Google Speech-to-Text API and generates text output. The input to this process is the audio data received from the device, and the output is the converted text.

[0214] Step 3:

[0215] The server analyzes the converted text data using a natural language processing engine. Specifically, it uses NLTK or spaCy to extract important keywords and phrases from the text. It receives text data as input and extracts important lexical information as output.

[0216] Step 4:

[0217] The server uses an emotion recognition engine to identify emotional states and stress levels from the analyzed text. Using tools such as IBM Watson Tone Analyzer, it evaluates the user's emotions based on the input text and outputs a quantified emotion evaluation result.

[0218] Step 5:

[0219] The server generates alerts and suggestions for the user based on the emotion assessment results and important vocabulary information. If the user is assessed as having high stress levels, an AI model is used to devise behavioral suggestions that promote relaxation. The input is the emotion assessment results and extracted vocabulary information, and the output is alert messages and behavioral suggestions.

[0220] Step 6:

[0221] The user receives system suggestions and alerts and selects the appropriate action. For example, they might approve playing a suggested relaxation song, or perform an action based on the instructions on their device. This step includes the user's action selection as output.

[0222] 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.

[0223] Data generation model 58 is a type of 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] The system of this invention effectively monitors communication data in the workplace environment and detects and responds to risks related to labor law violations and harassment in real time. This system consists of three main components: terminals, servers, and users.

[0239] First, the terminal continuously collects voice and text communication data through applications installed on devices within the workplace. This data is transmitted to a server via the company network, allowing for analysis without delay.

[0240] The server uses speech recognition technology to convert the received data into text. Then, natural language processing technology is used to analyze the converted text data and check for the presence of specific keywords or phrases. Through this analysis, the server assesses the risk of labor law violations and harassment.

[0241] If a risk is detected, the server will send an alert to quickly notify the relevant users. This alert will include details about the problem and specific countermeasures, designed to enable users to take appropriate action.

[0242] Furthermore, the server is integrated with project management tools to monitor the quality of communication and schedule management during project progress. This allows the server to suggest effective project management strategies to the user when project risks increase.

[0243] For example, if a statement suggesting "reducing break times" is made during a meeting, it will be immediately detected as a risk by the server. The server will then alert the relevant project leader and HR department users to support swift action on the issue. Based on the detected risks and their frequency, the server will also propose educational programs and provide mental health support to improve the work environment in the long term.

[0244] Thus, the present invention provides an effective solution for continuously monitoring workplace communication and supporting legal compliance and the realization of a safe working environment.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The terminals use applications installed on each device within the workplace to collect voice and text communication data in real time. This data is securely transmitted to a server via the corporate network.

[0248] Step 2:

[0249] The server performs initial processing on the received data, standardizing the format and removing noise. This preprocessing improves the quality of the data.

[0250] Step 3:

[0251] The server uses speech recognition technology to convert the audio data into text. This process is a preparatory step for a detailed analysis of the audio content.

[0252] Step 4:

[0253] The server uses natural language processing techniques to analyze text data and detect pre-configured keywords and phrases. This analysis determines whether specific language patterns are associated with risk.

[0254] Step 5:

[0255] The server performs a risk assessment based on the analysis results. Here, it checks whether the detected language patterns constitute violations of labor laws or harassment, and determines the risk level.

[0256] Step 6:

[0257] The server will issue an alert to the relevant users if it detects high-risk statements or situations. The alert will include specific details about the risks and countermeasures.

[0258] Step 7:

[0259] The server works in conjunction with project management tools to monitor project progress and detect signs of communication risks and schedule delays.

[0260] Step 8:

[0261] Based on the project's risk assessment, the server will propose to the user, as needed, new project members and revised schedules.

[0262] Step 9:

[0263] Users take appropriate action based on the alerts and suggestions they receive. The results of user actions and feedback are recorded on the server and used to improve the accuracy of risk assessments and countermeasures.

[0264] (Example 1)

[0265] 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."

[0266] In modern workplaces, the risk of legal violations and harassment in employee communication is increasing. Furthermore, there are challenges in the early detection and response to risks during project progress. In addition, there are concerns about the deterioration of the work environment due to a lack of appropriate training and support. Addressing these challenges and achieving legal compliance while ensuring a safe and healthy work environment is essential.

[0267] 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.

[0268] In this invention, the server includes means for collecting information in the workplace environment in real time, means for analyzing the collected information using speech processing technology and language processing technology, and means for evaluating risks related to compliance and mental burden based on the analysis results. This makes it possible to detect risks of legal violations and harassment at an early stage and take effective countermeasures.

[0269] "Work environment" refers to the physical or virtual space in which employees perform their work, and the activities within this space are directly related to communication and the progress of work.

[0270] "Information" refers to data expressed in audio, text, and other formats, including conversations and interactions within the workplace.

[0271] "Means of collecting data in real time" refers to mechanisms and technologies that can instantly capture and analyze information without prior storage.

[0272] "Speech processing technology" refers to the technology used to convert speech data into text data, and includes speech recognition software and algorithms.

[0273] "Language processing technology" refers to the technology used to semantically analyze text data and transform it into a form that humans can understand, and includes natural language processing technology.

[0274] "Compliance with the law" refers to a state of being in accordance with the Labor Standards Act and other laws and regulations, and aims to prevent and ensure compliance with violations.

[0275] "Mental burden" refers to the psychological pressure and stress that workplace communication and work environment impose on employees.

[0276] "Means of risk assessment" refers to methods and technologies for detecting potential legal violations and harassment based on analyzed data, and for evaluating the probability of these occurring.

[0277] This invention is a system that supports legal compliance and a safe work environment by effectively collecting and analyzing communication data in the workplace and assessing risks. This system is mainly implemented with three components: terminals, servers, and users.

[0278] The terminal, acting as a workplace device, continuously collects voice and text data through a dedicated application. This application can, for example, capture text data in real time from voice input devices and chat applications. The terminal then transmits the acquired data to a server via the company network.

[0279] The server plays a crucial role in analyzing the received data. First, it converts audio data to text using speech processing technologies such as the Google Cloud Speech-to-Text API. Next, it applies natural language processing, utilizing Python's NLTK library and SpaCy, to the converted text data to attempt to detect specific keywords and phrases. Based on these results, the server accurately assesses risks related to labor law violations and harassment.

[0280] If a specific risk is detected, project managers and HR personnel (the users) receive an immediate alert from the server. This alert is delivered via email or a dedicated application, clearly outlining the specific risk and recommended countermeasures. This enables a swift and appropriate response.

[0281] Furthermore, the server also provides support for project progress. It integrates with the business management system to monitor project progress and risk trends, and provides appropriate management suggestions. This process aims to improve overall work efficiency and reduce risks throughout the workplace.

[0282] As a specific example, suppose there is a statement suggesting a reduction in break time during a certain meeting. In this case, the server immediately detects this information as a risk and sends a warning to users such as the human resources department. Furthermore, the server attempts to achieve long-term workplace improvement by proposing educational programs or psychological care support according to the situation.

[0283] As an example of a prompt sentence for the generative AI model, "Please design a system that analyzes voice data in the workplace in real time and checks whether it contains specific keywords. Also, please propose the voice recognition technology and natural language processing tools to be used." can be cited. With this prompt, the system provides an optimal technical configuration to achieve the goal.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] As a device within the workplace, the terminal collects communication data in real time from voice input devices, keyboards, etc. Input includes statements during meetings and chat messages. The terminal appropriately formats this data and transmits it to the server via the internal network.

[0287] Step 2:

[0288] The server receives the data transmitted from the terminal. In the case of voice data, it is first converted to text using voice processing technology. For this process, for example, the Google Cloud Speech-to-Text API is used, and an audio file is used as the input, and the corresponding text is obtained as the output.

[0289] Step 3:

[0290] The server applies natural language processing to the text data. It uses Python's NLTK library and SpaCy to analyze keywords and important phrases. Input is either text converted from speech or directly entered text, and output is the analysis result including the detected keywords and phrases.

[0291] Step 4:

[0292] Based on the analysis results, the server performs a risk assessment. If specific keywords are determined to be related to harassment or violations of labor laws, a risk is recognized. The input is the analysis results, and the output is evaluation data indicating the presence and degree of risk.

[0293] Step 5:

[0294] If a risk is detected, the server immediately notifies the relevant users. Notifications are sent via email or a dedicated application, with risk assessment data as input and an alert message as output.

[0295] Step 6:

[0296] The server integrates with project management tools to monitor project progress data and communication trends. Inputs include progress data obtained from the project management tools, and outputs include reports containing improvement suggestions.

[0297] Step 7:

[0298] In response to detected risks, the server proposes educational programs and mental health support from a long-term perspective. This allows users to take concrete actions to improve their work environment. Input is historical risk data and trends, and output is provided as a proposal document.

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] Workplace communication often involves issues related to labor law violations and harassment, and it is crucial to detect these early and take appropriate action. However, traditional methods make it difficult to monitor these risks in real time and respond quickly in large workplace environments. Furthermore, it is important to provide flexible response methods using smart devices for detected risks.

[0302] 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.

[0303] In this invention, the server includes means for collecting communication information in the workplace environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for realizing risk notifications using smart devices based on risk assessment. This makes it possible to accurately detect potential labor law violations and harassment-related problems that may occur in the workplace and to provide a rapid and efficient solution for taking immediate countermeasures.

[0304] "Work environment" refers to the physical or virtual environment in which work is performed, and the place where staff members communicate with each other.

