system

Abstract

The system according to this embodiment aims to provide customized news based on the user's interests and to realize an interactive experience. [Solution] The system according to the embodiment comprises a generation unit, a provision unit, and a response unit. The generation unit generates news based on the user's interests. The provision unit provides the news generated by the generation unit in a presentation format by a virtual news caster. The response unit responds in real time to questions regarding the news provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, there is a problem that news provision based on the user's interests is not sufficiently performed and the interactive experience is lacking.

[0005] The system according to the embodiment aims to provide customized news based on the user's interests and realize an interactive experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a provision unit, and a response unit. The generation unit generates news based on the user's interests. The provision unit provides the news generated by the generation unit in a presentation format by a virtual newscaster. The response unit responds in real time to questions regarding the news provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide customized news based on the user's interests and realize an interactive experience. [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 system that generates individually customized news based on the user's interests and delivers it in a presentation format by a virtual news caster. This news delivery system generates news based on the user's interests and delivers it in a presentation format by a virtual news caster. Furthermore, it features a real-time question-and-answer function to provide an interactive experience. For example, the news delivery system generates news based on the user's interests. In this process, natural language processing and machine learning are used to generate news tailored to the user's interests. For example, if the user is interested in technology, the system generates the latest technology-related news. This allows the user to quickly receive news that matches their interests. Next, the news delivery system delivers the generated news in a presentation format by a virtual news caster. The virtual news caster uses facial recognition and speech synthesis technology to create a realistic caster. For example, when the virtual caster reads the news, they can change their facial expressions and tone of voice to create a more realistic presentation. This allows the user to enjoy the news both visually and aurally. Furthermore, the news delivery system features a real-time question-and-answer function to provide an interactive experience. When the user inputs a question about the news, the AI ​​responds to that question in real time. For example, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. This allows users to deepen their understanding of the news. This system is expected to improve user engagement, reduce the time spent consuming news, and enhance information comprehension. Users can quickly receive news that matches their interests, reducing stress caused by information overload. Furthermore, the interactive experience helps maintain interest in the news. In addition, realistic presentations by virtual newscasters improve news comprehension.This allows the news delivery system to provide customized news based on user interests and offer an interactive experience.

[0029] The news delivery system according to this embodiment comprises a generation unit, a delivery unit, and a response unit. The generation unit generates news based on the user's interests. The generation unit generates news tailored to the user's interests, for example, by utilizing natural language processing and machine learning. For example, if the user is interested in technology, the generation unit generates the latest technology-related news. The generation unit can also generate the latest sports-related news if the user is interested in sports. Furthermore, if the user is interested in entertainment, the generation unit can also generate the latest entertainment-related news. The delivery unit delivers the news generated by the generation unit in a presentation format by a virtual news caster. The delivery unit realizes a realistic caster, for example, by utilizing facial recognition and speech synthesis technology. For example, the delivery unit can make the presentation more realistic by changing the facial expressions and tone of voice when the virtual caster reads the news. The delivery unit can also add visual effects when the virtual caster reads the news. Furthermore, the delivery unit can display background images or videos when the virtual caster reads the news. The response unit responds in real time to questions about the news provided by the delivery unit. The response unit, for example, responds in real time to questions a user might input about the news using AI. For instance, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. The response unit can also provide relevant background information if a user asks, "Tell me the background of this news." Furthermore, if a user asks, "Tell me the impact of this news," the AI ​​can provide relevant impact information. This allows the news delivery system according to this embodiment to provide customized news based on the user's interests and offer an interactive experience.

[0030] The generation unit generates news based on the user's interests. For example, it uses natural language processing and machine learning to create news tailored to the user's interests. Specifically, the generation unit analyzes the user's past browsing history, search history, and social media activity to identify their interests. For example, if the user is interested in technology, the generation unit generates the latest technology-related news. In this case, the generation unit collects data from sources such as technology-related news articles, blogs, and research papers, summarizes this information using natural language processing techniques, and extracts the points that are important to the user. The generation unit can also generate the latest sports-related news if the user is interested in sports. In this case, the generation unit collects information such as the results of sporting events, interviews with athletes, and match highlights to provide content that is interesting to the user. Furthermore, if the user is interested in entertainment, the generation unit can generate the latest entertainment-related news. For example, it collects information such as movie releases, interviews with actors, and music album releases to provide news that is appealing to the user. When generating this news, the generation unit can adjust the content and tone of the news based on the user's interests to provide a more personalized news experience.

[0031] The delivery unit presents news generated by the generation unit in a presentation format by a virtual news anchor. The delivery unit uses technologies such as facial recognition and speech synthesis to create a realistic anchor. Specifically, the delivery unit can make the presentation more realistic by changing the virtual anchor's facial expressions and tone of voice when reading the news. For example, the virtual anchor can display a serious expression when delivering important news and a smile when delivering lighter news. The delivery unit can also add visual effects when the virtual anchor reads the news. For example, by displaying relevant images or videos in the background depending on the news content, the presentation can be made easier for viewers to understand. Furthermore, the delivery unit can display background images or videos when the virtual anchor reads the news. For example, when delivering a weather forecast, weather maps or satellite images can be displayed in the background to provide information that is visually easy for viewers to understand. By utilizing these technologies, the delivery unit can provide viewers with an engaging and interactive news experience.

[0032] The response unit responds in real time to questions about news provided by the news provider. For example, when a user inputs a question about the news, the AI ​​responds to that question in real time. Specifically, the response unit uses natural language processing technology to analyze the user's question, searches for relevant information, and generates a response. For example, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. In this case, the response unit searches the full text of the news article and related data, extracting and providing information that is important to the user. The response unit can also provide relevant background information if the user asks, "Tell me the background of this news." For example, by providing the cause of the news, related historical background, and comments from those involved, the user can gain a deeper understanding of the news. Furthermore, if the user asks, "Tell me the impact of this news," the AI ​​can provide relevant impact information. For example, by providing the impact of the news on the economy and society, and predictions for future developments, the user can understand the importance of the news. Through these functions, the response unit provides users with an interactive news experience and deepens their understanding of the news.

