system

Abstract

The system according to this embodiment aims to automatically generate and provide to users opinions from different backgrounds and perspectives regarding news articles. [Solution] The system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes news articles. The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of the news articles analyzed by the analysis unit. The provision unit provides the opinions automatically generated by the generation unit to the user.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that information bias easily occurs in news distribution, and it is difficult for users to understand news from multiple perspectives.

[0005] The system according to the embodiment aims to automatically generate opinions from different backgrounds and positions for news articles and provide them to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes news articles. The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of the news articles analyzed by the analysis unit. The provision unit provides the opinions automatically generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate and provide to users opinions from different backgrounds and perspectives regarding news articles. [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 opinion generation system according to an embodiment of the present invention is a system that utilizes a generating AI to automatically generate opinions on news articles from different backgrounds and perspectives and provide them to the user. This news opinion generation system analyzes news articles and generates opinions from different backgrounds and perspectives based on the content. For example, it provides opinions from different perspectives such as business people, sports fans, university professors, and engineers. The generating AI analyzes the content of news articles and automatically generates opinions based on each perspective. This allows users to understand the same news article from multiple perspectives. Next, the system provides the user with the automatically generated opinions from multiple perspectives. By referring to opinions from different backgrounds and perspectives on a news article, users can promote a multifaceted understanding of the news. For example, it provides opinions from various perspectives, such as the economic impact from the perspective of a business person, the impact of a sports event from the perspective of a sports fan, and an educational perspective from the perspective of a university professor. This system can reduce information bias and promote a multifaceted understanding for users. For example, by providing opinions from different perspectives on a news article, users can gain a deeper understanding of the background and impact of the news. Furthermore, by utilizing the generating AI, opinions on the content of news articles can be generated quickly and efficiently. Furthermore, this system allows users to listen to news articles in a radio-like manner. For example, by using the function to have news articles read aloud, users can hear opinions from different perspectives in audio format. This allows users to understand the news by utilizing not only visual information but also auditory information. Thus, the present invention provides a system that reduces information bias and promotes multifaceted understanding by automatically generating opinions from different backgrounds and perspectives on news articles using generational AI and providing them to the user. In this way, the news opinion generation system can reduce information bias and promote multifaceted understanding by automatically generating opinions from different backgrounds and perspectives on news articles and providing them to the user.

[0029] The news opinion generation system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes news articles. News articles include, but are not limited to, online news, newspaper articles, and blog posts. The analysis unit analyzes the content of news articles using, for example, text analysis technology. The analysis unit can also analyze the sentiment of news articles using sentiment analysis technology. Furthermore, the analysis unit can analyze the topics of news articles using topic modeling technology. For example, the analysis unit takes text data of a news article as input and analyzes the content of the article using text analysis technology. Sentiment analysis technology analyzes the sentiment of news articles and calculates sentiment scores such as positive, negative, and neutral. Topic modeling technology analyzes the topics of news articles and extracts the main topics. The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of the news articles analyzed by the analysis unit. The generation unit automatically generates opinions from different perspectives, such as those of business people, sports fans, university professors, and engineers. The generation unit uses generation AI to analyze the content of news articles and automatically generate opinions based on various perspectives. For example, the generation unit takes the content of a news article as input and uses generation AI to generate opinions from the perspective of a business person. The generation unit can also generate opinions from the perspective of a sports fan. Furthermore, the generation unit can generate opinions from the perspective of a university professor. For example, the generation unit analyzes the content of a news article and generates opinions from the perspective of a business person that consider the economic impact. From the perspective of a sports fan, it generates opinions that consider the impact of a sports event. From the perspective of a university professor, it generates opinions that consider educational aspects. The delivery unit provides the opinions automatically generated by the generation unit to the user. The delivery unit, for example, has a function that allows the user to listen to news articles like a radio program. The delivery unit can use generation AI to read the content of news articles aloud. For example, the delivery unit takes the content of a news article as input, uses generation AI to generate audio data, and provides it to the user. The delivery unit can also provide the user with a function to refer to opinions from different backgrounds and positions.For example, the information provider displays opinions from the perspective of a business person, a sports fan, and a university professor regarding a news article. This allows the news opinion generation system according to this embodiment to automatically generate and provide users with opinions from different backgrounds and perspectives regarding a news article, thereby reducing information bias and promoting a multifaceted understanding of the user.

[0030] The analysis unit analyzes news articles. News articles include, but are not limited to, online news, newspaper articles, and blog posts. The analysis unit analyzes the content of news articles using, for example, text analysis techniques. Specifically, it uses natural language processing (NLP) techniques to analyze the grammatical structure and meaning of news articles and extract the article's theme and important information. For example, it uses morphological analysis to identify the part of speech of words and dependency structure analysis to analyze the sentence structure. The analysis unit can also analyze the sentiment of news articles using sentiment analysis techniques. Sentiment analysis techniques take the text data of news articles as input and calculate sentiment scores such as positive, negative, and neutral. For example, it uses a word sentiment dictionary to calculate the sentiment score of each word and then calculates the sentiment score for the entire article. Furthermore, the analysis unit can also analyze the topics of news articles using topic modeling techniques. Topic modeling techniques take the text data of news articles as input and extract the main topics. For example, algorithms such as Latent Dirichlet Allocation (LDA) are used to analyze the topic distribution of news articles and identify which topics each article relates to. This allows the analysis unit to analyze the content, sentiment, and topics of news articles in detail, providing foundational data for opinion generation, which is the next step.

