System

Abstract

An object of a system according to an embodiment is to detect a sign of a pregnancy discrimination from communication in a workplace and take an appropriate action.SOLUTION: A system includes a monitoring unit, a detection unit, and a warning unit. The monitoring unit monitors communication in the workplace using the generated AI. The detection unit detects a sign of pregnancy discrimination from the intra-workplace communication monitored by the monitoring unit. The warning unit issues a warning message based on the sign of pregnancy discrimination detected by the detection unit.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 the drawback of making it difficult to detect early signs of maternity harassment from workplace communications and deal with them appropriately.

[0005] The system according to the embodiment aims to detect signs of maternity harassment from communications within the workplace and deal with them appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a detection unit, and a warning unit. The monitoring unit monitors communications within the workplace using a generation AI. The detection unit detects signs of maternity harassment from the communications within the workplace monitored by the monitoring unit. The warning unit issues a warning message based on the signs of maternity harassment detected by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect signs of maternity harassment from communications within the workplace and deal with them appropriately. [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 maternity harassment detection system according to an embodiment of the present invention is a system in which a generation AI monitors communication in the workplace, detects signs of maternity harassment, and issues a warning. As a result, the maternity harassment detection system can quickly detect maternity harassment in the workplace and immediately stop it.

[0029] A maternity harassment detection system according to an embodiment includes a monitoring unit, a detection unit, and a warning unit. The monitoring unit monitors workplace communications using a generation AI. For example, the monitoring unit monitors workplace emails in real time. The monitoring unit can also monitor workplace chats in real time. The monitoring unit can also monitor conference audio in real time. For example, the monitoring unit analyzes the content of emails to detect specific keywords. The monitoring unit analyzes the content of chats to detect specific phrases. The monitoring unit analyzes the conference audio to detect specific utterances. The detection unit detects signs of maternity harassment from workplace communications monitored by the monitoring unit. For example, the detection unit detects signs of maternity harassment from email content using a generation AI. The detection unit can also detect signs of maternity harassment from chat content using a generation AI. The detection unit can also detect signs of maternity harassment from conference audio using a generation AI. For example, the detection unit analyzes the content of emails to detect utterances such as, "Maybe you can't work because you're pregnant?" The detection unit analyzes the content of chats and detects statements such as, "She should be removed from the project because she's pregnant." The detection unit analyzes audio from meetings and detects statements such as, "It's inevitable that she'll be late with work because she's pregnant." The warning unit issues a warning message based on signs of maternity harassment detected by the detection unit. For example, the warning unit sends a warning message to the person involved. The warning unit can also send a warning message to a manager. The warning unit can also send a warning message to both the person involved and the manager. For example, the warning unit sends a warning message such as, "This statement may be maternity harassment." The warning unit sends a warning message such as, "This statement has been detected as a sign of maternity harassment." The warning unit sends a warning message such as, "This statement is likely to be maternity harassment." In this way, the maternity harassment detection system according to the embodiment can quickly detect maternity harassment in the workplace and issue a warning, thereby preventing the occurrence of maternity harassment.

[0030] The monitoring unit can monitor at least one of the communication data of emails, chats, and conference audio in the workplace in real time. In the monitoring unit, for example, the generation AI analyzes conference audio data in real time and analyzes the speaker's tone of voice. For example, if the tone of voice is authoritative, it detects this as a sign of maternity harassment. In addition, the monitoring unit has the generation AI analyze video data from video conferences and analyze the facial expressions of participants in real time. For example, if the facial expressions are stern, it detects this as a sign of maternity harassment. In addition, the generation AI combines the tone of voice and facial expression analysis to comprehensively evaluate the content of remarks and the emotional state. For example, if the tone of voice is authoritative and the facial expressions are stern, it determines that there is a high possibility of maternity harassment. In this way, by monitoring communication data in the workplace in real time, signs of maternity harassment can be quickly detected.

