system

Abstract

The system according to this embodiment aims to provide quick and accurate answers to questions about the news. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from the user. The generation unit generates answers based on the questions received by the reception unit. The provision unit provides the answers generated by the generation unit to the user.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide a quick and accurate answer to a question regarding news.

[0005] The system according to the embodiment aims to provide a quick and accurate answer to a question regarding news.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a question from a user. The generation unit generates an answer based on the question received by the reception unit. The provision unit provides the answer generated by the generation unit to the user.

Effects of the Invention

[0007] The system according to this embodiment can provide quick and accurate answers to questions about the news. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 Q&A system according to an embodiment of the present invention is a system in which a generative AI, which has thoroughly learned about news, answers what the user wants to know about the news in a Q&A format. The news Q&A system allows the user to input what they want to know, and the generative AI generates an answer in response. For example, the generative AI provides appropriate answers to questions such as, "Please tell me the 10 most recent dates of Russian airspace violations in Japan" or "Please tell me the events and names of the Japanese gold medalists at the Paris Olympics." This system utilizes data on articles, comments, and public opinion related to the news, increasing opportunities for users to engage with the news and promoting their knowledge and understanding of the news. Furthermore, by displaying the source, it is expected that traffic to news sites will increase. In addition, a UI using characters is adopted to make it appealing to children. For example, by using characters, it provides an environment in which children can learn about the news in a fun way. In terms of specific usage, the user inputs the information they want to know, and the generative AI generates an answer based on that information. If the input information is insufficient or the answer information is too much, the generative AI will ask the user for additional information, and by inputting additional information, the user can obtain a more accurate answer. This system is proposed not only as an aid for reading news, but also as a new way of reading news, and is intended to be offered as a standalone system or app. For example, it can be used to say, "Show me 10 articles about disaster information in XX city," or "Show me 3 of the latest articles related to XX (genre, etc.)." This mechanism will reduce the time users spend searching for news articles and deepen their knowledge and understanding. In addition, the system can collect information that users are interested in and seek in news, which is expected to promote the use of news sites. As a result, the news Q&A system will be able to provide appropriate answers to user questions using a generated AI.

[0029] The news Q&A system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. The reception unit can receive questions in, for example, text format, audio format, or on a specific topic. The generation unit generates answers based on the questions received by the reception unit. The generation unit uses a generation AI to learn from news data and generate answers based on user questions. The generation unit generates answers using data such as news articles, news feeds, or specific news sources. The provision unit provides the answers generated by the generation unit to the user. The provision unit can, for example, attach links to the source of the generated answers. The provision unit can also provide a UI using characters. As a result, the news Q&A system enables the generation AI to provide appropriate answers to user questions.

[0030] The reception desk receives questions from users. The reception desk can accept questions in various formats, such as text, voice, and on specific topics. Specifically, text questions can be entered by users using a keyboard or the touchscreen of a smartphone or tablet. Voice questions are entered by users speaking through a microphone and converted to text using speech recognition technology. This allows users to easily enter questions. For questions on specific topics, users can select a news topic of interest and enter questions related to that topic. The reception desk centrally manages these questions and sends them to the generation desk. Furthermore, the reception desk saves the user's question history and allows them to refer to past questions and answers. This allows users to review previously asked questions and add related questions. The reception desk is designed to allow flexible selection of question input methods and formats through its user interface, improving user convenience.

[0031] The generation unit generates answers based on questions received by the reception unit. The generation unit uses a generation AI to learn from news data and generate answers based on user questions. Specifically, the generation AI collects data from a large volume of news articles, news feeds, and specific news sources, and analyzes this data using natural language processing technology. The generation AI understands the content of the question, searches for relevant news information, and generates an appropriate answer. For example, if a user asks, "What's the latest news on climate change?", the generation AI searches for the latest news articles on climate change, summarizes their content, and generates an answer. The generation AI has an algorithm to select reliable information from news data and provide accurate answers. Furthermore, the generation AI can understand the context and intent of the user's question and generate answers in an appropriate tone and style. This allows the generation unit to provide quick and accurate answers to a wide range of user questions. In addition, the generation unit has the ability to evaluate the quality of the generated answers and make corrections or improvements as needed. This allows the generation unit to consistently provide high-quality answers and improve user satisfaction.

[0032] The provider unit provides users with the answers generated by the generator unit. For example, the provider unit can attach links to the source of the generated answers. Specifically, if the generated answer is based on a specific news article or news source, it can attach a link to that source to the answer, allowing users to refer to more detailed information. This allows users to verify the reliability of the answer and obtain more detailed information. The provider unit can also provide a UI using characters. For example, it can use animated characters or virtual assistants to provide users with a user-friendly interface. This allows users to have a fun and interactive experience. The provider unit can provide generated answers not only in text format but also in audio and video format. In audio format, the generated answers are read aloud using speech synthesis technology, allowing users to understand them by listening. In video format, the generated answers are provided as video clips, allowing information to be conveyed visually. Furthermore, the provider unit can collect user feedback and continuously improve the quality and method of providing answers. For example, users can provide ratings and comments on answers, and the provider unit can use that feedback to review and improve the accuracy and method of providing answers. This allows the service provider to consistently provide users with the most optimal information, improving the reliability and user satisfaction of the news Q&A system.

