System

Abstract

An object of a system according to an embodiment is to detect signs of sexual harassment at an early stage and take appropriate measures.SOLUTION: A system includes a sexual harassment detection part, a warning part, a recording part, and a reporting part. The sexual harassment detection unit detects a sexual harassment sign using the generated AI. The warning unit issues a warning based on the sexual harassment sign detected by the sexual harassment detection unit. The recording part records evidence on the basis of the sexual harassment sign detected by the sexual harassment detection part. The reporting unit reports the evidence recorded by the recording unit to an appropriate institution.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to detect signs of sexual harassment early and take appropriate measures.

[0005] The system according to the embodiment aims to detect signs of sexual harassment early and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a sexual harassment detection unit, a warning unit, a recording unit, and a reporting unit. The sexual harassment detection unit detects signs of sexual harassment using generative AI. The warning unit issues a warning based on the signs of sexual harassment detected by the sexual harassment detection unit. The recording unit records evidence based on the signs of sexual harassment detected by the sexual harassment detection unit. The reporting unit reports the evidence recorded by the recording unit to an appropriate institution. [Effects of the Invention]

[0007] The system according to the embodiment can detect signs of sexual harassment early and take appropriate measures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The sexual harassment detection tool according to an embodiment of the present invention is a system in which a generative AI analyzes the content of conversations and messages, detects signs of sexual harassment, issues a warning, interrupts the conversation as necessary, records evidence, and reports the incident to the appropriate authorities. This enables the sexual harassment detection tool to quickly detect sexual harassment and respond immediately.

[0029] A sexual harassment detection tool according to an embodiment includes a sexual harassment detection unit, a warning unit, a recording unit, and a reporting unit. The sexual harassment detection unit detects signs of sexual harassment using a generation AI. For example, the generation AI analyzes text data of conversations and messages and determines the possibility of sexual harassment based on specific keywords, phrases, and context. The generation AI can also use natural language processing technology to analyze the content of conversations in real time and detect signs of sexual harassment. For example, the generation AI may determine that "inappropriate physical comments" or "sexual requests" constitute sexual harassment. The warning unit issues a warning based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, the warning unit displays a warning message such as "This comment is inappropriate and may constitute sexual harassment." The warning unit can also interrupt the conversation and urge the other party to stop engaging in sexually harassing behavior as necessary. The recording unit records evidence based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, the recording unit saves the content of conversations or messages in which signs of sexual harassment are detected so that they can be reviewed later. The recording unit can also encrypt and store evidence data to prevent data tampering. The reporting unit reports the evidence recorded by the recording unit to the appropriate authorities. For example, the reporting unit may report the occurrence of sexual harassment to a superior, the human resources department, or a legal agency. This allows the sexual harassment detection tool to quickly detect sexual harassment and respond immediately. For example, when sexual harassment occurs, it can protect the victim and help resolve the problem. In addition, by recording evidence of sexual harassment and reporting it to the appropriate authorities, it can help resolve sexual harassment issues.

[0030] The sexual harassment detection unit analyzes the tone of conversation and voice intonation, and can detect signs of sexual harassment from voice data as well. For example, the generation AI analyzes the tone of conversation and voice intonation and detects signs of sexual harassment based on specific voice patterns. For example, a sudden change in voice tone or an unnatural rise in voice intonation can be detected as a possible sexual harassment. The sexual harassment detection unit also analyzes voice data and detects signs of sexual harassment if specific keywords or phrases are included. For example, it issues a warning if "inappropriate physical comments" or "sexual requests" are made in the voice. The sexual harassment detection unit also monitors the tone of conversation and voice intonation in real time, and immediately issues a warning if signs of sexual harassment are detected. For example, it displays a warning message if the voice tone becomes aggressive. This enables more accurate sexual harassment detection by detecting signs of sexual harassment from voice data as well.

[0031] The sexual harassment detection unit can refer to past conversation history and detect recurring patterns between specific users, thereby increasing the possibility of sexual harassment. For example, the generation AI analyzes past conversation history to detect recurring patterns of sexual harassment between specific users. For example, it issues a warning if the same user repeatedly makes inappropriate comments. The sexual harassment detection unit also increases the possibility of sexual harassment if specific phrases or keywords are repeatedly used based on past conversation history. For example, it issues a warning if "sexual requests" are made multiple times. The sexual harassment detection unit also refers to conversation history, and the generation AI automatically issues a warning if sexual harassment behavior is repeated between specific users. For example, it issues a warning if the same user makes inappropriate comments multiple times. This makes it easier to detect recurring sexual harassment behavior by referring to past conversation history.

[0032] The sexual harassment detection unit can also analyze image or video data to detect visual signs of sexual harassment. For example, the sexual harassment detection unit uses generative AI to analyze image data and detect visual signs of sexual harassment. For example, it issues a warning if inappropriate physical contact or sexual gestures are included. The sexual harassment detection unit also analyzes video data and issues a warning if specific actions or facial expressions are detected as signs of sexual harassment. For example, it issues a warning if a video contains sexual gestures or inappropriate physical contact. The sexual harassment detection unit also monitors image or video data in real time and immediately issues a warning if visual signs of sexual harassment are detected. For example, it analyzes camera footage and displays a warning message if inappropriate behavior is detected. In this way, by analyzing visual data as well, signs of sexual harassment can be detected from a more multifaceted perspective.

[0033] The sexual harassment detection unit can be implemented with a multilingual sexual harassment detection algorithm to accommodate different languages ​​and cultural spheres. For example, the sexual harassment detection unit may implement a multilingual sexual harassment detection algorithm using a generation AI to detect signs of sexual harassment in different languages. For example, it may support multiple languages ​​such as English, French, and Chinese. In addition, the sexual harassment detection unit may learn culturally specific phrases and behavioral patterns to detect signs of sexual harassment in different cultural spheres. For example, it may detect inappropriate comments or behavior in a particular cultural sphere. Furthermore, the sexual harassment detection unit may use a multilingual sexual harassment detection algorithm to detect signs of sexual harassment in different languages ​​and cultural spheres in real time. For example, it may analyze conversations and messages in different languages ​​to detect signs of sexual harassment. In this way, by implementing a multilingual algorithm, signs of sexual harassment can be detected in different languages ​​and cultural spheres.

