system

Abstract

The system according to the embodiment aims to detect potentially sexually harassing language in real time and issue a warning to the user. [Solution] A system according to an embodiment includes a construction unit, a learning unit, an analysis unit, a matching unit, and a warning unit. The construction unit constructs a database of past sexual harassment cases. The learning unit learns the database constructed by the construction unit. The analysis unit analyzes text entered by a user. The matching unit matches the text analyzed by the analysis unit with past cases. The warning unit detects text that is suspected of being sexual harassment based on the results of matching by the matching unit, and issues a warning to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to detect potentially sexually harassing language in real time and issue a warning.

[0005] The system according to the embodiment aims to detect potentially sexually harassing language in real time and issue a warning to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a construction unit, a learning unit, an analysis unit, a matching unit, and a warning unit. The construction unit constructs a database of past sexual harassment cases. The learning unit learns the database constructed by the construction unit. The analysis unit analyzes the text entered by the user. The matching unit matches the text analyzed by the analysis unit with past cases. The warning unit detects text that is suspected of being sexual harassment based on the results of matching by the matching unit, and issues a warning to the user. [Effects of the Invention]

[0007] The system according to the embodiment can detect potentially sexually harassing language in real time and issue a warning to the user. [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 sexual harassment detection system according to an embodiment of the present invention is a system that uses AI that has learned from past cases of sexual harassment to detect potentially sexually harassing statements. This sexual harassment detection system builds a database of past sexual harassment cases and trains AI on this database. Next, the AI ​​analyzes statements entered by the user and compares them with past cases. Based on the comparison results, potentially sexually harassing statements are detected and a warning is issued to the user. For example, the system may include the following steps: building a database of past sexual harassment cases, learning from past sexual harassment cases using AI, analyzing statements entered by the user, comparing them with past cases, detecting potentially sexually harassing statements, and issuing a warning to the user. This system makes it possible to detect potentially sexually harassing statements in advance and prevent them from occurring. As a result, the sexual harassment detection system can detect potentially sexually harassing statements in advance and prevent them from occurring.

[0029] A sexual harassment detection system according to an embodiment includes a construction unit, a learning unit, an analysis unit, a matching unit, and a warning unit. The construction unit constructs a database of past sexual harassment cases. The database of past sexual harassment cases includes, for example, specific case content and case collection methods, but is not limited to these examples. The learning unit uses AI to learn the database constructed by the construction unit. The learning unit performs learning based on, for example, the algorithm to be used and the selection criteria for the learning data. The analysis unit analyzes text entered by a user. The analysis unit analyzes the text using, for example, natural language processing technology, and understands its content. The matching unit matches the text analyzed by the analysis unit with past cases. The matching unit performs matching using, for example, cosine similarity or Jaccard coefficient. The warning unit detects text that may be sexual harassment based on the results of matching by the matching unit and issues a warning to the user. The warning unit issues the warning based on, for example, the format and timing of the warning. As a result, the sexual harassment detection system according to the embodiment can detect potentially sexually harassing language based on past cases of sexual harassment and issue a warning to the user.

[0030] The construction unit can add detailed metadata to past sexual harassment cases to improve search accuracy. For example, the construction unit can add detailed metadata to each case, such as the date and time of occurrence, location, and attributes of the people involved. The construction unit can also add keywords and tags based on the content of the case to improve search accuracy. Furthermore, the construction unit can set priorities and adjust the display order of search results based on the severity and impact of the case. By adding detailed metadata, search accuracy can be improved and more accurate information can be provided. Some or all of the above-described processing by the construction unit can be performed using, or without, AI. For example, the construction unit can input data on past sexual harassment cases into a generation AI and have the generation AI add metadata.

[0031] When constructing the database, the construction unit can include a variety of cases, including cases from different industries and cultural spheres. For example, the construction unit can collect sexual harassment cases from different industries (e.g., IT, manufacturing, services, etc.) and include them in the database. The construction unit can also collect sexual harassment cases from different cultural spheres (e.g., Asia, Europe, America, etc.) and include them in the database. Furthermore, the construction unit can collect sexual harassment cases from different occupations (e.g., managerial positions, general positions, part-time positions, etc.) and include them in the database. This makes it possible to address a variety of sexual harassment cases by including cases from different industries and cultural spheres. Some or all of the above-described processing in the construction unit may be performed using, or without, AI, for example. For example, the construction unit can input sexual harassment case data from different industries and cultural spheres into the generation AI and have the generation AI construct the database.

[0032] When constructing the database, the construction unit can prioritize collecting region-specific cases by taking into account the user's geographical location information. For example, the construction unit can prioritize collecting sexual harassment cases in the user's region and include them in the database. The construction unit can also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the construction unit can prioritize displaying region-specific cases in search results by taking into account the user's geographical location information. In this way, by collecting region-specific cases preferentially, appropriate information according to the region can be provided. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's geographical location information into the generation AI and cause the generation AI to collect region-specific cases.

[0033] When constructing the database, the construction unit can automatically collect cases from social media and news articles. For example, the construction unit can analyze social media posts and automatically collect cases of sexual harassment. The construction unit can also analyze news articles and automatically collect cases of sexual harassment. Furthermore, the construction unit can assign detailed metadata to the cases collected from social media and news articles and include them in the database. This allows the database to reflect the latest information by automatically collecting cases from social media and news articles. Some or all of the above-mentioned processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input data from social media and news articles into a generation AI and have the generation AI collect cases.

