system

Abstract

The system according to this embodiment aims to provide quick and accurate answers based on various information sources. [Solution] The system according to the embodiment comprises a registration unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers information sources. The analysis unit analyzes questions based on the information sources registered by the registration unit. The generation unit generates answers based on the questions analyzed by the analysis unit. The provision unit provides the answers generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to quickly find the necessary information, and there is room for improvement in the efficiency of information retrieval.

[0005] The system according to the embodiment aims to provide an answer quickly and accurately based on various information sources.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers information sources. The analysis unit analyzes a question based on the information sources registered by the registration unit. The generation unit generates an answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can provide quick and accurate answers based on various information sources. [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 information provision system according to an embodiment of the present invention is a system that utilizes generative AI technology to solve problems that rely on many manuals and guidelines in daily life and business. This information provision system provides a mechanism in which users register various information sources as RAGs (Retrieval-Augmented Generations), and based on that, it provides answers like an expert. This system can also be incorporated as a function of a messaging application. First, the user registers the necessary information sources as RAGs. For example, this could include company work rules and customer service manuals, product-specific errors, sports manuals, local government rules, and laws. This allows the user to customize the information to their needs and update it at any time. Next, the user inputs a specific question using text, voice, photos, or videos. For example, they might input a question such as, "How do I deal with error code 123 for this product?" This information is input into the generative AI. The generative AI analyzes the input question and generates the optimal answer by referring to the registered information sources. For example, it provides detailed instructions on how to deal with error code 123. This allows the user to quickly and accurately obtain the necessary information. Furthermore, this system can maintain consistency and accuracy. Because responses are generated based on registered information, consistency and accuracy of the answers are maintained. Furthermore, the information can be customized to user needs and updated at any time, giving it high adaptability. This system solves the problem of relying on numerous manuals and guidelines in daily life and business, allowing users to quickly and accurately obtain the information they need. It is also expected to be used by even more users by being integrated as a function of messaging applications. As a result, the information provision system enables users to quickly and accurately obtain the information they need.

[0029] The information provision system according to the embodiment comprises a registration unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers information sources. Information sources include, but are not limited to, databases, websites, and literature. The registration unit can register, for example, company work rules and customer service manuals, product errors, sports manuals, local government rules, and laws. The analysis unit analyzes questions based on the information sources registered by the registration unit. Questions include, but are not limited to, text format, audio format, and image format. The analysis unit analyzes questions using, for example, natural language processing technology. The analysis unit can also analyze questions using machine learning algorithms. The generation unit generates answers based on the questions analyzed by the analysis unit. Answers include, for example, text format, audio format, and image format. The generation unit generates answers using, for example, a generation AI. The generation AI generates answers using a text generation AI (e.g., LLM). The generation unit can also generate answers using a multimodal generation AI. The providing unit provides the answers generated by the generating unit. The providing unit provides the answers, for example, through a web application or a mobile application. The providing unit can also send answers by email. Furthermore, the providing unit can print the answers using a printer. This allows the information provision system to consistently perform everything from registering information sources to analyzing questions, generating answers, and providing them. Some or all of the above processes in the registration unit, analysis unit, generation unit, and providing unit may be performed using AI, for example, or not using AI. For example, the registration unit registers information sources in a database, the analysis unit retrieves information from the database and analyzes questions, the generation unit generates answers based on the analysis results, and the providing unit provides the generated answers to the user.

[0030] The registration unit registers information sources. These sources include, but are not limited to, databases, websites, and literature. Specifically, the registration unit can register things like company work rules and customer service manuals, product errors, sports manuals, local government rules, and laws. These information sources are centrally managed in the system's database and made quickly accessible as needed. The registration unit has the functionality to automatically extract, classify, and tag metadata from information sources. For example, when a document regarding company work rules is registered, the document's title, creation date, and related keywords are automatically extracted and stored in the database. This allows subsequent analysis and generation units to efficiently search and utilize the information. The registration unit also manages the updating and deletion of information sources. For example, when a new law comes into effect, the information on the old law is deleted and the new information is registered, ensuring that the system always provides the latest information. Furthermore, the registration unit has the functionality to evaluate the reliability of information sources. For example, it verifies whether the information source is provided by a reliable institution and avoids registering unreliable sources. This improves the overall reliability and accuracy of the system.

[0031] The analysis unit analyzes questions based on information sources registered by the registration unit. Questions may include, but are not limited to, text, audio, and image formats. The analysis unit uses natural language processing techniques to analyze questions. Specifically, it performs morphological and contextual analysis to understand the intent of the question and identify relevant information sources. For example, for text-based questions, it uses morphological analysis to segment words and contextual analysis to understand the intent of the question. For audio-based questions, it uses speech recognition technology to convert them to text, and then applies natural language processing. For image-based questions, it uses image recognition technology to extract text and objects from the image and analyzes the question based on that. The analysis unit can also analyze questions using machine learning algorithms. For example, it learns from past question and answer data and selects the optimal analysis method for new questions. This enables the analysis unit to perform highly accurate analysis for various question formats. Furthermore, the analysis unit has the ability to evaluate the priority of questions and respond quickly to urgent questions. For example, it prioritizes analysis of urgent inquiries and important legal questions, and generates answers quickly. This allows the analysis unit to respond to the diverse needs of users and provide information quickly and accurately.

[0032] The generation unit generates answers based on questions analyzed by the analysis unit. These answers may include, but are not limited to, text, audio, and image formats. The generation unit uses a generation AI to generate answers. Specifically, it uses a text generation AI (e.g., LLM) to generate appropriate answers to questions. For example, for legal questions, it generates detailed answers based on relevant laws and precedents. The generation unit can also generate answers using a multimodal generation AI. For example, for questions about product errors, it generates images or videos showing the error correction procedure in addition to a text-based answer. The generation unit also has the ability to evaluate the quality of the generated answers and make corrections as needed. For example, if a generated answer is incomplete or potentially misleading, it provides additional information or modifies the wording of the answer. Furthermore, the generation unit continuously improves the accuracy and quality of answers based on user feedback. For example, it collects user ratings and comments and updates the generation AI's training data based on them. This allows the generation unit to consistently provide high-quality answers and improve user satisfaction. Furthermore, the generation unit also has the function of generating multiple answer candidates and selecting the optimal answer. For example, it generates multiple answer candidates, evaluates the reliability and relevance of each answer, and selects the most appropriate answer. In this way, the generation unit can provide the user with the most suitable answer and improve the reliability and accuracy of the information provision system.

