system

Abstract

An object of a system according to an embodiment is to quantify a risk of a post content in an SNS and make a poster aware of the risk.SOLUTION: A system according to an embodiment includes a post analysis unit, a risk digitizing unit, and a risk presentation unit. The post analysis unit analyzes a post content. The risk quantifying section quantifies the risk based on the post content analyzed by the post analyzing section. The risk presenting unit presents the risk quantified by the risk quantifying unit to the poster.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] With conventional technology, it is difficult to grasp the risks posed by careless posts on social media in advance, and there is a risk of problems such as online outrage.

[0005] The system according to the embodiment aims to quantify the risk of content posted on SNS and make posters aware of the risk. [Means for solving the problem]

[0006] The system according to the embodiment includes a post analysis unit, a risk quantification unit, and a risk presentation unit. The post analysis unit analyzes the content of a post. The risk quantification unit quantifies the risk based on the content of the post analyzed by the post analysis unit. The risk presentation unit presents the risk quantified by the risk quantification unit to the poster. [Effects of the Invention]

[0007] The system according to the embodiment can quantify the risk of content posted on SNS and make posters aware of the risk. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) The risk assessment system according to an embodiment of the present invention is a system that uses a generation AI to quantify the risk of a post and make posters aware of the risk in order to prevent flaming caused by careless posts on social media. This allows posters to avoid rash behavior.

[0029] A risk assessment system according to an embodiment includes a post analysis unit, a risk quantification unit, and a risk presentation unit. The post analysis unit analyzes the content of a post. For example, the generation AI analyzes the content of the post using natural language processing technology to extract risk factors. The post analysis unit can also detect emotional expressions contained in the content of the post. For example, the generation AI calculates an emotional score for the content of the post and identifies extreme expressions. The risk quantification unit quantifies risk based on the content of the post analyzed by the post analysis unit. For example, the generation AI calculates a risk score based on the risk factors. The risk quantification unit can also analyze risk trends by referring to past posting history. For example, the generation AI learns past posting patterns and assigns a high risk score to posts that pose similar risks. The risk presentation unit presents the risk quantified by the risk quantification unit to the poster. For example, the generation AI displays the risk score and the reason for the score to the poster, encouraging them to reconsider their post. In addition to presenting the risk score, the risk presentation unit can also suggest specific modifications to reduce the risk. For example, the generation AI suggests alternative expressions to avoid offensive language. This allows the risk assessment system according to the embodiment to make posters aware of the risks and avoid reckless behavior. For example, the generation AI analyzes the content of a post, calculates a risk score, and presents it to the poster, making it easier for the poster to recognize the risks. The generation AI calculates an emotional score for the content of the post and identifies extreme language. The generation AI learns past posting patterns and assigns a high risk score to posts that pose a similar risk. The generation AI displays the risk score and the reason for it to the poster, encouraging them to reconsider their post. The generation AI suggests alternative expressions to avoid offensive language.

[0030] The post analysis unit performs an emotional analysis of the posted content, detects emotionally extreme expressions, and can quantify the risk. For example, the post analysis unit uses a generative AI to perform an emotional analysis of the posted content and detects emotionally extreme expressions. For example, it identifies expressions of anger or hatred and quantifies the risk. Specifically, it calculates an emotional score contained in the posted content, and assigns a high risk score if there are many extreme expressions. In this way, by detecting emotionally extreme expressions and quantifying the risk, posters can be more easily aware of the risk.

[0031] The post analysis unit can refer to the poster's past posting history, analyze risk trends, and quantify them. For example, the generation AI in the post analysis unit refers to the poster's past posting history and analyzes risk trends. For example, it can learn the patterns of posts that have caused controversy in the past and assign a high risk score to posts that pose a similar risk. This makes it easier to understand risk trends by referring to past posting history.

[0032] The post analysis unit can analyze images and videos included in the posted content and quantify visual risk elements. For example, the post analysis unit uses a generative AI to analyze images and videos included in the posted content and quantify visual risk elements. For example, it can detect violent images or inappropriate videos and quantify their risk. In this way, visual risks can also be understood by quantifying the risk elements of images and videos.

