system

Abstract

The system according to this embodiment aims to efficiently collect and analyze relevant information from a large amount of data. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit takes research themes and questions as input. The collection unit automatically collects relevant information based on the information entered by the reception unit. The analysis unit analyzes the data collected by the collection unit and extracts relevant information. The provision unit provides the analysis results obtained by the analysis unit.

Description

Technical Field

[0006]

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently collect and analyze relevant information from a large amount of data.

[0005] The system according to the embodiment aims to efficiently collect and analyze relevant information from a large amount of data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit takes research themes and questions as input. The collection unit automatically collects relevant information based on the information entered by the reception unit. The analysis unit analyzes the data collected by the collection unit and extracts relevant information. The provision unit provides the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze relevant information from a large amount of data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) An automated research platform according to an embodiment of the present invention is a system that assists researchers and business analysts in efficiently collecting and analyzing information. The system begins with the user inputting a research topic or question. Next, an AI agent automatically collects relevant information through internet-based information gathering methods. The collected data is analyzed by the AI ​​agent, and relevant information is extracted. Furthermore, the AI ​​agent uses machine learning to analyze data trends and patterns, and provides the analysis results to the user. For example, this platform reduces research time, improves the accuracy of data analysis, and enhances user engagement. It also utilizes a cloud-based platform and applies cutting-edge natural language processing technology to provide an intuitive user interface. This enables researchers and business analysts to efficiently collect and analyze information. This platform is useful for researchers who spend a lot of time gathering information, such as university researchers, market analysts, and corporate strategy planners, as well as business analysts who require rapid market analysis and business executives who make data-driven decisions. Furthermore, the AI ​​agent updates information in real time, providing analysis results based on the latest information. This ensures that research is always based on the most up-to-date information. By leveraging this AI research assistant agent, challenges such as the difficulty of analysis due to information overload and the lack of efficient information gathering due to time constraints can be addressed, accelerating research and business decision-making and enabling more effective strategies. This automated research platform allows researchers and business analysts to efficiently collect and analyze information.

[0029] The automated research platform according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives research themes and questions from the user. For example, the reception unit accepts the research themes and questions entered by the user in text format. The reception unit can also accept voice input. For example, the reception unit converts the user's voice into text using speech recognition technology. Furthermore, the reception unit can refer to the user's past research history and suggest the optimal input format. The collection unit automatically collects relevant information based on the information entered by the reception unit. For example, the collection unit collects news articles and academic papers from the internet using web scraping technology. The collection unit can also collect data such as corporate financial reports using APIs. Furthermore, the collection unit can prioritize the collection of relevant information based on the user's current areas of interest. The analysis unit analyzes the data collected by the collection unit and extracts relevant information. For example, the analysis unit analyzes the collected text data using natural language processing technology. The analysis unit can also analyze data trends and patterns using machine learning algorithms. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The provision unit provides the analysis results obtained by the analysis unit. For example, the provision unit provides the analysis results to the user in report format. The provision unit can also provide the analysis results visually using a dashboard display. Furthermore, the provision unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. As a result, the automated research platform according to this embodiment can automate everything from inputting research themes and questions to collecting, analyzing, and providing relevant information, enabling efficient information collection and analysis.

[0030] The reception desk accepts user input of research themes and questions. For example, the reception desk accepts user input of research themes and questions in text format. Users can input specific research themes and questions through a dedicated input form. This input form is designed for intuitive operation, and it is easy to check and correct the input content. The reception desk can also accept voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text. Voice input is highly convenient because it allows users to input research themes and questions without using their hands. Speech recognition technology can learn the user's pronunciation and speaking habits to improve accuracy. Furthermore, the reception desk can refer to the user's past research history and suggest the optimal input format. For example, users who have previously entered similar research themes will be provided with input assistance based on that history. This allows users to input research themes and questions efficiently. The reception desk also has a function to analyze the user's input content in real time and suggest corrections or supplements to the input content as needed. For example, if the input content contains ambiguous expressions or typos, the reception desk will point them out and suggest corrections. This allows users to input accurate and clear research topics and questions.

[0031] The data collection unit automatically collects relevant information based on the information entered by the reception unit. For example, the data collection unit uses web scraping technology to collect news articles and academic papers from the internet. Web scraping technology is a technique that automatically extracts necessary information from specified websites and stores it in a database. The data collection unit searches for relevant web pages based on specific keywords or themes and extracts the necessary information. The data collection unit can also collect data such as corporate financial reports using APIs. APIs are interfaces for exchanging data between different systems, and the data collection unit can obtain the latest data in real time through APIs. Furthermore, the data collection unit can prioritize the collection of relevant information based on the user's current areas of interest. For example, if a user is interested in a particular industry or topic, the data collection unit will prioritize the collection of information related to that field and provide it to the user. The data collection unit centrally manages the collected information and can collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis unit and the provision unit. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0032] The analysis unit analyzes the data collected by the collection unit and extracts relevant information. For example, the analysis unit uses natural language processing (NLP) technology to analyze collected text data. NLP is a technology that understands meaning and context from text data and extracts important information. The analysis unit analyzes text data and performs tasks such as keyword extraction, document classification, and summary generation. The analysis unit can also use machine learning algorithms to analyze data trends and patterns. Machine learning algorithms are technologies that learn from large amounts of data and automatically identify data features and patterns. The analysis unit can use machine learning algorithms to analyze data trends and patterns and perform future predictions and anomaly detection. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on high-importance data and simpler analysis on low-importance data. This allows the analysis unit to analyze data efficiently and effectively and extract relevant information. The analysis unit can update the analysis results in real time and provide the latest information. For example, every time new data is collected, the analysis unit analyzes that data and updates the analysis results. This ensures that users always have access to the latest information.

[0033] The service provider provides the analysis results obtained by the analysis provider. For example, the service provider provides the analysis results to the user in report format. The report is a document that summarizes the analysis results in an easy-to-understand manner, allowing the user to quickly grasp the information they need. The service provider can also provide the analysis results visually using a dashboard display. A dashboard is a tool that displays the analysis results in a visual format such as graphs and charts, allowing the user to intuitively understand data trends and patterns. Furthermore, the service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is feeling stressed, the service provider will provide information concisely and clearly to reduce the user's burden. Also, if the user is interested, the service provider will provide detailed information and additional resources to keep the user engaged. The service provider can collect user feedback and continuously improve the quality and presentation of the information it provides. For example, by having users rate and comment on the information provided, the service provider can identify areas for improvement based on that feedback and reflect them in future deliveries. This allows the service provider to provide high-quality information to users and improve user satisfaction.