[0033] The service provider provides answers generated by the generator. The service provider delivers answers through web and mobile applications. Specifically, users input questions via a web browser or smartphone app, and the generated answers are displayed in real time. The service provider can also send answers via email. For example, after a user submits a question, the generated answer is automatically sent via email. Furthermore, the service provider can print answers using a printer. For example, answers to important documents or manuals can be printed and provided physically. The service provider prioritizes user-friendliness, employing an intuitive and easy-to-understand design. For example, it provides a function to customize the display format of answers, allowing users to choose their preferred display method. The service provider also collects user feedback to improve its delivery methods. For example, based on user ratings and comments, it reviews the display format and delivery methods of answers to build a more user-friendly system. This allows the service provider to provide users with quick and appropriate information, improving the convenience and satisfaction of the information delivery system. Furthermore, the service provider has enhanced security measures and features to protect user privacy. For example, it encrypts data when providing answers to prevent unauthorized access and information leaks. This enables the service provider to deliver safe and reliable information to users.

[0034] The customization section allows users to customize information sources. The customization section provides, for example, a filtering function, allowing users to select information sources based on specific criteria. It also provides a prioritization function, allowing users to set priorities based on the importance of information sources. For example, the customization section allows users to filter information sources based on specific keywords. It also allows users to filter information sources based on specific categories. Furthermore, the customization section allows users to set priorities based on the importance of information sources. This allows users to customize information sources to suit their needs. Some or all of the above processing in the customization section may be performed using, for example, AI, or not. For example, the customization section can suggest the optimal customization method based on the user's past customization history.

[0035] The update unit updates information sources. The update unit can update information sources. For example, the update unit can provide a periodic update function and update information sources regularly. The update unit can also update information sources based on user requests. For example, the update unit can update information sources on a regular schedule such as daily, weekly, or monthly. The update unit can also instantly update information sources when a user requests that a specific information source be updated. This allows users to update information sources as needed. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can suggest the optimal update method based on the user's past update history.

[0036] The input method unit diversifies the methods for inputting questions. The input method unit can diversify the methods for inputting questions. For example, the input method unit provides a text input function, allowing users to input questions in text format. It also provides a voice input function, allowing users to input questions in voice format. Furthermore, the input method unit provides a photo / video recording function, allowing users to input questions using photos or videos. For example, the input method unit provides an interface for users to input questions in text format. It also provides a microphone input function for users to input questions in voice format. Furthermore, it provides a camera input function for users to take photos or videos and input questions. This allows users to input questions in a variety of ways. Some or all of the above-described processes in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can suggest the optimal input method based on the user's past input history.

[0037] The customization unit can customize information sources to meet user needs. For example, the customization unit can customize information sources based on the user's business needs. It can also customize information sources based on the user's personal interests. For example, the customization unit can filter information sources based on the user's specific business needs. It can also filter information sources based on the user's specific interests. This makes it possible to customize information sources to meet user needs. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can suggest the optimal customization method based on the user's past customization history.

[0038] The update unit can allow users to update information sources at any time. For example, the update unit can provide a 24-hour update function, allowing users to update information sources at any time. Alternatively, the update unit can provide an update function limited to specific time periods. For example, the update unit can allow users to update information sources 24 hours a day. Alternatively, the update unit can limit the updating of information sources to specific time periods. This allows users to update information sources as needed. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can suggest the optimal update method based on the user's past update history.

[0039] The input method unit can enable users to input questions using text, voice, photos, or videos. For example, the input method unit can provide a text input function, allowing users to input questions in text format. It can also provide a voice input function, enabling users to input questions in voice format. Furthermore, it can provide a photo / video capture function, allowing users to input questions using photos or videos. For example, the input method unit can provide an interface for users to input questions in text format. It can also provide a microphone input function for users to input questions in voice format. Furthermore, it can provide a camera input function for users to take photos or videos to input questions. This allows users to input questions in a variety of ways. Some or all of the above-described processes in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can suggest the optimal input method based on the user's past input history.

[0040] The registration unit can analyze a user's past registration history and select the optimal registration method. For example, it can automatically display information sources that the user has frequently registered with in the past as candidates. It can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information sources that the user will use at specific times based on their past registration history. This allows the system to provide the optimal registration method based on the user's past registration history. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past registration history data into a generating AI and have the generating AI select the optimal registration method.

[0041] The registration unit can filter information sources based on the user's current projects and areas of interest when registering them. For example, it can prioritize displaying information sources related to the user's current projects. It can also suggest highly relevant information sources based on the user's areas of interest. Furthermore, it can analyze the user's past project history and suggest the most suitable information sources. This allows for filtering of information sources based on the user's current projects and areas of interest. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's project history data into a generating AI and have the generating AI perform the filtering.

[0042] The registration unit can prioritize registering information sources that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize registering information sources related to that region. It can also suggest region-specific information sources based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize registering information sources related to their travel destination. This allows for the priority registration of highly relevant information sources based on the user's geographical location. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information sources.

[0043] The registration unit can analyze a user's social media activity and register relevant information sources when registering information sources. For example, it can prioritize registering information sources related to topics that the user frequently mentions on social media. It can also suggest information sources shared by the user's social media followers. Furthermore, it can analyze the user's social media activity history and suggest the most suitable information sources. This allows for the registration of relevant information sources based on the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information sources.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during question analysis. For example, it can perform a detailed analysis for high-importance questions and a concise analysis for low-importance questions. Furthermore, it can determine the priority of the analysis according to the importance of the question. This allows the level of detail of the analysis to be adjusted according to the importance of the question. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the question during question analysis. For example, a specialized analysis algorithm can be applied to technical questions. A simpler analysis algorithm can be applied to general questions. Furthermore, the optimal analysis algorithm can be selected depending on the category of the question. This ensures that the most suitable analysis algorithm is applied according to the category of the question. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question category data into a generating AI and have the generating AI select an analysis algorithm based on the category.

