system

Abstract

The system according to this embodiment aims to centrally manage information on politicians, policies, and central government ministries, and to generate policy risks and patterns. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides information based on the analysis results obtained by the analysis unit. The generation unit generates political risks and patterns based on the information provided by the provision 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that information on politicians, policies, and central government agencies is not sufficiently managed in a unified manner, and policy risks and patterns are not sufficiently generated.

[0005] The system according to the embodiment aims to manage information on politicians, policies, and central government agencies in a unified manner and generate policy risks and patterns.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides information based on the analysis results obtained by the analysis unit. The generation unit generates political risks and patterns based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally manage information on politicians, policies, and central government ministries, and generate policy risks and patterns. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An information management system according to an embodiment of the present invention is a system for centrally managing information on politicians, policies, and central government ministries. This information management system collects data on the policy preferences, private lives, and social relationships of politicians both domestically and internationally, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. AI analyzes this data to incorporate and systematize information on the direction of politics and policies. As a result, information is compiled objectively, providing unbiased information to the general public and generating political risks and patterns for corporations. For example, the information management system can collect data from newspaper articles and official government announcements, and the AI ​​analyzes it to understand politicians' policy preferences and social relationships. Next, the AI ​​analyzes the collected data to incorporate and systematize information on the direction of politics and policies. The AI ​​analyzes the collected data to determine the direction of politicians' policies and risks. For example, based on past policy data, it can predict the possibility and probability of future policies and regulations. As a result, information is compiled objectively, providing unbiased information to the general public. Furthermore, it generates political risks and patterns for corporations. Based on collected data, AI assesses political risks and predicts future policy risks and patterns. For example, it can evaluate the impact of specific policies on businesses and predict the risks. This allows businesses to accurately assess political risks and take appropriate measures. This system enables the provision of unbiased political information to the general public and the generation of political risks and patterns for corporations. For example, it can provide a social media service that clearly explains politicians and policies to the general public before elections, and a service that accurately assesses and predicts the political risks to companies, as well as the evaluation of politics and policies in Japan and around the world. In this way, by utilizing AI, information on politicians, policies, and central government ministries can be centrally managed and compiled objectively. This makes politics more transparent, providing unbiased information to the general public and generating political risks and patterns for corporations.This allows the information management system to centrally manage information on politicians, policies, and central government ministries, providing unbiased information to the general public and generating political risk and pattern analysis for corporations.

[0029] The information management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects data. For example, the collection unit collects data such as the policy preferences, private lives, and social relationships of domestic and international politicians, as well as data on policies, white papers, councils and council members, and laws published by newspapers and government agencies. The collection unit collects data from sources such as newspaper articles and official government announcements and stores it in a database. The collection unit can also automatically collect data using AI. For example, the collection unit uses AI to analyze newspaper articles and understand politicians' policy preferences and social relationships. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and determines the direction and risks of politicians' policies. The analysis unit can also analyze data using AI. For example, the analysis unit predicts the possibility and likelihood of future policies and regulations based on past policy data. The provision unit provides information based on the analysis results obtained by the analysis unit. The information provision unit provides, for example, unbiased information to the general public and generates political risks and patterns for corporations. The information provision unit can also provide information using AI. For example, the information provision unit evaluates the impact of a particular policy on a company and predicts the risk based on the analysis results. The information generation unit generates political risks and patterns based on the information provided by the information provision unit. For example, the information generation unit evaluates the impact of a particular policy on a company and predicts the risk. The information generation unit can also generate political risks and patterns using AI. For example, the information generation unit evaluates political risks based on collected data and predicts future policy risks and patterns. As a result, the information management system according to this embodiment can centrally manage information on politicians, policies, and central government ministries, provide unbiased information to the general public, and generate political risks and patterns for corporations.

[0030] The data collection department collects data. For example, it collects data on the policy preferences, private lives, and social circles of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. Specifically, the department collects data from a wide range of sources, including newspaper articles, official government announcements, academic papers, policy reports, interviews, and social media posts. Because this data exists in various formats, such as text, image, and audio, the department possesses advanced technology to efficiently collect and integrate it. The department can also automatically collect data using AI. For example, it uses natural language processing to analyze newspaper articles and understand politicians' policy preferences and social circles. The AI ​​uses algorithms to understand the content of articles and extract important information. Furthermore, the department uses web scraping technology to automatically collect publicly available information from the internet and store it in a database. This allows the department to efficiently collect vast amounts of data and update it in real time. Furthermore, the data collection unit is equipped with a filtering function to evaluate the reliability of the data, allowing it to eliminate unreliable information and collect only accurate and reliable data. This enables the data collection unit to provide high-quality data that forms the foundation of the information management system.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data to determine the direction and risks of politicians' policies. Specifically, the analysis unit analyzes the collected data using statistical methods and machine learning algorithms to analyze politicians' past statements and behavioral patterns. This allows it to predict the direction and future actions of politicians. The analysis unit can also analyze data using AI. For example, based on past policy data, the analysis unit predicts the possibility and likelihood of future policies and regulations. The AI ​​uses deep learning technology to learn complex patterns from large amounts of data and make highly accurate predictions. Furthermore, the analysis unit uses natural language processing technology to analyze collected text data and understand the intentions behind politicians' statements and policies. This allows the analysis unit to more accurately determine the direction and risks of politicians' policies. The analysis unit can also analyze the correlation between data and identify politicians' relationships and influential figures. This allows the analysis unit to reveal the factors behind politicians' actions and policies and provide deeper insights. In addition, the analysis unit can analyze data in real time and make rapid decisions based on the latest information. This allows the analysis unit, as the core of the information management system, to provide accurate and reliable analysis results.