[0034] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, adjusts the parameters of the learning algorithm based on the past learning data. The learning unit can also evaluate the accuracy of the past learning data and select the optimal learning algorithm. Furthermore, the learning unit can analyze trends in the past learning data and identify areas for improvement in the learning algorithm. This makes it possible to optimize the learning algorithm by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the algorithm.

[0035] The learning unit can achieve multilingual support by including examples from different languages ​​and cultural spheres during learning. For example, the learning unit can achieve multilingual support by including sexual harassment examples from different languages ​​in the learning data. The learning unit can also include sexual harassment examples from different cultural spheres in the learning data to take cultural differences into account. Furthermore, the learning unit can develop a multilingual learning algorithm to effectively learn examples from different languages ​​and cultural spheres. This enables multilingual support by including examples from different languages ​​and cultural spheres. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data from different languages ​​and cultural spheres into the generation AI and cause the generation AI to perform multilingual learning.

[0036] During learning, the learning unit can weight the learning data based on the time when the sexual harassment cases occurred. For example, the learning unit can weight recent sexual harassment cases highly to reflect the latest trends. The learning unit can also weight older sexual harassment cases less heavily to reduce the influence of outdated information. Furthermore, the learning unit can balance the learning data by weighting appropriately based on the time when the sexual harassment cases occurred. This makes it possible to learn data that reflects the latest trends by weighting based on the time when the sexual harassment cases occurred. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input data on the time when sexual harassment cases occurred into a generation AI and have the generation AI perform weighting.

[0037] During learning, the learning unit can build a learning model specialized for the user's industry or occupation. For example, the learning unit can build a learning model specialized for the IT industry and learn industry-specific sexual harassment cases. The learning unit can also build a learning model specialized for the manufacturing industry and learn industry-specific sexual harassment cases. Furthermore, the learning unit can build a learning model specialized for the service industry and learn industry-specific sexual harassment cases. In this way, by building a learning model specialized for the user's industry or occupation, it is possible to respond to industry-specific sexual harassment cases. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data specialized for the industry or occupation into the generation AI and have the generation AI build a learning model.

[0038] The analysis unit can improve analysis accuracy by taking into account the context and nuances of the text during analysis. For example, the analysis unit can improve analysis accuracy based on the context by taking into account the context of the text. The analysis unit can also analyze the nuances of the text and consider subtle differences in meaning. Furthermore, the analysis unit can improve analysis accuracy based on the frequency of use and general meaning of the text. In this way, by taking into account the context and nuances of the text, analysis accuracy is improved and more accurate analysis results can be provided. 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 input text data into a generation AI and have the generation AI perform analysis of the context and nuances.

[0039] The analysis unit can add a multilingual analysis function to support different languages ​​and dialects during analysis. For example, the analysis unit can analyze text in different languages ​​to achieve multilingual support. The analysis unit can also analyze text in different dialects to take into account regional nuances. Furthermore, the analysis unit can develop a multilingual analysis algorithm to effectively analyze text in different languages ​​and dialects. This enables multilingual analysis by supporting different languages ​​and dialects. 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 input data in different languages ​​and dialects into the generation AI and have the generation AI perform multilingual analysis.

[0040] During analysis, the analysis unit can apply an analysis algorithm specialized for the user's industry or occupation. For example, the analysis unit can apply an analysis algorithm specialized for the IT industry to analyze industry-specific wording. The analysis unit can also apply an analysis algorithm specialized for the manufacturing industry to analyze industry-specific wording. Furthermore, the analysis unit can apply an analysis algorithm specialized for the service industry to analyze industry-specific wording. In this way, by applying an analysis algorithm specialized for the user's industry or occupation, it is possible to handle industry-specific wording. 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 input data specialized for the industry or occupation into the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] During analysis, the analysis unit can improve analysis accuracy by referring to the user's past input history. The analysis unit, for example, adjusts parameters of the analysis algorithm based on the user's past input history. The analysis unit can also analyze the user's past input history and extract specific patterns to improve analysis accuracy. Furthermore, the analysis unit can refer to the user's past input history and prioritize analysis of similar phrases. By referring to the user's past input history, analysis accuracy can be improved and more accurate analysis results can be provided. 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 input past input history data into a generation AI and have the generation AI improve analysis accuracy.

[0042] The matching unit can improve matching accuracy by taking into account the interrelationships between words during matching. The matching unit can improve matching accuracy based on the context, for example, by taking into account the context of the words. The matching unit can also analyze the interrelationships between words and prioritize matching highly related words. Furthermore, the matching unit can improve matching accuracy based on the frequency of use and general meaning of the words. In this way, by taking the interrelationships between words into consideration, matching accuracy is improved and more accurate matching results can be provided. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input text data to a generation AI and have the generation AI analyze the interrelationships.

[0043] When performing matching, the matching unit can set diverse matching criteria by taking into account cases from different industries and cultural spheres. For example, the matching unit can set industry-specific matching criteria by taking into account cases of sexual harassment from different industries. The matching unit can also set matching criteria that reflect cultural differences by taking into account cases of sexual harassment from different cultural spheres. Furthermore, the matching unit can set job-specific matching criteria by taking into account cases of sexual harassment from different occupations. This allows for setting diverse matching criteria by taking into account cases from different industries and cultural spheres, enabling more appropriate matching. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, AI, for example. For example, the matching unit can input data from different industries and cultural spheres into the generation AI and have the generation AI set the matching criteria.

