system

Abstract

The system according to this embodiment aims to analyze the content of news and provide preventative measures and countermeasures. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a provision unit, a reception unit, and a collection unit. The analysis unit analyzes the content of the news. The generation unit generates preventive measures and countermeasures based on the results analyzed by the analysis unit. The provision unit provides the preventive measures and countermeasures generated by the generation unit to viewers. The reception unit receives input from viewers. The collection unit collects news data.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, preventive measures and countermeasures based on news content are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze news content and provide preventive measures and countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a provision unit, a reception unit, and a collection unit. The analysis unit analyzes the content of news. The generation unit generates preventive measures and countermeasures based on the results analyzed by the analysis unit. The provision unit provides the preventive measures and countermeasures generated by the generation unit to viewers. The reception unit receives input from viewers. The collection unit collects news data. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the content of news and provide preventative measures and countermeasures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The news delivery system according to an embodiment of the present invention is a mechanism that uses a generating AI to provide preventive measures and countermeasures to prevent incidents and accidents from occurring when reporting news. This news delivery system can provide viewers not only with the facts of incidents and accidents, but also with specific methods to prevent similar incidents from occurring. For example, the news delivery system uses a generating AI to analyze the content of the news being reported and generates preventive measures and countermeasures to prevent the incident from occurring. Next, the generated preventive measures and countermeasures are provided as news. Through this mechanism, viewers can learn not only the facts of incidents and accidents, but also specific methods to prevent similar incidents from occurring. First, the news delivery system uses a generating AI to analyze the content of the news being reported. In this process, news video and text data are used as input, and the generating AI understands the content. For example, if news of a traffic accident is reported, the generating AI analyzes the cause and circumstances of the accident and generates preventive measures to prevent similar accidents. Next, the news delivery system uses the generating AI to generate preventive measures and countermeasures based on the analysis results. For example, for news of a traffic accident, it generates points to be careful of while driving, methods of safe driving, and specific advice to avoid dangerous situations. Furthermore, the system can generate information on crime prevention measures, natural disaster predictions and evacuation guidelines, and lifestyle tips for maintaining physical and mental health. The generated preventative measures and countermeasures are then provided to viewers as news. For example, news programs can include segments such as "a segment suggesting effective preventative measures to protect oneself from accidents" or "a segment providing information on strengthening crime prevention measures and crime prevention," thereby providing viewers with specific information. This system allows viewers to learn not only the facts of incidents and accidents, but also specific ways to prevent similar incidents from happening again. As a result, viewers can gain knowledge to protect themselves and their families, leading to a safer and happier life. For example, the news provision system can use a generating AI to analyze news about traffic accidents and suggest points to be aware of while driving and methods for safe driving, allowing viewers to raise their awareness of their own driving. In addition, by providing information on crime prevention measures, viewers can understand the crime patterns and trends in their area and take appropriate measures.Furthermore, by providing predictions of natural disasters and evacuation guidelines, viewers can improve their ability to respond to disasters. By suggesting lifestyle tips for maintaining physical and mental health, viewers can resolve stress and mental health issues and live happier lives. In this way, news delivery systems can provide viewers not only with the facts of incidents and accidents, but also with concrete methods to prevent similar events from happening again.

[0029] The news delivery system according to this embodiment comprises an analysis unit, a generation unit, a delivery unit, a reception unit, and a collection unit. The analysis unit analyzes the content of the news. The content of the news includes, but is not limited to, text, audio, and video. The analysis unit analyzes the text data of the news using, for example, natural language processing technology. The analysis unit can also analyze the video data of the news using image analysis technology. The analysis unit can also analyze the audio data of the news using speech analysis technology. For example, the analysis unit analyzes the text data of the news using natural language processing technology and extracts important information. Image analysis technology is used to detect specific objects or scenes from the video data of the news. Speech analysis technology is used to analyze the emotions and intentions of speakers from the audio data of the news. The generation unit generates preventive measures and countermeasures based on the results analyzed by the analysis unit. The generation unit generates, for example, traffic accident preventive measures using generation AI. The generation unit can also generate crime prevention measures. The generation unit can also generate natural disaster predictions and evacuation guidelines. For example, the generation unit uses generation AI to generate traffic accident prevention measures and proposes points to be aware of while driving and methods for safe driving. The generation unit also proposes the installation of security cameras and the setting of restricted areas as crime prevention measures. The generation unit proposes the analysis of weather data and the setting of evacuation routes as predictions of natural disasters and evacuation guidelines. The provision unit provides the prevention measures and countermeasures generated by the generation unit to viewers. The provision unit can, for example, set up a segment in a news program that proposes prevention measures and countermeasures. The provision unit can also provide prevention measures and countermeasures through internet distribution. The provision unit can also provide prevention measures and countermeasures through a mobile app. For example, the provision unit can set up a segment in a news program that proposes effective prevention measures to protect oneself from accidents. The provision unit provides crime prevention information through internet distribution. The provision unit provides predictions of natural disasters and evacuation guidelines through a mobile app. The reception unit receives input from viewers. The reception unit collects viewer opinions, for example, through surveys. The reception unit can also receive viewer feedback through a comment function.Furthermore, the reception department can also collect viewer opinions through a feedback form. For example, the reception department can collect viewer opinions through a survey and use them to improve the news delivery system. The reception department accepts viewer feedback through a comment function and provides information that meets viewer needs. The reception department collects viewer opinions through a feedback form and uses them to improve the news delivery system. The collection department collects news data. For example, the collection department collects news articles. The collection department can also collect video clips. The collection department can also collect audio files. For example, the collection department collects news articles using web scraping technology. The collection department collects video clips and incorporates them into the news delivery system. The collection department collects audio files and incorporates them into the news delivery system. As a result, the news delivery system according to this embodiment can provide viewers not only with the facts of incidents and accidents, but also with specific methods to prevent similar incidents from happening again.

[0030] The analysis unit analyzes the content of news. News content includes, but is not limited to, text, audio, and video. For example, the analysis unit uses natural language processing technology to analyze the text data of news. Specifically, it uses natural language processing technology to understand the context of news articles and extract important keywords and phrases. This allows for a quick grasp of the main points and important information of the news. The analysis unit can also analyze the video data of news using image analysis technology. For example, it can detect specific objects or scenes from video data and identify important events within the video. This allows for efficient analysis of video news content and improves the quality of information provided to viewers. Furthermore, the analysis unit can analyze the audio data of news using speech analysis technology. For example, it can analyze the speaker's emotions and intentions from the audio data to gain a deeper understanding of the news content. Speech analysis technology can be combined with speech recognition technology to convert audio data into text data for further detailed analysis. This allows the analysis unit to comprehensively analyze the text, video, and audio data of news and provide comprehensive information to viewers.

[0031] The generation unit generates preventative measures and countermeasures based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate preventative measures for traffic accidents. Specifically, the generation AI learns from past traffic accident data and road condition data to identify locations and times with a high risk of accidents. Based on this, it proposes points to be aware of while driving and methods for safe driving. The generation unit can also generate crime prevention measures. For example, the generation AI analyzes crime occurrence data and regional characteristics to propose locations for security cameras and the setting of restricted areas. Furthermore, the generation unit can generate predictions of natural disasters and evacuation guidelines. For example, the generation AI analyzes weather data and topographic data to assess the risk of natural disasters in a specific area and proposes evacuation routes and locations. In this way, the generation unit can provide viewers with concrete and practical preventative measures and countermeasures. The generation unit regularly updates the learning data of the generation AI, enabling it to generate preventative measures and countermeasures based on the latest information. In this way, the generation unit can always provide highly accurate preventative measures and countermeasures based on the latest information, ensuring the safety of viewers.

