system

Abstract

The system according to this embodiment aims to automate information gathering and analysis, and to quickly grasp the impact on business operations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The identification unit identifies the impact on business operations based on the information analyzed by the analysis unit. The provision unit provides a customizable report based on the information identified by the identification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it takes a great deal of time and labor for information collection and analysis, and there is a risk of delay in grasping the latest information.

[0005] The system according to the embodiment aims to automate information collection and analysis and quickly grasp the impact on business.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a pointing unit, and a providing unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The pointing unit points out the impact on business based on the information analyzed by the analysis unit. The providing unit provides a customizable report based on the information pointed out by the pointing unit. [Effects of the Invention]

[0007] The system according to this embodiment automates information gathering and analysis, enabling rapid assessment of the impact on business 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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device, 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An information gathering and analysis system according to an embodiment of the present invention is a system that automatically collects the latest information from government agencies and industries and provides summaries and impacts on operations in real time. This information gathering and analysis system reduces the time and effort required for manual information gathering and enables faster decision-making by allowing immediate grasp of updated research results and policy guidelines. Furthermore, it provides customizable reports tailored to the industry, thereby increasing its applicability to operations. For example, the information gathering and analysis system uses an AI agent to gather information and point out its impact on operations. Specifically, first, the AI ​​agent automatically collects a wide range of information published by government agencies and industry associations. In this process, the AI ​​agent uses a natural language processing (NLP) model to extract relevant information. For example, it collects information on the latest research meetings and policy guidelines. Next, the AI ​​analyzes the collected information. The AI ​​summarizes the collected information and extracts key points. For example, it summarizes the discussions of research meetings and changes in policy guidelines. Furthermore, the AI ​​points out the impact on operations based on the summarized information. For example, it points out how new policy guidelines will affect operations. Since this information is provided in real time, rapid decision-making is possible. Finally, we provide customizable reports tailored to specific industries. This allows companies and organizations to obtain information best suited to their operations. For example, we offer reports for the financial industry and reports for the IT industry. This reduces the time and effort required for manual information gathering and allows for immediate access to updated research findings and policy guidelines. This enables faster decision-making and improved operational efficiency. Our information gathering and analysis system can automatically collect the latest information from government agencies and industries, providing summaries and impacts on operations in real time.

[0029] The information gathering and analysis system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects information. For example, the collection unit automatically collects information published by government agencies and industry associations. The collection unit can extract relevant information using natural language processing (NLP) models. For example, the collection unit collects information on the latest research meetings and policy guidelines. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit summarizes the collected information and extracts key points. The analysis unit can summarize information using natural language processing techniques. For example, the analysis unit summarizes discussions at research meetings and changes to policy guidelines. The identification unit identifies the impact on operations based on the information analyzed by the analysis unit. For example, the identification unit identifies how new policy guidelines will affect operations based on the summarized information. The identification unit can provide the impact on operations in real time. The provision unit provides customizable reports based on the information identified by the identification unit. For example, the provision unit provides customizable reports tailored to specific industries. The service provider can provide reports for the financial industry and reports for the IT industry. This enables the information gathering and analysis system according to the embodiment to automate information gathering, analysis, analysis, and report provision, thereby improving operational efficiency and facilitating rapid decision-making.

[0030] The data collection unit collects information. For example, it automatically collects information published by government agencies and industry associations. Specifically, the data collection unit uses web scraping technology to regularly obtain the latest announcements and reports from the websites of government agencies and industry associations. This also includes methods of obtaining information in real time using RSS feeds and APIs. The data collection unit can use natural language processing (NLP) models to extract relevant information. For example, the data collection unit collects information on the latest research meetings and policy guidelines. The NLP model extracts keywords and important phrases from the collected text data and evaluates the relevance of the information. This allows the data collection unit to efficiently extract necessary information from large amounts of data and store it in a database. Furthermore, the data collection unit automatically adds metadata (publication date, source, category, etc.) to the collected information, making it easier to manage and search for information. This allows the data collection unit to collect diverse data from a wide range of sources and strengthen the information infrastructure of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit summarizes the collected information and extracts key points. Specifically, the analysis unit can use natural language processing (NLP) techniques to summarize information. For instance, it can summarize discussions at research meetings or changes in policy guidelines. Using NLP techniques, the analysis unit extracts key content from long texts and generates short summaries. This includes methods for structuring and classifying information using topic modeling and text classification algorithms. Furthermore, the analysis unit implements a scoring system to evaluate the reliability and importance of information and prioritize it. This allows the analysis unit to quickly provide the most important information to the user. The analysis unit can also analyze changes and trends in new information by comparing it with historical data, providing future predictions and strategic insights. This allows the analysis unit to maximize the value of information and support user decision-making.