[0046] The analysis unit can determine the priority of analysis based on when the questions were submitted during question analysis. For example, it can determine the priority of analysis based on the time period in which the questions were submitted. It can also adjust the level of detail of the analysis according to when the questions were submitted. Furthermore, it can select the optimal analysis algorithm based on when the questions were submitted. This allows the analysis priority to be determined based on when the questions were submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question submission time data into a generating AI and have the generating AI perform the priority determination based on the submission time.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the questions during question analysis. For example, it can determine the priority of analysis based on the relevance of the questions. It can also adjust the level of detail of the analysis according to the relevance of the questions. Furthermore, it can select the optimal analysis algorithm based on the relevance of the questions. This allows the order of analysis to be adjusted based on the relevance of the questions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0048] The generation unit can adjust the level of detail in the answers based on the importance of the questions when generating responses. For example, it can generate detailed answers for high-importance questions and concise answers for low-importance questions. Furthermore, it can also determine the priority of the answers according to the importance of the questions. This allows for adjusting the level of detail in the answers according to the importance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the answers based on importance.

[0049] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, a specialized generation algorithm can be applied to technical questions, while a simpler generation algorithm can be applied to general questions. Furthermore, the optimal generation algorithm can be selected depending on the question category. This ensures that the most suitable generation algorithm is applied according to the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select a generation algorithm based on the category.

[0050] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, it can determine the priority of answers based on the time period in which the questions were submitted. It can also adjust the level of detail in the answers according to when the questions were submitted. Furthermore, it can select the optimal generation algorithm based on when the questions were submitted. This allows the priority of answers to be determined based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the question submission time data into a generation AI and have the generation AI perform the priority determination based on the submission time.

[0051] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, it can determine the priority of answers based on the relevance of the questions. It can also adjust the level of detail of the answers according to the relevance of the questions. Furthermore, it can select the optimal generation algorithm based on the relevance of the questions. This allows the order of answers to be adjusted based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into a generation AI and have the generation AI perform the adjustment of the answer order based on relevance.

[0052] The service provider can select the optimal service method by referring to the user's past usage history when providing responses. For example, it can prioritize suggesting service methods the user has used in the past. It can also select the optimal service method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and suggest the most efficient service method. This allows the service provider to offer the optimal service method based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history data into a generating AI and have the generating AI select the optimal service method.

[0053] The delivery unit can customize the delivery method based on the user's current situation when providing answers. For example, if the user is on the move, voice delivery may be prioritized. If the user is working at a desk, text delivery may be prioritized. Furthermore, if the user is in a meeting, a concise delivery method summarizing the key points may be selected. This allows the delivery method to be customized according to the user's current situation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user situation data into a generating AI and have the generating AI perform customization of the delivery method based on the situation.

[0054] The information delivery unit can select the optimal delivery method when providing responses, taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize providing information related to that region. It can also suggest region-specific delivery methods based on the user's geographical location information. Furthermore, if the user is traveling, it can prioritize providing information related to their travel destination. This allows for the provision of the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location data into a generating AI and have the generating AI select a delivery method based on geographical location.

[0055] The information delivery unit can analyze the user's social media activity and propose delivery methods when providing responses. For example, it can prioritize providing information related to topics that the user frequently mentions on social media. It can also propose information shared by the user's social media followers. Furthermore, it can analyze the user's social media activity history and propose the most suitable delivery method. This allows the system to propose the most suitable delivery method based on the user's social media activity. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's social media activity data into a generating AI and have the generating AI propose delivery methods based on social media activity.

[0056] The customization unit can select the optimal customization method when customizing information sources by referring to the user's past customization history. For example, it can propose the optimal customization method based on the user's past customizations. It can also select the most efficient customization method from the user's past customization history. Furthermore, it can analyze the user's past customization history and propose the optimal customization method. This allows the unit to provide the optimal customization method based on the user's past customization history. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's customization history data into a generating AI and have the generating AI select the optimal customization method.

[0057] The customization unit can select the optimal customization method when customizing information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize customizing information sources related to that region. It can also suggest region-specific customization methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize customizing information sources related to their travel destination. This allows the system to provide the optimal customization method based on the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select a customization method based on geographical location.

[0058] The update unit can select the optimal update method by referring to the user's past update history when updating information sources. For example, it can suggest the optimal update method based on the user's past updates. It can also select the most efficient update method from the user's past update history. Furthermore, it can analyze the user's past update history and suggest the optimal update method. This allows the update unit to provide the optimal update method based on the user's past update history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's update history data into a generating AI and have the generating AI select the optimal update method.

[0059] The update unit can select the optimal update method when updating information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize updating information sources related to that region. It can also suggest region-specific update methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize updating information sources related to their travel destination. This allows the update unit to provide the optimal update method based on the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location data into a generating AI and have the generating AI select an update method based on geographical location.

[0060] The input method unit can select the optimal input method when a question is entered by referring to the user's past input history. For example, it can automatically display questions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions that the user will use at a specific time of day based on the user's past input history. This allows the system to provide the optimal input method based on the user's past input history. Some or all of the above processing in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0061] The input method unit can select the optimal input method when a question is entered, taking into account the user's device information. For example, if the user is using a smartphone, it can provide an input method that matches the screen size. If the user is using a tablet, it can also provide an input method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible input method. This allows the system to provide the optimal input method based on the user's device information. Some or all of the above-described processing in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can input the user's device information into a generating AI and have the generating AI select an input method based on the device information.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during question analysis. For example, it can perform a detailed analysis for high-importance questions and a concise analysis for low-importance questions. Furthermore, it can determine the priority of the analysis according to the importance of the question. This allows the level of detail of the analysis to be adjusted according to the importance of the question. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0064] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, a specialized generation algorithm can be applied to technical questions, while a simpler generation algorithm can be applied to general questions. Furthermore, the optimal generation algorithm can be selected depending on the question category. This ensures that the most suitable generation algorithm is applied according to the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select a generation algorithm based on the category.

[0065] The service provider can select the optimal service method by referring to the user's past usage history when providing responses. For example, it can prioritize suggesting service methods the user has used in the past. It can also select the optimal service method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and suggest the most efficient service method. This allows the service provider to offer the optimal service method based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history data into a generating AI and have the generating AI select the optimal service method.

