system

Abstract

The system according to the embodiment aims to efficiently detect statements that may be maternity harassment. [Solution] A system according to an embodiment includes a storage unit, an analysis unit, and a determination unit. The storage unit stores past cases of maternity harassment in a database. The analysis unit analyzes newly inputted text based on the cases stored by the storage unit. The determination unit determines the possibility of maternity harassment based on the text analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently detect language that may be constituting maternity harassment.

[0005] The system according to the embodiment aims to efficiently detect statements that may be maternity harassment. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, an analysis unit, and a determination unit. The storage unit stores past cases of maternity harassment in a database. The analysis unit analyzes newly input text based on the cases stored by the storage unit. The determination unit determines the possibility of maternity harassment based on the text analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect language that may be maternity harassment. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A maternity harassment detection system according to an embodiment of the present invention accumulates past cases of maternity harassment in a database, analyzes newly entered text using AI, and detects potentially harassing text. This maternity harassment detection system accumulates past cases of maternity harassment in a database, analyzes newly entered text using AI, and compares it with the cases in the database. The AI ​​understands the meaning and context of the text and determines whether it may be maternity harassment. For example, when workplace comments or email content are entered, the AI ​​compares them with past cases and detects potentially harassing text. This system enables early detection of maternity harassment in the workplace and the implementation of appropriate countermeasures. First, cases are collected from past court records, reports, news articles, etc. and accumulated in a database. Next, natural language processing technology is used as the AI's analysis algorithm to understand the meaning and context of the text and determine whether it may be maternity harassment. The system's elements are defined as a "storage unit," "analysis unit," and "determination unit," and the relationship between these elements is clearly described. This allows the maternity harassment detection system to detect maternity harassment in the workplace early and take appropriate measures.

[0029] A maternity harassment detection system according to an embodiment includes a storage unit, an analysis unit, and a determination unit. The storage unit stores past cases of maternity harassment in a database. Examples of past cases of maternity harassment include, but are not limited to, court records, reports, and news articles. For example, the storage unit collects past court records and stores them in a database. The storage unit can also collect reports and news articles and store them in a database. The storage unit can also collect public information on the Internet and store it in a database. For example, the storage unit digitizes court records and stores them in a database. Reports and news articles are stored in a database as text data. Public information on the Internet is collected using web scraping technology and stored in a database. The analysis unit uses AI to analyze newly input text based on the cases stored by the storage unit. For example, the analysis unit understands the meaning and context of text using natural language processing technology. For example, the analysis unit divides text into parts and analyzes the meaning of each word using morphological analysis. The analysis unit can also analyze the structure of text using grammatical analysis. Furthermore, the analysis unit can also understand the meaning of text using semantic analysis. For example, the analysis unit divides text using morphological analysis and analyzes the meaning of each word. Grammatical analysis analyzes the structure of text and clarifies grammatical relationships. Semantic analysis understands the meaning of text and performs analysis based on the context. The determination unit determines the possibility of maternity harassment based on the text analyzed by the analysis unit. The determination unit uses, for example, AI to understand the meaning and context of text and determine the possibility of maternity harassment. For example, when the determination unit receives input of workplace remarks or email content, it compares the input with past cases and detects text that may be maternity harassment. The determination unit uses, for example, AI to understand the meaning and context of text and determine the possibility of maternity harassment. For example, when the determination unit receives input of workplace remarks or email content, it compares the input with past cases and detects text that may be maternity harassment. As a result, the maternity harassment detection system according to the embodiment can detect maternity harassment in the workplace early and take appropriate measures.

[0030] The storage unit can collect cases from at least one of past court records, reports, and news articles and store them in a database. The storage unit, for example, collects past court records and stores them in a database. For example, the storage unit collects published court decisions and court records and stores them in a database. The storage unit can also collect reports and store them in a database. For example, the storage unit collects internal corporate reports and investigative reports and stores them in a database. The storage unit can also collect news articles and store them in a database. For example, the storage unit collects online news and newspaper articles and stores them in a database. In this way, by collecting cases from past court records, reports, news articles, etc. and storing them in a database, a highly reliable database can be constructed. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can efficiently collect data using an AI model for collecting court records, reports, and news articles.

[0031] The analysis unit can use natural language processing technology to understand the meaning and context of the text and determine the possibility of maternity harassment. The analysis unit, for example, uses natural language processing technology to understand the meaning and context of the text. For example, the analysis unit uses morphological analysis to divide the text and analyze the meaning of each word. The analysis unit can also analyze the structure of the text using grammatical analysis. The analysis unit can also understand the meaning of the text using semantic analysis. For example, the analysis unit uses morphological analysis to divide the text and analyze the meaning of each word. Grammatical analysis analyzes the structure of the text and clarifies grammatical relationships. Semantic analysis understands the meaning of the text and performs analysis based on the context. In this way, by using natural language processing technology, the meaning and context of the text can be understood and the possibility of maternity harassment can be determined with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the meaning and context of the text using an AI model that uses natural language processing technology.

[0032] When workplace remarks or email content are input, the determination unit can compare them with past cases and detect language that may be maternity harassment. For example, when workplace remarks or email content are input, the determination unit compares them with past cases and detects language that may be maternity harassment. For example, the determination unit receives workplace remarks as voice input and converts them into text data using voice recognition technology. The determination unit then analyzes the text data and compares it with past cases. The determination unit can also input email content as text data and compare it with past cases. For example, the determination unit analyzes the email content and compares it with past cases. In this way, by inputting workplace remarks or email content, it can compare it with past cases and detect language that may be maternity harassment. Some or all of the above-mentioned processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can efficiently analyze language using an AI model for analyzing voice input and text data.

