system

Abstract

An object of the system according to the embodiment is to understand the intention of a slang used on an SNS and identify a dangerous activity.SOLUTION: A system according to an embodiment includes a context understanding unit, an intention analysis unit, and a danger determination unit. The context understanding unit analyzes a post or a message on the SNS. The intention analyzer analyzes the intention of the slang based on the context of the post or the message analyzed by the context understanding unit. The danger determining part determines whether the account using the slang is involved in a dangerous activity based on the intention of the slang analyzed by the intention analyzing part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately understand the intent behind the slang used on social networking sites and identify dangerous activities.

[0005] The system according to the embodiment aims to understand the intent behind the slang used on social networking sites and identify risky activities. [Means for solving the problem]

[0006] A system according to an embodiment includes a context understanding unit, an intention analysis unit, and a risk determination unit. The context understanding unit analyzes posts or messages on an SNS. The intention analysis unit analyzes the intention of the slang based on the context of the post or message analyzed by the context understanding unit. The risk determination unit determines whether an account using the slang is involved in risky activities based on the intention of the slang analyzed by the intention analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can understand the intent behind the slang used on social networking sites and identify risky activities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A monitoring system according to an embodiment of the present invention utilizes generative AI to monitor the use of slang on the Internet, particularly on social networking sites, and identify crime-related activities. The system uses generative AI to understand the context, analyze the intention behind the use of slang, and determine whether accounts using the slang are involved in dangerous activities. This enables the monitoring system to monitor the use of slang on social networking sites and identify crime-related activities.

[0029] A monitoring system according to an embodiment includes a context understanding unit, an intention analysis unit, and a risk determination unit. The context understanding unit analyzes posts or messages on social media. For example, the context understanding unit analyzes text posts on social media using natural language processing technology. The context understanding unit can also analyze posts with images and direct messages. For example, the context understanding unit analyzes the content of posts with images using image recognition technology and the text data of direct messages. The intention analysis unit analyzes the intention of slang based on the context of the post or message analyzed by the context understanding unit. For example, the intention analysis unit refers to a slang dictionary and identifies the intention of the slang based on past usage examples. The intention analysis unit can also analyze the intention of the slang using a machine learning algorithm. For example, the intention analysis unit learns the frequency and context of use of the slang and identifies its intention. The risk determination unit determines whether an account using slang is engaged in risky activities based on the intention of the slang analyzed by the intention analysis unit. For example, the risk determination unit determines risky activities based on criteria such as illegal activities, violent activities, and fraudulent activities. The risk determination unit can also analyze patterns of slang usage and account posting histories to identify possible risky activities. For example, the risk determination unit determines that accounts that frequently use specific slang or accounts that use slang in a specific pattern are risky. This allows the monitoring system according to the embodiment to monitor the use of slang on SNS and identify crime-related activities. For example, the monitoring system can detect crime-related activities, such as illegal drug trafficking and fraudulent solicitations, early on and take measures. This is expected to improve safety on SNS.

[0030] The context understanding unit can learn patterns of slang usage by referring to the poster's past posting history or message history. For example, the context understanding unit uses a generation AI to analyze the poster's past posting history and learn the frequency and context of slang usage. For example, if a specific slang is frequently used in a specific situation, the intention of that slang can be identified. The context understanding unit can also analyze message history and learn patterns of slang usage. For example, it can analyze messages containing specific keywords and identify the intention of that slang. In this way, by learning patterns of slang usage, the intention of the slang can be more accurately identified.

[0031] The context understanding unit can take geographical information or time of day into consideration and analyze whether the use of slang is concentrated in a particular region or time of day. For example, the context understanding unit uses the generation AI to analyze the poster's geographical information and determine whether slang is frequently used in a particular region. For example, if a particular slang is frequently used in a particular city or region, the context understanding unit can identify the intention. The context understanding unit can also analyze the time of day of the post and determine whether the use of slang is concentrated in a particular time of day. For example, if slang is frequently used in the late night hours, the intention can be identified. This allows for more accurate identification of the intention of the slang by analyzing whether the use of slang is concentrated in a particular region or time of day.