[0033] The post analysis unit can analyze the voice input and quantify the risk at the same time as converting the voice to text. For example, the generative AI analyzes the voice input and quantifies the risk at the same time as converting the voice to text. For example, the voice input content is converted into text and a risk score is assigned based on that content. This allows the voice input to be analyzed and the risk to be quantified at the same time as converting the voice to text.

[0034] The post analysis unit cross-references the content posted across different SNS platforms and can comprehensively quantify the risk. For example, the generation AI cross-references the content posted across different SNS platforms and can comprehensively quantify the risk. For example, it compares the content posted across multiple platforms and assigns a high risk score if the risk is consistently high. This makes it possible to cross-reference the content posted across different SNS platforms and comprehensively quantify the risk.

[0035] When presenting a risk, the risk presentation unit can compare it with the poster's past risk score and present specific areas for improvement. For example, when the generation AI presents a risk, the risk presentation unit compares it with the poster's past risk score and presents specific areas for improvement. For example, it compares past posts with current posts and indicates areas where risks have been reduced and areas where improvement is required. This allows the poster to specifically understand areas for improvement by comparing with past risk scores.

[0036] In addition to presenting a risk score, the risk presentation unit allows the generation AI to suggest specific revision suggestions to reduce the risk. For example, in addition to presenting a risk score, the risk presentation unit allows the generation AI to suggest specific revision suggestions to reduce the risk. For example, the risk presentation unit suggests alternative expressions to avoid offensive language. This makes it easier for posters to understand how to reduce the risk by suggesting specific revision suggestions.

[0037] The risk presentation unit can provide an interactive dashboard for visually displaying the risk score, allowing the poster to intuitively understand the risk. The risk presentation unit, for example, provides an interactive dashboard for the generation AI to visually display the risk score. For example, the risk score may be displayed in a graph or chart, allowing the poster to intuitively understand. In this way, visually displaying the risk makes it easier for the poster to intuitively understand the risk.

[0038] The risk presentation unit can simultaneously display feedback from other users when presenting a risk, thereby reflecting the opinions of the community. For example, when the generation AI presents a risk, the risk presentation unit can simultaneously display feedback from other users and reflect the opinions of the community. For example, it can display comments and ratings from other users. In this way, by displaying feedback from other users, the poster can easily understand the opinions of the community.

[0039] The post analysis unit performs trend analysis on UGC both inside and outside the group, and can identify high-risk trends. For example, the generation AI performs trend analysis on UGC both inside and outside the group, and identifies high-risk trends. For example, if a specific keyword or phrase is frequently used, the risk of that trend is quantified. This makes it easier to identify high-risk trends by performing trend analysis.

[0040] The post analysis unit can evaluate the influence of UGC posters and focus on quantifying the risks of highly influential posters. For example, the post analysis unit uses a generation AI to evaluate the influence of UGC posters and focus on quantifying the risks of highly influential posters. For example, it evaluates influence based on the number of followers and engagement rate and quantifies the risks of highly influential posters. This allows for effective risk management by focusing on quantifying the risks of highly influential posters.

[0041] The post analysis unit can analyze the context of UGC posts and quantify the risk. For example, the post analysis unit uses a generation AI to analyze the context of UGC posts and quantify the risk. For example, it analyzes the background and intent of the post content, and identifies and quantifies high-risk elements. By analyzing the context, it is possible to understand the background and intent of the post content and accurately quantify the risk.

[0042] The post analysis unit can quantify the risk of UGC in different languages ​​and build a multilingual risk assessment system. For example, the post analysis unit uses a generation AI to quantify the risk of UGC in different languages ​​and build a multilingual risk assessment system. For example, it supports multiple languages ​​such as English, French, and Chinese. This allows for the construction of a multilingual risk assessment system, making it possible to assess risk from an international perspective.

[0043] The post analysis unit can quantify risks in real time and provide instant feedback. For example, the generation AI can quantify risks in real time for UGC both inside and outside the group and provide instant feedback. For example, the post analysis unit can calculate a risk score the moment the post content is entered and present it to the poster. This allows the poster to quickly recognize risks by quantifying risks in real time and providing instant feedback.

[0044] The post analysis unit can analyze the content posted by students and conduct exercises in which the results of risk assessments are fed back. For example, the post analysis unit conducts exercises in which the generation AI analyzes the content posted by students and feeds back the results of risk assessments. For example, it presents a risk score for the content posted by students and indicates specific areas for improvement. In this way, the educational effectiveness is enhanced by analyzing the content posted by students and feeding back the results of risk assessments.