[0033] The storage unit can evaluate the reliability of past cases when collecting them and prioritize storing highly reliable cases. For example, the storage unit evaluates the reliability of past cases when collecting them and prioritizes storing highly reliable cases. For example, the storage unit evaluates the sources of collected cases and prioritizes storing cases from highly reliable sources. The storage unit can also analyze the content of the cases using AI to extract and store highly reliable information. Furthermore, the storage unit can cross-check information from multiple sources to evaluate the reliability of the cases. For example, the storage unit evaluates the sources of collected cases and prioritizes storing cases from highly reliable sources. The content of the cases can be analyzed using AI to extract and store highly reliable information. Information from multiple sources can be cross-checked and highly reliable cases can be stored preferentially. This improves the reliability of the database by evaluating the reliability of cases and preferentially storing highly reliable cases. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can efficiently select highly reliable cases using an AI model for evaluating the reliability of cases.

[0034] The accumulation unit can classify cases based on their categories when collecting them and store them in a database. For example, the accumulation unit classifies cases based on their categories when collecting them and stores them in a database. For example, the accumulation unit classifies the collected cases by industry and stores them in a database. The accumulation unit can also classify the collected cases by job type and store them in a database. The accumulation unit can also classify the collected cases by time of occurrence and store them in a database. For example, the accumulation unit classifies the collected cases by industry and stores them in a database. The accumulation unit can classify the collected cases by job type and store them in a database. The accumulation unit can classify the collected cases by time of occurrence and store them in a database. In this way, classification based on case category and storage in a database facilitates data organization. Some or all of the above-described processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can efficiently organize data using an AI model for automatically classifying case categories.

[0035] The accumulation unit can prioritize accumulation of highly relevant cases in consideration of the geographical distribution of the cases when collecting cases. For example, the accumulation unit prioritizes accumulation of highly relevant cases in consideration of the geographical distribution of the cases when collecting cases. For example, the accumulation unit analyzes the geographical distribution of the collected cases and prioritizes accumulation of cases from highly relevant regions. The accumulation unit can also prioritize collection of geographically close cases and accumulation in the database. Furthermore, the accumulation unit can focus collection of cases from specific regions in consideration of the geographical distribution. For example, the accumulation unit analyzes the geographical distribution of the collected cases and prioritizes accumulation of cases from highly relevant regions. Geographically close cases are prioritized and accumulated in the database. Cases from specific regions are prioritized in consideration of the geographical distribution. In this way, by prioritizing accumulation of highly relevant cases in consideration of the geographical distribution of the cases, a database that reflects the characteristics of each region can be constructed. Some or all of the above-mentioned processing in the accumulation unit may be performed, for example, using AI, or may be performed without using AI. For example, the repository can use AI models to analyze the geographic distribution of cases to efficiently select highly relevant cases.

[0036] The storage unit can classify cases based on the time of occurrence when collecting them and store them in the database. For example, when collecting cases, the storage unit classifies them based on the time of occurrence and stores them in the database. For example, the storage unit classifies collected cases by the time of occurrence and stores them in the database. The storage unit can also classify cases based on the time of occurrence and store the most recent cases preferentially. Furthermore, the storage unit can classify cases based on the time of occurrence and store past cases and the most recent cases in a balanced manner. For example, the storage unit classifies collected cases by the time of occurrence and stores them in the database. The storage unit classifies cases based on the time of occurrence and stores the most recent cases preferentially. The storage unit classifies cases based on the time of occurrence and stores past cases and the most recent cases in a balanced manner. In this way, by classifying cases based on the time of occurrence and storing them in the database, it is possible to organize data in chronological order. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can efficiently organize data using an AI model for automatically classifying the time of occurrence of cases.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the words during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the words during analysis. For example, the analysis unit performs a detailed analysis on words with high importance. The analysis unit can also perform a simplified analysis on words with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the words. For example, the analysis unit performs a detailed analysis on words with high importance. The analysis unit performs a simplified analysis on words with low importance. The analysis unit determines the priority of the analysis based on the importance of the words. In this way, the level of detail of the analysis can be adjusted based on the importance of the words, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can efficiently adjust the level of detail of the analysis using an AI model for evaluating the importance of words.

[0038] The analysis unit can apply different analysis algorithms depending on the category of the text during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of the text during analysis. For example, the analysis unit selects an appropriate analysis algorithm depending on the category of the text. The analysis unit can also apply different analysis algorithms to each category of the text to improve accuracy. Furthermore, the analysis unit can dynamically switch analysis algorithms based on the category of the text. For example, the analysis unit selects an appropriate analysis algorithm depending on the category of the text. The analysis unit applies different analysis algorithms to each category of the text to improve accuracy. The analysis unit dynamically switches analysis algorithms based on the category of the text. In this way, by applying different analysis algorithms depending on the category of the text, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently perform analysis by automatically classifying text categories and using an AI model to select an appropriate analysis algorithm.

[0039] The analysis unit can determine the analysis priority based on the submission time of the text during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the text during analysis. For example, the analysis unit prioritizes analysis of the most recent text. The analysis unit can also classify text based on the submission time and determine the analysis priority. The analysis unit can also analyze text based on the submission time, and analyze past text and the most recent text in a balanced manner. For example, the analysis unit prioritizes analysis of the most recent text. Classify text based on the submission time and determine the analysis priority. Analyze text based on the submission time, and analyze past text and the most recent text in a balanced manner. In this way, by determining the analysis priority based on the submission time of the text, the most recent text can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently perform analysis by automatically evaluating the submission time of text and using an AI model for determining the analysis priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the words during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the words during analysis. For example, the analysis unit prioritizes analysis of highly relevant words. The analysis unit can also determine the order of analysis based on the relevance of the words. Furthermore, the analysis unit can postpone analysis of less relevant words. For example, the analysis unit prioritizes analysis of highly relevant words. The analysis unit determines the order of analysis based on the relevance of the words. The analysis unit postpones analysis of less relevant words. In this way, by adjusting the order of analysis based on the relevance of the words, highly relevant words can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently perform analysis using an AI model for automatically evaluating the relevance of words and determining the order of analysis.