[0044] During matching, the matching unit can prioritize matching of region-specific cases by taking into account the user's geographical location information. For example, the matching unit prioritizes matching of sexual harassment cases in the user's region and provides region-specific information. The matching unit can also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the matching unit can prioritize displaying region-specific cases in search results by taking into account the user's geographical location information. In this way, by prioritizing matching of region-specific cases, appropriate information according to the region can be provided. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location information to the generation AI and cause the generation AI to perform matching of region-specific cases.

[0045] During matching, the matching unit can apply a matching algorithm specialized for the user's industry or occupation. For example, the matching unit can apply a matching algorithm specialized for the IT industry to match industry-specific wording. The matching unit can also apply a matching algorithm specialized for the manufacturing industry to match industry-specific wording. Furthermore, the matching unit can apply a matching algorithm specialized for the service industry to match industry-specific wording. In this way, by applying a matching algorithm specialized for the user's industry or occupation, it is possible to handle industry-specific wording. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input data specialized for the industry or occupation into the generation AI and cause the generation AI to apply the matching algorithm.

[0046] When issuing a warning, the warning unit can improve the accuracy of the warning by referring to past warning history. The warning unit, for example, adjusts warning parameters based on past warning history. The warning unit can also analyze past warning history and extract specific patterns to improve warning accuracy. Furthermore, the warning unit can refer to past warning history and issue appropriate warnings for similar wording. In this way, by referring to past warning history, the accuracy of the warning can be improved and more appropriate warnings can be issued. Some or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or without AI. For example, the warning unit can input past warning history data into a generation AI and cause the generation AI to improve the accuracy of the warning.

[0047] When issuing a warning, the warning unit can generate a warning message specialized for the user's industry or occupation. The warning unit can generate a warning message specialized for the IT industry, for example, and issue a warning for industry-specific wording. The warning unit can also generate a warning message specialized for the manufacturing industry and issue a warning for industry-specific wording. The warning unit can also generate a warning message specialized for the service industry and issue a warning for industry-specific wording. In this way, by generating a warning message specialized for the user's industry or occupation, it is possible to issue an appropriate warning for industry-specific wording. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input data specialized for the industry or occupation into a generation AI and cause the generation AI to generate a warning message.

[0048] When issuing a warning, the warning unit can generate a region-specific warning message taking into account the user's geographical location information. The warning unit can generate a region-specific warning message based on, for example, sexual harassment cases in the user's region. The warning unit can also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the warning unit can also display a region-specific warning message taking into account the user's geographical location information. In this way, by generating a region-specific warning message, it is possible to issue an appropriate warning according to the region. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's geographical location information to a generation AI and cause the generation AI to generate a region-specific warning message.

[0049] When issuing a warning, the warning unit can analyze the user's social media activity and customize the content of the warning. For example, the warning unit can analyze the user's social media posts and generate a relevant warning message. The warning unit can also extract specific patterns from the user's social media activity and customize the content of the warning. Furthermore, the warning unit can generate an appropriate warning message based on the user's social media activity. In this way, an appropriate warning message can be generated by analyzing the user's social media activity. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's social media activity data into a generation AI and have the generation AI customize the content of the warning.

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

[0051] The sexual harassment detection system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit, for example, analyzes the user's past behavioral patterns and identifies behavior that is likely to be sexual harassment. The behavior analysis unit can also evaluate the risk of sexual harassment at specific times or locations based on the user's behavioral history. Furthermore, the behavior analysis unit can detect changes in the user's behavioral patterns and issue a warning if abnormal behavior occurs. In this way, by analyzing the user's behavioral history, it becomes possible to more accurately evaluate the risk of sexual harassment and prevent it from occurring.

[0052] The sexual harassment detection system may further include a region specialization unit that prioritizes collecting region-specific cases by taking into account the user's geographical location information. For example, the region specialization unit may prioritize collecting sexual harassment cases in the user's region and include them in the database. The region specialization unit may also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the region specialization unit may prioritize displaying region-specific cases in search results by taking into account the user's geographical location information. This allows the system to provide appropriate information tailored to the region by prioritizing collection of region-specific cases.

[0053] The sexual harassment detection system may further include a social media analysis unit that analyzes a user's social media activity and customizes the content of the warning. The social media analysis unit, for example, analyzes the user's social media posts and generates a related warning message. The social media analysis unit may also extract specific patterns from the user's social media activity and customize the content of the warning. Furthermore, the social media analysis unit may also generate an appropriate warning message based on the user's social media activity. In this way, an appropriate warning message can be generated by analyzing the user's social media activity.

[0054] The sexual harassment detection system can further include an industry specialization unit that builds a learning model specialized for the user's industry or occupation. The industry specialization unit can build a learning model specialized for the IT industry, for example, to learn industry-specific sexual harassment cases. It can also build a learning model specialized for the manufacturing industry to learn industry-specific sexual harassment cases. It can also build a learning model specialized for the service industry to learn industry-specific sexual harassment cases. In this way, by building a learning model specialized for the user's industry or occupation, it is possible to respond to industry-specific sexual harassment cases.