[0032] The information provision department provides information based on the analysis results obtained by the analysis department. For example, the information provision department provides unbiased information to the general public and generates political risks and patterns for corporations. Specifically, based on the analysis results, the information provision department provides basic information on politicians' policies and activities to the general public, and more detailed risk assessments and impact analyses to corporations. The information provision department can also provide information using AI. For example, based on the analysis results, the information provision department evaluates the impact of a particular policy on a company and predicts the risk. AI can perform individual risk assessments considering characteristics such as the industry, size, and region of the company. Furthermore, the information provision department can provide customized information according to the user's needs. For example, it can create risk assessment reports specific to a particular industry or region to support corporate management decisions. The information provision department also puts effort into the methods of providing information, delivering information through various channels such as websites, mobile apps, and email newsletters. This allows users to access the necessary information anytime, anywhere. Furthermore, the information provision department can collect feedback from users and continuously improve the quality of the information it provides. This allows the information provision department to provide valuable information to users and enhance the reliability and usefulness of the information management system.

[0033] The generation unit generates political risks and patterns based on information provided by the supply unit. For example, the generation unit evaluates the impact of a specific policy on a company and predicts the risks. Specifically, the generation unit performs simulations to predict future policy risks and patterns based on collected data and analysis results. The generation unit can also generate political risks and patterns using AI. For example, the generation unit uses machine learning algorithms to learn risk patterns from past data and predict future risks. Furthermore, the generation unit can perform scenario analysis and conduct risk assessments based on multiple scenarios. This allows the generation unit to provide risk assessments that address various situations. The generation unit also visualizes the results of the risk assessment so that users can understand them intuitively. For example, it uses risk maps, graphs, charts, etc., to visually display the distribution and fluctuations of risks. This makes it easier for users to grasp the overall picture of the risks and take appropriate countermeasures. Furthermore, the generation unit can update the results of the risk assessment in real time and provide risk assessments based on the latest information. As a result, the generation unit, as part of an information management system, can provide users with highly accurate and reliable risk assessments and support corporate risk management.

[0034] The data collection unit collects data on the policy preferences, private lives, and social relationships of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. For example, the data collection unit can collect the policy preferences of domestic and international politicians. The data collection unit can also collect data on politicians' private lives and social relationships. The data collection unit can also collect policy data published by newspapers and government agencies. By collecting diverse data, information on politicians and policies can be managed comprehensively. Some or all of the processing described above in the data collection unit may or may not be performed using AI. For example, the data collection unit can collect data from newspaper articles and official government announcements, and have AI analyze it to understand politicians' policy preferences and social relationships.

[0035] The analysis unit can analyze the collected data and determine the direction and risks of politicians' policies. For example, the analysis unit can analyze the collected data and determine the direction of politicians' policies. The analysis unit can also analyze the collected data and determine the risks of politicians. The analysis unit can also analyze the data using AI. For example, the analysis unit can predict the possibility and likelihood of future policies and regulations based on past policy data. This allows for an understanding of politicians' policy direction and risks through data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit determines the direction and risks of politicians' policies based on the collected data.

[0036] The information provider can provide unbiased information to the general public and generate political risks and patterns for corporate clients. For example, the information provider can provide unbiased information to the general public. The information provider can also generate political risks and patterns for corporate clients. The information provider can also provide information using AI. For example, the information provider can evaluate the impact of a specific policy on a company and predict the risks based on the analysis results. This makes it possible to provide information suitable for both the general public and corporate clients. Some or all of the above-described processes in the information provider may be performed using AI or not. For example, the information provider can evaluate the impact of a specific policy on a company and predict the risks based on the analysis results.

[0037] The generation unit can assess the impact of specific policies on businesses and predict risks. For example, the generation unit assesses the impact of specific policies on businesses. The generation unit can also predict risks. The generation unit can also generate political risks and patterns using AI. For example, the generation unit assesses political risks and predicts future policy risks and patterns based on collected data. This allows for the assessment and prediction of policy risks to businesses. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit assesses political risks and predicts future policy risks and patterns based on collected data.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can perform efficient data collection by collecting data at specific time periods based on past data collection history. The data collection unit can also prioritize data collection from specific information sources based on past data collection history. For example, the data collection unit can analyze past data collection history and optimize the collection frequency. This enables efficient data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit selects the optimal collection method based on past data collection history.