[0031] The warning unit can send warning messages to the parties and administrators based on signs of maternity harassment detected by the detection unit. For example, the generation AI in the warning unit analyzes past email and chat history to learn keywords and phrases related to maternity harassment. For example, it detects phrases such as "pregnant" and "unable to work." The generation AI in the warning unit also analyzes audio data from past meetings to learn specific speech patterns. For example, if a specific person repeatedly makes overbearing statements, it will detect that pattern. The generation AI in the warning unit also analyzes past communication data in chronological order to detect trends of increasing signs of maternity harassment. For example, if there is an increase in statements related to maternity harassment over a specific period, it will report that trend. This makes it possible to prevent maternity harassment from occurring by quickly issuing warnings to the parties and administrators when signs of maternity harassment are detected.

[0032] When the system detects signs of maternity harassment, it can provide the parties involved with educational content to prevent it. For example, the system uses a generation AI to analyze text data from chats and emails and use an emotion estimation function to detect negative emotions. For example, it issues a warning if emotions such as "anger" or "sadness" are strong. The system also uses a generation AI to analyze conference audio data and estimate the speaker's emotional state in real time. For example, it issues a warning if the audio tone is low and the emotion score is negative. The system also uses a generation AI to analyze video conference video data and estimate emotions from participants' facial expressions. For example, it issues a warning if the expressions are stern and the emotion score is negative. This makes it possible to promote the prevention of maternity harassment by providing educational content to the parties involved when signs of maternity harassment are detected.

[0033] When the system detects signs of maternity harassment, it can suggest that the person concerned receive counseling. For example, the system's generating AI monitors the temperature data of a conference room in real time, and issues a warning if the temperature is too high, indicating that this is a stressor. For example, it issues a warning if the temperature exceeds 30 degrees. The system also monitors the lighting data of a conference room, and issues a warning if the lighting is too dim, indicating that this is a stressor. For example, it issues a warning if the illuminance is below 300 lux. The system also monitors the noise level in a conference room, and issues a warning if the noise is too high, indicating that this is a stressor. For example, it issues a warning if the noise level exceeds 70 decibels. This makes it possible to provide psychological support by suggesting that the person concerned receive counseling when signs of maternity harassment are detected.

[0034] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0035] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0036] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0037] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0038] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0039] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0040] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0041] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0042] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0043] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0044] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0045] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0046] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0047] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0048] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0049] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0050] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0051] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0052] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0053] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0054] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0055] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0056] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0057] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0058] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0059] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0060] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0061] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0062] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0063] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0064] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0065] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0066] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0067] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0068] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] When the system detects signs of maternity harassment, it can provide the person involved with resources for stress management. For example, the system uses a generating AI to analyze workplace environmental data and identify stress factors. For example, if the noise level is high, it will suggest the use of earplugs to manage stress. The system also uses a generating AI to analyze workplace lighting data and suggest an appropriate lighting environment. For example, if the lighting is too dim, it will suggest adjusting the brightness. The system also uses a generating AI to analyze workplace temperature data and suggest appropriate temperature management. For example, if the temperature is too high, it will suggest using an air conditioner. In this way, when signs of maternity harassment are detected, the system can promote the improvement of the work environment by providing the person involved with resources for stress management.

[0071] When the system detects signs of maternity harassment, it can suggest relaxation techniques to the person involved. For example, the system's generating AI can analyze music data in the workplace and suggest music suitable for relaxation. For example, it can suggest classical music or natural sounds. The system can also analyze scent data in the workplace and suggest aromas suitable for relaxation. For example, it can suggest lavender or chamomile scents. The system can also analyze lighting data in the workplace and suggest a lighting environment suitable for relaxation. For example, it can suggest warm-colored lighting. In this way, when signs of maternity harassment are detected, the system can suggest relaxation techniques to the person involved, helping to reduce stress.

[0072] When the system detects signs of maternity harassment, it can provide the person concerned with health management advice. For example, the system's generating AI can analyze workplace meal data and suggest healthy meals. For example, it can suggest balanced meals and nutritious foods. The system can also analyze workplace exercise data and suggest appropriate exercise. For example, it can suggest light stretching or walking. The system can also analyze workplace sleep data and suggest an appropriate sleeping environment. For example, it can suggest comfortable bedding and appropriate sleep hours. In this way, when signs of maternity harassment are detected, the system can provide the person concerned with health management advice, thereby improving their overall health.