[0032] The content provider will provide viewers with preventative measures and countermeasures generated by the content generation unit. For example, the content provider may include a segment in news programs that suggests preventative measures and countermeasures. Specifically, they may include a segment in news programs that suggests effective preventative measures to protect oneself from accidents, providing viewers with concrete advice. The content provider can also provide preventative measures and countermeasures through internet distribution. For example, they may provide crime prevention information, natural disaster predictions, and evacuation guidelines through news websites and video distribution platforms. Furthermore, the content provider can also provide preventative measures and countermeasures through mobile apps. For example, they can notify users in real time of the latest preventative measures and countermeasures through mobile apps, and encourage immediate action in emergencies. In this way, the content provider can provide viewers with preventative measures and countermeasures in a variety of ways, ensuring their safety. The content provider can collect feedback from viewers and continuously improve the quality of the information they provide. In this way, the content provider can provide information that meets the needs of viewers and gain their trust.

[0033] The reception department receives input from viewers. For example, the reception department collects viewer opinions through surveys. Specifically, it conducts surveys through news programs and mobile apps to collect viewer opinions and requests. The reception department can also receive viewer feedback through comment functions. For example, it collects viewer opinions and impressions through comment sections on news websites and video streaming platforms. Furthermore, the reception department can collect viewer opinions through feedback forms. For example, it can install feedback forms on news websites and mobile apps, allowing viewers to freely post their opinions. This allows the reception department to collect diverse viewer opinions and requests and use them to improve the news delivery system. The reception department can analyze the collected feedback to understand viewer needs and interests. This allows the reception department to provide information that meets viewer needs and improve viewer satisfaction.

[0034] The data collection unit collects news data. For example, it collects news articles. Specifically, it uses web scraping technology to automatically collect the latest news articles from news sites on the internet. The data collection unit can also collect video clips. For example, it collects video clips from news programs and video streaming platforms and incorporates them into the news delivery system. Furthermore, the data collection unit can also collect audio files. For example, it collects audio files from radio news and podcasts and incorporates them into the news delivery system. This allows the data collection unit to efficiently collect news data in various formats and incorporate it into the news delivery system. The data collection unit centrally manages the collected data and makes it accessible to the analysis and generation units. This allows the data collection unit to streamline data management across the entire news delivery system and improve system performance. The data collection unit can also filter and clean the collected data to ensure its quality. This allows the data collection unit to provide high-quality data to the news delivery system and reliable information to viewers.

[0035] The generation unit can generate measures to prevent traffic accidents. For example, the generation unit can raise awareness of driving manners. It can also raise awareness of traffic rules. Furthermore, it can recommend safe driving. For example, as part of raising awareness of driving manners, the generation unit can provide specific advice on points to watch out for while driving and how to avoid dangerous situations. As part of raising awareness of traffic rules, the generation unit can explain the dangers of running red lights and speeding. As part of recommending safe driving, the generation unit can suggest maintaining an appropriate distance between vehicles and adhering to speed limits. By generating measures to prevent traffic accidents, it is possible to prevent traffic accidents from occurring in the first place.

[0036] The generation unit can generate crime prevention measures. For example, the generation unit can propose the installation of security cameras. It can also propose the designation of a restricted area. Furthermore, it can propose strengthening community patrols. For example, as an example of installing security cameras, the generation unit can install security cameras in a specific area to enhance the deterrent effect on crime. As an example of designating a restricted area, the generation unit can designate areas with a high crime rate as restricted areas and strengthen security. As an example of strengthening community patrols, the generation unit can encourage patrol activities by local residents to prevent crime from occurring. In this way, by generating crime prevention measures, it is possible to prevent crime from occurring in the first place.

[0037] The generation unit can generate predictions for natural disasters and evacuation guidelines. For example, the generation unit can analyze meteorological data. It can also use earthquake prediction systems. It can also use flood prediction models. For example, as a meteorological data analysis unit, it collects meteorological data and predicts changes in weather. As an earthquake prediction system, it detects precursory phenomena for earthquakes and predicts the occurrence of earthquakes. As a flood prediction model, it analyzes river water level data and predicts the occurrence of floods. As evacuation guidelines, it sets evacuation routes and designates evacuation sites. As precautions during evacuation, it provides safe evacuation methods and lists of items to bring during evacuation. By generating predictions for natural disasters and evacuation guidelines, the ability to respond to disasters can be enhanced.

[0038] The generation function can generate lifestyle tips for maintaining physical and mental health. For example, it can suggest a balanced diet, exercise habits, and stress management methods. For instance, it can suggest nutritionally balanced meal menus, explain the importance of regular exercise and provide specific exercise plans, and suggest relaxation techniques and mental health care methods for stress management. By generating lifestyle tips for maintaining physical and mental health, it can help maintain the health of its users.

[0039] The sponsorship department can include a segment within the news program that proposes preventative measures and countermeasures. For example, the sponsorship department could create a special feature segment. They could also propose preventative measures and countermeasures in an interview format. Furthermore, they could create a segment that encourages viewer participation. For example, the sponsorship department could create a special feature segment that proposes preventative measures against traffic accidents. They could invite crime prevention experts to propose countermeasures in an interview format. They could propose preventative measures and countermeasures in a viewer participation segment by answering questions from viewers. In this way, by including a segment that proposes preventative measures and countermeasures within the news program, specific information can be provided to viewers.

[0040] The analysis unit can improve the accuracy of news analysis by referring to data on similar past events. For example, the generation AI can refer to past traffic accident data to more accurately analyze the cause of a current accident. The analysis unit can also refer to past crime data to analyze current crime patterns. Furthermore, the analysis unit can refer to past natural disaster data to analyze the impact of a current disaster. For example, the analysis unit can refer to past traffic accident data to analyze the cause of a current accident. The analysis unit can refer to past crime data to analyze current crime patterns. The analysis unit can refer to past natural disaster data to analyze the impact of a current disaster. In this way, the accuracy of news analysis is improved by referring to data on similar past events.

[0041] The analysis unit can apply different analysis algorithms depending on the news category when analyzing news. For example, the analysis unit can apply a traffic data analysis algorithm to news about traffic accidents. It can also apply a crime data analysis algorithm to news about crimes. Furthermore, it can apply a disaster data analysis algorithm to news about natural disasters. By applying different analysis algorithms depending on the news category, the analysis accuracy is improved.

[0042] The analysis unit can perform news analysis while considering geographical information of the news source's location. For example, the analysis unit's generating AI can consider geographical information of the location of a traffic accident to analyze the cause of the accident. The analysis unit can also have the generating AI consider geographical information of the location of a crime to analyze crime patterns. Furthermore, the analysis unit can have the generating AI consider geographical information of the location of a natural disaster to analyze the impact of the disaster. For example, the analysis unit's generating AI can consider geographical information of the location of a traffic accident to analyze the cause of the accident. The analysis unit's generating AI can consider geographical information of the location of a crime to analyze crime patterns. The analysis unit's generating AI can consider geographical information of the location of a natural disaster to analyze the impact of the disaster. By considering geographical information of the news source's location, the accuracy of the analysis is improved.

[0043] The analysis unit can improve the accuracy of news analysis by referring to relevant literature. For example, the generation AI can refer to relevant literature on traffic accidents to analyze the causes of accidents. The analysis unit can also refer to relevant literature on crimes to analyze crime patterns. Furthermore, the analysis unit can refer to relevant literature on natural disasters to analyze the impact of disasters. For example, the analysis unit can refer to relevant literature on traffic accidents to analyze the causes of accidents. The analysis unit can refer to relevant literature on crimes to analyze crime patterns. The analysis unit can refer to relevant literature on natural disasters to analyze the impact of disasters. By referring to relevant literature on news, the accuracy of the analysis is improved.

[0044] The generation unit can improve the accuracy of generating preventive measures and countermeasures by referring to past successful cases. For example, the generation unit's AI can refer to past successful cases of traffic accident prevention to generate preventive measures. The generation unit's AI can also refer to past successful cases of crime prevention to generate preventive measures. The generation unit's AI can also refer to past successful cases of natural disaster prevention to generate preventive measures. In this way, the accuracy of generating preventive measures and countermeasures is improved by referring to past successful cases.