[0041] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between words during the determination. The determination unit, for example, improves the accuracy of the determination by taking into account the interrelationships between words during the determination. For example, the determination unit analyzes the interrelationships between words and prioritizes determining highly related words. The determination unit can also improve the accuracy of the determination by taking into account the interrelationships between words. Furthermore, the determination unit can determine the priority of the determination based on the interrelationships between words. For example, the determination unit analyzes the interrelationships between words and prioritizes determining highly related words. The accuracy of the determination is improved by taking into account the interrelationships between words. The priority of the determination is determined based on the interrelationships between words. In this way, the accuracy of the determination can be improved by taking into account the interrelationships between words. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently perform the determination by automatically evaluating the interrelationships between words and using an AI model for improving the accuracy of the determination.

[0042] The determination unit can make a determination taking into account attribute information of the person who submitted the text. For example, the determination unit makes a determination taking into account attribute information of the person who submitted the text. For example, the determination unit improves the accuracy of the determination based on the attribute information of the person who submitted the text. The determination unit can also determine a priority of the determination taking into account the attribute information of the person who submitted the text. Furthermore, the determination unit can apply appropriate determination criteria based on the attribute information of the person who submitted the text. For example, the determination unit improves the accuracy of the determination based on the attribute information of the person who submitted the text. The determination unit determines a priority of the determination taking into account the attribute information of the person who submitted the text. The determination unit applies appropriate determination criteria based on the attribute information of the person who submitted the text. In this way, the accuracy of the determination can be improved by taking into account the attribute information of the person who submitted the text. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently make a determination using an AI model that automatically evaluates the attribute information of the person who submitted the text and improves the accuracy of the determination.

[0043] The determination unit can make a determination taking into account the geographical distribution of the wording. The determination unit can make a determination taking into account the geographical distribution of the wording, for example. For example, the determination unit analyzes the geographical distribution of the wording and prioritizes determining words in highly relevant regions. The determination unit can also prioritize determining words that are geographically close. Furthermore, the determination unit can focus on determining words in a specific region by taking into account the geographical distribution. For example, the determination unit analyzes the geographical distribution of the wording and prioritizes determining words in highly relevant regions. The determination unit prioritizes determining words that are geographically close. The determination unit can focus on determining words in a specific region by taking into account the geographical distribution. In this way, by taking the geographical distribution of the wording into account, it is possible to make a determination that reflects the characteristics of each region. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently make a determination by automatically evaluating the geographical distribution of the wording and using an AI model to improve the accuracy of the determination.

[0044] The determination unit can improve the accuracy of the determination based on related literature of the wording during the determination. For example, the determination unit improves the accuracy of the determination by referring to related literature of the wording during the determination. For example, the determination unit improves the accuracy of the determination by referring to related literature of the wording. The determination unit can also determine the priority of the determination based on the related literature. Furthermore, the determination unit can also apply appropriate determination criteria by referring to related literature. For example, the determination unit improves the accuracy of the determination by referring to related literature of the wording. The priority of the determination is determined based on the related literature. The appropriate determination criteria are applied by referring to related literature. In this way, the accuracy of the determination can be improved by referring to related literature of the wording. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently perform the determination using an AI model that automatically evaluates related literature and improves the accuracy of the determination.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The determination unit can make a determination taking into account attribute information of the person who submitted the text. For example, the determination unit can improve the accuracy of the determination based on attribute information such as the submitter's job title, position, age, and gender. The determination unit can also determine a priority order for the determination taking into account the submitter's attribute information. Furthermore, the determination unit can apply appropriate determination criteria based on the submitter's attribute information. This allows the accuracy of the determination to be improved by taking into account the attribute information of the submitter of the text. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can efficiently make a determination using an AI model that automatically evaluates the submitter's attribute information and improves the accuracy of the determination.

[0047] The storage unit can evaluate the reliability of cases when collecting cases and prioritize storing highly reliable cases. For example, the storage unit can evaluate the sources of collected cases and prioritize storing cases from highly reliable sources. The storage unit can also analyze the content of cases using AI to extract and store highly reliable information. Furthermore, the storage unit can cross-check information from multiple sources to evaluate the reliability of cases. This allows the reliability of the database to be improved by evaluating the reliability of cases and preferentially storing highly reliable cases. Some or all of the above-mentioned processing in the storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit can efficiently select highly reliable cases using an AI model for evaluating the reliability of cases.

[0048] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis on text with high importance. The analysis unit can also perform a simplified analysis on text with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the text. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the text. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can efficiently adjust the level of detail of the analysis using an AI model for evaluating the importance of the text.

[0049] When collecting cases, the accumulation unit can classify them based on the category of the case and store them in a database. For example, the accumulation unit can classify the collected cases by industry and store them in a database. The accumulation unit can also classify the collected cases by job type and store them in a database. Furthermore, the accumulation unit can classify the collected cases by the time of occurrence and store them in a database. In this way, classification based on the category of the case and storage in a database makes it easy to organize data. Some or all of the above-mentioned processing in the accumulation unit may be performed using AI, or may be performed without using AI. For example, the accumulation unit can efficiently organize data using an AI model for automatically classifying case categories.

