system

Abstract

The system according to this embodiment aims to streamline research operations by automating the collection and summarization of relevant information. [Solution] The system according to the embodiment comprises an information gathering unit, an analysis unit, a summary generation unit, a report generation unit, and an update unit. The information gathering unit collects information based on specified keywords and topics. The analysis unit analyzes the information collected by the information gathering unit. The summary generation unit summarizes the information analyzed by the analysis unit. The report generation unit creates a report based on the summary generated by the summary generation unit. The update unit periodically collects update information.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, collection of related information and creation of summaries are often performed manually, which has a problem of low efficiency.

[0005] The system according to the embodiment aims to automate the collection of related information and the creation of summaries to improve the efficiency of research work.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an information gathering unit, an analysis unit, a summary generation unit, a report generation unit, and an update unit. The information gathering unit collects information based on specified keywords and topics. The analysis unit analyzes the information collected by the information gathering unit. The summary generation unit summarizes the information analyzed by the analysis unit. The report generation unit creates a report based on the summary generated by the summary generation unit. The update unit periodically collects update information. [Effects of the Invention]

[0007] The system according to this embodiment can automate the collection and summarization of relevant information, thereby streamlining research operations. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM (Random Access Memory) 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 information gathering agent system according to an embodiment of the present invention is a system that automatically collects relevant information from various websites and internal databases and creates reports and summaries. This information gathering agent system collects relevant information from websites and internal databases based on specified keywords and topics. Next, it analyzes the collected information and automatically generates summaries and reports. This system can collect updated information on a regular basis, significantly improving the efficiency of employees' research work. For example, when a marketing person conducts market research on a new product, this system can collect relevant news articles and market reports. Next, it analyzes the collected information, extracts key points, and creates a summary. For example, it can summarize important trends and market developments from collected news articles. This system can also create detailed reports based on the collected information. As a result, employees can significantly reduce the time spent on information gathering and concentrate on strategic decision-making and creative work. Furthermore, this system has a function to collect updated information on a regular basis. For example, it can collect the latest information related to specified keywords and topics based on a regular daily or weekly schedule. As a result, employees can always have access to the latest information, improving the efficiency of research work. This system is extremely useful for business people, researchers, marketing personnel, consultants, planners, and others who need to gather information and conduct research. By reducing the time spent on information gathering and providing accurate and up-to-date data, this system can simultaneously improve the efficiency and quality of operations. For example, in today's world, where the importance of utilizing online information has increased due to the spread of remote work, this system is extremely useful. As companies increasingly demand efficiency and automation, automating information gathering and summarization tasks can provide users with an environment where they can concentrate on strategic decision-making and creative work. Furthermore, the information management and analysis tools market is growing rapidly, with the business intelligence market alone estimated at 100 billion yen. This system has great potential to expand its market share by utilizing AI.This allows the information gathering agent system to significantly streamline employee research tasks, enabling them to focus on strategic decision-making and creative work.

[0029] The information gathering agent system according to this embodiment comprises an information gathering unit, an analysis unit, a summary generation unit, a report generation unit, and an update unit. The information gathering unit collects information based on specified keywords or topics. The information gathering unit collects information from, for example, websites and internal databases. The information gathering unit uses AI to search across websites and databases and extract highly relevant information. For example, when a marketing person conducts market research on a new product, the information gathering unit can collect relevant news articles and market reports. The information gathering unit can collect information from websites using, for example, web scraping technology. The information gathering unit can also obtain information from internal databases using APIs. The analysis unit analyzes the collected information and extracts important points. The analysis unit analyzes the information using, for example, text mining technology. The analysis unit uses AI to analyze the collected information and extract important points. For example, the analysis unit can extract important trends and market movements from collected news articles. The analysis unit can analyze the information using, for example, data mining technology. Furthermore, the analysis unit can analyze information using statistical analysis techniques. The summary generation unit generates a summary based on the extracted key points. The summary generation unit generates a summary using, for example, natural language processing techniques. The summary generation unit generates a summary based on key points extracted using AI. For example, the summary generation unit can summarize important trends and market developments from collected news articles. The summary generation unit can generate a summary using, for example, rule-based summary generation techniques. The summary generation unit can also generate a summary using generation AI. The report generation unit creates a detailed report based on the generated summary. For example, the report generation unit creates a detailed report based on the generated summary. The report generation unit creates a detailed report based on a summary generated using AI. For example, the report generation unit can create a detailed report based on collected information. The report generation unit can create a report based on, for example, the report structure and the templates used.Furthermore, the report generation unit can also create reports using generation AI. The update unit periodically collects the latest information related to specified keywords and topics. The update unit collects the latest information based on a regular schedule, such as daily or weekly. The update unit periodically collects the latest information related to specified keywords and topics using AI. For example, the update unit can collect the latest information related to specified keywords and topics based on a regular schedule, such as daily or weekly. The update unit can collect the latest information, for example, when a specific event occurs. The update unit can also collect the latest information using generation AI. As a result, the information gathering agent system according to the embodiment can streamline research operations by automatically collecting information based on specified keywords and topics, and performing analysis, summarization, report creation, and collection of update information.

[0030] The Information Gathering Department collects information based on specified keywords and topics. For example, it collects information from websites and internal databases. The Department uses AI to search across websites and databases and extract highly relevant information. Specifically, it uses web scraping technology to collect information from websites. Web scraping technology analyzes the HTML structure of a specified webpage and extracts the necessary data. For example, when a marketing team conducts market research for a new product, the Information Gathering Department can collect relevant news articles and market reports. This allows the marketing team to understand the latest market trends and the actions of competitors. The Information Gathering Department can also obtain information from internal databases using APIs. By using APIs, it can access various internal databases and efficiently collect necessary information. For example, it can obtain past sales performance and customer feedback from the internal sales database to help with market research for new products. Furthermore, the Information Gathering Department can use AI to evaluate the relevance of the collected information and prioritize the extraction of highly important information. This allows the Information Gathering Department to efficiently collect necessary information from a vast amount of data, improving the efficiency of research operations.

[0031] The analysis department analyzes the collected information and extracts key points. For example, the analysis department uses text mining technology to analyze the information. Text mining technology is a technique for extracting useful information from large amounts of text data, and it analyzes text data using natural language processing technology. Specifically, the analysis department can extract important trends and market movements from collected news articles and market reports. For example, by using AI to analyze the collected information and analyzing the frequency of occurrence of specific keywords and phrases, it can identify important trends. Furthermore, the analysis department can analyze information using data mining technology. Data mining technology is a technique for discovering patterns and regularities from large amounts of data, and it analyzes information using statistical analysis technology. For example, by analyzing collected market data, it can predict fluctuations in demand for specific products and services. In addition, the analysis department uses AI to analyze the collected information and extract key points. AI can analyze data using machine learning algorithms and automatically extract important information. For example, it can extract important trends and market movements from collected news articles. This allows the analysis department to efficiently analyze the collected information and improve the efficiency of research operations.

[0032] The summary generation unit generates a summary based on the extracted key points. For example, the summary generation unit can generate summaries using natural language processing (NLP) technology. Natural language processing technology is a technique that analyzes text data, extracts important information, and generates summaries. Specifically, the summary generation unit can summarize important trends and market developments from collected news articles and market reports. For example, it can generate summaries based on key points extracted using AI. AI can analyze data using machine learning algorithms, automatically extract important information, and generate summaries. For example, it can summarize important trends and market developments from collected news articles. Furthermore, the summary generation unit can generate summaries using rule-based summarization technology. Rule-based summarization technology is a technique that analyzes text data based on predefined rules and generates summaries. For example, it can generate summaries based on the frequency of occurrence of specific keywords or phrases. Additionally, the summary generation unit can generate summaries using generative AI. Generative AI is a technique that analyzes text data using deep learning algorithms and generates summaries in natural language. For example, it can summarize important trends and market developments in natural language from collected news articles. This allows the summary generation unit to generate summaries efficiently and accurately, thereby improving the efficiency of research work.