[0034] The service provider includes a real-time update unit that updates information in real time. This real-time update unit updates information in real time, for example, using WebSocket technology. For instance, it collects relevant information and provides analysis results immediately after a user inputs a research topic or question. The real-time update unit can also periodically retrieve the latest information and update analysis results using an API. Furthermore, the real-time update unit can estimate the user's emotions and adjust the frequency of real-time updates based on the estimated emotions. For example, if the user is stressed, the real-time update unit reduces the update frequency and updates only important information. Conversely, if the user is relaxed, the real-time update unit can frequently update detailed information. This real-time information updates ensure that research is always based on the latest information.

[0035] The service provider includes a user interface unit that provides an intuitive user interface. The user interface unit adopts an intuitive design based, for example, the results of usability tests. For instance, the user interface unit provides a simple and highly visible design. Furthermore, the user interface unit can select the optimal display method by referring to the user's operation history. For example, the user interface unit selects the optimal display method based on interface designs the user has used in the past. The user interface unit can also estimate the user's emotions and adjust the interface display method based on the estimated emotions. For example, if the user is stressed, the user interface unit provides an interface with calming colors to reduce visual stress. Conversely, if the user is enjoying themselves, the user interface unit provides an interface with bright colors to make the input process more enjoyable. This improves user engagement by providing an intuitive user interface.

[0036] The analysis unit includes a trend analysis unit that uses machine learning to analyze data trends and patterns. The trend analysis unit can analyze data trends using, for example, regression analysis. For instance, it constructs a regression model to predict changes in data. The trend analysis unit can also analyze data patterns using clustering. For example, it divides data into clusters and analyzes the characteristics of each cluster. Furthermore, the trend analysis unit can analyze data trends and patterns with high accuracy using neural networks. For example, it can perform advanced trend analysis by constructing a deep learning model and training it with a large amount of data. This allows for highly accurate analysis of data trends and patterns using machine learning.

[0037] The data collection unit collects data from various sources, including news articles, academic papers, and corporate financial reports on the internet. For example, it might collect news articles from specific news websites. For instance, it might use web scraping techniques to retrieve the latest articles from news sites. The data collection unit can also collect academic papers from academic databases. For example, it might use APIs to retrieve paper data from academic databases. Furthermore, the data collection unit can collect financial reports from corporate websites. For example, it might access corporate websites and download financial reports. This allows for comprehensive information gathering by collecting data from diverse sources.

[0038] The analysis unit analyzes data using natural language processing techniques and extracts relevant information. For example, the analysis unit analyzes text data using morphological analysis. For instance, it divides text data into words and identifies the part of speech of each word. The analysis unit can also analyze sentence structure using grammatical analysis. For example, it analyzes the structure of a sentence, such as its subject, predicate, and object. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis. For example, it understands the context of the text data and extracts relevant information. As a result, the accuracy of data analysis is improved by using natural language processing techniques.

[0039] The reception desk analyzes the user's past research history and proposes the optimal input format. For example, the reception desk automatically displays research themes and questions that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest research themes and questions to be used at specific times based on the user's past research history. In this way, by analyzing past research history, the reception desk can propose the optimal input format for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past research history into a generating AI and have the generating AI propose the optimal input format.

[0040] The input section presents input suggestions based on the user's current areas of interest when they input research themes or questions. For example, the input section may suggest relevant research themes or questions based on keywords the user has recently searched for. For example, the input section may suggest relevant research themes or questions based on news or topics the user follows. The input section may also suggest relevant research themes or questions based on topics in online communities or forums the user participates in. This enables efficient input by presenting input suggestions based on the user's areas of interest. Some or all of the above processing in the input section may be performed using AI, for example, or not using AI. For example, the input section may input the user's current areas of interest into a generating AI and have the generating AI perform the task of presenting input suggestions.

[0041] The reception desk prioritizes presenting highly relevant themes and questions when users input research themes and questions, taking into account their geographical location. For example, if a user is in a specific region, the reception desk prioritizes presenting research themes and questions related to that region. For example, if a user is traveling, the reception desk prioritizes presenting research themes and questions related to their travel destination. Furthermore, if a user is participating in a specific event, the reception desk can prioritize presenting research themes and questions related to that event. In this way, by considering the user's geographical location, highly relevant themes and questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the task of presenting highly relevant themes and questions.

[0042] The reception desk analyzes the user's social media activity when they input research themes or questions, and suggests relevant themes and questions. For example, the reception desk suggests relevant research themes and questions based on topics the user frequently mentions on social media. For example, the reception desk suggests relevant research themes and questions based on posts from influencers and experts the user follows. The reception desk can also suggest relevant research themes and questions based on topics in social media groups and communities the user participates in. In this way, relevant themes and questions can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant themes and questions.

[0043] The data collection unit analyzes the user's past research history to select the optimal data collection method. For example, the data collection unit selects the optimal data collection method based on the information sources the user has used in the past. For example, the data collection unit prioritizes collecting highly reliable information sources from the user's past research history. The data collection unit can also analyze the user's past research history to select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing past research history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past research history into a generating AI and have the generating AI select the optimal data collection method.

[0044] The data collection unit filters information based on the user's current areas of interest. For example, the data collection unit prioritizes collecting relevant information based on keywords the user has recently searched for. For example, the data collection unit collects relevant information based on news and topics the user follows. The data collection unit can also collect relevant information based on topics in online communities and forums the user participates in. This allows for the collection of highly relevant information by filtering based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI and have the generating AI perform the filtering.

[0045] The data collection unit prioritizes collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit prioritizes collecting information related to that region. For example, if the user is traveling, the data collection unit prioritizes collecting information related to the travel destination. The data collection unit can also prioritize collecting information related to an event if the user is participating in a specific event. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0046] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit collects relevant information based on topics that the user frequently mentions on social media. For example, the data collection unit collects relevant information based on posts from influencers and experts that the user follows. The data collection unit can also collect relevant information based on topics in social media groups and communities that the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0047] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0048] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a statistical analysis algorithm to numerical data. The analysis unit can also apply an image analysis algorithm to image data. By applying the most appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0049] The analysis unit determines the priority of analysis based on the data collection period during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. For example, the analysis unit analyzes the most recent data while referring to past data. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0050] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0051] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, the provider provides a detailed explanation for highly important information. For example, the provider provides a simplified explanation for less important information. The provider can also adjust the level of detail provided according to the importance of the information. This allows for efficient information provision by adjusting the level of detail according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0052] The information provider applies different information provision algorithms depending on the information category at the time of provision. For example, the provider applies a natural language generation algorithm to text information. For example, the provider applies a visual provision method using graphs and charts to numerical information. The provider can also apply an image analysis algorithm to image information. By applying the most suitable information provision algorithm according to the information category, the accuracy of information provision is improved. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0053] The information provider determines the priority of information provision based on the information collection timing at the time of provision. For example, the information provider may prioritize providing the latest information. For example, the information provider may provide the latest information while referring to past information. The information provider may also adjust the priority of information provision according to the information collection timing. This allows for the provision of the latest information by determining the priority of information provision based on the information collection timing. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider may input the information collection timing into a generating AI and have the generating AI perform the determination of the provision priority.