[0305] "Communication information" refers to audio or text data related to information exchange within the workplace, such as conversations, messages, and emails.

[0306] "Methods for collecting information in real time" refers to a system that acquires communication information instantly, enabling processing of information without delay.

[0307] "Voice recognition technology" refers to the technology of converting voice data into text data, which is a technology that automatically interprets the content of voice data and converts it into characters.

[0308] "Natural language processing technology" refers to the technology of interpreting text data, performing semantic analysis and information extraction, and is a technology that processes the content written in natural language in a form that can be understood by machines.

[0309] "Risk assessment" refers to the process of judging the possibility of labor law violations or harassment in the workplace environment based on the collected communication information.

[0310] "Risk notification using smart devices" refers to the means of immediately notifying users of risk information via digital terminals such as smartphones and tablets.

[0311] "Project management means" refers to the mechanism for monitoring the progress of a project and effectively managing the plan and progress.

[0312] The system of the present invention is for monitoring communication information in the workplace environment in real time, quickly detecting risks related to labor law violations and harassment, and providing necessary countermeasures. The system is mainly composed of three main elements: a terminal, a server, and a user.

[0313] The terminal continuously collects voice and text communication information through applications installed on various devices installed in the workplace. These data are transmitted to the server in real time through the Internet or the company's internal network. The server uses voice recognition software such as Google Cloud Speech-to-Text to convert voice data into text. The converted text data is analyzed by natural language processing tools such as spaCy to check for any risky keywords or phrases.

[0314] When a risk is detected, the server quickly generates a risk alert and notifies the user via smart devices such as smartphones and tablets. This allows users to take prompt action. The system is also integrated with workplace project management tools, continuously monitoring the risk status during project progress and generating suggestions to improve project management efficiency. For example, if a statement suggests "reducing break times," the server immediately detects this and sends an alert to the administrator.

[0315] An example of a prompt in a generated AI model is, "This statement may violate labor laws. Please investigate thoroughly and take appropriate action." This allows the user to recognize the problem and take necessary measures quickly.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The device collects voice and text communication information within the workplace. The collected input includes communication information such as conversations and messages. This data is sent directly to the server, so the device performs the operation of transferring data over the network.

[0319] Step 2:

[0320] The server converts the received audio data into text using a speech recognition API such as Google Cloud Speech-to-Text. The input for this step is communication data in audio format. Text data is generated as output. This conversion involves transforming audio data into machine-readable text information.

[0321] Step 3:

[0322] The server analyzes text data using natural language processing technology (e.g., spaCy). The input is text converted from audio data, and the output is the result of the analysis. This process identifies risky keywords and phrases and assigns meaning to the text data.

[0323] Step 4:

[0324] The server generates a risk alert when a risk is detected based on the analysis results. The alert input is the analysis result, and the output is a notification message. This operation determines what should be notified based on the identified risk.

[0325] Step 5:

[0326] The server notifies the user of the generated risk alerts via a smart device. The input for this step is the alert message, and the output is a notification to the user's device. The user receives and acknowledges these notifications and takes appropriate action regarding the risks.

[0327] Step 6:

[0328] The server interacts with the project management system to monitor project risks and generate suggestions to support effective management. Inputs are various project-related data, and outputs are suggestions for management improvement. This suggestion generation process involves comparative analysis and evaluation of the data.

[0329] 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.

[0330] This invention combines an existing system that monitors communication in the workplace and assesses risks with an emotion engine that recognizes the emotional state of users. This system allows for a more comprehensive analysis of information obtained from communication data, making it possible to understand not only the risks of labor law violations and harassment, but also the state of mental health.

[0331] The device collects voice and text communication data within the workplace and sends it to the server in real time. This data includes conversations, emails, and chats between employees. This data is processed on the server.

[0332] The server first uses speech recognition technology to convert audio data into text. Next, it uses natural language processing technology to analyze the text data and detect pre-configured keywords and phrases. This analysis allows for the assessment of risks such as violations of labor laws and harassment.

[0333] Furthermore, the server uses an emotion engine to analyze the user's emotional state within the communication data. This allows for a quantitative assessment of the user's stress level and psychological state. The results of the emotion analysis are used to assess mental health risks and, if necessary, to send appropriate alerts to the user.

[0334] For example, if emotional fluctuations are detected in a particular member's remarks during a project meeting, the server will use this information to send an alert to the project manager. The alert will include the nature of the emotional change and measures to minimize its impact. Furthermore, measures will be taken to reduce the emotional burden on employees through proposed educational programs and mental health support.

[0335] This system supports project management more effectively by using emotional data collected from users and the results of risk assessments. It monitors project progress and enables suggestions that take into account communication risks, stress indicators, and other factors. As a result, the workplace environment is expected to become safer and more constructive, leading to improved employee mental health.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The devices collect voice and text data within the workplace in real time through applications installed on each device. The collected data is transferred to a server via the corporate network.

[0339] Step 2:

[0340] The server converts the received audio data into text using speech recognition technology, and then analyzes the text data using natural language processing technology. The analysis checks whether specific keywords or phrases are included.

[0341] Step 3:

[0342] The server assesses the risk of labor law violations and harassment based on the results of analysis using natural language processing. This assessment process is based on historical data and existing legal regulations.

[0343] Step 4:

[0344] In parallel, the server utilizes an emotion engine to analyze the user's emotions from the analyzed text data. The emotion engine detects emotions such as stress and anxiety based on word choice and context.

[0345] Step 5:

[0346] Based on the results of risk assessment and sentiment analysis, the server also assesses the user's mental health risks. If deemed necessary, it sends an alert to the user, which includes the detected risks and recommended actions.

[0347] Step 6:

[0348] The server integrates with project management tools to monitor project progress. This monitoring includes taking into account the quality of communication and the emotional state of team members, and notifies the project manager if any risks are detected.

[0349] Step 7:

[0350] Based on the collected sentiment data and risk assessment results, the server proposes new project member compositions and schedule adjustments as needed, thereby supporting the smooth progress of the project.

[0351] Step 8:

[0352] Users receive alerts and suggestions from the server and implement the suggested countermeasures. This feedback contributes to improving the system's accuracy and is used for future risk assessments and sentiment analysis.

[0353] (Example 2)

[0354] 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".

[0355] In traditional workplace environments, it has been difficult to timely detect and manage the risks of legal violations and harassment in communication. Furthermore, understanding psychological health risks based on employees' emotional states was insufficient, potentially leading to delays in appropriate responses. Therefore, there is a growing need for an integrated system to improve the work environment and achieve effective work management.

[0356] 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.

[0357] In this invention, the server includes means for collecting information in the work environment in real time, means for analyzing the collected information using acoustic recognition technology and natural language processing technology, and means for issuing warnings to users according to the assessed risk. This makes it possible to quickly identify risks in employee communication and take appropriate action. Furthermore, it is possible to assess psychological health risks through the analysis of emotional states and provide a safer and more constructive work environment.

[0358] "Work environment" refers to the physical and labor-related space where employees perform their duties, including offices and remote work locations.

[0359] "Information" is a general term for data exchanged in the workplace, including voice, text, email, and chat messages.

[0360] "Acoustic recognition technology" is a technology for converting audio data into text data, and includes the process of extracting linguistic information from audio.

[0361] "Natural language processing technology" refers to computer technologies used to analyze text data and perform semantic analysis and keyword detection.

[0362] "Risk" refers to elements or situations that may be related to legal violations or harassment, and includes potential problems in the work environment.

[0363] A "warning" is a notification issued when a risk is detected, and it includes information to prompt appropriate action.

[0364] "Emotional state" refers to information that indicates an employee's psychological state and stress level, and is obtained through sentiment analysis.

[0365] "Psychological health risks" refer to factors that may negatively impact employees' mental health and are related to stress and emotional fluctuations.

[0366] This system is designed to comprehensively monitor workplace communication data and assess risks. The terminals collect voice and text communications within the workplace in real time. This includes conversations, emails, and chat messages, and this data is transmitted from the terminals to the server using a secure protocol.

[0367] The server converts audio data into text data using acoustic recognition technology. This process utilizes speech recognition tools such as the Google Speech-to-Text API. The server then analyzes this text data using natural language processing techniques. Specifically, it leverages libraries such as the Natural Language Toolkit (NLTK) to detect pre-configured keywords and phrases and assess the risk of legal violations and harassment hidden within the communication.

[0368] Furthermore, the server incorporates an emotion engine that can analyze the user's emotional state. This analysis quantifies the user's stress level and psychological health risks based on specific phrases and contexts.

[0369] For example, if a particular member speaks in an unusual tone during a project meeting, the server will detect this emotional shift and send an alert to the project manager. This alert will include details about the emotional change and specific measures to mitigate its impact.

[0370] An example of a prompt sentence to input into the generating AI model is as follows: "Please tell me how to detect emotional fluctuations from business conversations and assess mental health risks."

[0371] This system enables the rapid identification of communication risks in the workplace and the proposal of specific measures to protect employees' mental health. This, in turn, makes it possible to create a safer and more constructive work environment.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The device collects voice and text communication data within the workplace. Specifically, it monitors meeting speeches, emails, and chat messages in real time and records this data digitally. The input is voice and text data from within the company, and the output is digital communication data.

[0375] Step 2:

[0376] The terminal sends the collected data to the server using a secure communication protocol. Specifically, it encrypts the data using protocols such as SSL / TLS before transmission. The input is digital communication data, and the output is the encrypted data that arrives on the server.