[0033] The report generation unit creates a detailed report based on the generated summary. Specifically, the report generation unit can create a detailed report based on collected information. For example, if a marketing person is conducting market research on a new product, the report generation unit can create a detailed market research report based on collected news articles and market reports. The report generation unit creates a detailed report based on a summary generated using AI. AI can analyze data using machine learning algorithms, automatically extract important information, and generate a report. For example, it can extract important trends and market developments from collected news articles and create a detailed market research report based on them. Furthermore, the report generation unit can create reports based on the report's structure and the templates used. For example, it can create reports according to a specific format, organizing and providing the necessary information. In addition, the report generation unit can also create reports using generative AI. Generative AI is a technology that uses deep learning algorithms to analyze text data and generate detailed reports in natural language. For example, it can create a market research report that describes important trends and market developments in detail in natural language from collected news articles. This allows the report generation unit to efficiently and accurately create detailed reports, thereby improving the efficiency of research operations.

[0034] The update department regularly collects the latest information related to specified keywords and topics. For example, it collects information based on a regular daily or weekly schedule. Specifically, the update department uses AI to regularly collect the latest information related to specified keywords and topics. The AI ​​uses machine learning algorithms to search across websites and databases and extract highly relevant and up-to-date information. For example, if a marketer is conducting market research for a new product, the update department can collect relevant news articles and market reports based on a regular daily or weekly schedule. This allows the marketer to stay informed about the latest market trends and competitor movements. The update department can also collect information when specific events occur. For example, in the event of a new product announcement or significant market fluctuations, the update department can quickly collect relevant information and provide it to the marketer. Furthermore, the update department can also collect information using generative AI. Generative AI is a technology that uses deep learning algorithms to analyze websites and databases and collect information in natural language. For example, it can collect relevant news articles and market reports in natural language and provide them to the marketer. This allows the update department to efficiently and accurately collect the latest information and improve the efficiency of research operations.

[0035] The information gathering department can collect information from websites and internal databases. For example, the information gathering department can collect information from websites. The information gathering department uses AI to search across websites and extract highly relevant information. For example, when a marketing person is conducting market research for a new product, the information gathering department can collect relevant news articles and market reports. For example, the information gathering department can collect information from websites using web scraping technology. The information gathering department can also collect information from internal databases. The information gathering department uses AI to search across internal databases and extract highly relevant information. For example, the information gathering department can collect relevant information from the company's knowledge base. For example, the information gathering department can retrieve information from internal databases using APIs. This allows the information gathering department to obtain relevant information from a wide range of sources by collecting information from websites and internal databases. Some or all of the above processes in the information gathering department may be performed using AI or not. For example, the information gathering department can input information collected from websites into a generating AI and have the generating AI perform information extraction.

[0036] The analysis unit can analyze the collected information and extract key points. For example, the analysis unit can analyze the information using text mining techniques. The analysis unit can also analyze the collected information using AI and extract key points. For example, the analysis unit can extract important trends and market developments from collected news articles. For example, the analysis unit can analyze the information using data mining techniques. Furthermore, the analysis unit can analyze the information using statistical analysis techniques. This allows the analysis unit to analyze the collected information and extract key points, making it easier to grasp the essence of the information. Key points include, but are not limited to, frequently occurring keywords or information related to specific themes. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the analysis of the information.

[0037] The summary generation unit can generate a summary based on the extracted key points. The summary generation unit can generate a summary using, for example, natural language processing technology. The summary generation unit can generate a summary based on the extracted key points using AI. For example, the summary generation unit can summarize important trends and market developments from collected news articles. The summary generation unit can generate a summary using, for example, rule-based summary generation technology. Furthermore, the summary generation unit can generate a summary using a generation AI. This allows the summary generation unit to concisely summarize the key points of information by generating a summary based on key points. Some or all of the above-described processes in the summary generation unit may be performed using AI or not. For example, the summary generation unit can input the extracted key points into a generation AI and have the generation AI perform summary generation.

[0038] The report generation unit can create a detailed report based on the generated summary. For example, the report generation unit can create a detailed report based on the generated summary. The report generation unit can create a detailed report based on a summary generated using AI. For example, the report generation unit can create a detailed report based on the collected information. For example, the report generation unit can create a report based on the report's structure and the templates used. The report generation unit can also create a report using a generation AI. This allows the report generation unit to provide detailed analysis results of the information by creating a detailed report based on the summary. Some or all of the above-described processes in the report generation unit may be performed using AI or not. For example, the report generation unit can input the generated summary into a generation AI and have the generation AI create the report.

[0039] The update unit can periodically collect the latest information related to specified keywords and topics. For example, the update unit can collect the latest information based on a regular schedule, such as daily or weekly. The update unit can periodically collect the latest information related to specified keywords and topics using AI. For example, the update unit can collect the latest information related to specified keywords and topics based on a regular schedule, such as daily or weekly. For example, the update unit can collect the latest information when a specific event occurs. The update unit can also collect the latest information using generative AI. This allows the update unit to always provide the latest information by periodically collecting it. Some or all of the above-described processes in the update unit may be performed using AI or not. For example, the update unit can input the latest information related to specified keywords and topics into a generative AI and have the generative AI collect the information.

[0040] The information gathering unit can analyze the user's past information gathering history and select the optimal gathering method. For example, the information gathering unit prioritizes searching for information sources that the user has frequently used in the past. The information gathering unit uses AI to analyze the user's past information gathering history and select the optimal gathering method. For example, the information gathering unit prioritizes suggesting gathering methods (websites, databases, etc.) that the user has used in the past. For example, the information gathering unit predicts and suggests information sources to be collected at a specific time period based on the user's past information gathering history. In this way, the information gathering unit can select the optimal gathering method by analyzing the user's past information gathering history, enabling efficient information gathering. The optimal gathering method is selected based on, for example, analysis of past gathering results or user feedback, but is not limited to such examples. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's past information gathering history into a generating AI and have the generating AI select the optimal gathering method.

[0041] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit prioritizes collecting information related to the user's current projects. The information gathering unit uses AI to filter information based on the user's current projects and areas of interest. For example, the information gathering unit filters highly relevant information based on the user's areas of interest. For example, the information gathering unit appropriately filters necessary information according to the progress of the user's projects. In this way, the information gathering unit can efficiently collect highly relevant information by filtering information based on the user's current projects and areas of interest. Current projects and areas of interest are identified based on, for example, data from project management tools or the user's search history, but are not limited to such examples. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform information filtering.

[0042] The information gathering unit can prioritize collecting highly relevant information based on the user's geographical location information during information gathering. For example, if the user is in a specific region, the information gathering unit will prioritize collecting information related to that region. The information gathering unit uses AI to collect highly relevant information based on the user's geographical location information. For example, if the user is on a business trip, the information gathering unit will prioritize collecting information related to the destination. For example, if the user is working remotely, the information gathering unit will prioritize collecting information about the area around their home. In this way, the information gathering unit can collect information tailored to the user's situation by prioritizing the collection of highly relevant information based on the user's geographical location information. Geographical location information is obtained based on, for example, GPS data or location information services, but is not limited to these examples. Some or all of the above processing in the information gathering unit may be performed using AI or not using AI. For example, the information gathering 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.