[0066] The customization unit can select the optimal customization method when customizing information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize customizing information sources related to that region. It can also suggest region-specific customization methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize customizing information sources related to their travel destination. This allows the system to provide the optimal customization method based on the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select a customization method based on geographical location.

[0067] The update unit can select the optimal update method when updating information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize updating information sources related to that region. It can also suggest region-specific update methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize updating information sources related to their travel destination. This allows the update unit to provide the optimal update method based on the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location data into a generating AI and have the generating AI select an update method based on geographical location.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The registration department registers information sources. Information sources include databases, websites, literature, company work rules, customer service manuals, product-specific errors, sports manuals, local government rules, laws, etc. Step 2: The analysis unit analyzes the questions based on the information sources registered by the registration unit. The questions include text, audio, and image formats, and natural language processing techniques and machine learning algorithms are used for the analysis. Step 3: The generation unit generates answers based on the questions analyzed by the analysis unit. The answers may be in text format, audio format, image format, etc., and generation AI or multimodal generation AI is used for generation. Step 4: The delivery unit provides the response generated by the generation unit. Delivery methods include web applications, mobile applications, email, and printers.

[0070] (Example of form 2) The information provision system according to an embodiment of the present invention is a system that utilizes generative AI technology to solve problems that rely on many manuals and guidelines in daily life and business. This information provision system provides a mechanism in which users register various information sources as RAGs (Retrieval-Augmented Generations), and based on that, it provides answers like an expert. This system can also be incorporated as a function of a messaging application. First, the user registers the necessary information sources as RAGs. For example, this could include company work rules and customer service manuals, product-specific errors, sports manuals, local government rules, and laws. This allows the user to customize the information to their needs and update it at any time. Next, the user inputs a specific question using text, voice, photos, or videos. For example, they might input a question such as, "How do I deal with error code 123 for this product?" This information is input into the generative AI. The generative AI analyzes the input question and generates the optimal answer by referring to the registered information sources. For example, it provides detailed instructions on how to deal with error code 123. This allows the user to quickly and accurately obtain the necessary information. Furthermore, this system can maintain consistency and accuracy. Because responses are generated based on registered information, consistency and accuracy of the answers are maintained. Furthermore, the information can be customized to user needs and updated at any time, giving it high adaptability. This system solves the problem of relying on numerous manuals and guidelines in daily life and business, allowing users to quickly and accurately obtain the information they need. It is also expected to be used by even more users by being integrated as a function of messaging applications. As a result, the information provision system enables users to quickly and accurately obtain the information they need.

[0071] The information provision system according to the embodiment comprises a registration unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers information sources. Information sources include, but are not limited to, databases, websites, and literature. The registration unit can register, for example, company work rules and customer service manuals, product errors, sports manuals, local government rules, and laws. The analysis unit analyzes questions based on the information sources registered by the registration unit. Questions include, but are not limited to, text format, audio format, and image format. The analysis unit analyzes questions using, for example, natural language processing technology. The analysis unit can also analyze questions using machine learning algorithms. The generation unit generates answers based on the questions analyzed by the analysis unit. Answers include, for example, text format, audio format, and image format. The generation unit generates answers using, for example, a generation AI. The generation AI generates answers using a text generation AI (e.g., LLM). The generation unit can also generate answers using a multimodal generation AI. The providing unit provides the answers generated by the generating unit. The providing unit provides the answers, for example, through a web application or a mobile application. The providing unit can also send answers by email. Furthermore, the providing unit can print the answers using a printer. This allows the information provision system to consistently perform everything from registering information sources to analyzing questions, generating answers, and providing them. Some or all of the above processes in the registration unit, analysis unit, generation unit, and providing unit may be performed using AI, for example, or not using AI. For example, the registration unit registers information sources in a database, the analysis unit retrieves information from the database and analyzes questions, the generation unit generates answers based on the analysis results, and the providing unit provides the generated answers to the user.

[0072] The registration unit registers information sources. These sources include, but are not limited to, databases, websites, and literature. Specifically, the registration unit can register things like company work rules and customer service manuals, product errors, sports manuals, local government rules, and laws. These information sources are centrally managed in the system's database and made quickly accessible as needed. The registration unit has the functionality to automatically extract, classify, and tag metadata from information sources. For example, when a document regarding company work rules is registered, the document's title, creation date, and related keywords are automatically extracted and stored in the database. This allows subsequent analysis and generation units to efficiently search and utilize the information. The registration unit also manages the updating and deletion of information sources. For example, when a new law comes into effect, the information on the old law is deleted and the new information is registered, ensuring that the system always provides the latest information. Furthermore, the registration unit has the functionality to evaluate the reliability of information sources. For example, it verifies whether the information source is provided by a reliable institution and avoids registering unreliable sources. This improves the overall reliability and accuracy of the system.

[0073] The analysis unit analyzes questions based on information sources registered by the registration unit. Questions may include, but are not limited to, text, audio, and image formats. The analysis unit uses natural language processing techniques to analyze questions. Specifically, it performs morphological and contextual analysis to understand the intent of the question and identify relevant information sources. For example, for text-based questions, it uses morphological analysis to segment words and contextual analysis to understand the intent of the question. For audio-based questions, it uses speech recognition technology to convert them to text, and then applies natural language processing. For image-based questions, it uses image recognition technology to extract text and objects from the image and analyzes the question based on that. The analysis unit can also analyze questions using machine learning algorithms. For example, it learns from past question and answer data and selects the optimal analysis method for new questions. This enables the analysis unit to perform highly accurate analysis for various question formats. Furthermore, the analysis unit has the ability to evaluate the priority of questions and respond quickly to urgent questions. For example, it prioritizes analysis of urgent inquiries and important legal questions, and generates answers quickly. This allows the analysis unit to respond to the diverse needs of users and provide information quickly and accurately.