[0377] Step 3:

[0378] The server converts audio data into text data using acoustic recognition technology. Specifically, it processes audio files using speech recognition software such as the Google Speech-to-Text API and outputs the spoken content as text. In this process, the input is audio data, and the output is text data.

[0379] Step 4:

[0380] The server analyzes text data using natural language processing techniques. Specifically, it extracts keywords and phrases from the text data and assesses risks related to labor laws and harassment. The input is text data, and the output is risk assessment data as a result of the analysis.

[0381] Step 5:

[0382] The server analyzes the user's emotional state using an emotion engine. Specifically, it applies an emotion analysis algorithm based on the text analysis results to quantify stress levels and psychological health risks. The input in this process is the text analysis results, and the output is emotion evaluation data.

[0383] Step 6:

[0384] The server issues alerts to users based on risk assessment data and sentiment assessment data. Specifically, it generates and sends alerts to responsible parties such as project managers, providing warnings as needed. The inputs for this process are risk assessment data and sentiment assessment data, and the output is an alert message.

[0385] (Application Example 2)

[0386] 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."

[0387] In modern workplaces and home environments, communication-related stress and misunderstandings are increasing, negatively impacting productivity and mental health. Furthermore, inadequate management of stress and emotional states makes it difficult to provide appropriate support promptly. This invention aims to solve these problems and provide a safer and more comfortable communication environment.

[0388] 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.

[0389] In this invention, the server includes means for collecting communication information in the workplace and daily environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for evaluating emotional states using the analysis results and emotion recognition technology, and quantifying stress and psychological states. This makes it possible to appropriately grasp the emotional state of users in a communication environment, reduce stress and risk, and achieve smooth communication.

[0390] "Work environment" refers to the place and conditions in which work is performed, and is the space in which employees engage in their daily activities.

[0391] The "everyday environment" refers to the place where a home or individual lives, and the space where daily communication takes place.

[0392] "Communication information" refers to the exchange of information in the form of conversations, chats, emails, etc.

[0393] "Methods of collecting information in real time" refers to methods of collecting information in a cumulative manner as soon as communication occurs.

[0394] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data.

[0395] "Natural language processing technology" refers to computer science techniques used to analyze and understand human language.

[0396] "Analysis results" refers to the results of analyzing data obtained using speech recognition or natural language processing.

[0397] "Emotion recognition technology" is a technology primarily aimed at enabling computers to analyze and understand human emotions.

[0398] "Methods for quantifying stress and psychological state" refers to methods that numerically evaluate a user's stress and psychological state based on emotional data.

[0399] "Users" refers to individuals or organizations that use this system.

[0400] "Means of providing emotional support" refers to methods of providing psychological support to users based on analyzed emotional data.

[0401] "Environmental management" refers to methods for controlling and improving factors related to communication and stress in the workplace and home.

[0402] To implement this invention, several elements constituting the system are necessary. First, a terminal is needed to collect voice and text communication information, and it is desirable that this terminal be equipped with a high-sensitivity microphone and network connectivity. This terminal transmits the collected information to a server. The server is responsible for converting the voice data into text using a speech recognition engine (e.g., Google Speech-to-Text API). This process makes it possible to analyze voice communication as text.

[0403] Next, the server analyzes the converted text data using natural language processing techniques to detect pre-configured important keywords and phrases. In this case, natural language processing engines such as spaCy or NLTK are used. Furthermore, as an emotion recognition technique, an analysis engine such as IBM Watson Tone Analyzer is used to analyze the user's emotional state and stress level from the conversation content.

[0404] Based on this information, the server assesses potential risks and stressors and issues alerts to the user as needed. It also provides real-time suggestions and emotional support to maintain effective communication.

[0405] For example, when the system recognizes that a conversation within the family is tense, it might suggest, "Why don't you try to relax a little? I'll play some recommended music." This kind of support is effective in reducing stress in home and work environments.

[0406] Another example of a prompt for a generative AI model is, "Please suggest ideas on how a robot can use emotional data to improve communication within the home." Using such prompts allows the system to explore further suggestions and improvements.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The device collects voice and text communication information in real time. Specifically, it uses a microphone to acquire voice data and simultaneously transmits that data to a server via the network. As a result of collecting voice data as input, an audio file is sent to the server.

[0410] Step 2:

[0411] The server converts the received audio data into text data using a speech recognition engine. It analyzes the audio file using the Google Speech-to-Text API and generates text output. The input to this process is the audio data received from the device, and the output is the converted text.

[0412] Step 3:

[0413] The server analyzes the converted text data using a natural language processing engine. Specifically, it uses NLTK or spaCy to extract important keywords and phrases from the text. It receives text data as input and extracts important lexical information as output.

[0414] Step 4:

[0415] The server uses an emotion recognition engine to identify emotional states and stress levels from the analyzed text. Using tools such as IBM Watson Tone Analyzer, it evaluates the user's emotions based on the input text and outputs a quantified emotion evaluation result.

[0416] Step 5:

[0417] The server generates alerts and suggestions for the user based on the emotion assessment results and important vocabulary information. If the user is assessed as having high stress levels, an AI model is used to devise behavioral suggestions that promote relaxation. The input is the emotion assessment results and extracted vocabulary information, and the output is alert messages and behavioral suggestions.

[0418] Step 6:

[0419] The user receives system suggestions and alerts and selects the appropriate action. For example, they might approve playing a suggested relaxation song, or perform an action based on the instructions on their device. This step includes the user's action selection as output.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] The system of this invention effectively monitors communication data in the workplace environment and detects and responds to risks related to labor law violations and harassment in real time. This system consists of three main components: terminals, servers, and users.

[0437] First, the terminal continuously collects voice and text communication data through applications installed on devices within the workplace. This data is transmitted to a server via the company network, allowing for analysis without delay.

[0438] The server uses speech recognition technology to convert the received data into text. Then, natural language processing technology is used to analyze the converted text data and check for the presence of specific keywords or phrases. Through this analysis, the server assesses the risk of labor law violations and harassment.

[0439] If a risk is detected, the server will send an alert to quickly notify the relevant users. This alert will include details about the problem and specific countermeasures, designed to enable users to take appropriate action.

[0440] Furthermore, the server is integrated with project management tools to monitor the quality of communication and schedule management during project progress. This allows the server to suggest effective project management strategies to the user when project risks increase.

[0441] For example, if a statement suggesting "reducing break times" is made during a meeting, it will be immediately detected as a risk by the server. The server will then alert the relevant project leader and HR department users to support swift action on the issue. Based on the detected risks and their frequency, the server will also propose educational programs and provide mental health support to improve the work environment in the long term.

[0442] Thus, the present invention provides an effective solution for continuously monitoring workplace communication and supporting legal compliance and the realization of a safe working environment.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The terminals use applications installed on each device within the workplace to collect voice and text communication data in real time. This data is securely transmitted to a server via the corporate network.

[0446] Step 2:

[0447] The server performs initial processing on the received data, standardizing the format and removing noise. This preprocessing improves the quality of the data.

[0448] Step 3:

[0449] The server uses speech recognition technology to convert the audio data into text. This process is a preparatory step for a detailed analysis of the audio content.

[0450] Step 4:

[0451] The server uses natural language processing techniques to analyze text data and detect pre-configured keywords and phrases. This analysis determines whether specific language patterns are associated with risk.

[0452] Step 5:

[0453] The server performs a risk assessment based on the analysis results. Here, it checks whether the detected language patterns constitute violations of labor laws or harassment, and determines the risk level.

[0454] Step 6:

[0455] The server will issue an alert to the relevant users if it detects high-risk statements or situations. The alert will include specific details about the risks and countermeasures.

[0456] Step 7:

[0457] The server works in conjunction with project management tools to monitor project progress and detect signs of communication risks and schedule delays.

[0458] Step 8:

[0459] Based on the project's risk assessment, the server will propose to the user, as needed, new project members and revised schedules.

[0460] Step 9:

[0461] Users take appropriate action based on the alerts and suggestions they receive. The results of user actions and feedback are recorded on the server and used to improve the accuracy of risk assessments and countermeasures.

[0462] (Example 1)

[0463] 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."

[0464] In modern workplaces, the risk of legal violations and harassment in employee communication is increasing. Furthermore, there are challenges in the early detection and response to risks during project progress. In addition, there are concerns about the deterioration of the work environment due to a lack of appropriate training and support. Addressing these challenges and achieving legal compliance while ensuring a safe and healthy work environment is essential.

[0465] 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.

[0466] In this invention, the server includes means for collecting information in the workplace environment in real time, means for analyzing the collected information using speech processing technology and language processing technology, and means for evaluating risks related to compliance and mental burden based on the analysis results. This makes it possible to detect risks of legal violations and harassment at an early stage and take effective countermeasures.

[0467] "Work environment" refers to the physical or virtual space in which employees perform their work, and the activities within this space are directly related to communication and the progress of work.

[0468] "Information" refers to data expressed in audio, text, and other formats, including conversations and interactions within the workplace.

[0469] "Means of collecting data in real time" refers to mechanisms and technologies that can instantly capture and analyze information without prior storage.

[0470] "Speech processing technology" refers to the technology used to convert speech data into text data, and includes speech recognition software and algorithms.

[0471] "Language processing technology" refers to the technology used to semantically analyze text data and transform it into a form that humans can understand, and includes natural language processing technology.