[0034] When issuing a warning message, the warning unit can refer to the other party's past behavioral history and provide more specific warning content. For example, the warning unit uses a generation AI to analyze the other party's past behavioral history and provide specific warning content. For example, if there is a history of inappropriate comments being made in the past, a warning message including the specific content is displayed. The warning unit also customizes the content of the warning message based on the other party's behavioral history. For example, if a person has committed similar sexual harassment in the past, a specific warning to prevent recurrence is issued. The warning unit also refers to the behavioral history and provides the other party with a warning message including specific areas for improvement. For example, it specifically indicates what actions are inappropriate based on past behavior. In this way, by referring to the past behavioral history, more specific and effective warnings can be issued.

[0035] The warning unit can introduce an interactive process that asks for user confirmation before issuing a warning message. For example, the warning unit introduces an interactive process that asks for user confirmation before the generation AI issues a warning message. For example, it displays a pop-up that asks for user confirmation before displaying the warning message. The warning unit also provides an interface that allows the user to select whether or not to issue a warning message. For example, it allows the user to check the content of the warning message and select whether or not to issue it. The warning unit also allows the user to customize the content of the warning message through an interactive process. For example, it allows the user to adjust the wording and tone of the warning message. By asking for user confirmation, this prevents false warnings and enables responses that are in line with the user's intentions.

[0036] When issuing a warning message, the warning unit can issue a warning by voice through a voice assistant. For example, the warning unit builds a system in which, when the generation AI issues a warning message, the warning unit issues a warning by voice through a voice assistant. For example, the voice assistant issues a warning such as, "This statement is inappropriate." The warning unit also uses the voice assistant to issue a warning message by voice, in combination with a visual warning. For example, the warning unit displays a warning message on a screen and also issues a warning by voice. When issuing a warning message through the voice assistant, the warning unit also issues a warning by voice in a tone that matches the user's emotional state. For example, the warning is issued in a gentle tone. This makes it possible to use a voice assistant to provide an effective warning in combination with a visual warning.

[0037] The warning unit can use a visual alert when issuing a warning message. For example, the warning unit can use a visual alert that changes the color of the screen when issuing a warning message. For example, the warning unit can change the background color of the screen to red at the same time as the warning message is displayed. The warning unit can also use the visual alert to enhance the effectiveness of the warning message. For example, the warning unit can add a flash effect to the screen at the same time as the warning message is displayed. The warning unit can also customize the visual alert when issuing a warning message. For example, the user can select how the warning message is displayed and the type of alert. In this way, the effectiveness of the warning message can be enhanced by using a visual alert in combination.

[0038] The recording unit can encrypt and store evidence data to prevent data tampering. For example, the recording unit constructs a system in which the generation AI encrypts and stores evidence data of sexual harassment. For example, the evidence data is securely stored using AES encryption technology. In addition, the generation AI adds a digital signature to the recording unit to prevent tampering with the evidence data. For example, a digital signature is generated for the evidence data using a hash function to detect tampering. In addition, the recording unit uses cloud storage to securely store the encrypted evidence data. For example, the evidence data is encrypted and stored in the cloud, and access control is performed. In this way, by encrypting and storing the evidence data, data tampering can be prevented and the reliability of the evidence can be increased.

[0039] The recording unit can add a timestamp and location information when saving evidence data to increase the reliability of the evidence. For example, the recording unit can add a timestamp when saving evidence data to increase the reliability of the evidence. For example, the recording unit can record the date and time when the evidence data was created so that it can be checked later. The recording unit can also add location information to the evidence data to identify the location where the sexual harassment occurred. For example, GPS data can be used to add location information to the evidence data. The recording unit can also build a system that improves the reliability of evidence data by adding a timestamp and location information. For example, the recording unit can automatically add a timestamp and location information to evidence data. In this way, adding a timestamp and location information can improve the reliability of the evidence data.

[0040] The recording unit can automatically back up evidence data to cloud storage to prevent data loss. The recording unit, for example, builds a system that automatically backs up evidence data to cloud storage. For example, each time evidence data is generated, it is automatically saved in the cloud. The recording unit also periodically backs up evidence data using cloud storage. For example, it backs up evidence data to the cloud at a fixed time every day. The recording unit also performs access control on evidence data backed up to cloud storage. For example, it sets access permissions for evidence data to prevent unauthorized access. In this way, data loss can be prevented by automatically backing up evidence data to cloud storage.

[0041] The recording unit can provide a dashboard for visually displaying evidence data, allowing users to intuitively understand it. The recording unit, for example, builds a system that provides a dashboard for visually displaying evidence data. For example, the recording unit displays the evidence data in graphs or charts, allowing users to intuitively understand it. The recording unit also displays a timeline of the evidence data on the dashboard, allowing users to visually confirm when sexual harassment occurred. For example, the timeline displays the date and time when the evidence data was generated. The recording unit also displays the evidence data on a map, allowing users to visually confirm the location where sexual harassment occurred. For example, the location information included in the evidence data is plotted on the map. In this way, visually displaying the evidence data makes it easier for users to intuitively understand it.

[0042] The sexual harassment prevention education department can analyze a user's learning history and provide customized educational content according to each individual's level of understanding. For example, the sexual harassment prevention education department builds a system in which a generative AI analyzes a user's learning history and provides customized educational content according to each individual's level of understanding. For example, it selects appropriate learning materials based on the user's past learning data. The sexual harassment prevention education department also provides quizzes and tests according to the user's level of understanding based on the user's learning history. For example, it presents questions that are specialized in areas in which the user is weak. The sexual harassment prevention education department also improves the user's learning effectiveness by providing customized educational content. For example, it adjusts the difficulty of the learning materials according to the user's level of understanding. In this way, by analyzing the user's learning history, it is possible to provide educational content according to each individual's level of understanding.

[0043] Sexual harassment prevention education departments can introduce interactive simulations into their educational content, allowing users to experience real-life situations. For example, they can introduce interactive simulations into their educational content and build a system that allows users to experience real-life situations. For example, they can simulate sexual harassment scenarios and allow users to learn appropriate responses. Furthermore, sexual harassment prevention education departments can use interactive simulations to improve users' skills in detecting signs of sexual harassment. For example, they can provide tasks within the simulation in which users can find signs of sexual harassment. Furthermore, sexual harassment prevention education departments can use simulations to teach users appropriate ways to respond to sexual harassment. For example, they can practice issuing warning messages within the simulation. In this way, the introduction of interactive simulations allows users to experience real-life situations and improve their sexual harassment prevention skills.