[0045] The post analysis unit analyzes students' posting history and can learn risk trends. For example, the generative AI in the post analysis unit analyzes students' posting history and can learn risk trends. For example, it can identify high-risk patterns based on the content of past posts and provide feedback to students. This increases the effectiveness of education by analyzing students' posting history and learning risk trends.

[0046] The post analysis unit analyzes student posts in multiple languages ​​and can perform risk assessments from an international perspective. For example, the generative AI in the post analysis unit analyzes student posts in multiple languages ​​and performs risk assessments from an international perspective. For example, it supports multiple languages ​​such as English, French, and Chinese. This makes it possible to perform risk assessments from an international perspective by analyzing in multiple languages.

[0047] The post analysis unit visualizes the content posted by students, allowing them to understand the risks visually. For example, the generative AI in the post analysis unit visualizes the content posted by students, allowing them to understand the risks visually. For example, the risk score can be displayed in a graph or chart, allowing students to understand intuitively. This visualization makes it easier for students to intuitively understand the risks.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The risk assessment system can also analyze the poster's geographical information and quantify risk based on the cultural background of each region. For example, expressions that are acceptable in certain regions may be problematic in others, so taking geographical information into account enables more accurate risk assessment. Specifically, the post analysis unit obtains the poster's IP address and location information and adjusts the risk score based on the cultural background and social norms of that region. This enables risk assessment by region, making it easier for posters to recognize risks specific to their region.

[0050] The risk assessment system can also take into account the poster's age group and perform age-appropriate risk assessment. For example, since younger posters tend to be more emotionally expressive, assigning a risk score according to age allows for more appropriate risk assessment. Specifically, the post analysis unit obtains the poster's age information, learns the risk trends for each age group, and adjusts the risk score accordingly. This enables age-appropriate risk assessment, making it easier for posters to recognize risks appropriate for their age.

[0051] The risk assessment system can also analyze the topic of the post and quantify the risk associated with a specific topic. For example, posts about politics or religion have a high risk of causing outrage, so these topics are assigned a high risk score. Specifically, the post analysis unit automatically classifies the topics of the post, learns the risk trends for each topic, and adjusts the risk score accordingly. This enables risk assessment according to the topic, making it easier for posters to recognize the risks associated with specific topics.

[0052] The risk assessment system can also analyze the reactions of a poster's followers and quantify the risk based on their responses. For example, a high risk score can be assigned to posts to which followers have negative reactions. Specifically, the post analysis unit analyzes followers' comments and reactions, and adjusts the risk score if there are many negative reactions. This makes it possible to assess risk based on follower reactions, making it easier for posters to recognize risks that take into account the opinions of their followers.

[0053] The risk assessment system can also take into account the time of day the content is posted and quantify the risk during specific time periods. For example, late-night posts have a higher risk of causing a controversy, so content posted late at night is assigned a higher risk score. Specifically, the post analysis unit obtains the time of posting, learns risk trends by time period, and adjusts the risk score accordingly. This makes it possible to assess risk according to the time of day, making it easier for posters to recognize the risks associated with specific time periods.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The post analysis unit analyzes the content of the post. For example, the generation AI analyzes the content of the post using natural language processing technology and extracts risk factors. The post analysis unit can also detect emotional expressions contained in the content of the post. For example, the generation AI calculates an emotional score for the content of the post and identifies extreme expressions. Step 2: The risk quantification unit quantifies the risk based on the post content analyzed by the post analysis unit. For example, the generation AI calculates a risk score based on risk factors. The risk quantification unit can also analyze risk trends by referring to past post history. For example, the generation AI learns past post patterns and assigns a high risk score to posts that pose similar risks. Step 3: The risk presentation unit presents the poster with the risk quantified by the risk quantification unit. For example, the generation AI may display the risk score and the reason for it to the poster, encouraging them to reconsider their post. In addition to presenting the risk score, the risk presentation unit may also suggest specific modifications to reduce the risk. For example, the generation AI may suggest alternative expressions to avoid offensive language.