[0039] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to policy areas that the user is interested in. For example, the data collection unit can prioritize collecting data related to politicians that the user is interested in. For example, the data collection unit can prioritize collecting data related to regions that the user is interested in. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit filters data based on the user's current areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, if a user is interested in a particular region, the data collection unit can prioritize the collection of data related to that region. For example, if a user is interested in the politics of a particular country, the data collection unit can prioritize the collection of data related to that country. For example, if a user is interested in the policies of a particular city, the data collection unit can prioritize the collection of data related to that city. In this way, highly relevant data can be prioritized by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information.

[0041] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can collect social media posts from politicians that the user follows. The data collection unit can also collect social media discussions on policies that the user is interested in. The data collection unit can also collect posts from political social media groups that the user participates in. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit collects relevant data based on social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important policy data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can apply multiple analytical methods to highly important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a policy analysis algorithm to policy data. For example, the analysis unit can apply a character analysis algorithm to politician data. For example, the analysis unit can apply a legal risk analysis algorithm to legal data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit applies different analysis algorithms depending on the data category.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also perform trend analysis based on historical data. The analysis unit can also prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit determines the priority of analysis based on the data collection period.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the order of analysis based on the relevance of the data.

[0046] The information provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide a detailed explanation for important information. For example, the provider can provide a simplified explanation for less important information. For example, the provider can provide explanations from multiple perspectives for highly important information. By adjusting the level of detail based on the importance of the information, efficient information provision becomes possible. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider adjusts the level of detail based on the importance of the information.

[0047] The information provider can apply different information provision algorithms depending on the information category when providing information. For example, the information provider can apply a policy analysis algorithm to policy information. For example, the information provider can apply a character analysis algorithm to information about politicians. For example, the information provider can apply a legal risk analysis algorithm to legal information. By applying an appropriate information provision algorithm according to the information category, the accuracy of information provision is improved. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can apply different information provision algorithms depending on the information category.

[0048] The information provider can determine the priority of information provision based on when the information was collected. For example, the provider may prioritize providing the latest information. The provider may also provide trend information based on past data. The provider may also prioritize providing information collected during a specific period. This enables efficient information provision by determining the priority of information provision based on when the information was collected. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider may determine the priority of information provision based on when the information was collected.

[0049] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. For example, the provider can postpone the delivery of less relevant information. The provider can also dynamically adjust the order of information delivery based on the relevance of the information. This enables efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider adjusts the order of delivery based on the relevance of the information.

[0050] The generation unit can improve the accuracy of generation by considering the interrelationships between data during generation. For example, the generation unit can generate risks by considering the interrelationships between policy data and politician data. The generation unit can also generate patterns by considering the interrelationships between legal data and policy data. The generation unit can also generate risks by considering the interrelationships between politicians' social network data and policy data. This improves the accuracy of generation by considering the interrelationships between data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit improves the accuracy of generation based on the interrelationships between data.

[0051] The generation unit can perform generation while considering the attribute information of the data submitter. For example, if the data submitter is a government agency, the generation unit can generate risk considering its reliability. For example, if the data submitter is a media outlet, the generation unit can also generate patterns considering its bias. For example, if the data submitter is an individual, the generation unit can also generate risk considering their background information. This improves the accuracy of generation by considering the attribute information of the data submitter. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit performs generation based on the attribute information of the data submitter.

[0052] The generation unit can perform generation while considering the geographical distribution of the data. For example, the generation unit can generate risk based on data related to a specific region. For example, the generation unit can also generate patterns based on geographically widespread data. For example, the generation unit can generate risk by comparing data from different regions. This improves the accuracy of generation by considering the geographical distribution of the data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit performs generation based on the geographical distribution of the data.

[0053] The generation unit can improve the accuracy of its generation by referring to relevant literature on the data during the generation process. For example, the generation unit can generate risks by referring to relevant academic papers. The generation unit can also generate patterns by referring to past research on policies, for example. The generation unit can also generate risks by referring to relevant reports, for example. This improves the accuracy of the generation by referring to relevant literature on the data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit improves the accuracy of its generation based on relevant literature on the data.

[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 the user's past behavior history and select the optimal data collection method. For example, by collecting data at specific time periods based on past behavior history, efficient data collection can be achieved. Prioritizing data collection from specific information sources is also possible. The collection frequency can also be optimized. This enables efficient data collection by selecting the optimal data collection method based on past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit selects the optimal data collection method based on past behavior history.

[0056] The analysis unit can adjust the level of detail of the analysis based on the reliability of the data during the analysis. For example, a detailed analysis can be performed on highly reliable data. A simplified analysis can be performed on less reliable data. Multiple analysis methods can also be applied to multiple highly reliable data points. This allows for efficient analysis by adjusting the level of detail of the analysis based on the reliability of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the level of detail of the analysis based on the reliability of the data.