[0472] "Compliance with the law" refers to a state of being in accordance with the Labor Standards Act and other laws and regulations, and aims to prevent and ensure compliance with violations.

[0473] "Mental burden" refers to the psychological pressure and stress that workplace communication and work environment impose on employees.

[0474] "Means of risk assessment" refers to methods and technologies for detecting potential legal violations and harassment based on analyzed data, and for evaluating the probability of these occurring.

[0475] This invention is a system that supports legal compliance and a safe work environment by effectively collecting and analyzing communication data in the workplace and assessing risks. This system is mainly implemented with three components: terminals, servers, and users.

[0476] The terminal, acting as a workplace device, continuously collects voice and text data through a dedicated application. This application can, for example, capture text data in real time from voice input devices and chat applications. The terminal then transmits the acquired data to a server via the company network.

[0477] The server plays a crucial role in analyzing the received data. First, it converts audio data to text using speech processing technologies such as the Google Cloud Speech-to-Text API. Next, it applies natural language processing, utilizing Python's NLTK library and SpaCy, to the converted text data to attempt to detect specific keywords and phrases. Based on these results, the server accurately assesses risks related to labor law violations and harassment.

[0478] If a specific risk is detected, project managers and HR personnel (the users) receive an immediate alert from the server. This alert is delivered via email or a dedicated application, clearly outlining the specific risk and recommended countermeasures. This enables a swift and appropriate response.

[0479] Furthermore, the server also provides support for project progress. It integrates with the business management system to monitor project progress and risk trends, and provides appropriate management suggestions. This process aims to improve overall work efficiency and reduce risks throughout the workplace.

[0480] As a concrete example, suppose a statement suggesting a reduction in break times is made during a meeting. In this case, the server immediately detects this information as a risk and issues a warning to users such as the human resources department. Furthermore, the server proposes appropriate educational programs and psychological support to promote long-term workplace improvement.

[0481] An example of a prompt for a generative AI model is: "Design a system that analyzes audio data within the workplace in real time and checks for the presence of specific keywords. Also, suggest the speech recognition technology and natural language processing tools to use." This prompt allows the system to provide the optimal technical configuration to achieve its objective.

[0482] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0483] Step 1:

[0484] As a workplace device, the terminal collects communication data in real time from voice input devices, keyboards, and other sources. This input includes meeting contributions and chat messages. The terminal formats this data appropriately and transmits it to the server via the company network.

[0485] Step 2:

[0486] The server receives data sent from the terminal. In the case of audio data, it is first converted to text using speech processing technology. For example, the Google Cloud Speech-to-Text API is used for this process, with an audio file as input and the corresponding text as output.

[0487] Step 3:

[0488] The server applies natural language processing to the text data. It uses Python's NLTK library and SpaCy to analyze keywords and important phrases. Input is either text converted from speech or directly entered text, and output is the analysis result including the detected keywords and phrases.

[0489] Step 4:

[0490] Based on the analysis results, the server performs a risk assessment. If specific keywords are determined to be related to harassment or violations of labor laws, a risk is recognized. The input is the analysis results, and the output is evaluation data indicating the presence and degree of risk.

[0491] Step 5:

[0492] If a risk is detected, the server immediately notifies the relevant users. Notifications are sent via email or a dedicated application, with risk assessment data as input and an alert message as output.

[0493] Step 6:

[0494] The server integrates with project management tools to monitor project progress data and communication trends. Inputs include progress data obtained from the project management tools, and outputs include reports containing improvement suggestions.

[0495] Step 7:

[0496] In response to detected risks, the server proposes educational programs and mental health support from a long-term perspective. This allows users to take concrete actions to improve their work environment. Input is historical risk data and trends, and output is provided as a proposal document.

[0497] (Application Example 1)

[0498] 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."

[0499] Workplace communication often involves issues related to labor law violations and harassment, and it is crucial to detect these early and take appropriate action. However, traditional methods make it difficult to monitor these risks in real time and respond quickly in large workplace environments. Furthermore, it is important to provide flexible response methods using smart devices for detected risks.

[0500] 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.

[0501] In this invention, the server includes means for collecting communication information in the workplace environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for realizing risk notifications using smart devices based on risk assessment. This makes it possible to accurately detect potential labor law violations and harassment-related problems that may occur in the workplace and to provide a rapid and efficient solution for taking immediate countermeasures.

[0502] "Work environment" refers to the physical or virtual environment in which work is performed, and the place where staff members communicate with each other.

[0503] "Communication information" refers to audio or text data related to information exchange within the workplace, such as conversations, messages, and emails.

[0504] "Methods for collecting information in real time" refers to a system that acquires communication information instantly, enabling processing of information without delay.

[0505] "Speech recognition technology" is a technology that converts speech data into text data, and it is a technology that automatically interprets the content of speech data and turns it into text.

[0506] "Natural language processing technology" is a technology that interprets text data, performs semantic analysis and information extraction, and processes content written in natural language in a way that machines can understand.

[0507] "Risk assessment" refers to the process of determining the possibility of labor law violations or harassment in the workplace environment based on collected communication information.

[0508] "Risk notification using smart devices" refers to a method of instantly informing users of risk information via digital devices such as smartphones and tablets.

[0509] "Project management tools" refer to systems for monitoring the progress of a project and effectively managing its plan and progress.

[0510] The system of this invention is designed to monitor communication information in the workplace environment in real time, quickly detect risks related to violations of labor laws and harassment, and provide necessary countermeasures. The system mainly consists of three main elements: terminals, servers, and users.

[0511] The terminals continuously collect voice and text communication information through applications installed on various devices within the workplace. This data is transmitted in real time to a server via the internet or the company network. The server converts the voice data into text using speech recognition software such as Google Cloud Speech-to-Text. The converted text data is then analyzed by natural language processing tools such as spaCy to check for risky keywords or phrases.

[0512] When a risk is detected, the server quickly generates a risk alert and notifies the user via smart devices such as smartphones and tablets. This allows users to take prompt action. The system is also integrated with workplace project management tools, continuously monitoring the risk status during project progress and generating suggestions to improve project management efficiency. For example, if a statement suggests "reducing break times," the server immediately detects this and sends an alert to the administrator.

[0513] An example of a prompt in a generated AI model is, "This statement may violate labor laws. Please investigate thoroughly and take appropriate action." This allows the user to recognize the problem and take necessary measures quickly.

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

[0515] Step 1:

[0516] The device collects voice and text communication information within the workplace. The collected input includes communication information such as conversations and messages. This data is sent directly to the server, so the device performs the operation of transferring data over the network.

[0517] Step 2:

[0518] The server converts the received audio data into text using a speech recognition API such as Google Cloud Speech-to-Text. The input for this step is communication data in audio format. Text data is generated as output. This conversion involves transforming audio data into machine-readable text information.

[0519] Step 3:

[0520] The server analyzes text data using natural language processing technology (e.g., spaCy). The input is text converted from audio data, and the output is the result of the analysis. This process identifies risky keywords and phrases and assigns meaning to the text data.

[0521] Step 4:

[0522] The server generates a risk alert when a risk is detected based on the analysis results. The alert input is the analysis result, and the output is a notification message. This operation determines what should be notified based on the identified risk.

[0523] Step 5:

[0524] The server notifies the user of the generated risk alerts via a smart device. The input for this step is the alert message, and the output is a notification to the user's device. The user receives and acknowledges these notifications and takes appropriate action regarding the risks.

[0525] Step 6:

[0526] The server interacts with the project management system to monitor project risks and generate suggestions to support effective management. Inputs are various project-related data, and outputs are suggestions for management improvement. This suggestion generation process involves comparative analysis and evaluation of the data.

[0527] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0528] This invention combines an existing system that monitors communication in the workplace and assesses risks with an emotion engine that recognizes the emotional state of users. This system allows for a more comprehensive analysis of information obtained from communication data, making it possible to understand not only the risks of labor law violations and harassment, but also the state of mental health.

[0529] The device collects voice and text communication data within the workplace and sends it to the server in real time. This data includes conversations, emails, and chats between employees. This data is processed on the server.

[0530] The server first uses speech recognition technology to convert audio data into text. Next, it uses natural language processing technology to analyze the text data and detect pre-configured keywords and phrases. This analysis allows for the assessment of risks such as violations of labor laws and harassment.

[0531] Furthermore, the server uses an emotion engine to analyze the user's emotional state within the communication data. This allows for a quantitative assessment of the user's stress level and psychological state. The results of the emotion analysis are used to assess mental health risks and, if necessary, to send appropriate alerts to the user.

[0532] For example, if emotional fluctuations are detected in a particular member's remarks during a project meeting, the server will use this information to send an alert to the project manager. The alert will include the nature of the emotional change and measures to minimize its impact. Furthermore, measures will be taken to reduce the emotional burden on employees through proposed educational programs and mental health support.

[0533] This system supports project management more effectively by using emotional data collected from users and the results of risk assessments. It monitors project progress and enables suggestions that take into account communication risks, stress indicators, and other factors. As a result, the workplace environment is expected to become safer and more constructive, leading to improved employee mental health.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The devices collect voice and text data within the workplace in real time through applications installed on each device. The collected data is transferred to a server via the corporate network.

[0537] Step 2:

[0538] The server converts the received audio data into text using speech recognition technology, and then analyzes the text data using natural language processing technology. The analysis checks whether specific keywords or phrases are included.

[0539] Step 3:

[0540] The server assesses the risk of labor law violations and harassment based on the results of analysis using natural language processing. This assessment process is based on historical data and existing legal regulations.