[0033] The learning unit can learn user behavior patterns. For example, the learning unit can analyze a user's news browsing history to identify user interests. For instance, it can identify news categories that a user frequently views and prioritize providing news related to those categories. The learning unit can also analyze a user's click history to identify news that the user is interested in. For example, it can analyze the content of news articles that a user clicks on to identify user interests. Furthermore, the learning unit can analyze a user's search history to identify topics that the user is interested in. For example, it can analyze keywords that a user searches for to identify user interests. By learning user behavior patterns, the learning unit can provide more relevant news. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input a user's news browsing history into an AI and have the AI ​​identify user interests.

[0034] The adjustment unit can change the facial expressions and tone of voice of the virtual news anchor. For example, the adjustment unit can provide a more realistic presentation by changing the facial expressions of the virtual anchor when they read the news. For example, the adjustment unit can make the virtual anchor read the news with a smile. It can also make the virtual anchor read the news with a surprised expression. Furthermore, the adjustment unit can make the virtual anchor read the news with a sad expression. The adjustment unit can also change the tone of voice of the virtual anchor. For example, the adjustment unit can make the virtual anchor read the news in a cheerful tone. It can also make the virtual anchor read the news in a calm tone. Furthermore, the adjustment unit can make the virtual anchor read the news in an excited tone. In this way, the adjustment unit can provide a more realistic presentation by changing the facial expressions and tone of voice of the virtual news anchor. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input changes in the virtual caster's facial expressions and voice tone into the AI, and then have the AI ​​perform adjustments to those expressions and voice tone.

[0035] The customization unit can generate news tailored to the user's interests. For example, the customization unit selects and customizes news based on the user's interests. For instance, if the user is interested in technology, the customization unit will prioritize providing the latest technology-related news. Similarly, if the user is interested in sports, the customization unit can prioritize providing the latest sports-related news. Furthermore, if the user is interested in entertainment, the customization unit can prioritize providing the latest entertainment-related news. This allows the customization unit to provide more relevant news by generating news tailored to the user's interests. Some or all of the above-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can input the user's interests into an AI and have the AI ​​perform news selection and customization.

[0036] The generation unit can analyze the user's past news browsing history and select the most relevant news when generating news. For example, the generation unit can prioritize generating news categories that the user has frequently viewed in the past. For example, the generation unit can also select news on specific topics from the user's past browsing history. Furthermore, the generation unit can generate relevant news based on news that the user has previously rated highly. In this way, the generation unit can provide more appropriate news by analyzing the user's past news browsing history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's news browsing history into AI and have the AI ​​perform the selection of the most relevant news.

[0037] The generation unit can prioritize news based on the user's current interests when generating news. For example, the generation unit can prioritize news based on keywords the user has recently searched for. For example, the generation unit can also prioritize news based on content the user has shared on social media. Furthermore, the generation unit can prioritize news based on the content of newsletters the user has recently subscribed to. This allows the generation unit to provide more relevant news by prioritizing news based on the user's current interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current interests into AI and have AI perform the task of determining news priorities.

[0038] The generation unit can generate highly relevant news by considering the user's geographical location information when generating news. For example, the generation unit can generate local news related to the user's current location. For example, if the user is traveling, the generation unit can also generate news about the travel destination. Furthermore, the generation unit can generate news that includes local event information based on the user's geographical location. In this way, the generation unit can provide more appropriate news by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and have the AI ​​perform the generation of highly relevant news.

[0039] The generation unit can analyze a user's social media activity and generate relevant news when generating news. For example, the generation unit can generate news based on the content of posts from accounts that the user follows on social media. For example, the generation unit can also generate news related to posts that the user has "liked" on social media. Furthermore, the generation unit can generate news based on content that the user has shared on social media. In this way, the generation unit can provide more appropriate news by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into AI and have the AI ​​generate relevant news.

[0040] The news delivery unit can select the most suitable presentation method by referring to the user's past viewing history when delivering news. For example, the unit may prioritize presentation styles that the user has previously enjoyed watching. For example, the unit may select a presentation method for a specific topic based on the user's past viewing history. The unit may also select a relevant presentation method based on presentation styles that the user has previously given high ratings to. In this way, the unit can provide a more appropriate presentation method by referring to the user's past viewing history. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not using AI. For example, the unit may input the user's viewing history into AI and have the AI ​​select the most suitable presentation method.

[0041] The news delivery unit can apply different presentation styles depending on the user's interests when delivering news. For example, if the user is interested in technology, the unit can apply a presentation style that includes technical details. For example, if the user is interested in sports, the unit can apply a dynamic presentation style. Furthermore, if the user is interested in entertainment, the unit can apply a highly entertaining presentation style. In this way, the unit can provide more relevant news by applying presentation styles according to the user's interests. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not using AI. For example, the news delivery unit can input the user's interests into AI and have the AI ​​perform the application of presentation styles.

[0042] The news delivery unit can select the optimal display method when delivering news, taking into account the user's device information. For example, if the user is using a smartphone, the unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the unit can provide a concise and highly visible display method. In this way, the unit can provide a more appropriate display method by taking into account the user's device information. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not. For example, the news delivery unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0043] The news delivery unit can adjust the volume and image quality of a presentation based on the user's viewing environment when delivering news. For example, if the user is in a quiet environment, the unit can set the volume low. For example, if the user is in a noisy environment, the unit can set the volume high. The unit can also set the image quality high if the user is using a high-definition device. In this way, the unit can provide more appropriate news by adjusting the volume and image quality of the presentation based on the user's viewing environment. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not using AI. For example, the news delivery unit can input user viewing environment data into AI and have the AI ​​perform the volume and image quality adjustments.

[0044] The response unit can select the most appropriate response method by referring to the user's past question history when responding. For example, the response unit can provide relevant information based on the content of questions the user has asked in the past. For example, the response unit can also select a response method for a specific topic from the user's past question history. Furthermore, the response unit can provide relevant responses based on response methods that the user has previously given high ratings to. In this way, the response unit can provide more appropriate responses by referring to the user's past question history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's question history into AI and have the AI ​​select the most appropriate response method.