[0045] The generation unit can apply different generation algorithms depending on the category of the event when generating preventive measures and countermeasures. For example, the generation unit can apply a traffic data analysis algorithm to preventive measures for traffic accidents. It can also apply a crime data analysis algorithm to preventive measures for crimes. Furthermore, it can apply a disaster data analysis algorithm to preventive measures for natural disasters. By applying different generation algorithms depending on the category of the event, the generation accuracy is improved.

[0046] The generation unit can consider geographical information of the location where an event occurs when generating preventive measures and countermeasures. For example, the generation unit's AI can consider geographical information of the location where a traffic accident occurred and generate preventive measures. The generation unit's AI can also consider geographical information of the location where a crime occurred and generate preventive measures. Furthermore, the generation unit's AI can also consider geographical information of the location where a natural disaster occurred and generate preventive measures. For example, the generation unit's AI can consider geographical information of the location where a traffic accident occurred and generate preventive measures. The generation unit's AI can consider geographical information of the location where a crime occurred and generate preventive measures. The generation unit's AI can consider geographical information of the location where a natural disaster occurred and generate preventive measures. By considering geographical information of the location where an event occurs, the generation accuracy is improved.

[0047] The generation unit can improve the accuracy of generating preventive measures and countermeasures by referring to the opinions of relevant experts. For example, the generation unit's AI can refer to the opinions of experts in traffic accident prevention to generate preventive measures. The generation unit's AI can also refer to the opinions of experts in crime prevention to generate preventive measures. The generation unit's AI can also refer to the opinions of experts in natural disaster prevention to generate preventive measures. For example, the generation unit's AI can refer to the opinions of experts in traffic accident prevention to generate preventive measures. The generation unit's AI can refer to the opinions of experts in crime prevention to generate preventive measures. The generation unit's AI can refer to the opinions of experts in natural disaster prevention to generate preventive measures. This improves the accuracy of generation by referring to the opinions of relevant experts.

[0048] The news delivery unit can select the optimal delivery method by referring to the viewer's past viewing history when delivering news. For example, the news delivery unit can use a generative AI to refer to the viewer's past viewing history and deliver the most relevant news. The news delivery unit can also use a generative AI to refer to the viewer's past viewing history and deliver news that is of interest to the viewer. The news delivery unit can also use a generative AI to refer to the viewer's past viewing history and deliver relevant news. In this way, the news delivery unit can deliver the most relevant news by referring to the viewer's past viewing history.

[0049] The news delivery unit can apply different delivery algorithms depending on the news category when delivering news. For example, the unit can apply a traffic data analysis algorithm to news about traffic accidents. It can also apply a crime data analysis algorithm to news about crimes. Furthermore, it can apply a disaster data analysis algorithm to news about natural disasters. By applying different delivery algorithms depending on the news category, the accuracy of news delivery is improved.

[0050] The news delivery department can select the optimal delivery method by considering the viewer's device information when delivering news. For example, the news delivery department can use a generating AI to refer to the viewer's device information and deliver the most relevant news. The news delivery department can also use a generating AI to refer to the viewer's device information and deliver news that is of interest to the viewer. The news delivery department can also use a generating AI to refer to the viewer's device information and deliver relevant news. In this way, by considering the viewer's device information, the news delivery department can deliver the most relevant news. The news delivery department can use a generating AI to refer to the viewer's device information and deliver news that is of interest to the viewer. The news delivery department can use a generating AI to refer to the viewer's device information and deliver relevant news.

[0051] The news delivery department can analyze viewers' social media activity and provide relevant news when delivering news. For example, the news delivery department can use a generative AI to refer to viewers' social media activity and provide the most relevant news. The news delivery department can also use a generative AI to refer to viewers' social media activity and provide news that will pique their interest. The news delivery department can also use a generative AI to refer to viewers' social media activity and provide relevant news. For example, the news delivery department can use a generative AI to refer to viewers' social media activity and provide the most relevant news. The news delivery department can use a generative AI to refer to viewers' social media activity and provide news that will pique their interest. The news delivery department can use a generative AI to refer to viewers' social media activity and provide relevant news. This allows the news delivery department to provide relevant news by analyzing viewers' social media activity.

[0052] The reception unit can select the optimal reception method by referring to the viewer's past input history when receiving input. For example, the reception unit's generating AI can refer to the viewer's past input history and provide the optimal input method. The reception unit can also refer to the viewer's past input history and provide an interesting input method. Furthermore, the reception unit can refer to the viewer's past input history and provide a relevant input method. For example, the reception unit's generating AI can refer to the viewer's past input history and provide the optimal input method. The reception unit's generating AI can refer to the viewer's past input history and provide an interesting input method. The reception unit's generating AI can refer to the viewer's past input history and provide a relevant input method. This allows the reception unit to provide the optimal input method by referring to the viewer's past input history.

[0053] The reception unit can select the optimal reception method when receiving input, taking into account the viewer's device information. For example, the reception unit's generating AI can refer to the viewer's device information and provide the optimal input method. The reception unit can also refer to the viewer's device information and provide an interesting input method. Furthermore, the reception unit can refer to the viewer's device information and provide a relevant input method. For example, the reception unit's generating AI can refer to the viewer's device information and provide the optimal input method. The reception unit's generating AI can refer to the viewer's device information and provide an interesting input method. The reception unit's generating AI can refer to the viewer's device information and provide a relevant input method. This allows the reception unit to provide the optimal input method by taking the viewer's device information into consideration.

[0054] The data collection unit can improve collection accuracy by referring to past collected data when collecting news data. For example, the data collection unit's generating AI can refer to past traffic accident data to collect current accident data. The data collection unit can also refer to past crime data to collect current crime data. The data collection unit can also refer to past natural disaster data to collect current disaster data. For example, the data collection unit's generating AI can refer to past traffic accident data to collect current accident data. The data collection unit's generating AI can refer to past crime data to collect current crime data. The data collection unit's generating AI can refer to past natural disaster data to collect current disaster data. By referring to past collected data, collection accuracy is improved.

[0055] The data collection unit can consider geographical information of the news source's location when collecting news data. For example, the data collection unit's generating AI can consider geographical information of the location of a traffic accident and collect accident data. The data collection unit can also consider geographical information of the location of a crime and collect crime data. The data collection unit can also consider geographical information of the location of a natural disaster and collect disaster data. By considering geographical information of the news source's location, the data collection accuracy is improved.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] A news delivery system can provide the most relevant news by referencing a viewer's past viewing history. For example, the delivery department can analyze a viewer's past viewing history and prioritize providing news that is likely to interest them. It can also analyze trends in news that viewers have previously watched and provide related news. Furthermore, based on the viewer's viewing history, it can suggest news that the viewer has not yet seen but may be interested in. This enables news delivery tailored to the viewer's interests, thereby increasing viewer satisfaction.

[0058] The news delivery system can select the optimal delivery method when delivering news, taking into account the viewer's device information. For example, if the viewer is using a smartphone, the delivery department can select a mobile-friendly news delivery method. If the viewer is using a tablet, it can select a news delivery method suitable for a large screen. Furthermore, if the viewer is using a desktop, it can select a news delivery method that includes detailed information. This enables optimal news delivery tailored to the viewer's device, thereby improving viewer convenience.

[0059] A news delivery system can analyze viewers' social media activity and provide relevant news. For example, the system can analyze viewers' social media posts and "likes" to provide news that is likely to interest them. It can also provide relevant news based on information about accounts that viewers follow and groups they participate in. Furthermore, it can analyze viewers' social media trends and provide the latest news that is likely to interest them. This makes it possible to provide news tailored to viewers' interests and increases viewer satisfaction.