[0055] The sexual harassment detection system can further include a history analysis unit that refers to the user's past input history to improve analysis accuracy. The history analysis unit, for example, adjusts the parameters of the analysis algorithm based on the user's past input history. The history analysis unit can also analyze the user's past input history and extract specific patterns to improve analysis accuracy. Furthermore, the history analysis unit can also refer to the user's past input history and prioritize analysis of similar phrases. In this way, by referring to the user's past input history, analysis accuracy can be improved and more accurate analysis results can be provided.

[0056] The sexual harassment detection system may further include a region warning unit that generates a region-specific warning message taking into account the user's geographical location information. The region warning unit generates the region-specific warning message based on, for example, sexual harassment cases in the user's region. The region warning unit may also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the region warning unit may also display a region-specific warning message taking into account the user's geographical location information. In this way, by generating a region-specific warning message, it is possible to issue an appropriate warning according to the region.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The Construction Department will create a database of past sexual harassment cases. The database of past sexual harassment cases will include the details of specific cases and the methods for collecting the cases. Step 2: The learning unit uses AI to learn the database constructed by the construction unit. The learning unit performs learning based on the algorithm to be used and the criteria for selecting the training data. Step 3: The analysis unit analyzes the text entered by the user. The analysis unit uses natural language processing technology to analyze the text and understand its content. Step 4: The matching unit matches the text analyzed by the analysis unit with past cases. The matching unit performs matching using methods such as cosine similarity or Jaccard coefficient. Step 5: The warning unit detects potentially sexually harassing text based on the results of the comparison by the comparison unit, and issues a warning to the user. The warning unit issues the warning based on the format and timing of the warning.

[0059] (Example 2) A sexual harassment detection system according to an embodiment of the present invention is a system that uses AI that has learned from past cases of sexual harassment to detect potentially sexually harassing statements. This sexual harassment detection system builds a database of past sexual harassment cases and trains AI on this database. Next, the AI ​​analyzes statements entered by the user and compares them with past cases. Based on the comparison results, potentially sexually harassing statements are detected and a warning is issued to the user. For example, the system may include the following steps: building a database of past sexual harassment cases, learning from past sexual harassment cases using AI, analyzing statements entered by the user, comparing them with past cases, detecting potentially sexually harassing statements, and issuing a warning to the user. This system makes it possible to detect potentially sexually harassing statements in advance and prevent them from occurring. As a result, the sexual harassment detection system can detect potentially sexually harassing statements in advance and prevent them from occurring.

[0060] A sexual harassment detection system according to an embodiment includes a construction unit, a learning unit, an analysis unit, a matching unit, and a warning unit. The construction unit constructs a database of past sexual harassment cases. The database of past sexual harassment cases includes, for example, specific case content and case collection methods, but is not limited to these examples. The learning unit uses AI to learn the database constructed by the construction unit. The learning unit performs learning based on, for example, the algorithm to be used and the selection criteria for the learning data. The analysis unit analyzes text entered by a user. The analysis unit analyzes the text using, for example, natural language processing technology, and understands its content. The matching unit matches the text analyzed by the analysis unit with past cases. The matching unit performs matching using, for example, cosine similarity or Jaccard coefficient. The warning unit detects text that may be sexual harassment based on the results of matching by the matching unit and issues a warning to the user. The warning unit issues the warning based on, for example, the format and timing of the warning. As a result, the sexual harassment detection system according to the embodiment can detect potentially sexually harassing language based on past cases of sexual harassment and issue a warning to the user.

[0061] The construction unit can estimate the user's emotions and adjust the database update frequency based on the estimated user emotions. For example, if the user is feeling stressed, the construction unit can set the database update frequency low to reduce the load on the system. Furthermore, if the user is relaxed, the construction unit can set the database update frequency high to reflect the latest information. Furthermore, if the user is in a hurry, the construction unit can prioritize only important updates and quickly update the database. This reduces the load on the system by adjusting the database update frequency according to the user's emotions, enabling a system to respond according to the user's situation. 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 construction unit can be performed using, for example, an AI. For example, the construction unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0062] The construction unit can add detailed metadata to past sexual harassment cases to improve search accuracy. For example, the construction unit can add detailed metadata to each case, such as the date and time of occurrence, location, and attributes of the people involved. The construction unit can also add keywords and tags based on the content of the case to improve search accuracy. Furthermore, the construction unit can set priorities and adjust the display order of search results based on the severity and impact of the case. By adding detailed metadata, search accuracy can be improved and more accurate information can be provided. Some or all of the above-described processing by the construction unit can be performed using, or without, AI. For example, the construction unit can input data on past sexual harassment cases into a generation AI and have the generation AI add metadata.

[0063] When constructing the database, the construction unit can include a variety of cases, including cases from different industries and cultural spheres. For example, the construction unit can collect sexual harassment cases from different industries (e.g., IT, manufacturing, services, etc.) and include them in the database. The construction unit can also collect sexual harassment cases from different cultural spheres (e.g., Asia, Europe, America, etc.) and include them in the database. Furthermore, the construction unit can collect sexual harassment cases from different occupations (e.g., managerial positions, general positions, part-time positions, etc.) and include them in the database. This makes it possible to address a variety of sexual harassment cases by including cases from different industries and cultural spheres. Some or all of the above-described processing in the construction unit may be performed using, or without, AI, for example. For example, the construction unit can input sexual harassment case data from different industries and cultural spheres into the generation AI and have the generation AI construct the database.