[0045] The response unit can apply different response algorithms depending on the user's interests when responding. For example, if the user is interested in technology, the response unit will provide a response that includes technical details. For example, if the user is interested in sports, the response unit may provide a response that includes sports-related information. Furthermore, if the user is interested in entertainment, the response unit may provide a highly entertaining response. In this way, the response unit can provide a more appropriate response by applying a response algorithm according to the user's interests. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's interests into AI and have AI perform the application of the response algorithm.

[0046] The response unit can provide relevant information when responding, taking into account the context of the user's question. For example, if the user asks a question about a specific news story, the response unit will provide information related to that news story. For example, the response unit can also provide relevant information based on questions the user has asked in the past. Furthermore, the response unit can analyze the context of the user's question and provide the most relevant information. In this way, the response unit can provide more appropriate information by taking into account the context of the user's question. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the context of the user's question into AI and have the AI ​​perform the task of providing relevant information.

[0047] The response unit can determine the priority of responses based on the frequency of the user's questions. For example, the response unit will prioritize responses to topics that the user frequently asks about. For example, the response unit can also determine the priority of responses based on the topics the user has asked about frequently in the past. Furthermore, the response unit can analyze the frequency of the user's questions and prioritize responses to the most important questions. In this way, the response unit can prioritize responses to more important questions by determining the priority of responses based on the frequency of the user's questions. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the frequency of the user's questions into the AI ​​and have the AI ​​perform the determination of the response priority.

[0048] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can extract specific patterns from past learning data and optimize the learning algorithm. The learning unit can also analyze past learning data and apply the most effective learning algorithm. In this way, the learning unit can provide a more appropriate learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0049] The learning unit can weight the training data based on the user's behavior patterns during training. For example, the learning unit can weight the training data based on actions that the user frequently performs. For example, the learning unit can analyze the user's behavior patterns and weight the most important data. The learning unit can also determine the priority of the training data based on the user's behavior patterns. This allows the learning unit to provide more appropriate training by weighting the training data based on the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior patterns into the AI ​​and have the AI ​​perform the weighting of the training data.

[0050] The adjustment unit can select the optimal facial expressions and voice tones by referring to the user's past viewing history during the adjustment process. For example, the adjustment unit may prioritize facial expressions and voice tones that the user has previously enjoyed watching. For example, the adjustment unit may also select facial expressions and voice tones related to a specific topic from the user's past viewing history. Furthermore, the adjustment unit may select relevant facial expressions and voice tones based on facial expressions and voice tones that the user has previously given high ratings to. In this way, the adjustment unit can provide more appropriate facial expressions and voice tones by referring to the user's past viewing history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit may input the user's viewing history into AI and have the AI ​​perform the selection of the optimal facial expressions and voice tones.

[0051] The adjustment unit can adjust the display method of the virtual news anchor based on the user's viewing environment during the adjustment process. For example, if the user is in a quiet environment, the adjustment unit can provide a calm display method. For example, if the user is in a noisy environment, the adjustment unit can also provide a highly visible display method. Furthermore, if the user is using a high-definition device, the adjustment unit can provide a high-definition display method. In this way, the adjustment unit can provide more appropriate news by adjusting the display method of the virtual news anchor based on the user's viewing environment. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's viewing environment data into the AI ​​and have the AI ​​perform the adjustment of the display method.

[0052] The customization unit can select the optimal customization method by referring to the user's past news browsing history during the customization process. For example, the customization unit can prioritize news categories that the user has frequently viewed in the past. For example, the customization unit can also customize news on specific topics based on the user's past browsing history. Furthermore, the customization unit can customize related news based on news that the user has previously rated highly. In this way, the customization unit can provide more appropriate news by referring to the user's past news browsing history. Some or all of the above processes in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the user's news browsing history into AI and have the AI ​​select the optimal customization method.

[0053] The customization unit can customize news that is highly relevant to the user, taking into account the user's geographical location during the customization process. For example, the customization unit can customize local news related to the user's current location. For example, if the user is traveling, the customization unit can also customize news related to the user's travel destination. Furthermore, the customization unit can customize news that includes local event information based on the user's geographical location. In this way, the customization unit can provide more appropriate news by taking the user's geographical location into consideration. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the user's geographical location information into AI and have the AI ​​perform the customization of highly relevant news.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The news delivery system can also include a feedback section. This section allows users to input feedback on the news they receive. For example, users can rate the news content as "helpful" or "interesting." The feedback section can also allow users to provide feedback on the news presentation, such as "easy to understand" or "needs more detail." Furthermore, the feedback section can allow users to provide feedback on the timing of the news delivery, such as "appropriate" or "late." This allows the feedback section to collect user feedback and use it to improve the news delivery system.

[0056] The news delivery system can also include a notification unit. This notification unit can notify users of important and urgent news in real time. For example, it can send push notifications when news occurs in categories of interest to the user. It can also send notifications when news occurs related to keywords set by the user. Furthermore, it can notify users of local emergency information based on their geographical location. This ensures that users do not miss important news.

[0057] The news delivery system can also include a translation unit. This unit can translate the generated news into multiple languages. For example, it can translate news generated in English into Japanese. It can also translate news generated in Spanish into French. Furthermore, the translation unit can automatically translate the news based on the user's selected language. This allows the translation unit to enable users to understand the news in their native language.

[0058] The news delivery system can also include an archive section. The archive section can store previously delivered news, allowing users to refer to it later. For example, the archive section allows users to search and view news from a specific date. It can also display a list of past news from a specific category. Furthermore, the archive section can store news that users have previously rated highly, making it available for re-viewing. This allows the archive section to easily access past news.