[0060] The news delivery system can apply different delivery algorithms depending on the news category when delivering news. For example, a traffic data analysis algorithm can be applied to news about traffic accidents. Similarly, a crime data analysis algorithm can be applied to news about crimes. Furthermore, a disaster data analysis algorithm can be applied to news about natural disasters. By applying the most appropriate delivery algorithm for each news category, the accuracy of news delivery is improved, and more accurate and relevant news can be provided to viewers.

[0061] The news delivery system can select the optimal delivery method by referring to the viewer's past viewing history when delivering news. For example, the delivery department can analyze the viewer's past viewing history and prioritize delivering news that is likely to interest the viewer. It can also analyze the trends of news that the viewer has watched in the past and provide related news. Furthermore, based on the viewer's viewing history, it can suggest news that the viewer has not yet seen but may be interested in. This makes it possible to deliver news that is tailored to the viewer's interests and can increase viewer satisfaction.

[0062] The news delivery system can select the optimal delivery method when delivering news, taking into account the viewer's device information. For example, if the viewer is using a smartphone, the delivery department can select a mobile-friendly news delivery method. If the viewer is using a tablet, it can select a news delivery method suitable for a large screen. Furthermore, if the viewer is using a desktop, it can select a news delivery method that includes detailed information. This enables optimal news delivery tailored to the viewer's device, thereby improving viewer convenience.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects news data. For example, the collection unit uses web scraping techniques to collect news articles, as well as video clips and audio files. Step 2: The analysis unit analyzes the content of the news. For example, it uses natural language processing technology to analyze the text data of the news, image analysis technology to analyze the video data of the news, and audio analysis technology to analyze the audio data of the news. Step 3: The generation unit generates preventive measures and countermeasures based on the results analyzed by the analysis unit. For example, it uses generation AI to generate traffic accident prevention measures, crime prevention measures, and natural disaster predictions and evacuation guidelines. Step 4: The providing unit provides viewers with the preventative measures and countermeasures generated by the generating unit. For example, this could be done by creating a segment in a news program to suggest them, or by providing them through internet distribution or mobile apps. Step 5: The reception desk receives input from viewers. For example, it collects viewer opinions and feedback through surveys, comment functions, and feedback forms.

[0065] (Example of form 2) The news delivery system according to an embodiment of the present invention is a mechanism that uses a generating AI to provide preventive measures and countermeasures to prevent incidents and accidents from occurring when reporting news. This news delivery system can provide viewers not only with the facts of incidents and accidents, but also with specific methods to prevent similar incidents from occurring. For example, the news delivery system uses a generating AI to analyze the content of the news being reported and generates preventive measures and countermeasures to prevent the incident from occurring. Next, the generated preventive measures and countermeasures are provided as news. Through this mechanism, viewers can learn not only the facts of incidents and accidents, but also specific methods to prevent similar incidents from occurring. First, the news delivery system uses a generating AI to analyze the content of the news being reported. In this process, news video and text data are used as input, and the generating AI understands the content. For example, if news of a traffic accident is reported, the generating AI analyzes the cause and circumstances of the accident and generates preventive measures to prevent similar accidents. Next, the news delivery system uses the generating AI to generate preventive measures and countermeasures based on the analysis results. For example, for news of a traffic accident, it generates points to be careful of while driving, methods of safe driving, and specific advice to avoid dangerous situations. Furthermore, the system can generate information on crime prevention measures, natural disaster predictions and evacuation guidelines, and lifestyle tips for maintaining physical and mental health. The generated preventative measures and countermeasures are then provided to viewers as news. For example, news programs can include segments such as "a segment suggesting effective preventative measures to protect oneself from accidents" or "a segment providing information on strengthening crime prevention measures and crime prevention," thereby providing viewers with specific information. This system allows viewers to learn not only the facts of incidents and accidents, but also specific ways to prevent similar incidents from happening again. As a result, viewers can gain knowledge to protect themselves and their families, leading to a safer and happier life. For example, the news provision system can use a generating AI to analyze news about traffic accidents and suggest points to be aware of while driving and methods for safe driving, allowing viewers to raise their awareness of their own driving. In addition, by providing information on crime prevention measures, viewers can understand the crime patterns and trends in their area and take appropriate measures.Furthermore, by providing predictions of natural disasters and evacuation guidelines, viewers can improve their ability to respond to disasters. By suggesting lifestyle tips for maintaining physical and mental health, viewers can resolve stress and mental health issues and live happier lives. In this way, news delivery systems can provide viewers not only with the facts of incidents and accidents, but also with concrete methods to prevent similar events from happening again.

[0066] The news delivery system according to this embodiment comprises an analysis unit, a generation unit, a delivery unit, a reception unit, and a collection unit. The analysis unit analyzes the content of the news. The content of the news includes, but is not limited to, text, audio, and video. The analysis unit analyzes the text data of the news using, for example, natural language processing technology. The analysis unit can also analyze the video data of the news using image analysis technology. The analysis unit can also analyze the audio data of the news using speech analysis technology. For example, the analysis unit analyzes the text data of the news using natural language processing technology and extracts important information. Image analysis technology is used to detect specific objects or scenes from the video data of the news. Speech analysis technology is used to analyze the emotions and intentions of speakers from the audio data of the news. The generation unit generates preventive measures and countermeasures based on the results analyzed by the analysis unit. The generation unit generates, for example, traffic accident preventive measures using generation AI. The generation unit can also generate crime prevention measures. The generation unit can also generate natural disaster predictions and evacuation guidelines. For example, the generation unit uses generation AI to generate traffic accident prevention measures and proposes points to be aware of while driving and methods for safe driving. The generation unit also proposes the installation of security cameras and the setting of restricted areas as crime prevention measures. The generation unit proposes the analysis of weather data and the setting of evacuation routes as predictions of natural disasters and evacuation guidelines. The provision unit provides the prevention measures and countermeasures generated by the generation unit to viewers. The provision unit can, for example, set up a segment in a news program that proposes prevention measures and countermeasures. The provision unit can also provide prevention measures and countermeasures through internet distribution. The provision unit can also provide prevention measures and countermeasures through a mobile app. For example, the provision unit can set up a segment in a news program that proposes effective prevention measures to protect oneself from accidents. The provision unit provides crime prevention information through internet distribution. The provision unit provides predictions of natural disasters and evacuation guidelines through a mobile app. The reception unit receives input from viewers. The reception unit collects viewer opinions, for example, through surveys. The reception unit can also receive viewer feedback through a comment function.Furthermore, the reception department can also collect viewer opinions through a feedback form. For example, the reception department can collect viewer opinions through a survey and use them to improve the news delivery system. The reception department accepts viewer feedback through a comment function and provides information that meets viewer needs. The reception department collects viewer opinions through a feedback form and uses them to improve the news delivery system. The collection department collects news data. For example, the collection department collects news articles. The collection department can also collect video clips. The collection department can also collect audio files. For example, the collection department collects news articles using web scraping technology. The collection department collects video clips and incorporates them into the news delivery system. The collection department collects audio files and incorporates them into the news delivery system. As a result, the news delivery system according to this embodiment can provide viewers not only with the facts of incidents and accidents, but also with specific methods to prevent similar incidents from happening again.

[0067] The analysis unit analyzes the content of news. News content includes, but is not limited to, text, audio, and video. For example, the analysis unit uses natural language processing technology to analyze the text data of news. Specifically, it uses natural language processing technology to understand the context of news articles and extract important keywords and phrases. This allows for a quick grasp of the main points and important information of the news. The analysis unit can also analyze the video data of news using image analysis technology. For example, it can detect specific objects or scenes from video data and identify important events within the video. This allows for efficient analysis of video news content and improves the quality of information provided to viewers. Furthermore, the analysis unit can analyze the audio data of news using speech analysis technology. For example, it can analyze the speaker's emotions and intentions from the audio data to gain a deeper understanding of the news content. Speech analysis technology can be combined with speech recognition technology to convert audio data into text data for further detailed analysis. This allows the analysis unit to comprehensively analyze the text, video, and audio data of news and provide comprehensive information to viewers.