[0044] The sexual harassment prevention education department can make educational content available on different devices. For example, the sexual harassment prevention education department can build a system that makes educational content available on different devices. For example, it can make educational content viewable on smartphones and tablets. The sexual harassment prevention education department can also provide educational content that is compatible with different devices. For example, it can provide an interface optimized for smartphones. The sexual harassment prevention education department can also make educational content compatible with multiple devices, allowing users to study anytime, anywhere. For example, it can make the same content available on desktop PCs, smartphones, and tablets. By making educational content available on different devices, users can study anytime, anywhere.

[0045] The Sexual Harassment Prevention Education Department can make educational content multilingual and accommodate international users. For example, the Sexual Harassment Prevention Education Department builds a system to make educational content multilingual. For example, it provides educational content in multiple languages, such as English, French, and Chinese. The Sexual Harassment Prevention Education Department can also accommodate international users by providing multilingual educational content. For example, it can provide sexual harassment prevention education in different languages. The Sexual Harassment Prevention Education Department can also use a generative AI to make educational content multilingual. For example, it can automatically translate educational content and provide it in different languages. This makes the educational content multilingual and accommodate international users.

[0046] The risk assessment unit allows the generation AI to analyze past sexual harassment case data and improve the accuracy of risk assessment. For example, the risk assessment unit builds a system in which the generation AI analyzes past sexual harassment case data and improves the accuracy of risk assessment. For example, it trains a risk assessment model based on past case data. The risk assessment unit also improves the risk assessment algorithm based on past sexual harassment case data. For example, it detects specific patterns and trends and reflects them in risk assessment. The risk assessment unit also builds a feedback loop in which the generation AI analyzes past case data and improves the accuracy of risk assessment. For example, it continuously improves the model based on the results of risk assessment. In this way, the accuracy of risk assessment can be improved by analyzing past sexual harassment case data.

[0047] The risk assessment unit can automatically generate specific preventive measures based on the results of risk assessment and suggest them to the user. For example, the risk assessment unit builds a system that automatically generates specific preventive measures based on the results of risk assessment. For example, it suggests avoiding certain actions when the risk is high. The risk assessment unit also analyzes the results of risk assessment using a generation AI and suggests specific preventive measures to the user. For example, it provides guidelines for behavior in high-risk environments. The risk assessment unit also develops a system that automatically generates preventive measures based on the results of risk assessment and notifies the user. For example, it sends a message recommending certain actions when the risk is high. In this way, sexual harassment can be prevented from occurring by automatically generating specific preventive measures based on the results of risk assessment and suggesting them to the user.

[0048] The risk assessment unit can provide a dashboard for visually displaying the results of the risk assessment, allowing the user to intuitively understand. The risk assessment unit, for example, builds a system that provides a dashboard for visually displaying the results of the risk assessment. For example, the risk assessment unit displays risk scores in graphs or charts, allowing the user to intuitively understand. The risk assessment unit also displays a timeline of the risk assessment on the dashboard, allowing the user to visually check fluctuations in risk. For example, the risk assessment results are displayed in chronological order. The risk assessment unit also displays the results of the risk assessment on a map, allowing the user to visually check high-risk locations. For example, the risk assessment results are plotted on a map. In this way, visually displaying the results of the risk assessment makes it easier for the user to intuitively understand.

[0049] The risk assessment unit can integrate the results of the risk assessment with other datasets to perform a more comprehensive risk assessment. The risk assessment unit, for example, builds a system that integrates the results of the risk assessment with other datasets. For example, it integrates workplace environmental data and employee feedback data to perform a comprehensive risk assessment. The risk assessment unit also improves the accuracy of the risk assessment based on other datasets. For example, it analyzes workplace environmental data and reflects it in the risk assessment. The risk assessment unit also integrates the results of the risk assessment with other datasets to identify risk factors. For example, it identifies high-risk factors based on workplace environmental data and proposes preventive measures. In this way, by integrating with other datasets, a more comprehensive risk assessment is possible.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The sexual harassment detection unit can analyze a user's behavioral patterns and issue a warning if a specific behavior is detected as a sign of sexual harassment. For example, a warning can be issued if a user frequently makes inappropriate comments during a specific time period. The sexual harassment detection unit can also identify behaviors that pose a high risk of sexual harassment based on the user's behavioral patterns and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior can be displayed. The sexual harassment detection unit can also identify users who are at high risk of sexual harassment based on the results of the behavioral pattern analysis and notify the administrator. In this way, by analyzing user behavioral patterns, it is possible to detect the risk of sexual harassment early and take appropriate measures.

[0052] The sexual harassment detection unit can analyze a user's social media activity and detect signs of sexual harassment. For example, it can issue a warning if a user makes an inappropriate comment on social media. The sexual harassment detection unit can also identify users who are at high risk of sexual harassment based on the content of their social media posts and notify administrators. For example, it can issue a warning if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations where there is a high risk of sexual harassment by analyzing social media activity and suggest preventive measures. This makes it possible to detect the risk of sexual harassment early and respond appropriately by analyzing social media activity.

[0053] The sexual harassment detection unit can identify behaviors that pose a high risk of sexual harassment based on a user's behavioral history and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior is displayed. The sexual harassment detection unit can also identify users who pose a high risk of sexual harassment based on the behavioral history and notify an administrator. For example, a warning is issued if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations that pose a high risk of sexual harassment by analyzing the behavioral history and suggest preventive measures. In this way, analyzing the behavioral history makes it possible to detect the risk of sexual harassment early and take appropriate measures.

[0054] The sexual harassment detection unit can identify behaviors that pose a high risk of sexual harassment based on a user's behavioral patterns and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior is displayed. The sexual harassment detection unit can also identify users who pose a high risk of sexual harassment based on behavioral patterns and notify an administrator. For example, a warning is issued if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations that pose a high risk of sexual harassment based on the results of analyzing behavioral patterns and suggest preventive measures. In this way, analyzing behavioral patterns makes it possible to detect the risk of sexual harassment early and take appropriate measures.