[0059] The news delivery system can also include a recommendation section. This recommendation section can suggest relevant news based on the user's past news viewing history and ratings. For example, it can recommend new news related to news the user has previously rated highly. It can also prioritize recommending news from categories the user frequently views. Furthermore, it can recommend news that the user hasn't yet viewed but might be interested in, based on their interests. This allows the recommendation section to efficiently find news that the user is interested in.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The generation unit generates news based on the user's interests. The generation unit uses natural language processing and machine learning to generate news tailored to the user's interests. For example, if the user is interested in technology, it will generate the latest technology-related news; if they are interested in sports, it will generate the latest sports-related news. Furthermore, if they are interested in entertainment, it can also generate the latest entertainment-related news. Step 2: The delivery unit presents the news generated by the generation unit in a presentation format by a virtual news anchor. The delivery unit uses facial recognition and speech synthesis technology to create a realistic anchor. The virtual anchor can change its facial expressions and tone of voice when reading the news to create a more realistic presentation. It can also add visual effects and display background images and videos. Step 3: The response unit responds in real time to questions about the news provided by the delivery unit. When a user enters a question about the news, the AI ​​responds to that question in real time. For example, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. If the user asks, "Tell me the background of this news," the AI ​​will provide relevant background information. It can also provide relevant impact information if the user asks, "Tell me the impact of this news."

[0062] (Example of form 2) The news delivery system according to an embodiment of the present invention is a system that generates individually customized news based on the user's interests and delivers it in a presentation format by a virtual news caster. This news delivery system generates news based on the user's interests and delivers it in a presentation format by a virtual news caster. Furthermore, it features a real-time question-and-answer function to provide an interactive experience. For example, the news delivery system generates news based on the user's interests. In this process, natural language processing and machine learning are used to generate news tailored to the user's interests. For example, if the user is interested in technology, the system generates the latest technology-related news. This allows the user to quickly receive news that matches their interests. Next, the news delivery system delivers the generated news in a presentation format by a virtual news caster. The virtual news caster uses facial recognition and speech synthesis technology to create a realistic caster. For example, when the virtual caster reads the news, they can change their facial expressions and tone of voice to create a more realistic presentation. This allows the user to enjoy the news both visually and aurally. Furthermore, the news delivery system features a real-time question-and-answer function to provide an interactive experience. When the user inputs a question about the news, the AI ​​responds to that question in real time. For example, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. This allows users to deepen their understanding of the news. This system is expected to improve user engagement, reduce the time spent consuming news, and enhance information comprehension. Users can quickly receive news that matches their interests, reducing stress caused by information overload. Furthermore, the interactive experience helps maintain interest in the news. In addition, realistic presentations by virtual newscasters improve news comprehension.This allows the news delivery system to provide customized news based on user interests and offer an interactive experience.

[0063] The news delivery system according to this embodiment comprises a generation unit, a delivery unit, and a response unit. The generation unit generates news based on the user's interests. The generation unit generates news tailored to the user's interests, for example, by utilizing natural language processing and machine learning. For example, if the user is interested in technology, the generation unit generates the latest technology-related news. The generation unit can also generate the latest sports-related news if the user is interested in sports. Furthermore, if the user is interested in entertainment, the generation unit can also generate the latest entertainment-related news. The delivery unit delivers the news generated by the generation unit in a presentation format by a virtual news caster. The delivery unit realizes a realistic caster, for example, by utilizing facial recognition and speech synthesis technology. For example, the delivery unit can make the presentation more realistic by changing the facial expressions and tone of voice when the virtual caster reads the news. The delivery unit can also add visual effects when the virtual caster reads the news. Furthermore, the delivery unit can display background images or videos when the virtual caster reads the news. The response unit responds in real time to questions about the news provided by the delivery unit. The response unit, for example, responds in real time to questions a user might input about the news using AI. For instance, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. The response unit can also provide relevant background information if a user asks, "Tell me the background of this news." Furthermore, if a user asks, "Tell me the impact of this news," the AI ​​can provide relevant impact information. This allows the news delivery system according to this embodiment to provide customized news based on the user's interests and offer an interactive experience.

[0064] The generation unit generates news based on the user's interests. For example, it uses natural language processing and machine learning to create news tailored to the user's interests. Specifically, the generation unit analyzes the user's past browsing history, search history, and social media activity to identify their interests. For example, if the user is interested in technology, the generation unit generates the latest technology-related news. In this case, the generation unit collects data from sources such as technology-related news articles, blogs, and research papers, summarizes this information using natural language processing techniques, and extracts the points that are important to the user. The generation unit can also generate the latest sports-related news if the user is interested in sports. In this case, the generation unit collects information such as the results of sporting events, interviews with athletes, and match highlights to provide content that is interesting to the user. Furthermore, if the user is interested in entertainment, the generation unit can generate the latest entertainment-related news. For example, it collects information such as movie releases, interviews with actors, and music album releases to provide news that is appealing to the user. When generating this news, the generation unit can adjust the content and tone of the news based on the user's interests to provide a more personalized news experience.

[0065] The delivery unit presents news generated by the generation unit in a presentation format by a virtual news anchor. The delivery unit uses technologies such as facial recognition and speech synthesis to create a realistic anchor. Specifically, the delivery unit can make the presentation more realistic by changing the virtual anchor's facial expressions and tone of voice when reading the news. For example, the virtual anchor can display a serious expression when delivering important news and a smile when delivering lighter news. The delivery unit can also add visual effects when the virtual anchor reads the news. For example, by displaying relevant images or videos in the background depending on the news content, the presentation can be made easier for viewers to understand. Furthermore, the delivery unit can display background images or videos when the virtual anchor reads the news. For example, when delivering a weather forecast, weather maps or satellite images can be displayed in the background to provide information that is visually easy for viewers to understand. By utilizing these technologies, the delivery unit can provide viewers with an engaging and interactive news experience.

[0066] The response unit responds in real time to questions about news provided by the news provider. For example, when a user inputs a question about the news, the AI ​​responds to that question in real time. Specifically, the response unit uses natural language processing technology to analyze the user's question, searches for relevant information, and generates a response. For example, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. In this case, the response unit searches the full text of the news article and related data, extracting and providing information that is important to the user. The response unit can also provide relevant background information if the user asks, "Tell me the background of this news." For example, by providing the cause of the news, related historical background, and comments from those involved, the user can gain a deeper understanding of the news. Furthermore, if the user asks, "Tell me the impact of this news," the AI ​​can provide relevant impact information. For example, by providing the impact of the news on the economy and society, and predictions for future developments, the user can understand the importance of the news. Through these functions, the response unit provides users with an interactive news experience and deepens their understanding of the news.