[0033] The reception desk can accept additional information from users. For example, the reception desk can accept additional information such as detailed explanations, relevant data, and supplementary questions. This allows users to obtain more accurate answers by providing additional information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input additional information from users into an AI, which can then analyze the information and perform appropriate processing.

[0034] The generation unit can learn from news data and generate answers based on user questions. The generation unit learns from data such as news articles, news feeds, and specific news sources. Using a generative AI, the generation unit generates appropriate answers based on user questions. This means that by learning from news data, the generative AI can provide more appropriate answers. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit inputs news data into the generative AI, and the generative AI generates answers based on that data.

[0035] The provider may attach a link to the source of the citation to the generated answer. The provider may attach the link to the source in the form of, for example, a URL link, a hyperlink, or a QR code (registered trademark). This allows the user to verify the reliability of the information by attaching the link to the source. Some or all of the above processing in the provider may be performed using AI or not. For example, the provider may use an AI model that automatically attaches a link to the source of the citation to the generated answer.

[0036] The provider can provide a UI using characters. For example, the provider can provide a UI using animated characters, characters with voices, interactive characters, etc. By providing a UI using characters, children will also want to use it. Some or all of the above processing in the provider may be performed using AI or not using AI. For example, the provider can use an AI model that generates character movements and voices.

[0037] The generation unit can generate responses using data such as articles, comments, and public opinions related to news. The generation unit collects data on articles, comments, and public opinions through methods such as obtaining it from social media and analyzing user posts. The generation unit uses a generation AI to generate responses based on this data. This allows for the provision of richer information by utilizing diverse data. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs data on articles, comments, and public opinions related to news into the generation AI, and the generation AI generates responses based on that data.

[0038] The reception desk can analyze a user's past question history and select the optimal question reception method. For example, the reception desk can analyze past question history stored in a database using machine learning. The reception desk can automatically display frequently asked questions as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest questions to be asked at specific times based on the user's past question history. In this way, by analyzing past question history, the reception desk can provide the user with the most suitable question reception method. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past question history into AI, and the AI ​​can select the optimal question reception method based on that data.

[0039] The reception desk can filter questions based on the user's current areas of interest when they are received. The reception desk identifies the user's current areas of interest, for example, using survey results or browsing history. The reception desk prioritizes receiving relevant questions based on the news categories the user has recently been interested in. If the user has shown interest in a particular topic, the reception desk filters and displays questions related to that topic. The reception desk analyzes the user's past search history and filters questions based on areas of interest. This allows for more appropriate answers by providing questions based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's areas of interest data into the AI, and the AI ​​filters questions based on that data.

[0040] The reception desk can prioritize receiving questions based on the user's geographical location when a question is received. The reception desk obtains the user's geographical location using, for example, GPS data or an IP address. If the user is in a specific region, the reception desk prioritizes receiving questions about news related to that region. If the user is traveling, the reception desk prioritizes receiving questions about news related to their travel destination. If the user is at home, the reception desk prioritizes receiving questions about local news. By prioritizing questions based on geographical location, the reception desk can provide users with information that is highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's geographical location into an AI, and the AI ​​selects highly relevant questions based on that data.

[0041] The reception desk can analyze a user's social media activity when receiving a question and accept relevant questions. The reception desk analyzes a user's social media activity, for example, by analyzing the content of posts or the number of followers. The reception desk prioritizes accepting questions related to topics that the user frequently mentions on social media. The reception desk accepts relevant questions based on the news sources that the user follows on social media. The reception desk analyzes the user's social media activity history and accepts questions related to topics of high interest. This allows the reception desk to provide information tailored to the user's interests by accepting questions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's social media activity data into an AI, which then selects relevant questions based on that data.

[0042] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. The generation unit evaluates the importance of the question, for example, by user specification or automatic system evaluation. The generation unit generates detailed answers for high-importance questions. The generation unit generates concise answers for low-importance questions. The generation unit adjusts the length and content of the answer according to the importance of the question. This allows the user to receive optimal information by providing answers that match the importance of the question. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question importance data into the generation AI, and the generation AI adjusts the level of detail in the answer based on that data.

[0043] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit applies a generation algorithm based on categories such as technical questions and general questions. For political questions, the generation unit applies a specialized political generation algorithm. For sports questions, the generation unit applies a specialized sports generation algorithm. For entertainment questions, the generation unit applies a specialized entertainment generation algorithm. This allows for the provision of more appropriate answers by applying a generation algorithm appropriate to the question category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question category data into the generation AI, and the generation AI applies an appropriate generation algorithm based on that data.

[0044] The generation unit can determine the priority of answers based on when the questions were submitted when generating responses. The generation unit evaluates the submission timing of questions, for example, by considering factors such as the submission date and urgency. The generation unit prioritizes generating answers for the most recent questions. The generation unit postpones generating answers for older questions. The generation unit adjusts the priority of answers according to when the questions were submitted. This allows for a quick response by setting a priority for answers based on when the questions were submitted. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question submission timing data into the generation AI, and the generation AI determines the priority of answers based on that data.

[0045] The generation unit can adjust the order of answers based on the relevance of the questions when generating responses. The generation unit determines the relevance of questions by evaluating factors such as similarity of content and user interest. If the relevance of a question is high, the generation unit prioritizes generating the answer. If the relevance of a question is low, the generation unit postpones generating the answer. The generation unit adjusts the order of answers according to the relevance of the questions. This allows the system to provide the user with the most relevant information by setting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question relevance data into the generation AI, and the generation AI adjusts the order of answers based on that data.