[0057] The information delivery unit can analyze the user's past usage history and select the optimal delivery method when providing information. For example, by providing information at specific time periods based on past usage history, efficient information delivery can be achieved. Prioritization of information delivery from specific information sources can also be prioritized. The frequency of information delivery can also be optimized. This enables efficient information delivery by selecting the optimal delivery method based on past usage history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit selects the optimal delivery method based on past usage history.

[0058] The generation unit can adjust the level of detail of the generated data based on the reliability of the data. For example, it can generate detailed risks and patterns for highly reliable data, and simplified risks and patterns for less reliable data. Multiple generation methods can also be applied to multiple highly reliable data sets. This allows for efficient generation by adjusting the level of detail based on the reliability of the data. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit adjusts the level of detail of the generated data based on the reliability of the data.

[0059] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, it can prioritize the collection of data related to policy areas in which the user is interested. It can also prioritize the collection of data related to politicians in which the user is interested. It can also prioritize the collection of data related to regions in which the user is interested. This allows for the collection of highly relevant data by filtering the data based on the user's areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit filters data based on the user's current areas of interest.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data. The data collection unit collects data such as the policy preferences, private lives, and social relationships of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. The data collection unit collects data from sources such as newspaper articles and official government announcements and stores it in a database. The data collection unit can also automatically collect data using AI. For example, the data collection unit can use AI to analyze newspaper articles and understand politicians' policy preferences and social relationships. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to determine the direction and risks of politicians' policies. The analysis unit can also use AI to analyze the data. For example, the analysis unit can predict the possibility and likelihood of future policies and regulations based on past policy data. Step 3: The service provider provides information based on the analysis results obtained by the analysis provider. For example, the service provider provides unbiased information to the general public and generates political risks and patterns for corporate clients. The service provider can also provide information using AI. For example, based on the analysis results, the service provider can evaluate the impact of a specific policy on a company and predict the risks. Step 4: The generation unit generates political risks and patterns based on the information provided by the supply unit. For example, the generation unit evaluates the impact of a particular policy on a company and predicts the risks. The generation unit can also use AI to generate political risks and patterns. For example, the generation unit evaluates political risks based on collected data and predicts future policy risks and patterns.

[0062] (Example of form 2) An information management system according to an embodiment of the present invention is a system for centrally managing information on politicians, policies, and central government ministries. This information management system collects data on the policy preferences, private lives, and social relationships of politicians both domestically and internationally, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. AI analyzes this data to incorporate and systematize information on the direction of politics and policies. As a result, information is compiled objectively, providing unbiased information to the general public and generating political risks and patterns for corporations. For example, the information management system can collect data from newspaper articles and official government announcements, and the AI ​​analyzes it to understand politicians' policy preferences and social relationships. Next, the AI ​​analyzes the collected data to incorporate and systematize information on the direction of politics and policies. The AI ​​analyzes the collected data to determine the direction of politicians' policies and risks. For example, based on past policy data, it can predict the possibility and probability of future policies and regulations. As a result, information is compiled objectively, providing unbiased information to the general public. Furthermore, it generates political risks and patterns for corporations. Based on collected data, AI assesses political risks and predicts future policy risks and patterns. For example, it can evaluate the impact of specific policies on businesses and predict the risks. This allows businesses to accurately assess political risks and take appropriate measures. This system enables the provision of unbiased political information to the general public and the generation of political risks and patterns for corporations. For example, it can provide a social media service that clearly explains politicians and policies to the general public before elections, and a service that accurately assesses and predicts the political risks to companies, as well as the evaluation of politics and policies in Japan and around the world. In this way, by utilizing AI, information on politicians, policies, and central government ministries can be centrally managed and compiled objectively. This makes politics more transparent, providing unbiased information to the general public and generating political risks and patterns for corporations.This allows the information management system to centrally manage information on politicians, policies, and central government ministries, providing unbiased information to the general public and generating political risk and pattern analysis for corporations.

[0063] The information management system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects data. For example, the collection unit collects data such as the policy preferences, private lives, and social relationships of domestic and international politicians, as well as data on policies, white papers, councils and council members, and laws published by newspapers and government agencies. The collection unit collects data from sources such as newspaper articles and official government announcements and stores it in a database. The collection unit can also automatically collect data using AI. For example, the collection unit uses AI to analyze newspaper articles and understand politicians' policy preferences and social relationships. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and determines the direction and risks of politicians' policies. The analysis unit can also analyze data using AI. For example, the analysis unit predicts the possibility and likelihood of future policies and regulations based on past policy data. The provision unit provides information based on the analysis results obtained by the analysis unit. The information provision unit provides, for example, unbiased information to the general public and generates political risks and patterns for corporations. The information provision unit can also provide information using AI. For example, the information provision unit evaluates the impact of a particular policy on a company and predicts the risk based on the analysis results. The information generation unit generates political risks and patterns based on the information provided by the information provision unit. For example, the information generation unit evaluates the impact of a particular policy on a company and predicts the risk. The information generation unit can also generate political risks and patterns using AI. For example, the information generation unit evaluates political risks based on collected data and predicts future policy risks and patterns. As a result, the information management system according to this embodiment can centrally manage information on politicians, policies, and central government ministries, provide unbiased information to the general public, and generate political risks and patterns for corporations.