[0055] The sexual harassment detection unit can identify behaviors that pose a high risk of sexual harassment based on a user's behavioral history and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior is displayed. The sexual harassment detection unit can also identify users who pose a high risk of sexual harassment based on the behavioral history and notify an administrator. For example, a warning is issued if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations that pose a high risk of sexual harassment by analyzing the behavioral history and suggest preventive measures. In this way, analyzing the behavioral history makes it possible to detect the risk of sexual harassment early and take appropriate measures.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The sexual harassment detection unit uses generation AI to detect signs of sexual harassment. For example, the generation AI analyzes text data from conversations and messages, and determines the possibility of sexual harassment based on specific keywords, phrases, and context. The generation AI can also use natural language processing technology to analyze the content of conversations in real time and detect signs of sexual harassment. For example, it can determine that "inappropriate physical comments" and "sexual requests" constitute sexual harassment. Step 2: The warning unit issues a warning based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, it displays a warning message such as, "This comment is inappropriate. It may constitute sexual harassment." If necessary, it can also interrupt the conversation and urge the other person to stop the sexually harassing behavior. Step 3: The recording unit records evidence based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, the recording unit can save the content of conversations or messages in which signs of sexual harassment were detected so that they can be reviewed later. The recording unit can also encrypt and store the evidence data to prevent data tampering. Step 4: The Reporting Department reports the evidence recorded by the Recording Department to the appropriate authorities, such as a supervisor, the human resources department, or legal authorities, so that sexual harassment can be detected quickly and dealt with immediately.

[0058] (Example 2) The sexual harassment detection tool according to an embodiment of the present invention is a system in which a generative AI analyzes the content of conversations and messages, detects signs of sexual harassment, issues a warning, interrupts the conversation as necessary, records evidence, and reports the incident to the appropriate authorities. This enables the sexual harassment detection tool to quickly detect sexual harassment and respond immediately.

[0059] A sexual harassment detection tool according to an embodiment includes a sexual harassment detection unit, a warning unit, a recording unit, and a reporting unit. The sexual harassment detection unit detects signs of sexual harassment using a generation AI. For example, the generation AI analyzes text data of conversations and messages and determines the possibility of sexual harassment based on specific keywords, phrases, and context. The generation AI can also use natural language processing technology to analyze the content of conversations in real time and detect signs of sexual harassment. For example, the generation AI may determine that "inappropriate physical comments" or "sexual requests" constitute sexual harassment. The warning unit issues a warning based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, the warning unit displays a warning message such as "This comment is inappropriate and may constitute sexual harassment." The warning unit can also interrupt the conversation and urge the other party to stop engaging in sexually harassing behavior as necessary. The recording unit records evidence based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, the recording unit saves the content of conversations or messages in which signs of sexual harassment are detected so that they can be reviewed later. The recording unit can also encrypt and store evidence data to prevent data tampering. The reporting unit reports the evidence recorded by the recording unit to the appropriate authorities. For example, the reporting unit may report the occurrence of sexual harassment to a superior, the human resources department, or a legal agency. This allows the sexual harassment detection tool to quickly detect sexual harassment and respond immediately. For example, when sexual harassment occurs, it can protect the victim and help resolve the problem. In addition, by recording evidence of sexual harassment and reporting it to the appropriate authorities, it can help resolve sexual harassment issues.

[0060] The sexual harassment detection unit analyzes the tone of conversation and voice intonation, and can detect signs of sexual harassment from voice data as well. For example, the generation AI analyzes the tone of conversation and voice intonation and detects signs of sexual harassment based on specific voice patterns. For example, a sudden change in voice tone or an unnatural rise in voice intonation can be detected as a possible sexual harassment. The sexual harassment detection unit also analyzes voice data and detects signs of sexual harassment if specific keywords or phrases are included. For example, it issues a warning if "inappropriate physical comments" or "sexual requests" are made in the voice. The sexual harassment detection unit also monitors the tone of conversation and voice intonation in real time, and immediately issues a warning if signs of sexual harassment are detected. For example, it displays a warning message if the voice tone becomes aggressive. This enables more accurate sexual harassment detection by detecting signs of sexual harassment from voice data as well.

[0061] The sexual harassment detection unit can refer to past conversation history and detect recurring patterns between specific users, thereby increasing the possibility of sexual harassment. For example, the generation AI analyzes past conversation history to detect recurring patterns of sexual harassment between specific users. For example, it issues a warning if the same user repeatedly makes inappropriate comments. The sexual harassment detection unit also increases the possibility of sexual harassment if specific phrases or keywords are repeatedly used based on past conversation history. For example, it issues a warning if "sexual requests" are made multiple times. The sexual harassment detection unit also refers to conversation history, and the generation AI automatically issues a warning if sexual harassment behavior is repeated between specific users. For example, it issues a warning if the same user makes inappropriate comments multiple times. This makes it easier to detect recurring sexual harassment behavior by referring to past conversation history.

[0062] The sexual harassment detection unit uses the emotion estimation function to analyze changes in emotions during a conversation and can detect a sudden increase in negative emotions as a sign of sexual harassment. The sexual harassment detection unit, for example, uses the emotion estimation function to analyze changes in emotions during a conversation in real time and detect a sudden increase in negative emotions as a sign of sexual harassment. For example, it issues a warning if anger or discomfort suddenly increases. The sexual harassment detection unit also analyzes emotional data during a conversation and determines the possibility of sexual harassment if a specific emotional pattern is detected. For example, it issues a warning if anxiety or fear suddenly increases during a conversation. The sexual harassment detection unit also uses the emotion estimation function to monitor changes in emotions during a conversation and immediately issues a warning if negative emotions suddenly increase. For example, it displays a warning message if discomfort suddenly increases during a conversation. In this way, by analyzing changes in emotions, signs of sexual harassment can be detected more quickly.

[0063] The sexual harassment detection unit can also analyze image or video data to detect visual signs of sexual harassment. For example, the sexual harassment detection unit uses generative AI to analyze image data and detect visual signs of sexual harassment. For example, it issues a warning if inappropriate physical contact or sexual gestures are included. The sexual harassment detection unit also analyzes video data and issues a warning if specific actions or facial expressions are detected as signs of sexual harassment. For example, it issues a warning if a video contains sexual gestures or inappropriate physical contact. The sexual harassment detection unit also monitors image or video data in real time and immediately issues a warning if visual signs of sexual harassment are detected. For example, it analyzes camera footage and displays a warning message if inappropriate behavior is detected. In this way, by analyzing visual data as well, signs of sexual harassment can be detected from a more multifaceted perspective.

[0064] The sexual harassment detection unit can be implemented with a multilingual sexual harassment detection algorithm to accommodate different languages ​​and cultural spheres. For example, the sexual harassment detection unit may implement a multilingual sexual harassment detection algorithm using a generation AI to detect signs of sexual harassment in different languages. For example, it may support multiple languages ​​such as English, French, and Chinese. In addition, the sexual harassment detection unit may learn culturally specific phrases and behavioral patterns to detect signs of sexual harassment in different cultural spheres. For example, it may detect inappropriate comments or behavior in a particular cultural sphere. Furthermore, the sexual harassment detection unit may use a multilingual sexual harassment detection algorithm to detect signs of sexual harassment in different languages ​​and cultural spheres in real time. For example, it may analyze conversations and messages in different languages ​​to detect signs of sexual harassment. In this way, by implementing a multilingual algorithm, signs of sexual harassment can be detected in different languages ​​and cultural spheres.