[0046] The service provider can select the optimal display method by referring to the user's past operation history when providing responses. The service provider can store data such as click history and browsing history in a database and analyze it using machine learning. The service provider prioritizes providing display methods that the user has preferred to use in the past. The service provider proposes the optimal display method based on the user's past operation history. The service provider analyzes the user's past operation history and provides a highly visible display method. By providing a display method based on past operation history, the service provider can provide the user with the most relevant information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past operation history data into AI, and the AI ​​can select the optimal display method based on that data.

[0047] The service provider can customize the displayed content based on the user's current areas of interest when providing responses. For example, the service provider can identify the user's areas of interest from survey results and past behavioral data. The service provider can prioritize displaying relevant responses based on the news categories the user has recently been interested in. If the user has shown interest in a particular topic, the service provider can customize and display responses related to that topic. The service provider can analyze the user's past search history and customize the displayed content based on their areas of interest. This improves the user experience by providing content based on the user's areas of interest. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's areas of interest data into AI, and the AI ​​can customize the displayed content based on that data.

[0048] The information provider can select the optimal display method based on the user's device information when providing responses. For example, the information provider can acquire device information such as device type, OS, and browser. If the user is using a smartphone, the information provider can provide a display method that matches the screen size. If the user is using a tablet, the information provider can provide a display method optimized for a large screen. If the user is using a smartwatch, the information provider can provide a concise and highly visible display method. In this way, by providing a display method based on device information, the information provider can provide the most suitable information for the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input device information into the AI, and the AI ​​can select the optimal display method based on that data.

[0049] The service provider can analyze the user's social media activity and customize the displayed content when providing responses. The service provider analyzes the user's social media activity, for example, by analyzing the content of posts or the number of followers. The service provider prioritizes displaying responses related to topics that the user frequently mentions on social media. The service provider displays relevant responses based on the news sources that the user follows on social media. The service provider analyzes the user's social media activity history and displays responses related to topics of high interest. This improves the user experience by providing content based on social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs the user's social media activity data into AI, and the AI ​​customizes the displayed content based on that data.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The news Q&A system may further include a reliability evaluation unit that evaluates the reliability of answers to user questions. The reliability evaluation unit evaluates the reliability of answers based on, for example, the reliability score of the news source, the number of times the article has been cited, and the reliability of the author. The reliability evaluation unit assigns a high rating to highly reliable answers, providing users with reliable information. The reliability evaluation unit displays a warning for low-reliability answers, alerting the user. This allows users to verify the reliability of the information provided and use the system with confidence. Some or all of the above processing in the reliability evaluation unit may be performed using AI or not. For example, the reliability evaluation unit inputs reliability data of news sources into an AI, and the AI ​​evaluates reliability based on that data.

[0052] The news Q&A system may further include a diversity evaluation unit that assesses the diversity of answers to user questions. The diversity evaluation unit evaluates answers that include information from different news sources, different perspectives, and different opinions, for example. The diversity evaluation unit assigns high ratings to highly diverse answers, providing users with multifaceted information. The diversity evaluation unit displays supplementary information to answers with low diversity, providing users with additional perspectives. This allows users to confirm the diversity of the information provided and understand the news from a broader perspective. Some or all of the processing described above in the diversity evaluation unit may be performed using AI or not. For example, the diversity evaluation unit inputs diversity data of news sources into an AI, and the AI ​​evaluates the diversity based on that data.

[0053] The news Q&A system may further include a relevance evaluation unit that evaluates the relevance of answers to user questions. The relevance evaluation unit, for example, evaluates the degree of agreement between the user's question and the answer. The relevance evaluation unit assigns a high rating to highly relevant answers and provides the user with appropriate information. The relevance evaluation unit displays supplementary information to less relevant answers and provides the user with additional information. This allows the user to confirm the relevance of the information provided and obtain more appropriate information. Some or all of the above processing in the relevance evaluation unit may be performed using AI or not. For example, the relevance evaluation unit inputs question-and-answer relevance data into the AI, and the AI ​​evaluates the relevance based on that data.

[0054] The news Q&A system may further include a visual evaluation unit that assesses the visual appeal of the answers to user questions. The visual evaluation unit evaluates, for example, the layout, color scheme, and presence or absence of images in the answers. The visual evaluation unit assigns high ratings to visually appealing answers, providing users with a sense of visual satisfaction. The visual evaluation unit displays improvement suggestions for answers that are not visually appealing, providing users with a better visual experience. This allows users to confirm the visual appeal of the information provided and obtain more satisfying information. Some or all of the above processing in the visual evaluation unit may be performed using AI or not. For example, the visual evaluation unit inputs the visual data of the answer into the AI, and the AI ​​evaluates the visual appeal based on that data.

[0055] The news Q&A system may further include a reliability evaluation unit that evaluates the reliability of answers to user questions. The reliability evaluation unit evaluates the reliability of answers based on, for example, the reliability score of the news source, the number of times the article has been cited, and the reliability of the author. The reliability evaluation unit assigns a high rating to highly reliable answers, providing users with reliable information. The reliability evaluation unit displays a warning for low-reliability answers, alerting the user. This allows users to verify the reliability of the information provided and use the system with confidence. Some or all of the above processing in the reliability evaluation unit may be performed using AI or not. For example, the reliability evaluation unit inputs reliability data of news sources into an AI, and the AI ​​evaluates reliability based on that data.