[0064] The data collection department collects data. For example, it collects data on the policy preferences, private lives, and social circles of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. Specifically, the department collects data from a wide range of sources, including newspaper articles, official government announcements, academic papers, policy reports, interviews, and social media posts. Because this data exists in various formats, such as text, image, and audio, the department possesses advanced technology to efficiently collect and integrate it. The department can also automatically collect data using AI. For example, it uses natural language processing to analyze newspaper articles and understand politicians' policy preferences and social circles. The AI ​​uses algorithms to understand the content of articles and extract important information. Furthermore, the department uses web scraping technology to automatically collect publicly available information from the internet and store it in a database. This allows the department to efficiently collect vast amounts of data and update it in real time. Furthermore, the data collection unit is equipped with a filtering function to evaluate the reliability of the data, allowing it to eliminate unreliable information and collect only accurate and reliable data. This enables the data collection unit to provide high-quality data that forms the foundation of the information management system.

[0065] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data to determine the direction and risks of politicians' policies. Specifically, the analysis unit analyzes the collected data using statistical methods and machine learning algorithms to analyze politicians' past statements and behavioral patterns. This allows it to predict the direction and future actions of politicians. The analysis unit can also analyze data using AI. For example, based on past policy data, the analysis unit predicts the possibility and likelihood of future policies and regulations. The AI ​​uses deep learning technology to learn complex patterns from large amounts of data and make highly accurate predictions. Furthermore, the analysis unit uses natural language processing technology to analyze collected text data and understand the intentions behind politicians' statements and policies. This allows the analysis unit to more accurately determine the direction and risks of politicians' policies. The analysis unit can also analyze the correlation between data and identify politicians' relationships and influential figures. This allows the analysis unit to reveal the factors behind politicians' actions and policies and provide deeper insights. In addition, the analysis unit can analyze data in real time and make rapid decisions based on the latest information. This allows the analysis unit, as the core of the information management system, to provide accurate and reliable analysis results.

[0066] The information provision department provides information based on the analysis results obtained by the analysis department. For example, the information provision department provides unbiased information to the general public and generates political risks and patterns for corporations. Specifically, based on the analysis results, the information provision department provides basic information on politicians' policies and activities to the general public, and more detailed risk assessments and impact analyses to corporations. The information provision department can also provide information using AI. For example, based on the analysis results, the information provision department evaluates the impact of a particular policy on a company and predicts the risk. AI can perform individual risk assessments considering characteristics such as the industry, size, and region of the company. Furthermore, the information provision department can provide customized information according to the user's needs. For example, it can create risk assessment reports specific to a particular industry or region to support corporate management decisions. The information provision department also puts effort into the methods of providing information, delivering information through various channels such as websites, mobile apps, and email newsletters. This allows users to access the necessary information anytime, anywhere. Furthermore, the information provision department can collect feedback from users and continuously improve the quality of the information it provides. This allows the information provision department to provide valuable information to users and enhance the reliability and usefulness of the information management system.

[0067] The generation unit generates political risks and patterns based on information provided by the supply unit. For example, the generation unit evaluates the impact of a specific policy on a company and predicts the risks. Specifically, the generation unit performs simulations to predict future policy risks and patterns based on collected data and analysis results. The generation unit can also generate political risks and patterns using AI. For example, the generation unit uses machine learning algorithms to learn risk patterns from past data and predict future risks. Furthermore, the generation unit can perform scenario analysis and conduct risk assessments based on multiple scenarios. This allows the generation unit to provide risk assessments that address various situations. The generation unit also visualizes the results of the risk assessment so that users can understand them intuitively. For example, it uses risk maps, graphs, charts, etc., to visually display the distribution and fluctuations of risks. This makes it easier for users to grasp the overall picture of the risks and take appropriate countermeasures. Furthermore, the generation unit can update the results of the risk assessment in real time and provide risk assessments based on the latest information. As a result, the generation unit, as part of an information management system, can provide users with highly accurate and reliable risk assessments and support corporate risk management.

[0068] The data collection unit collects data on the policy preferences, private lives, and social relationships of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. For example, the data collection unit can collect the policy preferences of domestic and international politicians. The data collection unit can also collect data on politicians' private lives and social relationships. The data collection unit can also collect policy data published by newspapers and government agencies. By collecting diverse data, information on politicians and policies can be managed comprehensively. Some or all of the processing described above in the data collection unit may or may not be performed using AI. For example, the data collection unit can collect data from newspaper articles and official government announcements, and have AI analyze it to understand politicians' policy preferences and social relationships.