[0032] The context understanding unit can also analyze the content of images or videos and identify the intention of slang even from media other than text. For example, the context understanding unit uses a generative AI to analyze images included in a post and identify the intention of the slang. For example, if a specific image is used in association with a slang, the intention can be identified. The context understanding unit can also analyze the content of a video and identify the intention of the slang. For example, a video analysis algorithm can be used to analyze the content of a video and identify the intention of the slang. This makes it possible to identify the intention of the slang even from media other than text.

[0033] The context understanding unit can compare slang usage patterns across different social media platforms and identify common slang. For example, the context understanding unit uses a generation AI to analyze posts on different social media platforms and compare slang usage patterns. For example, if the same slang is used on Twitter and Instagram, the intention can be identified. The context understanding unit can also perform frequency analysis and co-occurrence network analysis to identify common slang. For example, frequency analysis can be used to compare the frequency of slang usage across different social media platforms and identify common slang. This makes it possible to compare slang usage patterns across different social media platforms and identify common slang.

[0034] The risk determination unit can analyze information about an account's followers or the accounts it follows and identify groups of highly related accounts. For example, the risk determination unit uses a generation AI to analyze information about an account's followers and identify groups of highly related accounts. For example, if many followers use the same slang, it identifies the risk of that account. The risk determination unit can also analyze information about followed accounts and identify groups of highly related accounts. For example, it can identify accounts with common interests and identify the risk of those accounts. In this way, by identifying groups of highly related accounts, it is possible to more accurately identify accounts involved in risky activities.

[0035] The risk determination unit can analyze an account's posting frequency or activity time to detect abnormal patterns. For example, the risk determination unit uses a generation AI to analyze an account's posting frequency to detect abnormal patterns. For example, if there is a large number of posts in a short period of time, the risk determination unit identifies the account as risky. The risk determination unit can also analyze the account's activity time to detect abnormal patterns. For example, if there is frequent activity during the late night hours, the risk determination unit identifies the account as risky. By detecting abnormal patterns in this way, accounts involved in risky activities can be identified more accurately.

[0036] The risk determination unit can compare the content of an account's past posts with the content of its current posts and determine that an account is dangerous if there is a sudden change. For example, the generation AI can compare the content of an account's past posts with the content of its current posts and determine that an account is dangerous if there is a sudden change. For example, if there is a sudden increase in the use of slang, the risk of that account can be identified. The risk determination unit can also analyze changes in themes of posted content or sudden increases in posting frequency and determine that an account is dangerous if there is a sudden change. For example, it can compare the content of past posts with the content of current posts and identify changes in themes or sudden increases in frequency. This allows accounts involved in dangerous activities to be identified more accurately by determining that a sudden change is dangerous.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The monitoring system may further include a real-time translation unit. The real-time translation unit automatically translates slang posted in different languages ​​and provides the translation to the context understanding unit. For example, slang posted in English may be translated into Japanese to identify its intent. The real-time translation unit may also support multiple languages ​​and analyze slang usage patterns across different languages. This allows the use of slang in different languages ​​to be monitored and criminal activity to be identified.

[0039] The monitoring system may further include an audio analysis unit. The audio analysis unit analyzes voice messages and phone call content to identify the intent of slang. For example, it may use voice recognition technology to convert voice messages into text and analyze the content. The audio analysis unit may also analyze phone call content in real time to identify the use of slang. This makes it possible to identify the intent of slang even from audio media and monitor crime-related activities.

[0040] The monitoring system may further include a behavior prediction unit. The behavior prediction unit predicts a user's future behavior based on past posting history and message history. For example, the behavior prediction unit may learn a pattern of a specific behavior being taken after a specific slang term is used, and predict future behavior based on that pattern. The behavior prediction unit may also use a machine learning algorithm to analyze a user's behavioral patterns and predict future behavior. This makes it possible to predict crime-related activities in advance and take countermeasures.

[0041] The monitoring system can further include a location information analysis unit. The location information analysis unit analyzes the poster's location information and identifies the usage pattern of slang in a specific area. For example, if slang is frequently used in a specific city or area, the intention behind it can be identified. The location information analysis unit can also identify areas where slang use is concentrated based on the location information. This makes it possible to analyze the usage pattern of slang in a specific area and identify crime-related activities.