[0031] The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of news articles analyzed by the analysis unit. For example, the generation unit automatically generates opinions from different viewpoints such as business people, sports fans, university professors, and engineers. The generation unit uses a generation AI to analyze the content of news articles and automatically generate opinions based on each perspective. Specifically, a large-scale language model (LLM) is used as the generation AI, and the content of the news article is input as a prompt. For example, a prompt such as "Please give your opinion on this news article from the perspective of a business person" is input to the generation AI, and an opinion from the perspective of a business person is generated. Similarly, to generate an opinion from the perspective of a sports fan, a prompt such as "Please give your opinion on this news article from the perspective of a sports fan" is used. Furthermore, to generate an opinion from the perspective of a university professor, a prompt such as "Please give your opinion on this news article from the perspective of a university professor" is used. Based on these prompts, the generation AI analyzes the content of the news article and generates opinions based on each perspective. For example, from a business person's perspective, it can generate opinions on economic impacts and market trends; from a sports fan's perspective, it can generate opinions on the impact of sporting events and athlete performance; and from a university professor's perspective, it can generate opinions on educational aspects and social impacts. This allows the generation unit to automatically generate opinions from diverse perspectives on news articles and prepare them for provision to users.

[0032] The service provider provides users with opinions automatically generated by the generation unit. For example, the service provider includes a function that allows users to listen to news articles in a radio-like format. The service provider can use a generation AI to read the content of news articles aloud. Specifically, it uses speech synthesis technology to convert the opinions generated by the generation AI from text to speech. For example, it takes the content of a news article and the generated opinions as input and generates audio data using speech synthesis technology. The generated audio data can be listened to by the user through devices such as smartphones and personal computers. Furthermore, the service provider can provide users with a function to refer to opinions from different backgrounds and perspectives. For example, it can display opinions on a news article from the perspective of a business person, a sports fan, and a university professor. This allows users to refer to diverse perspectives on news articles, reducing information bias. In addition, the service provider can collect user feedback and continuously improve the quality of the generated opinions. For example, it can provide a function that allows users to rate and comment on the provided opinions, and update the generation AI's learning data based on this feedback. This allows the service provider to continue providing users with high-quality opinions.

[0033] The generation unit automatically generates opinions from different perspectives, such as those of business professionals, sports fans, university professors, and engineers. For example, the generation unit can generate opinions from a business professional's perspective that consider the economic impact. For example, the generation unit can generate opinions from a sports fan's perspective that consider the impact of a sports event. For example, the generation unit can generate opinions from a university professor's perspective that consider the educational aspects. The generation unit uses a generation AI to analyze the content of news articles and automatically generate opinions based on each perspective. For example, the generation unit takes the content of a news article as input and uses the generation AI to generate opinions from a business professional's perspective. The generation unit can also use the generation AI to generate opinions from a sports fan's perspective. The generation unit can also use the generation AI to generate opinions from a university professor's perspective. This allows users to understand news articles from multiple perspectives by automatically generating opinions from different viewpoints.

[0034] The delivery unit has a function that allows users to listen to news articles like a radio program. The delivery unit can read the content of news articles aloud using generative AI. For example, the delivery unit takes the content of a news article as input, generates audio data using generative AI, and provides it to the user. This allows users to understand the news by utilizing not only visual information but also auditory information.

[0035] The analysis unit optimizes the analysis algorithm by referring to past analysis results of news articles. The analysis unit can, for example, analyze user reactions to specific topics from past analysis results and adjust the algorithm. The analysis unit can also, for example, re-evaluate the importance of specific keywords based on past analysis results to improve analysis accuracy. The analysis unit can also, for example, optimize analysis methods for similar articles by referring to past analysis results. This allows the accuracy of the analysis algorithm to be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0036] The analysis unit applies different analysis methods depending on the category of the news article. For example, for business-related articles, the analysis unit applies analysis methods that emphasize economic indicators and market trends. For sports-related articles, the analysis unit may also apply analysis methods that emphasize match results and athlete performance. For education-related articles, the analysis unit may also apply analysis methods that emphasize educational policies and academic research. By applying analysis methods appropriate to the category of the news article, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article category data into a generating AI and have the generating AI execute the application of analysis methods appropriate to the category.

[0037] The analysis unit prioritizes analyzing highly relevant information based on the user's geographical location when analyzing news articles. For example, if the user is in a specific region, the analysis unit prioritizes analyzing news related to that region. For example, if the user is traveling, the analysis unit can also prioritize analyzing news related to their travel destination. For example, if the user is at home, the analysis unit can also prioritize analyzing local news. This allows for the provision of more appropriate information by prioritizing the analysis of highly relevant information based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority analysis of highly relevant information.