[0054] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the provider may prioritize the delivery of highly relevant information. For example, it may postpone the delivery of less relevant information. The provider can also adjust the order of information delivery according to the relevance of the information. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0055] The real-time update unit selects the optimal update timing by referring to past update history during real-time updates. For example, the real-time update unit selects the most effective update timing from past update history. For example, the real-time update unit analyzes past update history and adjusts the update timing based on the user's usage patterns. The real-time update unit can also select an update timing that meets the user's needs by referring to past update history. In this way, the optimal update timing can be selected by referring to past update history. Some or all of the above processing in the real-time update unit may be performed using AI, for example, or without using AI. For example, the real-time update unit can input past update history into a generating AI and have the generating AI perform the selection of the optimal update timing.

[0056] The real-time update unit selects the optimal update timing by considering the user's geographical location information during real-time updates. For example, if the user is in a specific region, the real-time update unit prioritizes updating information related to that region. For example, if the user is traveling, the real-time update unit prioritizes updating information related to the travel destination. Furthermore, if the user is participating in a specific event, the real-time update unit can also prioritize updating information related to that event. This allows the system to select the optimal update timing by considering the user's geographical location information. Some or all of the above processing in the real-time update unit may be performed using AI, for example, or without AI. For example, the real-time update unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal update timing.

[0057] The user interface unit selects the optimal display method when displaying the interface by referring to the user's past operation history. For example, the user interface unit selects the optimal display method based on interface designs previously used by the user. For example, the user interface unit prioritizes displaying user-friendly interfaces based on the user's past operation history. The user interface unit can also analyze the user's past operation history and select the most efficient display method. In this way, the optimal display method can be selected by referring to past operation history. Some or all of the above processing in the user interface unit may be performed using AI, for example, or without AI. For example, the user interface unit can input the user's past operation history into a generating AI and have the generating AI perform the selection of the optimal display method.

[0058] The user interface unit selects the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the user interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the user interface unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the user interface unit can also provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the user interface unit may be performed using AI, for example, or without AI. For example, the user interface unit can input the user's device information into a generating AI and have the generating AI perform the selection of the optimal display method.

[0059] The trend analysis unit predicts current trends by referring to past trend data during trend analysis. For example, the trend analysis unit predicts current trends based on past trend data. For example, the trend analysis unit analyzes past trend data and predicts future trends. The trend analysis unit can also predict current trends by referring to past trend data. In this way, by referring to past trend data, current trends can be predicted with high accuracy. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without using AI. For example, the trend analysis unit can input past trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0060] The trend analysis unit applies different trend analysis methods to each data category during trend analysis. For example, for text data, the trend analysis unit applies a trend analysis method using natural language processing. For numerical data, the trend analysis unit applies a trend analysis method using statistical analysis. Furthermore, the trend analysis unit can also apply a trend analysis method using image analysis to image data. This improves the accuracy of trend analysis by applying the most suitable trend analysis method for each data category. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input the data categories into a generating AI and have the generating AI execute the application of the trend analysis method.

[0061] The trend analysis unit analyzes changes in trends based on the data collection period during trend analysis. For example, the trend analysis unit analyzes changes in trends based on the latest data. For example, the trend analysis unit analyzes the latest trends while referring to past data. The trend analysis unit can also analyze changes in trends according to the data collection period. This allows for the understanding of the latest trends by analyzing changes in trends based on the data collection period. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input the data collection period into a generating AI and have the generating AI perform the analysis of changes in trends.

[0062] The trend analysis unit analyzes trends by referring to relevant market data during trend analysis. For example, the trend analysis unit analyzes trends based on relevant market data. For example, the trend analysis unit analyzes trends while referring to relevant market data. The trend analysis unit can also analyze changes in trends by referring to relevant market data. This allows for highly accurate analysis of changes in trends by referring to relevant market data. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input relevant market data into a generating AI and have the generating AI perform the trend analysis.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception system can automatically suggest relevant past research results based on user input. For example, if a user enters a question about a specific topic, the reception system will present research results from similar topics conducted in the past. The reception system can also extract keywords related to the user's question and filter past research results accordingly. Furthermore, the reception system can refer to the user's past research history and prioritize the presentation of highly relevant research results. This allows users to efficiently conduct new research while referring to past research results.

[0065] The information provider can automatically collect and provide the latest trend information related to the user's research topic. For example, it can collect the latest trend information from news sites and social media on the internet and provide it to the user. It can also collect and provide the latest research results and technological trends in specific industries or fields. Furthermore, it can collect information on events and conferences related to the user's research topic and notify the user. This allows the user to proceed with their research based on the latest trend information.

[0066] The analytics unit can visualize and provide data related to the user's research topic. For example, it can convert collected data into graphs and charts, providing them in a visually easy-to-understand format. It can also visualize and present data trends and patterns. Furthermore, it can visualize data correlations, allowing users to intuitively understand the relationships between data. This makes it easier for users to visually grasp the data, improving research efficiency.

[0067] The data collection unit can collect and provide data related to the user's research topic in multiple languages. For example, the unit can collect news articles and academic papers written in multiple languages, such as English, Japanese, and Chinese. The unit can also automatically translate data in each language and provide it to the user. Furthermore, the unit can prioritize data collection in the language specified by the user. This allows users to conduct research using multilingual data and gather information from a global perspective.