[0056] (Example 2) The risk assessment system according to an embodiment of the present invention is a system that uses a generation AI to quantify the risk of a post and make posters aware of the risk in order to prevent flaming caused by careless posts on social media. This allows posters to avoid rash behavior.

[0057] A risk assessment system according to an embodiment includes a post analysis unit, a risk quantification unit, and a risk presentation unit. The post analysis unit analyzes the content of a post. For example, the generation AI analyzes the content of the post using natural language processing technology to extract risk factors. The post analysis unit can also detect emotional expressions contained in the content of the post. For example, the generation AI calculates an emotional score for the content of the post and identifies extreme expressions. The risk quantification unit quantifies risk based on the content of the post analyzed by the post analysis unit. For example, the generation AI calculates a risk score based on the risk factors. The risk quantification unit can also analyze risk trends by referring to past posting history. For example, the generation AI learns past posting patterns and assigns a high risk score to posts that pose similar risks. The risk presentation unit presents the risk quantified by the risk quantification unit to the poster. For example, the generation AI displays the risk score and the reason for the score to the poster, encouraging them to reconsider their post. In addition to presenting the risk score, the risk presentation unit can also suggest specific modifications to reduce the risk. For example, the generation AI suggests alternative expressions to avoid offensive language. This allows the risk assessment system according to the embodiment to make posters aware of the risks and avoid reckless behavior. For example, the generation AI analyzes the content of a post, calculates a risk score, and presents it to the poster, making it easier for the poster to recognize the risks. The generation AI calculates an emotional score for the content of the post and identifies extreme language. The generation AI learns past posting patterns and assigns a high risk score to posts that pose a similar risk. The generation AI displays the risk score and the reason for it to the poster, encouraging them to reconsider their post. The generation AI suggests alternative expressions to avoid offensive language.

[0058] The post analysis unit performs an emotional analysis of the posted content, detects emotionally extreme expressions, and can quantify the risk. For example, the post analysis unit uses a generative AI to perform an emotional analysis of the posted content and detects emotionally extreme expressions. For example, it identifies expressions of anger or hatred and quantifies the risk. Specifically, it calculates an emotional score contained in the posted content, and assigns a high risk score if there are many extreme expressions. In this way, by detecting emotionally extreme expressions and quantifying the risk, posters can be more easily aware of the risk.

[0059] The post analysis unit can refer to the poster's past posting history, analyze risk trends, and quantify them. For example, the generation AI in the post analysis unit refers to the poster's past posting history and analyzes risk trends. For example, it can learn the patterns of posts that have caused controversy in the past and assign a high risk score to posts that pose a similar risk. This makes it easier to understand risk trends by referring to past posting history.

[0060] The post analysis unit can analyze images and videos included in the posted content and quantify visual risk elements. For example, the post analysis unit uses a generative AI to analyze images and videos included in the posted content and quantify visual risk elements. For example, it can detect violent images or inappropriate videos and quantify their risk. In this way, visual risks can also be understood by quantifying the risk elements of images and videos.

[0061] The post analysis unit can analyze the voice input and quantify the risk at the same time as converting the voice to text. For example, the generative AI analyzes the voice input and quantifies the risk at the same time as converting the voice to text. For example, the voice input content is converted into text and a risk score is assigned based on that content. This allows the voice input to be analyzed and the risk to be quantified at the same time as converting the voice to text.

[0062] The post analysis unit cross-references the content posted across different SNS platforms and can comprehensively quantify the risk. For example, the generation AI cross-references the content posted across different SNS platforms and can comprehensively quantify the risk. For example, it compares the content posted across multiple platforms and assigns a high risk score if the risk is consistently high. This makes it possible to cross-reference the content posted across different SNS platforms and comprehensively quantify the risk.

[0063] The post analysis unit uses the emotion estimation function to analyze the poster's emotional state in real time and quantify the risk based on that emotion. For example, the generative AI uses the emotion estimation function to analyze the poster's emotional state in real time and quantify the risk based on that emotion. For example, a high risk score is assigned to a post made in an angry or excited state. This makes it possible to analyze the poster's emotional state in real time and quantify the risk based on that emotion.