[0042] The monitoring system can further include a device information analysis unit. The device information analysis unit analyzes device information used by the poster and identifies the usage pattern of slang from a specific device. For example, if slang is frequently used from a specific smartphone or computer, the intention can be identified. The device information analysis unit can also identify the usage pattern of slang based on the type of device and usage status. This makes it possible to analyze the usage pattern of slang from a specific device and identify crime-related activities.

[0043] The monitoring system can further include a network analysis unit. The network analysis unit analyzes the poster's network connection information and identifies the use pattern of slang from a specific network. For example, if slang is frequently used from a specific Wi-Fi network or IP address, the intention can be identified. The network analysis unit can also identify the use pattern of slang based on the type of network and connection status. This makes it possible to analyze the use pattern of slang from a specific network and identify criminal activity.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The context understanding unit analyzes posts or messages on the SNS. For example, the context understanding unit analyzes text posts on the SNS using natural language processing technology. The context understanding unit can also analyze posts with images and direct messages. For example, the context understanding unit analyzes the content of posts with images using image recognition technology and analyzes the text data of direct messages. Step 2: The intent analysis unit analyzes the intent of the slang based on the context of the post or message analyzed by the context understanding unit. For example, the intent analysis unit refers to a dictionary of slang and identifies the intent of the slang based on past usage examples. The intent analysis unit can also analyze the intent of the slang using a machine learning algorithm. For example, the intent analysis unit learns the frequency and context of slang usage and identifies its intent. Step 3: The risk determination unit determines whether an account using slang is involved in risky activities based on the intent of the slang analyzed by the intent analysis unit. For example, the risk determination unit determines risky activities based on criteria such as illegal activities, violent activities, and fraudulent activities. The risk determination unit can also analyze slang usage patterns and account posting history to identify possible risky activities. For example, the risk determination unit determines accounts that frequently use specific slang or accounts that use slang in a specific pattern as risky.

[0046] (Example 2) A monitoring system according to an embodiment of the present invention utilizes generative AI to monitor the use of slang on the Internet, particularly on social networking sites, and identify crime-related activities. The system uses generative AI to understand the context, analyze the intention behind the use of slang, and determine whether accounts using the slang are involved in dangerous activities. This enables the monitoring system to monitor the use of slang on social networking sites and identify crime-related activities.

[0047] A monitoring system according to an embodiment includes a context understanding unit, an intention analysis unit, and a risk determination unit. The context understanding unit analyzes posts or messages on social media. For example, the context understanding unit analyzes text posts on social media using natural language processing technology. The context understanding unit can also analyze posts with images and direct messages. For example, the context understanding unit analyzes the content of posts with images using image recognition technology and the text data of direct messages. The intention analysis unit analyzes the intention of slang based on the context of the post or message analyzed by the context understanding unit. For example, the intention analysis unit refers to a slang dictionary and identifies the intention of the slang based on past usage examples. The intention analysis unit can also analyze the intention of the slang using a machine learning algorithm. For example, the intention analysis unit learns the frequency and context of use of the slang and identifies its intention. The risk determination unit determines whether an account using slang is engaged in risky activities based on the intention of the slang analyzed by the intention analysis unit. For example, the risk determination unit determines risky activities based on criteria such as illegal activities, violent activities, and fraudulent activities. The risk determination unit can also analyze patterns of slang usage and account posting histories to identify possible risky activities. For example, the risk determination unit determines that accounts that frequently use specific slang or accounts that use slang in a specific pattern are risky. This allows the monitoring system according to the embodiment to monitor the use of slang on SNS and identify crime-related activities. For example, the monitoring system can detect crime-related activities, such as illegal drug trafficking and fraudulent solicitations, early on and take measures. This is expected to improve safety on SNS.

[0048] The context understanding unit can learn patterns of slang usage by referring to the poster's past posting history or message history. For example, the context understanding unit uses a generation AI to analyze the poster's past posting history and learn the frequency and context of slang usage. For example, if a specific slang is frequently used in a specific situation, the intention of that slang can be identified. The context understanding unit can also analyze message history and learn patterns of slang usage. For example, it can analyze messages containing specific keywords and identify the intention of that slang. In this way, by learning patterns of slang usage, the intention of the slang can be more accurately identified.