[0032] The identification team identifies the impact on operations based on information analyzed by the analysis team. For example, the identification team identifies how new policy guidelines will affect operations based on summarized information. Specifically, the identification team uses an expert system with expertise in business processes and regulatory requirements to assess the impact of the information. The identification team can provide the impact on operations in real time. For example, if new policy guidelines are introduced, it will analyze how those guidelines will affect the company's compliance requirements and propose necessary countermeasures. The identification team can identify changes in business processes and new risk factors and provide concrete action plans. Furthermore, the identification team uses simulation tools to conduct impact assessments based on different scenarios and select the optimal countermeasures. This allows the identification team to help users respond quickly and appropriately, thereby enhancing operational efficiency and risk management.

[0033] The service provider will provide customizable reports based on the information identified by the feedback provider. The service provider will offer customized reports tailored to specific industries, for example. Specifically, the service provider can flexibly adjust the content and format of reports according to user needs and industry characteristics. The service provider can provide reports for the financial industry and reports for the IT industry. For example, reports for the financial industry can include analysis of the impact of new regulations and market trends, while reports for the IT industry can assess technology trends and security risks. The service provider employs a template-based approach in generating reports, allowing users to easily customize them. Furthermore, the service provider diversifies report delivery methods, providing information in ways that suit user convenience, such as email, dashboards, and mobile apps. This allows the service provider to help users quickly and efficiently obtain the information they need, supporting their business decision-making.

[0034] The data collection unit can automatically collect information released by government agencies and industry associations. For example, the data collection unit can automatically collect information released by government agencies and industry associations. The data collection unit can use natural language processing (NLP) models to extract relevant information. For example, the data collection unit collects information on the latest research meetings and policy guidelines. This allows for the rapid acquisition of the latest information by automatically collecting information from government agencies and industry associations. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input information released by government agencies and industry associations into a generative AI, which can then analyze the information and extract relevant information.

[0035] The analysis unit can summarize the collected information and extract key points. For example, the analysis unit can summarize the collected information and extract key points. The analysis unit can use natural language processing techniques to summarize the information. For example, the analysis unit can summarize the discussions of a research group or changes in policy guidelines. This makes it easier to understand the information and make decisions by summarizing the information and extracting key points. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected information into a generative AI, which can then summarize the information and extract key points.

[0036] The feedback function can identify the impact on operations based on the summarized information. For example, the feedback function can identify the impact on operations based on the summarized information. The feedback function can provide the impact on operations in real time. This enables rapid decision-making by identifying the impact on operations. Some or all of the above processing in the feedback function may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback function can input summarized information into a generative AI, and the generative AI can identify the impact on operations.

[0037] The service provider can provide customizable reports tailored to specific industries. For example, the service provider can provide customizable reports tailored to specific industries. The service provider can provide reports for the financial industry or reports for the IT industry. By providing customizable reports, the applicability to business operations is increased. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input industry-specific information into a generative AI, which can then generate a customizable report.

[0038] The data collection unit can analyze the user's past information gathering history and select the optimal collection method. For example, the data collection unit can prioritize collecting information from sources that the user has frequently collected in the past. For example, the data collection unit can optimize the information to be collected at specific time periods based on the user's past information gathering history. For example, the data collection unit can analyze the user's past information gathering history and propose the most efficient collection method. This allows the optimal collection method to be selected by analyzing the past information gathering history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past information gathering history into a generative AI, which can then select the optimal collection method.

[0039] The data collection unit can filter information based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to projects the user is currently working on. For example, the data collection unit can filter highly relevant information based on the user's areas of interest. For example, the data collection unit can collect only the necessary information according to the user's work situation. This allows the collection of only the necessary information by filtering information based on work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input data on the user's work situation and areas of interest into a generating AI, which can then filter the information.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can collect necessary information based on their current location. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the user's social media activity and filter out highly relevant information. For example, the data collection unit can prioritize collecting information on accounts the user follows. This allows for efficient collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant information.