[0064] The construction unit can estimate the user's emotions and adjust the database construction method based on the estimated user emotions. For example, when the user is stressed, the construction unit can adopt a simple database construction method to reduce the burden on the user. Furthermore, when the user is relaxed, the construction unit can adopt a detailed database construction method to improve accuracy. Furthermore, when the user is in a hurry, the construction unit can prioritize construction of only important data and quickly complete the database. This reduces the burden on the user by adjusting the database construction method according to the user's emotions, enabling efficient database construction. 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 construction unit can be performed using, for example, an AI, or without an AI. For example, the construction unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0065] When constructing the database, the construction unit can prioritize collecting region-specific cases by taking into account the user's geographical location information. For example, the construction unit can prioritize collecting sexual harassment cases in the user's region and include them in the database. The construction unit can also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the construction unit can prioritize displaying region-specific cases in search results by taking into account the user's geographical location information. In this way, by collecting region-specific cases preferentially, appropriate information according to the region can be provided. Some or all of the above-described processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input the user's geographical location information into the generation AI and cause the generation AI to collect region-specific cases.

[0066] When constructing the database, the construction unit can automatically collect cases from social media and news articles. For example, the construction unit can analyze social media posts and automatically collect cases of sexual harassment. The construction unit can also analyze news articles and automatically collect cases of sexual harassment. Furthermore, the construction unit can assign detailed metadata to the cases collected from social media and news articles and include them in the database. This allows the database to reflect the latest information by automatically collecting cases from social media and news articles. Some or all of the above-mentioned processing in the construction unit may be performed using, for example, AI, or may be performed without using AI. For example, the construction unit can input data from social media and news articles into a generation AI and have the generation AI collect cases.

[0067] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit selects simple, easy-to-understand training data. Furthermore, if the user is relaxed, the learning unit can select detailed and diverse training data. Furthermore, if the user is in a hurry, the learning unit can prioritize selecting only important training data. This enables efficient learning by selecting training data according to the user's emotions. 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 learning unit can be performed using, for example, an AI, or without an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0068] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, adjusts the parameters of the learning algorithm based on the past learning data. The learning unit can also evaluate the accuracy of the past learning data and select the optimal learning algorithm. Furthermore, the learning unit can analyze trends in the past learning data and identify areas for improvement in the learning algorithm. This makes it possible to optimize the learning algorithm by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the algorithm.

[0069] The learning unit can achieve multilingual support by including examples from different languages ​​and cultural spheres during learning. For example, the learning unit can achieve multilingual support by including sexual harassment examples from different languages ​​in the learning data. The learning unit can also include sexual harassment examples from different cultural spheres in the learning data to take cultural differences into account. Furthermore, the learning unit can develop a multilingual learning algorithm to effectively learn examples from different languages ​​and cultural spheres. This enables multilingual support by including examples from different languages ​​and cultural spheres. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data from different languages ​​and cultural spheres into the generation AI and cause the generation AI to perform multilingual learning.

[0070] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. For example, if the user is feeling stressed, the learning unit can set the learning frequency low to reduce the load on the system. Furthermore, if the user is relaxed, the learning unit can set the learning frequency high to reflect the latest information. Furthermore, if the user is in a hurry, the learning unit can prioritize only important learning and quickly complete the learning. This reduces the load on the system and enables efficient learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 learning unit can be performed using, for example, an AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0071] During learning, the learning unit can weight the learning data based on the time when the sexual harassment cases occurred. For example, the learning unit can weight recent sexual harassment cases highly to reflect the latest trends. The learning unit can also weight older sexual harassment cases less heavily to reduce the influence of outdated information. Furthermore, the learning unit can balance the learning data by weighting appropriately based on the time when the sexual harassment cases occurred. This makes it possible to learn data that reflects the latest trends by weighting based on the time when the sexual harassment cases occurred. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input data on the time when sexual harassment cases occurred into a generation AI and have the generation AI perform weighting.

[0072] During learning, the learning unit can build a learning model specialized for the user's industry or occupation. For example, the learning unit can build a learning model specialized for the IT industry and learn industry-specific sexual harassment cases. The learning unit can also build a learning model specialized for the manufacturing industry and learn industry-specific sexual harassment cases. Furthermore, the learning unit can build a learning model specialized for the service industry and learn industry-specific sexual harassment cases. In this way, by building a learning model specialized for the user's industry or occupation, it is possible to respond to industry-specific sexual harassment cases. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data specialized for the industry or occupation into the generation AI and have the generation AI build a learning model.

[0073] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can set the analysis accuracy low to reduce the load on the system. Furthermore, if the user is relaxed, the analysis unit can set the analysis accuracy high to provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize only important analyses and quickly complete the analysis. This reduces the load on the system and enables efficient analysis by adjusting the analysis accuracy according to the user's emotions. 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 analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0074] The analysis unit can improve analysis accuracy by taking into account the context and nuances of the text during analysis. For example, the analysis unit can improve analysis accuracy based on the context by taking into account the context of the text. The analysis unit can also analyze the nuances of the text and consider subtle differences in meaning. Furthermore, the analysis unit can improve analysis accuracy based on the frequency of use and general meaning of the text. In this way, by taking into account the context and nuances of the text, analysis accuracy is improved and more accurate analysis results can be provided. 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 input text data into a generation AI and have the generation AI perform analysis of the context and nuances.