[0038] The analysis unit analyzes the user's social media activity and analyzes relevant information when analyzing news articles. For example, the analysis unit prioritizes analyzing news related to topics that the user follows on social media. The analysis unit can also analyze information related to news that the user has shared on social media. For example, the analysis unit can analyze news of high interest based on the user's comments and reactions on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis of relevant information.

[0039] The generation unit adjusts the level of detail in its opinion generation based on the importance of the news article. For example, for important news articles, the generation unit generates detailed and insightful opinions. For general news articles, the generation unit can also generate concise and to-the-point opinions. For light news articles, the generation unit can also generate visually stimulating opinions. By adjusting the level of detail in the generation based on the importance of the news article, it is possible to provide more appropriate opinions. 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 news article importance data into a generation AI and have the generation AI perform opinion generation based on importance.

[0040] The generation unit applies different generation algorithms depending on the category of the news article when generating opinions. For example, for business-category articles, the generation unit applies an algorithm that generates opinions from an economic perspective. For example, for sports-category articles, the generation unit may apply an algorithm that generates opinions from the perspective of a sports fan. For example, for education-category articles, the generation unit may apply an algorithm that generates opinions from an educational perspective. By applying a generation algorithm appropriate to the category of the news article, more appropriate opinions can be provided. 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 news article category data into a generation AI and have the generation AI execute the application of a generation algorithm appropriate to the category.

[0041] The generation unit determines the generation priority based on the submission date of the news article when generating opinions. For example, the generation unit quickly generates opinions for the latest news articles. The generation unit can also generate detailed opinions for past news articles. For example, the generation unit can generate different opinions before and after a specific event for news articles related to that event. This allows for the provision of more appropriate opinions by determining the generation priority based on the submission date of the news article. 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 news article submission date data into a generation AI and have the generation AI generate opinions based on the submission date.

[0042] The generation unit adjusts the order of opinion generation based on the relevance of news articles. For example, the generation unit prioritizes generating opinions for news articles related to topics the user is interested in. The generation unit can also prioritize generating opinions related to news articles the user has read in the past. The generation unit can also generate opinions for highly relevant news articles based on the user's social media activity. This allows for the provision of more appropriate opinions by adjusting the order of generation based on the relevance of news articles. 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 news article relevance data into a generation AI and have the generation AI perform opinion generation based on relevance.

[0043] The service provider selects the optimal service delivery method by referring to the user's past browsing history at the time of delivery. For example, the service provider may prioritize providing opinions related to topics the user has frequently viewed in the past. The service provider may also provide opinions related to topics of high interest to the user based on their past browsing history. The service provider may also select the optimal service delivery method based on the user's past browsing history. This allows for the provision of more appropriate information by referring to the user's past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past browsing history data into a generating AI and have the generating AI select the optimal service delivery method.

[0044] The service provider customizes the content offered based on the user's current areas of interest at the time of delivery. For example, the service provider prioritizes providing opinions related to topics the user is currently interested in. The service provider can also customize and provide relevant opinions based on the user's current areas of interest. The service provider can also select the most appropriate content based on the user's current areas of interest. This allows for the provision of more relevant information by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current areas of interest data into a generating AI and have the generating AI perform the customization of the content based on those areas of interest.

[0045] The service provider selects the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. By selecting the optimal display method based on the user's device information, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0046] The service provider analyzes the user's social media activity and customizes the content provided at the time of delivery. For example, the service provider may prioritize providing opinions related to topics the user follows on social media. The service provider may also provide opinions related to news shared by the user on social media. The service provider may also provide opinions of high interest based on the user's comments and reactions on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content provided.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The analytics unit can also generate relevant opinions based on the content of news articles, referencing the user's past browsing history. For example, it can generate more detailed opinions on news articles related to topics the user has frequently viewed in the past. Furthermore, it can generate opinions with particularly deep insights on news articles related to specific themes the user has shown interest in in the past. It can also generate more neutral opinions on news articles related to topics the user has previously reacted negatively to. This allows for the provision of more personalized opinions by leveraging the user's past browsing history.

[0049] The news service can also prioritize providing relevant news articles based on the user's geographical location. For example, if a user is in a specific region, news articles related to that region will be displayed preferentially. Furthermore, if a user is traveling, news articles related to their travel destination can be prioritized. Also, if a user is at home, local news articles can be prioritized. This allows the service to provide more relevant news articles by utilizing the user's geographical location.

[0050] The analytics unit can also analyze users' social media activity based on the content of news articles and generate relevant opinions. For example, it can generate more detailed opinions on news articles related to topics that users follow on social media. Furthermore, it can generate opinions related to news articles that users have shared on social media. It can also generate opinions on news articles of high interest based on users' comments and reactions on social media. This allows for the provision of more personalized opinions by leveraging users' social media activity.

[0051] The service provider can also select the optimal display method based on the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, it can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the service provider to utilize the user's device information to provide a more appropriate display method.