[0069] The analysis unit can analyze the collected data and determine the direction and risks of politicians' policies. For example, the analysis unit can analyze the collected data and determine the direction of politicians' policies. The analysis unit can also analyze the collected data and determine the risks of politicians. The analysis unit can also analyze the data using AI. For example, the analysis unit can predict the possibility and likelihood of future policies and regulations based on past policy data. This allows for an understanding of politicians' policy direction and risks through data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit determines the direction and risks of politicians' policies based on the collected data.

[0070] The information provider can provide unbiased information to the general public and generate political risks and patterns for corporate clients. For example, the information provider can provide unbiased information to the general public. The information provider can also generate political risks and patterns for corporate clients. The information provider can also provide information using AI. For example, the information provider can evaluate the impact of a specific policy on a company and predict the risks based on the analysis results. This makes it possible to provide information suitable for both the general public and corporate clients. Some or all of the above-described processes in the information provider may be performed using AI or not. For example, the information provider can evaluate the impact of a specific policy on a company and predict the risks based on the analysis results.

[0071] The generation unit can assess the impact of specific policies on businesses and predict risks. For example, the generation unit assesses the impact of specific policies on businesses. The generation unit can also predict risks. The generation unit can also generate political risks and patterns using AI. For example, the generation unit assesses political risks and predicts future policy risks and patterns based on collected data. This allows for the assessment and prediction of policy risks to businesses. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit assesses political risks and predicts future policy risks and patterns based on collected data.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is excited, the data collection unit can collect the latest political information in real time and provide it immediately. For example, if the user is relaxed, the data collection unit can collect data periodically and provide it in batches. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and provide only important information. This allows for data collection at a more appropriate time by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit adjusts the timing of data collection based on the user's emotion data.

[0073] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can perform efficient data collection by collecting data at specific time periods based on past data collection history. The data collection unit can also prioritize data collection from specific information sources based on past data collection history. For example, the data collection unit can analyze past data collection history and optimize the collection frequency. This enables efficient data collection by selecting the optimal collection method based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit selects the optimal collection method based on past data collection history.

[0074] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to policy areas that the user is interested in. For example, the data collection unit can prioritize collecting data related to politicians that the user is interested in. For example, the data collection unit can prioritize collecting data related to regions that the user is interested in. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit filters data based on the user's current areas of interest.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting the latest political news. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed policy analysis data. For example, if the user is stressed, the data collection unit may prioritize collecting only important information. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit determines the priority of data to collect based on the user's emotion data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, if a user is interested in a particular region, the data collection unit can prioritize the collection of data related to that region. For example, if a user is interested in the politics of a particular country, the data collection unit can prioritize the collection of data related to that country. For example, if a user is interested in the policies of a particular city, the data collection unit can prioritize the collection of data related to that city. In this way, highly relevant data can be prioritized by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit prioritizes the collection of highly relevant data based on geographical location information.

[0077] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can collect social media posts from politicians that the user follows. The data collection unit can also collect social media discussions on policies that the user is interested in. The data collection unit can also collect posts from political social media groups that the user participates in. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit collects relevant data based on social media activity.

[0078] 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 excited, the analysis unit can display the analysis results using visually stimulating graphs or charts. For example, if the user is relaxed, the analysis unit can also provide a detailed text analysis. For example, if the user is stressed, the analysis unit can also provide a concise and to-the-point analysis result. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the presentation of the analysis based on the user's emotion data.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on important policy data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can apply multiple analytical methods to highly important data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a policy analysis algorithm to policy data. For example, the analysis unit can apply a character analysis algorithm to politician data. For example, the analysis unit can apply a legal risk analysis algorithm to legal data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit applies different analysis algorithms depending on the data category.

[0081] 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 also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also 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 generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the length of the analysis based on the user's emotion data.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also perform trend analysis based on historical data. The analysis unit can also prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit determines the priority of analysis based on the data collection period.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the order of analysis based on the relevance of the data.

[0084] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is excited, the information provider may provide information in a visually stimulating way. For example, if the user is relaxed, the information provider may provide detailed text information. For example, if the user is stressed, the information provider may provide concise and to-the-point information. This allows for the provision of more appropriate information by adjusting the method of information delivery 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 information provider may be performed using AI or not. For example, the information provider adjusts the method of information delivery based on the user's emotion data.

[0085] The information provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide a detailed explanation for important information. For example, the provider can provide a simplified explanation for less important information. For example, the provider can provide explanations from multiple perspectives for highly important information. By adjusting the level of detail based on the importance of the information, efficient information provision becomes possible. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider adjusts the level of detail based on the importance of the information.

[0086] The information provider can apply different information provision algorithms depending on the information category when providing information. For example, the information provider can apply a policy analysis algorithm to policy information. For example, the information provider can apply a character analysis algorithm to information about politicians. For example, the information provider can apply a legal risk analysis algorithm to legal information. By applying an appropriate information provision algorithm according to the information category, the accuracy of information provision is improved. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can apply different information provision algorithms depending on the information category.