[0043] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit collects information related to topics the user has shown interest in on social media. The information gathering unit uses AI to analyze a user's social media activity and collect relevant information. For example, the information gathering unit collects information based on the content of posts from accounts the user follows. For example, the information gathering unit predicts and collects information that the user might be interested in based on their social media activity. This enables the information gathering unit to collect information based on the user's interests by analyzing their social media activity. Social media activity is analyzed based on, for example, the content of posts, likes, and share history, but is not limited to these examples. Some or all of the above-described processes in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit uses AI to evaluate the importance of the information and adjust the level of detail of the analysis. For example, the analysis unit performs a concise analysis on information of low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of the information. In this way, the analysis unit can perform a detailed analysis on important information by adjusting the level of detail of the analysis based on the importance of the information. The importance of the information is evaluated based on, for example, the source of the information or the recency of the information, but is not limited to such examples. 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 information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a trend analysis algorithm to news articles. The analysis unit uses AI to classify information categories and apply appropriate analysis algorithms. For example, the analysis unit applies a statistical analysis algorithm to market reports. For example, the analysis unit applies a text mining algorithm to technical documents. This allows the analysis unit to perform appropriate analysis according to the characteristics of the information by applying different analysis algorithms depending on the category of information. Information categories are classified into, for example, technical information and business information, but are not limited to such examples. 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 information category data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit prioritizes the analysis of information with high urgency. The analysis unit uses AI to evaluate the information submission timing and determine the analysis priority. For example, the analysis unit quickly analyzes information with an approaching submission deadline. For example, the analysis unit adjusts the analysis priority in stages according to the submission timing. This allows the analysis unit to prioritize the analysis of information with high urgency by determining the analysis priority based on the information submission timing. The information submission timing is evaluated based on, for example, the submission date and time or submission frequency, but is not limited to such examples. 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 information submission timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit uses AI to evaluate the relevance of the information and adjust the order of analysis. For example, the analysis unit postpones the analysis of less relevant information. The analysis unit adjusts the order of analysis step by step according to the relevance of the information. In this way, the analysis unit can prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the information. The relevance of the information is evaluated based on, for example, co-occurrence network analysis or relevance scores, but is not limited to such examples. 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 information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The summary generation unit can adjust the level of detail in the summary based on the importance of the information during summary generation. For example, the summary generation unit will provide a detailed summary for information of high importance. The summary generation unit uses AI to evaluate the importance of information and adjust the level of detail in the summary. For example, the summary generation unit will provide a concise summary for information of low importance. For example, the summary generation unit can adjust the level of detail in the summary in stages according to the importance of the information. In this way, the summary generation unit can provide a detailed summary for important information by adjusting the level of detail in the summary based on the importance of the information. The importance of information is evaluated based on, for example, the source of the information or the recency of the information, but is not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not using AI. For example, the summary generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the summary.

[0049] The summary generation unit can apply different summarization algorithms depending on the information category when generating summaries. For example, the summary generation unit applies a trend summarization algorithm to news articles. The summary generation unit uses AI to classify information categories and apply an appropriate summarization algorithm. For example, the summary generation unit applies a statistical summarization algorithm to market reports. For example, the summary generation unit applies a text mining summarization algorithm to technical documents. By applying different summarization algorithms depending on the information category, the summary generation unit can produce appropriate summaries that are appropriate to the characteristics of the information. Information categories include, for example, technical information and business information, but are not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not. For example, the summary generation unit can input information category data into a generation AI and cause the generation AI to apply an appropriate summarization algorithm.

[0050] The summary generation unit can determine the priority of summaries based on the information submission timing when generating summaries. For example, the summary generation unit prioritizes summarizing information with high urgency. The summary generation unit uses AI to evaluate the information submission timing and determine the priority of summaries. For example, the summary generation unit quickly summarizes information with an approaching submission deadline. For example, the summary generation unit adjusts the priority of summaries in stages according to the submission timing. In this way, the summary generation unit can prioritize summarizing information with high urgency by determining the priority of summaries based on the information submission timing. The information submission timing is evaluated based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not using AI. For example, the summary generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of the summary priority.

[0051] The summary generation unit can adjust the order of summaries based on the relevance of the information during the summary generation process. For example, the summary generation unit prioritizes summarizing highly relevant information. The summary generation unit uses AI to evaluate the relevance of the information and adjust the order of summaries. For example, the summary generation unit postpones summarizing less relevant information. For example, the summary generation unit adjusts the order of summaries in stages according to the relevance of the information. In this way, the summary generation unit can prioritize summarizing highly relevant information by adjusting the order of summaries based on the relevance of the information. The relevance of the information is evaluated based on, for example, co-occurrence network analysis or relevance scores, but is not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not using AI. For example, the summary generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of summaries.

[0052] The report generation unit can adjust the level of detail in a report based on the importance of the summary during report generation. For example, the report generation unit creates a detailed report for summaries of high importance. The report generation unit uses AI to evaluate the importance of summaries and adjust the level of detail in the report. For example, the report generation unit creates a concise report for summaries of low importance. The report generation unit adjusts the level of detail in stages according to the importance of the summary. In this way, the report generation unit can create detailed reports for important summaries by adjusting the level of detail in the report based on the importance of the summary. The importance of a summary is evaluated based on, for example, the source of the summary or the recency of the summary, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input summary importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0053] The report generation unit can apply different report generation algorithms depending on the summary category when generating a report. For example, the report generation unit applies a trend report generation algorithm to news summaries. The report generation unit uses AI to classify the summary category and apply the appropriate report generation algorithm. For example, the report generation unit applies a statistical report generation algorithm to market report summaries. For example, the report generation unit applies a text mining report generation algorithm to technical document summaries. In this way, the report generation unit can produce appropriate reports according to the characteristics of the summary by applying different report generation algorithms depending on the summary category. Summary categories are classified into, for example, technical information and business information, but are not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input summary category data into a generation AI and cause the generation AI to apply the appropriate report generation algorithm.

[0054] The report generation unit can determine the priority of reports based on the submission timing of the summaries when generating reports. For example, the report generation unit will prioritize creating reports for summaries with high urgency. The report generation unit uses AI to evaluate the submission timing of summaries and determine the priority of reports. For example, the report generation unit will quickly create reports for summaries with approaching submission deadlines. The report generation unit will adjust the priority of reports in stages according to the submission timing, for example. In this way, the report generation unit can prioritize the creation of reports for high-urgency summaries by determining the priority of reports based on the submission timing of the summaries. The submission timing of summaries is evaluated based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input the submission timing data of summaries into the generation AI and have the generation AI perform the determination of report priority.

[0055] The report generation unit can adjust the order of reports based on the relevance of the summaries during report generation. For example, the report generation unit prioritizes creating reports for highly relevant summaries. The report generation unit uses AI to evaluate the relevance of summaries and adjust the order of reports. For example, the report generation unit postpones creating reports for less relevant summaries. For example, the report generation unit adjusts the order of reports in stages according to the relevance of the summaries. In this way, the report generation unit can prioritize reporting highly relevant summaries by adjusting the order of reports based on the relevance of the summaries. The relevance of summaries is evaluated based on, for example, co-occurrence network analysis or relevance scores, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input summary relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0056] The update unit can select the optimal collection method by referring to past update information when collecting update information. For example, the update unit prioritizes searching for information sources that have been frequently collected in the past. The update unit uses AI to analyze past update information and select the optimal collection method. For example, the update unit prioritizes suggesting collection methods (websites, databases, etc.) that have been used in the past. For example, the update unit predicts and suggests information sources to be collected at a specific time period based on past update information. This allows the update unit to select the optimal collection method by referring to past update information, enabling efficient information collection. The optimal collection method is selected based on, for example, analysis of past collection results or user feedback, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input past update information data into a generating AI and have the generating AI select the optimal collection method.

[0057] The update unit can apply different collection algorithms depending on the information category when collecting update information. For example, the update unit applies a trend collection algorithm to news information. The update unit uses AI to classify information categories and apply an appropriate collection algorithm. For example, the update unit applies a statistical collection algorithm to market information. For example, the update unit applies a text mining collection algorithm to technical information. This allows the update unit to collect information appropriately according to its characteristics by applying different collection algorithms depending on the information category. The collection algorithm is applied based on, for example, web scraping or data acquisition using APIs, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input information category data into a generating AI and have the generating AI apply an appropriate collection algorithm.

[0058] The update unit can determine the priority of information collection based on the submission timing when collecting update information. For example, the update unit prioritizes the collection of information that is of high urgency. The update unit uses AI to evaluate the submission timing of information and determine the priority of collection. For example, the update unit quickly collects information with an approaching submission deadline. For example, the update unit adjusts the collection priority in stages according to the submission timing. In this way, the update unit can prioritize the collection of information that is of high urgency by determining the priority of collection based on the submission timing of information. The submission timing of information is evaluated based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input information submission timing data into a generating AI and have the generating AI perform the determination of collection priority.