[0052] The service provider can also select the most appropriate delivery method by referring to the user's past browsing history. For example, it can prioritize providing opinions related to topics the user has frequently viewed in the past. Furthermore, it can provide opinions related to topics of high interest to the user based on their past browsing history. It can also select the most appropriate delivery method based on the user's past browsing history. This allows for the provision of more relevant information by utilizing the user's past browsing history.

[0053] The service provider can also customize the content offered based on the user's current areas of interest. For example, it can prioritize providing opinions related to topics the user is currently interested in. Furthermore, it can customize and provide relevant opinions based on the user's current areas of interest. It can also select the most appropriate content based on the user's current areas of interest. This allows for the provision of more relevant information by leveraging the user's current areas of interest.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The analysis unit analyzes news articles. News articles include online news, newspaper articles, blog posts, etc. The analysis unit uses text analysis technology, sentiment analysis technology, and topic modeling technology to analyze the content, sentiment, and topics of the news articles. For example, text data is taken as input, the content is analyzed using text analysis technology, sentiment analysis technology is used to calculate sentiment scores, and topic modeling technology is used to extract the main topics. Step 2: The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of the news article analyzed by the analysis unit. The generation unit uses generation AI to generate opinions from the perspectives of business people, sports fans, university professors, engineers, etc. For example, it generates opinions that consider the economic impact from the perspective of business people, opinions that consider the impact of sports events from the perspective of sports fans, and opinions that consider educational aspects from the perspective of university professors. Step 3: The providing unit provides the user with opinions automatically generated by the generating unit. The providing unit has the functionality to read the content of news articles aloud using generating AI, and to display opinions from different backgrounds and perspectives. For example, it provides the user with opinions from the perspective of a business person, a sports fan, and a university professor.

[0056] (Example of form 2) The news opinion generation system according to an embodiment of the present invention is a system that utilizes a generating AI to automatically generate opinions on news articles from different backgrounds and perspectives and provide them to the user. This news opinion generation system analyzes news articles and generates opinions from different backgrounds and perspectives based on the content. For example, it provides opinions from different perspectives such as business people, sports fans, university professors, and engineers. The generating AI analyzes the content of news articles and automatically generates opinions based on each perspective. This allows users to understand the same news article from multiple perspectives. Next, the system provides the user with the automatically generated opinions from multiple perspectives. By referring to opinions from different backgrounds and perspectives on a news article, users can promote a multifaceted understanding of the news. For example, it provides opinions from various perspectives, such as the economic impact from the perspective of a business person, the impact of a sports event from the perspective of a sports fan, and an educational perspective from the perspective of a university professor. This system can reduce information bias and promote a multifaceted understanding for users. For example, by providing opinions from different perspectives on a news article, users can gain a deeper understanding of the background and impact of the news. Furthermore, by utilizing the generating AI, opinions on the content of news articles can be generated quickly and efficiently. Furthermore, this system allows users to listen to news articles in a radio-like manner. For example, by using the function to have news articles read aloud, users can hear opinions from different perspectives in audio format. This allows users to understand the news by utilizing not only visual information but also auditory information. Thus, the present invention provides a system that reduces information bias and promotes multifaceted understanding by automatically generating opinions from different backgrounds and perspectives on news articles using generational AI and providing them to the user. In this way, the news opinion generation system can reduce information bias and promote multifaceted understanding by automatically generating opinions from different backgrounds and perspectives on news articles and providing them to the user.

[0057] The news opinion generation system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes news articles. News articles include, but are not limited to, online news, newspaper articles, and blog posts. The analysis unit analyzes the content of news articles using, for example, text analysis technology. The analysis unit can also analyze the sentiment of news articles using sentiment analysis technology. Furthermore, the analysis unit can analyze the topics of news articles using topic modeling technology. For example, the analysis unit takes text data of a news article as input and analyzes the content of the article using text analysis technology. Sentiment analysis technology analyzes the sentiment of news articles and calculates sentiment scores such as positive, negative, and neutral. Topic modeling technology analyzes the topics of news articles and extracts the main topics. The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of the news articles analyzed by the analysis unit. The generation unit automatically generates opinions from different perspectives, such as those of business people, sports fans, university professors, and engineers. The generation unit uses generation AI to analyze the content of news articles and automatically generate opinions based on various perspectives. For example, the generation unit takes the content of a news article as input and uses generation AI to generate opinions from the perspective of a business person. The generation unit can also generate opinions from the perspective of a sports fan. Furthermore, the generation unit can generate opinions from the perspective of a university professor. For example, the generation unit analyzes the content of a news article and generates opinions from the perspective of a business person that consider the economic impact. From the perspective of a sports fan, it generates opinions that consider the impact of a sports event. From the perspective of a university professor, it generates opinions that consider educational aspects. The delivery unit provides the opinions automatically generated by the generation unit to the user. The delivery unit, for example, has a function that allows the user to listen to news articles like a radio program. The delivery unit can use generation AI to read the content of news articles aloud. For example, the delivery unit takes the content of a news article as input, uses generation AI to generate audio data, and provides it to the user. The delivery unit can also provide the user with a function to refer to opinions from different backgrounds and positions.For example, the information provider displays opinions from the perspective of a business person, a sports fan, and a university professor regarding a news article. This allows the news opinion generation system according to this embodiment to automatically generate and provide users with opinions from different backgrounds and perspectives regarding a news article, thereby reducing information bias and promoting a multifaceted understanding of the user.