[0068] The data provider can update and provide data related to the user's research topic in real time. For example, immediately after the user enters their research topic, the data provider can collect and provide the latest relevant data. Furthermore, the data provider can update and provide newly collected data in real time as the user progresses with their research. In addition, when the user reviews their research results, the data provider can provide analysis results based on the latest data. This ensures that the user can always conduct their research based on the most up-to-date information.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives the user's research topic or question. For example, the reception desk accepts the research topic or question entered by the user in text format. The reception desk can also accept voice input, using speech recognition technology to convert the user's voice into text. Furthermore, the reception desk can refer to the user's past research history to suggest the most suitable input format. Step 2: The collection unit automatically collects relevant information based on the information entered by the reception unit. For example, the collection unit can use web scraping techniques to collect news articles and academic papers from the internet. It can also use APIs to collect data such as corporate financial reports. Furthermore, the collection unit can prioritize the collection of relevant information based on the user's current areas of interest. Step 3: The analysis unit analyzes the data collected by the collection unit and extracts relevant information. For example, the analysis unit analyzes the collected text data using natural language processing techniques. It can also analyze data trends and patterns using machine learning algorithms. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Step 4: The service provider provides the analysis results obtained by the analysis unit. For example, the service provider provides the analysis results to the user in report format. Alternatively, the analysis results can be provided visually using a dashboard display. Furthermore, the service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions.

[0071] (Example of form 2) An automated research platform according to an embodiment of the present invention is a system that assists researchers and business analysts in efficiently collecting and analyzing information. The system begins with the user inputting a research topic or question. Next, an AI agent automatically collects relevant information through internet-based information gathering methods. The collected data is analyzed by the AI ​​agent, and relevant information is extracted. Furthermore, the AI ​​agent uses machine learning to analyze data trends and patterns, and provides the analysis results to the user. For example, this platform reduces research time, improves the accuracy of data analysis, and enhances user engagement. It also utilizes a cloud-based platform and applies cutting-edge natural language processing technology to provide an intuitive user interface. This enables researchers and business analysts to efficiently collect and analyze information. This platform is useful for researchers who spend a lot of time gathering information, such as university researchers, market analysts, and corporate strategy planners, as well as business analysts who require rapid market analysis and business executives who make data-driven decisions. Furthermore, the AI ​​agent updates information in real time, providing analysis results based on the latest information. This ensures that research is always based on the most up-to-date information. By leveraging this AI research assistant agent, challenges such as the difficulty of analysis due to information overload and the lack of efficient information gathering due to time constraints can be addressed, accelerating research and business decision-making and enabling more effective strategies. This automated research platform allows researchers and business analysts to efficiently collect and analyze information.

[0072] The automated research platform according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives research themes and questions from the user. For example, the reception unit accepts the research themes and questions entered by the user in text format. The reception unit can also accept voice input. For example, the reception unit converts the user's voice into text using speech recognition technology. Furthermore, the reception unit can refer to the user's past research history and suggest the optimal input format. The collection unit automatically collects relevant information based on the information entered by the reception unit. For example, the collection unit collects news articles and academic papers from the internet using web scraping technology. The collection unit can also collect data such as corporate financial reports using APIs. Furthermore, the collection unit can prioritize the collection of relevant information based on the user's current areas of interest. The analysis unit analyzes the data collected by the collection unit and extracts relevant information. For example, the analysis unit analyzes the collected text data using natural language processing technology. The analysis unit can also analyze data trends and patterns using machine learning algorithms. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The provision unit provides the analysis results obtained by the analysis unit. For example, the provision unit provides the analysis results to the user in report format. The provision unit can also provide the analysis results visually using a dashboard display. Furthermore, the provision unit can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. As a result, the automated research platform according to this embodiment can automate everything from inputting research themes and questions to collecting, analyzing, and providing relevant information, enabling efficient information collection and analysis.

[0073] The reception desk accepts user input of research themes and questions. For example, the reception desk accepts user input of research themes and questions in text format. Users can input specific research themes and questions through a dedicated input form. This input form is designed for intuitive operation, and it is easy to check and correct the input content. The reception desk can also accept voice input. For example, the reception desk uses speech recognition technology to convert the user's voice into text. Voice input is highly convenient because it allows users to input research themes and questions without using their hands. Speech recognition technology can learn the user's pronunciation and speaking habits to improve accuracy. Furthermore, the reception desk can refer to the user's past research history and suggest the optimal input format. For example, users who have previously entered similar research themes will be provided with input assistance based on that history. This allows users to input research themes and questions efficiently. The reception desk also has a function to analyze the user's input content in real time and suggest corrections or supplements to the input content as needed. For example, if the input content contains ambiguous expressions or typos, the reception desk will point them out and suggest corrections. This allows users to input accurate and clear research topics and questions.

[0074] The data collection unit automatically collects relevant information based on the information entered by the reception unit. For example, the data collection unit uses web scraping technology to collect news articles and academic papers from the internet. Web scraping technology is a technique that automatically extracts necessary information from specified websites and stores it in a database. The data collection unit searches for relevant web pages based on specific keywords or themes and extracts the necessary information. The data collection unit can also collect data such as corporate financial reports using APIs. APIs are interfaces for exchanging data between different systems, and the data collection unit can obtain the latest data in real time through APIs. Furthermore, the data collection unit can prioritize the collection of relevant information based on the user's current areas of interest. For example, if a user is interested in a particular industry or topic, the data collection unit will prioritize the collection of information related to that field and provide it to the user. The data collection unit centrally manages the collected information and can collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis unit and the provision unit. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0075] The analysis unit analyzes the data collected by the collection unit and extracts relevant information. For example, the analysis unit uses natural language processing (NLP) technology to analyze collected text data. NLP is a technology that understands meaning and context from text data and extracts important information. The analysis unit analyzes text data and performs tasks such as keyword extraction, document classification, and summary generation. The analysis unit can also use machine learning algorithms to analyze data trends and patterns. Machine learning algorithms are technologies that learn from large amounts of data and automatically identify data features and patterns. The analysis unit can use machine learning algorithms to analyze data trends and patterns and perform future predictions and anomaly detection. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on high-importance data and simpler analysis on low-importance data. This allows the analysis unit to analyze data efficiently and effectively and extract relevant information. The analysis unit can update the analysis results in real time and provide the latest information. For example, every time new data is collected, the analysis unit analyzes that data and updates the analysis results. This ensures that users always have access to the latest information.

[0076] The service provider provides the analysis results obtained by the analysis provider. For example, the service provider provides the analysis results to the user in report format. The report is a document that summarizes the analysis results in an easy-to-understand manner, allowing the user to quickly grasp the information they need. The service provider can also provide the analysis results visually using a dashboard display. A dashboard is a tool that displays the analysis results in a visual format such as graphs and charts, allowing the user to intuitively understand data trends and patterns. Furthermore, the service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated emotions. For example, if the user is feeling stressed, the service provider will provide information concisely and clearly to reduce the user's burden. Also, if the user is interested, the service provider will provide detailed information and additional resources to keep the user engaged. The service provider can collect user feedback and continuously improve the quality and presentation of the information it provides. For example, by having users rate and comment on the information provided, the service provider can identify areas for improvement based on that feedback and reflect them in future deliveries. This allows the service provider to provide high-quality information to users and improve user satisfaction.