[0067] The learning unit can learn user behavior patterns. For example, the learning unit can analyze a user's news browsing history to identify user interests. For instance, it can identify news categories that a user frequently views and prioritize providing news related to those categories. The learning unit can also analyze a user's click history to identify news that the user is interested in. For example, it can analyze the content of news articles that a user clicks on to identify user interests. Furthermore, the learning unit can analyze a user's search history to identify topics that the user is interested in. For example, it can analyze keywords that a user searches for to identify user interests. By learning user behavior patterns, the learning unit can provide more relevant news. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input a user's news browsing history into an AI and have the AI ​​identify user interests.

[0068] The adjustment unit can change the facial expressions and tone of voice of the virtual news anchor. For example, the adjustment unit can provide a more realistic presentation by changing the facial expressions of the virtual anchor when they read the news. For example, the adjustment unit can make the virtual anchor read the news with a smile. It can also make the virtual anchor read the news with a surprised expression. Furthermore, the adjustment unit can make the virtual anchor read the news with a sad expression. The adjustment unit can also change the tone of voice of the virtual anchor. For example, the adjustment unit can make the virtual anchor read the news in a cheerful tone. It can also make the virtual anchor read the news in a calm tone. Furthermore, the adjustment unit can make the virtual anchor read the news in an excited tone. In this way, the adjustment unit can provide a more realistic presentation by changing the facial expressions and tone of voice of the virtual news anchor. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input changes in the virtual caster's facial expressions and voice tone into the AI, and then have the AI ​​perform adjustments to those expressions and voice tone.

[0069] The customization unit can generate news tailored to the user's interests. For example, the customization unit selects and customizes news based on the user's interests. For instance, if the user is interested in technology, the customization unit will prioritize providing the latest technology-related news. Similarly, if the user is interested in sports, the customization unit can prioritize providing the latest sports-related news. Furthermore, if the user is interested in entertainment, the customization unit can prioritize providing the latest entertainment-related news. This allows the customization unit to provide more relevant news by generating news tailored to the user's interests. Some or all of the above-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can input the user's interests into an AI and have the AI ​​perform news selection and customization.

[0070] The generation unit can estimate the user's emotions and adjust the news content based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating positive news. For example, if the user is relaxed, the generation unit may also generate news including detailed analysis articles. Furthermore, if the user is excited, the generation unit may generate breaking news. In this way, the generation unit can provide more relevant news by adjusting the news content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the news content.

[0071] The generation unit can analyze the user's past news browsing history and select the most relevant news when generating news. For example, the generation unit can prioritize generating news categories that the user has frequently viewed in the past. For example, the generation unit can also select news on specific topics from the user's past browsing history. Furthermore, the generation unit can generate relevant news based on news that the user has previously rated highly. In this way, the generation unit can provide more appropriate news by analyzing the user's past news browsing history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's news browsing history into AI and have the AI ​​perform the selection of the most relevant news.

[0072] The generation unit can prioritize news based on the user's current interests when generating news. For example, the generation unit can prioritize news based on keywords the user has recently searched for. For example, the generation unit can also prioritize news based on content the user has shared on social media. Furthermore, the generation unit can prioritize news based on the content of newsletters the user has recently subscribed to. This allows the generation unit to provide more relevant news by prioritizing news based on the user's current interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current interests into AI and have AI perform the task of determining news priorities.

[0073] The generation unit can estimate the user's emotions and adjust the way the news is presented based on those emotions. For example, if the user is sad, the generation unit can present the news in a gentle tone. For example, if the user is happy, the generation unit can present the news in a cheerful tone. Also, if the user is angry, the generation unit can present the news in a calm tone. In this way, the generation unit can provide more appropriate news by adjusting the way the news is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the news is presented.

[0074] The generation unit can generate highly relevant news by considering the user's geographical location information when generating news. For example, the generation unit can generate local news related to the user's current location. For example, if the user is traveling, the generation unit can also generate news about the travel destination. Furthermore, the generation unit can generate news that includes local event information based on the user's geographical location. In this way, the generation unit can provide more appropriate news by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and have the AI ​​perform the generation of highly relevant news.

[0075] The generation unit can analyze a user's social media activity and generate relevant news when generating news. For example, the generation unit can generate news based on the content of posts from accounts that the user follows on social media. For example, the generation unit can also generate news related to posts that the user has "liked" on social media. Furthermore, the generation unit can generate news based on content that the user has shared on social media. In this way, the generation unit can provide more appropriate news by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into AI and have the AI ​​generate relevant news.

[0076] The delivery unit can estimate the user's emotions and adjust the presentation method based on the estimated emotions. For example, if the user is nervous, the delivery unit can deliver the presentation in a calm tone. For example, if the user is relaxed, the delivery unit can deliver the presentation in a cheerful tone. Also, if the user is in a hurry, the delivery unit can deliver a quick and concise presentation. In this way, the delivery unit can provide more relevant news by adjusting the presentation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the presentation method.

[0077] The news delivery unit can select the most suitable presentation method by referring to the user's past viewing history when delivering news. For example, the unit may prioritize presentation styles that the user has previously enjoyed watching. For example, the unit may select a presentation method for a specific topic based on the user's past viewing history. The unit may also select a relevant presentation method based on presentation styles that the user has previously given high ratings to. In this way, the unit can provide a more appropriate presentation method by referring to the user's past viewing history. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not using AI. For example, the unit may input the user's viewing history into AI and have the AI ​​select the most suitable presentation method.

[0078] The news delivery unit can apply different presentation styles depending on the user's interests when delivering news. For example, if the user is interested in technology, the unit can apply a presentation style that includes technical details. For example, if the user is interested in sports, the unit can apply a dynamic presentation style. Furthermore, if the user is interested in entertainment, the unit can apply a highly entertaining presentation style. In this way, the unit can provide more relevant news by applying presentation styles according to the user's interests. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not using AI. For example, the news delivery unit can input the user's interests into AI and have the AI ​​perform the application of presentation styles.

[0079] The delivery unit can estimate the user's emotions and adjust the length of the presentation based on the estimated emotions. For example, if the user is in a hurry, the delivery unit can provide a short, to-the-point presentation. For example, if the user is relaxed, the delivery unit can provide a longer presentation with detailed explanations. Furthermore, if the user is excited, the delivery unit can provide a presentation with visually stimulating effects. In this way, the delivery unit can provide more relevant news by adjusting the length of the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the length of the presentation.