[0056] The news Q&A system may further include a diversity evaluation unit that assesses the diversity of answers to user questions. The diversity evaluation unit evaluates answers that include information from different news sources, different perspectives, and different opinions, for example. The diversity evaluation unit assigns high ratings to highly diverse answers, providing users with multifaceted information. The diversity evaluation unit displays supplementary information to answers with low diversity, providing users with additional perspectives. This allows users to confirm the diversity of the information provided and understand the news from a broader perspective. Some or all of the processing described above in the diversity evaluation unit may be performed using AI or not. For example, the diversity evaluation unit inputs diversity data of news sources into an AI, and the AI ​​evaluates the diversity based on that data.

[0057] The news Q&A system may further include a relevance evaluation unit that evaluates the relevance of answers to user questions. The relevance evaluation unit, for example, evaluates the degree of agreement between the user's question and the answer. The relevance evaluation unit assigns a high rating to highly relevant answers and provides the user with appropriate information. The relevance evaluation unit displays supplementary information to less relevant answers and provides the user with additional information. This allows the user to confirm the relevance of the information provided and obtain more appropriate information. Some or all of the above processing in the relevance evaluation unit may be performed using AI or not. For example, the relevance evaluation unit inputs question-and-answer relevance data into the AI, and the AI ​​evaluates the relevance based on that data.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives questions from users. The reception desk can accept questions in text format, audio format, or on specific topics. Step 2: The generation unit generates answers based on the questions received by the reception unit. The generation unit uses generation AI to learn from news data and generates answers based on user questions. The generation unit generates answers using data such as news articles, news feeds, and specific news sources. Step 3: The provider unit provides the user with the answer generated by the generator unit. The provider unit can attach a link to the source of the generated answer. The provider unit can also provide a UI using characters.

[0060] (Example of form 2) The news Q&A system according to an embodiment of the present invention is a system in which a generative AI, which has thoroughly learned about news, answers what the user wants to know about the news in a Q&A format. The news Q&A system allows the user to input what they want to know, and the generative AI generates an answer in response. For example, the generative AI provides appropriate answers to questions such as, "Please tell me the 10 most recent dates of Russian airspace violations in Japan" or "Please tell me the events and names of the Japanese gold medalists at the Paris Olympics." This system utilizes data on articles, comments, and public opinion related to the news, increasing opportunities for users to engage with the news and promoting their knowledge and understanding of the news. Furthermore, by displaying the source, it is expected that traffic to news sites will increase. In addition, a UI using characters is adopted to make it appealing to children. For example, by using characters, it provides an environment in which children can learn about the news in a fun way. In terms of specific usage, the user inputs the information they want to know, and the generative AI generates an answer based on that information. If the input information is insufficient or the answer information is too much, the generative AI will ask the user for additional information, and by inputting additional information, the user can obtain a more accurate answer. This system is proposed not only as an aid for reading news, but also as a new way of reading news, and is intended to be offered as a standalone system or app. For example, it can be used to say, "Show me 10 articles about disaster information in XX city," or "Show me 3 of the latest articles related to XX (genre, etc.)." This mechanism will reduce the time users spend searching for news articles and deepen their knowledge and understanding. In addition, the system can collect information that users are interested in and seek in news, which is expected to promote the use of news sites. As a result, the news Q&A system will be able to provide appropriate answers to user questions using a generated AI.

[0061] The news Q&A system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives questions from users. The reception unit can receive questions in, for example, text format, audio format, or on a specific topic. The generation unit generates answers based on the questions received by the reception unit. The generation unit uses a generation AI to learn from news data and generate answers based on user questions. The generation unit generates answers using data such as news articles, news feeds, or specific news sources. The provision unit provides the answers generated by the generation unit to the user. The provision unit can, for example, attach links to the source of the generated answers. The provision unit can also provide a UI using characters. As a result, the news Q&A system enables the generation AI to provide appropriate answers to user questions.

[0062] The reception desk receives questions from users. The reception desk can accept questions in various formats, such as text, voice, and on specific topics. Specifically, text questions can be entered by users using a keyboard or the touchscreen of a smartphone or tablet. Voice questions are entered by users speaking through a microphone and converted to text using speech recognition technology. This allows users to easily enter questions. For questions on specific topics, users can select a news topic of interest and enter questions related to that topic. The reception desk centrally manages these questions and sends them to the generation desk. Furthermore, the reception desk saves the user's question history and allows them to refer to past questions and answers. This allows users to review previously asked questions and add related questions. The reception desk is designed to allow flexible selection of question input methods and formats through its user interface, improving user convenience.