[0065] The sexual harassment detection unit uses the emotion estimation function to monitor the emotions of a user when sexually harassed in real time and respond immediately. The sexual harassment detection unit, for example, uses the emotion estimation function to build a system that monitors the emotions of a user when sexually harassed in real time and responds immediately. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The sexual harassment detection unit also analyzes the user's emotional reactions in real time and immediately issues a warning if signs of sexual harassment are detected. For example, it displays a warning message if the user's emotions suddenly change to a negative state. The sexual harassment detection unit also monitors the emotions of a user when sexually harassed in real time based on the emotion estimation data and interrupts the conversation as necessary. For example, it interrupts the conversation if the user's emotions change to discomfort or fear. This makes it possible to immediately respond to sexual harassment by monitoring the user's emotions in real time.

[0066] When issuing a warning message, the warning unit can refer to the other party's past behavioral history and provide more specific warning content. For example, the warning unit uses a generation AI to analyze the other party's past behavioral history and provide specific warning content. For example, if there is a history of inappropriate comments being made in the past, a warning message including the specific content is displayed. The warning unit also customizes the content of the warning message based on the other party's behavioral history. For example, if a person has committed similar sexual harassment in the past, a specific warning to prevent recurrence is issued. The warning unit also refers to the behavioral history and provides the other party with a warning message including specific areas for improvement. For example, it specifically indicates what actions are inappropriate based on past behavior. In this way, by referring to the past behavioral history, more specific and effective warnings can be issued.

[0067] The warning unit can introduce an interactive process that asks for user confirmation before issuing a warning message. For example, the warning unit introduces an interactive process that asks for user confirmation before the generation AI issues a warning message. For example, it displays a pop-up that asks for user confirmation before displaying the warning message. The warning unit also provides an interface that allows the user to select whether or not to issue a warning message. For example, it allows the user to check the content of the warning message and select whether or not to issue it. The warning unit also allows the user to customize the content of the warning message through an interactive process. For example, it allows the user to adjust the wording and tone of the warning message. By asking for user confirmation, this prevents false warnings and enables responses that are in line with the user's intentions.

[0068] The warning unit can use the emotion estimation function to adjust the tone and content of the warning message to match the emotional state of the user. For example, the warning unit uses the emotion estimation function to adjust the tone and content of the warning message to match the emotional state of the user. For example, if the user is feeling anger or annoyance, the warning message is displayed in a gentler tone. The warning unit also customizes the content of the warning message based on the user's emotion data. For example, if the user is feeling anxious, the warning message is displayed to give a sense of security. The warning unit also analyzes the emotion estimation data in real time and dynamically adjusts the tone and content of the warning message according to the user's emotional state. For example, the warning message is adjusted each time the user's emotion changes. This allows for more effective warnings by adjusting the warning message to match the user's emotional state.

[0069] When issuing a warning message, the warning unit can issue a warning by voice through a voice assistant. For example, the warning unit builds a system in which, when the generation AI issues a warning message, the warning unit issues a warning by voice through a voice assistant. For example, the voice assistant issues a warning such as, "This statement is inappropriate." The warning unit also uses the voice assistant to issue a warning message by voice, in combination with a visual warning. For example, the warning unit displays a warning message on a screen and also issues a warning by voice. When issuing a warning message through the voice assistant, the warning unit also issues a warning by voice in a tone that matches the user's emotional state. For example, the warning is issued in a gentle tone. This makes it possible to use a voice assistant to provide an effective warning in combination with a visual warning.

[0070] The warning unit can use a visual alert when issuing a warning message. For example, the warning unit can use a visual alert that changes the color of the screen when issuing a warning message. For example, the warning unit can change the background color of the screen to red at the same time as the warning message is displayed. The warning unit can also use the visual alert to enhance the effectiveness of the warning message. For example, the warning unit can add a flash effect to the screen at the same time as the warning message is displayed. The warning unit can also customize the visual alert when issuing a warning message. For example, the user can select how the warning message is displayed and the type of alert. In this way, the effectiveness of the warning message can be enhanced by using a visual alert in combination.

[0071] The warning unit can use the emotion estimation function to monitor the effectiveness of the warning message in real time and adjust the message content as needed. The warning unit, for example, uses the emotion estimation function to build a system that monitors the effectiveness of the warning message in real time and adjusts the message content as needed. For example, if the warning message is ineffective, the tone or content of the message is changed. The warning unit also analyzes the user's emotional response in real time and evaluates the effectiveness of the warning message. For example, it monitors changes in the user's emotions after the warning message is displayed and adjusts the message as needed. The warning unit also develops a system that dynamically adjusts the effectiveness of the warning message based on the emotion estimation data. For example, if the user's emotions do not improve, it regenerates the content of the warning message. This enables more effective warnings by monitoring the effectiveness of the warning message in real time and adjusting it as needed.

[0072] The recording unit can encrypt and store evidence data to prevent data tampering. For example, the recording unit constructs a system in which the generation AI encrypts and stores evidence data of sexual harassment. For example, the evidence data is securely stored using AES encryption technology. In addition, the generation AI adds a digital signature to the recording unit to prevent tampering with the evidence data. For example, a digital signature is generated for the evidence data using a hash function to detect tampering. In addition, the recording unit uses cloud storage to securely store the encrypted evidence data. For example, the evidence data is encrypted and stored in the cloud, and access control is performed. In this way, by encrypting and storing the evidence data, data tampering can be prevented and the reliability of the evidence can be increased.

[0073] The recording unit can add a timestamp and location information when saving evidence data to increase the reliability of the evidence. For example, the recording unit can add a timestamp when saving evidence data to increase the reliability of the evidence. For example, the recording unit can record the date and time when the evidence data was created so that it can be checked later. The recording unit can also add location information to the evidence data to identify the location where the sexual harassment occurred. For example, GPS data can be used to add location information to the evidence data. The recording unit can also build a system that improves the reliability of evidence data by adding a timestamp and location information. For example, the recording unit can automatically add a timestamp and location information to evidence data. In this way, adding a timestamp and location information can improve the reliability of the evidence data.