[0058] The analysis unit analyzes news articles. News articles include, but are not limited to, online news, newspaper articles, and blog posts. The analysis unit analyzes the content of news articles using, for example, text analysis techniques. Specifically, it uses natural language processing (NLP) techniques to analyze the grammatical structure and meaning of news articles and extract the article's theme and important information. For example, it uses morphological analysis to identify the part of speech of words and dependency structure analysis to analyze the sentence structure. The analysis unit can also analyze the sentiment of news articles using sentiment analysis techniques. Sentiment analysis techniques take the text data of news articles as input and calculate sentiment scores such as positive, negative, and neutral. For example, it uses a word sentiment dictionary to calculate the sentiment score of each word and then calculates the sentiment score for the entire article. Furthermore, the analysis unit can also analyze the topics of news articles using topic modeling techniques. Topic modeling techniques take the text data of news articles as input and extract the main topics. For example, algorithms such as Latent Dirichlet Allocation (LDA) are used to analyze the topic distribution of news articles and identify which topics each article relates to. This allows the analysis unit to analyze the content, sentiment, and topics of news articles in detail, providing foundational data for opinion generation, which is the next step.

[0059] The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of news articles analyzed by the analysis unit. For example, the generation unit automatically generates opinions from different viewpoints such as business people, sports fans, university professors, and engineers. The generation unit uses a generation AI to analyze the content of news articles and automatically generate opinions based on each perspective. Specifically, a large-scale language model (LLM) is used as the generation AI, and the content of the news article is input as a prompt. For example, a prompt such as "Please give your opinion on this news article from the perspective of a business person" is input to the generation AI, and an opinion from the perspective of a business person is generated. Similarly, to generate an opinion from the perspective of a sports fan, a prompt such as "Please give your opinion on this news article from the perspective of a sports fan" is used. Furthermore, to generate an opinion from the perspective of a university professor, a prompt such as "Please give your opinion on this news article from the perspective of a university professor" is used. Based on these prompts, the generation AI analyzes the content of the news article and generates opinions based on each perspective. For example, from a business person's perspective, it can generate opinions on economic impacts and market trends; from a sports fan's perspective, it can generate opinions on the impact of sporting events and athlete performance; and from a university professor's perspective, it can generate opinions on educational aspects and social impacts. This allows the generation unit to automatically generate opinions from diverse perspectives on news articles and prepare them for provision to users.

[0060] The service provider provides users with opinions automatically generated by the generation unit. For example, the service provider includes a function that allows users to listen to news articles in a radio-like format. The service provider can use a generation AI to read the content of news articles aloud. Specifically, it uses speech synthesis technology to convert the opinions generated by the generation AI from text to speech. For example, it takes the content of a news article and the generated opinions as input and generates audio data using speech synthesis technology. The generated audio data can be listened to by the user through devices such as smartphones and personal computers. Furthermore, the service provider can provide users with a function to refer to opinions from different backgrounds and perspectives. For example, it can display opinions on a news article from the perspective of a business person, a sports fan, and a university professor. This allows users to refer to diverse perspectives on news articles, reducing information bias. In addition, the service provider can collect user feedback and continuously improve the quality of the generated opinions. For example, it can provide a function that allows users to rate and comment on the provided opinions, and update the generation AI's learning data based on this feedback. This allows the service provider to continue providing users with high-quality opinions.

[0061] The generation unit automatically generates opinions from different perspectives, such as those of business professionals, sports fans, university professors, and engineers. For example, the generation unit can generate opinions from a business professional's perspective that consider the economic impact. For example, the generation unit can generate opinions from a sports fan's perspective that consider the impact of a sports event. For example, the generation unit can generate opinions from a university professor's perspective that consider the educational aspects. The generation unit uses a generation AI to analyze the content of news articles and automatically generate opinions based on each perspective. For example, the generation unit takes the content of a news article as input and uses the generation AI to generate opinions from a business professional's perspective. The generation unit can also use the generation AI to generate opinions from a sports fan's perspective. The generation unit can also use the generation AI to generate opinions from a university professor's perspective. This allows users to understand news articles from multiple perspectives by automatically generating opinions from different viewpoints.

[0062] The delivery unit has a function that allows users to listen to news articles like a radio program. The delivery unit can read the content of news articles aloud using generative AI. For example, the delivery unit takes the content of a news article as input, generates audio data using generative AI, and provides it to the user. This allows users to understand the news by utilizing not only visual information but also auditory information.