[0050] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between words during the determination. For example, the determination unit analyzes the interrelationships between words and prioritizes determining highly related words. The determination unit can also improve the accuracy of the determination by taking into account the interrelationships between words. Furthermore, the determination unit can determine the priority of the determination based on the interrelationships between words. In this way, the accuracy of the determination can be improved by taking the interrelationships between words into account. Some or all of the above-described processing in the determination unit may be performed using AI or may be performed without using AI. For example, the determination unit can efficiently perform the determination by automatically evaluating the interrelationships between words and using an AI model to improve the accuracy of the determination.

[0051] The determination unit can improve the accuracy of the determination by referring to related literature of the wording during the determination. For example, the determination unit can improve the accuracy of the determination by referring to related literature of the wording. The determination unit can also determine the priority of the determination based on the related literature. Furthermore, the determination unit can also apply appropriate determination criteria by referring to related literature. In this way, the accuracy of the determination can be improved by referring to related literature of the wording. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can efficiently perform the determination by automatically evaluating related literature and using an AI model to improve the accuracy of the determination.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The storage unit stores past cases of maternity harassment in a database. Past cases of maternity harassment include court records, reports, news articles, etc. The storage unit collects this information and stores it in a database. For example, it digitizes court records, stores reports and news articles as text data, and uses web scraping technology to collect publicly available information on the internet. Step 2: The analysis unit uses AI to analyze the newly input text based on the examples accumulated by the storage unit. The analysis unit uses natural language processing technology to understand the meaning and context of the text. For example, it uses morphological analysis to divide the text and analyze the meaning of each word, grammatical analysis to analyze the structure of the text, and semantic analysis to understand the meaning of the text. Step 3: The judgment unit determines the possibility of maternity harassment based on the wording analyzed by the analysis unit. The judgment unit uses AI to understand the meaning and context of the words, and compares them with past cases to detect words that may be maternity harassment. This allows for early detection of maternity harassment in the workplace and the implementation of appropriate measures.

[0054] (Example 2) A maternity harassment detection system according to an embodiment of the present invention accumulates past cases of maternity harassment in a database, analyzes newly entered text using AI, and detects potentially harassing text. This maternity harassment detection system accumulates past cases of maternity harassment in a database, analyzes newly entered text using AI, and compares it with the cases in the database. The AI ​​understands the meaning and context of the text and determines whether it may be maternity harassment. For example, when workplace comments or email content are entered, the AI ​​compares them with past cases and detects potentially harassing text. This system enables early detection of maternity harassment in the workplace and the implementation of appropriate countermeasures. First, cases are collected from past court records, reports, news articles, etc. and accumulated in a database. Next, natural language processing technology is used as the AI's analysis algorithm to understand the meaning and context of the text and determine whether it may be maternity harassment. The system's elements are defined as a "storage unit," "analysis unit," and "determination unit," and the relationship between these elements is clearly described. This allows the maternity harassment detection system to detect maternity harassment in the workplace early and take appropriate measures.

[0055] A maternity harassment detection system according to an embodiment includes a storage unit, an analysis unit, and a determination unit. The storage unit stores past cases of maternity harassment in a database. Examples of past cases of maternity harassment include, but are not limited to, court records, reports, and news articles. For example, the storage unit collects past court records and stores them in a database. The storage unit can also collect reports and news articles and store them in a database. The storage unit can also collect public information on the Internet and store it in a database. For example, the storage unit digitizes court records and stores them in a database. Reports and news articles are stored in a database as text data. Public information on the Internet is collected using web scraping technology and stored in a database. The analysis unit uses AI to analyze newly input text based on the cases stored by the storage unit. For example, the analysis unit understands the meaning and context of text using natural language processing technology. For example, the analysis unit divides text into parts and analyzes the meaning of each word using morphological analysis. The analysis unit can also analyze the structure of text using grammatical analysis. Furthermore, the analysis unit can also understand the meaning of text using semantic analysis. For example, the analysis unit divides text using morphological analysis and analyzes the meaning of each word. Grammatical analysis analyzes the structure of text and clarifies grammatical relationships. Semantic analysis understands the meaning of text and performs analysis based on the context. The determination unit determines the possibility of maternity harassment based on the text analyzed by the analysis unit. The determination unit uses, for example, AI to understand the meaning and context of text and determine the possibility of maternity harassment. For example, when the determination unit receives input of workplace remarks or email content, it compares the input with past cases and detects text that may be maternity harassment. The determination unit uses, for example, AI to understand the meaning and context of text and determine the possibility of maternity harassment. For example, when the determination unit receives input of workplace remarks or email content, it compares the input with past cases and detects text that may be maternity harassment. As a result, the maternity harassment detection system according to the embodiment can detect maternity harassment in the workplace early and take appropriate measures.

[0056] The storage unit can collect cases from at least one of past court records, reports, and news articles and store them in a database. The storage unit, for example, collects past court records and stores them in a database. For example, the storage unit collects published court decisions and court records and stores them in a database. The storage unit can also collect reports and store them in a database. For example, the storage unit collects internal corporate reports and investigative reports and stores them in a database. The storage unit can also collect news articles and store them in a database. For example, the storage unit collects online news and newspaper articles and stores them in a database. In this way, by collecting cases from past court records, reports, news articles, etc. and storing them in a database, a highly reliable database can be constructed. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can efficiently collect data using an AI model for collecting court records, reports, and news articles.