[0064] When presenting a risk, the risk presentation unit can compare it with the poster's past risk score and present specific areas for improvement. For example, when the generation AI presents a risk, the risk presentation unit compares it with the poster's past risk score and presents specific areas for improvement. For example, it compares past posts with current posts and indicates areas where risks have been reduced and areas where improvement is required. This allows the poster to specifically understand areas for improvement by comparing with past risk scores.

[0065] In addition to presenting a risk score, the risk presentation unit allows the generation AI to suggest specific revision suggestions to reduce the risk. For example, in addition to presenting a risk score, the risk presentation unit allows the generation AI to suggest specific revision suggestions to reduce the risk. For example, the risk presentation unit suggests alternative expressions to avoid offensive language. This makes it easier for posters to understand how to reduce the risk by suggesting specific revision suggestions.

[0066] The risk presentation unit can take into account the poster's emotional state when presenting a risk and provide feedback that takes those emotions into consideration. For example, when the generation AI presents a risk, the risk presentation unit can take into account the poster's emotional state and provide feedback that takes those emotions into consideration. For example, if negative emotions are strong, the risk can be communicated in gentle words. In this way, providing feedback that takes emotions into consideration makes it easier for the poster to accept the risk.

[0067] The risk presentation unit can provide an interactive dashboard for visually displaying the risk score, allowing the poster to intuitively understand the risk. The risk presentation unit, for example, provides an interactive dashboard for the generation AI to visually display the risk score. For example, the risk score may be displayed in a graph or chart, allowing the poster to intuitively understand. In this way, visually displaying the risk makes it easier for the poster to intuitively understand the risk.

[0068] The risk presentation unit can simultaneously display feedback from other users when presenting a risk, thereby reflecting the opinions of the community. For example, when the generation AI presents a risk, the risk presentation unit can simultaneously display feedback from other users and reflect the opinions of the community. For example, it can display comments and ratings from other users. In this way, by displaying feedback from other users, the poster can easily understand the opinions of the community.

[0069] The risk presentation unit uses the emotion estimation function to monitor the poster's emotional response when a risk is presented, and can adjust the method of feedback. For example, the generation AI uses the emotion estimation function to monitor the poster's emotional response when a risk is presented, and adjusts the method of feedback. For example, if negative emotions are strong, the risk can be communicated in gentler terms. In this way, by monitoring emotional responses, appropriate feedback can be provided to the poster.

[0070] The post analysis unit performs trend analysis on UGC both inside and outside the group, and can identify high-risk trends. For example, the generation AI performs trend analysis on UGC both inside and outside the group, and identifies high-risk trends. For example, if a specific keyword or phrase is frequently used, the risk of that trend is quantified. This makes it easier to identify high-risk trends by performing trend analysis.

[0071] The post analysis unit can evaluate the influence of UGC posters and focus on quantifying the risks of highly influential posters. For example, the post analysis unit uses a generation AI to evaluate the influence of UGC posters and focus on quantifying the risks of highly influential posters. For example, it evaluates influence based on the number of followers and engagement rate and quantifies the risks of highly influential posters. This allows for effective risk management by focusing on quantifying the risks of highly influential posters.

[0072] The post analysis unit can analyze the context of UGC posts and quantify the risk. For example, the post analysis unit uses a generation AI to analyze the context of UGC posts and quantify the risk. For example, it analyzes the background and intent of the post content, and identifies and quantifies high-risk elements. By analyzing the context, it is possible to understand the background and intent of the post content and accurately quantify the risk.

[0073] The post analysis unit can quantify the risk of UGC in different languages ​​and build a multilingual risk assessment system. For example, the post analysis unit uses a generation AI to quantify the risk of UGC in different languages ​​and build a multilingual risk assessment system. For example, it supports multiple languages ​​such as English, French, and Chinese. This allows for the construction of a multilingual risk assessment system, making it possible to assess risk from an international perspective.

[0074] The post analysis unit can quantify risks in real time and provide instant feedback. For example, the generation AI can quantify risks in real time for UGC both inside and outside the group and provide instant feedback. For example, the post analysis unit can calculate a risk score the moment the post content is entered and present it to the poster. This allows the poster to quickly recognize risks by quantifying risks in real time and providing instant feedback.