[0087] The information provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is in a hurry, the provider can provide short, concise information. For example, if the user is relaxed, the provider can also provide detailed information. For example, if the user is excited, the provider can also provide visually stimulating information. By adjusting the length of the information provided according to the user's emotions, more appropriate information 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 processing described above in the information provider may be performed using AI or not. For example, the provider adjusts the length of the information provided based on the user's emotion data.

[0088] The information provider can determine the priority of information provision based on when the information was collected. For example, the provider may prioritize providing the latest information. The provider may also provide trend information based on past data. The provider may also prioritize providing information collected during a specific period. This enables efficient information provision by determining the priority of information provision based on when the information was collected. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider may determine the priority of information provision based on when the information was collected.

[0089] The information provider can adjust the order of information delivery based on the relevance of the information. For example, the provider can prioritize the delivery of highly relevant information. For example, the provider can postpone the delivery of less relevant information. The provider can also dynamically adjust the order of information delivery based on the relevance of the information. This enables efficient information delivery by adjusting the order of delivery based on the relevance of the information. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider adjusts the order of delivery based on the relevance of the information.

[0090] The generation unit can estimate the user's emotions and determine the priority of risks and patterns to generate based on the estimated user emotions. For example, if the user is excited, the generation unit will prioritize generating the most important risks and patterns. For example, if the user is relaxed, the generation unit can also generate detailed risks and patterns. For example, if the user is stressed, the generation unit can also generate concise and to-the-point risks and patterns. This allows for the priority generation of important risks and patterns by determining the priority of risks and patterns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit determines the priority of risks and patterns to generate based on the user's emotion data.

[0091] The generation unit can improve the accuracy of generation by considering the interrelationships between data during generation. For example, the generation unit can generate risks by considering the interrelationships between policy data and politician data. The generation unit can also generate patterns by considering the interrelationships between legal data and policy data. The generation unit can also generate risks by considering the interrelationships between politicians' social network data and policy data. This improves the accuracy of generation by considering the interrelationships between data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit improves the accuracy of generation based on the interrelationships between data.

[0092] The generation unit can perform generation while considering the attribute information of the data submitter. For example, if the data submitter is a government agency, the generation unit can generate risk considering its reliability. For example, if the data submitter is a media outlet, the generation unit can also generate patterns considering its bias. For example, if the data submitter is an individual, the generation unit can also generate risk considering their background information. This improves the accuracy of generation by considering the attribute information of the data submitter. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit performs generation based on the attribute information of the data submitter.

[0093] The generation unit can estimate the user's emotions and adjust how risks and patterns are displayed based on the estimated emotions. For example, if the user is excited, the generation unit may use visually stimulating graphs or charts to display risks and patterns. If the user is relaxed, the generation unit may also use detailed text information to display risks and patterns. If the user is stressed, the generation unit may also use a concise and to-the-point display method to display risks and patterns. This allows for more appropriate display by adjusting how risks and patterns are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit adjusts how risks and patterns are displayed based on the user's emotion data.

[0094] The generation unit can perform generation while considering the geographical distribution of the data. For example, the generation unit can generate risk based on data related to a specific region. For example, the generation unit can also generate patterns based on geographically widespread data. For example, the generation unit can generate risk by comparing data from different regions. This improves the accuracy of generation by considering the geographical distribution of the data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit performs generation based on the geographical distribution of the data.

[0095] The generation unit can improve the accuracy of its generation by referring to relevant literature on the data during the generation process. For example, the generation unit can generate risks by referring to relevant academic papers. The generation unit can also generate patterns by referring to past research on policies, for example. The generation unit can also generate risks by referring to relevant reports, for example. This improves the accuracy of the generation by referring to relevant literature on the data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit improves the accuracy of its generation based on relevant literature on the data.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the latest data can be prioritized for analysis. If the user is relaxed, a detailed analysis can be performed. If the user is stressed, only important data can be prioritized for analysis. This allows for efficient analysis by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit determines the priority of analysis based on the user's emotion data.

[0098] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the user is excited, it can provide the latest information in real time. If the user is relaxed, it can provide information periodically. If the user is stressed, it can provide only important information. By adjusting the timing of information delivery according to the user's emotions, information can be delivered at a more appropriate time. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit adjusts the timing of information delivery based on the user's emotion data.

[0099] The generation unit can estimate the user's emotions and adjust the level of detail of the risks and patterns it generates based on the estimated emotions. For example, if the user is excited, it can generate concise and to-the-point risks and patterns. If the user is relaxed, it can generate detailed risks and patterns. If the user is stressed, it can generate only important risks and patterns. By adjusting the level of detail of the risks and patterns generated according to the user's emotions, more appropriate risks and patterns can be generated. Emotion estimation is achieved using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit adjusts the level of detail of the risks and patterns it generates based on the user's emotion data.

[0100] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is excited, it can collect the latest data in real time. If the user is relaxed, it can collect data periodically. If the user is stressed, it can collect only important data. This allows for more appropriate data collection by adjusting the data collection method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit adjusts the data collection method based on the user's emotion data.