[0057] The analysis unit can use natural language processing technology to understand the meaning and context of the text and determine the possibility of maternity harassment. The analysis unit, for example, uses natural language processing technology to understand the meaning and context of the text. For example, the analysis unit uses morphological analysis to divide the text and analyze the meaning of each word. The analysis unit can also analyze the structure of the text using grammatical analysis. The analysis unit can also understand the meaning of the text using semantic analysis. For example, the analysis unit uses morphological analysis to divide the text and analyze the meaning of each word. Grammatical analysis analyzes the structure of the text and clarifies grammatical relationships. Semantic analysis understands the meaning of the text and performs analysis based on the context. In this way, by using natural language processing technology, the meaning and context of the text can be understood and the possibility of maternity harassment can be determined with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can analyze the meaning and context of the text using an AI model that uses natural language processing technology.

[0058] When workplace remarks or email content are input, the determination unit can compare them with past cases and detect language that may be maternity harassment. For example, when workplace remarks or email content are input, the determination unit compares them with past cases and detects language that may be maternity harassment. For example, the determination unit receives workplace remarks as voice input and converts them into text data using voice recognition technology. The determination unit then analyzes the text data and compares it with past cases. The determination unit can also input email content as text data and compare it with past cases. For example, the determination unit analyzes the email content and compares it with past cases. In this way, by inputting workplace remarks or email content, it can compare it with past cases and detect language that may be maternity harassment. Some or all of the above-mentioned processing by the determination unit may be performed using, for example, AI, or may be performed without AI. For example, the determination unit can efficiently analyze language using an AI model for analyzing voice input and text data.

[0059] The storage unit can estimate the user's emotions and adjust the timing of case collection based on the estimated user emotions. The storage unit, for example, estimates the user's emotions and adjusts the timing of case collection based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit delays the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the storage unit can also accelerate the collection timing to efficiently store data. Furthermore, if the user is busy, the storage unit can adjust the collection timing to match the user's schedule. For example, the storage unit monitors the user's emotions in real time and adjusts the collection timing according to changes in emotions. This allows for adjusting the timing of case collection based on the user's emotions, reducing the user's burden and enabling efficient data storage. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can efficiently estimate emotions using an AI model for analyzing user emotion data.

[0060] The storage unit can evaluate the reliability of past cases when collecting them and prioritize storing highly reliable cases. For example, the storage unit evaluates the reliability of past cases when collecting them and prioritizes storing highly reliable cases. For example, the storage unit evaluates the sources of collected cases and prioritizes storing cases from highly reliable sources. The storage unit can also analyze the content of the cases using AI to extract and store highly reliable information. Furthermore, the storage unit can cross-check information from multiple sources to evaluate the reliability of the cases. For example, the storage unit evaluates the sources of collected cases and prioritizes storing cases from highly reliable sources. The content of the cases can be analyzed using AI to extract and store highly reliable information. Information from multiple sources can be cross-checked and highly reliable cases can be stored preferentially. This improves the reliability of the database by evaluating the reliability of cases and preferentially storing highly reliable cases. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can efficiently select highly reliable cases using an AI model for evaluating the reliability of cases.

[0061] The accumulation unit can classify cases based on their categories when collecting them and store them in a database. For example, the accumulation unit classifies cases based on their categories when collecting them and stores them in a database. For example, the accumulation unit classifies the collected cases by industry and stores them in a database. The accumulation unit can also classify the collected cases by job type and store them in a database. The accumulation unit can also classify the collected cases by time of occurrence and store them in a database. For example, the accumulation unit classifies the collected cases by industry and stores them in a database. The accumulation unit can classify the collected cases by job type and store them in a database. The accumulation unit can classify the collected cases by time of occurrence and store them in a database. In this way, classification based on case category and storage in a database facilitates data organization. Some or all of the above-described processing in the accumulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the accumulation unit can efficiently organize data using an AI model for automatically classifying case categories.

[0062] The storage unit can estimate the user's emotions and determine the priority of the cases to be collected based on the estimated user emotions. The storage unit, for example, estimates the user's emotions and determines the priority of the cases to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the storage unit postpones cases of low importance. Furthermore, when the user is relaxed, the storage unit can also prioritize collecting cases of high importance. Furthermore, when the user is busy, the storage unit can adjust the priority of the cases to be collected to efficiently store data. For example, the storage unit monitors the user's emotions in real time and determines the priority of the cases to be collected according to changes in emotions. Thus, by determining the priority of the cases to be collected based on the user's emotions, data can be efficiently stored. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can efficiently estimate emotions using an AI model for analyzing user emotion data.

[0063] The accumulation unit can prioritize accumulation of highly relevant cases in consideration of the geographical distribution of the cases when collecting cases. For example, the accumulation unit prioritizes accumulation of highly relevant cases in consideration of the geographical distribution of the cases when collecting cases. For example, the accumulation unit analyzes the geographical distribution of the collected cases and prioritizes accumulation of cases from highly relevant regions. The accumulation unit can also prioritize collection of geographically close cases and accumulation in the database. Furthermore, the accumulation unit can focus collection of cases from specific regions in consideration of the geographical distribution. For example, the accumulation unit analyzes the geographical distribution of the collected cases and prioritizes accumulation of cases from highly relevant regions. Geographically close cases are prioritized and accumulated in the database. Cases from specific regions are prioritized in consideration of the geographical distribution. In this way, by prioritizing accumulation of highly relevant cases in consideration of the geographical distribution of the cases, a database that reflects the characteristics of each region can be constructed. Some or all of the above-mentioned processing in the accumulation unit may be performed, for example, using AI, or may be performed without using AI. For example, the repository can use AI models to analyze the geographic distribution of cases to efficiently select highly relevant cases.