[0049] The context understanding unit can take geographical information or time of day into consideration and analyze whether the use of slang is concentrated in a particular region or time of day. For example, the context understanding unit uses the generation AI to analyze the poster's geographical information and determine whether slang is frequently used in a particular region. For example, if a particular slang is frequently used in a particular city or region, the context understanding unit can identify the intention. The context understanding unit can also analyze the time of day of the post and determine whether the use of slang is concentrated in a particular time of day. For example, if slang is frequently used in the late night hours, the intention can be identified. This allows for more accurate identification of the intention of the slang by analyzing whether the use of slang is concentrated in a particular region or time of day.

[0050] The intent analysis unit can use an emotion estimation function to analyze the emotion of posts containing slang and determine that there is a high possibility of illegal activity if the emotion is strong negative. For example, the intent analysis unit uses a generative AI to analyze the emotion of posts containing slang and identify the possibility of illegal activity if the emotion is strong negative. For example, if a post containing strong emotions of anger or sadness contains slang, it can identify the intention. The intent analysis unit can also use an emotion estimation algorithm to analyze the emotion of posts containing slang. For example, it can use text mining technology to analyze emotion and identify the possibility of illegal activity if the emotion is strong negative. This makes it possible to identify the possibility of illegal activity if the emotion is strong negative.

[0051] The context understanding unit can also analyze the content of images or videos and identify the intention of slang even from media other than text. For example, the context understanding unit uses a generative AI to analyze images included in a post and identify the intention of the slang. For example, if a specific image is used in association with a slang, the intention can be identified. The context understanding unit can also analyze the content of a video and identify the intention of the slang. For example, a video analysis algorithm can be used to analyze the content of a video and identify the intention of the slang. This makes it possible to identify the intention of the slang even from media other than text.

[0052] The context understanding unit can compare slang usage patterns across different social media platforms and identify common slang. For example, the context understanding unit uses a generation AI to analyze posts on different social media platforms and compare slang usage patterns. For example, if the same slang is used on Twitter and Instagram, the intention can be identified. The context understanding unit can also perform frequency analysis and co-occurrence network analysis to identify common slang. For example, frequency analysis can be used to compare the frequency of slang usage across different social media platforms and identify common slang. This makes it possible to compare slang usage patterns across different social media platforms and identify common slang.

[0053] The intent analysis unit uses an emotion estimation function to analyze other users' reactions to posts containing slang, and can determine that illegal activity is likely when there are few positive reactions. For example, the intent analysis unit uses a generation AI to analyze other users' reactions to posts containing slang, and identifies the possibility of illegal activity when there are few positive reactions. For example, if a post with many negative comments contains slang, the intention can be identified. The intent analysis unit can also analyze other users' reactions using an emotion estimation algorithm. For example, it can analyze other users' reactions using text mining technology, and identify the possibility of illegal activity when there are few positive reactions. This makes it possible to identify the possibility of illegal activity when there are few positive reactions.

[0054] The risk determination unit can analyze information about an account's followers or the accounts it follows and identify groups of highly related accounts. For example, the risk determination unit uses a generation AI to analyze information about an account's followers and identify groups of highly related accounts. For example, if many followers use the same slang, it identifies the risk of that account. The risk determination unit can also analyze information about followed accounts and identify groups of highly related accounts. For example, it can identify accounts with common interests and identify the risk of those accounts. In this way, by identifying groups of highly related accounts, it is possible to more accurately identify accounts involved in risky activities.

[0055] The risk determination unit can analyze an account's posting frequency or activity time to detect abnormal patterns. For example, the risk determination unit uses a generation AI to analyze an account's posting frequency to detect abnormal patterns. For example, if there is a large number of posts in a short period of time, the risk determination unit identifies the account as risky. The risk determination unit can also analyze the account's activity time to detect abnormal patterns. For example, if there is frequent activity during the late night hours, the risk determination unit identifies the account as risky. By detecting abnormal patterns in this way, accounts involved in risky activities can be identified more accurately.