[0541] Step 4:

[0542] In parallel, the server utilizes an emotion engine to analyze the user's emotions from the analyzed text data. The emotion engine detects emotions such as stress and anxiety based on word choice and context.

[0543] Step 5:

[0544] Based on the results of risk assessment and sentiment analysis, the server also assesses the user's mental health risks. If deemed necessary, it sends an alert to the user, which includes the detected risks and recommended actions.

[0545] Step 6:

[0546] The server integrates with project management tools to monitor project progress. This monitoring includes taking into account the quality of communication and the emotional state of team members, and notifies the project manager if any risks are detected.

[0547] Step 7:

[0548] Based on the collected sentiment data and risk assessment results, the server proposes new project member compositions and schedule adjustments as needed, thereby supporting the smooth progress of the project.

[0549] Step 8:

[0550] Users receive alerts and suggestions from the server and implement the suggested countermeasures. This feedback contributes to improving the system's accuracy and is used for future risk assessments and sentiment analysis.

[0551] (Example 2)

[0552] 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."

[0553] In traditional workplace environments, it has been difficult to timely detect and manage the risks of legal violations and harassment in communication. Furthermore, understanding psychological health risks based on employees' emotional states was insufficient, potentially leading to delays in appropriate responses. Therefore, there is a growing need for an integrated system to improve the work environment and achieve effective work management.

[0554] 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.

[0555] In this invention, the server includes means for collecting information in the work environment in real time, means for analyzing the collected information using acoustic recognition technology and natural language processing technology, and means for issuing warnings to users according to the assessed risk. This makes it possible to quickly identify risks in employee communication and take appropriate action. Furthermore, it is possible to assess psychological health risks through the analysis of emotional states and provide a safer and more constructive work environment.

[0556] "Work environment" refers to the physical and labor-related space where employees perform their duties, including offices and remote work locations.

[0557] "Information" is a general term for data exchanged in the workplace, including voice, text, email, and chat messages.

[0558] "Acoustic recognition technology" is a technology for converting audio data into text data, and includes the process of extracting linguistic information from audio.

[0559] "Natural language processing technology" refers to computer technologies used to analyze text data and perform semantic analysis and keyword detection.

[0560] "Risk" refers to elements or situations that may be related to legal violations or harassment, and includes potential problems in the work environment.

[0561] A "warning" is a notification issued when a risk is detected, and it includes information to prompt appropriate action.

[0562] "Emotional state" refers to information that indicates an employee's psychological state and stress level, and is obtained through sentiment analysis.

[0563] "Psychological health risks" refer to factors that may negatively impact employees' mental health and are related to stress and emotional fluctuations.

[0564] This system is designed to comprehensively monitor workplace communication data and assess risks. The terminals collect voice and text communications within the workplace in real time. This includes conversations, emails, and chat messages, and this data is transmitted from the terminals to the server using a secure protocol.

[0565] The server converts audio data into text data using acoustic recognition technology. This process utilizes speech recognition tools such as the Google Speech-to-Text API. The server then analyzes this text data using natural language processing techniques. Specifically, it leverages libraries such as the Natural Language Toolkit (NLTK) to detect pre-configured keywords and phrases and assess the risk of legal violations and harassment hidden within the communication.

[0566] Furthermore, the server incorporates an emotion engine that can analyze the user's emotional state. This analysis quantifies the user's stress level and psychological health risks based on specific phrases and contexts.

[0567] For example, if a particular member speaks in an unusual tone during a project meeting, the server will detect this emotional shift and send an alert to the project manager. This alert will include details about the emotional change and specific measures to mitigate its impact.

[0568] An example of a prompt sentence to input into the generating AI model is as follows: "Please tell me how to detect emotional fluctuations from business conversations and assess mental health risks."

[0569] This system enables the rapid identification of communication risks in the workplace and the proposal of specific measures to protect employees' mental health. This, in turn, makes it possible to create a safer and more constructive work environment.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The device collects voice and text communication data within the workplace. Specifically, it monitors meeting speeches, emails, and chat messages in real time and records this data digitally. The input is voice and text data from within the company, and the output is digital communication data.

[0573] Step 2:

[0574] The terminal sends the collected data to the server using a secure communication protocol. Specifically, it encrypts the data using protocols such as SSL / TLS before transmission. The input is digital communication data, and the output is the encrypted data that arrives on the server.

[0575] Step 3:

[0576] The server converts audio data into text data using acoustic recognition technology. Specifically, it processes audio files using speech recognition software such as the Google Speech-to-Text API and outputs the spoken content as text. In this process, the input is audio data, and the output is text data.

[0577] Step 4:

[0578] The server analyzes text data using natural language processing techniques. Specifically, it extracts keywords and phrases from the text data and assesses risks related to labor laws and harassment. The input is text data, and the output is risk assessment data as a result of the analysis.

[0579] Step 5:

[0580] The server analyzes the user's emotional state using an emotion engine. Specifically, it applies an emotion analysis algorithm based on the text analysis results to quantify stress levels and psychological health risks. The input in this process is the text analysis results, and the output is emotion evaluation data.

[0581] Step 6:

[0582] The server issues alerts to users based on risk assessment data and sentiment assessment data. Specifically, it generates and sends alerts to responsible parties such as project managers, providing warnings as needed. The inputs for this process are risk assessment data and sentiment assessment data, and the output is an alert message.

[0583] (Application Example 2)

[0584] 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."

[0585] In modern workplaces and home environments, communication-related stress and misunderstandings are increasing, negatively impacting productivity and mental health. Furthermore, inadequate management of stress and emotional states makes it difficult to provide appropriate support promptly. This invention aims to solve these problems and provide a safer and more comfortable communication environment.

[0586] 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.

[0587] In this invention, the server includes means for collecting communication information in the workplace and daily environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for evaluating emotional states using the analysis results and emotion recognition technology, and quantifying stress and psychological states. This makes it possible to appropriately grasp the emotional state of users in a communication environment, reduce stress and risk, and achieve smooth communication.

[0588] "Work environment" refers to the place and conditions in which work is performed, and is the space in which employees engage in their daily activities.

[0589] The "everyday environment" refers to the place where a home or individual lives, and the space where daily communication takes place.

[0590] "Communication information" refers to the exchange of information in the form of conversations, chats, emails, etc.

[0591] "Methods of collecting information in real time" refers to methods of collecting information in a cumulative manner as soon as communication occurs.

[0592] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data.

[0593] "Natural language processing technology" refers to computer science techniques used to analyze and understand human language.

[0594] "Analysis results" refers to the results of analyzing data obtained using speech recognition or natural language processing.

[0595] "Emotion recognition technology" is a technology primarily aimed at enabling computers to analyze and understand human emotions.

[0596] "Methods for quantifying stress and psychological state" refers to methods that numerically evaluate a user's stress and psychological state based on emotional data.

[0597] "Users" refers to individuals or organizations that use this system.

[0598] "Means of providing emotional support" refers to methods of providing psychological support to users based on analyzed emotional data.

[0599] "Environmental management" refers to methods for controlling and improving factors related to communication and stress in the workplace and home.

[0600] To implement this invention, several elements constituting the system are necessary. First, a terminal is needed to collect voice and text communication information, and it is desirable that this terminal be equipped with a high-sensitivity microphone and network connectivity. This terminal transmits the collected information to a server. The server is responsible for converting the voice data into text using a speech recognition engine (e.g., Google Speech-to-Text API). This process makes it possible to analyze voice communication as text.

[0601] Next, the server analyzes the converted text data using natural language processing techniques to detect pre-configured important keywords and phrases. In this case, natural language processing engines such as spaCy or NLTK are used. Furthermore, as an emotion recognition technique, an analysis engine such as IBM Watson Tone Analyzer is used to analyze the user's emotional state and stress level from the conversation content.

[0602] Based on this information, the server assesses potential risks and stressors and issues alerts to the user as needed. It also provides real-time suggestions and emotional support to maintain effective communication.

[0603] For example, when the system recognizes that a conversation within the family is tense, it might suggest, "Why don't you try to relax a little? I'll play some recommended music." This kind of support is effective in reducing stress in home and work environments.

[0604] Another example of a prompt for a generative AI model is, "Please suggest ideas on how a robot can use emotional data to improve communication within the home." Using such prompts allows the system to explore further suggestions and improvements.

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The device collects voice and text communication information in real time. Specifically, it uses a microphone to acquire voice data and simultaneously transmits that data to a server via the network. As a result of collecting voice data as input, an audio file is sent to the server.

[0608] Step 2:

[0609] The server converts the received audio data into text data using a speech recognition engine. It analyzes the audio file using the Google Speech-to-Text API and generates text output. The input to this process is the audio data received from the device, and the output is the converted text.

[0610] Step 3:

[0611] The server analyzes the converted text data using a natural language processing engine. Specifically, it uses NLTK or spaCy to extract important keywords and phrases from the text. It receives text data as input and extracts important lexical information as output.

[0612] Step 4:

[0613] The server uses an emotion recognition engine to identify emotional states and stress levels from the analyzed text. Using tools such as IBM Watson Tone Analyzer, it evaluates the user's emotions based on the input text and outputs a quantified emotion evaluation result.

[0614] Step 5:

[0615] The server generates alerts and suggestions for the user based on the emotion assessment results and important vocabulary information. If the user is assessed as having high stress levels, an AI model is used to devise behavioral suggestions that promote relaxation. The input is the emotion assessment results and extracted vocabulary information, and the output is alert messages and behavioral suggestions.