[0042] 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 can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the information. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input information importance data into a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific analysis algorithm to information about policy guidelines. For example, the analysis unit can apply a different analysis algorithm to information about research results. For example, the analysis unit can select the optimal analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information category data into a generative AI, and the generative AI can apply the optimal analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the information collection timing during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can adjust the priority of analysis based on the information collection timing. For example, the analysis unit can perform analysis on older information as needed. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the information collection timing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input information collection timing data into a generating AI, and the generating AI can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can perform analysis on less relevant information as needed. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0046] The feedback unit can improve the accuracy of its feedback by considering the interrelationships of information when providing feedback. For example, the feedback unit analyzes the interrelationships of information and provides relevant feedback. For example, the feedback unit can improve the accuracy of its feedback based on the interrelationships of information. For example, the feedback unit can provide optimal feedback by considering the interrelationships of information. This improves the accuracy of feedback by considering the interrelationships of information. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input interrelationship data of information into a generative AI, which can then improve the accuracy of its feedback.

[0047] The feedback function can make feedback by considering the attribute information of the information provider. The feedback function can improve the accuracy of the feedback based on, for example, the reliability of the information provider. The feedback function can adjust the content of the feedback based on, for example, the expertise of the information provider. The feedback function can make optimal feedback by considering the attribute information of the information provider. This improves the accuracy of the feedback by considering the attribute information of the information provider. Some or all of the above processing in the feedback function may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback function can input the attribute information of the information provider into a generating AI, and the generating AI can adjust the content of the feedback.

[0048] The feedback function can make feedback while considering the geographical distribution of information. For example, the feedback function can make relevant feedback based on the geographical distribution of information. For example, the feedback function can adjust the content of the feedback while considering the geographical distribution of information. For example, the feedback function can make optimal feedback based on the geographical distribution of information. This allows the content of the feedback to be adjusted by considering the geographical distribution of information. Some or all of the above processing in the feedback function may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback function can input geographical distribution data of information into a generating AI, and the generating AI can adjust the content of the feedback.

[0049] The feedback function can improve the accuracy of its feedback by referring to relevant literature when providing feedback. For example, the feedback function can improve the accuracy of its feedback by referring to relevant literature. For example, the feedback function can adjust the content of its feedback based on relevant literature. For example, the feedback function can make optimal feedback by considering relevant literature. As a result, the accuracy of feedback is improved by referring to relevant literature. Some or all of the above processing in the feedback function may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback function can input relevant literature data into a generating AI, and the generating AI can adjust the content of its feedback.

[0050] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider can prioritize providing display methods that the user has previously preferred. For example, the service provider can suggest the optimal display method based on the user's past operation history. For example, the service provider can analyze the user's past operation history and select the most efficient display method. This allows the service provider to select the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's past operation history data into a generating AI, and the generating AI can select the optimal display method.

[0051] The service provider can customize the content of reports based on the user's work situation when providing them. For example, the service provider can prioritize providing information related to projects the user is currently working on. The service provider can customize the content of reports based on the user's work situation. For example, the service provider can provide only the necessary information according to the user's work situation. This allows the service provider to provide only the necessary information by customizing the report content based on the work situation. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's work situation data into a generating AI, and the generating AI can customize the content of the report.

[0052] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can provide a method that displays detailed information. This allows the service provider to select the optimal display method by considering device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0053] The service provider can adjust the content of reports based on the user's industry characteristics when providing them. For example, a report for the financial industry might include economic indicators and market trends. A report for the IT industry might include technology trends and new product information. A report for the healthcare industry might include the latest research findings and regulatory information. By adjusting the content of reports based on industry characteristics, the service provider can provide industry-specific information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's industry characteristics data into a generative AI, which can then adjust the content of the report.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze a user's past search history and prioritize the collection of highly relevant information. For example, it can collect information based on keywords that the user has frequently searched for in the past. The data collection unit can analyze a user's search history and prioritize the collection of information related to specific topics. This allows for the efficient collection of more relevant information based on the user's past search history.

[0056] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, it can evaluate the reliability of the information sources and prioritize the analysis of highly reliable information. The analysis unit can determine the priority of analysis based on the reliability of the information. By prioritizing the analysis of highly reliable information, it can provide more accurate analysis results.