[0064] The storage unit can classify cases based on the time of occurrence when collecting them and store them in the database. For example, when collecting cases, the storage unit classifies them based on the time of occurrence and stores them in the database. For example, the storage unit classifies collected cases by the time of occurrence and stores them in the database. The storage unit can also classify cases based on the time of occurrence and store the most recent cases preferentially. Furthermore, the storage unit can classify cases based on the time of occurrence and store past cases and the most recent cases in a balanced manner. For example, the storage unit classifies collected cases by the time of occurrence and stores them in the database. The storage unit classifies cases based on the time of occurrence and stores the most recent cases preferentially. The storage unit classifies cases based on the time of occurrence and stores past cases and the most recent cases in a balanced manner. In this way, by classifying cases based on the time of occurrence and storing them in the database, it is possible to organize data in chronological order. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can efficiently organize data using an AI model for automatically classifying the time of occurrence of cases.

[0065] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit can monitor the user's emotions in real time and adjust the presentation method of the analysis according to changes in emotions. By adjusting the presentation method of the analysis based on the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can efficiently estimate emotions using an AI model for analyzing user emotion data.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the words during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the words during analysis. For example, the analysis unit performs a detailed analysis on words with high importance. The analysis unit can also perform a simplified analysis on words with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the words. For example, the analysis unit performs a detailed analysis on words with high importance. The analysis unit performs a simplified analysis on words with low importance. The analysis unit determines the priority of the analysis based on the importance of the words. In this way, the level of detail of the analysis can be adjusted based on the importance of the words, thereby enabling efficient analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can efficiently adjust the level of detail of the analysis using an AI model for evaluating the importance of words.

[0067] The analysis unit can apply different analysis algorithms depending on the category of the text during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of the text during analysis. For example, the analysis unit selects an appropriate analysis algorithm depending on the category of the text. The analysis unit can also apply different analysis algorithms to each category of the text to improve accuracy. Furthermore, the analysis unit can dynamically switch analysis algorithms based on the category of the text. For example, the analysis unit selects an appropriate analysis algorithm depending on the category of the text. The analysis unit applies different analysis algorithms to each category of the text to improve accuracy. The analysis unit dynamically switches analysis algorithms based on the category of the text. In this way, by applying different analysis algorithms depending on the category of the text, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently perform analysis by automatically classifying text categories and using an AI model to select an appropriate analysis algorithm.

[0068] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. For example, the analysis unit monitors the user's emotions in real time and adjusts the length of the analysis according to changes in emotions. By adjusting the length of the analysis based on the user's emotions, optimal analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can efficiently estimate emotions using an AI model for analyzing user emotion data.

[0069] The analysis unit can determine the analysis priority based on the submission time of the text during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the text during analysis. For example, the analysis unit prioritizes analysis of the most recent text. The analysis unit can also classify text based on the submission time and determine the analysis priority. The analysis unit can also analyze text based on the submission time, and analyze past text and the most recent text in a balanced manner. For example, the analysis unit prioritizes analysis of the most recent text. Classify text based on the submission time and determine the analysis priority. Analyze text based on the submission time, and analyze past text and the most recent text in a balanced manner. In this way, by determining the analysis priority based on the submission time of the text, the most recent text can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently perform analysis by automatically evaluating the submission time of text and using an AI model for determining the analysis priority.

[0070] The analysis unit can adjust the order of analysis based on the relevance of the words during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the words during analysis. For example, the analysis unit prioritizes analysis of highly relevant words. The analysis unit can also determine the order of analysis based on the relevance of the words. Furthermore, the analysis unit can postpone analysis of less relevant words. For example, the analysis unit prioritizes analysis of highly relevant words. The analysis unit determines the order of analysis based on the relevance of the words. The analysis unit postpones analysis of less relevant words. In this way, by adjusting the order of analysis based on the relevance of the words, highly relevant words can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can efficiently perform analysis using an AI model for automatically evaluating the relevance of words and determining the order of analysis.

[0071] The determination unit can estimate the user's emotions and adjust the determination criteria based on the estimated user emotions. The determination unit, for example, estimates the user's emotions and adjusts the determination criteria based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit relaxes the determination criteria to reduce the user's burden. The determination unit can also apply stricter determination criteria when the user is relaxed. Furthermore, if the user is in a hurry, the determination unit can adjust the criteria to make a quick determination. For example, the determination unit monitors the user's emotions in real time and adjusts the determination criteria according to changes in emotions. By adjusting the determination criteria based on the user's emotions, it is possible to provide an optimal determination result for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can efficiently estimate emotions using an AI model for analyzing user emotion data.

[0072] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between words during the determination. The determination unit, for example, improves the accuracy of the determination by taking into account the interrelationships between words during the determination. For example, the determination unit analyzes the interrelationships between words and prioritizes determining highly related words. The determination unit can also improve the accuracy of the determination by taking into account the interrelationships between words. Furthermore, the determination unit can determine the priority of the determination based on the interrelationships between words. For example, the determination unit analyzes the interrelationships between words and prioritizes determining highly related words. The accuracy of the determination is improved by taking into account the interrelationships between words. The priority of the determination is determined based on the interrelationships between words. In this way, the accuracy of the determination can be improved by taking into account the interrelationships between words. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently perform the determination by automatically evaluating the interrelationships between words and using an AI model for improving the accuracy of the determination.