[0075] The analysis unit can add a multilingual analysis function to support different languages ​​and dialects during analysis. For example, the analysis unit can analyze text in different languages ​​to achieve multilingual support. The analysis unit can also analyze text in different dialects to take into account regional nuances. Furthermore, the analysis unit can develop a multilingual analysis algorithm to effectively analyze text in different languages ​​and dialects. This enables multilingual analysis by supporting different languages ​​and dialects. 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 input data in different languages ​​and dialects into the generation AI and have the generation AI perform multilingual analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, enabling a display that is easy for the user to view. 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 can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can apply an analysis algorithm specialized for the user's industry or occupation. For example, the analysis unit can apply an analysis algorithm specialized for the IT industry to analyze industry-specific wording. The analysis unit can also apply an analysis algorithm specialized for the manufacturing industry to analyze industry-specific wording. Furthermore, the analysis unit can apply an analysis algorithm specialized for the service industry to analyze industry-specific wording. In this way, by applying an analysis algorithm specialized for the user's industry or occupation, it is possible to handle industry-specific wording. 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 input data specialized for the industry or occupation into the generation AI and cause the generation AI to apply the analysis algorithm.

[0078] During analysis, the analysis unit can improve analysis accuracy by referring to the user's past input history. The analysis unit, for example, adjusts parameters of the analysis algorithm based on the user's past input history. The analysis unit can also analyze the user's past input history and extract specific patterns to improve analysis accuracy. Furthermore, the analysis unit can refer to the user's past input history and prioritize analysis of similar phrases. By referring to the user's past input history, analysis accuracy can be improved and more accurate analysis results can be provided. 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 input past input history data into a generation AI and have the generation AI improve analysis accuracy.

[0079] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, if the user is stressed, the matching unit can set the matching criteria loosely to reduce the system load. Alternatively, if the user is relaxed, the matching unit can set the matching criteria tightly to provide detailed matching results. Furthermore, if the user is in a hurry, the matching unit can prioritize only important matches and quickly complete the matching. This reduces the system load and enables efficient matching by adjusting the matching criteria according to the user's emotions. 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 matching unit can be performed using, for example, an AI. For example, the matching unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0080] The matching unit can improve matching accuracy by taking into account the interrelationships between words during matching. The matching unit can improve matching accuracy based on the context, for example, by taking into account the context of the words. The matching unit can also analyze the interrelationships between words and prioritize matching highly related words. Furthermore, the matching unit can improve matching accuracy based on the frequency of use and general meaning of the words. In this way, by taking the interrelationships between words into consideration, matching accuracy is improved and more accurate matching results can be provided. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input text data to a generation AI and have the generation AI analyze the interrelationships.

[0081] When performing matching, the matching unit can set diverse matching criteria by taking into account cases from different industries and cultural spheres. For example, the matching unit can set industry-specific matching criteria by taking into account cases of sexual harassment from different industries. The matching unit can also set matching criteria that reflect cultural differences by taking into account cases of sexual harassment from different cultural spheres. Furthermore, the matching unit can set job-specific matching criteria by taking into account cases of sexual harassment from different occupations. This allows for setting diverse matching criteria by taking into account cases from different industries and cultural spheres, enabling more appropriate matching. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, AI, for example. For example, the matching unit can input data from different industries and cultural spheres into the generation AI and have the generation AI set the matching criteria.

[0082] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated user emotions. For example, when the user is stressed, the matching unit prioritizes displaying important matching results to reduce the user's burden. Furthermore, when the user is relaxed, the matching unit can display detailed matching results to provide information. Furthermore, when the user is in a hurry, the matching unit can prioritize displaying key points. This allows the display order of the matching results to be adjusted according to the user's emotions, resulting in a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 matching unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the matching unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0083] During matching, the matching unit can prioritize matching of region-specific cases by taking into account the user's geographical location information. For example, the matching unit prioritizes matching of sexual harassment cases in the user's region and provides region-specific information. The matching unit can also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the matching unit can prioritize displaying region-specific cases in search results by taking into account the user's geographical location information. In this way, by prioritizing matching of region-specific cases, appropriate information according to the region can be provided. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location information to the generation AI and cause the generation AI to perform matching of region-specific cases.

[0084] During matching, the matching unit can apply a matching algorithm specialized for the user's industry or occupation. For example, the matching unit can apply a matching algorithm specialized for the IT industry to match industry-specific wording. The matching unit can also apply a matching algorithm specialized for the manufacturing industry to match industry-specific wording. Furthermore, the matching unit can apply a matching algorithm specialized for the service industry to match industry-specific wording. In this way, by applying a matching algorithm specialized for the user's industry or occupation, it is possible to handle industry-specific wording. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input data specialized for the industry or occupation into the generation AI and cause the generation AI to apply the matching algorithm.

[0085] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the warning unit issues a warning in a calm manner. Furthermore, if the user is relaxed, the warning unit can also issue a warning with detailed information. Furthermore, if the user is in a hurry, the warning unit can issue a concise and quick warning. By adjusting the way the warning is expressed based on the user's emotions, it is possible to issue an appropriate warning for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] When issuing a warning, the warning unit can improve the accuracy of the warning by referring to past warning history. The warning unit, for example, adjusts warning parameters based on past warning history. The warning unit can also analyze past warning history and extract specific patterns to improve warning accuracy. Furthermore, the warning unit can refer to past warning history and issue appropriate warnings for similar wording. In this way, by referring to past warning history, the accuracy of the warning can be improved and more appropriate warnings can be issued. Some or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or without AI. For example, the warning unit can input past warning history data into a generation AI and cause the generation AI to improve the accuracy of the warning.