[0063] The generation unit generates answers based on questions received by the reception unit. The generation unit uses a generation AI to learn from news data and generate answers based on user questions. Specifically, the generation AI collects data from a large volume of news articles, news feeds, and specific news sources, and analyzes this data using natural language processing technology. The generation AI understands the content of the question, searches for relevant news information, and generates an appropriate answer. For example, if a user asks, "What's the latest news on climate change?", the generation AI searches for the latest news articles on climate change, summarizes their content, and generates an answer. The generation AI has an algorithm to select reliable information from news data and provide accurate answers. Furthermore, the generation AI can understand the context and intent of the user's question and generate answers in an appropriate tone and style. This allows the generation unit to provide quick and accurate answers to a wide range of user questions. In addition, the generation unit has the ability to evaluate the quality of the generated answers and make corrections or improvements as needed. This allows the generation unit to consistently provide high-quality answers and improve user satisfaction.

[0064] The provider unit provides users with the answers generated by the generator unit. For example, the provider unit can attach links to the source of the generated answers. Specifically, if the generated answer is based on a specific news article or news source, it can attach a link to that source to the answer, allowing users to refer to more detailed information. This allows users to verify the reliability of the answer and obtain more detailed information. The provider unit can also provide a UI using characters. For example, it can use animated characters or virtual assistants to provide users with a user-friendly interface. This allows users to have a fun and interactive experience. The provider unit can provide generated answers not only in text format but also in audio and video format. In audio format, the generated answers are read aloud using speech synthesis technology, allowing users to understand them by listening. In video format, the generated answers are provided as video clips, allowing information to be conveyed visually. Furthermore, the provider unit can collect user feedback and continuously improve the quality and method of providing answers. For example, users can provide ratings and comments on answers, and the provider unit can use that feedback to review and improve the accuracy and method of providing answers. This allows the service provider to consistently provide users with the most optimal information, improving the reliability and user satisfaction of the news Q&A system.

[0065] The reception desk can accept additional information from users. For example, the reception desk can accept additional information such as detailed explanations, relevant data, and supplementary questions. This allows users to obtain more accurate answers by providing additional information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input additional information from users into an AI, which can then analyze the information and perform appropriate processing.

[0066] The generation unit can learn from news data and generate answers based on user questions. The generation unit learns from data such as news articles, news feeds, and specific news sources. Using a generative AI, the generation unit generates appropriate answers based on user questions. This means that by learning from news data, the generative AI can provide more appropriate answers. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit inputs news data into the generative AI, and the generative AI generates answers based on that data.

[0067] The provider may attach a link to the source of the generated answer. The provider may attach the link to the source in the form of, for example, a URL link, hyperlink, or QR code. This allows the user to verify the reliability of the information by attaching the link to the source. Some or all of the above processing in the provider may be performed using AI or not. For example, the provider may use an AI model that automatically attaches a link to the source of the generated answer.

[0068] The provider can provide a UI using characters. For example, the provider can provide a UI using animated characters, characters with voices, interactive characters, etc. By providing a UI using characters, children will also want to use it. Some or all of the above processing in the provider may be performed using AI or not using AI. For example, the provider can use an AI model that generates character movements and voices.

[0069] The generation unit can generate responses using data such as articles, comments, and public opinions related to news. The generation unit collects data on articles, comments, and public opinions through methods such as obtaining it from social media and analyzing user posts. The generation unit uses a generation AI to generate responses based on this data. This allows for the provision of richer information by utilizing diverse data. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs data on articles, comments, and public opinions related to news into the generation AI, and the generation AI generates responses based on that data.

[0070] The reception desk can estimate the user's emotions and adjust the question reception method based on the estimated emotions. The reception desk estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. If the user is excited, the reception desk provides a simple and intuitive interface and quickly accepts questions. If the user is relaxed, the reception desk provides detailed input options and suggests a customizable question reception method. If the user is stressed, the reception desk prioritizes voice input and makes it easy to enter questions. This improves the user experience by providing a question reception method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception desk may be performed using AI or not. For example, the reception desk inputs the user's emotion data into a generative AI, which then adjusts the question reception method based on that data.

[0071] The reception desk can analyze a user's past question history and select the optimal question reception method. For example, the reception desk can analyze past question history stored in a database using machine learning. The reception desk can automatically display frequently asked questions as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest questions to be asked at specific times based on the user's past question history. In this way, by analyzing past question history, the reception desk can provide the user with the most suitable question reception method. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past question history into AI, and the AI ​​can select the optimal question reception method based on that data.

[0072] The reception desk can filter questions based on the user's current areas of interest when they are received. The reception desk identifies the user's current areas of interest, for example, using survey results or browsing history. The reception desk prioritizes receiving relevant questions based on the news categories the user has recently been interested in. If the user has shown interest in a particular topic, the reception desk filters and displays questions related to that topic. The reception desk analyzes the user's past search history and filters questions based on areas of interest. This allows for more appropriate answers by providing questions based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's areas of interest data into the AI, and the AI ​​filters questions based on that data.

[0073] The reception desk can estimate the user's emotions and determine the priority of questions based on the estimated emotions. The reception desk estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. If the user feels urgent, the reception desk prioritizes that question. If the user is relaxed, the reception desk processes that question with the same priority as other questions. If the user is agitated, the reception desk sets a priority for quick processing of that question. This enables a rapid response by setting question priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception desk may be performed using AI or not. For example, the reception desk inputs the user's emotion data into a generative AI, and the generative AI determines the priority of questions based on that data.

