System

Abstract

An object of a system according to an embodiment is to evaluate a fire risk of content posted by a user and appropriately correct the content.SOLUTION: A system according to an embodiment includes a post-analysis unit, a risk evaluation unit, and a correction proposal unit. The post analysis unit analyzes the content to be posted by the user. The risk evaluation unit evaluates a fire risk based on the content analyzed by the post-analysis unit. The modification suggester suggests a modification to reduce the risk assessed by the risk assessor.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 assess in advance the risk of a user posting content causing a controversy and to make appropriate corrections.

[0005] The system according to the embodiment aims to evaluate the risk of content posted by users causing a controversy and to make appropriate corrections. [Means for solving the problem]

[0006] The system according to the embodiment includes a post analysis unit, a risk assessment unit, and a revision suggestion unit. The post analysis unit analyzes the content a user intends to post. The risk assessment unit assesses the risk of a flame war based on the content analyzed by the post analysis unit. The revision suggestion unit proposes a revision to reduce the risk assessed by the risk assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the risk of a user posting content causing a controversy and make appropriate corrections. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system for reducing the risk of a social media outcry according to an embodiment of the present invention uses a generation AI to analyze content that a user intends to post on social media, evaluates the risk of the content causing an outcry, and proposes a revised version. This allows the system to appropriately manage the content of users' posts and reduce the risk of the content causing an outcry.

[0029] A flame war risk reduction system according to an embodiment includes a post analysis unit, a risk assessment unit, and a revision suggestion unit. The post analysis unit analyzes the content a user intends to post. For example, the post analysis unit uses a generation AI to analyze input text using natural language processing technology and understand the content. The post analysis unit can also identify emotional expressions and potentially misleading parts. The risk assessment unit evaluates the risk of a flame war based on the content analyzed by the post analysis unit. For example, the risk assessment unit evaluates the risk based on past flame war cases and the frequency of occurrence of specific keywords. The risk assessment unit can also refer to the user's past post history and learn past flame war patterns to evaluate risk. The revision suggestion unit proposes a revision to reduce the risk assessed by the risk assessment unit. For example, the revision suggestion unit uses a generation AI to revise the content the user intends to post to more neutral and calm expression. The revision suggestion unit can also learn patterns of past successful posts and generate revision suggestions based on them. This allows the flame war risk reduction system according to an embodiment to appropriately manage the content of users' posts and reduce the risk of a flame war. For example, the generation AI analyzes the content a user is about to post, assesses the risk of it causing a backlash, and suggests appropriate revisions, thereby reducing the risk of it causing a backlash.

[0030] The post analysis unit can reference a user's past post history, learn flaming patterns, and perform risk assessment. For example, the post analysis unit uses a generation AI to analyze a user's past post history and learn the patterns of posts that have caused flaming in the past. For example, if a specific keyword or phrase has caused a flaming, the unit can perform a risk assessment based on that pattern. The post analysis unit also clarifies the specific content of the past post history and how to reference it. For example, what period of time posts should be targeted, and how to collect the data. This allows the system to learn past flaming patterns and improve the accuracy of risk assessment.

[0031] The risk assessment unit can refer to news trends and public opinion trends in real time and conduct risk assessments taking social sentiment into account. For example, the risk assessment unit uses a generative AI to collect news trends in real time and compare them with the content of posts. For example, posts related to topics that are currently generating high social sentiment may be assessed as high risk. The risk assessment unit also clarifies the specific methods for collecting news trends and reference standards, such as news sources and how frequently trends are updated. This improves the accuracy of risk assessments by taking news trends and public opinion trends into account in real time.

[0032] The post analysis unit can analyze images and videos included in the post content and reflect this in the risk assessment. For example, the post analysis unit uses a generation AI to analyze images and videos included in the post content and reflect this in the risk assessment. For example, if a specific symbol or expression in an image is the cause of a controversy, the risk assessment is based on that information. The post analysis unit also clarifies the specific analysis method and technology used for images and videos, such as image recognition algorithms and video analysis tools. This allows for the analysis of images and videos to improve the accuracy of risk assessment.