[0068] The generation unit generates preventative measures and countermeasures based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate preventative measures for traffic accidents. Specifically, the generation AI learns from past traffic accident data and road condition data to identify locations and times with a high risk of accidents. Based on this, it proposes points to be aware of while driving and methods for safe driving. The generation unit can also generate crime prevention measures. For example, the generation AI analyzes crime occurrence data and regional characteristics to propose locations for security cameras and the setting of restricted areas. Furthermore, the generation unit can generate predictions of natural disasters and evacuation guidelines. For example, the generation AI analyzes weather data and topographic data to assess the risk of natural disasters in a specific area and proposes evacuation routes and locations. In this way, the generation unit can provide viewers with concrete and practical preventative measures and countermeasures. The generation unit regularly updates the learning data of the generation AI, enabling it to generate preventative measures and countermeasures based on the latest information. In this way, the generation unit can always provide highly accurate preventative measures and countermeasures based on the latest information, ensuring the safety of viewers.

[0069] The content provider will provide viewers with preventative measures and countermeasures generated by the content generation unit. For example, the content provider may include a segment in news programs that suggests preventative measures and countermeasures. Specifically, they may include a segment in news programs that suggests effective preventative measures to protect oneself from accidents, providing viewers with concrete advice. The content provider can also provide preventative measures and countermeasures through internet distribution. For example, they may provide crime prevention information, natural disaster predictions, and evacuation guidelines through news websites and video distribution platforms. Furthermore, the content provider can also provide preventative measures and countermeasures through mobile apps. For example, they can notify users in real time of the latest preventative measures and countermeasures through mobile apps, and encourage immediate action in emergencies. In this way, the content provider can provide viewers with preventative measures and countermeasures in a variety of ways, ensuring their safety. The content provider can collect feedback from viewers and continuously improve the quality of the information they provide. In this way, the content provider can provide information that meets the needs of viewers and gain their trust.

[0070] The reception department receives input from viewers. For example, the reception department collects viewer opinions through surveys. Specifically, it conducts surveys through news programs and mobile apps to collect viewer opinions and requests. The reception department can also receive viewer feedback through comment functions. For example, it collects viewer opinions and impressions through comment sections on news websites and video streaming platforms. Furthermore, the reception department can collect viewer opinions through feedback forms. For example, it can install feedback forms on news websites and mobile apps, allowing viewers to freely post their opinions. This allows the reception department to collect diverse viewer opinions and requests and use them to improve the news delivery system. The reception department can analyze the collected feedback to understand viewer needs and interests. This allows the reception department to provide information that meets viewer needs and improve viewer satisfaction.

[0071] The data collection unit collects news data. For example, it collects news articles. Specifically, it uses web scraping technology to automatically collect the latest news articles from news sites on the internet. The data collection unit can also collect video clips. For example, it collects video clips from news programs and video streaming platforms and incorporates them into the news delivery system. Furthermore, the data collection unit can also collect audio files. For example, it collects audio files from radio news and podcasts and incorporates them into the news delivery system. This allows the data collection unit to efficiently collect news data in various formats and incorporate it into the news delivery system. The data collection unit centrally manages the collected data and makes it accessible to the analysis and generation units. This allows the data collection unit to streamline data management across the entire news delivery system and improve system performance. The data collection unit can also filter and clean the collected data to ensure its quality. This allows the data collection unit to provide high-quality data to the news delivery system and reliable information to viewers.

[0072] The generation unit can generate measures to prevent traffic accidents. For example, the generation unit can raise awareness of driving manners. It can also raise awareness of traffic rules. Furthermore, it can recommend safe driving. For example, as part of raising awareness of driving manners, the generation unit can provide specific advice on points to watch out for while driving and how to avoid dangerous situations. As part of raising awareness of traffic rules, the generation unit can explain the dangers of running red lights and speeding. As part of recommending safe driving, the generation unit can suggest maintaining an appropriate distance between vehicles and adhering to speed limits. By generating measures to prevent traffic accidents, it is possible to prevent traffic accidents from occurring in the first place.

[0073] The generation unit can generate crime prevention measures. For example, the generation unit can propose the installation of security cameras. It can also propose the designation of a restricted area. Furthermore, it can propose strengthening community patrols. For example, as an example of installing security cameras, the generation unit can install security cameras in a specific area to enhance the deterrent effect on crime. As an example of designating a restricted area, the generation unit can designate areas with a high crime rate as restricted areas and strengthen security. As an example of strengthening community patrols, the generation unit can encourage patrol activities by local residents to prevent crime from occurring. In this way, by generating crime prevention measures, it is possible to prevent crime from occurring in the first place.

[0074] The generation unit can generate predictions for natural disasters and evacuation guidelines. For example, the generation unit can analyze meteorological data. It can also use earthquake prediction systems. It can also use flood prediction models. For example, as a meteorological data analysis unit, it collects meteorological data and predicts changes in weather. As an earthquake prediction system, it detects precursory phenomena for earthquakes and predicts the occurrence of earthquakes. As a flood prediction model, it analyzes river water level data and predicts the occurrence of floods. As evacuation guidelines, it sets evacuation routes and designates evacuation sites. As precautions during evacuation, it provides safe evacuation methods and lists of items to bring during evacuation. By generating predictions for natural disasters and evacuation guidelines, the ability to respond to disasters can be enhanced.

[0075] The generation function can generate lifestyle tips for maintaining physical and mental health. For example, it can suggest a balanced diet, exercise habits, and stress management methods. For instance, it can suggest nutritionally balanced meal menus, explain the importance of regular exercise and provide specific exercise plans, and suggest relaxation techniques and mental health care methods for stress management. By generating lifestyle tips for maintaining physical and mental health, it can help maintain the health of its users.

[0076] The sponsorship department can include a segment within the news program that proposes preventative measures and countermeasures. For example, the sponsorship department could create a special feature segment. They could also propose preventative measures and countermeasures in an interview format. Furthermore, they could create a segment that encourages viewer participation. For example, the sponsorship department could create a special feature segment that proposes preventative measures against traffic accidents. They could invite crime prevention experts to propose countermeasures in an interview format. They could propose preventative measures and countermeasures in a viewer participation segment by answering questions from viewers. In this way, by including a segment that proposes preventative measures and countermeasures within the news program, specific information can be provided to viewers.

[0077] The analysis unit can estimate the viewer's emotions and adjust the news analysis method based on the estimated viewer emotions. For example, if the viewer is feeling anxious, the generation AI will analyze the news content in a way that provides a sense of reassurance. The analysis unit can also analyze the news content in a way that allows the viewer to receive it calmly if they are excited. Furthermore, if the viewer is indifferent, the generation AI will analyze the news content in a way that makes it interesting. For example, if the viewer is feeling anxious, the generation AI will adjust the news content to provide a sense of reassurance. If the viewer is excited, the generation AI will adjust the news content in a way that allows the viewer to receive it calmly. If the viewer is indifferent, the generation AI will adjust the news content in a way that makes it interesting. In this way, by adjusting the news analysis method according to the viewer's emotions, news that is suitable for the viewer can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] The analysis unit can improve the accuracy of news analysis by referring to data on similar past events. For example, the generation AI can refer to past traffic accident data to more accurately analyze the cause of a current accident. The analysis unit can also refer to past crime data to analyze current crime patterns. Furthermore, the analysis unit can refer to past natural disaster data to analyze the impact of a current disaster. For example, the analysis unit can refer to past traffic accident data to analyze the cause of a current accident. The analysis unit can refer to past crime data to analyze current crime patterns. The analysis unit can refer to past natural disaster data to analyze the impact of a current disaster. In this way, the accuracy of news analysis is improved by referring to data on similar past events.