[0057] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize collecting information related to that region. The data collection unit can also filter highly relevant information based on the user's geographical location. This allows for the efficient collection of highly relevant information by considering geographical location.

[0058] The analysis unit can apply different analysis algorithms depending on the category of the collected information. For example, a specific analysis algorithm can be applied to information about policy guidelines. The analysis unit can apply a different analysis algorithm to information about research results. This allows for improved accuracy of the analysis by applying the most suitable analysis algorithm according to the category of information.

[0059] The service provider can customize the report content based on the user's work situation. For example, it can prioritize providing information related to the project the user is currently working on. This allows the service provider to customize the report content based on the user's work situation, providing only the necessary information.

[0060] The service provider can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for the larger screen. In this way, the optimal display method can be selected by considering the device information.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit gathers information. The collection unit automatically collects information released by government agencies and industry associations, for example. The collection unit can use natural language processing (NLP) models to extract relevant information. For example, the collection unit collects information on the latest research meetings and policy guidelines. Step 2: The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit summarizes the collected information and extracts key points. The analysis unit can use natural language processing techniques to summarize the information. For example, the analysis unit can summarize the discussions of a research group or changes in policy guidelines. Step 3: The reporting unit identifies the impact on operations based on the information analyzed by the analysis unit. For example, the reporting unit identifies how new policy guidelines will affect operations based on summarized information. The reporting unit can provide the impact on operations in real time. Step 4: The service provider provides a customizable report based on the information pointed out by the feedback provider. The service provider provides customizable reports tailored to specific industries, for example. The service provider can provide reports for the financial industry or reports for the IT industry.

[0063] (Example of form 2) An information gathering and analysis system according to an embodiment of the present invention is a system that automatically collects the latest information from government agencies and industries and provides summaries and impacts on operations in real time. This information gathering and analysis system reduces the time and effort required for manual information gathering and enables faster decision-making by allowing immediate grasp of updated research results and policy guidelines. Furthermore, it provides customizable reports tailored to the industry, thereby increasing its applicability to operations. For example, the information gathering and analysis system uses an AI agent to gather information and point out its impact on operations. Specifically, first, the AI ​​agent automatically collects a wide range of information published by government agencies and industry associations. In this process, the AI ​​agent uses a natural language processing (NLP) model to extract relevant information. For example, it collects information on the latest research meetings and policy guidelines. Next, the AI ​​analyzes the collected information. The AI ​​summarizes the collected information and extracts key points. For example, it summarizes the discussions of research meetings and changes in policy guidelines. Furthermore, the AI ​​points out the impact on operations based on the summarized information. For example, it points out how new policy guidelines will affect operations. Since this information is provided in real time, rapid decision-making is possible. Finally, we provide customizable reports tailored to specific industries. This allows companies and organizations to obtain information best suited to their operations. For example, we offer reports for the financial industry and reports for the IT industry. This reduces the time and effort required for manual information gathering and allows for immediate access to updated research findings and policy guidelines. This enables faster decision-making and improved operational efficiency. Our information gathering and analysis system can automatically collect the latest information from government agencies and industries, providing summaries and impacts on operations in real time.

[0064] The information gathering and analysis system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a provision unit. The collection unit collects information. For example, the collection unit automatically collects information published by government agencies and industry associations. The collection unit can extract relevant information using natural language processing (NLP) models. For example, the collection unit collects information on the latest research meetings and policy guidelines. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit summarizes the collected information and extracts key points. The analysis unit can summarize information using natural language processing techniques. For example, the analysis unit summarizes discussions at research meetings and changes to policy guidelines. The identification unit identifies the impact on operations based on the information analyzed by the analysis unit. For example, the identification unit identifies how new policy guidelines will affect operations based on the summarized information. The identification unit can provide the impact on operations in real time. The provision unit provides customizable reports based on the information identified by the identification unit. For example, the provision unit provides customizable reports tailored to specific industries. The service provider can provide reports for the financial industry and reports for the IT industry. This enables the information gathering and analysis system according to the embodiment to automate information gathering, analysis, analysis, and report provision, thereby improving operational efficiency and facilitating rapid decision-making.