[0033] The post analysis unit can also analyze post content in different languages ​​and perform risk assessments from an international perspective. For example, the generative AI analyzes post content in different languages ​​and performs risk assessments from an international perspective. For example, if an expression in a particular language is misleading in other cultural spheres, risk assessments are performed based on that information. The post analysis unit also clarifies the specific types of different languages ​​and analysis methods. For example, major languages, use of translation tools, etc. This makes it possible to perform risk assessments from an international perspective by analyzing post content in different languages.

[0034] The risk assessment unit can automatically collect related news articles and background information and reflect it in risk assessment. For example, when the generative AI analyzes text, the risk assessment unit automatically collects related news articles and background information and reflects it in risk assessment. For example, it collects information related to a specific incident or event and performs risk assessment based on that information. The risk assessment unit also clarifies the specific collection method and reference criteria for news articles, such as news sources and how often articles are updated. This improves the accuracy of risk assessment by collecting related news articles and background information.

[0035] The risk assessment unit can take into account the user's expertise and industry experience to conduct risk assessments to prevent technical misunderstandings. For example, the generative AI conducts risk assessments of texts by taking into account the user's expertise and industry experience. For example, if technical terms or industry-specific expressions are misleading, risk assessments are conducted based on that information. The risk assessment unit also clarifies specific evaluation criteria and reference methods for expertise, such as qualifications and past performance. This enables risk assessments that prevent technical misunderstandings by taking into account the user's expertise and industry experience.

[0036] The risk assessment unit can predict reactions on different social media platforms and perform risk assessments for each platform. For example, the risk assessment unit uses a generation AI to predict reactions on different social media platforms and perform risk assessments for each platform. For example, it identifies topics that are likely to cause controversy on specific platforms and performs risk assessments based on that information. The risk assessment unit also clarifies the specific types of social media platforms and how to reference them. For example, major social media platforms and the characteristics of each platform. This makes it possible to predict reactions on different social media platforms and perform risk assessments for each platform.

[0037] The risk assessment unit can conduct risk assessment based on the target audience, taking into account the attributes and past responses of the user's followers. For example, the risk assessment unit uses the generation AI to analyze the attributes of the user's followers and conduct risk assessment based on the target audience. For example, if there are many followers with specific attributes, the risk assessment unit conducts risk assessment based on that information. The risk assessment unit also clarifies specific assessment criteria and reference methods for follower attributes, such as age, gender, and interests. This makes it possible to conduct risk assessment based on the target audience by taking into account the attributes and past responses of the user's followers.

[0038] The correction suggestion unit can learn patterns of past successful posts and generate correction suggestions based on them. For example, the generation AI of the correction suggestion unit learns patterns of past successful posts and generates correction suggestions based on them. For example, it creates correction suggestions by referring to the expressions and structures of posts that have received high ratings in the past. The correction suggestion unit also clarifies the specific definition of successful post patterns and the learning method. For example, it may use past successful cases or a machine learning algorithm. In this way, by learning patterns of past successful posts, it is possible to generate more effective correction suggestions.

[0039] The revision suggestion unit can take into account different expression styles and generate revision suggestions in the most appropriate style. For example, the generation AI can take into account different expression styles and generate revision suggestions in the most appropriate style. For example, it can present humorous expressions and formal expressions as options. The revision suggestion unit also clarifies the specific types and evaluation criteria of the expression styles. For example, humorous, formal, casual, etc. This allows for the generation of revision suggestions in the most appropriate style by taking into account different expression styles.

[0040] The revision suggestion unit can generate internationally appropriate revision suggestions by taking into account the sensitivities of different cultural spheres and regions. For example, the generation AI generates revision suggestions that are internationally appropriate by taking into account the sensitivities of different cultural spheres and regions. For example, it creates revision suggestions that avoid expressions that are misleading in specific cultures or regions. The revision suggestion unit also clarifies specific definitions and evaluation criteria for cultural spheres and regions, such as specific countries, regions, or cultural backgrounds. This allows for the generation of internationally appropriate revision suggestions by taking into account the sensitivities of different cultural spheres and regions.