[0077] The service provider includes a real-time update unit that updates information in real time. This real-time update unit updates information in real time, for example, using WebSocket technology. For instance, it collects relevant information and provides analysis results immediately after a user inputs a research topic or question. The real-time update unit can also periodically retrieve the latest information and update analysis results using an API. Furthermore, the real-time update unit can estimate the user's emotions and adjust the frequency of real-time updates based on the estimated emotions. For example, if the user is stressed, the real-time update unit reduces the update frequency and updates only important information. Conversely, if the user is relaxed, the real-time update unit can frequently update detailed information. This real-time information updates ensure that research is always based on the latest information.

[0078] The service provider includes a user interface unit that provides an intuitive user interface. The user interface unit adopts an intuitive design based, for example, the results of usability tests. For instance, the user interface unit provides a simple and highly visible design. Furthermore, the user interface unit can select the optimal display method by referring to the user's operation history. For example, the user interface unit selects the optimal display method based on interface designs the user has used in the past. The user interface unit can also estimate the user's emotions and adjust the interface display method based on the estimated emotions. For example, if the user is stressed, the user interface unit provides an interface with calming colors to reduce visual stress. Conversely, if the user is enjoying themselves, the user interface unit provides an interface with bright colors to make the input process more enjoyable. This improves user engagement by providing an intuitive user interface.

[0079] The analysis unit includes a trend analysis unit that uses machine learning to analyze data trends and patterns. The trend analysis unit can analyze data trends using, for example, regression analysis. For instance, it constructs a regression model to predict changes in data. The trend analysis unit can also analyze data patterns using clustering. For example, it divides data into clusters and analyzes the characteristics of each cluster. Furthermore, the trend analysis unit can analyze data trends and patterns with high accuracy using neural networks. For example, it can perform advanced trend analysis by constructing a deep learning model and training it with a large amount of data. This allows for highly accurate analysis of data trends and patterns using machine learning.

[0080] The data collection unit collects data from various sources, including news articles, academic papers, and corporate financial reports on the internet. For example, it might collect news articles from specific news websites. For instance, it might use web scraping techniques to retrieve the latest articles from news sites. The data collection unit can also collect academic papers from academic databases. For example, it might use APIs to retrieve paper data from academic databases. Furthermore, the data collection unit can collect financial reports from corporate websites. For example, it might access corporate websites and download financial reports. This allows for comprehensive information gathering by collecting data from diverse sources.

[0081] The analysis unit analyzes data using natural language processing techniques and extracts relevant information. For example, the analysis unit analyzes text data using morphological analysis. For instance, it divides text data into words and identifies the part of speech of each word. The analysis unit can also analyze sentence structure using grammatical analysis. For example, it analyzes the structure of a sentence, such as its subject, predicate, and object. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis. For example, it understands the context of the text data and extracts relevant information. As a result, the accuracy of data analysis is improved by using natural language processing techniques.

[0082] The reception desk estimates the user's emotions and adjusts the input method for research themes and questions based on the estimated emotions. The reception desk can estimate the user's emotions using, for example, facial recognition technology. For example, the reception desk can analyze the user's facial expressions captured by a camera and estimate their emotions. The reception desk can also estimate the user's emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the user's voice and estimate their emotions. The reception desk can also estimate the user's emotions using text analysis technology. For example, the reception desk can analyze the content of the text entered by the user and estimate their emotions. By adjusting the input method according to the user's emotions, it is possible to reduce user stress and enable efficient input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The reception desk analyzes the user's past research history and proposes the optimal input format. For example, the reception desk automatically displays research themes and questions that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest research themes and questions to be used at specific times based on the user's past research history. In this way, by analyzing past research history, the reception desk can propose the optimal input format for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past research history into a generating AI and have the generating AI propose the optimal input format.

[0084] The input section presents input suggestions based on the user's current areas of interest when they input research themes or questions. For example, the input section may suggest relevant research themes or questions based on keywords the user has recently searched for. For example, the input section may suggest relevant research themes or questions based on news or topics the user follows. The input section may also suggest relevant research themes or questions based on topics in online communities or forums the user participates in. This enables efficient input by presenting input suggestions based on the user's areas of interest. Some or all of the above processing in the input section may be performed using AI, for example, or not using AI. For example, the input section may input the user's current areas of interest into a generating AI and have the generating AI perform the task of presenting input suggestions.

[0085] The reception desk estimates the user's emotions and prioritizes the submitted research topics and questions based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize high-priority research topics and questions. For example, if the user is relaxed, the reception desk will prioritize detailed research topics and questions. Also, if the user is in a hurry, the reception desk can prioritize research topics and questions that can be answered quickly. In this way, important research topics and questions can be prioritized by determining priorities 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of research topics and questions.

[0086] The reception desk prioritizes presenting highly relevant themes and questions when users input research themes and questions, taking into account their geographical location. For example, if a user is in a specific region, the reception desk prioritizes presenting research themes and questions related to that region. For example, if a user is traveling, the reception desk prioritizes presenting research themes and questions related to their travel destination. Furthermore, if a user is participating in a specific event, the reception desk can prioritize presenting research themes and questions related to that event. In this way, by considering the user's geographical location, highly relevant themes and questions can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the task of presenting highly relevant themes and questions.

[0087] The reception desk analyzes the user's social media activity when they input research themes or questions, and suggests relevant themes and questions. For example, the reception desk suggests relevant research themes and questions based on topics the user frequently mentions on social media. For example, the reception desk suggests relevant research themes and questions based on posts from influencers and experts the user follows. The reception desk can also suggest relevant research themes and questions based on topics in social media groups and communities the user participates in. In this way, relevant themes and questions can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant themes and questions.

[0088] The data collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of information collection and collects only important information. For example, if the user is relaxed, the data collection unit collects detailed information and provides a wide range of data. Also, if the user is in a hurry, the data collection unit can collect information quickly and provide results in a short time. This allows for efficient information collection by adjusting the timing of information collection 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0089] The data collection unit analyzes the user's past research history to select the optimal data collection method. For example, the data collection unit selects the optimal data collection method based on the information sources the user has used in the past. For example, the data collection unit prioritizes collecting highly reliable information sources from the user's past research history. The data collection unit can also analyze the user's past research history to select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing past research history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past research history into a generating AI and have the generating AI select the optimal data collection method.