[0059] The update unit can adjust the collection order based on the relevance of the information when collecting update information. For example, the update unit prioritizes the collection of highly relevant information. The update unit uses AI to evaluate the relevance of the information and adjust the collection order. For example, the update unit postpones the collection of less relevant information. The update unit adjusts the collection order in stages according to the relevance of the information. In this way, the update unit can prioritize the collection of highly relevant information by adjusting the collection order based on the relevance of the information. The relevance of the information is evaluated based on, for example, co-occurrence network analysis or relevance score, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the collection order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The information gathering unit can analyze a user's past search history and prioritize searching for information sources that the user has previously shown interest in. For example, by prioritizing searches of websites and databases that the user has frequently accessed in the past, it can efficiently collect more relevant information. The information gathering unit can also predict and suggest information sources to collect at specific times based on the user's past search history. This allows the information gathering unit to select the optimal information gathering method based on the user's past behavior patterns, enabling efficient information collection.

[0062] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, by evaluating the reliability of the information source and author and prioritizing the analysis of highly reliable information, it can provide more accurate analysis results. The analysis unit can also adjust the level of detail of the analysis based on the reliability of the information. This allows the analysis unit to perform detailed analysis on highly reliable information and simplified analysis on less reliable information.

[0063] The summary generation unit can adjust the content of the summary based on the user's areas of interest. For example, if the user is interested in a particular topic, the summary can prioritize the inclusion of information related to that topic. The summary generation unit can also adjust the way the summary is presented based on the user's areas of interest. This allows the summary generation unit to provide a summary that is easy for the user to understand by tailoring it to their interests.

[0064] The report generation unit can adjust the report content based on the user's project progress. For example, it can provide an overall overview in the early stages of a project and then provide detailed analysis results as the project progresses. The report generation unit can also prioritize reports based on the project progress. This allows the report generation unit to provide appropriate reports according to the project's stage.

[0065] The update unit can adjust the timing of update information collection based on the user's schedule. For example, it can reduce the frequency of update information collection during busy periods and increase it during less busy periods. The update unit can also prioritize update information based on the user's schedule. This enables the update unit to efficiently collect information according to the user's schedule.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The information gathering department collects information based on specified keywords and topics. For example, it collects information from websites and internal databases, uses AI to perform cross-sectional searches, and extracts highly relevant information. Specifically, it can collect information using web scraping technology and APIs. Step 2: The analysis unit analyzes the information collected by the information collection unit and extracts key points. For example, it analyzes the information using text mining, data mining, and statistical analysis techniques. Step 3: The summary generation unit generates a summary based on the key points extracted by the analysis unit. For example, it may use natural language processing technology, rule-based summarization technology, or generative AI to generate the summary. Step 4: The report generation unit creates a detailed report based on the summary generated by the summary generation unit. For example, it creates a report based on the report structure and the templates used, and then uses a generation AI to create a detailed report. Step 5: The update unit regularly collects the latest information related to specified keywords and topics. For example, it collects the latest information based on a regular daily or weekly schedule, using AI to gather the information.

[0068] (Example of form 2) An information gathering agent system according to an embodiment of the present invention is a system that automatically collects relevant information from various websites and internal databases and creates reports and summaries. This information gathering agent system collects relevant information from websites and internal databases based on specified keywords and topics. Next, it analyzes the collected information and automatically generates summaries and reports. This system can collect updated information on a regular basis, significantly improving the efficiency of employees' research work. For example, when a marketing person conducts market research on a new product, this system can collect relevant news articles and market reports. Next, it analyzes the collected information, extracts key points, and creates a summary. For example, it can summarize important trends and market developments from collected news articles. This system can also create detailed reports based on the collected information. As a result, employees can significantly reduce the time spent on information gathering and concentrate on strategic decision-making and creative work. Furthermore, this system has a function to collect updated information on a regular basis. For example, it can collect the latest information related to specified keywords and topics based on a regular daily or weekly schedule. As a result, employees can always have access to the latest information, improving the efficiency of research work. This system is extremely useful for business people, researchers, marketing personnel, consultants, planners, and others who need to gather information and conduct research. By reducing the time spent on information gathering and providing accurate and up-to-date data, this system can simultaneously improve the efficiency and quality of operations. For example, in today's world, where the importance of utilizing online information has increased due to the spread of remote work, this system is extremely useful. As companies increasingly demand efficiency and automation, automating information gathering and summarization tasks can provide users with an environment where they can concentrate on strategic decision-making and creative work. Furthermore, the information management and analysis tools market is growing rapidly, with the business intelligence market alone estimated at 100 billion yen. This system has great potential to expand its market share by utilizing AI.This allows the information gathering agent system to significantly streamline employee research tasks, enabling them to focus on strategic decision-making and creative work.

[0069] The information gathering agent system according to this embodiment comprises an information gathering unit, an analysis unit, a summary generation unit, a report generation unit, and an update unit. The information gathering unit collects information based on specified keywords or topics. The information gathering unit collects information from, for example, websites and internal databases. The information gathering unit uses AI to search across websites and databases and extract highly relevant information. For example, when a marketing person conducts market research on a new product, the information gathering unit can collect relevant news articles and market reports. The information gathering unit can collect information from websites using, for example, web scraping technology. The information gathering unit can also obtain information from internal databases using APIs. The analysis unit analyzes the collected information and extracts important points. The analysis unit analyzes the information using, for example, text mining technology. The analysis unit uses AI to analyze the collected information and extract important points. For example, the analysis unit can extract important trends and market movements from collected news articles. The analysis unit can analyze the information using, for example, data mining technology. Furthermore, the analysis unit can analyze information using statistical analysis techniques. The summary generation unit generates a summary based on the extracted key points. The summary generation unit generates a summary using, for example, natural language processing techniques. The summary generation unit generates a summary based on key points extracted using AI. For example, the summary generation unit can summarize important trends and market developments from collected news articles. The summary generation unit can generate a summary using, for example, rule-based summary generation techniques. The summary generation unit can also generate a summary using generation AI. The report generation unit creates a detailed report based on the generated summary. For example, the report generation unit creates a detailed report based on the generated summary. The report generation unit creates a detailed report based on a summary generated using AI. For example, the report generation unit can create a detailed report based on collected information. The report generation unit can create a report based on, for example, the report structure and the templates used.Furthermore, the report generation unit can also create reports using generation AI. The update unit periodically collects the latest information related to specified keywords and topics. The update unit collects the latest information based on a regular schedule, such as daily or weekly. The update unit periodically collects the latest information related to specified keywords and topics using AI. For example, the update unit can collect the latest information related to specified keywords and topics based on a regular schedule, such as daily or weekly. The update unit can collect the latest information, for example, when a specific event occurs. The update unit can also collect the latest information using generation AI. As a result, the information gathering agent system according to the embodiment can streamline research operations by automatically collecting information based on specified keywords and topics, and performing analysis, summarization, report creation, and collection of update information.

[0070] The Information Gathering Department collects information based on specified keywords and topics. For example, it collects information from websites and internal databases. The Department uses AI to search across websites and databases and extract highly relevant information. Specifically, it uses web scraping technology to collect information from websites. Web scraping technology analyzes the HTML structure of a specified webpage and extracts the necessary data. For example, when a marketing team conducts market research for a new product, the Information Gathering Department can collect relevant news articles and market reports. This allows the marketing team to understand the latest market trends and the actions of competitors. The Information Gathering Department can also obtain information from internal databases using APIs. By using APIs, it can access various internal databases and efficiently collect necessary information. For example, it can obtain past sales performance and customer feedback from the internal sales database to help with market research for new products. Furthermore, the Information Gathering Department can use AI to evaluate the relevance of the collected information and prioritize the extraction of highly important information. This allows the Information Gathering Department to efficiently collect necessary information from a vast amount of data, improving the efficiency of research operations.