[0041] The revision suggestion unit can generate revision suggestions to maintain consistency with the content of the user's past posts. For example, the generation AI analyzes the content of the user's past posts and generates revision suggestions to maintain consistency. For example, revision suggestions are created by referring to the style and tone of past posts. The revision suggestion unit also clarifies specific evaluation criteria for consistency and how to maintain it. For example, consistency with the content of past posts, maintaining the brand image, etc. This makes it possible to generate reliable revision suggestions by maintaining consistency with the content of the user's past posts.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Flaming Risk Reduction System can also learn a user's posting style and tone based on their past posts and suggest revisions based on that. For example, it can generate revision suggestions based on specific expressions and phrases used by the user in the past. It can also suggest revisions that are consistent with past posts to ensure consistency in the user's posting style. This makes the user's posts more consistent and increases their credibility.

[0044] The flame war risk reduction system can also analyze the attributes of a user's followers and suggest revisions based on the target audience. For example, it can suggest expressions that match the age group and interests of followers. It can also generate revisions that will elicit positive responses based on followers' past reactions. This ensures that users' posts resonate appropriately with their target audience, reducing the risk of flame wars.

[0045] The flame war risk reduction system can also predict reactions on different social media platforms and perform risk assessments for each platform. For example, it can identify topics that are likely to cause flame wars on a particular platform and perform risk assessments based on that information. It can also propose appropriate revisions taking into account the user demographics and cultural background of each platform. This effectively reduces the risk of flame wars on different platforms.

[0046] The flame war risk reduction system can also automatically collect related news articles and background information and reflect this in risk assessments. For example, it can collect information related to a specific incident or event and use that information to conduct risk assessments. Furthermore, by clarifying the specific methods for collecting news articles and the reference standards, the accuracy of real-time risk assessments can be improved. This allows risk assessments to be based on the most up-to-date information.

[0047] The Flame Risk Reduction System can also analyze posts in different languages ​​to assess risk from an international perspective. For example, if an expression in a particular language is misleading in other cultures, the system can assess risk based on that information. Furthermore, by clarifying the specific types of different languages ​​and their analysis methods, risk assessment from an international perspective becomes possible. This allows appropriate risk assessments to be provided to global users.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The post analysis unit analyzes the content the user is about to post. For example, the post analysis unit uses generative AI to analyze the input text with natural language processing technology and understand the content. The post analysis unit can also identify emotional expressions and parts that may be misleading. Step 2: The risk assessment unit assesses the risk of a flaming incident based on the content analyzed by the post analysis unit. For example, the risk assessment unit assesses the risk based on past flaming incidents and the frequency of occurrence of specific keywords. The risk assessment unit can also refer to the user's past post history and learn past flaming patterns to assess risk. Step 3: The revision suggestion module proposes revisions to reduce the risks assessed by the risk assessment module. For example, the revision suggestion module uses generative AI to revise the content the user is about to post to make it more neutral and calm. The revision suggestion module can also learn patterns of past successful posts and generate revision suggestions based on them.

[0050] (Example 2) The system for reducing the risk of a social media outcry according to an embodiment of the present invention uses a generation AI to analyze content that a user intends to post on social media, evaluates the risk of the content causing an outcry, and proposes a revised version. This allows the system to appropriately manage the content of users' posts and reduce the risk of the content causing an outcry.