[0056] The risk determination unit can use the emotion estimation function to analyze the overall emotional trends of posts from accounts that use slang and determine accounts with strong negative emotions as risky. For example, the risk determination unit uses a generation AI to analyze the overall emotional trends of posts from accounts that use slang and determine accounts with strong negative emotions as risky. For example, if there are many posts with strong emotions of anger or sadness, the risk determination unit identifies the account as risky. The risk determination unit can also use an emotion estimation algorithm to analyze the emotional trends of accounts. For example, it can use text mining technology to analyze emotions and determine accounts with strong negative emotions as risky. This allows accounts with strong negative emotions to be determined as risky, making it possible to more accurately identify accounts involved in risky activities.

[0057] The risk determination unit can compare the content of an account's past posts with the content of its current posts and determine that an account is dangerous if there is a sudden change. For example, the generation AI can compare the content of an account's past posts with the content of its current posts and determine that an account is dangerous if there is a sudden change. For example, if there is a sudden increase in the use of slang, the risk of that account can be identified. The risk determination unit can also analyze changes in themes of posted content or sudden increases in posting frequency and determine that an account is dangerous if there is a sudden change. For example, it can compare the content of past posts with the content of current posts and identify changes in themes or sudden increases in frequency. This allows accounts involved in dangerous activities to be identified more accurately by determining that a sudden change is dangerous.

[0058] The risk determination unit can use an emotion estimation function to analyze the emotional reactions of followers of an account that uses slang, and determine that the account is dangerous if there are many negative reactions. For example, the risk determination unit uses a generation AI to analyze the emotional reactions of followers of an account that uses slang, and determine that the account is dangerous if there are many negative reactions. For example, if a follower's comments or reactions are negative, the risk of the account is identified. The risk determination unit can also analyze the emotional reactions of followers using an emotion estimation algorithm. For example, it can analyze the emotions of followers using text mining technology, and determine that the account is dangerous if there are many negative reactions. This makes it possible to more accurately identify accounts involved in dangerous activities by determining that an account is dangerous if there are many negative reactions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The monitoring system may further include a real-time translation unit. The real-time translation unit automatically translates slang posted in different languages ​​and provides the translation to the context understanding unit. For example, slang posted in English may be translated into Japanese to identify its intent. The real-time translation unit may also support multiple languages ​​and analyze slang usage patterns across different languages. This allows the use of slang in different languages ​​to be monitored and criminal activity to be identified.

[0061] The monitoring system may further include an audio analysis unit. The audio analysis unit analyzes voice messages and phone call content to identify the intent of slang. For example, it may use voice recognition technology to convert voice messages into text and analyze the content. The audio analysis unit may also analyze phone call content in real time to identify the use of slang. This makes it possible to identify the intent of slang even from audio media and monitor crime-related activities.

[0062] The monitoring system may further include a behavior prediction unit. The behavior prediction unit predicts a user's future behavior based on past posting history and message history. For example, the behavior prediction unit may learn a pattern of a specific behavior being taken after a specific slang term is used, and predict future behavior based on that pattern. The behavior prediction unit may also use a machine learning algorithm to analyze a user's behavioral patterns and predict future behavior. This makes it possible to predict crime-related activities in advance and take countermeasures.

[0063] The monitoring system can further use emotion estimation capabilities to analyze the emotions of posts containing slang and determine that the likelihood of illegal activity is low if the emotion is strong and positive. For example, if a post containing slang expresses strong emotions of joy or excitement, the system can identify the intention. The monitoring system can also use emotion estimation algorithms to analyze the emotions of posts containing slang and determine that the likelihood of illegal activity is low if the emotion is strong and positive. This reduces false positives by lowering the likelihood of illegal activity if the emotion is strong and positive.

[0064] The monitoring system can also use emotion estimation to analyze the emotion of posts containing slang and determine that sudden changes in emotion indicate a high possibility of illegal activity. For example, if a poster's emotion suddenly changes to a negative one, the system can identify their intention. The system can also use emotion estimation algorithms to analyze the emotion of posts containing slang and identify possible illegal activity if sudden changes in emotion occur. This allows the system to identify possible illegal activity if sudden changes in emotion occur and take early action.