[0075] The post analysis unit can use the emotion estimation function to analyze the emotional state of the UGC poster and perform emotion-based risk assessment. For example, the generative AI in the post analysis unit uses the emotion estimation function to analyze the emotional state of the UGC poster and perform emotion-based risk assessment. For example, a high risk score is assigned to a post made in an angry or excited state. This makes it possible to use the emotion estimation function to perform risk assessment based on the poster's emotional state.

[0076] The post analysis unit can analyze the content posted by students and conduct exercises in which the results of risk assessments are fed back. For example, the post analysis unit conducts exercises in which the generation AI analyzes the content posted by students and feeds back the results of risk assessments. For example, it presents a risk score for the content posted by students and indicates specific areas for improvement. In this way, the educational effectiveness is enhanced by analyzing the content posted by students and feeding back the results of risk assessments.

[0077] The post analysis unit analyzes students' posting history and can learn risk trends. For example, the generative AI in the post analysis unit analyzes students' posting history and can learn risk trends. For example, it can identify high-risk patterns based on the content of past posts and provide feedback to students. This increases the effectiveness of education by analyzing students' posting history and learning risk trends.

[0078] The post analysis unit can analyze the emotional state of students and perform risk assessment based on their emotions. For example, the generative AI in the post analysis unit analyzes the emotional state of students and performs risk assessment based on their emotions. For example, it assigns a high risk score to posts made in an angry or excited state. This allows the educational effect to be improved by analyzing the emotional state of students and performing risk assessment based on their emotions.

[0079] The post analysis unit analyzes student posts in multiple languages ​​and can perform risk assessments from an international perspective. For example, the generative AI in the post analysis unit analyzes student posts in multiple languages ​​and performs risk assessments from an international perspective. For example, it supports multiple languages ​​such as English, French, and Chinese. This makes it possible to perform risk assessments from an international perspective by analyzing in multiple languages.

[0080] The post analysis unit visualizes the content posted by students, allowing them to understand the risks visually. For example, the generative AI in the post analysis unit visualizes the content posted by students, allowing them to understand the risks visually. For example, the risk score can be displayed in a graph or chart, allowing students to understand intuitively. This visualization makes it easier for students to intuitively understand the risks.

[0081] The post analysis unit uses the emotion estimation function to analyze students' emotional responses in educational programs and provide feedback based on their emotions. For example, the generative AI uses the emotion estimation function to analyze students' emotional responses in educational programs and provide feedback based on their emotions. For example, if negative emotions are strong, the risk can be communicated in gentle words. In this way, using the emotion estimation function makes it possible to provide feedback that takes students' emotions into consideration.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The risk assessment system can also analyze the poster's geographical information and quantify risk based on the cultural background of each region. For example, expressions that are acceptable in certain regions may be problematic in others, so taking geographical information into account enables more accurate risk assessment. Specifically, the post analysis unit obtains the poster's IP address and location information and adjusts the risk score based on the cultural background and social norms of that region. This enables risk assessment by region, making it easier for posters to recognize risks specific to their region.

[0084] The risk assessment system can also take into account the poster's age group and perform age-appropriate risk assessment. For example, since younger posters tend to be more emotionally expressive, assigning a risk score according to age allows for more appropriate risk assessment. Specifically, the post analysis unit obtains the poster's age information, learns the risk trends for each age group, and adjusts the risk score accordingly. This enables age-appropriate risk assessment, making it easier for posters to recognize risks appropriate for their age.

[0085] The risk assessment system can also analyze the topic of the post and quantify the risk associated with a specific topic. For example, posts about politics or religion have a high risk of causing outrage, so these topics are assigned a high risk score. Specifically, the post analysis unit automatically classifies the topics of the post, learns the risk trends for each topic, and adjusts the risk score accordingly. This enables risk assessment according to the topic, making it easier for posters to recognize the risks associated with specific topics.

[0086] The risk assessment system can also analyze the reactions of a poster's followers and quantify the risk based on their responses. For example, a high risk score can be assigned to posts to which followers have negative reactions. Specifically, the post analysis unit analyzes followers' comments and reactions, and adjusts the risk score if there are many negative reactions. This makes it possible to assess risk based on follower reactions, making it easier for posters to recognize risks that take into account the opinions of their followers.