[0051] A flame war risk reduction system according to an embodiment includes a post analysis unit, a risk assessment unit, and a revision suggestion unit. The post analysis unit analyzes the content a user intends to post. For example, the post analysis unit uses a generation AI to analyze input text using natural language processing technology and understand the content. The post analysis unit can also identify emotional expressions and potentially misleading parts. The risk assessment unit evaluates the risk of a flame war based on the content analyzed by the post analysis unit. For example, the risk assessment unit evaluates the risk based on past flame war cases and the frequency of occurrence of specific keywords. The risk assessment unit can also refer to the user's past post history and learn past flame war patterns to evaluate risk. The revision suggestion unit proposes a revision to reduce the risk assessed by the risk assessment unit. For example, the revision suggestion unit uses a generation AI to revise the content the user intends to post to more neutral and calm expression. The revision suggestion unit can also learn patterns of past successful posts and generate revision suggestions based on them. This allows the flame war risk reduction system according to an embodiment to appropriately manage the content of users' posts and reduce the risk of a flame war. For example, the generation AI analyzes the content a user is about to post, assesses the risk of it causing a backlash, and suggests appropriate revisions, thereby reducing the risk of it causing a backlash.

[0052] The post analysis unit can reference a user's past post history, learn flaming patterns, and perform risk assessment. For example, the post analysis unit uses a generation AI to analyze a user's past post history and learn the patterns of posts that have caused flaming in the past. For example, if a specific keyword or phrase has caused a flaming, the unit can perform a risk assessment based on that pattern. The post analysis unit also clarifies the specific content of the past post history and how to reference it. For example, what period of time posts should be targeted, and how to collect the data. This allows the system to learn past flaming patterns and improve the accuracy of risk assessment.

[0053] The risk assessment unit can refer to news trends and public opinion trends in real time and conduct risk assessments taking social sentiment into account. For example, the risk assessment unit uses a generative AI to collect news trends in real time and compare them with the content of posts. For example, posts related to topics that are currently generating high social sentiment may be assessed as high risk. The risk assessment unit also clarifies the specific methods for collecting news trends and reference standards, such as news sources and how frequently trends are updated. This improves the accuracy of risk assessments by taking news trends and public opinion trends into account in real time.

[0054] The risk assessment unit can use the emotion estimation function to estimate the user's emotional state, identify posts made in an emotionally unstable state, and perform risk assessment. The risk assessment unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, if the user is feeling anger or sadness, the risk of that post is assessed as high. The risk assessment unit also clarifies the specific implementation method of the emotion estimation function and the technology to be used, such as the use of emotion analysis algorithms and sensor data. This improves the accuracy of risk assessment by taking the user's emotional state into account.

[0055] The post analysis unit can analyze images and videos included in the post content and reflect this in the risk assessment. For example, the post analysis unit uses a generation AI to analyze images and videos included in the post content and reflect this in the risk assessment. For example, if a specific symbol or expression in an image is the cause of a controversy, the risk assessment is based on that information. The post analysis unit also clarifies the specific analysis method and technology used for images and videos, such as image recognition algorithms and video analysis tools. This allows for the analysis of images and videos to improve the accuracy of risk assessment.

[0056] The post analysis unit can also analyze post content in different languages ​​and perform risk assessments from an international perspective. For example, the generative AI analyzes post content in different languages ​​and performs risk assessments from an international perspective. For example, if an expression in a particular language is misleading in other cultural spheres, risk assessments are performed based on that information. The post analysis unit also clarifies the specific types of different languages ​​and analysis methods. For example, major languages, use of translation tools, etc. This makes it possible to perform risk assessments from an international perspective by analyzing post content in different languages.

[0057] The risk assessment unit can estimate the user's emotional state in real time and make suggestions that will elicit positive emotions. For example, the risk assessment unit uses an emotion estimation function to analyze the user's emotions in real time when they enter post content. For example, if the user has negative emotions, it makes positive suggestions. The risk assessment unit also clarifies specific evaluation criteria and methods for eliciting positive emotions, such as providing an emotion score or positive feedback. This allows the risk assessment unit to estimate the user's emotional state in real time and make suggestions that will elicit positive emotions, thereby improving the accuracy of risk assessment.