[0065] The monitoring system can further use emotion estimation capabilities to analyze the emotion of posts containing code words and determine that inconsistent emotions indicate a high likelihood of illegal activity. For example, if a poster's emotions are inconsistent, their intentions can be identified. The monitoring system can also use emotion estimation algorithms to analyze the emotion of posts containing code words and identify possible illegal activity if the emotions are inconsistent. This allows for the identification of possible illegal activity if the emotions are inconsistent and for early countermeasures to be taken.

[0066] The monitoring system can further use emotion estimation capabilities to analyze the emotions of posts containing code words and determine that illegal activity is likely when the emotion intensity is high. For example, if the poster's emotion is very strong, the system can identify their intention. The monitoring system can also use emotion estimation algorithms to analyze the emotions of posts containing code words and identify the possibility of illegal activity when the emotion intensity is high. This allows the system to identify the possibility of illegal activity when the emotion intensity is high and take early action.

[0067] The monitoring system can further include a location information analysis unit. The location information analysis unit analyzes the poster's location information and identifies the usage pattern of slang in a specific area. For example, if slang is frequently used in a specific city or area, the intention behind it can be identified. The location information analysis unit can also identify areas where slang use is concentrated based on the location information. This makes it possible to analyze the usage pattern of slang in a specific area and identify crime-related activities.

[0068] The monitoring system can further include a device information analysis unit. The device information analysis unit analyzes device information used by the poster and identifies the usage pattern of slang from a specific device. For example, if slang is frequently used from a specific smartphone or computer, the intention can be identified. The device information analysis unit can also identify the usage pattern of slang based on the type of device and usage status. This makes it possible to analyze the usage pattern of slang from a specific device and identify crime-related activities.

[0069] The monitoring system can further include a network analysis unit. The network analysis unit analyzes the poster's network connection information and identifies the use pattern of slang from a specific network. For example, if slang is frequently used from a specific Wi-Fi network or IP address, the intention can be identified. The network analysis unit can also identify the use pattern of slang based on the type of network and connection status. This makes it possible to analyze the use pattern of slang from a specific network and identify criminal activity.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The context understanding unit analyzes posts or messages on the SNS. For example, the context understanding unit analyzes text posts on the SNS using natural language processing technology. The context understanding unit can also analyze posts with images and direct messages. For example, the context understanding unit analyzes the content of posts with images using image recognition technology and analyzes the text data of direct messages. Step 2: The intent analysis unit analyzes the intent of the slang based on the context of the post or message analyzed by the context understanding unit. For example, the intent analysis unit refers to a dictionary of slang and identifies the intent of the slang based on past usage examples. The intent analysis unit can also analyze the intent of the slang using a machine learning algorithm. For example, the intent analysis unit learns the frequency and context of slang usage and identifies its intent. Step 3: The risk determination unit determines whether an account using slang is involved in risky activities based on the intent of the slang analyzed by the intent analysis unit. For example, the risk determination unit determines risky activities based on criteria such as illegal activities, violent activities, and fraudulent activities. The risk determination unit can also analyze slang usage patterns and account posting history to identify possible risky activities. For example, the risk determination unit determines accounts that frequently use specific slang or accounts that use slang in a specific pattern as risky.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] 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.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] 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.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0116] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0120] The data processing system 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 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 context understanding unit that analyzes posts or messages on SNS; an intention analysis unit that analyzes the intention of slang based on the context of the post or message analyzed by the context understanding unit; a risk determination unit that determines whether an account using slang is involved in risky activities based on the intent of the slang analyzed by the intent analysis unit. A system characterized by:

2. The context understanding unit Refer to the poster's past posting history or message history to learn the usage pattern of the slang.

2. The system of claim 1.

3. The context understanding unit Analyze the content of images or videos to identify the intent of the code words from media other than text.

2. The system of claim 1.

4. The danger determination unit Analyzing information about the followers of the account or the accounts that follow the account, and identifying a group of highly relevant accounts 2. The system of claim 1.

5. The intention analysis unit Analyzing the sentiment of the post containing the code word, and determining that there is a high possibility of illegal activity if the sentiment is strongly negative 2. The system of claim 1.

Images