[0065] The data collection unit collects information. For example, it automatically collects information published by government agencies and industry associations. Specifically, the data collection unit uses web scraping technology to regularly obtain the latest announcements and reports from the websites of government agencies and industry associations. This also includes methods of obtaining information in real time using RSS feeds and APIs. The data collection unit can use natural language processing (NLP) models to extract relevant information. For example, the data collection unit collects information on the latest research meetings and policy guidelines. The NLP model extracts keywords and important phrases from the collected text data and evaluates the relevance of the information. This allows the data collection unit to efficiently extract necessary information from large amounts of data and store it in a database. Furthermore, the data collection unit automatically adds metadata (publication date, source, category, etc.) to the collected information, making it easier to manage and search for information. This allows the data collection unit to collect diverse data from a wide range of sources and strengthen the information infrastructure of the entire system.

[0066] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit summarizes the collected information and extracts key points. Specifically, the analysis unit can use natural language processing (NLP) techniques to summarize information. For instance, it can summarize discussions at research meetings or changes in policy guidelines. Using NLP techniques, the analysis unit extracts key content from long texts and generates short summaries. This includes methods for structuring and classifying information using topic modeling and text classification algorithms. Furthermore, the analysis unit implements a scoring system to evaluate the reliability and importance of information and prioritize it. This allows the analysis unit to quickly provide the most important information to the user. The analysis unit can also analyze changes and trends in new information by comparing it with historical data, providing future predictions and strategic insights. This allows the analysis unit to maximize the value of information and support user decision-making.

[0067] The identification team identifies the impact on operations based on information analyzed by the analysis team. For example, the identification team identifies how new policy guidelines will affect operations based on summarized information. Specifically, the identification team uses an expert system with expertise in business processes and regulatory requirements to assess the impact of the information. The identification team can provide the impact on operations in real time. For example, if new policy guidelines are introduced, it will analyze how those guidelines will affect the company's compliance requirements and propose necessary countermeasures. The identification team can identify changes in business processes and new risk factors and provide concrete action plans. Furthermore, the identification team uses simulation tools to conduct impact assessments based on different scenarios and select the optimal countermeasures. This allows the identification team to help users respond quickly and appropriately, thereby enhancing operational efficiency and risk management.

[0068] The service provider will provide customizable reports based on the information identified by the feedback provider. The service provider will offer customized reports tailored to specific industries, for example. Specifically, the service provider can flexibly adjust the content and format of reports according to user needs and industry characteristics. The service provider can provide reports for the financial industry and reports for the IT industry. For example, reports for the financial industry can include analysis of the impact of new regulations and market trends, while reports for the IT industry can assess technology trends and security risks. The service provider employs a template-based approach in generating reports, allowing users to easily customize them. Furthermore, the service provider diversifies report delivery methods, providing information in ways that suit user convenience, such as email, dashboards, and mobile apps. This allows the service provider to help users quickly and efficiently obtain the information they need, supporting their business decision-making.

[0069] The data collection unit can automatically collect information released by government agencies and industry associations. For example, the data collection unit can automatically collect information released by government agencies and industry associations. The data collection unit can use natural language processing (NLP) models to extract relevant information. For example, the data collection unit collects information on the latest research meetings and policy guidelines. This allows for the rapid acquisition of the latest information by automatically collecting information from government agencies and industry associations. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input information released by government agencies and industry associations into a generative AI, which can then analyze the information and extract relevant information.

[0070] The analysis unit can summarize the collected information and extract key points. For example, the analysis unit can summarize the collected information and extract key points. The analysis unit can use natural language processing techniques to summarize the information. For example, the analysis unit can summarize the discussions of a research group or changes in policy guidelines. This makes it easier to understand the information and make decisions by summarizing the information and extracting key points. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected information into a generative AI, which can then summarize the information and extract key points.

[0071] The feedback function can identify the impact on operations based on the summarized information. For example, the feedback function can identify the impact on operations based on the summarized information. The feedback function can provide the impact on operations in real time. This enables rapid decision-making by identifying the impact on operations. Some or all of the above processing in the feedback function may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback function can input summarized information into a generative AI, and the generative AI can identify the impact on operations.

[0072] The service provider can provide customizable reports tailored to specific industries. For example, the service provider can provide customizable reports tailored to specific industries. The service provider can provide reports for the financial industry or reports for the IT industry. By providing customizable reports, the applicability to business operations is increased. Some or all of the above-described processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input industry-specific information into a generative AI, which can then generate a customizable report.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only important information. For example, if the user is relaxed, the data collection unit can increase the frequency of information collection and collect detailed information. For example, if the user is in a hurry, the data collection unit can adjust the timing of information collection to quickly provide the necessary information. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of information collection.