[0073] When the system detects signs of maternity harassment, it can provide mental health resources to the person involved. For example, the system uses a generative AI to analyze workplace mental health data and suggest appropriate counseling services. For example, it may suggest online counseling or mental health apps. The system also uses a generative AI to analyze workplace stress level data and provide resources for stress management. For example, it may suggest workshops or seminars for stress management. The system also uses a generative AI to analyze workplace communication data and suggest communication techniques that are useful for improving mental health. For example, it may suggest assertive communication or empathy techniques. In this way, when signs of maternity harassment are detected, mental health resources can be provided to the person involved, thereby strengthening psychological support.

[0074] When the system detects signs of maternity harassment, it can suggest changes to the workplace layout to the person involved. For example, the system uses a generation AI to analyze workplace layout data and suggest layout changes to ensure privacy. For example, it could suggest installing partitions or rearranging desks. The system also uses a generation AI to analyze workplace traffic flow data and suggest layout changes to ensure efficient traffic flow. For example, it could suggest widening corridors or shortening traffic lines. The system also uses a generation AI to analyze workplace lighting data and suggest layout changes to ensure an appropriate lighting environment. For example, it could suggest rearranging windows to let in natural light. In this way, when signs of maternity harassment are detected, the system can provide a comfortable working environment by suggesting changes to the workplace layout to the person involved.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The monitoring unit uses generative AI to monitor workplace communications. For example, the monitoring unit monitors emails, chats, and audio from meetings in the workplace in real time to detect specific keywords, phrases, and statements. Step 2: The detection unit detects signs of maternity harassment from workplace communications monitored by the monitoring unit. For example, the detection unit uses generative AI to analyze the content of emails, chats, and audio from meetings, and detects statements such as, "Maybe she can't do her job because she's pregnant?" or "She should be removed from the project because she's pregnant." Step 3: The warning unit issues a warning message based on the signs of maternity harassment detected by the detection unit. For example, the warning unit sends a warning message to the person involved or an administrator, such as "This comment may be maternity harassment" or "This comment has been detected as a sign of maternity harassment."

[0077] (Example 2) The maternity harassment detection system according to an embodiment of the present invention is a system in which a generation AI monitors communication in the workplace, detects signs of maternity harassment, and issues a warning. As a result, the maternity harassment detection system can quickly detect maternity harassment in the workplace and immediately stop it.

[0078] A maternity harassment detection system according to an embodiment includes a monitoring unit, a detection unit, and a warning unit. The monitoring unit monitors workplace communications using a generation AI. For example, the monitoring unit monitors workplace emails in real time. The monitoring unit can also monitor workplace chats in real time. The monitoring unit can also monitor conference audio in real time. For example, the monitoring unit analyzes the content of emails to detect specific keywords. The monitoring unit analyzes the content of chats to detect specific phrases. The monitoring unit analyzes the conference audio to detect specific utterances. The detection unit detects signs of maternity harassment from workplace communications monitored by the monitoring unit. For example, the detection unit detects signs of maternity harassment from email content using a generation AI. The detection unit can also detect signs of maternity harassment from chat content using a generation AI. The detection unit can also detect signs of maternity harassment from conference audio using a generation AI. For example, the detection unit analyzes the content of emails to detect utterances such as, "Maybe you can't work because you're pregnant?" The detection unit analyzes the content of chats and detects statements such as, "She should be removed from the project because she's pregnant." The detection unit analyzes audio from meetings and detects statements such as, "It's inevitable that she'll be late with work because she's pregnant." The warning unit issues a warning message based on signs of maternity harassment detected by the detection unit. For example, the warning unit sends a warning message to the person involved. The warning unit can also send a warning message to a manager. The warning unit can also send a warning message to both the person involved and the manager. For example, the warning unit sends a warning message such as, "This statement may be maternity harassment." The warning unit sends a warning message such as, "This statement has been detected as a sign of maternity harassment." The warning unit sends a warning message such as, "This statement is likely to be maternity harassment." In this way, the maternity harassment detection system according to the embodiment can quickly detect maternity harassment in the workplace and issue a warning, thereby preventing the occurrence of maternity harassment.