[0079] The analysis unit can apply different analysis algorithms depending on the news category when analyzing news. For example, the analysis unit can apply a traffic data analysis algorithm to news about traffic accidents. It can also apply a crime data analysis algorithm to news about crimes. Furthermore, it can apply a disaster data analysis algorithm to news about natural disasters. By applying different analysis algorithms depending on the news category, the analysis accuracy is improved.

[0080] The analysis unit can estimate the viewer's emotions and determine the priority of analysis results based on the estimated viewer emotions. For example, if the viewer is feeling anxious, the generation AI will prioritize analysis results that provide a sense of security. The analysis unit can also prioritize analysis results that the generation AI can calmly process if the viewer is excited. Furthermore, if the viewer is indifferent, the analysis unit can prioritize analysis results that the generation AI can find interesting. For example, if the viewer is feeling anxious, the generation AI will prioritize analysis results that provide a sense of security. If the viewer is excited, the analysis unit will prioritize analysis results that the generation AI can calmly process if the viewer is excited. If the viewer is indifferent, the analysis unit will prioritize analysis results that the generation AI can find interesting if the viewer is indifferent. In this way, by determining the priority of analysis results according to the viewer's emotions, information appropriate to the viewer can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can perform news analysis while considering geographical information of the news source's location. For example, the analysis unit's generating AI can consider geographical information of the location of a traffic accident to analyze the cause of the accident. The analysis unit can also have the generating AI consider geographical information of the location of a crime to analyze crime patterns. Furthermore, the analysis unit can have the generating AI consider geographical information of the location of a natural disaster to analyze the impact of the disaster. For example, the analysis unit's generating AI can consider geographical information of the location of a traffic accident to analyze the cause of the accident. The analysis unit's generating AI can consider geographical information of the location of a crime to analyze crime patterns. The analysis unit's generating AI can consider geographical information of the location of a natural disaster to analyze the impact of the disaster. By considering geographical information of the news source's location, the accuracy of the analysis is improved.

[0082] The analysis unit can improve the accuracy of news analysis by referring to relevant literature. For example, the generation AI can refer to relevant literature on traffic accidents to analyze the causes of accidents. The analysis unit can also refer to relevant literature on crimes to analyze crime patterns. Furthermore, the analysis unit can refer to relevant literature on natural disasters to analyze the impact of disasters. For example, the analysis unit can refer to relevant literature on traffic accidents to analyze the causes of accidents. The analysis unit can refer to relevant literature on crimes to analyze crime patterns. The analysis unit can refer to relevant literature on natural disasters to analyze the impact of disasters. By referring to relevant literature on news, the accuracy of the analysis is improved.

[0083] The generation unit can estimate the viewer's emotions and adjust the method of generating preventative measures and countermeasures based on the estimated viewer emotions. For example, if the viewer is feeling anxious, the generation AI can generate preventative measures that provide a sense of security. The generation unit can also generate preventative measures that can be received calmly if the viewer is excited. Furthermore, if the viewer is indifferent, the generation AI can generate preventative measures that will pique their interest. For example, if the viewer is feeling anxious, the generation AI can generate preventative measures that provide a sense of security. If the viewer is excited, the generation AI can generate preventative measures that can be received calmly. If the viewer is indifferent, the generation AI can generate preventative measures that will pique their interest. In this way, by adjusting the method of generating preventative measures and countermeasures according to the viewer's emotions, appropriate preventative measures and countermeasures can be provided to the viewer. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] The generation unit can improve the accuracy of generating preventive measures and countermeasures by referring to past successful cases. For example, the generation unit's AI can refer to past successful cases of traffic accident prevention to generate preventive measures. The generation unit's AI can also refer to past successful cases of crime prevention to generate preventive measures. The generation unit's AI can also refer to past successful cases of natural disaster prevention to generate preventive measures. In this way, the accuracy of generating preventive measures and countermeasures is improved by referring to past successful cases.

[0085] The generation unit can apply different generation algorithms depending on the category of the event when generating preventive measures and countermeasures. For example, the generation unit can apply a traffic data analysis algorithm to preventive measures for traffic accidents. It can also apply a crime data analysis algorithm to preventive measures for crimes. Furthermore, it can apply a disaster data analysis algorithm to preventive measures for natural disasters. By applying different generation algorithms depending on the category of the event, the generation accuracy is improved.

[0086] The generation unit can estimate the viewer's emotions and determine the priority of preventative measures and countermeasures to generate based on the estimated viewer emotions. For example, if the viewer is feeling anxious, the generation unit will prioritize preventative measures that provide a sense of security. If the viewer is excited, the generation unit can also prioritize preventative measures that the generation AI can receive calmly. If the viewer is indifferent, the generation unit can also prioritize preventative measures that the generation AI can capture the viewer's interest. For example, if the viewer is feeling anxious, the generation unit will prioritize preventative measures that provide a sense of security. If the viewer is excited, the generation unit will prioritize preventative measures that the generation AI can receive calmly. If the viewer is indifferent, the generation unit will prioritize preventative measures that the generation AI can capture the viewer's interest. This allows for the provision of information tailored to the viewer by determining the priority of preventative measures and countermeasures according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The generation unit can consider geographical information of the location where an event occurs when generating preventive measures and countermeasures. For example, the generation unit's AI can consider geographical information of the location where a traffic accident occurred and generate preventive measures. The generation unit's AI can also consider geographical information of the location where a crime occurred and generate preventive measures. Furthermore, the generation unit's AI can also consider geographical information of the location where a natural disaster occurred and generate preventive measures. For example, the generation unit's AI can consider geographical information of the location where a traffic accident occurred and generate preventive measures. The generation unit's AI can consider geographical information of the location where a crime occurred and generate preventive measures. The generation unit's AI can consider geographical information of the location where a natural disaster occurred and generate preventive measures. By considering geographical information of the location where an event occurs, the generation accuracy is improved.

[0088] The generation unit can improve the accuracy of generating preventive measures and countermeasures by referring to the opinions of relevant experts. For example, the generation unit's AI can refer to the opinions of experts in traffic accident prevention to generate preventive measures. The generation unit's AI can also refer to the opinions of experts in crime prevention to generate preventive measures. The generation unit's AI can also refer to the opinions of experts in natural disaster prevention to generate preventive measures. For example, the generation unit's AI can refer to the opinions of experts in traffic accident prevention to generate preventive measures. The generation unit's AI can refer to the opinions of experts in crime prevention to generate preventive measures. The generation unit's AI can refer to the opinions of experts in natural disaster prevention to generate preventive measures. This improves the accuracy of generation by referring to the opinions of relevant experts.

[0089] The news provider can estimate the viewer's emotions and adjust how the news is displayed based on those estimated emotions. For example, if a viewer is feeling anxious, the generating AI can provide a reassuring display method. Similarly, if a viewer is excited, the generating AI can provide a calm and rational display method. Furthermore, if a viewer is indifferent, the generating AI can provide an engaging display method. This allows the news provider to deliver news tailored to the viewer's emotions by adjusting how the news is displayed. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The news delivery unit can select the optimal delivery method by referring to the viewer's past viewing history when delivering news. For example, the news delivery unit can use a generative AI to refer to the viewer's past viewing history and deliver the most relevant news. The news delivery unit can also use a generative AI to refer to the viewer's past viewing history and deliver news that is of interest to the viewer. The news delivery unit can also use a generative AI to refer to the viewer's past viewing history and deliver relevant news. In this way, the news delivery unit can deliver the most relevant news by referring to the viewer's past viewing history.

[0091] The news delivery unit can apply different delivery algorithms depending on the news category when delivering news. For example, the unit can apply a traffic data analysis algorithm to news about traffic accidents. It can also apply a crime data analysis algorithm to news about crimes. Furthermore, it can apply a disaster data analysis algorithm to news about natural disasters. By applying different delivery algorithms depending on the news category, the accuracy of news delivery is improved.