[0074] The data collection unit can analyze the user's past information gathering history and select the optimal collection method. For example, the data collection unit can prioritize collecting information from sources that the user has frequently collected in the past. For example, the data collection unit can optimize the information to be collected at specific time periods based on the user's past information gathering history. For example, the data collection unit can analyze the user's past information gathering history and propose the most efficient collection method. This allows the optimal collection method to be selected by analyzing the past information gathering history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past information gathering history into a generative AI, which can then select the optimal collection method.

[0075] The data collection unit can filter information based on the user's current work situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to projects the user is currently working on. For example, the data collection unit can filter highly relevant information based on the user's areas of interest. For example, the data collection unit can collect only the necessary information according to the user's work situation. This allows the collection of only the necessary information by filtering information based on work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input data on the user's work situation and areas of interest into a generating AI, which can then filter the information.

[0076] The data collection 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 data collection unit can prioritize collecting only important information. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit can prioritize collecting information that is needed quickly. In this way, important information can be prioritized by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of information.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, the data collection unit can filter highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can collect necessary information based on their current location. This allows for the priority collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant information.

[0078] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user has shown interest in on social media. For example, the data collection unit can analyze the user's social media activity and filter out highly relevant information. For example, the data collection unit can prioritize collecting information on accounts the user follows. This allows for efficient collection of relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant information.

[0079] 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, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the presentation of the analysis.

[0080] 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 can perform a detailed analysis on information of high importance. For example, the analysis unit can perform a concise analysis on information of low importance. The analysis unit can adjust the level of detail of the analysis according to the importance of the information. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input information importance data into a generation AI, and the generation AI can adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific analysis algorithm to information about policy guidelines. For example, the analysis unit can apply a different analysis algorithm to information about research results. For example, the analysis unit can select the optimal analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input information category data into a generative AI, and the generative AI can apply the optimal analysis algorithm.

[0082] 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 can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can then adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the information collection timing during the analysis. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can adjust the priority of analysis based on the information collection timing. For example, the analysis unit can perform analysis on older information as needed. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the information collection timing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input information collection timing data into a generating AI, and the generating AI can determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can perform analysis on less relevant information as needed. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input information relevance data into a generating AI, and the generating AI can adjust the order of analysis.

[0085] The feedback function can estimate the user's emotions and adjust its feedback criteria based on those emotions. For example, if the user is tense, the feedback function will provide simple and easily understandable feedback. If the user is relaxed, the feedback function can provide detailed feedback. If the user is in a hurry, the feedback function can provide concise feedback. By adjusting the feedback criteria according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback function may be performed using a generative AI, or not. For example, the feedback function can input user emotion data into a generative AI, which can then adjust its feedback criteria.

[0086] The feedback unit can improve the accuracy of its feedback by considering the interrelationships of information when providing feedback. For example, the feedback unit analyzes the interrelationships of information and provides relevant feedback. For example, the feedback unit can improve the accuracy of its feedback based on the interrelationships of information. For example, the feedback unit can provide optimal feedback by considering the interrelationships of information. This improves the accuracy of feedback by considering the interrelationships of information. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit can input interrelationship data of information into a generative AI, which can then improve the accuracy of its feedback.

[0087] The feedback function can make feedback by considering the attribute information of the information provider. The feedback function can improve the accuracy of the feedback based on, for example, the reliability of the information provider. The feedback function can adjust the content of the feedback based on, for example, the expertise of the information provider. The feedback function can make optimal feedback by considering the attribute information of the information provider. This improves the accuracy of the feedback by considering the attribute information of the information provider. Some or all of the above processing in the feedback function may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback function can input the attribute information of the information provider into a generating AI, and the generating AI can adjust the content of the feedback.

[0088] The feedback section can estimate the user's emotions and adjust the order in which the feedback results are displayed based on the estimated emotions. For example, if the user is tense, the feedback section can prioritize displaying important feedback. If the user is relaxed, the feedback section can prioritize displaying detailed feedback in a sequential manner. If the user is in a hurry, the feedback section can prioritize displaying concise feedback. By adjusting the order in which the feedback results are displayed according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback section may be performed using a generative AI, or not using a generative AI. For example, the feedback section can input user emotion data into a generative AI, which can then adjust the order in which the generative AI displays the feedback results.