[0074] The recording unit can use the emotion estimation function to analyze emotional information contained in the evidence data and reflect it in the report content. The recording unit, for example, uses the emotion estimation function to analyze emotional information contained in the evidence data and build a system that reflects it in the report content. For example, it calculates an emotion score from text or audio contained in the evidence data. The recording unit also analyzes the emotional information contained in the evidence data and describes changes in emotion in the report. For example, it reflects the emotional state of a victim when sexual harassment occurs in the report. The recording unit also evaluates the reliability of the evidence data based on the emotion estimation data. For example, if the emotional information matches, it determines that the reliability of the evidence data is high. In this way, by analyzing the emotional information, changes in emotion can be reflected in the report content, enabling more detailed reports.

[0075] The recording unit can automatically back up evidence data to cloud storage to prevent data loss. The recording unit, for example, builds a system that automatically backs up evidence data to cloud storage. For example, each time evidence data is generated, it is automatically saved in the cloud. The recording unit also periodically backs up evidence data using cloud storage. For example, it backs up evidence data to the cloud at a fixed time every day. The recording unit also performs access control on evidence data backed up to cloud storage. For example, it sets access permissions for evidence data to prevent unauthorized access. In this way, data loss can be prevented by automatically backing up evidence data to cloud storage.

[0076] The recording unit can provide a dashboard for visually displaying evidence data, allowing users to intuitively understand it. The recording unit, for example, builds a system that provides a dashboard for visually displaying evidence data. For example, the recording unit displays the evidence data in graphs or charts, allowing users to intuitively understand it. The recording unit also displays a timeline of the evidence data on the dashboard, allowing users to visually confirm when sexual harassment occurred. For example, the timeline displays the date and time when the evidence data was generated. The recording unit also displays the evidence data on a map, allowing users to visually confirm the location where sexual harassment occurred. For example, the location information included in the evidence data is plotted on the map. In this way, visually displaying the evidence data makes it easier for users to intuitively understand it.

[0077] The recording unit uses the emotion estimation function to collect the user's emotional reactions to the evidence data, which can be used to improve the content of the report. The recording unit, for example, uses the emotion estimation function to build a system that collects the user's emotional reactions to the evidence data. For example, it analyzes the emotions of the user when viewing the evidence data in real time. The recording unit also identifies areas in the report that need improvement based on the user's emotional reaction data. For example, it identifies parts where the user felt anxiety or discomfort, and modifies the report content. The recording unit also improves the display method of the evidence data based on the emotion estimation data. For example, it changes the display format of the evidence data depending on the user's emotional reaction. In this way, collecting the user's emotional reactions can be used to improve the content of the report.

[0078] The sexual harassment prevention education department can analyze a user's learning history and provide customized educational content according to each individual's level of understanding. For example, the sexual harassment prevention education department builds a system in which a generative AI analyzes a user's learning history and provides customized educational content according to each individual's level of understanding. For example, it selects appropriate learning materials based on the user's past learning data. The sexual harassment prevention education department also provides quizzes and tests according to the user's level of understanding based on the user's learning history. For example, it presents questions that are specialized in areas in which the user is weak. The sexual harassment prevention education department also improves the user's learning effectiveness by providing customized educational content. For example, it adjusts the difficulty of the learning materials according to the user's level of understanding. In this way, by analyzing the user's learning history, it is possible to provide educational content according to each individual's level of understanding.

[0079] Sexual harassment prevention education departments can introduce interactive simulations into their educational content, allowing users to experience real-life situations. For example, they can introduce interactive simulations into their educational content and build a system that allows users to experience real-life situations. For example, they can simulate sexual harassment scenarios and allow users to learn appropriate responses. Furthermore, sexual harassment prevention education departments can use interactive simulations to improve users' skills in detecting signs of sexual harassment. For example, they can provide tasks within the simulation in which users can find signs of sexual harassment. Furthermore, sexual harassment prevention education departments can use simulations to teach users appropriate ways to respond to sexual harassment. For example, they can practice issuing warning messages within the simulation. In this way, the introduction of interactive simulations allows users to experience real-life situations and improve their sexual harassment prevention skills.

[0080] The sexual harassment prevention education department can use the emotion estimation function to evaluate the effectiveness of educational content in real time and adjust the content as needed. The sexual harassment prevention education department, for example, uses the emotion estimation function to build a system that evaluates the effectiveness of educational content in real time. For example, it analyzes the emotions of users when they are learning educational content and evaluates the effectiveness. The sexual harassment prevention education department also evaluates the effectiveness of the educational content and adjusts the content as needed. For example, it identifies parts that are difficult for users to understand and improves the content of the teaching materials. The sexual harassment prevention education department also develops a system that dynamically adjusts the effectiveness of educational content based on emotion estimation data. For example, it changes the content of the teaching materials if the user's emotion changes to negative. This makes it possible to evaluate the effectiveness of educational content in real time and adjust the content as needed, thereby improving learning effectiveness.

[0081] The sexual harassment prevention education department can make educational content available on different devices. For example, the sexual harassment prevention education department can build a system that makes educational content available on different devices. For example, it can make educational content viewable on smartphones and tablets. The sexual harassment prevention education department can also provide educational content that is compatible with different devices. For example, it can provide an interface optimized for smartphones. The sexual harassment prevention education department can also make educational content compatible with multiple devices, allowing users to study anytime, anywhere. For example, it can make the same content available on desktop PCs, smartphones, and tablets. By making educational content available on different devices, users can study anytime, anywhere.

[0082] The Sexual Harassment Prevention Education Department can make educational content multilingual and accommodate international users. For example, the Sexual Harassment Prevention Education Department builds a system to make educational content multilingual. For example, it provides educational content in multiple languages, such as English, French, and Chinese. The Sexual Harassment Prevention Education Department can also accommodate international users by providing multilingual educational content. For example, it can provide sexual harassment prevention education in different languages. The Sexual Harassment Prevention Education Department can also use a generative AI to make educational content multilingual. For example, it can automatically translate educational content and provide it in different languages. This makes the educational content multilingual and accommodate international users.

[0083] The sexual harassment prevention education department can use the emotion estimation function to monitor the emotional state of a user when learning educational content, thereby improving learning effectiveness. For example, the sexual harassment prevention education department uses the emotion estimation function to build a system that monitors the emotional state of a user when learning educational content. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The sexual harassment prevention education department also improves the learning effectiveness of educational content based on the user's emotional state. For example, if the user is feeling anxious, it provides content that gives a sense of security. The sexual harassment prevention education department also develops a system that dynamically adjusts the content of educational content based on emotion estimation data. For example, if the user's emotion changes to negative, it adjusts the difficulty of the content. In this way, learning effectiveness can be improved by monitoring the user's emotional state.