[0058] The risk assessment unit can automatically collect related news articles and background information and reflect it in risk assessment. For example, when the generative AI analyzes text, the risk assessment unit automatically collects related news articles and background information and reflects it in risk assessment. For example, it collects information related to a specific incident or event and performs risk assessment based on that information. The risk assessment unit also clarifies the specific collection method and reference criteria for news articles, such as news sources and how often articles are updated. This improves the accuracy of risk assessment by collecting related news articles and background information.

[0059] The risk assessment unit can take into account the user's expertise and industry experience to conduct risk assessments to prevent technical misunderstandings. For example, the generative AI conducts risk assessments of texts by taking into account the user's expertise and industry experience. For example, if technical terms or industry-specific expressions are misleading, risk assessments are conducted based on that information. The risk assessment unit also clarifies specific evaluation criteria and reference methods for expertise, such as qualifications and past performance. This enables risk assessments that prevent technical misunderstandings by taking into account the user's expertise and industry experience.

[0060] The risk assessment unit can use the emotion estimation function to identify emotional expressions in the text and perform risk assessment based on the emotional intensity. For example, the risk assessment unit uses the emotion estimation function to identify emotional expressions in the text and perform risk assessment based on the emotional intensity. For example, if an expression with a strong emotion is the cause of a flame war, the risk assessment is performed based on that information. The risk assessment unit also clarifies the specific method for identifying emotional expressions and the assessment criteria, such as specific keywords or emotion scores. This allows for identifying emotional expressions in the text and performing risk assessment based on the emotional intensity, thereby improving the accuracy of the risk assessment.

[0061] The risk assessment unit can predict reactions on different social media platforms and perform risk assessments for each platform. For example, the risk assessment unit uses a generation AI to predict reactions on different social media platforms and perform risk assessments for each platform. For example, it identifies topics that are likely to cause controversy on specific platforms and performs risk assessments based on that information. The risk assessment unit also clarifies the specific types of social media platforms and how to reference them. For example, major social media platforms and the characteristics of each platform. This makes it possible to predict reactions on different social media platforms and perform risk assessments for each platform.

[0062] The risk assessment unit can conduct risk assessment based on the target audience, taking into account the attributes and past responses of the user's followers. For example, the risk assessment unit uses the generation AI to analyze the attributes of the user's followers and conduct risk assessment based on the target audience. For example, if there are many followers with specific attributes, the risk assessment unit conducts risk assessment based on that information. The risk assessment unit also clarifies specific assessment criteria and reference methods for follower attributes, such as age, gender, and interests. This makes it possible to conduct risk assessment based on the target audience by taking into account the attributes and past responses of the user's followers.

[0063] The risk assessment unit can estimate the emotions of a user when entering post content in real time and make suggestions to alleviate negative emotions. For example, the risk assessment unit uses an emotion estimation function to analyze the emotions of a user when entering post content in real time and make suggestions to alleviate negative emotions. For example, if a user is feeling anger or sadness, the risk assessment unit makes suggestions to help the user stay calm. The risk assessment unit also clarifies specific evaluation criteria for negative emotions and methods to alleviate them, such as providing an emotion score or positive feedback. This improves the accuracy of risk assessment by estimating the user's emotions in real time and making suggestions to alleviate negative emotions.

[0064] The correction suggestion unit can learn patterns of past successful posts and generate correction suggestions based on them. For example, the generation AI of the correction suggestion unit learns patterns of past successful posts and generates correction suggestions based on them. For example, it creates correction suggestions by referring to the expressions and structures of posts that have received high ratings in the past. The correction suggestion unit also clarifies the specific definition of successful post patterns and the learning method. For example, it may use past successful cases or a machine learning algorithm. In this way, by learning patterns of past successful posts, it is possible to generate more effective correction suggestions.

[0065] The revision suggestion unit can take into account different expression styles and generate revision suggestions in the most appropriate style. For example, the generation AI can take into account different expression styles and generate revision suggestions in the most appropriate style. For example, it can present humorous expressions and formal expressions as options. The revision suggestion unit also clarifies the specific types and evaluation criteria of the expression styles. For example, humorous, formal, casual, etc. This allows for the generation of revision suggestions in the most appropriate style by taking into account different expression styles.