[0087] When issuing a warning, the warning unit can generate a warning message specialized for the user's industry or occupation. The warning unit can generate a warning message specialized for the IT industry, for example, and issue a warning for industry-specific wording. The warning unit can also generate a warning message specialized for the manufacturing industry and issue a warning for industry-specific wording. The warning unit can also generate a warning message specialized for the service industry and issue a warning for industry-specific wording. In this way, by generating a warning message specialized for the user's industry or occupation, it is possible to issue an appropriate warning for industry-specific wording. Some or all of the above-described processing in the warning unit can be performed using, for example, AI, or can be performed without using AI. For example, the warning unit can input data specialized for the industry or occupation into a generation AI and cause the generation AI to generate a warning message.

[0088] The warning unit can estimate the user's emotions and adjust the timing of the warning based on the estimated user emotions. For example, if the user is feeling stressed, the warning unit can delay the timing of the warning to reduce the user's burden. Furthermore, if the user is relaxed, the warning unit can also accelerate the timing of the warning and provide more detailed information. Furthermore, if the user is in a hurry, the warning unit can prioritize only important warnings and issue them quickly. This allows the warning timing to be adjusted according to the user's emotions, so that the warning can be issued at an appropriate time for the user. The emotion estimation is realized 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 warning unit may be performed using, for example, an AI. For example, the warning unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0089] When issuing a warning, the warning unit can generate a region-specific warning message taking into account the user's geographical location information. The warning unit can generate a region-specific warning message based on, for example, sexual harassment cases in the user's region. The warning unit can also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the warning unit can also display a region-specific warning message taking into account the user's geographical location information. In this way, by generating a region-specific warning message, it is possible to issue an appropriate warning according to the region. Some or all of the above-described processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's geographical location information to a generation AI and cause the generation AI to generate a region-specific warning message.

[0090] When issuing a warning, the warning unit can analyze the user's social media activity and customize the content of the warning. For example, the warning unit can analyze the user's social media posts and generate a relevant warning message. The warning unit can also extract specific patterns from the user's social media activity and customize the content of the warning. Furthermore, the warning unit can generate an appropriate warning message based on the user's social media activity. In this way, an appropriate warning message can be generated by analyzing the user's social media activity. Some or all of the above-mentioned processing in the warning unit may be performed using, for example, AI, or may be performed without using AI. For example, the warning unit can input the user's social media activity data into a generation AI and have the generation AI customize the content of the warning. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned construction unit, learning unit, analysis unit, matching unit, and warning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a database of past sexual harassment cases. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the database using AI. The analysis unit is realized, for example, by the control unit 46A of the smart device 14 and analyzes words entered by the user. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the analyzed words with past cases. The warning unit is realized, for example, by the control unit 46A of the smart device 14 and detects words that may be sexual harassment and issues a warning to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned construction unit, learning unit, analysis unit, matching unit, and warning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a database of past sexual harassment cases. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the database using AI. The analysis unit is realized, for example, by the control unit 46A of the smart glasses 214 and analyzes words entered by the user. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the analyzed words with past cases. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214 and detects words that may be sexual harassment and issues a warning to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned construction unit, learning unit, analysis unit, matching unit, and warning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a database of past sexual harassment cases. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the database using AI. The analysis unit is realized, for example, by the control unit 46A of the headset type terminal 314 and analyzes words entered by the user. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the analyzed words with past cases. The warning unit is realized, for example, by the control unit 46A of the headset type terminal 314 and detects words that may be sexual harassment and issues a warning to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned construction unit, learning unit, analysis unit, matching unit, and warning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the construction unit is realized by the specific processing unit 290 of the data processing device 12 and constructs a database of past sexual harassment cases. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and learns the database using AI. The analysis unit is realized, for example, by the control unit 46A of the robot 414 and analyzes words entered by the user. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the analyzed words with past cases. The warning unit is realized, for example, by the control unit 46A of the robot 414 and detects words that may be sexual harassment and issues a warning to the user.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The sexual harassment detection system can further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit, for example, analyzes the user's past behavioral patterns and identifies behavior that is likely to be sexual harassment. The behavior analysis unit can also evaluate the risk of sexual harassment at specific times or locations based on the user's behavioral history. Furthermore, the behavior analysis unit can detect changes in the user's behavioral patterns and issue a warning if abnormal behavior occurs. In this way, by analyzing the user's behavioral history, it becomes possible to more accurately evaluate the risk of sexual harassment and prevent it from occurring.

[0093] The sexual harassment detection system may further include a warning customization unit that estimates the user's emotions and customizes the content of the warning based on the estimated emotions. For example, the warning customization unit may issue a warning in a gentle manner if the user is feeling stressed. Alternatively, the warning customization unit may issue a warning containing detailed information if the user is relaxed. Furthermore, the warning customization unit may issue a concise and quick warning if the user is in a hurry. In this way, by customizing the content of the warning according to the user's emotions, it is possible to issue a warning that is appropriate for the user.