[0074] The generation unit generates answers based on questions analyzed by the analysis unit. These answers may include, but are not limited to, text, audio, and image formats. The generation unit uses a generation AI to generate answers. Specifically, it uses a text generation AI (e.g., LLM) to generate appropriate answers to questions. For example, for legal questions, it generates detailed answers based on relevant laws and precedents. The generation unit can also generate answers using a multimodal generation AI. For example, for questions about product errors, it generates images or videos showing the error correction procedure in addition to a text-based answer. The generation unit also has the ability to evaluate the quality of the generated answers and make corrections as needed. For example, if a generated answer is incomplete or potentially misleading, it provides additional information or modifies the wording of the answer. Furthermore, the generation unit continuously improves the accuracy and quality of answers based on user feedback. For example, it collects user ratings and comments and updates the generation AI's training data based on them. This allows the generation unit to consistently provide high-quality answers and improve user satisfaction. Furthermore, the generation unit also has the function of generating multiple answer candidates and selecting the optimal answer. For example, it generates multiple answer candidates, evaluates the reliability and relevance of each answer, and selects the most appropriate answer. In this way, the generation unit can provide the user with the most suitable answer and improve the reliability and accuracy of the information provision system.

[0075] The service provider provides answers generated by the generator. The service provider delivers answers through web and mobile applications. Specifically, users input questions via a web browser or smartphone app, and the generated answers are displayed in real time. The service provider can also send answers via email. For example, after a user submits a question, the generated answer is automatically sent via email. Furthermore, the service provider can print answers using a printer. For example, answers to important documents or manuals can be printed and provided physically. The service provider prioritizes user-friendliness, employing an intuitive and easy-to-understand design. For example, it provides a function to customize the display format of answers, allowing users to choose their preferred display method. The service provider also collects user feedback to improve its delivery methods. For example, based on user ratings and comments, it reviews the display format and delivery methods of answers to build a more user-friendly system. This allows the service provider to provide users with quick and appropriate information, improving the convenience and satisfaction of the information delivery system. Furthermore, the service provider has enhanced security measures and features to protect user privacy. For example, it encrypts data when providing answers to prevent unauthorized access and information leaks. This enables the service provider to deliver safe and reliable information to users.

[0076] The customization section allows users to customize information sources. The customization section provides, for example, a filtering function, allowing users to select information sources based on specific criteria. It also provides a prioritization function, allowing users to set priorities based on the importance of information sources. For example, the customization section allows users to filter information sources based on specific keywords. It also allows users to filter information sources based on specific categories. Furthermore, the customization section allows users to set priorities based on the importance of information sources. This allows users to customize information sources to suit their needs. Some or all of the above processing in the customization section may be performed using, for example, AI, or not. For example, the customization section can suggest the optimal customization method based on the user's past customization history.

[0077] The update unit updates information sources. The update unit can update information sources. For example, the update unit can provide a periodic update function and update information sources regularly. The update unit can also update information sources based on user requests. For example, the update unit can update information sources on a regular schedule such as daily, weekly, or monthly. The update unit can also instantly update information sources when a user requests that a specific information source be updated. This allows users to update information sources as needed. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can suggest the optimal update method based on the user's past update history.

[0078] The input method unit diversifies the methods for inputting questions. The input method unit can diversify the methods for inputting questions. For example, the input method unit provides a text input function, allowing users to input questions in text format. It also provides a voice input function, allowing users to input questions in voice format. Furthermore, the input method unit provides a photo / video recording function, allowing users to input questions using photos or videos. For example, the input method unit provides an interface for users to input questions in text format. It also provides a microphone input function for users to input questions in voice format. Furthermore, it provides a camera input function for users to take photos or videos and input questions. This allows users to input questions in a variety of ways. Some or all of the above-described processes in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can suggest the optimal input method based on the user's past input history.

[0079] The customization unit can customize information sources to meet user needs. For example, the customization unit can customize information sources based on the user's business needs. It can also customize information sources based on the user's personal interests. For example, the customization unit can filter information sources based on the user's specific business needs. It can also filter information sources based on the user's specific interests. This makes it possible to customize information sources to meet user needs. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can suggest the optimal customization method based on the user's past customization history.

[0080] The update unit can allow users to update information sources at any time. For example, the update unit can provide a 24-hour update function, allowing users to update information sources at any time. Alternatively, the update unit can provide an update function limited to specific time periods. For example, the update unit can allow users to update information sources 24 hours a day. Alternatively, the update unit can limit the updating of information sources to specific time periods. This allows users to update information sources as needed. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can suggest the optimal update method based on the user's past update history.

[0081] The input method unit can enable users to input questions using text, voice, photos, or videos. For example, the input method unit can provide a text input function, allowing users to input questions in text format. It can also provide a voice input function, enabling users to input questions in voice format. Furthermore, it can provide a photo / video capture function, allowing users to input questions using photos or videos. For example, the input method unit can provide an interface for users to input questions in text format. It can also provide a microphone input function for users to input questions in voice format. Furthermore, it can provide a camera input function for users to take photos or videos to input questions. This allows users to input questions in a variety of ways. Some or all of the above-described processes in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can suggest the optimal input method based on the user's past input history.

[0082] The registration unit estimates the user's emotions and adjusts the timing of information source registration based on the estimated user emotions. For example, if the user is stressed, the registration procedure can be simplified to allow for quick information source registration. If the user is relaxed, detailed registration options can be provided, and a customizable registration method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick information source registration. This allows for adjustment of information source registration timing 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the registration unit may be performed using AI or not. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform emotion-based adjustments to registration timing.

[0083] The registration unit can analyze a user's past registration history and select the optimal registration method. For example, it can automatically display information sources that the user has frequently registered with in the past as candidates. It can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information sources that the user will use at specific times based on their past registration history. This allows the system to provide the optimal registration method based on the user's past registration history. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's past registration history data into a generating AI and have the generating AI select the optimal registration method.

[0084] The registration unit can filter information sources based on the user's current projects and areas of interest when registering them. For example, it can prioritize displaying information sources related to the user's current projects. It can also suggest highly relevant information sources based on the user's areas of interest. Furthermore, it can analyze the user's past project history and suggest the most suitable information sources. This allows for filtering of information sources based on the user's current projects and areas of interest. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's project history data into a generating AI and have the generating AI perform the filtering.

[0085] The registration unit can estimate the user's emotions and determine the priority of information sources to register based on the estimated emotions. For example, if the user is stressed, high-importance information sources can be registered first. If the user is relaxed, detailed information sources can be registered first. Furthermore, if the user is in a hurry, information sources that can be accessed quickly can be registered first. This allows for the prioritization of information sources according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 registration unit may be performed using AI or not using AI. For example, the registration unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0086] The registration unit can prioritize registering information sources that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize registering information sources related to that region. It can also suggest region-specific information sources based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize registering information sources related to their travel destination. This allows for the priority registration of highly relevant information sources based on the user's geographical location. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant information sources.