[0071] The analysis department analyzes the collected information and extracts key points. For example, the analysis department uses text mining technology to analyze the information. Text mining technology is a technique for extracting useful information from large amounts of text data, and it analyzes text data using natural language processing technology. Specifically, the analysis department can extract important trends and market movements from collected news articles and market reports. For example, by using AI to analyze the collected information and analyzing the frequency of occurrence of specific keywords and phrases, it can identify important trends. Furthermore, the analysis department can analyze information using data mining technology. Data mining technology is a technique for discovering patterns and regularities from large amounts of data, and it analyzes information using statistical analysis technology. For example, by analyzing collected market data, it can predict fluctuations in demand for specific products and services. In addition, the analysis department uses AI to analyze the collected information and extract key points. AI can analyze data using machine learning algorithms and automatically extract important information. For example, it can extract important trends and market movements from collected news articles. This allows the analysis department to efficiently analyze the collected information and improve the efficiency of research operations.

[0072] The summary generation unit generates a summary based on the extracted key points. For example, the summary generation unit can generate summaries using natural language processing (NLP) technology. Natural language processing technology is a technique that analyzes text data, extracts important information, and generates summaries. Specifically, the summary generation unit can summarize important trends and market developments from collected news articles and market reports. For example, it can generate summaries based on key points extracted using AI. AI can analyze data using machine learning algorithms, automatically extract important information, and generate summaries. For example, it can summarize important trends and market developments from collected news articles. Furthermore, the summary generation unit can generate summaries using rule-based summarization technology. Rule-based summarization technology is a technique that analyzes text data based on predefined rules and generates summaries. For example, it can generate summaries based on the frequency of occurrence of specific keywords or phrases. Additionally, the summary generation unit can generate summaries using generative AI. Generative AI is a technique that analyzes text data using deep learning algorithms and generates summaries in natural language. For example, it can summarize important trends and market developments in natural language from collected news articles. This allows the summary generation unit to generate summaries efficiently and accurately, thereby improving the efficiency of research work.

[0073] The report generation unit creates a detailed report based on the generated summary. Specifically, the report generation unit can create a detailed report based on collected information. For example, if a marketing person is conducting market research on a new product, the report generation unit can create a detailed market research report based on collected news articles and market reports. The report generation unit creates a detailed report based on a summary generated using AI. AI can analyze data using machine learning algorithms, automatically extract important information, and generate a report. For example, it can extract important trends and market developments from collected news articles and create a detailed market research report based on them. Furthermore, the report generation unit can create reports based on the report's structure and the templates used. For example, it can create reports according to a specific format, organizing and providing the necessary information. In addition, the report generation unit can also create reports using generative AI. Generative AI is a technology that uses deep learning algorithms to analyze text data and generate detailed reports in natural language. For example, it can create a market research report that describes important trends and market developments in detail in natural language from collected news articles. This allows the report generation unit to efficiently and accurately create detailed reports, thereby improving the efficiency of research operations.

[0074] The update department regularly collects the latest information related to specified keywords and topics. For example, it collects information based on a regular daily or weekly schedule. Specifically, the update department uses AI to regularly collect the latest information related to specified keywords and topics. The AI ​​uses machine learning algorithms to search across websites and databases and extract highly relevant and up-to-date information. For example, if a marketer is conducting market research for a new product, the update department can collect relevant news articles and market reports based on a regular daily or weekly schedule. This allows the marketer to stay informed about the latest market trends and competitor movements. The update department can also collect information when specific events occur. For example, in the event of a new product announcement or significant market fluctuations, the update department can quickly collect relevant information and provide it to the marketer. Furthermore, the update department can also collect information using generative AI. Generative AI is a technology that uses deep learning algorithms to analyze websites and databases and collect information in natural language. For example, it can collect relevant news articles and market reports in natural language and provide them to the marketer. This allows the update department to efficiently and accurately collect the latest information and improve the efficiency of research operations.

[0075] The information gathering department can collect information from websites and internal databases. For example, the information gathering department can collect information from websites. The information gathering department uses AI to search across websites and extract highly relevant information. For example, when a marketing person is conducting market research for a new product, the information gathering department can collect relevant news articles and market reports. For example, the information gathering department can collect information from websites using web scraping technology. The information gathering department can also collect information from internal databases. The information gathering department uses AI to search across internal databases and extract highly relevant information. For example, the information gathering department can collect relevant information from the company's knowledge base. For example, the information gathering department can retrieve information from internal databases using APIs. This allows the information gathering department to obtain relevant information from a wide range of sources by collecting information from websites and internal databases. Some or all of the above processes in the information gathering department may be performed using AI or not. For example, the information gathering department can input information collected from websites into a generating AI and have the generating AI perform information extraction.

[0076] The analysis unit can analyze the collected information and extract key points. For example, the analysis unit can analyze the information using text mining techniques. The analysis unit can also analyze the collected information using AI and extract key points. For example, the analysis unit can extract important trends and market developments from collected news articles. For example, the analysis unit can analyze the information using data mining techniques. Furthermore, the analysis unit can analyze the information using statistical analysis techniques. This allows the analysis unit to analyze the collected information and extract key points, making it easier to grasp the essence of the information. Key points include, but are not limited to, frequently occurring keywords or information related to specific themes. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the analysis of the information.

[0077] The summary generation unit can generate a summary based on the extracted key points. The summary generation unit can generate a summary using, for example, natural language processing technology. The summary generation unit can generate a summary based on the extracted key points using AI. For example, the summary generation unit can summarize important trends and market developments from collected news articles. The summary generation unit can generate a summary using, for example, rule-based summary generation technology. Furthermore, the summary generation unit can generate a summary using a generation AI. This allows the summary generation unit to concisely summarize the key points of information by generating a summary based on key points. Some or all of the above-described processes in the summary generation unit may be performed using AI or not. For example, the summary generation unit can input the extracted key points into a generation AI and have the generation AI perform summary generation.

[0078] The report generation unit can create a detailed report based on the generated summary. For example, the report generation unit can create a detailed report based on the generated summary. The report generation unit can create a detailed report based on a summary generated using AI. For example, the report generation unit can create a detailed report based on the collected information. For example, the report generation unit can create a report based on the report's structure and the templates used. The report generation unit can also create a report using a generation AI. This allows the report generation unit to provide detailed analysis results of the information by creating a detailed report based on the summary. Some or all of the above-described processes in the report generation unit may be performed using AI or not. For example, the report generation unit can input the generated summary into a generation AI and have the generation AI create the report.

[0079] The update unit can periodically collect the latest information related to specified keywords and topics. For example, the update unit can collect the latest information based on a regular schedule, such as daily or weekly. The update unit can periodically collect the latest information related to specified keywords and topics using AI. For example, the update unit can collect the latest information related to specified keywords and topics based on a regular schedule, such as daily or weekly. For example, the update unit can collect the latest information when a specific event occurs. The update unit can also collect the latest information using generative AI. This allows the update unit to always provide the latest information by periodically collecting it. Some or all of the above-described processes in the update unit may be performed using AI or not. For example, the update unit can input the latest information related to specified keywords and topics into a generative AI and have the generative AI collect the information.

[0080] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, if the user is stressed, the information gathering unit will reduce the frequency of information gathering and collect only important information. The information gathering unit estimates the user's emotions using an emotion estimation algorithm. For example, the information gathering unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the information gathering unit will increase the frequency of information gathering and collect more detailed information. The information gathering unit estimates the user's emotions using an emotion estimation algorithm. For example, the information gathering unit can estimate the user's emotions using survey results. For example, if the user is in a hurry, the information gathering unit will speed up the timing of information gathering and provide the necessary information immediately. In this way, the information gathering unit can adjust the timing of information gathering according to the user's emotions, enabling information gathering that is appropriate to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the information gathering unit may be performed using AI or not. For example, the information gathering unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0081] The information gathering unit can analyze the user's past information gathering history and select the optimal gathering method. For example, the information gathering unit prioritizes searching for information sources that the user has frequently used in the past. The information gathering unit uses AI to analyze the user's past information gathering history and select the optimal gathering method. For example, the information gathering unit prioritizes suggesting gathering methods (websites, databases, etc.) that the user has used in the past. For example, the information gathering unit predicts and suggests information sources to be collected at a specific time period based on the user's past information gathering history. In this way, the information gathering unit can select the optimal gathering method by analyzing the user's past information gathering history, enabling efficient information gathering. The optimal gathering method is selected based on, for example, analysis of past gathering results or user feedback, but is not limited to such examples. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's past information gathering history into a generating AI and have the generating AI select the optimal gathering method.