[0101] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, a concise and to-the-point analysis can be performed. If the user is relaxed, a detailed analysis can be performed. If the user is stressed, only important data can be analyzed. In this way, by adjusting the accuracy of the analysis according to the user's emotions, a more appropriate analysis can be performed. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit adjusts the accuracy of the analysis based on the user's emotion data.

[0102] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, by collecting data at specific time periods based on past behavior history, efficient data collection can be achieved. Prioritizing data collection from specific information sources is also possible. The collection frequency can also be optimized. This enables efficient data collection by selecting the optimal data collection method based on past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit selects the optimal data collection method based on past behavior history.

[0103] The analysis unit can adjust the level of detail of the analysis based on the reliability of the data during the analysis. For example, a detailed analysis can be performed on highly reliable data. A simplified analysis can be performed on less reliable data. Multiple analysis methods can also be applied to multiple highly reliable data points. This allows for efficient analysis by adjusting the level of detail of the analysis based on the reliability of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit adjusts the level of detail of the analysis based on the reliability of the data.

[0104] The information delivery unit can analyze the user's past usage history and select the optimal delivery method when providing information. For example, by providing information at specific time periods based on past usage history, efficient information delivery can be achieved. Prioritization of information delivery from specific information sources can also be prioritized. The frequency of information delivery can also be optimized. This enables efficient information delivery by selecting the optimal delivery method based on past usage history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit selects the optimal delivery method based on past usage history.

[0105] The generation unit can adjust the level of detail of the generated data based on the reliability of the data. For example, it can generate detailed risks and patterns for highly reliable data, and simplified risks and patterns for less reliable data. Multiple generation methods can also be applied to multiple highly reliable data sets. This allows for efficient generation by adjusting the level of detail based on the reliability of the data. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit adjusts the level of detail of the generated data based on the reliability of the data.

[0106] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, it can prioritize the collection of data related to policy areas in which the user is interested. It can also prioritize the collection of data related to politicians in which the user is interested. It can also prioritize the collection of data related to regions in which the user is interested. This allows for the collection of highly relevant data by filtering the data based on the user's areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit filters data based on the user's current areas of interest.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data. The data collection unit collects data such as the policy preferences, private lives, and social relationships of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. The data collection unit collects data from sources such as newspaper articles and official government announcements and stores it in a database. The data collection unit can also automatically collect data using AI. For example, the data collection unit can use AI to analyze newspaper articles and understand politicians' policy preferences and social relationships. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to determine the direction and risks of politicians' policies. The analysis unit can also use AI to analyze the data. For example, the analysis unit can predict the possibility and likelihood of future policies and regulations based on past policy data. Step 3: The service provider provides information based on the analysis results obtained by the analysis provider. For example, the service provider provides unbiased information to the general public and generates political risks and patterns for corporate clients. The service provider can also provide information using AI. For example, based on the analysis results, the service provider can evaluate the impact of a specific policy on a company and predict the risks. Step 4: The generation unit generates political risks and patterns based on the information provided by the supply unit. For example, the generation unit evaluates the impact of a particular policy on a company and predicts the risks. The generation unit can also use AI to generate political risks and patterns. For example, the generation unit evaluates political risks based on collected data and predicts future policy risks and patterns.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit provides information using, for example, the output device 40 of the smart device 14. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates political risks and patterns. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit provides information using, for example, the speaker 240 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates political risks and patterns. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit provides information using, for example, the speaker 240 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates political risks and patterns. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit provides information using, for example, the speaker 240 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates political risks and patterns. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides information based on the analysis results obtained by the aforementioned analysis unit, The system comprises a generation unit that generates political risks and patterns based on the information provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is This involves collecting data on the policy preferences, private lives, and social circles of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to determine the direction and risks of politicians' policies. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide unbiased information to the general public and generate political risk and pattern analysis for corporate clients. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is To assess the impact of specific policies on businesses and predict risks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection 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 data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data 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 data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. 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 data. 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 data category. 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 data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, 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 20) The aforementioned supply unit is, When providing information, different provision algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the length of information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, the priority of provision will be determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, adjust the order of presentation based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates user emotions and determines the priority of risks and patterns to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the accuracy of the generation is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the data is generated while taking into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and adjusts how risks and patterns are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, we refer to relevant literature to improve the accuracy of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides information based on the analysis results obtained by the aforementioned analysis unit, The system comprises a generation unit that generates political risks and patterns based on the information provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is This involves collecting data on the policy preferences, private lives, and social circles of domestic and international politicians, as well as data on policies, white papers, councils and their members, and laws published by newspapers and government agencies. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to determine the direction and risks of politicians' policies. The system according to feature 1.

4. The aforementioned supply unit is, We provide unbiased information to the general public and generate political risk and pattern analysis for corporate clients. The system according to feature 1.

5. The generating unit is To assess the impact of specific policies on businesses and predict risks. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

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

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

10. The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system according to feature 1.

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