[0616] Step 6:

[0617] The user receives system suggestions and alerts and selects the appropriate action. For example, they might approve playing a suggested relaxation song, or perform an action based on the instructions on their device. This step includes the user's action selection as output.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] [Fourth Embodiment]

[0622] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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".

[0635] The system of this invention effectively monitors communication data in the workplace environment and detects and responds to risks related to labor law violations and harassment in real time. This system consists of three main components: terminals, servers, and users.

[0636] First, the terminal continuously collects voice and text communication data through applications installed on devices within the workplace. This data is transmitted to a server via the company network, allowing for analysis without delay.

[0637] The server uses speech recognition technology to convert the received data into text. Then, natural language processing technology is used to analyze the converted text data and check for the presence of specific keywords or phrases. Through this analysis, the server assesses the risk of labor law violations and harassment.

[0638] If a risk is detected, the server will send an alert to quickly notify the relevant users. This alert will include details about the problem and specific countermeasures, designed to enable users to take appropriate action.

[0639] Furthermore, the server is integrated with project management tools to monitor the quality of communication and schedule management during project progress. This allows the server to suggest effective project management strategies to the user when project risks increase.

[0640] For example, if a statement suggesting "reducing break times" is made during a meeting, it will be immediately detected as a risk by the server. The server will then alert the relevant project leader and HR department users to support swift action on the issue. Based on the detected risks and their frequency, the server will also propose educational programs and provide mental health support to improve the work environment in the long term.

[0641] Thus, the present invention provides an effective solution for continuously monitoring workplace communication and supporting legal compliance and the realization of a safe working environment.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The terminals use applications installed on each device within the workplace to collect voice and text communication data in real time. This data is securely transmitted to a server via the corporate network.

[0645] Step 2:

[0646] The server performs initial processing on the received data, standardizing the format and removing noise. This preprocessing improves the quality of the data.

[0647] Step 3:

[0648] The server uses speech recognition technology to convert the audio data into text. This process is a preparatory step for a detailed analysis of the audio content.

[0649] Step 4:

[0650] The server uses natural language processing techniques to analyze text data and detect pre-configured keywords and phrases. This analysis determines whether specific language patterns are associated with risk.

[0651] Step 5:

[0652] The server performs a risk assessment based on the analysis results. Here, it checks whether the detected language patterns constitute violations of labor laws or harassment, and determines the risk level.

[0653] Step 6:

[0654] The server will issue an alert to the relevant users if it detects high-risk statements or situations. The alert will include specific details about the risks and countermeasures.

[0655] Step 7:

[0656] The server works in conjunction with project management tools to monitor project progress and detect signs of communication risks and schedule delays.

[0657] Step 8:

[0658] Based on the project's risk assessment, the server will propose to the user, as needed, new project members and revised schedules.

[0659] Step 9:

[0660] Users take appropriate action based on the alerts and suggestions they receive. The results of user actions and feedback are recorded on the server and used to improve the accuracy of risk assessments and countermeasures.

[0661] (Example 1)

[0662] 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".

[0663] In modern workplaces, the risk of legal violations and harassment in employee communication is increasing. Furthermore, there are challenges in the early detection and response to risks during project progress. In addition, there are concerns about the deterioration of the work environment due to a lack of appropriate training and support. Addressing these challenges and achieving legal compliance while ensuring a safe and healthy work environment is essential.

[0664] 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.

[0665] In this invention, the server includes means for collecting information in the workplace environment in real time, means for analyzing the collected information using speech processing technology and language processing technology, and means for evaluating risks related to compliance and mental burden based on the analysis results. This makes it possible to detect risks of legal violations and harassment at an early stage and take effective countermeasures.

[0666] "Work environment" refers to the physical or virtual space in which employees perform their work, and the activities within this space are directly related to communication and the progress of work.

[0667] "Information" refers to data expressed in audio, text, and other formats, including conversations and interactions within the workplace.

[0668] "Means of collecting data in real time" refers to mechanisms and technologies that can instantly capture and analyze information without prior storage.

[0669] "Speech processing technology" refers to the technology used to convert speech data into text data, and includes speech recognition software and algorithms.

[0670] "Language processing technology" refers to the technology used to semantically analyze text data and transform it into a form that humans can understand, and includes natural language processing technology.

[0671] "Compliance with the law" refers to a state of being in accordance with the Labor Standards Act and other laws and regulations, and aims to prevent and ensure compliance with violations.

[0672] "Mental burden" refers to the psychological pressure and stress that workplace communication and work environment impose on employees.

[0673] "Means of risk assessment" refers to methods and technologies for detecting potential legal violations and harassment based on analyzed data, and for evaluating the probability of these occurring.

[0674] This invention is a system that supports legal compliance and a safe work environment by effectively collecting and analyzing communication data in the workplace and assessing risks. This system is mainly implemented with three components: terminals, servers, and users.

[0675] The terminal, acting as a workplace device, continuously collects voice and text data through a dedicated application. This application can, for example, capture text data in real time from voice input devices and chat applications. The terminal then transmits the acquired data to a server via the company network.

[0676] The server plays a crucial role in analyzing the received data. First, it converts audio data to text using speech processing technologies such as the Google Cloud Speech-to-Text API. Next, it applies natural language processing, utilizing Python's NLTK library and SpaCy, to the converted text data to attempt to detect specific keywords and phrases. Based on these results, the server accurately assesses risks related to labor law violations and harassment.

[0677] If a specific risk is detected, project managers and HR personnel (the users) receive an immediate alert from the server. This alert is delivered via email or a dedicated application, clearly outlining the specific risk and recommended countermeasures. This enables a swift and appropriate response.

[0678] Furthermore, the server also provides support for project progress. It integrates with the business management system to monitor project progress and risk trends, and provides appropriate management suggestions. This process aims to improve overall work efficiency and reduce risks throughout the workplace.

[0679] As a concrete example, suppose a statement suggesting a reduction in break times is made during a meeting. In this case, the server immediately detects this information as a risk and issues a warning to users such as the human resources department. Furthermore, the server proposes appropriate educational programs and psychological support to promote long-term workplace improvement.

[0680] An example of a prompt for a generative AI model is: "Design a system that analyzes audio data within the workplace in real time and checks for the presence of specific keywords. Also, suggest the speech recognition technology and natural language processing tools to use." This prompt allows the system to provide the optimal technical configuration to achieve its objective.

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

[0682] Step 1:

[0683] As a workplace device, the terminal collects communication data in real time from voice input devices, keyboards, and other sources. This input includes meeting contributions and chat messages. The terminal formats this data appropriately and transmits it to the server via the company network.

[0684] Step 2:

[0685] The server receives data sent from the terminal. In the case of audio data, it is first converted to text using speech processing technology. For example, the Google Cloud Speech-to-Text API is used for this process, with an audio file as input and the corresponding text as output.

[0686] Step 3:

[0687] The server applies natural language processing to the text data. It uses Python's NLTK library and SpaCy to analyze keywords and important phrases. Input is either text converted from speech or directly entered text, and output is the analysis result including the detected keywords and phrases.

[0688] Step 4:

[0689] Based on the analysis results, the server performs a risk assessment. If specific keywords are determined to be related to harassment or violations of labor laws, a risk is recognized. The input is the analysis results, and the output is evaluation data indicating the presence and degree of risk.

[0690] Step 5:

[0691] If a risk is detected, the server immediately notifies the relevant users. Notifications are sent via email or a dedicated application, with risk assessment data as input and an alert message as output.

[0692] Step 6:

[0693] The server integrates with project management tools to monitor project progress data and communication trends. Inputs include progress data obtained from the project management tools, and outputs include reports containing improvement suggestions.

[0694] Step 7:

[0695] In response to detected risks, the server proposes educational programs and mental health support from a long-term perspective. This allows users to take concrete actions to improve their work environment. Input is historical risk data and trends, and output is provided as a proposal document.

[0696] (Application Example 1)

[0697] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0698] Workplace communication often involves issues related to labor law violations and harassment, and it is crucial to detect these early and take appropriate action. However, traditional methods make it difficult to monitor these risks in real time and respond quickly in large workplace environments. Furthermore, it is important to provide flexible response methods using smart devices for detected risks.

[0699] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0700] In this invention, the server includes means for collecting communication information in the workplace environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for realizing risk notifications using smart devices based on risk assessment. This makes it possible to accurately detect potential labor law violations and harassment-related problems that may occur in the workplace and to provide a rapid and efficient solution for taking immediate countermeasures.

[0701] "Work environment" refers to the physical or virtual environment in which work is performed, and the place where staff members communicate with each other.

[0702] "Communication information" refers to audio or text data related to information exchange within the workplace, such as conversations, messages, and emails.

[0703] "Methods for collecting information in real time" refers to a system that acquires communication information instantly, enabling processing of information without delay.

[0704] "Speech recognition technology" is a technology that converts speech data into text data, and it is a technology that automatically interprets the content of speech data and turns it into text.

[0705] "Natural language processing technology" is a technology that interprets text data, performs semantic analysis and information extraction, and processes content written in natural language in a way that machines can understand.

[0706] "Risk assessment" refers to the process of determining the possibility of labor law violations or harassment in the workplace environment based on collected communication information.

[0707] "Risk notification using smart devices" refers to a method of instantly informing users of risk information via digital devices such as smartphones and tablets.

[0708] "Project management tools" refer to systems for monitoring the progress of a project and effectively managing its plan and progress.