[0082] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit prioritizes collecting information related to the user's current projects. The information gathering unit uses AI to filter information based on the user's current projects and areas of interest. For example, the information gathering unit filters highly relevant information based on the user's areas of interest. For example, the information gathering unit appropriately filters necessary information according to the progress of the user's projects. In this way, the information gathering unit can efficiently collect highly relevant information by filtering information based on the user's current projects and areas of interest. Current projects and areas of interest are identified based on, for example, data from project management tools or the user's search history, but are not limited to such examples. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform information filtering.

[0083] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the information gathering unit will prioritize collecting information of high importance. The information gathering unit estimates the user's emotions using an emotion estimation algorithm. For example, the information gathering unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the information gathering unit will prioritize collecting detailed information. The information gathering unit estimates the user's emotions using an emotion estimation algorithm. For example, the information gathering unit can estimate the user's emotions using survey results. For example, if the user is in a hurry, the information gathering unit will prioritize collecting information that can be collected quickly. In this way, the information gathering unit can collect information appropriate to the user's situation by determining the priority of information to collect 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 may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the information gathering unit may be performed using AI or not. For example, the information gathering unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0084] The information gathering unit can prioritize collecting highly relevant information based on the user's geographical location information during information gathering. For example, if the user is in a specific region, the information gathering unit will prioritize collecting information related to that region. The information gathering unit uses AI to collect highly relevant information based on the user's geographical location information. For example, if the user is on a business trip, the information gathering unit will prioritize collecting information related to the destination. For example, if the user is working remotely, the information gathering unit will prioritize collecting information about the area around their home. In this way, the information gathering unit can collect information tailored to the user's situation by prioritizing the collection of highly relevant information based on the user's geographical location information. Geographical location information is obtained based on, for example, GPS data or location information services, but is not limited to these examples. Some or all of the above processing in the information gathering unit may be performed using AI or not using AI. For example, the information gathering 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.

[0085] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit collects information related to topics the user has shown interest in on social media. The information gathering unit uses AI to analyze a user's social media activity and collect relevant information. For example, the information gathering unit collects information based on the content of posts from accounts the user follows. For example, the information gathering unit predicts and collects information that the user might be interested in based on their social media activity. This enables the information gathering unit to collect information based on the user's interests by analyzing their social media activity. Social media activity is analyzed based on, for example, the content of posts, likes, and share history, but is not limited to these examples. Some or all of the above-described processes in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit provides simple and easily understandable analysis results. The analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the analysis unit can estimate the user's emotions using facial recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the analysis unit provides detailed analysis results. The analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the analysis unit can estimate the user's emotions using survey results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis 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-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit uses AI to evaluate the importance of the information and adjust the level of detail of the analysis. For example, the analysis unit performs a concise analysis on information of low importance. For example, the analysis unit adjusts the level of detail of the analysis in stages according to the importance of the information. In this way, the analysis unit can perform a detailed analysis on important information by adjusting the level of detail of the analysis based on the importance of the information. The importance of the information is evaluated based on, for example, the source of the information or the recency of the information, but is not limited to such examples. 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 information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a trend analysis algorithm to news articles. The analysis unit uses AI to classify information categories and apply appropriate analysis algorithms. For example, the analysis unit applies a statistical analysis algorithm to market reports. For example, the analysis unit applies a text mining algorithm to technical documents. This allows the analysis unit to perform appropriate analysis according to the characteristics of the information by applying different analysis algorithms depending on the category of information. Information categories are classified into, for example, technical information and business information, but are not limited to such examples. 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 information category data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust 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 result. The analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the analysis unit can estimate the user's emotions using facial recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit estimates the user's emotions using an emotion estimation algorithm. For example, the analysis unit can estimate the user's emotions using survey results. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. In this way, the analysis unit can provide analysis results appropriate to the user's situation by adjusting the length of the analysis 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-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0090] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis process. For example, the analysis unit prioritizes the analysis of information with high urgency. The analysis unit uses AI to evaluate the information submission timing and determine the analysis priority. For example, the analysis unit quickly analyzes information with an approaching submission deadline. For example, the analysis unit adjusts the analysis priority in stages according to the submission timing. This allows the analysis unit to prioritize the analysis of information with high urgency by determining the analysis priority based on the information submission timing. The information submission timing is evaluated based on, for example, the submission date and time or submission frequency, but is not limited to such examples. 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 information submission timing data into a generating AI and have the generating AI perform the determination of analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit uses AI to evaluate the relevance of the information and adjust the order of analysis. For example, the analysis unit postpones the analysis of less relevant information. The analysis unit adjusts the order of analysis step by step according to the relevance of the information. In this way, the analysis unit can prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the information. The relevance of the information is evaluated based on, for example, co-occurrence network analysis or relevance scores, but is not limited to such examples. 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 information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The summary generation unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is nervous, the summary generation unit provides a simple and easy-to-read summary. The summary generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the summary generation unit can estimate the user's emotions using facial recognition technology. The summary generation unit can also estimate the user's emotions using speech analysis technology. For example, if the user is relaxed, the summary generation unit provides a detailed summary. The summary generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the summary generation unit can estimate the user's emotions using survey results. For example, if the user is in a hurry, the summary generation unit provides a concise summary. In this way, the summary generation unit can provide a summary that is easy for the user to understand by adjusting the way the summary is presented 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-described processes in the summary generation unit may be performed using AI or not. For example, the summary generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0093] The summary generation unit can adjust the level of detail in the summary based on the importance of the information during summary generation. For example, the summary generation unit will provide a detailed summary for information of high importance. The summary generation unit uses AI to evaluate the importance of information and adjust the level of detail in the summary. For example, the summary generation unit will provide a concise summary for information of low importance. For example, the summary generation unit can adjust the level of detail in the summary in stages according to the importance of the information. In this way, the summary generation unit can provide a detailed summary for important information by adjusting the level of detail in the summary based on the importance of the information. The importance of information is evaluated based on, for example, the source of the information or the recency of the information, but is not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not using AI. For example, the summary generation unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the summary.

[0094] The summary generation unit can apply different summarization algorithms depending on the information category when generating summaries. For example, the summary generation unit applies a trend summarization algorithm to news articles. The summary generation unit uses AI to classify information categories and apply an appropriate summarization algorithm. For example, the summary generation unit applies a statistical summarization algorithm to market reports. For example, the summary generation unit applies a text mining summarization algorithm to technical documents. By applying different summarization algorithms depending on the information category, the summary generation unit can produce appropriate summaries that are appropriate to the characteristics of the information. Information categories include, for example, technical information and business information, but are not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not. For example, the summary generation unit can input information category data into a generation AI and cause the generation AI to apply an appropriate summarization algorithm.

[0095] The summary generation unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summary generation unit provides a short, concise summary. The summary generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the summary generation unit can estimate the user's emotions using facial recognition technology. The summary generation unit can also estimate the user's emotions using speech analysis technology. For example, if the user is relaxed, the summary generation unit provides a detailed summary. The summary generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the summary generation unit can estimate the user's emotions using survey results. For example, if the user is excited, the summary generation unit provides a visually stimulating summary. In this way, the summary generation unit can provide a summary appropriate to the user's situation by adjusting the length of the summary 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-described processes in the summary generation unit may be performed using AI or not. For example, the summary generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0096] The summary generation unit can determine the priority of summaries based on the information submission timing when generating summaries. For example, the summary generation unit prioritizes summarizing information with high urgency. The summary generation unit uses AI to evaluate the information submission timing and determine the priority of summaries. For example, the summary generation unit quickly summarizes information with an approaching submission deadline. For example, the summary generation unit adjusts the priority of summaries in stages according to the submission timing. In this way, the summary generation unit can prioritize summarizing information with high urgency by determining the priority of summaries based on the information submission timing. The information submission timing is evaluated based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not using AI. For example, the summary generation unit can input information submission timing data into a generation AI and have the generation AI perform the determination of the summary priority.