[0080] The news delivery unit can select the optimal display method when delivering news, taking into account the user's device information. For example, if the user is using a smartphone, the unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the unit can also provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the unit can provide a concise and highly visible display method. In this way, the unit can provide a more appropriate display method by taking into account the user's device information. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not. For example, the news delivery unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0081] The news delivery unit can adjust the volume and image quality of a presentation based on the user's viewing environment when delivering news. For example, if the user is in a quiet environment, the unit can set the volume low. For example, if the user is in a noisy environment, the unit can set the volume high. The unit can also set the image quality high if the user is using a high-definition device. In this way, the unit can provide more appropriate news by adjusting the volume and image quality of the presentation based on the user's viewing environment. Some or all of the above processing in the news delivery unit may be performed using AI, for example, or not using AI. For example, the news delivery unit can input user viewing environment data into AI and have the AI ​​perform the volume and image quality adjustments.

[0082] The response unit can estimate the user's emotions and adjust the content of its response based on the estimated emotions. For example, if the user is tense, the response unit will respond in a gentle tone. For example, if the user is relaxed, the response unit may provide a response that includes detailed information. Also, if the user is in a hurry, the response unit may provide a quick and concise response. In this way, the response unit can provide a more appropriate response by adjusting the content of its response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI adjust the content of the response.

[0083] The response unit can select the most appropriate response method by referring to the user's past question history when responding. For example, the response unit can provide relevant information based on the content of questions the user has asked in the past. For example, the response unit can also select a response method for a specific topic from the user's past question history. Furthermore, the response unit can provide relevant responses based on response methods that the user has previously given high ratings to. In this way, the response unit can provide more appropriate responses by referring to the user's past question history. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's question history into AI and have the AI ​​select the most appropriate response method.

[0084] The response unit can apply different response algorithms depending on the user's interests when responding. For example, if the user is interested in technology, the response unit will provide a response that includes technical details. For example, if the user is interested in sports, the response unit may provide a response that includes sports-related information. Furthermore, if the user is interested in entertainment, the response unit may provide a highly entertaining response. In this way, the response unit can provide a more appropriate response by applying a response algorithm according to the user's interests. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the user's interests into AI and have AI perform the application of the response algorithm.

[0085] The response unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is sad, the response unit will respond in a gentle tone. For example, if the user is happy, the response unit may also respond in a cheerful tone. Furthermore, if the user is angry, the response unit may respond in a calm tone. In this way, the response unit can provide a more appropriate response by adjusting the way it expresses its response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses its response.

[0086] The response unit can provide relevant information when responding, taking into account the context of the user's question. For example, if the user asks a question about a specific news story, the response unit will provide information related to that news story. For example, the response unit can also provide relevant information based on questions the user has asked in the past. Furthermore, the response unit can analyze the context of the user's question and provide the most relevant information. In this way, the response unit can provide more appropriate information by taking into account the context of the user's question. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the context of the user's question into AI and have the AI ​​perform the task of providing relevant information.

[0087] The response unit can determine the priority of responses based on the frequency of the user's questions. For example, the response unit will prioritize responses to topics that the user frequently asks about. For example, the response unit can also determine the priority of responses based on the topics the user has asked about frequently in the past. Furthermore, the response unit can analyze the frequency of the user's questions and prioritize responses to the most important questions. In this way, the response unit can prioritize responses to more important questions by determining the priority of responses based on the frequency of the user's questions. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input the frequency of the user's questions into the AI ​​and have the AI ​​perform the determination of the response priority.

[0088] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select training data that includes detailed information. For example, if the user is in a hurry, the learning unit can select training data that is concise. Also, if the user is excited, the learning unit can select training data that includes visually stimulating information. In this way, the learning unit can provide more appropriate training data by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0089] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can extract specific patterns from past learning data and optimize the learning algorithm. The learning unit can also analyze past learning data and apply the most effective learning algorithm. In this way, the learning unit can provide a more appropriate learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0090] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit may increase the learning frequency if the user is relaxed. For example, it may decrease the learning frequency if the user is in a hurry. It may also adjust the learning frequency if the user is excited. In this way, the learning unit can provide more appropriate learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0091] The learning unit can weight the training data based on the user's behavior patterns during training. For example, the learning unit can weight the training data based on actions that the user frequently performs. For example, the learning unit can analyze the user's behavior patterns and weight the most important data. The learning unit can also determine the priority of the training data based on the user's behavior patterns. This allows the learning unit to provide more appropriate training by weighting the training data based on the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior patterns into the AI ​​and have the AI ​​perform the weighting of the training data.

[0092] The adjustment unit can estimate the user's emotions and adjust the virtual newscaster's facial expressions and voice tone based on the estimated emotions. For example, if the user is nervous, the adjustment unit can deliver the news with a calm facial expression and voice tone. For example, if the user is relaxed, the adjustment unit can deliver the news with a cheerful facial expression and voice tone. Also, if the user is in a hurry, the adjustment unit can deliver the news with a quick and concise facial expression and voice tone. In this way, the adjustment unit can provide more appropriate news by adjusting the virtual newscaster's facial expressions and voice tone based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of facial expressions and voice tone.

[0093] The adjustment unit can select the optimal facial expressions and voice tones by referring to the user's past viewing history during the adjustment process. For example, the adjustment unit may prioritize facial expressions and voice tones that the user has previously enjoyed watching. For example, the adjustment unit may also select facial expressions and voice tones related to a specific topic from the user's past viewing history. Furthermore, the adjustment unit may select relevant facial expressions and voice tones based on facial expressions and voice tones that the user has previously given high ratings to. In this way, the adjustment unit can provide more appropriate facial expressions and voice tones by referring to the user's past viewing history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit may input the user's viewing history into AI and have the AI ​​perform the selection of the optimal facial expressions and voice tones.