[0066] The revision suggestion unit can generate revision suggestions that have a positive emotional impact by taking into account the user's emotional state using the emotion estimation function. The revision suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate revision suggestions that have a positive emotional impact. For example, if the user has negative emotions, the revision suggestion unit suggests positive expressions. The revision suggestion unit also clarifies specific evaluation criteria and generation methods for the positive emotional impact. For example, emotion scores, provision of positive feedback, etc. This makes it possible to generate revision suggestions that have a positive emotional impact by taking into account the user's emotional state.

[0067] The revision suggestion unit can generate internationally appropriate revision suggestions by taking into account the sensitivities of different cultural spheres and regions. For example, the generation AI generates revision suggestions that are internationally appropriate by taking into account the sensitivities of different cultural spheres and regions. For example, it creates revision suggestions that avoid expressions that are misleading in specific cultures or regions. The revision suggestion unit also clarifies specific definitions and evaluation criteria for cultural spheres and regions, such as specific countries, regions, or cultural backgrounds. This allows for the generation of internationally appropriate revision suggestions by taking into account the sensitivities of different cultural spheres and regions.

[0068] The revision suggestion unit can generate revision suggestions to maintain consistency with the content of the user's past posts. For example, the generation AI analyzes the content of the user's past posts and generates revision suggestions to maintain consistency. For example, revision suggestions are created by referring to the style and tone of past posts. The revision suggestion unit also clarifies specific evaluation criteria for consistency and how to maintain it. For example, consistency with the content of past posts, maintaining the brand image, etc. This makes it possible to generate reliable revision suggestions by maintaining consistency with the content of the user's past posts.

[0069] The revision suggestion unit can monitor the emotional response of the user when reviewing the revision in real time and continuously suggest optimal revision suggestions. The revision suggestion unit, for example, uses an emotion estimation function to monitor the emotional response of the user when reviewing the revision in real time. For example, it preferentially suggests revision suggestions for which the user has positive emotions. The revision suggestion unit also clarifies specific evaluation criteria and monitoring methods for emotional responses, such as emotion scores and real-time feedback. In this way, by monitoring the user's emotional responses in real time, it is possible to continuously suggest optimal revision suggestions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The Flaming Risk Reduction System can also learn a user's posting style and tone based on their past posts and suggest revisions based on that. For example, it can generate revision suggestions based on specific expressions and phrases used by the user in the past. It can also suggest revisions that are consistent with past posts to ensure consistency in the user's posting style. This makes the user's posts more consistent and increases their credibility.

[0072] The flame war risk reduction system can also analyze the attributes of a user's followers and suggest revisions based on the target audience. For example, it can suggest expressions that match the age group and interests of followers. It can also generate revisions that will elicit positive responses based on followers' past reactions. This ensures that users' posts resonate appropriately with their target audience, reducing the risk of flame wars.

[0073] The flame war risk reduction system can also predict reactions on different social media platforms and perform risk assessments for each platform. For example, it can identify topics that are likely to cause flame wars on a particular platform and perform risk assessments based on that information. It can also propose appropriate revisions taking into account the user demographics and cultural background of each platform. This effectively reduces the risk of flame wars on different platforms.

[0074] The flame war risk reduction system can also automatically collect related news articles and background information and reflect this in risk assessments. For example, it can collect information related to a specific incident or event and use that information to conduct risk assessments. Furthermore, by clarifying the specific methods for collecting news articles and the reference standards, the accuracy of real-time risk assessments can be improved. This allows risk assessments to be based on the most up-to-date information.

[0075] The Flame Risk Reduction System can also analyze posts in different languages ​​to assess risk from an international perspective. For example, if an expression in a particular language is misleading in other cultures, the system can assess risk based on that information. Furthermore, by clarifying the specific types of different languages ​​and their analysis methods, risk assessment from an international perspective becomes possible. This allows appropriate risk assessments to be provided to global users.