[0094] The sexual harassment detection system may further include a region specialization unit that prioritizes collecting region-specific cases by taking into account the user's geographical location information. For example, the region specialization unit may prioritize collecting sexual harassment cases in the user's region and include them in the database. The region specialization unit may also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the region specialization unit may prioritize displaying region-specific cases in search results by taking into account the user's geographical location information. This allows the system to provide appropriate information tailored to the region by prioritizing collection of region-specific cases.

[0095] The sexual harassment detection system may further include a social media analysis unit that analyzes a user's social media activity and customizes the content of the warning. The social media analysis unit, for example, analyzes the user's social media posts and generates a related warning message. The social media analysis unit may also extract specific patterns from the user's social media activity and customize the content of the warning. Furthermore, the social media analysis unit may also generate an appropriate warning message based on the user's social media activity. In this way, an appropriate warning message can be generated by analyzing the user's social media activity.

[0096] The sexual harassment detection system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the database update frequency based on the estimated emotions. For example, if the user is feeling stressed, the emotion adjustment unit can set the database update frequency low to reduce the load on the system. Also, if the user is relaxed, the emotion adjustment unit can set the database update frequency high to reflect the latest information. Furthermore, if the user is in a hurry, the system can prioritize only important updates and update the database quickly. In this way, adjusting the database update frequency according to the user's emotions reduces the load on the system and enables responses according to the user's situation.

[0097] The sexual harassment detection system can further include an industry specialization unit that builds a learning model specialized for the user's industry or occupation. The industry specialization unit can build a learning model specialized for the IT industry, for example, to learn industry-specific sexual harassment cases. It can also build a learning model specialized for the manufacturing industry to learn industry-specific sexual harassment cases. It can also build a learning model specialized for the service industry to learn industry-specific sexual harassment cases. In this way, by building a learning model specialized for the user's industry or occupation, it is possible to respond to industry-specific sexual harassment cases.

[0098] The sexual harassment detection system can further include an emotion selection unit that estimates the user's emotion and selects learning data based on the estimated emotion. For example, if the user is feeling stressed, the emotion selection unit selects simple, easy-to-understand learning data. Alternatively, if the user is relaxed, the emotion selection unit can select detailed and diverse learning data. Furthermore, if the user is in a hurry, the emotion selection unit can prioritize the selection of only important learning data. This allows for efficient learning by selecting learning data according to the user's emotion.

[0099] The sexual harassment detection system can further include a history analysis unit that refers to the user's past input history to improve analysis accuracy. The history analysis unit, for example, adjusts the parameters of the analysis algorithm based on the user's past input history. The history analysis unit can also analyze the user's past input history and extract specific patterns to improve analysis accuracy. Furthermore, the history analysis unit can also refer to the user's past input history and prioritize analysis of similar phrases. In this way, by referring to the user's past input history, analysis accuracy can be improved and more accurate analysis results can be provided.

[0100] The sexual harassment detection system can further include a display adjustment unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, the display adjustment unit can provide a simple, highly visible display method when the user is feeling stressed. It can also provide a display method that includes detailed information when the user is relaxed. It can also provide a display method that focuses on the main points when the user is in a hurry. This allows the display method of the analysis results to be adjusted according to the user's emotions, making it easy for the user to see.

[0101] The sexual harassment detection system may further include a region warning unit that generates a region-specific warning message taking into account the user's geographical location information. The region warning unit generates the region-specific warning message based on, for example, sexual harassment cases in the user's region. The region warning unit may also assign region-specific keywords and tags based on the user's geographical location information. Furthermore, the region warning unit may also display a region-specific warning message taking into account the user's geographical location information. In this way, by generating a region-specific warning message, it is possible to issue an appropriate warning according to the region.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The Construction Department will create a database of past sexual harassment cases. The database of past sexual harassment cases will include the details of specific cases and the methods for collecting the cases. Step 2: The learning unit uses AI to learn the database constructed by the construction unit. The learning unit performs learning based on the algorithm to be used and the criteria for selecting the training data. Step 3: The analysis unit analyzes the text entered by the user. The analysis unit uses natural language processing technology to analyze the text and understand its content. Step 4: The matching unit matches the text analyzed by the analysis unit with past cases. The matching unit performs matching using methods such as cosine similarity or Jaccard coefficient. Step 5: The warning unit detects potentially sexually harassing text based on the results of the comparison by the comparison unit, and issues a warning to the user. The warning unit issues the warning based on the format and timing of the warning.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A construction department that builds a database of past sexual harassment cases, a learning unit that learns the database constructed by the construction unit; an analysis unit that analyzes the text entered by the user; a collation unit that compares the wording analyzed by the analysis unit with past cases; a warning unit that detects language that is suspected of being sexual harassment based on the result of the comparison by the comparison unit and issues a warning to the user. A system characterized by:

2. The construction unit Estimate the user's emotions and adjust the database update frequency based on the estimated user emotions. The system of claim 1 .

3. The construction unit Adding detailed metadata about past sexual harassment cases to improve search accuracy The system of claim 1 .

4. The construction unit When building a database, include diverse examples, including examples from different industries and cultural spheres. The system of claim 1 .

5. The construction unit Estimate the user's emotions and adjust the database construction method based on the estimated user emotions. The system of claim 1 .

6. The construction unit When building the database, consider the user's geographic location information to prioritize collecting area-specific cases. The system of claim 1 .

7. The construction unit Automatically collect examples from social media or news articles when building your database The system of claim 1 .

8. The learning unit Estimate the user's emotions and select training data based on the estimated user emotions. The system of claim 1 .

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