[0079] The monitoring unit can monitor at least one of the communication data of emails, chats, and conference audio in the workplace in real time. In the monitoring unit, for example, the generation AI analyzes conference audio data in real time and analyzes the speaker's tone of voice. For example, if the tone of voice is authoritative, it detects this as a sign of maternity harassment. In addition, the monitoring unit has the generation AI analyze video data from video conferences and analyze the facial expressions of participants in real time. For example, if the facial expressions are stern, it detects this as a sign of maternity harassment. In addition, the generation AI combines the tone of voice and facial expression analysis to comprehensively evaluate the content of remarks and the emotional state. For example, if the tone of voice is authoritative and the facial expressions are stern, it determines that there is a high possibility of maternity harassment. In this way, by monitoring communication data in the workplace in real time, signs of maternity harassment can be quickly detected.

[0080] The warning unit can send warning messages to the parties and administrators based on signs of maternity harassment detected by the detection unit. For example, the generation AI in the warning unit analyzes past email and chat history to learn keywords and phrases related to maternity harassment. For example, it detects phrases such as "pregnant" and "unable to work." The generation AI in the warning unit also analyzes audio data from past meetings to learn specific speech patterns. For example, if a specific person repeatedly makes overbearing statements, it will detect that pattern. The generation AI in the warning unit also analyzes past communication data in chronological order to detect trends of increasing signs of maternity harassment. For example, if there is an increase in statements related to maternity harassment over a specific period, it will report that trend. This makes it possible to prevent maternity harassment from occurring by quickly issuing warnings to the parties and administrators when signs of maternity harassment are detected.

[0081] When the system detects signs of maternity harassment, it can provide the parties involved with educational content to prevent it. For example, the system uses a generation AI to analyze text data from chats and emails and use an emotion estimation function to detect negative emotions. For example, it issues a warning if emotions such as "anger" or "sadness" are strong. The system also uses a generation AI to analyze conference audio data and estimate the speaker's emotional state in real time. For example, it issues a warning if the audio tone is low and the emotion score is negative. The system also uses a generation AI to analyze video conference video data and estimate emotions from participants' facial expressions. For example, it issues a warning if the expressions are stern and the emotion score is negative. This makes it possible to promote the prevention of maternity harassment by providing educational content to the parties involved when signs of maternity harassment are detected.

[0082] When the system detects signs of maternity harassment, it can suggest that the person concerned receive counseling. For example, the system's generating AI monitors the temperature data of a conference room in real time, and issues a warning if the temperature is too high, indicating that this is a stressor. For example, it issues a warning if the temperature exceeds 30 degrees. The system also monitors the lighting data of a conference room, and issues a warning if the lighting is too dim, indicating that this is a stressor. For example, it issues a warning if the illuminance is below 300 lux. The system also monitors the noise level in a conference room, and issues a warning if the noise is too high, indicating that this is a stressor. For example, it issues a warning if the noise level exceeds 70 decibels. This makes it possible to provide psychological support by suggesting that the person concerned receive counseling when signs of maternity harassment are detected.

[0083] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0084] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0085] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0086] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0087] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0088] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0089] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0090] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0091] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0092] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0093] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0094] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0095] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0096] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0097] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0098] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0099] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0100] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0101] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0102] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0103] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0104] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0105] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0106] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0107] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0108] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0109] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0110] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0111] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0112] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0113] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0114] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0115] When the system detects signs of maternity harassment, it can send the person involved a message suggesting that they seek counseling. For example, the system's generation AI analyzes text data from chats and emails and uses emotion estimation to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the speaker's emotional state in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it's good to speak with a smile." This makes it possible to provide psychological support by suggesting that the person involved seek counseling when signs of maternity harassment are detected.

[0116] When the system detects signs of maternity harassment, it can automatically send guidelines for preventing maternity harassment to the parties involved. For example, the system's generation AI learns communication data from different industries to detect industry-specific signs of maternity harassment. For example, it could learn by comparing data from the IT industry and the manufacturing industry. The system could also learn communication data from different cultural spheres to detect culture-specific signs of maternity harassment. For example, it could learn by comparing data from Asia and Europe. The system could also learn multilingual communication data to detect signs of maternity harassment in different languages. For example, it could learn data in English, Japanese, and Chinese. This makes it possible to promote the prevention of maternity harassment by providing guidelines to the parties involved when signs of maternity harassment are detected.