[0076] The risk assessment unit can estimate the user's emotional state in real time and make suggestions to elicit positive emotions. For example, if the user has negative emotions, it will make positive suggestions. The risk assessment unit also clarifies specific evaluation criteria and methods for eliciting positive emotions, such as providing an emotion score or positive feedback. This allows the unit to estimate the user's emotional state in real time and make suggestions to elicit positive emotions, thereby improving the accuracy of risk assessment.

[0077] The risk assessment unit can use the emotion estimation function to estimate the user's emotional state, identify posts that indicate an emotionally unstable state, and perform risk assessment. For example, if the user is feeling angry or sad, the risk of that post is assessed as high. The risk assessment unit also clarifies the specific implementation method of the emotion estimation function and the technology to be used, such as the use of emotion analysis algorithms and sensor data. This improves the accuracy of risk assessment by taking the user's emotional state into account.

[0078] The risk assessment unit can use the emotion estimation function to identify emotional expressions in text and perform risk assessment based on emotional intensity. For example, if an expression with a strong emotion is the cause of a controversy, the risk assessment unit performs risk assessment based on that information. The risk assessment unit also clarifies the specific method for identifying emotional expressions and the assessment criteria, such as specific keywords or emotion scores. This allows the unit to identify emotional expressions in text and perform risk assessment based on emotional intensity, thereby improving the accuracy of risk assessment.

[0079] The risk assessment unit can estimate the emotions of users when they post content in real time and make suggestions to alleviate negative emotions. For example, if a user is feeling angry or sad, the unit can make suggestions to help them stay calm. The risk assessment unit also clarifies specific evaluation criteria for negative emotions and ways to alleviate them, such as providing an emotion score or positive feedback. This improves the accuracy of risk assessment by estimating users' emotions in real time and making suggestions to alleviate negative emotions.

[0080] The risk assessment unit can use the emotion estimation function to estimate the user's emotional state and generate correction suggestions that have a positive emotional impact. For example, if the user has negative emotions, it can suggest positive expressions. The risk assessment unit also clarifies specific evaluation criteria and generation methods for the positive emotional impact. For example, it can provide an emotion score, positive feedback, etc. This allows it to generate correction suggestions that have a positive emotional impact by taking the user's emotional state into consideration.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The post analysis unit analyzes the content the user is about to post. For example, the post analysis unit uses generative AI to analyze the input text with natural language processing technology and understand the content. The post analysis unit can also identify emotional expressions and parts that may be misleading. Step 2: The risk assessment unit assesses the risk of a flaming incident based on the content analyzed by the post analysis unit. For example, the risk assessment unit assesses the risk based on past flaming incidents and the frequency of occurrence of specific keywords. The risk assessment unit can also refer to the user's past post history and learn past flaming patterns to assess risk. Step 3: The revision suggestion module proposes revisions to reduce the risks assessed by the risk assessment module. For example, the revision suggestion module uses generative AI to revise the content the user is about to post to make it more neutral and calm. The revision suggestion module can also learn patterns of past successful posts and generate revision suggestions based on them.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 content that a user is about to post; a risk assessment unit that assesses the risk of a controversy based on the content analyzed by the post analysis unit; a modification suggestion unit that proposes a modification to reduce the risk assessed by the risk assessment unit. A system characterized by:

2. The risk assessment unit See real-time news trends and public opinion trends, and take social sentiment into account when making risk assessments 2. The system of claim 1.

3. The post-analysis unit Analyze images and videos contained in posts and reflect them in risk assessments 2. The system of claim 1.

4. The risk assessment unit Automatically gather relevant news articles and background information to inform risk assessments 2. The system of claim 1.

5. The modification suggestion unit Learn patterns from past successful posts and generate revision suggestions based on them 2. The system of claim 1.

6. The risk assessment unit Inferring the emotional state of the user, identifying posts in an emotionally unstable state, and performing a risk assessment.

2. The system of claim 1.

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