[0084] The risk assessment unit allows the generation AI to analyze past sexual harassment case data and improve the accuracy of risk assessment. For example, the risk assessment unit builds a system in which the generation AI analyzes past sexual harassment case data and improves the accuracy of risk assessment. For example, it trains a risk assessment model based on past case data. The risk assessment unit also improves the risk assessment algorithm based on past sexual harassment case data. For example, it detects specific patterns and trends and reflects them in risk assessment. The risk assessment unit also builds a feedback loop in which the generation AI analyzes past case data and improves the accuracy of risk assessment. For example, it continuously improves the model based on the results of risk assessment. In this way, the accuracy of risk assessment can be improved by analyzing past sexual harassment case data.

[0085] The risk assessment unit can automatically generate specific preventive measures based on the results of risk assessment and suggest them to the user. For example, the risk assessment unit builds a system that automatically generates specific preventive measures based on the results of risk assessment. For example, it suggests avoiding certain actions when the risk is high. The risk assessment unit also analyzes the results of risk assessment using a generation AI and suggests specific preventive measures to the user. For example, it provides guidelines for behavior in high-risk environments. The risk assessment unit also develops a system that automatically generates preventive measures based on the results of risk assessment and notifies the user. For example, it sends a message recommending certain actions when the risk is high. In this way, sexual harassment can be prevented from occurring by automatically generating specific preventive measures based on the results of risk assessment and suggesting them to the user.

[0086] The risk assessment unit uses the emotion estimation function to consider the user's emotional state when assessing risk and can suggest more appropriate preventive measures. The risk assessment unit, for example, uses the emotion estimation function to build a system that considers the user's emotional state when assessing risk. For example, if the user is feeling anxious, it suggests preventive measures that give a sense of security. The risk assessment unit also adjusts the results of the risk assessment based on the user's emotional state and suggests appropriate preventive measures. For example, if the user is feeling stressed, it recommends actions to reduce risk. The risk assessment unit also develops a system that dynamically adjusts the results of the risk assessment based on the emotion estimation data and suggests appropriate preventive measures to the user. For example, it adjusts the preventive measures every time the user's emotions change. In this way, it is possible to suggest more appropriate preventive measures by considering the user's emotional state.

[0087] The risk assessment unit can provide a dashboard for visually displaying the results of the risk assessment, allowing the user to intuitively understand. The risk assessment unit, for example, builds a system that provides a dashboard for visually displaying the results of the risk assessment. For example, the risk assessment unit displays risk scores in graphs or charts, allowing the user to intuitively understand. The risk assessment unit also displays a timeline of the risk assessment on the dashboard, allowing the user to visually check fluctuations in risk. For example, the risk assessment results are displayed in chronological order. The risk assessment unit also displays the results of the risk assessment on a map, allowing the user to visually check high-risk locations. For example, the risk assessment results are plotted on a map. In this way, visually displaying the results of the risk assessment makes it easier for the user to intuitively understand.

[0088] The risk assessment unit can integrate the results of the risk assessment with other datasets to perform a more comprehensive risk assessment. The risk assessment unit, for example, builds a system that integrates the results of the risk assessment with other datasets. For example, it integrates workplace environmental data and employee feedback data to perform a comprehensive risk assessment. The risk assessment unit also improves the accuracy of the risk assessment based on other datasets. For example, it analyzes workplace environmental data and reflects it in the risk assessment. The risk assessment unit also integrates the results of the risk assessment with other datasets to identify risk factors. For example, it identifies high-risk factors based on workplace environmental data and proposes preventive measures. In this way, by integrating with other datasets, a more comprehensive risk assessment is possible.

[0089] The risk assessment unit uses the emotion estimation function to collect users' emotional reactions to the risk assessment results, which can be used to improve preventive measures. The risk assessment unit, for example, uses the emotion estimation function to build a system that collects users' emotional reactions to the risk assessment results. For example, it analyzes the emotions of users when they view the risk assessment results in real time. The risk assessment unit also identifies areas where preventive measures need to be improved based on the user's emotional reaction data. For example, it identifies areas where the user feels anxious or uncomfortable and modifies the preventive measures. The risk assessment unit also develops a system that dynamically adjusts the risk assessment results based on the emotion estimation data and improves the preventive measures. For example, it adjusts the preventive measures every time the user's emotions change. In this way, collecting users' emotional reactions can be used to improve the preventive measures.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The sexual harassment detection unit can analyze a user's behavioral patterns and issue a warning if a specific behavior is detected as a sign of sexual harassment. For example, a warning can be issued if a user frequently makes inappropriate comments during a specific time period. The sexual harassment detection unit can also identify behaviors that pose a high risk of sexual harassment based on the user's behavioral patterns and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior can be displayed. The sexual harassment detection unit can also identify users who are at high risk of sexual harassment based on the results of the behavioral pattern analysis and notify the administrator. In this way, by analyzing user behavioral patterns, it is possible to detect the risk of sexual harassment early and take appropriate measures.

[0092] The sexual harassment detection unit can analyze the user's biometric information to detect signs of stress or anxiety. For example, it can monitor the heart rate and electrodermal activity and issue a warning if the stress level rises sharply. The sexual harassment detection unit can also monitor the user's health based on the biometric information and suggest appropriate responses if the risk of sexual harassment increases. For example, it can provide advice on how to relax if the stress level is high. The sexual harassment detection unit can also analyze the biometric information to monitor the user's emotional state in real time and issue a warning as necessary. This makes it possible to detect the risk of sexual harassment early and take appropriate measures by analyzing the biometric information.

[0093] The sexual harassment detection unit can analyze a user's social media activity and detect signs of sexual harassment. For example, it can issue a warning if a user makes an inappropriate comment on social media. The sexual harassment detection unit can also identify users who are at high risk of sexual harassment based on the content of their social media posts and notify administrators. For example, it can issue a warning if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations where there is a high risk of sexual harassment by analyzing social media activity and suggest preventive measures. This makes it possible to detect the risk of sexual harassment early and respond appropriately by analyzing social media activity.