[0117] The system can record signs of maternity harassment detected by the detection unit and periodically create reports. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it suggests, "It's good to speak with a smile." By recording signs of maternity harassment and periodically creating reports, the actual state of maternity harassment can be more easily understood.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] When the system detects signs of maternity harassment, it can provide the person involved with resources for stress management. For example, the system uses a generating AI to analyze workplace environmental data and identify stress factors. For example, if the noise level is high, it will suggest the use of earplugs to manage stress. The system also uses a generating AI to analyze workplace lighting data and suggest an appropriate lighting environment. For example, if the lighting is too dim, it will suggest adjusting the brightness. The system also uses a generating AI to analyze workplace temperature data and suggest appropriate temperature management. For example, if the temperature is too high, it will suggest using an air conditioner. In this way, when signs of maternity harassment are detected, the system can promote the improvement of the work environment by providing the person involved with resources for stress management.

[0120] When the system detects signs of maternity harassment, it can suggest relaxation techniques to the person involved. For example, the system's generating AI can analyze music data in the workplace and suggest music suitable for relaxation. For example, it can suggest classical music or natural sounds. The system can also analyze scent data in the workplace and suggest aromas suitable for relaxation. For example, it can suggest lavender or chamomile scents. The system can also analyze lighting data in the workplace and suggest a lighting environment suitable for relaxation. For example, it can suggest warm-colored lighting. In this way, when signs of maternity harassment are detected, the system can suggest relaxation techniques to the person involved, helping to reduce stress.

[0121] When the system detects signs of maternity harassment, it can provide the person concerned with health management advice. For example, the system's generating AI can analyze workplace meal data and suggest healthy meals. For example, it can suggest balanced meals and nutritious foods. The system can also analyze workplace exercise data and suggest appropriate exercise. For example, it can suggest light stretching or walking. The system can also analyze workplace sleep data and suggest an appropriate sleeping environment. For example, it can suggest comfortable bedding and appropriate sleep hours. In this way, when signs of maternity harassment are detected, the system can provide the person concerned with health management advice, thereby improving their overall health.

[0122] When the system detects signs of maternity harassment, it can provide mental health resources to the person involved. For example, the system uses a generative AI to analyze workplace mental health data and suggest appropriate counseling services. For example, it may suggest online counseling or mental health apps. The system also uses a generative AI to analyze workplace stress level data and provide resources for stress management. For example, it may suggest workshops or seminars for stress management. The system also uses a generative AI to analyze workplace communication data and suggest communication techniques that are useful for improving mental health. For example, it may suggest assertive communication or empathy techniques. In this way, when signs of maternity harassment are detected, mental health resources can be provided to the person involved, thereby strengthening psychological support.

[0123] When the system detects signs of maternity harassment, it can suggest changes to the workplace layout to the person involved. For example, the system uses a generation AI to analyze workplace layout data and suggest layout changes to ensure privacy. For example, it could suggest installing partitions or rearranging desks. The system also uses a generation AI to analyze workplace traffic flow data and suggest layout changes to ensure efficient traffic flow. For example, it could suggest widening corridors or shortening traffic lines. The system also uses a generation AI to analyze workplace lighting data and suggest layout changes to ensure an appropriate lighting environment. For example, it could suggest rearranging windows to let in natural light. In this way, when signs of maternity harassment are detected, the system can provide a comfortable working environment by suggesting changes to the workplace layout to the person involved.

[0124] When the system detects signs of maternity harassment, it can provide the parties involved with emotional self-management techniques. For example, the system uses a generation AI to analyze text data from chats and emails and use an emotion estimation function to detect negative emotions. For example, it issues a warning if emotions such as "anger" or "sadness" are strong. The system also uses a generation AI to analyze conference audio data and estimate the speaker's emotional state in real time. For example, it issues a warning if the audio tone is low and the emotion score is negative. The system also uses a generation AI to analyze video conference video data and estimate emotions from participants' facial expressions. For example, it issues a warning if the facial expressions are stern and the emotion score is negative. This allows the system to promote emotional control by providing the parties involved with emotional self-management techniques when it detects signs of maternity harassment.

[0125] When the system detects signs of maternity harassment, it can provide positive feedback to the person involved. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions that elicit positive emotions. For example, it suggests positive phrases such as "thank you" and "good job." The system also analyzes audio data from meetings, estimates the emotional state of the speaker in real time, and makes suggestions that elicit positive emotions. For example, it suggests positive feedback such as "that's a great idea." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions that elicit positive emotions. For example, it makes a suggestion such as "it would be good to speak with a smile." This makes it possible to improve the workplace atmosphere by providing positive feedback to the person involved when signs of maternity harassment are detected.