[0063] The analysis unit estimates the user's emotions and adjusts the analysis method of the news article based on the estimated user emotions. For example, if the user is stressed, the analysis unit applies a concise and to-the-point analysis method. If the user is relaxed, for example, the analysis unit may apply a detailed analysis method to provide deeper insights. If the user is excited, for example, the analysis unit may provide visually stimulating analysis results. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0064] The analysis unit optimizes the analysis algorithm by referring to past analysis results of news articles. The analysis unit can, for example, analyze user reactions to specific topics from past analysis results and adjust the algorithm. The analysis unit can also, for example, re-evaluate the importance of specific keywords based on past analysis results to improve analysis accuracy. The analysis unit can also, for example, optimize analysis methods for similar articles by referring to past analysis results. This allows the accuracy of the analysis algorithm to be improved by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis result data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0065] The analysis unit applies different analysis methods depending on the category of the news article. For example, for business-related articles, the analysis unit applies analysis methods that emphasize economic indicators and market trends. For sports-related articles, the analysis unit may also apply analysis methods that emphasize match results and athlete performance. For education-related articles, the analysis unit may also apply analysis methods that emphasize educational policies and academic research. By applying analysis methods appropriate to the category of the news article, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input news article category data into a generating AI and have the generating AI execute the application of analysis methods appropriate to the category.

[0066] The analysis unit estimates the user's emotions and determines the priority of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying positive analysis results. For example, if the user is relaxed, the analysis unit may also prioritize displaying detailed analysis results. For example, if the user is excited, the analysis unit may also prioritize displaying visually stimulating analysis results. This allows for the provision of more appropriate information by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The analysis unit prioritizes analyzing highly relevant information based on the user's geographical location when analyzing news articles. For example, if the user is in a specific region, the analysis unit prioritizes analyzing news related to that region. For example, if the user is traveling, the analysis unit can also prioritize analyzing news related to their travel destination. For example, if the user is at home, the analysis unit can also prioritize analyzing local news. This allows for the provision of more appropriate information by prioritizing the analysis of highly relevant information based on the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the priority analysis of highly relevant information.

[0068] The analysis unit analyzes the user's social media activity and analyzes relevant information when analyzing news articles. For example, the analysis unit prioritizes analyzing news related to topics that the user follows on social media. The analysis unit can also analyze information related to news that the user has shared on social media. For example, the analysis unit can analyze news of high interest based on the user's comments and reactions on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis of relevant information.

[0069] The generation unit estimates the user's emotions and adjusts the method of generating opinions based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates opinions that include detailed and deep insights. For example, if the user is in a hurry, the generation unit can also generate concise and to-the-point opinions. For example, if the user is excited, the generation unit can also generate visually stimulating opinions. This allows for the provision of more appropriate opinions by adjusting the method of generating opinions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI generate opinions based on emotions.

[0070] The generation unit adjusts the level of detail in its opinion generation based on the importance of the news article. For example, for important news articles, the generation unit generates detailed and insightful opinions. For general news articles, the generation unit can also generate concise and to-the-point opinions. For light news articles, the generation unit can also generate visually stimulating opinions. By adjusting the level of detail in the generation based on the importance of the news article, it is possible to provide more appropriate opinions. 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 news article importance data into a generation AI and have the generation AI perform opinion generation based on importance.

[0071] The generation unit applies different generation algorithms depending on the category of the news article when generating opinions. For example, for business-category articles, the generation unit applies an algorithm that generates opinions from an economic perspective. For example, for sports-category articles, the generation unit may apply an algorithm that generates opinions from the perspective of a sports fan. For example, for education-category articles, the generation unit may apply an algorithm that generates opinions from an educational perspective. By applying a generation algorithm appropriate to the category of the news article, more appropriate opinions can be provided. 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 news article category data into a generation AI and have the generation AI execute the application of a generation algorithm appropriate to the category.

[0072] The generation unit estimates the user's emotions and determines the priority of opinions to generate based on the estimated user emotions. For example, if the user is stressed, the generation unit will prioritize generating positive opinions. For example, if the user is relaxed, the generation unit may also prioritize generating detailed opinions. For example, if the user is excited, the generation unit may also prioritize generating visually stimulating opinions. This allows for the provision of more appropriate opinions by prioritizing opinions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the determination of opinion priorities based on emotions.

[0073] The generation unit determines the generation priority based on the submission date of the news article when generating opinions. For example, the generation unit quickly generates opinions for the latest news articles. The generation unit can also generate detailed opinions for past news articles. For example, the generation unit can generate different opinions before and after a specific event for news articles related to that event. This allows for the provision of more appropriate opinions by determining the generation priority based on the submission date of the news article. 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 news article submission date data into a generation AI and have the generation AI generate opinions based on the submission date.

[0074] The generation unit adjusts the order of opinion generation based on the relevance of news articles. For example, the generation unit prioritizes generating opinions for news articles related to topics the user is interested in. The generation unit can also prioritize generating opinions related to news articles the user has read in the past. The generation unit can also generate opinions for highly relevant news articles based on the user's social media activity. This allows for the provision of more appropriate opinions by adjusting the order of generation based on the relevance of news articles. 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 news article relevance data into a generation AI and have the generation AI perform opinion generation based on relevance.