[0087] The registration unit can analyze a user's social media activity and register relevant information sources when registering information sources. For example, it can prioritize registering information sources related to topics that the user frequently mentions on social media. It can also suggest information sources shared by the user's social media followers. Furthermore, it can analyze the user's social media activity history and suggest the most suitable information sources. This allows for the registration of relevant information sources based on the user's social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information sources.

[0088] The analysis unit can estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. For example, if the user is stressed, it can provide a concise and clear analysis method. If the user is relaxed, it can provide a detailed analysis method. Furthermore, if the user is in a hurry, it can provide a method for rapid analysis. This allows the question analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method based on emotions.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during question analysis. For example, it can perform a detailed analysis for high-importance questions and a concise analysis for low-importance questions. Furthermore, it can determine the priority of the analysis according to the importance of the question. This allows the level of detail of the analysis to be adjusted according to the importance of the question. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0090] The analysis unit can apply different analysis algorithms depending on the category of the question during question analysis. For example, a specialized analysis algorithm can be applied to technical questions. A simpler analysis algorithm can be applied to general questions. Furthermore, the optimal analysis algorithm can be selected depending on the category of the question. This ensures that the most suitable analysis algorithm is applied according to the category of the question. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question category data into a generating AI and have the generating AI select an analysis algorithm based on the category.

[0091] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize analyzing high-importance questions. If the user is relaxed, it can prioritize detailed analysis. Furthermore, if the user is in a hurry, it can prioritize selecting questions that require quick analysis. This allows the analysis priority to be determined 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0092] The analysis unit can determine the priority of analysis based on when the questions were submitted during question analysis. For example, it can determine the priority of analysis based on the time period in which the questions were submitted. It can also adjust the level of detail of the analysis according to when the questions were submitted. Furthermore, it can select the optimal analysis algorithm based on when the questions were submitted. This allows the analysis priority to be determined based on when the questions were submitted. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question submission time data into a generating AI and have the generating AI perform the priority determination based on the submission time.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the questions during question analysis. For example, it can determine the priority of analysis based on the relevance of the questions. It can also adjust the level of detail of the analysis according to the relevance of the questions. Furthermore, it can select the optimal analysis algorithm based on the relevance of the questions. This allows the order of analysis to be adjusted based on the relevance of the questions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0094] The generation unit can estimate the user's emotions and adjust the response generation method based on the estimated user emotions. For example, if the user is stressed, it can generate a concise and clear response. If the user is relaxed, it can also generate a detailed response. Furthermore, if the user is in a hurry, it can provide a method for generating a response quickly. This allows the response generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform adjustments to the generation method based on emotions.

[0095] The generation unit can adjust the level of detail in the answers based on the importance of the questions when generating responses. For example, it can generate detailed answers for high-importance questions and concise answers for low-importance questions. Furthermore, it can also determine the priority of the answers according to the importance of the questions. This allows for adjusting the level of detail in the answers according to the importance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the answers based on importance.

[0096] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, a specialized generation algorithm can be applied to technical questions, while a simpler generation algorithm can be applied to general questions. Furthermore, the optimal generation algorithm can be selected depending on the question category. This ensures that the most suitable generation algorithm is applied according to the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select a generation algorithm based on the category.

[0097] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is stressed, it can generate a short, concise response. If the user is relaxed, it can generate a longer response that includes detailed explanations. Furthermore, if the user is in a hurry, it can provide a way to generate a response quickly. This allows the response length to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform the emotion-based adjustment of response length.

[0098] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, it can determine the priority of answers based on the time period in which the questions were submitted. It can also adjust the level of detail in the answers according to when the questions were submitted. Furthermore, it can select the optimal generation algorithm based on when the questions were submitted. This allows the priority of answers to be determined based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the question submission time data into a generation AI and have the generation AI perform the priority determination based on the submission time.

[0099] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, it can determine the priority of answers based on the relevance of the questions. It can also adjust the level of detail of the answers according to the relevance of the questions. Furthermore, it can select the optimal generation algorithm based on the relevance of the questions. This allows the order of answers to be adjusted based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question relevance data into a generation AI and have the generation AI perform the adjustment of the answer order based on relevance.

[0100] The delivery unit can estimate the user's emotions and adjust the method of providing responses based on the estimated emotions. For example, if the user is stressed, it can provide a concise and clear response. If the user is relaxed, it can provide a detailed response. Furthermore, if the user is in a hurry, it can provide a quick response. This allows the method of providing responses to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the delivery method based on the emotions.

[0101] The service provider can select the optimal service method by referring to the user's past usage history when providing responses. For example, it can prioritize suggesting service methods the user has used in the past. It can also select the optimal service method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and suggest the most efficient service method. This allows the service provider to offer the optimal service method based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history data into a generating AI and have the generating AI select the optimal service method.

[0102] The delivery unit can customize the delivery method based on the user's current situation when providing answers. For example, if the user is on the move, voice delivery may be prioritized. If the user is working at a desk, text delivery may be prioritized. Furthermore, if the user is in a meeting, a concise delivery method summarizing the key points may be selected. This allows the delivery method to be customized according to the user's current situation. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user situation data into a generating AI and have the generating AI perform customization of the delivery method based on the situation.

[0103] The service provider can estimate the user's emotions and determine the priority of the responses to be provided based on the estimated emotions. For example, if the user is stressed, it can prioritize providing high-importance responses. If the user is relaxed, it can also prioritize providing detailed responses. Furthermore, if the user is in a hurry, it can prioritize selecting responses that can be provided quickly. This allows the service provider to determine the priority of responses to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0104] The information delivery unit can select the optimal delivery method when providing responses, taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize providing information related to that region. It can also suggest region-specific delivery methods based on the user's geographical location information. Furthermore, if the user is traveling, it can prioritize providing information related to their travel destination. This allows for the provision of the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location data into a generating AI and have the generating AI select a delivery method based on geographical location.

[0105] The information delivery unit can analyze the user's social media activity and propose delivery methods when providing responses. For example, it can prioritize providing information related to topics that the user frequently mentions on social media. It can also propose information shared by the user's social media followers. Furthermore, it can analyze the user's social media activity history and propose the most suitable delivery method. This allows the system to propose the most suitable delivery method based on the user's social media activity. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input the user's social media activity data into a generating AI and have the generating AI propose delivery methods based on social media activity.