[0089] The feedback function can make feedback while considering the geographical distribution of information. For example, the feedback function can make relevant feedback based on the geographical distribution of information. For example, the feedback function can adjust the content of the feedback while considering the geographical distribution of information. For example, the feedback function can make optimal feedback based on the geographical distribution of information. This allows the content of the feedback to be adjusted by considering the geographical distribution of information. Some or all of the above processing in the feedback function may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback function can input geographical distribution data of information into a generating AI, and the generating AI can adjust the content of the feedback.

[0090] The feedback function can improve the accuracy of its feedback by referring to relevant literature when providing feedback. For example, the feedback function can improve the accuracy of its feedback by referring to relevant literature. For example, the feedback function can adjust the content of its feedback based on relevant literature. For example, the feedback function can make optimal feedback by considering relevant literature. As a result, the accuracy of feedback is improved by referring to relevant literature. Some or all of the above processing in the feedback function may be performed using, for example, a generating AI, or without using a generating AI. For example, the feedback function can input relevant literature data into a generating AI, and the generating AI can adjust the content of its feedback.

[0091] The service provider can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and easy-to-read report. For example, if the user is relaxed, the service provider can provide a detailed report. For example, if the user is in a hurry, the service provider can provide a concise report. By adjusting how the report is displayed according to the user's emotions, a more appropriate report can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust how the report is displayed.

[0092] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider can prioritize providing display methods that the user has previously preferred. For example, the service provider can suggest the optimal display method based on the user's past operation history. For example, the service provider can analyze the user's past operation history and select the most efficient display method. This allows the service provider to select the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's past operation history data into a generating AI, and the generating AI can select the optimal display method.

[0093] The service provider can customize the content of reports based on the user's work situation when providing them. For example, the service provider can prioritize providing information related to projects the user is currently working on. The service provider can customize the content of reports based on the user's work situation. For example, the service provider can provide only the necessary information according to the user's work situation. This allows the service provider to provide only the necessary information by customizing the report content based on the work situation. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's work situation data into a generating AI, and the generating AI can customize the content of the report.

[0094] The service provider can estimate the user's emotions and adjust the report's operation procedures based on the estimated emotions. For example, if the user is nervous, the service provider can provide simple and intuitive operation procedures. For example, if the user is relaxed, the service provider can provide detailed operation procedures. For example, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. This allows for more appropriate operation by adjusting the report's operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the operation procedures.

[0095] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can provide a method that displays detailed information. This allows the service provider to select the optimal display method by considering device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0096] The service provider can adjust the content of reports based on the user's industry characteristics when providing them. For example, a report for the financial industry might include economic indicators and market trends. A report for the IT industry might include technology trends and new product information. A report for the healthcare industry might include the latest research findings and regulatory information. By adjusting the content of reports based on industry characteristics, the service provider can provide industry-specific information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's industry characteristics data into a generative AI, which can then adjust the content of the report.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The data collection unit can analyze a user's past search history and prioritize the collection of highly relevant information. For example, it can collect information based on keywords that the user has frequently searched for in the past. The data collection unit can analyze a user's search history and prioritize the collection of information related to specific topics. This allows for the efficient collection of more relevant information based on the user's past search history.

[0099] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, it can evaluate the reliability of the information sources and prioritize the analysis of highly reliable information. The analysis unit can determine the priority of analysis based on the reliability of the information. By prioritizing the analysis of highly reliable information, it can provide more accurate analysis results.

[0100] The feedback function can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, the feedback will be concise and only the most important points will be highlighted. If the user is relaxed, the feedback function can provide more detailed feedback. In this way, by adjusting the content of the feedback according to the user's emotions, more appropriate feedback can be provided.

[0101] The service provider can estimate the user's emotions and adjust the report content based on those estimates. For example, if the user is feeling stressed, the report can be concise, providing only the essential points. If the user is relaxed, the service provider can provide a more detailed report. This allows for the provision of more relevant information by adjusting the report content according to the user's emotions.

[0102] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize collecting information related to that region. The data collection unit can also filter highly relevant information based on the user's geographical location. This allows for the efficient collection of highly relevant information by considering geographical location.