[0074] The reception desk can prioritize receiving questions based on the user's geographical location when a question is received. The reception desk obtains the user's geographical location using, for example, GPS data or an IP address. If the user is in a specific region, the reception desk prioritizes receiving questions about news related to that region. If the user is traveling, the reception desk prioritizes receiving questions about news related to their travel destination. If the user is at home, the reception desk prioritizes receiving questions about local news. By prioritizing questions based on geographical location, the reception desk can provide users with information that is highly relevant. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's geographical location into an AI, and the AI ​​selects highly relevant questions based on that data.

[0075] The reception desk can analyze a user's social media activity when receiving a question and accept relevant questions. The reception desk analyzes a user's social media activity, for example, by analyzing the content of posts or the number of followers. The reception desk prioritizes accepting questions related to topics that the user frequently mentions on social media. The reception desk accepts relevant questions based on the news sources that the user follows on social media. The reception desk analyzes the user's social media activity history and accepts questions related to topics of high interest. This allows the reception desk to provide information tailored to the user's interests by accepting questions based on social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's social media activity data into an AI, which then selects relevant questions based on that data.

[0076] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. The generation unit estimates the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. When the user is relaxed, the generation unit generates a detailed and polite response. When the user is in a hurry, the generation unit generates a concise and to-the-point response. When the user is excited, the generation unit generates a response with visually appealing effects. This improves the user experience by providing a way of expressing responses that is appropriate 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 is performed using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the way the response is expressed based on that data.

[0077] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. The generation unit evaluates the importance of the question, for example, by user specification or automatic system evaluation. The generation unit generates detailed answers for high-importance questions. The generation unit generates concise answers for low-importance questions. The generation unit adjusts the length and content of the answer according to the importance of the question. This allows the user to receive optimal information by providing answers that match the importance of the question. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question importance data into the generation AI, and the generation AI adjusts the level of detail in the answer based on that data.

[0078] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit applies a generation algorithm based on categories such as technical questions and general questions. For political questions, the generation unit applies a specialized political generation algorithm. For sports questions, the generation unit applies a specialized sports generation algorithm. For entertainment questions, the generation unit applies a specialized entertainment generation algorithm. This allows for the provision of more appropriate answers by applying a generation algorithm appropriate to the question category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question category data into the generation AI, and the generation AI applies an appropriate generation algorithm based on that data.

[0079] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. The generation unit estimates the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. If the user is in a hurry, the generation unit generates a short, to-the-point response. If the user is relaxed, the generation unit generates a longer response that includes detailed explanations. If the user is excited, the generation unit generates a response with visually stimulating effects. This improves the user experience by providing response lengths that match the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 is performed using generative AI. For example, the generation unit inputs user emotion data into the generative AI, and the generative AI adjusts the length of the response based on that data.

[0080] The generation unit can determine the priority of answers based on when the questions were submitted when generating responses. The generation unit evaluates the submission timing of questions, for example, by considering factors such as the submission date and urgency. The generation unit prioritizes generating answers for the most recent questions. The generation unit postpones generating answers for older questions. The generation unit adjusts the priority of answers according to when the questions were submitted. This allows for a quick response by setting a priority for answers based on when the questions were submitted. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question submission timing data into the generation AI, and the generation AI determines the priority of answers based on that data.

[0081] The generation unit can adjust the order of answers based on the relevance of the questions when generating responses. The generation unit determines the relevance of questions by evaluating factors such as similarity of content and user interest. If the relevance of a question is high, the generation unit prioritizes generating the answer. If the relevance of a question is low, the generation unit postpones generating the answer. The generation unit adjusts the order of answers according to the relevance of the questions. This allows the system to provide the user with the most relevant information by setting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit inputs question relevance data into the generation AI, and the generation AI adjusts the order of answers based on that data.

[0082] The service provider can estimate the user's emotions and adjust the display method of the response based on the estimated emotions. The service provider estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. If the user is nervous, the service provider provides a simple and highly visible display method. If the user is relaxed, the service provider provides a display method that includes detailed information. If the user is in a hurry, the service provider provides a display method that gets straight to the point. This improves the user experience by providing a display method that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. For example, the service provider inputs the user's emotion data into the generative AI, and the generative AI adjusts the display method based on that data.

[0083] The service provider can select the optimal display method by referring to the user's past operation history when providing responses. The service provider can store data such as click history and browsing history in a database and analyze it using machine learning. The service provider prioritizes providing display methods that the user has preferred to use in the past. The service provider proposes the optimal display method based on the user's past operation history. The service provider analyzes the user's past operation history and provides a highly visible display method. By providing a display method based on past operation history, the service provider can provide the user with the most relevant information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past operation history data into AI, and the AI ​​can select the optimal display method based on that data.

[0084] The service provider can customize the displayed content based on the user's current areas of interest when providing responses. For example, the service provider can identify the user's areas of interest from survey results and past behavioral data. The service provider can prioritize displaying relevant responses based on the news categories the user has recently been interested in. If the user has shown interest in a particular topic, the service provider can customize and display responses related to that topic. The service provider can analyze the user's past search history and customize the displayed content based on their areas of interest. This improves the user experience by providing content based on the user's areas of interest. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's areas of interest data into AI, and the AI ​​can customize the displayed content based on that data.