[0090] The data collection unit filters information based on the user's current areas of interest. For example, the data collection unit prioritizes collecting relevant information based on keywords the user has recently searched for. For example, the data collection unit collects relevant information based on news and topics the user follows. The data collection unit can also collect relevant information based on topics in online communities and forums the user participates in. This allows for the collection of highly relevant information by filtering based on the user's areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI and have the generating AI perform the filtering.

[0091] The data collection unit estimates the user's emotions and determines the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting information of high importance. For example, if the user is relaxed, the data collection unit prioritizes collecting detailed information. The data collection unit can also prioritize collecting information that can be quickly retrieved if the user is in a hurry. In this way, important information can be prioritized by determining the priority of information 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0092] The data collection unit prioritizes collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit prioritizes collecting information related to that region. For example, if the user is traveling, the data collection unit prioritizes collecting information related to the travel destination. The data collection unit can also prioritize collecting information related to an event if the user is participating in a specific event. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0093] The data collection unit analyzes the user's social media activity and collects relevant information during data collection. For example, the data collection unit collects relevant information based on topics that the user frequently mentions on social media. For example, the data collection unit collects relevant information based on posts from influencers and experts that the user follows. The data collection unit can also collect relevant information based on topics in social media groups and communities that the user participates in. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0094] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The analysis unit can also provide concise analysis results if the user is in a hurry. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide highly easy-to-understand analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0095] The analysis unit adjusts the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0096] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit applies a statistical analysis algorithm to numerical data. The analysis unit can also apply an image analysis algorithm to image data. By applying the most appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0097] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. The analysis unit can also provide an analysis with visually stimulating effects if the user is excited. By adjusting the length of the analysis according to the user's emotions, an analysis of appropriate length can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 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 adjust the length of the analysis.

[0098] The analysis unit determines the priority of analysis based on the data collection period during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. For example, the analysis unit analyzes the most recent data while referring to past data. The analysis unit can also adjust the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0099] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. For example, the analysis unit postpones the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0100] The information provider estimates the user's emotions and adjusts the presentation of the information based on the estimated emotions. For example, if the user is nervous, the provider provides simple and easily understandable information. For example, if the user is relaxed, the provider provides detailed information. The provider can also provide concise information if the user is in a hurry. In this way, by adjusting the presentation of information according to the user's emotions, it is possible to provide easily understandable information. 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 information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the presentation of the information.

[0101] The information provider adjusts the level of detail provided based on the importance of the information at the time of provision. For example, the provider provides a detailed explanation for highly important information. For example, the provider provides a simplified explanation for less important information. The provider can also adjust the level of detail provided according to the importance of the information. This allows for efficient information provision by adjusting the level of detail according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into a generating AI and have the generating AI perform the adjustment of the level of detail provided.

[0102] The information provider applies different information provision algorithms depending on the information category at the time of provision. For example, the provider applies a natural language generation algorithm to text information. For example, the provider applies a visual provision method using graphs and charts to numerical information. The provider can also apply an image analysis algorithm to image information. By applying the most suitable information provision algorithm according to the information category, the accuracy of information provision is improved. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into a generating AI and have the generating AI execute the application of the information provision algorithm.

[0103] The information provider estimates the user's emotions and adjusts the length of the information provided based on the estimated emotions. For example, if the user is in a hurry, the provider provides short, concise information. For example, if the user is relaxed, the provider provides detailed information. The provider can also provide information with visually stimulating effects if the user is excited. By adjusting the length of the information according to the user's emotions, the provider can provide information of an appropriate length. 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 information provider may be performed using AI or not. For example, the information provider can input user emotion data into a generative AI and have the generative AI adjust the length of the information.

[0104] The information provider determines the priority of information provision based on the information collection timing at the time of provision. For example, the information provider may prioritize providing the latest information. For example, the information provider may provide the latest information while referring to past information. The information provider may also adjust the priority of information provision according to the information collection timing. This allows for the provision of the latest information by determining the priority of information provision based on the information collection timing. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider may input the information collection timing into a generating AI and have the generating AI perform the determination of the provision priority.

[0105] The information provider adjusts the order of information delivery based on the relevance of the information. For example, the provider may prioritize the delivery of highly relevant information. For example, it may postpone the delivery of less relevant information. The provider can also adjust the order of information delivery according to the relevance of the information. This allows for efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the relevance of the information into a generating AI and have the generating AI perform the adjustment of the delivery order.

[0106] The real-time update unit estimates the user's emotions and adjusts the frequency of real-time updates based on the estimated emotions. For example, if the user is stressed, the real-time update unit reduces the update frequency and updates only important information. For example, if the user is relaxed, the real-time update unit updates detailed information frequently. Also, if the user is in a hurry, the real-time update unit can quickly update information and provide results in a short time. This allows for efficient information updates by adjusting the frequency of real-time updates 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 real-time update unit may be performed using AI or not. For example, the real-time update unit can input user emotion data into the generative AI and have the generative AI adjust the frequency of real-time updates.

[0107] The real-time update unit selects the optimal update timing by referring to past update history during real-time updates. For example, the real-time update unit selects the most effective update timing from past update history. For example, the real-time update unit analyzes past update history and adjusts the update timing based on the user's usage patterns. The real-time update unit can also select an update timing that meets the user's needs by referring to past update history. In this way, the optimal update timing can be selected by referring to past update history. Some or all of the above processing in the real-time update unit may be performed using AI, for example, or without using AI. For example, the real-time update unit can input past update history into a generating AI and have the generating AI perform the selection of the optimal update timing.

[0108] The real-time update unit estimates the user's emotions and determines the priority of real-time updates based on the estimated emotions. For example, if the user is stressed, the real-time update unit prioritizes updating information of high importance. For example, if the user is relaxed, the real-time update unit prioritizes updating detailed information. Also, if the user is in a hurry, the real-time update unit can prioritize updating information that can be updated quickly. In this way, by determining the priority of real-time updates according to the user's emotions, important information can be updated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 real-time update unit may be performed using AI, for example, or without AI. For example, the real-time update unit can input user emotion data into the generative AI and have the generative AI perform the determination of the real-time update priority.