[0126] When the system detects signs of maternity harassment, it can provide the parties involved with a platform to encourage them to share their feelings. For example, the system uses a generation AI to analyze text data from chats and emails and make suggestions to encourage them to share their feelings using an emotion estimation function. For example, it suggests questions such as, "How are you feeling today?" The system also uses a generation AI to analyze audio data from meetings, estimate the emotional state of the speaker in real time, and make suggestions to encourage them to share their feelings. For example, it suggests questions such as, "What do you think about this project?" The system also uses a generation AI to analyze video data from video conferences, estimate emotions from participants' facial expressions, and make suggestions to encourage them to share their feelings. For example, it suggests questions such as, "How do you feel about recent progress at work?" This makes it possible to improve workplace communication by providing the parties involved with a platform to encourage them to share their feelings when it detects signs of maternity harassment.

[0127] When the system detects signs of maternity harassment, it can provide the parties involved with tools to encourage self-evaluation of their emotions. For example, the system uses a generation AI to analyze text data from chats and emails and make suggestions to encourage self-evaluation of emotions using an emotion estimation function. For example, it suggests questions such as, "Please rate how you are feeling today on a scale of 1 to 10." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions to encourage self-evaluation of emotions. For example, it suggests questions such as, "Please rate how you are feeling after this meeting on a scale of 1 to 10." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions to encourage self-evaluation of emotions. For example, it suggests questions such as, "Please rate how you are feeling about this project on a scale of 1 to 10." This makes it possible to increase self-awareness by providing the parties involved with tools to encourage self-evaluation of emotions when signs of maternity harassment are detected.

[0128] When the system detects signs of maternity harassment, it can provide the parties involved with resources to encourage emotional reflection. For example, the system's generation AI analyzes text data from chats and emails and uses its emotion estimation function to make suggestions to encourage emotional reflection. For example, it may make a suggestion such as, "Let's reflect on what happened today." The system also analyzes audio data from meetings, estimates the emotional state of speakers in real time, and makes suggestions to encourage emotional reflection. For example, it may make a suggestion such as, "Let's reflect on what you felt in this meeting." The system also analyzes video data from video conferences, estimates emotions from participants' facial expressions, and makes suggestions to encourage emotional reflection. For example, it may make a suggestion such as, "Let's reflect on your experiences on this project." In this way, when signs of maternity harassment are detected, the system provides the parties involved with resources to encourage emotional reflection, thereby promoting self-growth.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The monitoring unit uses generative AI to monitor workplace communications. For example, the monitoring unit monitors emails, chats, and audio from meetings in the workplace in real time to detect specific keywords, phrases, and statements. Step 2: The detection unit detects signs of maternity harassment from workplace communications monitored by the monitoring unit. For example, the detection unit uses generative AI to analyze the content of emails, chats, and audio from meetings, and detects statements such as, "Maybe she can't do her job because she's pregnant?" or "She should be removed from the project because she's pregnant." Step 3: The warning unit issues a warning message based on the signs of maternity harassment detected by the detection unit. For example, the warning unit sends a warning message to the person involved or an administrator, such as "This comment may be maternity harassment" or "This comment has been detected as a sign of maternity harassment."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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]

[0198] 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 monitoring department that uses generative AI to monitor communication within the workplace; a detection unit that detects signs of maternity harassment from communications in the workplace monitored by the monitoring unit; a warning unit that issues a warning message based on the signs of maternity harassment detected by the detection unit. A system characterized by:

2. The monitoring unit At least one of the communication data of emails, chats, and conference voices within the workplace is monitored in real time.

2. The system of claim 1.

3. The warning unit The warning message is sent to the person concerned and an administrator based on the signs of maternity harassment detected by the detection unit.

2. The system of claim 1.

4. The system comprises: When signs of maternity harassment are detected, counseling will be offered to the person involved.

2. The system of claim 1.

5. The system comprises: When signs of maternity harassment are detected, guidelines for preventing maternity harassment will be automatically sent to the parties involved.

2. The system of claim 1.

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