[0085] The service provider can estimate the user's emotions and adjust the display order of responses based on the estimated emotions. The service provider estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. If the user is nervous, the service provider displays important information first. If the user is relaxed, the service provider displays detailed information later. If the user is in a hurry, the service provider displays concise information first. This improves the user experience by providing a display order that corresponds to the user's emotions. 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. For example, the service provider inputs user emotion data into a generative AI, and the generative AI adjusts the display order based on that data.

[0086] The information provider can select the optimal display method based on the user's device information when providing responses. For example, the information provider can acquire device information such as device type, OS, and browser. If the user is using a smartphone, the information provider can provide a display method that matches the screen size. If the user is using a tablet, the information provider can provide a display method optimized for a large screen. If the user is using a smartwatch, the information provider can provide a concise and highly visible display method. In this way, by providing a display method based on device information, the information provider can provide the most suitable information for the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input device information into the AI, and the AI ​​can select the optimal display method based on that data.

[0087] The service provider can analyze the user's social media activity and customize the displayed content when providing responses. The service provider analyzes the user's social media activity, for example, by analyzing the content of posts or the number of followers. The service provider prioritizes displaying responses related to topics that the user frequently mentions on social media. The service provider displays relevant responses based on the news sources that the user follows on social media. The service provider analyzes the user's social media activity history and displays responses related to topics of high interest. This improves the user experience by providing content based on social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider inputs the user's social media activity data into AI, and the AI ​​customizes the displayed content based on that data.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The news Q&A system may further include a reliability evaluation unit that evaluates the reliability of answers to user questions. The reliability evaluation unit evaluates the reliability of answers based on, for example, the reliability score of the news source, the number of times the article has been cited, and the reliability of the author. The reliability evaluation unit assigns a high rating to highly reliable answers, providing users with reliable information. The reliability evaluation unit displays a warning for low-reliability answers, alerting the user. This allows users to verify the reliability of the information provided and use the system with confidence. Some or all of the above processing in the reliability evaluation unit may be performed using AI or not. For example, the reliability evaluation unit inputs reliability data of news sources into an AI, and the AI ​​evaluates reliability based on that data.

[0090] The news Q&A system may further include a diversity evaluation unit that assesses the diversity of answers to user questions. The diversity evaluation unit evaluates answers that include information from different news sources, different perspectives, and different opinions, for example. The diversity evaluation unit assigns high ratings to highly diverse answers, providing users with multifaceted information. The diversity evaluation unit displays supplementary information to answers with low diversity, providing users with additional perspectives. This allows users to confirm the diversity of the information provided and understand the news from a broader perspective. Some or all of the processing described above in the diversity evaluation unit may be performed using AI or not. For example, the diversity evaluation unit inputs diversity data of news sources into an AI, and the AI ​​evaluates the diversity based on that data.

[0091] The news Q&A system may further include an emotional impact evaluation unit that evaluates the emotional impact of the answers to the user's questions. The emotional impact evaluation unit, for example, evaluates the emotional impact that the answers have on the user. The emotional impact evaluation unit gives a high rating to answers that have a positive impact, providing the user with a sense of security and satisfaction. The emotional impact evaluation unit displays a warning for answers that have a negative impact, drawing the user's attention. This allows the user to check the emotional impact of the information provided and use it with confidence. Some or all of the above processing in the emotional impact evaluation unit may be performed using AI or not. For example, the emotional impact evaluation unit inputs emotional data of the answers into the AI, and the AI ​​evaluates the emotional impact based on that data.

[0092] The news Q&A system may further include a relevance evaluation unit that evaluates the relevance of answers to user questions. The relevance evaluation unit, for example, evaluates the degree of agreement between the user's question and the answer. The relevance evaluation unit assigns a high rating to highly relevant answers and provides the user with appropriate information. The relevance evaluation unit displays supplementary information to less relevant answers and provides the user with additional information. This allows the user to confirm the relevance of the information provided and obtain more appropriate information. Some or all of the above processing in the relevance evaluation unit may be performed using AI or not. For example, the relevance evaluation unit inputs question-and-answer relevance data into the AI, and the AI ​​evaluates the relevance based on that data.

[0093] The news Q&A system may further include a visual evaluation unit that assesses the visual appeal of the answers to user questions. The visual evaluation unit evaluates, for example, the layout, color scheme, and presence or absence of images in the answers. The visual evaluation unit assigns high ratings to visually appealing answers, providing users with a sense of visual satisfaction. The visual evaluation unit displays improvement suggestions for answers that are not visually appealing, providing users with a better visual experience. This allows users to confirm the visual appeal of the information provided and obtain more satisfying information. Some or all of the above processing in the visual evaluation unit may be performed using AI or not. For example, the visual evaluation unit inputs the visual data of the answer into the AI, and the AI ​​evaluates the visual appeal based on that data.

[0094] The news Q&A system may further include an emotional tone adjustment unit that adjusts the emotional tone of the answers to user questions. The emotional tone adjustment unit adjusts, for example, the style and expression of the answers. When the user is relaxed, the emotional tone adjustment unit provides answers in a calm and polite tone. When the user is in a hurry, the emotional tone adjustment unit provides answers in a concise and to-the-point tone. When the user is excited, the emotional tone adjustment unit provides answers in a tone with visually stimulating effects. This improves the user experience by providing answers in a tone that matches the user's emotions. Some or all of the above processing in the emotional tone adjustment unit may be performed using AI or not. For example, the emotional tone adjustment unit inputs the user's emotional data into the AI, and the AI ​​adjusts the emotional tone based on that data.