[0109] The real-time update unit selects the optimal update timing by considering the user's geographical location information during real-time updates. For example, if the user is in a specific region, the real-time update unit prioritizes updating information related to that region. For example, if the user is traveling, the real-time update unit prioritizes updating information related to the travel destination. Furthermore, if the user is participating in a specific event, the real-time update unit can also prioritize updating information related to that event. This allows the system to select the optimal update timing by considering the user's geographical location information. Some or all of the above processing in the real-time update unit may be performed using AI, for example, or without AI. For example, the real-time update unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal update timing.

[0110] The user interface unit estimates the user's emotions and adjusts the interface display method based on the estimated user emotions. For example, if the user interface unit is tense, it provides an interface with calming colors to reduce visual stress. For example, if the user interface unit is enjoying itself, it provides an interface with bright colors to make the input process more enjoyable. Furthermore, if the user interface unit is tired, it can provide a simple and highly visible interface to facilitate the input process. In this way, a highly visible interface can be provided by adjusting the interface display method 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 user interface unit may be performed using AI, for example, or without AI. For example, the user interface unit can input user emotion data into the generative AI and have the generative AI adjust the interface display method.

[0111] The user interface unit selects the optimal display method when displaying the interface by referring to the user's past operation history. For example, the user interface unit selects the optimal display method based on interface designs previously used by the user. For example, the user interface unit prioritizes displaying user-friendly interfaces based on the user's past operation history. The user interface unit can also analyze the user's past operation history and select the most efficient display method. In this way, the optimal display method can be selected by referring to past operation history. Some or all of the above processing in the user interface unit may be performed using AI, for example, or without AI. For example, the user interface unit can input the user's past operation history into a generating AI and have the generating AI perform the selection of the optimal display method.

[0112] The user interface unit estimates the user's emotions and adjusts the interface's operation procedures based on the estimated emotions. For example, if the user is tense, the user interface unit simplifies the operation procedures to reduce stress. For example, if the user is relaxed, the user interface unit provides detailed operation procedures and suggests customizable operation methods. The user interface unit can also prioritize providing procedures that allow for quick operation if the user is in a hurry. This enables efficient operation by adjusting the operation procedures 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 user interface unit may be performed using AI, for example, or not using AI. For example, the user interface unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of operation procedures.

[0113] The user interface unit selects the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the user interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the user interface unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the user interface unit can also provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the user interface unit may be performed using AI, for example, or without AI. For example, the user interface unit can input the user's device information into a generating AI and have the generating AI perform the selection of the optimal display method.

[0114] The trend analysis unit estimates the user's emotions and adjusts the trend display method based on the estimated user emotions. For example, if the user is tense, the trend analysis unit provides a simple and highly visible trend display. For example, if the user is relaxed, the trend analysis unit provides a detailed trend display. The trend analysis unit can also provide a concise trend display if the user is in a hurry. In this way, a highly visible trend display can be provided by adjusting the trend display method 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 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 trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the trend display method.

[0115] The trend analysis unit predicts current trends by referring to past trend data during trend analysis. For example, the trend analysis unit predicts current trends based on past trend data. For example, the trend analysis unit analyzes past trend data and predicts future trends. The trend analysis unit can also predict current trends by referring to past trend data. In this way, by referring to past trend data, current trends can be predicted with high accuracy. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without using AI. For example, the trend analysis unit can input past trend data into a generating AI and have the generating AI perform a prediction of current trends.

[0116] The trend analysis unit applies different trend analysis methods to each data category during trend analysis. For example, for text data, the trend analysis unit applies a trend analysis method using natural language processing. For numerical data, the trend analysis unit applies a trend analysis method using statistical analysis. Furthermore, the trend analysis unit can also apply a trend analysis method using image analysis to image data. This improves the accuracy of trend analysis by applying the most suitable trend analysis method for each data category. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input the data categories into a generating AI and have the generating AI execute the application of the trend analysis method.

[0117] The trend analysis unit estimates the user's emotions and adjusts the importance of trends based on the estimated emotions. For example, if the user is stressed, the trend analysis unit will prioritize displaying high-importance trends. For example, if the user is relaxed, the trend analysis unit will display detailed trends. Furthermore, if the user is in a hurry, the trend analysis unit can prioritize displaying trends that can be quickly understood. In this way, important trends can be prioritized by adjusting the importance of trends 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 trend analysis unit may be performed using AI or not using AI. For example, the trend analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of trend importance.

[0118] The trend analysis unit analyzes changes in trends based on the data collection period during trend analysis. For example, the trend analysis unit analyzes changes in trends based on the latest data. For example, the trend analysis unit analyzes the latest trends while referring to past data. The trend analysis unit can also analyze changes in trends according to the data collection period. This allows for the understanding of the latest trends by analyzing changes in trends based on the data collection period. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input the data collection period into a generating AI and have the generating AI perform the analysis of changes in trends.

[0119] The trend analysis unit analyzes trends by referring to relevant market data during trend analysis. For example, the trend analysis unit analyzes trends based on relevant market data. For example, the trend analysis unit analyzes trends while referring to relevant market data. The trend analysis unit can also analyze changes in trends by referring to relevant market data. This allows for highly accurate analysis of changes in trends by referring to relevant market data. Some or all of the above processing in the trend analysis unit may be performed using AI, for example, or without AI. For example, the trend analysis unit can input relevant market data into a generating AI and have the generating AI perform the trend analysis.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The reception system can automatically suggest relevant past research results based on user input. For example, if a user enters a question about a specific topic, the reception system will present research results from similar topics conducted in the past. The reception system can also extract keywords related to the user's question and filter past research results accordingly. Furthermore, the reception system can refer to the user's past research history and prioritize the presentation of highly relevant research results. This allows users to efficiently conduct new research while referring to past research results.

[0122] The information provider can automatically collect and provide the latest trend information related to the user's research topic. For example, it can collect the latest trend information from news sites and social media on the internet and provide it to the user. It can also collect and provide the latest research results and technological trends in specific industries or fields. Furthermore, it can collect information on events and conferences related to the user's research topic and notify the user. This allows the user to proceed with their research based on the latest trend information.

[0123] The analytics unit can visualize and provide data related to the user's research topic. For example, it can convert collected data into graphs and charts, providing them in a visually easy-to-understand format. It can also visualize and present data trends and patterns. Furthermore, it can visualize data correlations, allowing users to intuitively understand the relationships between data. This makes it easier for users to visually grasp the data, improving research efficiency.