[0097] The summary generation unit can adjust the order of summaries based on the relevance of the information during the summary generation process. For example, the summary generation unit prioritizes summarizing highly relevant information. The summary generation unit uses AI to evaluate the relevance of the information and adjust the order of summaries. For example, the summary generation unit postpones summarizing less relevant information. For example, the summary generation unit adjusts the order of summaries in stages according to the relevance of the information. In this way, the summary generation unit can prioritize summarizing highly relevant information by adjusting the order of summaries based on the relevance of the information. The relevance of the information is evaluated based on, for example, co-occurrence network analysis or relevance scores, but is not limited to such examples. Some or all of the above processing in the summary generation unit may be performed using AI or not using AI. For example, the summary generation unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of summaries.

[0098] The report generation unit can estimate the user's emotions and adjust the report's presentation based on the estimated emotions. For example, if the user is nervous, the report generation unit provides a simple and highly visual report. The report generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the report generation unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the report generation unit provides a detailed report. The report generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the report generation unit can estimate the user's emotions using survey results. For example, if the user is in a hurry, the report generation unit provides a concise report. In this way, the report generation unit can provide a report that is easy for the user to understand by adjusting the report's presentation 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 processing described above in the report generation unit may be performed using AI or not. For example, the report generation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0099] The report generation unit can adjust the level of detail in a report based on the importance of the summary during report generation. For example, the report generation unit creates a detailed report for summaries of high importance. The report generation unit uses AI to evaluate the importance of summaries and adjust the level of detail in the report. For example, the report generation unit creates a concise report for summaries of low importance. The report generation unit adjusts the level of detail in stages according to the importance of the summary. In this way, the report generation unit can create detailed reports for important summaries by adjusting the level of detail in the report based on the importance of the summary. The importance of a summary is evaluated based on, for example, the source of the summary or the recency of the summary, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input summary importance data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0100] The report generation unit can apply different report generation algorithms depending on the summary category when generating a report. For example, the report generation unit applies a trend report generation algorithm to news summaries. The report generation unit uses AI to classify the summary category and apply the appropriate report generation algorithm. For example, the report generation unit applies a statistical report generation algorithm to market report summaries. For example, the report generation unit applies a text mining report generation algorithm to technical document summaries. In this way, the report generation unit can produce appropriate reports according to the characteristics of the summary by applying different report generation algorithms depending on the summary category. Summary categories are classified into, for example, technical information and business information, but are not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input summary category data into a generation AI and cause the generation AI to apply the appropriate report generation algorithm.

[0101] The report generation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, the report generation unit will provide a short, concise report. The report generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the report generation unit can estimate the user's emotions using facial recognition technology. The report generation unit can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the report generation unit will provide a detailed report. The report generation unit estimates the user's emotions using an emotion estimation algorithm. For example, the report generation unit can estimate the user's emotions using survey results. For example, if the user is excited, the report generation unit will provide a visually stimulating report. In this way, the report generation unit can provide a report that is appropriate to the user's situation by adjusting the length of the report 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 processing described above in the report generation unit may be performed using AI or not. For example, the report generation unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0102] The report generation unit can determine the priority of reports based on the submission timing of the summaries when generating reports. For example, the report generation unit will prioritize creating reports for summaries with high urgency. The report generation unit uses AI to evaluate the submission timing of summaries and determine the priority of reports. For example, the report generation unit will quickly create reports for summaries with approaching submission deadlines. The report generation unit will adjust the priority of reports in stages according to the submission timing, for example. In this way, the report generation unit can prioritize the creation of reports for high-urgency summaries by determining the priority of reports based on the submission timing of the summaries. The submission timing of summaries is evaluated based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input the submission timing data of summaries into the generation AI and have the generation AI perform the determination of report priority.

[0103] The report generation unit can adjust the order of reports based on the relevance of the summaries during report generation. For example, the report generation unit prioritizes creating reports for highly relevant summaries. The report generation unit uses AI to evaluate the relevance of summaries and adjust the order of reports. For example, the report generation unit postpones creating reports for less relevant summaries. For example, the report generation unit adjusts the order of reports in stages according to the relevance of the summaries. In this way, the report generation unit can prioritize reporting highly relevant summaries by adjusting the order of reports based on the relevance of the summaries. The relevance of summaries is evaluated based on, for example, co-occurrence network analysis or relevance scores, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input summary relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0104] The update unit can estimate the user's emotions and adjust the timing of update information collection based on the estimated emotions. For example, if the user is stressed, the update unit will reduce the frequency of update information collection and collect only important information. The update unit estimates the user's emotions using an emotion estimation algorithm. For example, the update unit can estimate the user's emotions using facial recognition technology. The update unit can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the update unit will increase the frequency of update information collection and collect more detailed information. The update unit estimates the user's emotions using an emotion estimation algorithm. For example, the update unit can estimate the user's emotions using survey results. For example, if the user is in a hurry, the update unit will speed up the timing of update information collection and provide the necessary information immediately. In this way, the update unit can collect information appropriate to the user's situation by adjusting the timing of update 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. The generative AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the update unit may be performed using AI or not using AI. For example, the update unit may input user sentiment data into the generative AI and have the generative AI perform sentiment estimation.

[0105] The update unit can select the optimal collection method by referring to past update information when collecting update information. For example, the update unit prioritizes searching for information sources that have been frequently collected in the past. The update unit uses AI to analyze past update information and select the optimal collection method. For example, the update unit prioritizes suggesting collection methods (websites, databases, etc.) that have been used in the past. For example, the update unit predicts and suggests information sources to be collected at a specific time period based on past update information. This allows the update unit to select the optimal collection method by referring to past update information, enabling efficient information collection. The optimal collection method is selected based on, for example, analysis of past collection results or user feedback, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input past update information data into a generating AI and have the generating AI select the optimal collection method.

[0106] The update unit can apply different collection algorithms depending on the information category when collecting update information. For example, the update unit applies a trend collection algorithm to news information. The update unit uses AI to classify information categories and apply an appropriate collection algorithm. For example, the update unit applies a statistical collection algorithm to market information. For example, the update unit applies a text mining collection algorithm to technical information. This allows the update unit to collect information appropriately according to its characteristics by applying different collection algorithms depending on the information category. The collection algorithm is applied based on, for example, web scraping or data acquisition using APIs, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input information category data into a generating AI and have the generating AI apply an appropriate collection algorithm.

[0107] The update unit can estimate the user's emotions and prioritize update information based on the estimated emotions. For example, if the user is stressed, the update unit will prioritize collecting information of high importance. The update unit estimates the user's emotions using an emotion estimation algorithm. For example, the update unit can estimate the user's emotions using facial recognition technology. The update unit can also estimate the user's emotions using voice analysis technology. For example, if the user is relaxed, the update unit will prioritize collecting detailed information. The update unit estimates the user's emotions using an emotion estimation algorithm. For example, the update unit can estimate the user's emotions using survey results. For example, if the user is in a hurry, the update unit will prioritize collecting information that can be collected quickly. In this way, the update unit can collect information appropriate to the user's situation by prioritizing update information according to the user's emotions. Emotion estimation is implemented 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-described processes in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0108] The update unit can determine the priority of information collection based on the submission timing when collecting update information. For example, the update unit prioritizes the collection of information that is of high urgency. The update unit uses AI to evaluate the submission timing of information and determine the priority of collection. For example, the update unit quickly collects information with an approaching submission deadline. For example, the update unit adjusts the collection priority in stages according to the submission timing. In this way, the update unit can prioritize the collection of information that is of high urgency by determining the priority of collection based on the submission timing of information. The submission timing of information is evaluated based on, for example, the submission date and time or the frequency of submission, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input information submission timing data into a generating AI and have the generating AI perform the determination of collection priority.