[0095] The news Q&A system may further include a reliability evaluation unit that evaluates the reliability of answers to user questions. The reliability evaluation unit evaluates the reliability of answers based on, for example, the reliability score of the news source, the number of times the article has been cited, and the reliability of the author. The reliability evaluation unit assigns a high rating to highly reliable answers, providing users with reliable information. The reliability evaluation unit displays a warning for low-reliability answers, alerting the user. This allows users to verify the reliability of the information provided and use the system with confidence. Some or all of the above processing in the reliability evaluation unit may be performed using AI or not. For example, the reliability evaluation unit inputs reliability data of news sources into an AI, and the AI ​​evaluates reliability based on that data.

[0096] The news Q&A system may further include a diversity evaluation unit that assesses the diversity of answers to user questions. The diversity evaluation unit evaluates answers that include information from different news sources, different perspectives, and different opinions, for example. The diversity evaluation unit assigns high ratings to highly diverse answers, providing users with multifaceted information. The diversity evaluation unit displays supplementary information to answers with low diversity, providing users with additional perspectives. This allows users to confirm the diversity of the information provided and understand the news from a broader perspective. Some or all of the processing described above in the diversity evaluation unit may be performed using AI or not. For example, the diversity evaluation unit inputs diversity data of news sources into an AI, and the AI ​​evaluates the diversity based on that data.

[0097] The news Q&A system may further include an emotional impact evaluation unit that evaluates the emotional impact of the answers to the user's questions. The emotional impact evaluation unit, for example, evaluates the emotional impact that the answers have on the user. The emotional impact evaluation unit gives a high rating to answers that have a positive impact, providing the user with a sense of security and satisfaction. The emotional impact evaluation unit displays a warning for answers that have a negative impact, drawing the user's attention. This allows the user to check the emotional impact of the information provided and use it with confidence. Some or all of the above processing in the emotional impact evaluation unit may be performed using AI or not. For example, the emotional impact evaluation unit inputs emotional data of the answers into the AI, and the AI ​​evaluates the emotional impact based on that data.

[0098] The news Q&A system may further include a relevance evaluation unit that evaluates the relevance of answers to user questions. The relevance evaluation unit, for example, evaluates the degree of agreement between the user's question and the answer. The relevance evaluation unit assigns a high rating to highly relevant answers and provides the user with appropriate information. The relevance evaluation unit displays supplementary information to less relevant answers and provides the user with additional information. This allows the user to confirm the relevance of the information provided and obtain more appropriate information. Some or all of the above processing in the relevance evaluation unit may be performed using AI or not. For example, the relevance evaluation unit inputs question-and-answer relevance data into the AI, and the AI ​​evaluates the relevance based on that data.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk receives questions from users. The reception desk can accept questions in text format, audio format, or on specific topics. Step 2: The generation unit generates answers based on the questions received by the reception unit. The generation unit uses generation AI to learn from news data and generates answers based on user questions. The generation unit generates answers using data such as news articles, news feeds, and specific news sources. Step 3: The provider unit provides the user with the answer generated by the generator unit. The provider unit can attach a link to the source of the generated answer. The provider unit can also provide a UI using characters.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives questions from the user in text or voice format. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates answers based on the user's questions using a generation AI that has learned from news data. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the generated answers to the user and can attach links to the source. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception 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 reception unit is implemented by the microphone 238 of the smart glasses 214 and receives questions from the user in voice format. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates answers based on the user's questions using a generation AI that has learned from news data. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated answers to the user and can attach links to the source. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives questions from the user in voice format. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates answers based on the user's questions using a generation AI that has learned from news data. The provision unit is implemented, for example, by the display 343 of the headset terminal 314 and provides the generated answers to the user and can attach links to the source. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the reception unit, generation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives questions from the user in voice format. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates answers based on the user's questions using a generation AI that has learned from news data. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated answers to the user and can attach links to the source. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A reception desk that handles questions from users, A generation unit that generates an answer based on a question received by the reception unit, The system includes a providing unit that provides the answer generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned reception unit is Accepts additional information from the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Learn from news data and generate answers based on user questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Attach a link to the source to the generated answer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides a UI using characters. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is We generate answers using data from news-related articles, comments, and public opinion. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past question history and select the most suitable method for receiving questions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a question is submitted, the content of the question is filtered based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and accepts relevant questions. 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 adjusts the way responses are expressed 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 answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating answers, different generation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. 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 responses 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 a response, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing responses, the displayed content is customized based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the display order of responses based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing responses, the system selects the optimal display method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When users provide responses, the system analyzes their social media activity to customize the displayed content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that handles questions from users, A generation unit that generates answers based on questions received by the reception unit, The system includes a providing unit that provides the answer generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned reception unit is Accepts additional information from the user. The system according to feature 1.

3. The generating unit is Learn from news data and generate answers based on user questions. The system according to feature 1.

4. The aforementioned supply unit is, Attach a link to the source to the generated answer. The system according to feature 1.

5. The aforementioned supply unit is, Provides a UI using characters. The system according to feature 1.

6. The generating unit is We generate answers using data from news-related articles, comments, and public opinion. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past question history and select the most suitable method for receiving questions. The system according to feature 1.

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