[0103] The analysis unit can apply different analysis algorithms depending on the category of the collected information. For example, a specific analysis algorithm can be applied to information about policy guidelines. The analysis unit can apply a different analysis algorithm to information about research results. This allows for improved accuracy of the analysis by applying the most suitable analysis algorithm according to the category of information.

[0104] The feedback section can estimate the user's emotions and adjust how the feedback is displayed based on those emotions. For example, if the user is stressed, the feedback can be simplified, highlighting only the most important points. If the user is relaxed, the feedback section can display more detailed feedback. This allows for more appropriate feedback by adjusting how the feedback is displayed according to the user's emotions.

[0105] The service provider can customize the report content based on the user's work situation. For example, it can prioritize providing information related to the project the user is currently working on. This allows the service provider to customize the report content based on the user's work situation, providing only the necessary information.

[0106] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on those emotions. For example, if the user is stressed, it will prioritize collecting only important information. If the user is relaxed, the data collection unit can prioritize collecting detailed information. This allows for the efficient collection of important information by prioritizing it according to the user's emotions.

[0107] The service provider can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for the larger screen. In this way, the optimal display method can be selected by considering the device information.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit gathers information. The collection unit automatically collects information released by government agencies and industry associations, for example. The collection unit can use natural language processing (NLP) models to extract relevant information. For example, the collection unit collects information on the latest research meetings and policy guidelines. Step 2: The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit summarizes the collected information and extracts key points. The analysis unit can use natural language processing techniques to summarize the information. For example, the analysis unit can summarize the discussions of a research group or changes in policy guidelines. Step 3: The reporting unit identifies the impact on operations based on the information analyzed by the analysis unit. For example, the reporting unit identifies how new policy guidelines will affect operations based on summarized information. The reporting unit can provide the impact on operations in real time. Step 4: The service provider provides a customizable report based on the information pointed out by the feedback provider. The service provider provides customizable reports tailored to specific industries, for example. The service provider can provide reports for the financial industry or reports for the IT industry.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and automatically collects information published by government agencies and industry associations. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the collected information and extracts important points. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and points out the impact on business operations based on the summarized information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides customizable reports tailored to the industry, etc. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The 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.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 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.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the 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.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 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.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and automatically collects information published by government agencies and industry associations. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the collected information and extracts important points. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and points out the impact on business operations based on the summarized information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides customizable reports tailored to the industry, etc. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and automatically collects information published by government agencies and industry associations. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes the collected information and extracts important points. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and points out the impact on business operations based on the summarized information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides customizable reports tailored to the industry, etc. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and automatically collects information published by government agencies and industry associations. The analysis unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and summarizes the collected information and extracts important points. The identification unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and points out the impact on business operations based on the summarized information. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides customizable reports tailored to the industry, etc. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, the identification unit points out the impact on business operations, A provision unit that provides a customizable report based on the information pointed out by the aforementioned pointing unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Automatically collects information released by government agencies and industry associations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Summarize the collected information and extract the key points. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned point is, Based on the summarized information, identify the impact on operations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide customizable reports tailored to specific industries and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is 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 8) The aforementioned collection unit is When collecting information, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) 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 14) 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 15) 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 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned point is, We estimate the user's emotions and adjust the criteria for feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned point is, When providing feedback, consider the interrelationships between pieces of information to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned point is, When making a complaint, the attribute information of the information provider should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned point is, It estimates the user's sentiment and adjusts the order in which the results of the suggestions are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned point is, When making a point of criticism, take into consideration the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned point is, When making a suggestion, refer to relevant literature to improve the accuracy of the suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing reports, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing reports, customize the report content based on the user's work situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the report's operation steps based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing reports, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the report, we adjust the report content based on the user's industry characteristics. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, Based on the information analyzed by the aforementioned analysis unit, the identification unit points out the impact on business operations, A provision unit that provides a customizable report based on the information pointed out by the aforementioned pointing unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Automatically collects information released by government agencies and industry associations. The system according to feature 1.

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

4. The aforementioned point is, Based on the summarized information, identify the impact on operations. The system according to feature 1.

5. The aforementioned supply unit is, We provide customizable reports tailored to specific industries and other factors. The system according to feature 1.

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

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

8. The aforementioned collection unit is When collecting information, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

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

10. The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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