[0094] The adjustment unit can estimate the user's emotions and adjust the virtual newscaster's actions based on the estimated emotions. For example, if the user is tense, the adjustment unit may deliver the news with calm actions. For example, if the user is relaxed, the adjustment unit may deliver the news with cheerful actions. Also, if the user is in a hurry, the adjustment unit may deliver the news with quick and concise actions. In this way, the adjustment unit can deliver more appropriate news by adjusting the virtual newscaster's actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of actions.

[0095] The adjustment unit can adjust the display method of the virtual news anchor based on the user's viewing environment during the adjustment process. For example, if the user is in a quiet environment, the adjustment unit can provide a calm display method. For example, if the user is in a noisy environment, the adjustment unit can also provide a highly visible display method. Furthermore, if the user is using a high-definition device, the adjustment unit can provide a high-definition display method. In this way, the adjustment unit can provide more appropriate news by adjusting the display method of the virtual news anchor based on the user's viewing environment. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's viewing environment data into the AI ​​and have the AI ​​perform the adjustment of the display method.

[0096] The customization unit can estimate the user's emotions and adjust how news is customized based on those emotions. For example, if the user is stressed, the customization unit will prioritize positive news. For example, if the user is relaxed, the customization unit may also customize news that includes detailed analysis articles. Furthermore, if the user is excited, the customization unit may customize news that is highly timely. In this way, the customization unit can provide more appropriate news by adjusting how news is customized based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not using AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the customization method.

[0097] The customization unit can select the optimal customization method by referring to the user's past news browsing history during the customization process. For example, the customization unit can prioritize news categories that the user has frequently viewed in the past. For example, the customization unit can also customize news on specific topics based on the user's past browsing history. Furthermore, the customization unit can customize related news based on news that the user has previously rated highly. In this way, the customization unit can provide more appropriate news by referring to the user's past news browsing history. Some or all of the above processes in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the user's news browsing history into AI and have the AI ​​select the optimal customization method.

[0098] The customization unit can estimate the user's emotions and determine the priority of news customization based on the estimated emotions. For example, if the user is stressed, the customization unit will prioritize positive news. For example, if the user is relaxed, the customization unit may prioritize news containing detailed analysis articles. Also, if the user is excited, the customization unit may prioritize breaking news. In this way, the customization unit can provide more appropriate news by determining the priority of news customization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not using AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform the determination of customization priorities.

[0099] The customization unit can customize news that is highly relevant to the user, taking into account the user's geographical location during the customization process. For example, the customization unit can customize local news related to the user's current location. For example, if the user is traveling, the customization unit can also customize news related to the user's travel destination. Furthermore, the customization unit can customize news that includes local event information based on the user's geographical location. In this way, the customization unit can provide more appropriate news by taking the user's geographical location into consideration. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input the user's geographical location information into AI and have the AI ​​perform the customization of highly relevant news.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The news delivery system can also include a feedback section. This section allows users to input feedback on the news they receive. For example, users can rate the news content as "helpful" or "interesting." The feedback section can also allow users to provide feedback on the news presentation, such as "easy to understand" or "needs more detail." Furthermore, the feedback section can allow users to provide feedback on the timing of the news delivery, such as "appropriate" or "late." This allows the feedback section to collect user feedback and use it to improve the news delivery system.

[0102] The news delivery system can also include a notification unit. This notification unit can notify users of important and urgent news in real time. For example, it can send push notifications when news occurs in categories of interest to the user. It can also send notifications when news occurs related to keywords set by the user. Furthermore, it can notify users of local emergency information based on their geographical location. This ensures that users do not miss important news.

[0103] The news delivery system can also include a translation unit. This unit can translate the generated news into multiple languages. For example, it can translate news generated in English into Japanese. It can also translate news generated in Spanish into French. Furthermore, the translation unit can automatically translate the news based on the user's selected language. This allows the translation unit to enable users to understand the news in their native language.

[0104] The news delivery system can also include an archive section. The archive section can store previously delivered news, allowing users to refer to it later. For example, the archive section allows users to search and view news from a specific date. It can also display a list of past news from a specific category. Furthermore, the archive section can store news that users have previously rated highly, making it available for re-viewing. This allows the archive section to easily access past news.

[0105] The news delivery system can also include a recommendation section. This recommendation section can suggest relevant news based on the user's past news viewing history and ratings. For example, it can recommend new news related to news the user has previously rated highly. It can also prioritize recommending news from categories the user frequently views. Furthermore, it can recommend news that the user hasn't yet viewed but might be interested in, based on their interests. This allows the recommendation section to efficiently find news that the user is interested in.

[0106] The news delivery system can also be equipped with a sentiment analysis unit. This unit can estimate the user's emotions and adjust the news delivery method based on the estimated emotions. For example, if the user is stressed, the sentiment analysis unit can prioritize providing relaxing news. It can also deliver news in a calm tone if the user is agitated. Furthermore, if the user is sad, the sentiment analysis unit can provide positive news. In this way, the sentiment analysis unit can deliver news tailored to the user's emotions.

[0107] The news delivery system can also include an emotional feedback section. This section allows users to input their emotions after viewing the news. For example, users can input emotions such as "enjoyed" or "sad" after viewing the news. It can also input emotions such as "excited" or "calm" after viewing the news. Furthermore, it can input emotions such as "helpful" or "indifferent" after viewing the news. This allows the emotional feedback section to collect user emotions and use them to improve the news delivery system.

[0108] The news delivery system can also include a sentiment history section. This section can store a user's past sentiment data and refer to it when delivering news. For example, the sentiment history section can store sentiment data from when a user has viewed news in the past. It can also store sentiment data from when a user has viewed news in a specific category. Furthermore, it can store sentiment data from when a user has viewed specific news articles. This allows the sentiment history section to provide more appropriate news by referencing the user's past sentiment data.

[0109] The news delivery system can also be equipped with an emotion prediction unit. This unit can predict future emotions based on the user's current situation and past emotion data. For example, it can predict how a user will feel when viewing a particular news item. It can also predict how a user will feel when viewing news in a specific category. Furthermore, it can predict how a user will feel when viewing news at a specific time of day. By predicting the user's future emotions, the emotion prediction unit can deliver more appropriate news.