[0124] The data collection unit can collect and provide data related to the user's research topic in multiple languages. For example, the unit can collect news articles and academic papers written in multiple languages, such as English, Japanese, and Chinese. The unit can also automatically translate data in each language and provide it to the user. Furthermore, the unit can prioritize data collection in the language specified by the user. This allows users to conduct research using multilingual data and gather information from a global perspective.

[0125] The data provider can update and provide data related to the user's research topic in real time. For example, immediately after the user enters their research topic, the data provider can collect and provide the latest relevant data. Furthermore, the data provider can update and provide newly collected data in real time as the user progresses with their research. In addition, when the user reviews their research results, the data provider can provide analysis results based on the latest data. This ensures that the user can always conduct their research based on the most up-to-date information.

[0126] The reception desk can estimate the user's emotions and adjust the research topic and question input method based on the estimated emotions. For example, if the reception desk is stressed, it can suggest a simple input method. If the user is relaxed, it can suggest a more detailed input method. Furthermore, if the user is in a hurry, it can suggest a method that allows for quick input. By providing the optimal input method according to the user's emotions, it reduces user stress and enables efficient input.

[0127] The information provider can estimate the user's emotions and adjust the way the information is presented based on those emotions. For example, if the user is stressed, the provider can provide simple, easy-to-understand information. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is in a hurry, the provider can provide concise information. By adjusting the way information is presented according to the user's emotions, the provider can deliver highly visible information.

[0128] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide highly understandable analysis results.

[0129] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of information collection and collect only essential information. Conversely, if the user is relaxed, the unit can collect detailed information and provide a wide range of data. Furthermore, if the user is in a hurry, the unit can collect information quickly and provide results in a short time. This allows for efficient information collection by adjusting the timing of information collection according to the user's emotions.

[0130] The real-time update unit can estimate the user's emotions and adjust the frequency of real-time updates based on those emotions. For example, if the user is stressed, the real-time update unit will reduce the update frequency and only update important information. Conversely, if the user is relaxed, the real-time update unit can frequently update detailed information. Furthermore, if the user is in a hurry, the real-time update unit can quickly update information and provide results in a short time. This allows for efficient information updates by adjusting the frequency of real-time updates according to the user's emotions.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The reception desk receives the user's research topic or question. For example, the reception desk accepts the research topic or question entered by the user in text format. The reception desk can also accept voice input, using speech recognition technology to convert the user's voice into text. Furthermore, the reception desk can refer to the user's past research history to suggest the most suitable input format. Step 2: The collection unit automatically collects relevant information based on the information entered by the reception unit. For example, the collection unit can use web scraping techniques to collect news articles and academic papers from the internet. It can also use APIs to collect data such as corporate financial reports. Furthermore, the collection unit can prioritize the collection of relevant information based on the user's current areas of interest. Step 3: The analysis unit analyzes the data collected by the collection unit and extracts relevant information. For example, the analysis unit analyzes the collected text data using natural language processing techniques. It can also analyze data trends and patterns using machine learning algorithms. Furthermore, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Step 4: The service provider provides the analysis results obtained by the analysis unit. For example, the service provider provides the analysis results to the user in report format. Alternatively, the analysis results can be provided visually using a dashboard display. Furthermore, the service provider can estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input of research themes and questions. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically collects information from the internet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data and extracts relevant information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The 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.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and accepts user input of research themes and questions. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically collects information from the internet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data and extracts relevant information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In 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.

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

[0165] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0167] The data processing system 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.

[0168] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input of research themes and questions. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically collects information from the internet. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data and extracts relevant information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the reception unit, collection unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input of research themes and questions. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically collects information from the internet. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data and extracts relevant information. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the analysis results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) A reception area where you can enter research themes and questions, A collection unit that automatically collects relevant information based on the information entered by the reception unit, An analysis unit analyzes the data collected by the aforementioned collection unit and extracts relevant information, A providing unit that provides the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned supply unit is, It features a real-time update unit that updates information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It features a user interface section that provides an intuitive user interface. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It includes a trend analysis unit that uses machine learning to analyze data trends and patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We collect data from online news articles, academic papers, corporate financial reports, and other sources. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We analyze data using natural language processing techniques and extract relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates user sentiment and adjusts research themes and question input methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past research history and suggest the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter research topics or questions, the system suggests input options based on their current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates user sentiment and prioritizes the entered research topics and questions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input research themes or questions, the system prioritizes presenting themes and questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When you enter research themes and questions, the system analyzes your social media activity and suggests related themes and questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, the system analyzes the user's past research history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, the order of provision will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned real-time update unit is: It estimates the user's sentiment and adjusts the frequency of real-time updates based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned real-time update unit is: During real-time updates, the system references past update history to select the optimal update timing. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned real-time update unit is: It estimates user sentiment and determines the priority of real-time updates based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned real-time update unit is: During real-time updates, the system selects the optimal update timing by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The user interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The user interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 37) The user interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The user interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned trend analysis unit, It estimates the user's emotions and adjusts how trends are displayed based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned trend analysis unit, When analyzing trends, we refer to past trend data to predict current trends. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned trend analysis unit, When performing trend analysis, different trend analysis methods are applied to each data category. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned trend analysis unit, It estimates the user's emotions and adjusts the importance of trends based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned trend analysis unit, When analyzing trends, analyze changes in trends based on the data collection period. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned trend analysis unit, When analyzing trends, we refer to relevant market data to analyze the trends. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where you can enter research themes and questions, A collection unit that automatically collects relevant information based on the information entered by the reception unit, An analysis unit analyzes the data collected by the aforementioned collection unit and extracts relevant information, A providing unit that provides the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned supply unit is, It features a real-time update unit that updates information in real time. The system according to feature 1.

3. The aforementioned supply unit is, It features a user interface section that provides an intuitive user interface. The system according to feature 1.

4. The aforementioned analysis unit, It includes a trend analysis unit that uses machine learning to analyze data trends and patterns. The system according to feature 1.

5. The aforementioned collection unit is We collect data from online news articles, academic papers, corporate financial reports, and other sources. The system according to feature 1.

6. The aforementioned analysis unit, We analyze data using natural language processing techniques and extract relevant information. The system according to feature 1.

7. The aforementioned reception unit is The system estimates user sentiment and adjusts research themes and question input methods based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned reception unit is We analyze the user's past research history and suggest the optimal input format. The system according to feature 1.

9. The aforementioned reception unit is When users enter research topics or questions, the system will suggest input options based on their current areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the entered research topics and questions based on those estimated emotions. The system according to feature 1.

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