[0709] The system of this invention is designed to monitor communication information in the workplace environment in real time, quickly detect risks related to violations of labor laws and harassment, and provide necessary countermeasures. The system mainly consists of three main elements: terminals, servers, and users.

[0710] The terminals continuously collect voice and text communication information through applications installed on various devices within the workplace. This data is transmitted in real time to a server via the internet or the company network. The server converts the voice data into text using speech recognition software such as Google Cloud Speech-to-Text. The converted text data is then analyzed by natural language processing tools such as spaCy to check for risky keywords or phrases.

[0711] When a risk is detected, the server quickly generates a risk alert and notifies the user via smart devices such as smartphones and tablets. This allows users to take prompt action. The system is also integrated with workplace project management tools, continuously monitoring the risk status during project progress and generating suggestions to improve project management efficiency. For example, if a statement suggests "reducing break times," the server immediately detects this and sends an alert to the administrator.

[0712] An example of a prompt in a generated AI model is, "This statement may violate labor laws. Please investigate thoroughly and take appropriate action." This allows the user to recognize the problem and take necessary measures quickly.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The device collects voice and text communication information within the workplace. The collected input includes communication information such as conversations and messages. This data is sent directly to the server, so the device performs the operation of transferring data over the network.

[0716] Step 2:

[0717] The server converts the received audio data into text using a speech recognition API such as Google Cloud Speech-to-Text. The input for this step is communication data in audio format. Text data is generated as output. This conversion involves transforming audio data into machine-readable text information.

[0718] Step 3:

[0719] The server analyzes text data using natural language processing technology (e.g., spaCy). The input is text converted from audio data, and the output is the result of the analysis. This process identifies risky keywords and phrases and assigns meaning to the text data.

[0720] Step 4:

[0721] The server generates a risk alert when a risk is detected based on the analysis results. The alert input is the analysis result, and the output is a notification message. This operation determines what should be notified based on the identified risk.

[0722] Step 5:

[0723] The server notifies the user of the generated risk alerts via a smart device. The input for this step is the alert message, and the output is a notification to the user's device. The user receives and acknowledges these notifications and takes appropriate action regarding the risks.

[0724] Step 6:

[0725] The server interacts with the project management system to monitor project risks and generate suggestions to support effective management. Inputs are various project-related data, and outputs are suggestions for management improvement. This suggestion generation process involves comparative analysis and evaluation of the data.

[0726] 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.

[0727] This invention combines an existing system that monitors communication in the workplace and assesses risks with an emotion engine that recognizes the emotional state of users. This system allows for a more comprehensive analysis of information obtained from communication data, making it possible to understand not only the risks of labor law violations and harassment, but also the state of mental health.

[0728] The device collects voice and text communication data within the workplace and sends it to the server in real time. This data includes conversations, emails, and chats between employees. This data is processed on the server.

[0729] The server first uses speech recognition technology to convert audio data into text. Next, it uses natural language processing technology to analyze the text data and detect pre-configured keywords and phrases. This analysis allows for the assessment of risks such as violations of labor laws and harassment.

[0730] Furthermore, the server uses an emotion engine to analyze the user's emotional state within the communication data. This allows for a quantitative assessment of the user's stress level and psychological state. The results of the emotion analysis are used to assess mental health risks and, if necessary, to send appropriate alerts to the user.

[0731] For example, if emotional fluctuations are detected in a particular member's remarks during a project meeting, the server will use this information to send an alert to the project manager. The alert will include the nature of the emotional change and measures to minimize its impact. Furthermore, measures will be taken to reduce the emotional burden on employees through proposed educational programs and mental health support.

[0732] This system supports project management more effectively by using emotional data collected from users and the results of risk assessments. It monitors project progress and enables suggestions that take into account communication risks, stress indicators, and other factors. As a result, the workplace environment is expected to become safer and more constructive, leading to improved employee mental health.

[0733] The following describes the processing flow.

[0734] Step 1:

[0735] The devices collect voice and text data within the workplace in real time through applications installed on each device. The collected data is transferred to a server via the corporate network.

[0736] Step 2:

[0737] The server converts the received audio data into text using speech recognition technology, and then analyzes the text data using natural language processing technology. The analysis checks whether specific keywords or phrases are included.

[0738] Step 3:

[0739] The server assesses the risk of labor law violations and harassment based on the results of analysis using natural language processing. This assessment process is based on historical data and existing legal regulations.

[0740] Step 4:

[0741] In parallel, the server utilizes an emotion engine to analyze the user's emotions from the analyzed text data. The emotion engine detects emotions such as stress and anxiety based on word choice and context.

[0742] Step 5:

[0743] Based on the results of risk assessment and sentiment analysis, the server also assesses the user's mental health risks. If deemed necessary, it sends an alert to the user, which includes the detected risks and recommended actions.

[0744] Step 6:

[0745] The server integrates with project management tools to monitor project progress. This monitoring includes taking into account the quality of communication and the emotional state of team members, and notifies the project manager if any risks are detected.

[0746] Step 7:

[0747] Based on the collected sentiment data and risk assessment results, the server proposes new project member compositions and schedule adjustments as needed, thereby supporting the smooth progress of the project.

[0748] Step 8:

[0749] Users receive alerts and suggestions from the server and implement the suggested countermeasures. This feedback contributes to improving the system's accuracy and is used for future risk assessments and sentiment analysis.

[0750] (Example 2)

[0751] 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".

[0752] In traditional workplace environments, it has been difficult to timely detect and manage the risks of legal violations and harassment in communication. Furthermore, understanding psychological health risks based on employees' emotional states was insufficient, potentially leading to delays in appropriate responses. Therefore, there is a growing need for an integrated system to improve the work environment and achieve effective work management.

[0753] 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.

[0754] In this invention, the server includes means for collecting information in the work environment in real time, means for analyzing the collected information using acoustic recognition technology and natural language processing technology, and means for issuing warnings to users according to the assessed risk. This makes it possible to quickly identify risks in employee communication and take appropriate action. Furthermore, it is possible to assess psychological health risks through the analysis of emotional states and provide a safer and more constructive work environment.

[0755] "Work environment" refers to the physical and labor-related space where employees perform their duties, including offices and remote work locations.

[0756] "Information" is a general term for data exchanged in the workplace, including voice, text, email, and chat messages.

[0757] "Acoustic recognition technology" is a technology for converting audio data into text data, and includes the process of extracting linguistic information from audio.

[0758] "Natural language processing technology" refers to computer technologies used to analyze text data and perform semantic analysis and keyword detection.

[0759] "Risk" refers to elements or situations that may be related to legal violations or harassment, and includes potential problems in the work environment.

[0760] A "warning" is a notification issued when a risk is detected, and it includes information to prompt appropriate action.

[0761] "Emotional state" refers to information that indicates an employee's psychological state and stress level, and is obtained through sentiment analysis.

[0762] "Psychological health risks" refer to factors that may negatively impact employees' mental health and are related to stress and emotional fluctuations.

[0763] This system is designed to comprehensively monitor workplace communication data and assess risks. The terminals collect voice and text communications within the workplace in real time. This includes conversations, emails, and chat messages, and this data is transmitted from the terminals to the server using a secure protocol.

[0764] The server converts audio data into text data using acoustic recognition technology. This process utilizes speech recognition tools such as the Google Speech-to-Text API. The server then analyzes this text data using natural language processing techniques. Specifically, it leverages libraries such as the Natural Language Toolkit (NLTK) to detect pre-configured keywords and phrases and assess the risk of legal violations and harassment hidden within the communication.

[0765] Furthermore, the server incorporates an emotion engine that can analyze the user's emotional state. This analysis quantifies the user's stress level and psychological health risks based on specific phrases and contexts.

[0766] For example, if a particular member speaks in an unusual tone during a project meeting, the server will detect this emotional shift and send an alert to the project manager. This alert will include details about the emotional change and specific measures to mitigate its impact.

[0767] An example of a prompt sentence to input into the generating AI model is as follows: "Please tell me how to detect emotional fluctuations from business conversations and assess mental health risks."

[0768] This system enables the rapid identification of communication risks in the workplace and the proposal of specific measures to protect employees' mental health. This, in turn, makes it possible to create a safer and more constructive work environment.

[0769] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0770] Step 1:

[0771] The device collects voice and text communication data within the workplace. Specifically, it monitors meeting speeches, emails, and chat messages in real time and records this data digitally. The input is voice and text data from within the company, and the output is digital communication data.

[0772] Step 2:

[0773] The terminal sends the collected data to the server using a secure communication protocol. Specifically, it encrypts the data using protocols such as SSL / TLS before transmission. The input is digital communication data, and the output is the encrypted data that arrives on the server.

[0774] Step 3:

[0775] The server converts audio data into text data using acoustic recognition technology. Specifically, it processes audio files using speech recognition software such as the Google Speech-to-Text API and outputs the spoken content as text. In this process, the input is audio data, and the output is text data.

[0776] Step 4:

[0777] The server analyzes text data using natural language processing techniques. Specifically, it extracts keywords and phrases from the text data and assesses risks related to labor laws and harassment. The input is text data, and the output is risk assessment data as a result of the analysis.

[0778] Step 5:

[0779] The server analyzes the user's emotional state using an emotion engine. Specifically, it applies an emotion analysis algorithm based on the text analysis results to quantify stress levels and psychological health risks. The input in this process is the text analysis results, and the output is emotion evaluation data.