[0087] The risk assessment system can also take into account the time of day the content is posted and quantify the risk during specific time periods. For example, late-night posts have a higher risk of causing a controversy, so content posted late at night is assigned a higher risk score. Specifically, the post analysis unit obtains the time of posting, learns risk trends by time period, and adjusts the risk score accordingly. This makes it possible to assess risk according to the time of day, making it easier for posters to recognize the risks associated with specific time periods.

[0088] The risk assessment system can also analyze the poster's emotional state in real time and quantify the risk based on that emotion. For example, a high risk score can be assigned to a post made in an angry or excited state. Specifically, the post analysis unit analyzes the poster's emotional state in real time and adjusts the risk score based on the emotional score. This enables risk assessment based on the poster's emotional state, making it easier for posters to recognize risks related to their own emotions.

[0089] The risk assessment system can also analyze the poster's emotional state and provide feedback based on their emotions. For example, if the poster has strong negative emotions, it can convey the risk in gentler terms. Specifically, the risk presentation unit analyzes the poster's emotional state and adjusts the feedback method based on the emotional score. This enables feedback that takes emotions into consideration, making it easier for the poster to accept the risk.

[0090] The risk assessment system can also analyze the poster's emotional state and suggest risk mitigation measures based on their emotions. For example, it can provide advice on staying calm in response to a post made in an angry or excited state. Specifically, the risk presentation unit analyzes the poster's emotional state and suggests risk mitigation measures based on their emotional score. This makes it possible to provide risk mitigation measures based on emotions, making it easier for posters to understand how to reduce risk.

[0091] The risk assessment system can also analyze the poster's emotional state and perform emotion-based risk assessment. For example, a high risk score can be assigned to a post made in an angry or excited state. Specifically, the post analysis unit analyzes the poster's emotional state and adjusts the risk score based on the emotional score. This enables emotion-based risk assessment, making it easier for posters to recognize risks related to their own emotions.

[0092] The risk assessment system can also analyze the poster's emotional state and display the fluctuations in the risk score based on the emotion in real time. For example, when the poster posts while feeling angry or excited, the system displays the increase in the risk score in real time. Specifically, the risk presentation unit analyzes the poster's emotional state and displays the fluctuations in the risk score based on the emotion score in real time. This makes it easier for the poster to recognize the risks related to their emotions in real time.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The post analysis unit analyzes the content of the post. For example, the generation AI analyzes the content of the post using natural language processing technology and extracts risk factors. The post analysis unit can also detect emotional expressions contained in the content of the post. For example, the generation AI calculates an emotional score for the content of the post and identifies extreme expressions. Step 2: The risk quantification unit quantifies the risk based on the post content analyzed by the post analysis unit. For example, the generation AI calculates a risk score based on risk factors. The risk quantification unit can also analyze risk trends by referring to past post history. For example, the generation AI learns past post patterns and assigns a high risk score to posts that pose similar risks. Step 3: The risk presentation unit presents the poster with the risk quantified by the risk quantification unit. For example, the generation AI may display the risk score and the reason for it to the poster, encouraging them to reconsider their post. In addition to presenting the risk score, the risk presentation unit may also suggest specific modifications to reduce the risk. For example, the generation AI may suggest alternative expressions to avoid offensive language.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a post analysis unit that analyzes the post content; a risk quantification unit that quantifies a risk based on the post content analyzed by the post analysis unit; a risk presentation unit that presents the risk quantified by the risk quantification unit to a poster. A system characterized by:

2. The post analysis unit Refer to the poster's past posting history, analyze risk trends, and quantify them 2. The system of claim 1.

3. The post analysis unit Analyzes voice input and converts it into text while simultaneously quantifying risk 2. The system of claim 1.

4. The risk presentation unit When presenting risks, compare them with the poster's past risk scores and provide specific suggestions for improvement.

2. The system of claim 1.

5. The post analysis unit Conduct trend analysis on UGC both within and outside the group to identify high-risk trends 2. The system of claim 1.

6. The post analysis unit Using emotion estimation functionality, the emotional state of the poster is analyzed in real time and the risk is quantified based on that emotion.

2. The system of claim 1.

7. The risk presentation unit When presenting risks, consider the poster's emotional state and provide feedback that takes their emotions into consideration.

2. The system of claim 1.

8. The post analysis unit Analyze students' emotional state and conduct emotion-based risk assessments 2. The system of claim 1.

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