[0109] The update unit can adjust the collection order based on the relevance of the information when collecting update information. For example, the update unit prioritizes the collection of highly relevant information. The update unit uses AI to evaluate the relevance of the information and adjust the collection order. For example, the update unit postpones the collection of less relevant information. The update unit adjusts the collection order in stages according to the relevance of the information. In this way, the update unit can prioritize the collection of highly relevant information by adjusting the collection order based on the relevance of the information. The relevance of the information is evaluated based on, for example, co-occurrence network analysis or relevance score, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the collection order.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The information gathering unit can analyze a user's past search history and prioritize searching for information sources that the user has previously shown interest in. For example, by prioritizing searches of websites and databases that the user has frequently accessed in the past, it can efficiently collect more relevant information. The information gathering unit can also predict and suggest information sources to collect at specific times based on the user's past search history. This allows the information gathering unit to select the optimal information gathering method based on the user's past behavior patterns, enabling efficient information collection.

[0112] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, by evaluating the reliability of the information source and author and prioritizing the analysis of highly reliable information, it can provide more accurate analysis results. The analysis unit can also adjust the level of detail of the analysis based on the reliability of the information. This allows the analysis unit to perform detailed analysis on highly reliable information and simplified analysis on less reliable information.

[0113] The summary generation unit can adjust the content of the summary based on the user's areas of interest. For example, if the user is interested in a particular topic, the summary can prioritize the inclusion of information related to that topic. The summary generation unit can also adjust the way the summary is presented based on the user's areas of interest. This allows the summary generation unit to provide a summary that is easy for the user to understand by tailoring it to their interests.

[0114] The report generation unit can adjust the report content based on the user's project progress. For example, it can provide an overall overview in the early stages of a project and then provide detailed analysis results as the project progresses. The report generation unit can also prioritize reports based on the project progress. This allows the report generation unit to provide appropriate reports according to the project's stage.

[0115] The update unit can adjust the timing of update information collection based on the user's schedule. For example, it can reduce the frequency of update information collection during busy periods and increase it during less busy periods. The update unit can also prioritize update information based on the user's schedule. This enables the update unit to efficiently collect information according to the user's schedule.

[0116] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on those emotions. For example, if the user is stressed, the frequency of information gathering can be reduced, and only important information can be collected. Conversely, if the user is relaxed, the frequency of information gathering can be increased, and more detailed information can be collected. This allows the information gathering unit to collect information in a way that is appropriate to the user's emotions.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, it can provide simple and easy-to-understand analysis results. Conversely, if the user is relaxed, it can provide detailed analysis results. In this way, the analysis unit can provide analysis results that are tailored to the user's emotions.

[0118] The summary generation unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is in a hurry, it can provide a short, concise summary. Conversely, if the user is relaxed, it can provide a more detailed summary. In this way, the summary generation unit can provide a summary that is tailored to the user's emotions.

[0119] The report generation unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read report. Conversely, if the user is relaxed, it can provide a more detailed report. This allows the report generation unit to provide reports tailored to the user's emotions.

[0120] The update unit can estimate the user's emotions and adjust the timing of update information collection based on those emotions. For example, if the user is stressed, the frequency of update information collection can be reduced, and only important information can be collected. Conversely, if the user is relaxed, the frequency of update information collection can be increased, and more detailed information can be collected. This allows the update unit to collect update information in accordance with the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The information gathering department collects information based on specified keywords and topics. For example, it collects information from websites and internal databases, uses AI to perform cross-sectional searches, and extracts highly relevant information. Specifically, it can collect information using web scraping technology and APIs. Step 2: The analysis unit analyzes the information collected by the information collection unit and extracts key points. For example, it analyzes the information using text mining, data mining, and statistical analysis techniques. Step 3: The summary generation unit generates a summary based on the key points extracted by the analysis unit. For example, it may use natural language processing technology, rule-based summarization technology, or generative AI to generate the summary. Step 4: The report generation unit creates a detailed report based on the summary generated by the summary generation unit. For example, it creates a report based on the report structure and the templates used, and then uses a generation AI to create a detailed report. Step 5: The update unit regularly collects the latest information related to specified keywords and topics. For example, it collects the latest information based on a regular daily or weekly schedule, using AI to gather the information.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the information gathering unit, analysis unit, summary generation unit, report generation unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the smart device 14 and collects information from websites and internal databases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The summary generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a summary based on the extracted important points. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a detailed report based on the generated summary. The update unit is implemented by the control unit 46A of the smart device 14 and can periodically collect the latest information related to specified keywords and topics. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the information gathering unit, analysis unit, summary generation unit, report generation unit, and update unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the smart glasses 214 and collects information from websites and internal databases. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The summary generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a summary based on the extracted important points. The report generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a detailed report based on the generated summary. The update unit is implemented by the control unit 46A of the smart glasses 214 and can periodically collect the latest information related to specified keywords and topics. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the information gathering unit, analysis unit, summary generation unit, report generation unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the headset terminal 314 and collects information from websites and internal databases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The summary generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a summary based on the extracted important points. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a detailed report based on the generated summary. The update unit is implemented by the control unit 46A of the headset terminal 314 and can periodically collect the latest information related to specified keywords and topics. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the information gathering unit, analysis unit, summary generation unit, report generation unit, and update unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the robot 414 and collects information from websites and internal databases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The summary generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a summary based on the extracted important points. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a detailed report based on the generated summary. The update unit is implemented by the control unit 46A of the robot 414 and can periodically collect the latest information related to specified keywords and topics. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) An information gathering unit that collects information based on specified keywords and topics, An analysis unit analyzes the information collected by the aforementioned information collection unit, A summary generation unit that summarizes the information analyzed by the aforementioned analysis unit, A report generation unit that creates a report based on the summary generated by the summary generation unit, It comprises an update unit that periodically collects update information. A system characterized by the following features. (Note 2) The aforementioned information gathering unit, Gather information from websites and internal databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected information and extract the key points. The system described in Appendix 1, characterized by the features described herein. (Note 4) The summary generation unit, Generate a summary based on the key points extracted. The system described in Appendix 1, characterized by the features described herein. (Note 5) The report generation unit, Create a detailed report based on the generated summary. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is, Regularly collect the latest information related to specified keywords and topics. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, 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 8) The aforementioned information gathering unit, Analyze the user's past information gathering history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information gathering unit, When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information gathering unit, 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 11) The aforementioned information gathering unit, When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information gathering unit, 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 13) 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 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The summary generation unit, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The summary generation unit, When generating a summary, adjust the level of detail in the summary based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The summary generation unit, When generating summaries, different summarization algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The summary generation unit, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The summary generation unit, When generating summaries, prioritize summaries based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The summary generation unit, When generating summaries, adjust the order of the summaries based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The report generation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The report generation unit, When generating a report, adjust the level of detail in the report based on the importance of the summary. The system described in Appendix 1, characterized by the features described herein. (Note 27) The report generation unit, When generating reports, different report generation algorithms are applied depending on the summary category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The report generation unit, It estimates the user's sentiment and adjusts the report length based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The report generation unit, When generating reports, prioritize reports based on the submission timing of the summaries. The system described in Appendix 1, characterized by the features described herein. (Note 30) The report generation unit, When generating reports, adjust the order of reports based on the relevance of the summaries. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned update unit is, It estimates the user's sentiment and adjusts the timing of update information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned update unit is, When collecting update information, the system selects the optimal collection method by referring to past update information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned update unit is, When collecting update information, different collection algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned update unit is, It estimates user sentiment and prioritizes update information based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned update unit is, When collecting update information, prioritize collection based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned update unit is, When collecting update information, adjust the collection order based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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. An information gathering unit that collects information based on specified keywords and topics, An analysis unit analyzes the information collected by the aforementioned information collection unit, A summary generation unit that summarizes the information analyzed by the aforementioned analysis unit, A report generation unit that creates a report based on the summary generated by the summary generation unit, It comprises an update unit that periodically collects update information. A system characterized by the following features.

2. The aforementioned information gathering unit, Gather information from websites and internal databases. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected information and extract the key points. The system according to feature 1.

4. The summary generation unit, Generate a summary based on the key points extracted. The system according to feature 1.

5. The report generation unit, Create a detailed report based on the generated summary. The system according to feature 1.

6. The aforementioned update unit is Regularly collect the latest information related to specified keywords and topics. The system according to feature 1.

7. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned information gathering unit, Analyze the user's past information gathering history and select the optimal collection method. The system according to feature 1.

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

10. The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

Images