[0106] The customization unit can estimate the user's emotions and adjust the information source customization method based on the estimated user emotions. For example, if the user is stressed, it can provide a concise and clear customization method. If the user is relaxed, it can provide a detailed customization method. Furthermore, if the user is in a hurry, it can provide a method for quick customization. This allows the information source customization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the customization method based on emotions.

[0107] The customization unit can select the optimal customization method when customizing information sources by referring to the user's past customization history. For example, it can propose the optimal customization method based on the user's past customizations. It can also select the most efficient customization method from the user's past customization history. Furthermore, it can analyze the user's past customization history and propose the optimal customization method. This allows the unit to provide the optimal customization method based on the user's past customization history. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's customization history data into a generating AI and have the generating AI select the optimal customization method.

[0108] The customization unit can estimate the user's emotions and determine the priority of customizations based on those emotions. For example, if the user is stressed, high-priority customizations can be prioritized. If the user is relaxed, more detailed customizations can be prioritized. Furthermore, if the user is in a hurry, a method for quick customization can be provided. This allows customization priorities to be determined 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 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 customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0109] The customization unit can select the optimal customization method when customizing information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize customizing information sources related to that region. It can also suggest region-specific customization methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize customizing information sources related to their travel destination. This allows the system to provide the optimal customization method based on the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select a customization method based on geographical location.

[0110] The update unit can estimate the user's emotions and adjust the timing of information updates based on the estimated user emotions. For example, if the user is stressed, the information source can be updated quickly. If the user is relaxed, a detailed update method can be provided. Furthermore, if the user is in a hurry, a concise and quick update method can be provided. This allows the timing of information source updates to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user emotion data into the generative AI and have the generative AI perform emotion-based adjustments to the update timing.

[0111] The update unit can select the optimal update method by referring to the user's past update history when updating information sources. For example, it can suggest the optimal update method based on the user's past updates. It can also select the most efficient update method from the user's past update history. Furthermore, it can analyze the user's past update history and suggest the optimal update method. This allows the update unit to provide the optimal update method based on the user's past update history. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's update history data into a generating AI and have the generating AI select the optimal update method.

[0112] The update unit can estimate the user's emotions and determine the priority of information sources to update based on the estimated emotions. For example, if the user is stressed, it can prioritize updating high-importance information sources. If the user is relaxed, it can also prioritize updating detailed information sources. Furthermore, if the user is in a hurry, it can prioritize selecting information sources that can be updated quickly. This allows the system to determine the priority of information sources to update according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 update unit may be performed using AI, or not using AI. For example, the update unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0113] The update unit can select the optimal update method when updating information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize updating information sources related to that region. It can also suggest region-specific update methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize updating information sources related to their travel destination. This allows the update unit to provide the optimal update method based on the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location data into a generating AI and have the generating AI select an update method based on geographical location.

[0114] The input method unit can estimate the user's emotions and adjust the input method for questions based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick question input. This allows the input method for questions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input method unit may be performed using AI or not. For example, the input method unit can input user emotion data into the generative AI and have the generative AI perform emotion-based adjustments to the input method.

[0115] The input method unit can select the optimal input method when a question is entered by referring to the user's past input history. For example, it can automatically display questions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest questions that the user will use at a specific time of day based on the user's past input history. This allows the system to provide the optimal input method based on the user's past input history. Some or all of the above processing in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0116] The input method unit can estimate the user's emotions and determine the priority of input methods based on the estimated user emotions. For example, if the user is stressed, it can provide a method that prioritizes answering high-importance questions. If the user is relaxed, it can also provide a method that prioritizes answering detailed questions. Furthermore, if the user is in a hurry, it can provide a method that allows for quick input. This allows the priority of input methods to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the input method unit may be performed using AI, or not using AI. For example, the input method unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0117] The input method unit can select the optimal input method when a question is entered, taking into account the user's device information. For example, if the user is using a smartphone, it can provide an input method that matches the screen size. If the user is using a tablet, it can also provide an input method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible input method. This allows the system to provide the optimal input method based on the user's device information. Some or all of the above-described processing in the input method unit may be performed using AI, for example, or without AI. For example, the input method unit can input the user's device information into a generating AI and have the generating AI select an input method based on the device information.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The analysis unit can estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. For example, if the user is stressed, it can provide a concise and clear analysis method. If the user is relaxed, it can provide a detailed analysis method. Furthermore, if the user is in a hurry, it can provide a method for rapid analysis. This allows the question analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the analysis method based on emotions.

[0120] The generation unit can estimate the user's emotions and adjust the response generation method based on the estimated user emotions. For example, if the user is stressed, it can generate a concise and clear response. If the user is relaxed, it can also generate a detailed response. Furthermore, if the user is in a hurry, it can provide a method for generating a response quickly. This allows the response generation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform adjustments to the generation method based on emotions.

[0121] The delivery unit can estimate the user's emotions and adjust the method of providing responses based on the estimated emotions. For example, if the user is stressed, it can provide a concise and clear response. If the user is relaxed, it can provide a detailed response. Furthermore, if the user is in a hurry, it can provide a quick response. This allows the method of providing responses to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the delivery method based on the emotions.

[0122] The customization unit can estimate the user's emotions and adjust the information source customization method based on the estimated user emotions. For example, if the user is stressed, it can provide a concise and clear customization method. If the user is relaxed, it can provide a detailed customization method. Furthermore, if the user is in a hurry, it can provide a method for quick customization. This allows the information source customization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, for example, or not using AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the customization method based on emotions.

[0123] The update unit can estimate the user's emotions and adjust the timing of information updates based on the estimated user emotions. For example, if the user is stressed, the information source can be updated quickly. If the user is relaxed, a detailed update method can be provided. Furthermore, if the user is in a hurry, a concise and quick update method can be provided. This allows the timing of information source updates to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user emotion data into the generative AI and have the generative AI perform emotion-based adjustments to the update timing.