[0075] The service provider estimates the user's emotions and adjusts the way opinions are presented based on the estimated emotions. For example, if the user is nervous, the service provider provides a simple and easy-to-understand presentation. If the user is relaxed, the service provider may also provide a presentation that includes detailed information. If the user is in a hurry, the service provider may also provide a presentation that gets straight to the point. By adjusting the way opinions are presented according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way opinions are presented based on emotions.

[0076] The service provider selects the optimal service delivery method by referring to the user's past browsing history at the time of delivery. For example, the service provider may prioritize providing opinions related to topics the user has frequently viewed in the past. The service provider may also provide opinions related to topics of high interest to the user based on their past browsing history. The service provider may also select the optimal service delivery method based on the user's past browsing history. This allows for the provision of more appropriate information by referring to the user's past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past browsing history data into a generating AI and have the generating AI select the optimal service delivery method.

[0077] The service provider customizes the content offered based on the user's current areas of interest at the time of delivery. For example, the service provider prioritizes providing opinions related to topics the user is currently interested in. The service provider can also customize and provide relevant opinions based on the user's current areas of interest. The service provider can also select the most appropriate content based on the user's current areas of interest. This allows for the provision of more relevant information by customizing the content based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current areas of interest data into a generating AI and have the generating AI perform the customization of the content based on those areas of interest.

[0078] The service provider estimates the user's emotions and adjusts the way opinions are displayed based on the estimated emotions. For example, if the user is nervous, the service provider provides a simple and highly visible display method. For example, if the user is relaxed, the service provider may also provide a display method that includes detailed information. For example, if the user is in a hurry, the service provider may also provide a display method that gets straight to the point. By adjusting the way opinions are displayed according to the user's emotions, more appropriate information can be provided. 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way opinions are displayed based on emotions.

[0079] The service provider selects the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. By selecting the optimal display method based on the user's device information, more appropriate information can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0080] The service provider analyzes the user's social media activity and customizes the content provided at the time of delivery. For example, the service provider may prioritize providing opinions related to topics the user follows on social media. The service provider may also provide opinions related to news shared by the user on social media. The service provider may also provide opinions of high interest based on the user's comments and reactions on social media. This allows for the provision of more relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the user's social media activity data into a generating AI and have the generating AI perform the customization of the content provided.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The analytics unit can also generate relevant opinions based on the content of news articles, referencing the user's past browsing history. For example, it can generate more detailed opinions on news articles related to topics the user has frequently viewed in the past. Furthermore, it can generate opinions with particularly deep insights on news articles related to specific themes the user has shown interest in in the past. It can also generate more neutral opinions on news articles related to topics the user has previously reacted negatively to. This allows for the provision of more personalized opinions by leveraging the user's past browsing history.

[0083] The generator can estimate the user's current emotional state based on the content of the news article and adjust the tone of the opinion based on that estimated emotion. For example, if the user is stressed, the generator will produce more positive and encouraging opinions. If the user is relaxed, the generator can also produce opinions that include detailed and deep insights. If the user is excited, the generator can also produce opinions that are visually stimulating. This allows the generator to provide more appropriate opinions depending on the user's emotional state.

[0084] The news service can also prioritize providing relevant news articles based on the user's geographical location. For example, if a user is in a specific region, news articles related to that region will be displayed preferentially. Furthermore, if a user is traveling, news articles related to their travel destination can be prioritized. Also, if a user is at home, local news articles can be prioritized. This allows the service to provide more relevant news articles by utilizing the user's geographical location.

[0085] The analytics unit can also analyze users' social media activity based on the content of news articles and generate relevant opinions. For example, it can generate more detailed opinions on news articles related to topics that users follow on social media. Furthermore, it can generate opinions related to news articles that users have shared on social media. It can also generate opinions on news articles of high interest based on users' comments and reactions on social media. This allows for the provision of more personalized opinions by leveraging users' social media activity.

[0086] The generation unit can estimate the user's emotions based on the content of the news article and adjust the way opinions are generated based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate opinions that include detailed and deep insights. If the user is in a hurry, the generation unit can also generate concise and to-the-point opinions. If the user is excited, the generation unit can also generate visually stimulating opinions. This allows for the provision of more appropriate opinions depending on the user's emotional state.

[0087] The service provider can also select the optimal display method based on the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, it can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, it can provide a concise and highly visible display method. This allows the service provider to utilize the user's device information to provide a more appropriate display method.

[0088] The analysis unit can estimate the user's emotions based on the content of news articles and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, positive analysis results will be displayed preferentially. If the user is relaxed, detailed analysis results may be displayed preferentially. Furthermore, if the user is excited, visually stimulating analysis results may be displayed preferentially. This allows for the provision of more appropriate analysis results according to the user's emotional state.