[0092] The news provider can estimate the viewer's emotions and determine the priority of news to deliver based on those estimated emotions. For example, if a viewer is feeling anxious, the generating AI will prioritize news that provides a sense of reassurance. If a viewer is excited, the generating AI can prioritize news that can be received calmly. If a viewer is indifferent, the generating AI can prioritize news that will capture their interest. For example, if a viewer is feeling anxious, the generating AI will prioritize news that provides a sense of reassurance. If a viewer is excited, the generating AI will prioritize news that can be received calmly. If a viewer is indifferent, the generating AI will prioritize news that will capture their interest. This allows the news provider to deliver news that is appropriate for the viewer by determining the priority of news according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The news delivery department can select the optimal delivery method by considering the viewer's device information when delivering news. For example, the news delivery department can use a generating AI to refer to the viewer's device information and deliver the most relevant news. The news delivery department can also use a generating AI to refer to the viewer's device information and deliver news that is of interest to the viewer. The news delivery department can also use a generating AI to refer to the viewer's device information and deliver relevant news. In this way, by considering the viewer's device information, the news delivery department can deliver the most relevant news. The news delivery department can use a generating AI to refer to the viewer's device information and deliver news that is of interest to the viewer. The news delivery department can use a generating AI to refer to the viewer's device information and deliver relevant news.

[0094] The news delivery department can analyze viewers' social media activity and provide relevant news when delivering news. For example, the news delivery department can use a generative AI to refer to viewers' social media activity and provide the most relevant news. The news delivery department can also use a generative AI to refer to viewers' social media activity and provide news that will pique their interest. The news delivery department can also use a generative AI to refer to viewers' social media activity and provide relevant news. For example, the news delivery department can use a generative AI to refer to viewers' social media activity and provide the most relevant news. The news delivery department can use a generative AI to refer to viewers' social media activity and provide news that will pique their interest. The news delivery department can use a generative AI to refer to viewers' social media activity and provide relevant news. This allows the news delivery department to provide relevant news by analyzing viewers' social media activity.

[0095] The reception unit can estimate the viewer's emotions and adjust the input reception method based on the estimated viewer emotions. For example, if the viewer is feeling anxious, the reception unit can provide an input reception method that provides reassurance through the generative AI. The reception unit can also provide an input reception method that allows the generative AI to receive calmly if the viewer is excited. Furthermore, if the viewer is indifferent, the reception unit can provide an input reception method that engages the generative AI. In this way, by adjusting the input reception method according to the viewer's emotions, an appropriate input method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The reception unit can select the optimal reception method by referring to the viewer's past input history when receiving input. For example, the reception unit's generating AI can refer to the viewer's past input history and provide the optimal input method. The reception unit can also refer to the viewer's past input history and provide an interesting input method. Furthermore, the reception unit can refer to the viewer's past input history and provide a relevant input method. For example, the reception unit's generating AI can refer to the viewer's past input history and provide the optimal input method. The reception unit's generating AI can refer to the viewer's past input history and provide an interesting input method. The reception unit's generating AI can refer to the viewer's past input history and provide a relevant input method. This allows the reception unit to provide the optimal input method by referring to the viewer's past input history.

[0097] The reception unit can estimate the viewer's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the viewer is feeling anxious, the reception unit can use a generative AI to provide a reassuring interface design. The reception unit can also use a generative AI to provide an interface design that the viewer can receive calmly if they are excited. Furthermore, if the viewer is indifferent, the reception unit can use a generative AI to provide an interface design that captures their interest. This allows for the provision of an appropriate interface to the viewer by adjusting the input interface design according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The reception unit can select the optimal reception method when receiving input, taking into account the viewer's device information. For example, the reception unit's generating AI can refer to the viewer's device information and provide the optimal input method. The reception unit can also refer to the viewer's device information and provide an interesting input method. Furthermore, the reception unit can refer to the viewer's device information and provide a relevant input method. For example, the reception unit's generating AI can refer to the viewer's device information and provide the optimal input method. The reception unit's generating AI can refer to the viewer's device information and provide an interesting input method. The reception unit's generating AI can refer to the viewer's device information and provide a relevant input method. This allows the reception unit to provide the optimal input method by taking the viewer's device information into consideration.

[0099] The data collection unit can estimate the viewer's emotions and adjust the news data collection method based on the estimated viewer emotions. For example, if a viewer is feeling anxious, the data collection unit can provide a news data collection method that provides reassurance through the generative AI. The data collection unit can also provide a news data collection method that allows the generative AI to calmly process the data if the viewer is excited. Furthermore, if the viewer is indifferent, the data collection unit can provide a news data collection method that engages the viewer's interest. This allows for the collection of news data appropriate to the viewer by adjusting the news data collection method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The data collection unit can improve collection accuracy by referring to past collected data when collecting news data. For example, the data collection unit's generating AI can refer to past traffic accident data to collect current accident data. The data collection unit can also refer to past crime data to collect current crime data. The data collection unit can also refer to past natural disaster data to collect current disaster data. For example, the data collection unit's generating AI can refer to past traffic accident data to collect current accident data. The data collection unit's generating AI can refer to past crime data to collect current crime data. The data collection unit's generating AI can refer to past natural disaster data to collect current disaster data. By referring to past collected data, collection accuracy is improved.

[0101] The data collection unit can estimate the viewer's emotions and determine the priority of news data to collect based on the estimated viewer emotions. For example, if the viewer is feeling anxious, the generating AI will prioritize collecting news data that provides a sense of reassurance. Similarly, if the viewer is excited, the generating AI will prioritize collecting news data that allows for calm processing. Furthermore, if the viewer is indifferent, the generating AI will prioritize collecting news data that captures their interest. This allows for the collection of news data tailored to the viewer's emotions by prioritizing news data accordingly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The data collection unit can consider geographical information of the news source's location when collecting news data. For example, the data collection unit's generating AI can consider geographical information of the location of a traffic accident and collect accident data. The data collection unit can also consider geographical information of the location of a crime and collect crime data. The data collection unit can also consider geographical information of the location of a natural disaster and collect disaster data. By considering geographical information of the news source's location, the data collection accuracy is improved.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The news delivery system can estimate the viewer's emotions and adjust the way the news is delivered based on those emotions. For example, if a viewer is feeling anxious, the system can choose a way to deliver the news that provides reassurance. If a viewer is excited, the system can choose a way to deliver the news that allows them to receive it calmly. Furthermore, if a viewer is indifferent, the system can choose a way to deliver the news that will pique their interest. This enables the delivery of news that is optimally tailored to the viewer's emotions, thereby capturing their attention. Emotion estimation is achieved using an emotion engine or generative AI.

[0105] A news delivery system can provide the most relevant news by referencing a viewer's past viewing history. For example, the delivery department can analyze a viewer's past viewing history and prioritize providing news that is likely to interest them. It can also analyze trends in news that viewers have previously watched and provide related news. Furthermore, based on the viewer's viewing history, it can suggest news that the viewer has not yet seen but may be interested in. This enables news delivery tailored to the viewer's interests, thereby increasing viewer satisfaction.

[0106] The news delivery system can select the optimal delivery method when delivering news, taking into account the viewer's device information. For example, if the viewer is using a smartphone, the delivery department can select a mobile-friendly news delivery method. If the viewer is using a tablet, it can select a news delivery method suitable for a large screen. Furthermore, if the viewer is using a desktop, it can select a news delivery method that includes detailed information. This enables optimal news delivery tailored to the viewer's device, thereby improving viewer convenience.

[0107] A news delivery system can analyze viewers' social media activity and provide relevant news. For example, the system can analyze viewers' social media posts and "likes" to provide news that is likely to interest them. It can also provide relevant news based on information about accounts that viewers follow and groups they participate in. Furthermore, it can analyze viewers' social media trends and provide the latest news that is likely to interest them. This makes it possible to provide news tailored to viewers' interests and increases viewer satisfaction.