[0073] The determination unit can make a determination taking into account attribute information of the person who submitted the text. For example, the determination unit makes a determination taking into account attribute information of the person who submitted the text. For example, the determination unit improves the accuracy of the determination based on the attribute information of the person who submitted the text. The determination unit can also determine a priority of the determination taking into account the attribute information of the person who submitted the text. Furthermore, the determination unit can apply appropriate determination criteria based on the attribute information of the person who submitted the text. For example, the determination unit improves the accuracy of the determination based on the attribute information of the person who submitted the text. The determination unit determines a priority of the determination taking into account the attribute information of the person who submitted the text. The determination unit applies appropriate determination criteria based on the attribute information of the person who submitted the text. In this way, the accuracy of the determination can be improved by taking into account the attribute information of the person who submitted the text. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently make a determination using an AI model that automatically evaluates the attribute information of the person who submitted the text and improves the accuracy of the determination.

[0074] The determination unit can estimate the user's emotions and adjust the order in which the determination results are displayed based on the estimated user emotions. The determination unit, for example, estimates the user's emotions and adjusts the order in which the determination results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit postpones results of lower importance. Furthermore, if the user is relaxed, the determination unit can prioritize displaying results of higher importance. Furthermore, if the user is in a hurry, the determination unit can prioritize displaying results that highlight the main points. For example, the determination unit monitors the user's emotions in real time and adjusts the order in which the determination results are displayed in response to changes in emotions. This allows for optimal display of results for the user by adjusting the order in which the determination results are displayed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can use an AI model for analyzing the user's emotion data to efficiently estimate the emotion and adjust the order in which the determination results are displayed.

[0075] The determination unit can make a determination taking into account the geographical distribution of the wording. The determination unit can make a determination taking into account the geographical distribution of the wording, for example. For example, the determination unit analyzes the geographical distribution of the wording and prioritizes determining words in highly relevant regions. The determination unit can also prioritize determining words that are geographically close. Furthermore, the determination unit can focus on determining words in a specific region by taking into account the geographical distribution. For example, the determination unit analyzes the geographical distribution of the wording and prioritizes determining words in highly relevant regions. The determination unit prioritizes determining words that are geographically close. The determination unit can focus on determining words in a specific region by taking into account the geographical distribution. In this way, by taking the geographical distribution of the wording into account, it is possible to make a determination that reflects the characteristics of each region. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently make a determination by automatically evaluating the geographical distribution of the wording and using an AI model to improve the accuracy of the determination.

[0076] The determination unit can improve the accuracy of the determination based on related literature of the wording during the determination. For example, the determination unit improves the accuracy of the determination by referring to related literature of the wording during the determination. For example, the determination unit improves the accuracy of the determination by referring to related literature of the wording. The determination unit can also determine the priority of the determination based on the related literature. Furthermore, the determination unit can also apply appropriate determination criteria by referring to related literature. For example, the determination unit improves the accuracy of the determination by referring to related literature of the wording. The priority of the determination is determined based on the related literature. The appropriate determination criteria are applied by referring to related literature. In this way, the accuracy of the determination can be improved by referring to related literature of the wording. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can efficiently perform the determination using an AI model that automatically evaluates related literature and improves the accuracy of the determination. === Hard Collateral 1-1 === Each of the multiple elements including the storage unit, analysis unit, and determination unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12, and stores past cases of maternity harassment in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes newly inputted text using AI. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the possibility of maternity harassment based on the analyzed text. === Hard Collateral 1-2 === Each of the multiple elements including the storage unit, analysis unit, and determination unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores past cases of maternity harassment in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes newly inputted text using AI. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the possibility of maternity harassment based on the analyzed text. === Hard Collateral 1-3 === Each of the multiple elements including the storage unit, analysis unit, and determination unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12, and stores past cases of maternity harassment in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes newly inputted wording using AI. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the possibility of maternity harassment based on the analyzed wording. === Hard Collateral 1-4 === Each of the multiple elements including the storage unit, analysis unit, and determination unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the storage unit is realized by the specific processing unit 290 of the data processing device 12, and stores past cases of maternity harassment in the database 24. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes newly inputted wording using AI. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the possibility of maternity harassment based on the analyzed wording.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit postpones analyzing less important phrases and prioritizes analyzing more important phrases. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis, while if the user is in a hurry, the analysis unit can perform a concise analysis that focuses on the main points. This allows the analysis priority to be adjusted based on the user's emotions, thereby providing optimal analysis results for the user. The estimation of emotions is achieved using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can efficiently estimate emotions using an AI model for analyzing the user's emotion data.

[0079] The determination unit can make a determination taking into account attribute information of the person who submitted the text. For example, the determination unit can improve the accuracy of the determination based on attribute information such as the submitter's job title, position, age, and gender. The determination unit can also determine a priority order for the determination taking into account the submitter's attribute information. Furthermore, the determination unit can apply appropriate determination criteria based on the submitter's attribute information. This allows the accuracy of the determination to be improved by taking into account the attribute information of the submitter of the text. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can efficiently make a determination using an AI model that automatically evaluates the submitter's attribute information and improves the accuracy of the determination.

[0080] The storage unit can evaluate the reliability of cases when collecting cases and prioritize storing highly reliable cases. For example, the storage unit can evaluate the sources of collected cases and prioritize storing cases from highly reliable sources. The storage unit can also analyze the content of cases using AI to extract and store highly reliable information. Furthermore, the storage unit can cross-check information from multiple sources to evaluate the reliability of cases. This allows the reliability of the database to be improved by evaluating the reliability of cases and preferentially storing highly reliable cases. Some or all of the above-mentioned processing in the storage unit may be performed using AI, or may be performed without using AI. For example, the storage unit can efficiently select highly reliable cases using an AI model for evaluating the reliability of cases.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis on text with high importance. The analysis unit can also perform a simplified analysis on text with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the text. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the text. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can efficiently adjust the level of detail of the analysis using an AI model for evaluating the importance of the text.