[0780] Step 6:

[0781] The server issues alerts to users based on risk assessment data and sentiment assessment data. Specifically, it generates and sends alerts to responsible parties such as project managers, providing warnings as needed. The inputs for this process are risk assessment data and sentiment assessment data, and the output is an alert message.

[0782] (Application Example 2)

[0783] 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".

[0784] In modern workplaces and home environments, communication-related stress and misunderstandings are increasing, negatively impacting productivity and mental health. Furthermore, inadequate management of stress and emotional states makes it difficult to provide appropriate support promptly. This invention aims to solve these problems and provide a safer and more comfortable communication environment.

[0785] 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.

[0786] In this invention, the server includes means for collecting communication information in the workplace and daily environment in real time, means for analyzing the collected communication information using speech recognition technology and natural language processing technology, and means for evaluating emotional states using the analysis results and emotion recognition technology, and quantifying stress and psychological states. This makes it possible to appropriately grasp the emotional state of users in a communication environment, reduce stress and risk, and achieve smooth communication.

[0787] "Work environment" refers to the place and conditions in which work is performed, and is the space in which employees engage in their daily activities.

[0788] The "everyday environment" refers to the place where a home or individual lives, and the space where daily communication takes place.

[0789] "Communication information" refers to the exchange of information in the form of conversations, chats, emails, etc.

[0790] "Methods of collecting information in real time" refers to methods of collecting information in a cumulative manner as soon as communication occurs.

[0791] "Speech recognition technology" is a technology that analyzes speech data and converts it into text data.

[0792] "Natural language processing technology" refers to computer science techniques used to analyze and understand human language.

[0793] "Analysis results" refers to the results of analyzing data obtained using speech recognition or natural language processing.

[0794] "Emotion recognition technology" is a technology primarily aimed at enabling computers to analyze and understand human emotions.

[0795] "Methods for quantifying stress and psychological state" refers to methods that numerically evaluate a user's stress and psychological state based on emotional data.

[0796] "Users" refers to individuals or organizations that use this system.

[0797] "Means of providing emotional support" refers to methods of providing psychological support to users based on analyzed emotional data.

[0798] "Environmental management" refers to methods for controlling and improving factors related to communication and stress in the workplace and home.

[0799] To implement this invention, several elements constituting the system are necessary. First, a terminal is needed to collect voice and text communication information, and it is desirable that this terminal be equipped with a high-sensitivity microphone and network connectivity. This terminal transmits the collected information to a server. The server is responsible for converting the voice data into text using a speech recognition engine (e.g., Google Speech-to-Text API). This process makes it possible to analyze voice communication as text.

[0800] Next, the server analyzes the converted text data using natural language processing techniques to detect pre-configured important keywords and phrases. In this case, natural language processing engines such as spaCy or NLTK are used. Furthermore, as an emotion recognition technique, an analysis engine such as IBM Watson Tone Analyzer is used to analyze the user's emotional state and stress level from the conversation content.

[0801] Based on this information, the server assesses potential risks and stressors and issues alerts to the user as needed. It also provides real-time suggestions and emotional support to maintain effective communication.

[0802] For example, when the system recognizes that a conversation within the family is tense, it might suggest, "Why don't you try to relax a little? I'll play some recommended music." This kind of support is effective in reducing stress in home and work environments.

[0803] Another example of a prompt for a generative AI model is, "Please suggest ideas on how a robot can use emotional data to improve communication within the home." Using such prompts allows the system to explore further suggestions and improvements.

[0804] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0805] Step 1:

[0806] The device collects voice and text communication information in real time. Specifically, it uses a microphone to acquire voice data and simultaneously transmits that data to a server via the network. As a result of collecting voice data as input, an audio file is sent to the server.

[0807] Step 2:

[0808] The server converts the received audio data into text data using a speech recognition engine. It analyzes the audio file using the Google Speech-to-Text API and generates text output. The input to this process is the audio data received from the device, and the output is the converted text.

[0809] Step 3:

[0810] The server analyzes the converted text data using a natural language processing engine. Specifically, it uses NLTK or spaCy to extract important keywords and phrases from the text. It receives text data as input and extracts important lexical information as output.

[0811] Step 4:

[0812] The server uses an emotion recognition engine to identify emotional states and stress levels from the analyzed text. Using tools such as IBM Watson Tone Analyzer, it evaluates the user's emotions based on the input text and outputs a quantified emotion evaluation result.

[0813] Step 5:

[0814] The server generates alerts and suggestions for the user based on the emotion assessment results and important vocabulary information. If the user is assessed as having high stress levels, an AI model is used to devise behavioral suggestions that promote relaxation. The input is the emotion assessment results and extracted vocabulary information, and the output is alert messages and behavioral suggestions.

[0815] Step 6:

[0816] The user receives system suggestions and alerts and selects the appropriate action. For example, they might approve playing a suggested relaxation song, or perform an action based on the instructions on their device. This step includes the user's action selection as output.

[0817] 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.

[0818] 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.

[0819] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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."

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] The following is further disclosed regarding the embodiments described above.

[0839] (Claim 1)

[0840] A means of collecting communication data in the workplace environment in real time,

[0841] A means for analyzing collected communication data using speech recognition technology and natural language processing technology,

[0842] A means of evaluating risks related to violations of labor laws and harassment based on the analysis results,

[0843] A means of sending alerts to users based on risk assessment,

[0844] A means of monitoring risks during project progress in conjunction with project management tools and providing suggestions to support effective project management,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, characterized in that it includes means for providing educational program suggestions and mental health support when a risk is detected.

[0848] (Claim 3)

[0849] The system according to claim 1, characterized by having means for proposing new project member compositions and schedule reorganization proposals in response to detected risks and problems.

[0850] "Example 1"

[0851] (Claim 1)

[0852] Means for collecting information in the workplace environment in real time,

[0853] A means for analyzing the collected information using speech processing technology and language processing technology,

[0854] A means of evaluating risks related to compliance and mental burden based on the analysis results,

[0855] A means of issuing warnings to workers based on risk assessment,

[0856] A means of monitoring risks during business operations in conjunction with a business management system and providing suggestions to support effective business management,

[0857] A method for quantifying risk based on conversation patterns,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, characterized in that it includes means for providing guidance programs and psychological care support when a risk is detected.

[0861] (Claim 3)

[0862] The system according to claim 1, characterized by having means for proposing new work team configurations and schedule adjustments in response to detected risks and problems.

[0863] "Application Example 1"

[0864] (Claim 1)

[0865] A means of collecting communication information in the workplace environment in real time,

[0866] A means for analyzing collected communication information using speech recognition technology and natural language processing technology,

[0867] A means of evaluating risks related to violations of labor laws and harassment based on the analysis results,

[0868] A means of issuing alerts to users based on risk assessment,

[0869] A means of monitoring risks during project progress in conjunction with project management tools, and providing suggestions to support effective project management.

[0870] Based on risk assessment, a means of realizing risk notification using smart devices,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, characterized in that it includes means for providing educational program suggestions and psychological support when a risk is detected.

[0874] (Claim 3)

[0875] The system according to claim 1, characterized in that it includes means for immediately notifying the user via a smart terminal based on the detected risk, thereby enabling a rapid response.

[0876] "Example 2 of combining an emotion engine"

[0877] (Claim 1)

[0878] Means for collecting information in the workplace environment in real time,

[0879] A means for analyzing the collected information using acoustic recognition technology and natural language processing technology,

[0880] A means of evaluating risks related to legal violations and human rights based on the analysis results,

[0881] A means of issuing warnings to users according to the assessed risk,

[0882] A means of monitoring risks during business operations in conjunction with information processing equipment and making suggestions to support effective business management,

[0883] A means of analyzing the emotional state of users and assessing psychological health risks,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, characterized in that it includes means for providing training program suggestions and mental health support when a risk is detected.

[0887] (Claim 3)

[0888] The system according to claim 1, characterized by having means for proposing new personnel assignments and plan reorganization proposals in response to detected risks and problems.

[0889] "Application example 2 when combining with an emotional engine"

[0890] (Claim 1)

[0891] A means of collecting communication information in the workplace and daily environment in real time,

[0892] A means for analyzing collected communication information using speech recognition technology and natural language processing technology,

[0893] A means of evaluating emotional states using analysis results and emotion recognition technology, and quantifying stress and psychological state,

[0894] A means of issuing alerts to users and providing emotional support based on risk assessment and emotional assessment,

[0895] A means of monitoring ongoing risks and stressors in conjunction with project management systems and home management systems, and providing suggestions to support effective environmental management.

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, characterized by comprising means for proposing educational programs, providing mental health support, and recommending activities to reduce emotional burden.

[0899] (Claim 3)

[0900] The system according to claim 1, characterized by comprising means for proposing new environmental adjustment plans and schedule reorganization plans in response to detected risks and emotional states. [Explanation of Symbols]

[0901] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting communication information in the workplace environment in real time, A means for analyzing collected communication information using speech recognition technology and natural language processing technology, A means of evaluating risks related to violations of labor laws and harassment based on the analysis results, A means of issuing alerts to users based on risk assessment, A means of monitoring risks during project progress in conjunction with project management tools, and providing suggestions to support effective project management. Based on risk assessment, a means of realizing risk notification using smart devices, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for providing educational program suggestions and psychological support when a risk is detected.

3. The system according to claim 1, characterized in that it includes means for immediately notifying the user via a smart terminal based on the detected risk, thereby enabling a rapid response.

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