[0108] The news delivery system can estimate the viewer's emotions and adjust how the news is displayed based on those emotions. For example, if a viewer is feeling anxious, the system can choose a display method that provides reassurance. If a viewer is excited, the system can choose a display method that allows for calm reception. Furthermore, if a viewer is indifferent, the system can choose a display method that attracts their interest. This enables optimal news display tailored to the viewer's emotions, thereby capturing their attention. Emotion estimation is achieved using an emotion engine or generative AI.

[0109] The news delivery system can apply different delivery algorithms depending on the news category when delivering news. For example, a traffic data analysis algorithm can be applied to news about traffic accidents. Similarly, a crime data analysis algorithm can be applied to news about crimes. Furthermore, a disaster data analysis algorithm can be applied to news about natural disasters. By applying the most appropriate delivery algorithm for each news category, the accuracy of news delivery is improved, and more accurate and relevant news can be provided to viewers.

[0110] The news delivery system can estimate the viewer's emotions and prioritize the news delivered based on those emotions. For example, if a viewer is feeling anxious, the system can prioritize news that provides reassurance. If a viewer is excited, the system can prioritize news that can be received calmly. Furthermore, if a viewer is indifferent, the system can prioritize news that will pique their interest. This enables the delivery of optimal news tailored to the viewer's emotions, thereby capturing their attention. Emotion estimation is achieved using an emotion engine or generative AI.

[0111] The news delivery system can select the optimal delivery method by referring to the viewer's past viewing history when delivering news. For example, the delivery department can analyze the viewer's past viewing history and prioritize delivering news that is likely to interest the viewer. It can also analyze the trends of news that the viewer has watched in the past and provide related news. Furthermore, based on the viewer's viewing history, it can suggest news that the viewer has not yet seen but may be interested in. This makes it possible to deliver news that is tailored to the viewer's interests and can increase viewer satisfaction.

[0112] The news delivery system can estimate the viewer's emotions and adjust how the news is displayed based on those emotions. For example, if a viewer is feeling anxious, the system can choose a display method that provides reassurance. If a viewer is excited, the system can choose a display method that allows for calm reception. Furthermore, if a viewer is indifferent, the system can choose a display method that attracts their interest. This enables optimal news display tailored to the viewer's emotions, thereby capturing their attention. Emotion estimation is achieved using an emotion engine or generative AI.

[0113] The news delivery system can select the optimal delivery method when delivering news, taking into account the viewer's device information. For example, if the viewer is using a smartphone, the delivery department can select a mobile-friendly news delivery method. If the viewer is using a tablet, it can select a news delivery method suitable for a large screen. Furthermore, if the viewer is using a desktop, it can select a news delivery method that includes detailed information. This enables optimal news delivery tailored to the viewer's device, thereby improving viewer convenience.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The collection unit collects news data. For example, the collection unit uses web scraping techniques to collect news articles, as well as video clips and audio files. Step 2: The analysis unit analyzes the content of the news. For example, it uses natural language processing technology to analyze the text data of the news, image analysis technology to analyze the video data of the news, and audio analysis technology to analyze the audio data of the news. Step 3: The generation unit generates preventive measures and countermeasures based on the results analyzed by the analysis unit. For example, it uses generation AI to generate traffic accident prevention measures, crime prevention measures, and natural disaster predictions and evacuation guidelines. Step 4: The providing unit provides viewers with the preventative measures and countermeasures generated by the generating unit. For example, this could be done by creating a segment in a news program to suggest them, or by providing them through internet distribution or mobile apps. Step 5: The reception desk receives input from viewers. For example, it collects viewer opinions and feedback through surveys, comment functions, and feedback forms.

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0119] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, reception unit, and collection unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes news text data using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates preventive measures and countermeasures using generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides information to viewers through news programs and internet distribution. The reception unit receives viewer input using the touch panel 38A or microphone 38B of the smart device 14. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects news articles and video clips. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 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.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, reception unit, and collection unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes news text data using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates preventive measures and countermeasures using generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides information to viewers through news programs and internet distribution. The reception unit receives viewer input using the microphone 238 of the smart glasses 214. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects news articles and video clips. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, reception unit, and collection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes news text data using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates preventive measures and countermeasures using generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides information to viewers through news programs and internet distribution. The reception unit receives viewer input using the microphone 238 of the headset terminal 314. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects news articles and video clips. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 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.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the analysis unit, generation unit, provision unit, reception unit, and collection unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes news text data using natural language processing technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates preventive measures and countermeasures using generation AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides information to viewers through news programs and internet distribution. The reception unit receives viewer input using the microphone 238 of the robot 414. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects news articles and video clips. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) The analysis department analyzes the content of the news, A generation unit that generates preventive measures and countermeasures based on the results of the analysis performed by the aforementioned analysis unit, A provisioning unit that provides preventive measures and countermeasures generated by the generation unit to viewers, A reception desk that accepts viewer input, It comprises a data collection unit that collects news data, A system characterized by the following features. (Note 2) The generating unit is Generate traffic accident prevention measures The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate crime prevention measures The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate natural disaster predictions and evacuation guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Creating lifestyle techniques to maintain physical and mental health The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, A segment offering preventative measures and countermeasures will be included in the news program. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate viewer sentiment and adjust news analysis methods based on the estimated viewer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing news, we improve the accuracy of the analysis by referring to data on similar past events. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing news, different analysis algorithms are applied depending on the news category. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the viewer's emotions and prioritizes the analysis results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing news, the analysis takes into account the geographical information of the news's origin. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing news, we improve the accuracy of the analysis by referring to related literature. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the emotions of viewers and adjust the methods for generating preventative measures and countermeasures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating preventative measures and countermeasures, we improve the accuracy of the generation process by referring to past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating preventative measures and countermeasures, different generation algorithms are applied depending on the category of the event. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the audience's emotions and determines the priority of preventative measures and countermeasures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating preventative measures and countermeasures, the geographical information of the location where the event occurred should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating preventive measures and countermeasures, we improve the accuracy of the generation by referring to the opinions of relevant experts. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the viewer's emotions and adjusts how news is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When delivering news, the system selects the most suitable delivery method by referring to the viewer's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing news, different delivery algorithms are applied depending on the news category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the audience's emotions and determines the priority of news to deliver based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When delivering news, the optimal delivery method is selected by considering the viewer's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When delivering news, we analyze viewers' social media activity to provide relevant news. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is The system estimates the viewer's emotions and adjusts the input processing method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is When receiving input, the system will refer to the viewer's past input history to select the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is It estimates the viewer's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is When receiving input, the system selects the optimal reception method considering the viewer's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned collection unit is We estimate the sentiment of our audience and adjust how we collect news data based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned collection unit is When collecting news data, we improve collection accuracy by referring to past collected data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned collection unit is It estimates the sentiment of viewers and determines the priority of news data to collect based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned collection unit is When collecting news data, geographical information about the location where the news originated should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes the content of the news, A generation unit that generates preventive measures and countermeasures based on the results of the analysis performed by the aforementioned analysis unit, A provisioning unit that provides preventive measures and countermeasures generated by the generation unit to viewers, A reception desk that accepts viewer input, It comprises a data collection unit that collects news data, A system characterized by the following features.

2. The generating unit is Generate traffic accident prevention measures The system according to feature 1.

3. The generating unit is Generate crime prevention measures The system according to feature 1.

4. The generating unit is Generate natural disaster predictions and evacuation guidelines. The system according to feature 1.

5. The generating unit is Creating lifestyle techniques to maintain physical and mental health The system according to feature 1.

6. The aforementioned supply unit is, A segment offering preventative measures and countermeasures will be included in the news program. The system according to feature 1.

7. The aforementioned analysis unit, We estimate viewer sentiment and adjust news analysis methods based on the estimated viewer sentiment. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing news, we improve the accuracy of the analysis by referring to data on similar past events. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing news, different analysis algorithms are applied depending on the news category. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the viewer's emotions and prioritizes the analysis results based on those estimated emotions. The system according to feature 1.

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