[0082] The determination unit can estimate the user's emotions and adjust the criteria for determination based on the estimated user emotions. For example, if the user is feeling stressed, the determination unit relaxes the criteria to reduce the user's burden. The determination unit can also apply stricter criteria when the user is relaxed. Furthermore, if the user is in a hurry, the determination unit can adjust the criteria to make a quick determination. By adjusting the criteria for determination based on the user's emotions, it is possible to provide an optimal determination result for the user. The estimation of emotions is achieved using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without AI. For example, the determination unit can efficiently estimate emotions using an AI model for analyzing the user's emotion data.

[0083] When collecting cases, the accumulation unit can classify them based on the category of the case and store them in a database. For example, the accumulation unit can classify the collected cases by industry and store them in a database. The accumulation unit can also classify the collected cases by job type and store them in a database. Furthermore, the accumulation unit can classify the collected cases by the time of occurrence and store them in a database. In this way, classification based on the category of the case and storage in a database makes it easy to organize data. Some or all of the above-mentioned processing in the accumulation unit may be performed using AI, or may be performed without using AI. For example, the accumulation unit can efficiently organize data using an AI model for automatically classifying case categories.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a summary analysis result can be provided. By adjusting the way the analysis is presented based on the user's emotions, analysis results that are easy for the user to understand can be provided. The estimation of emotions is achieved using an emotion engine or a generative AI. Examples of generative AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can efficiently estimate emotions using an AI model for analyzing user emotion data.

[0085] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between words during the determination. For example, the determination unit analyzes the interrelationships between words and prioritizes determining highly related words. The determination unit can also improve the accuracy of the determination by taking into account the interrelationships between words. Furthermore, the determination unit can determine the priority of the determination based on the interrelationships between words. In this way, the accuracy of the determination can be improved by taking the interrelationships between words into account. Some or all of the above-described processing in the determination unit may be performed using AI or may be performed without using AI. For example, the determination unit can efficiently perform the determination by automatically evaluating the interrelationships between words and using an AI model to improve the accuracy of the determination.

[0086] The storage unit can estimate the user's emotions and prioritize the cases to be collected based on the estimated user emotions. For example, if the user is feeling stressed, it can postpone collecting less important cases. Also, if the user is relaxed, it can prioritize collecting more important cases. Furthermore, if the user is busy, it can adjust the priority of the cases to be collected to efficiently store data. This allows for efficient data storage by determining the priority of the cases to be collected based on the user's emotions. The emotion estimation is achieved using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the storage unit may be performed using AI, or may be performed without AI. For example, the storage unit can efficiently estimate emotions using an AI model for analyzing the user's emotion data.

[0087] The determination unit can improve the accuracy of the determination by referring to related literature of the wording during the determination. For example, the determination unit can improve the accuracy of the determination by referring to related literature of the wording. The determination unit can also determine the priority of the determination based on the related literature. Furthermore, the determination unit can also apply appropriate determination criteria by referring to related literature. In this way, the accuracy of the determination can be improved by referring to related literature of the wording. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can efficiently perform the determination by automatically evaluating related literature and using an AI model to improve the accuracy of the determination.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The storage unit stores past cases of maternity harassment in a database. Past cases of maternity harassment include court records, reports, news articles, etc. The storage unit collects this information and stores it in a database. For example, it digitizes court records, stores reports and news articles as text data, and uses web scraping technology to collect publicly available information on the internet. Step 2: The analysis unit uses AI to analyze the newly input text based on the examples accumulated by the storage unit. The analysis unit uses natural language processing technology to understand the meaning and context of the text. For example, it uses morphological analysis to divide the text and analyze the meaning of each word, grammatical analysis to analyze the structure of the text, and semantic analysis to understand the meaning of the text. Step 3: The judgment unit determines the possibility of maternity harassment based on the wording analyzed by the analysis unit. The judgment unit uses AI to understand the meaning and context of the words, and compares them with past cases to detect words that may be maternity harassment. This allows for early detection of maternity harassment in the workplace and the implementation of appropriate measures.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0092] 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.

[0093] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] 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.

[0106] 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.

[0107] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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, in order to avoid confusion and to 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.

[0160] 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.

[0161] [Explanation of symbols]

[0162] 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. An accumulation department that accumulates past cases of maternity harassment in a database; an analysis unit that analyzes a newly input sentence based on the cases stored by the storage unit; a determination unit that determines the possibility of maternity harassment based on the wording analyzed by the analysis unit; Equipped with A system characterized by:

2. The storage unit is Collect cases from past court records, reports, and / or news articles and store them in a database 2. The system of claim 1.

3. The analysis unit Using natural language processing technology to understand the meaning and context of text and determine whether it is maternity harassment 2. The system of claim 1.

4. The determination unit When you enter the content of workplace remarks or emails, it compares them with past cases and detects any wording that may be maternity harassment.

2. The system of claim 1.

5. The storage unit is Inferring user emotions and adjusting the timing of case collection based on the estimated user emotions 2. The system of claim 1.

6. The storage unit is When collecting past cases, evaluate the reliability of the cases and prioritize storing the most reliable cases.

2. The system of claim 1.

7. The storage unit is When collecting cases, they are classified based on the category of the case and stored in a database.

2. The system of claim 1.

8. The storage unit is Estimate user sentiment and prioritize cases to collect based on the estimated sentiment 2. The system of claim 1.

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