[0094] The sexual harassment detection unit can use the emotion estimation function to assess the risk of sexual harassment based on the user's emotional state. For example, if the user is feeling anxious or fearful, it determines that there is a high risk of sexual harassment and issues a warning. The sexual harassment detection unit can also monitor the user's emotional state in real time based on the emotion estimation data and suggest appropriate responses if the risk of sexual harassment increases. For example, if the user is feeling uncomfortable, it can provide advice on how to relax. The sexual harassment detection unit can also use the emotion estimation function to assess the risk of sexual harassment based on the user's emotional state and issue a warning as necessary. As a result, the emotion estimation function enables early detection of the risk of sexual harassment and appropriate responses.

[0095] The sexual harassment detection unit can identify behaviors that pose a high risk of sexual harassment based on a user's behavioral history and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior is displayed. The sexual harassment detection unit can also identify users who pose a high risk of sexual harassment based on the behavioral history and notify an administrator. For example, a warning is issued if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations that pose a high risk of sexual harassment by analyzing the behavioral history and suggest preventive measures. In this way, analyzing the behavioral history makes it possible to detect the risk of sexual harassment early and take appropriate measures.

[0096] The sexual harassment detection unit can identify situations with a high risk of sexual harassment based on the user's emotional state and suggest preventive measures. For example, if the user feels anxious or fearful, it determines that there is a high risk of sexual harassment and issues a warning. The sexual harassment detection unit can also monitor the user's emotional state in real time based on emotion estimation data and suggest appropriate responses if the risk of sexual harassment increases. For example, if the user feels uncomfortable, it can provide advice on how to relax. The sexual harassment detection unit can also use the emotion estimation function to evaluate the risk of sexual harassment based on the user's emotional state and issue a warning as necessary. As a result, the emotion estimation function can be used to detect the risk of sexual harassment early and enable appropriate responses.

[0097] The sexual harassment detection unit can identify behaviors that pose a high risk of sexual harassment based on a user's behavioral patterns and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior is displayed. The sexual harassment detection unit can also identify users who pose a high risk of sexual harassment based on behavioral patterns and notify an administrator. For example, a warning is issued if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations that pose a high risk of sexual harassment based on the results of analyzing behavioral patterns and suggest preventive measures. In this way, analyzing behavioral patterns makes it possible to detect the risk of sexual harassment early and take appropriate measures.

[0098] The sexual harassment detection unit can use the emotion estimation function to assess the risk of sexual harassment based on the user's emotional state. For example, if the user is feeling anxious or fearful, it determines that there is a high risk of sexual harassment and issues a warning. The sexual harassment detection unit can also monitor the user's emotional state in real time based on the emotion estimation data and suggest appropriate responses if the risk of sexual harassment increases. For example, if the user is feeling uncomfortable, it can provide advice on how to relax. The sexual harassment detection unit can also use the emotion estimation function to assess the risk of sexual harassment based on the user's emotional state and issue a warning as necessary. As a result, the emotion estimation function enables early detection of the risk of sexual harassment and appropriate responses.

[0099] The sexual harassment detection unit can identify behaviors that pose a high risk of sexual harassment based on a user's behavioral history and suggest preventive measures. For example, if a specific behavior is repeated, a message urging the user to improve their behavior is displayed. The sexual harassment detection unit can also identify users who pose a high risk of sexual harassment based on the behavioral history and notify an administrator. For example, a warning is issued if a specific user repeatedly makes inappropriate comments. The sexual harassment detection unit can also identify situations that pose a high risk of sexual harassment by analyzing the behavioral history and suggest preventive measures. In this way, analyzing the behavioral history makes it possible to detect the risk of sexual harassment early and take appropriate measures.

[0100] The sexual harassment detection unit can use the emotion estimation function to assess the risk of sexual harassment based on the user's emotional state. For example, if the user is feeling anxious or fearful, it determines that there is a high risk of sexual harassment and issues a warning. The sexual harassment detection unit can also monitor the user's emotional state in real time based on the emotion estimation data and suggest appropriate responses if the risk of sexual harassment increases. For example, if the user is feeling uncomfortable, it can provide advice on how to relax. The sexual harassment detection unit can also use the emotion estimation function to assess the risk of sexual harassment based on the user's emotional state and issue a warning as necessary. As a result, the emotion estimation function enables early detection of the risk of sexual harassment and appropriate responses.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The sexual harassment detection unit uses generation AI to detect signs of sexual harassment. For example, the generation AI analyzes text data from conversations and messages, and determines the possibility of sexual harassment based on specific keywords, phrases, and context. The generation AI can also use natural language processing technology to analyze the content of conversations in real time and detect signs of sexual harassment. For example, it can determine that "inappropriate physical comments" and "sexual requests" constitute sexual harassment. Step 2: The warning unit issues a warning based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, it displays a warning message such as, "This comment is inappropriate. It may constitute sexual harassment." If necessary, it can also interrupt the conversation and urge the other person to stop the sexually harassing behavior. Step 3: The recording unit records evidence based on the signs of sexual harassment detected by the sexual harassment detection unit. For example, the recording unit can save the content of conversations or messages in which signs of sexual harassment were detected so that they can be reviewed later. The recording unit can also encrypt and store the evidence data to prevent data tampering. Step 4: The Reporting Department reports the evidence recorded by the Recording Department to the appropriate authorities, such as a supervisor, the human resources department, or legal authorities, so that sexual harassment can be detected quickly and dealt with immediately.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A sexual harassment detection unit that uses generative AI to detect signs of sexual harassment; a warning unit that issues a warning based on the signs of sexual harassment detected by the sexual harassment detection unit; a recording unit that records evidence based on the signs of sexual harassment detected by the sexual harassment detection unit; a reporting unit that reports the evidence recorded by the recording unit to an appropriate organization. A system characterized by:

2. The sexual harassment detection unit Image or video data may also be analyzed to detect visual signs of sexual harassment.

2. The system of claim 1.

3. The warning unit When issuing a warning message, the system will refer to the other party's behavioral history to provide more specific warning content.

2. The system of claim 1.

4. The recording unit Encrypt and store evidence data to prevent data tampering 2. The system of claim 1.

5. The Education Department for Sexual Harassment Prevention Analyze users' learning history and provide customized educational content according to their individual level of understanding 2. The system of claim 1.

6. The sexual harassment detection unit Analyze changes in emotions during conversations, and detect a sudden increase in negative emotions as a sign of sexual harassment.

2. The system of claim 1.

7. The warning unit Adapt the tone and content of your warning messages to suit the user's emotional state 2. The system of claim 1.

8. The recording unit Analyzing emotional information contained in evidence data and reflecting it in reports 2. The system of claim 1.

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