[0110] The news delivery system can also be equipped with an emotion adjustment unit. This unit can adjust the user's emotions in real time. For example, if the user is feeling stressed, it can provide content that helps them relax. Similarly, if the user is excited, it can provide content that helps them calm down. Furthermore, if the user is sad, it can provide content that promotes positive emotions. By adjusting the user's emotions in real time, the emotion adjustment unit can deliver more appropriate news.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The generation unit generates news based on the user's interests. The generation unit uses natural language processing and machine learning to generate news tailored to the user's interests. For example, if the user is interested in technology, it will generate the latest technology-related news; if they are interested in sports, it will generate the latest sports-related news. Furthermore, if they are interested in entertainment, it can also generate the latest entertainment-related news. Step 2: The delivery unit presents the news generated by the generation unit in a presentation format by a virtual news anchor. The delivery unit uses facial recognition and speech synthesis technology to create a realistic anchor. The virtual anchor can change its facial expressions and tone of voice when reading the news to create a more realistic presentation. It can also add visual effects and display background images and videos. Step 3: The response unit responds in real time to questions about the news provided by the delivery unit. When a user enters a question about the news, the AI ​​responds to that question in real time. For example, if a user asks, "Tell me more about this news," the AI ​​will provide relevant information. If the user asks, "Tell me the background of this news," the AI ​​will provide relevant background information. It can also provide relevant impact information if the user asks, "Tell me the impact of this news."

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the generation unit, provision unit, response unit, learning unit, adjustment unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates news tailored to the user's interests using natural language processing and machine learning. The provision unit is implemented by the control unit 46A of the smart device 14 and has a virtual news caster deliver the news in a presentation format. The response unit is implemented by the specific processing unit 290 of the data processing device 12 and responds to the user's questions in real time. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the user's behavior patterns. The adjustment unit is implemented by the control unit 46A of the smart device 14 and changes the virtual caster's facial expressions and voice tone. The customization unit is implemented by the specific processing unit 290 of the data processing device 12 and generates news tailored to the user's interests. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the generation unit, provision unit, response unit, learning unit, adjustment unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates news tailored to the user's interests using natural language processing and machine learning. The provision unit is implemented by the control unit 46A of the smart glasses 214 and has a virtual news caster present the news in a presentation format. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and responds to the user's questions in real time. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and changes the virtual caster's facial expressions and voice tone. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates news tailored to the user's interests. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the generation unit, provision unit, response unit, learning unit, adjustment unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates news tailored to the user's interests using natural language processing and machine learning. The provision unit is implemented by the control unit 46A of the headset terminal 314 and has a virtual news caster deliver the news in a presentation format. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and responds to the user's questions in real time. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and changes the virtual caster's facial expressions and voice tone. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates news tailored to the user's interests. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the generation unit, provision unit, response unit, learning unit, adjustment unit, and customization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates news tailored to the user's interests using natural language processing and machine learning. The provision unit is implemented by the control unit 46A of the robot 414 and has a virtual news caster deliver the news in a presentation format. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and responds to the user's questions in real time. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The adjustment unit is implemented by the control unit 46A of the robot 414 and changes the virtual caster's facial expressions and voice tone. The customization unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates news tailored to the user's interests. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A generation unit that generates news based on user interests, A distribution unit provides news generated by the generation unit in a presentation format by a virtual newscaster, The system includes a response unit that responds in real time to questions regarding news provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) It includes a learning unit that learns user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an adjustment section that changes the facial expressions and voice tone of the virtual news anchor. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a customization section that generates news tailored to the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It estimates the user's sentiment and adjusts the news content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is When generating news, the system analyzes the user's past news browsing history to select the most relevant news. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating news, we prioritize news based on the user's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We estimate user sentiment and adjust the way news is presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating news, the system takes the user's geographical location into consideration to generate highly relevant news. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating news, the system analyzes the user's social media activity and generates relevant news. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, It estimates the user's emotions and adjusts the presentation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When delivering news, the system selects the most suitable presentation method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When delivering news, apply different presentation styles depending on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing news, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When delivering news, the presentation volume and image quality are adjusted based on the user's viewing environment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The response unit is It estimates the user's emotions and adjusts the response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The response unit is When responding, the system will refer to the user's past question history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The response unit is When responding, apply different response algorithms depending on the user's concerns. The system described in Appendix 1, characterized by the features described herein. (Note 20) The response unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The response unit is When responding, provide relevant information considering the context of the user's question. The system described in Appendix 1, characterized by the features described herein. (Note 22) The response unit is When responding, prioritize responses based on the frequency of the user's questions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, the training data is weighted based on user behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, It estimates the user's emotions and adjusts the virtual newscaster's facial expressions and voice tone based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, During the adjustment process, the system references the user's past viewing history to select the optimal facial expressions and voice tone. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, It estimates the user's emotions and adjusts the virtual newscaster's behavior based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, the display method of the virtual newscaster is adjusted based on the user's viewing environment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned customization unit is It estimates the user's sentiment and adjusts how news is customized based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past news browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned customization unit is It estimates the user's sentiment and determines the priority of news customization based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned customization unit is During customization, the system takes the user's geographical location into account to tailor the news to be more relevant. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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. A generation unit that generates news based on user interests, A distribution unit provides news generated by the generation unit in a presentation format by a virtual newscaster, The system includes a response unit that responds in real time to questions regarding news provided by the aforementioned provision unit. A system characterized by the following features.

2. It includes a learning unit that learns user behavior patterns. The system according to feature 1.

3. It features an adjustment section that changes the facial expressions and voice tone of the virtual news anchor. The system according to feature 1.

4. It features a customization section that generates news tailored to the user's interests. The system according to feature 1.

5. The generating unit is It estimates the user's sentiment and adjusts the news content based on that estimated sentiment. The system according to feature 1.

6. The generating unit is When generating news, the system analyzes the user's past news browsing history to select the most relevant news. The system according to feature 1.

7. The generating unit is When generating news, we prioritize news based on the user's current interests. The system according to feature 1.

8. The generating unit is We estimate user sentiment and adjust the way news is presented based on that estimated sentiment. The system according to feature 1.

9. The generating unit is When generating news, the system takes the user's geographical location into consideration to generate highly relevant news. The system according to feature 1.

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