[0089] The service provider can also select the most appropriate delivery method by referring to the user's past browsing history. For example, it can prioritize providing opinions related to topics the user has frequently viewed in the past. Furthermore, it can provide opinions related to topics of high interest to the user based on their past browsing history. It can also select the most appropriate delivery method based on the user's past browsing history. This allows for the provision of more relevant information by utilizing the user's past browsing history.

[0090] The information delivery system can also estimate the user's emotions and adjust the way information is delivered based on those estimates. For example, if the user is nervous, a simple and highly visible delivery method can be provided. If the user is relaxed, a delivery method including detailed information can be provided. Furthermore, if the user is in a hurry, a concise delivery method can be provided. This allows for the delivery of more appropriate information according to the user's emotional state.

[0091] The service provider can also customize the content offered based on the user's current areas of interest. For example, it can prioritize providing opinions related to topics the user is currently interested in. Furthermore, it can customize and provide relevant opinions based on the user's current areas of interest. It can also select the most appropriate content based on the user's current areas of interest. This allows for the provision of more relevant information by leveraging the user's current areas of interest.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The analysis unit analyzes news articles. News articles include online news, newspaper articles, blog posts, etc. The analysis unit uses text analysis technology, sentiment analysis technology, and topic modeling technology to analyze the content, sentiment, and topics of the news articles. For example, text data is taken as input, the content is analyzed using text analysis technology, sentiment analysis technology is used to calculate sentiment scores, and topic modeling technology is used to extract the main topics. Step 2: The generation unit automatically generates opinions from different backgrounds and perspectives based on the content of the news article analyzed by the analysis unit. The generation unit uses generation AI to generate opinions from the perspectives of business people, sports fans, university professors, engineers, etc. For example, it generates opinions that consider the economic impact from the perspective of business people, opinions that consider the impact of sports events from the perspective of sports fans, and opinions that consider educational aspects from the perspective of university professors. Step 3: The providing unit provides the user with opinions automatically generated by the generating unit. The providing unit has the functionality to read the content of news articles aloud using generating AI, and to display opinions from different backgrounds and perspectives. For example, it provides the user with opinions from the perspective of a business person, a sports fan, and a university professor.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the smart device 14 and the processor 28 of the data processing unit 12, and analyzes the content of a news article. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates opinions from different backgrounds and perspectives based on the analyzed content. The provision unit is implemented by the control unit 46A of the smart device 14, and provides the generated opinions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0111] 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.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0113] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the smart glasses 214 and the processor 28 of the data processing unit 12, and analyzes the content of a news article. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates opinions from different backgrounds and perspectives based on the analyzed content. The provision unit is implemented by the control unit 46A of the smart glasses 214, and provides the generated opinions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the headset terminal 314 and the processor 28 of the data processing unit 12, and analyzes the content of a news article. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates opinions from different backgrounds and perspectives based on the analyzed content. The provision unit is implemented by the control unit 46A of the headset terminal 314, and provides the generated opinions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the computer 36 of the robot 414 and the processor 28 of the data processing unit 12, and analyzes the content of a news article. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates opinions from different backgrounds and perspectives based on the analyzed content. The provision unit is implemented by the control unit 46A of the robot 414, and provides the generated opinions to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] (Note 1) The analysis department analyzes news articles, A generation unit that automatically generates opinions from different backgrounds and perspectives based on the content of news articles analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides the user with opinions automatically generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Automatically generates opinions from diverse perspectives, such as those of business professionals, sports fans, university professors, and engineers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It features a function that allows users to listen to news articles like radio. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We estimate user sentiment and adjust the news article analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Referencing past analysis results of news articles optimizes the analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Apply different analysis methods depending on the category of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing news articles, the system prioritizes analyzing highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing news articles, the system analyzes users' social media activity and extracts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the opinion generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating opinions, adjust the level of detail based on the importance of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating opinions, different generation algorithms are applied depending on the category of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and determines the priority of opinions to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating opinions, the priority of generation is determined based on the timing of news article submissions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating opinions, the order of generation is adjusted based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and adjusts how opinions are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing content, the system will refer to the user's past browsing history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts how opinions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the optimal display method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity to customize the content offered. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes news articles, A generation unit that automatically generates opinions from different backgrounds and perspectives based on the content of news articles analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides the user with opinions automatically generated by the generation unit. A system characterized by the following features.

2. The generating unit is Automatically generates opinions from diverse perspectives, such as those of business professionals, sports fans, university professors, and engineers. The system according to feature 1.

3. The aforementioned supply unit is, It features a function that allows users to listen to news articles like radio. The system according to feature 1.

4. The aforementioned analysis unit, We estimate user sentiment and adjust the news article analysis method based on the estimated user sentiment. The system according to feature 1.

5. The aforementioned analysis unit, Referencing past analysis results of news articles optimizes the analysis algorithm. The system according to feature 1.

6. The aforementioned analysis unit, Apply different analysis methods depending on the category of the news article. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing news articles, the system prioritizes analyzing highly relevant information based on the user's geographical location. The system according to feature 1.

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