[0124] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during question analysis. For example, it can perform a detailed analysis for high-importance questions and a concise analysis for low-importance questions. Furthermore, it can determine the priority of the analysis according to the importance of the question. This allows the level of detail of the analysis to be adjusted according to the importance of the question. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input question importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0125] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, a specialized generation algorithm can be applied to technical questions, while a simpler generation algorithm can be applied to general questions. Furthermore, the optimal generation algorithm can be selected depending on the question category. This ensures that the most suitable generation algorithm is applied according to the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input question category data into a generation AI and have the generation AI select a generation algorithm based on the category.

[0126] The service provider can select the optimal service method by referring to the user's past usage history when providing responses. For example, it can prioritize suggesting service methods the user has used in the past. It can also select the optimal service method based on the user's past usage history. Furthermore, it can analyze the user's past usage history and suggest the most efficient service method. This allows the service provider to offer the optimal service method based on the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's usage history data into a generating AI and have the generating AI select the optimal service method.

[0127] The customization unit can select the optimal customization method when customizing information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize customizing information sources related to that region. It can also suggest region-specific customization methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize customizing information sources related to their travel destination. This allows the system to provide the optimal customization method based on the user's geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location data into a generating AI and have the generating AI select a customization method based on geographical location.

[0128] The update unit can select the optimal update method when updating information sources, taking into account the user's geographical location. For example, if the user is in a specific region, it can prioritize updating information sources related to that region. It can also suggest region-specific update methods based on the user's geographical location. Furthermore, if the user is traveling, it can prioritize updating information sources related to their travel destination. This allows the update unit to provide the optimal update method based on the user's geographical location. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location data into a generating AI and have the generating AI select an update method based on geographical location.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The registration department registers information sources. Information sources include databases, websites, literature, company work rules, customer service manuals, product-specific errors, sports manuals, local government rules, laws, etc. Step 2: The analysis unit analyzes the questions based on the information sources registered by the registration unit. The questions include text, audio, and image formats, and natural language processing techniques and machine learning algorithms are used for the analysis. Step 3: The generation unit generates answers based on the questions analyzed by the analysis unit. The answers may be in text format, audio format, image format, etc., and generation AI or multimodal generation AI is used for generation. Step 4: The delivery unit provides the response generated by the generation unit. Delivery methods include web applications, mobile applications, email, and printers.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the above-mentioned elements, including the registration unit, analysis unit, generation unit, provision unit, customization unit, update unit, input method unit, and emotion estimation function, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The customization unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The update unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The input method unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the above-mentioned elements, including the registration unit, analysis unit, generation unit, provision unit, customization unit, update unit, input method unit, and emotion estimation function, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The customization unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The update unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The input method unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the registration unit, analysis unit, generation unit, provision unit, customization unit, update unit, input method unit, and emotion estimation function, is implemented, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The customization unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The update unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The input method is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the registration unit, analysis unit, generation unit, provision unit, customization unit, update unit, input method unit, and emotion estimation function, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The customization unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The update unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The input method unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) A registration section for registering information sources, An analysis unit analyzes the question based on the information sources registered by the registration unit, A generation unit that generates an answer based on the question analyzed by the analysis unit, The system includes a providing unit that provides the answer generated by the generation unit. A system characterized by the following features. (Note 2) It includes a customization section that allows users to customize the information sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an update unit for updating the information source. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an input method section that diversifies the methods for entering questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned customization unit is Customize information sources to meet user needs. The system described in Appendix 2, characterized by the features described herein. (Note 6) The aforementioned update unit is, Allow users to update their information sources at any time. The system described in Appendix 3, characterized by the features described herein. (Note 7) The aforementioned input method unit is: It will be possible to input questions using text, voice, photos, and videos. The system described in Appendix 4, characterized by the features described herein. (Note 8) The aforementioned registration unit is The system estimates the user's sentiment and adjusts the timing of information source registration based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is Analyze the user's past registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is When registering information sources, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is It estimates the user's emotions and determines the priority of information sources to register based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering information sources, the system prioritizes registering highly relevant sources by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned registration unit is When registering information sources, the system analyzes the user's social media activity and registers relevant information sources. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During question analysis, adjust the level of detail based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing questions, different analysis algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During question analysis, the priority of the analysis is determined based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During question analysis, the order of analysis is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the response generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) 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 23) 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 24) 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 25) 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 26) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing responses, the system will select the most suitable method of delivery by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing responses, the method of delivery will be customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the responses to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing responses, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing responses, we analyze the user's social media activity and suggest methods for providing the responses. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned customization unit is It estimates the user's sentiment and adjusts how information sources are customized based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned customization unit is When customizing information sources, the system selects the optimal customization method by referring to the user's past customization history. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned customization unit is When customizing information sources, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned update unit is, It estimates user sentiment and adjusts the timing of information source updates based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned update unit is, When updating information sources, the system selects the optimal update method by referring to the user's past update history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned update unit is, It estimates the user's sentiment and determines the priority of information sources to update based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned update unit is, When updating information sources, the optimal update method is selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned input method unit is: It estimates the user's emotions and adjusts how questions are entered based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned input method unit is: When a question is entered, the system selects the most suitable input method by referring to the user's past input history. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned input method unit is: It estimates the user's emotions and determines the priority of input methods based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned input method unit is: When a question is entered, the system selects the optimal input method considering the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0203] 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 registration section for registering information sources, An analysis unit analyzes the question based on the information sources registered by the registration unit, A generation unit that generates an answer based on the question analyzed by the analysis unit, The system includes a providing unit that provides the answer generated by the generation unit. A system characterized by the following features.

2. It includes a customization section that allows users to customize the information sources. The system according to feature 1.

3. It includes an update unit for updating the information source. The system according to feature 1.

4. It includes an input method section that diversifies the methods for entering questions. The system according to feature 1.

5. The aforementioned customization unit is Customize information sources to meet user needs. The system according to feature 2.

6. The aforementioned update unit is, Allow users to update their information sources at any time. The system according to claim 3.

7. The aforementioned input method unit is: It will be possible to input questions using text, voice, photos, and videos. The system according to feature 4.

8. The aforementioned registration unit is The system estimates the user's sentiment and adjusts the timing of information source registration based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned registration unit is Analyze the user's past registration history and select the optimal registration method. The system according to feature 1.

10. The aforementioned registration unit is When registering information sources, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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