system

Abstract

The system according to this embodiment aims to predict future risks based on historical data and propose specific countermeasures. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects historical data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts future risks based on the data analyzed by the analysis unit. The proposal unit proposes specific countermeasures based on the risks predicted by the prediction unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it cannot be said that predicting future risks based on historical data and proposing specific countermeasures have been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to predict future risks based on historical data and propose specific countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects historical data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts future risks based on the data analyzed by the analysis unit. The proposal unit proposes specific countermeasures based on the risks predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict future risks based on historical data and propose specific countermeasures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The predictive model provision system according to an embodiment of the present invention is a system that uses AI to analyze historical failure cases and provides predictive models to prevent similar failures in the future. The predictive model provision system collects vast amounts of data from world history, Japanese history, corporate history, and startup history, and the AI ​​analyzes this data to learn from past failure cases, applying these patterns to the modern business environment to predict future risks. Furthermore, the predictive model provision system proposes specific countermeasures based on the predicted risks. This system is particularly targeted at management, strategic planning departments, and risk management departments of medium- and large-sized enterprises, and supports corporate strategic planning and risk management. For example, the predictive model provision system utilizes IDC Frontier Co., Ltd.'s hyperscale data center and cloud services to safely and efficiently manage and analyze historical and modern data. This enables high-speed and accurate data processing of the AI ​​model and improves the accuracy of future predictions. Furthermore, the predictive model provision system is estimated to have a global corporate strategy and risk management market of approximately 30 trillion yen, and an AI-powered corporate strategy and risk management market of approximately 3 trillion yen, with a target of 10% market share and 2 billion yen in sales in Japan by 2026. This allows the predictive model provision system to support companies in strategic planning and risk management.

[0029] The predictive model provision system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects historical data. For example, the data collection unit collects data from world history, Japanese history, corporate history, and startup history. The data collection unit can collect data from publicly available databases on the internet or internal corporate databases. The data collection unit can also use crawling technology for data collection. For example, the data collection unit crawls websites based on specific keywords and collects relevant data. Furthermore, the data collection unit can utilize APIs for data collection. For example, the data collection unit obtains data from databases through public APIs. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using AI. For example, the analysis unit extracts data patterns using machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit extracts keywords from text data and evaluates the relevance of the data. Furthermore, the analysis unit can perform data clustering. For example, the analysis unit clusters the data and groups similar data. The prediction unit predicts future risks based on data analyzed by the analysis unit. The prediction unit may use AI to predict risks, for example. The prediction unit may use algorithms that predict future risks based on historical data, for example. The prediction unit can also predict risks using simulation technology, for example. The prediction unit performs simulations to evaluate future risks. Furthermore, the prediction unit can perform data trend analysis, for example. The prediction unit analyzes data trends to predict future risks. The proposal unit proposes specific countermeasures based on the risks predicted by the prediction unit. The proposal unit may use AI to propose countermeasures, for example. The proposal unit may propose risk avoidance or risk mitigation measures, for example. The proposal unit can also propose risk transfer measures, for example. The proposal unit may propose methods for transferring risks to other companies or insurance companies. Furthermore, the proposal unit can propose risk response plans, for example. The proposal unit may propose response plans in the event of a risk occurring.As a result, the predictive model provisioning system according to this embodiment can support corporate strategic planning and risk management.

[0030] The data collection unit collects historical data. For example, it collects data from world history, Japanese history, corporate history, and startup history. Specifically, the data collection unit can collect data from publicly available databases on the internet and internal corporate databases. For example, data on world history includes historical events, economic conditions, and political changes in various countries. Data on Japanese history includes domestic historical events, cultural changes, and economic development. Corporate history includes management strategies, market fluctuations, and relationships with competitors from the company's founding to the present. Startup history includes the growth process of emerging companies, funding history, and technological innovation. The data collection unit can use crawling technology to efficiently collect this data. For example, it can crawl websites based on specific keywords and automatically collect relevant data. The data collection unit can also utilize APIs for data collection. For example, it can retrieve data from databases through public APIs. This allows the data collection unit to collect a wide range of data from diverse sources and enrich the system's overall database. Furthermore, the data collection unit centrally manages the collected data, making it easily accessible to the analysis and prediction units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. Specifically, it extracts data patterns using machine learning algorithms. For example, it can analyze historical economic data to identify fluctuation patterns in specific economic indicators. It can also analyze text data using natural language processing techniques. For example, it can extract keywords from historical documents and news articles and evaluate the relevance of the data. Furthermore, the analysis unit can perform data clustering. For example, it can cluster the growth patterns of companies and group companies with similar growth patterns. This allows the analysis unit to analyze the collected data from multiple perspectives and reveal the patterns and trends behind the data. Additionally, the analysis unit can compare historical and current data to detect unusual patterns and outliers. For example, it can detect unusual fluctuations in economic indicators during specific periods and identify their causes. Through this, the analysis unit can improve the overall accuracy and reliability of the system through data analysis.

[0032] The forecasting unit predicts future risks based on data analyzed by the analysis unit. For example, the forecasting unit uses AI to predict risks. Specifically, it employs algorithms that predict future risks based on historical data. For instance, it can predict future economic risks based on historical economic data. The forecasting unit can also predict risks using simulation technology. For example, it can perform simulations to evaluate future market fluctuations and corporate growth patterns. Furthermore, the forecasting unit can perform data trend analysis. For example, it can analyze trends in historical data to predict future risks. This allows the forecasting unit to predict future risks with high accuracy and provide information useful for corporate strategy planning and risk management. Moreover, the forecasting unit can continuously revise its prediction results based on real-time updated data to respond to the latest situations. For example, if economic indicators or market fluctuations change rapidly, the forecasting unit immediately incorporates new data and updates its prediction results. This allows the forecasting unit to always provide highly accurate risk predictions based on the latest information, supporting quick and appropriate responses.

[0033] The proposal department proposes specific countermeasures based on the risks predicted by the forecasting department. For example, the proposal department uses AI to propose countermeasures. Specifically, it proposes risk avoidance and risk mitigation measures. For example, if a particular market risk increases, it may propose withdrawal from that market or reduction of investment. The proposal department can also propose risk transfer measures. For example, it may propose methods for transferring risk to other companies or insurance companies. Furthermore, the proposal department can propose risk response plans. For example, it may propose a response plan in the event of a risk occurring. This allows the proposal department to help companies respond to risks quickly and appropriately. In addition, the proposal department can continuously evaluate the effectiveness of the proposed measures and revise them as needed. For example, after the proposed measures are implemented, it monitors their effectiveness and proposes new measures as needed. The proposal department can also present multiple countermeasures and help companies select the optimal one. In this way, the proposal department can support companies in strategic planning and risk management, enabling sustainable growth and stability for companies.

[0034] The data collection unit can collect data from world history, Japanese history, corporate history, and startup history. For example, the unit can collect data from world history. The unit can also focus on collecting data from specific eras or regions. For example, the unit can focus on collecting data from the 19th-century Industrial Revolution and apply it to modern technological innovation. It can also collect data from the post-World War II economic recovery period and use it to inform modern economic policy. Furthermore, the unit can collect success stories of Silicon Valley startups and apply them to modern corporate strategies. By collecting diverse historical data, a broader range of risk predictions becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on specific eras or regions into a generating AI and have the generating AI collect the relevant data.

[0035] The analysis unit can learn from past failures and apply those patterns to the current business environment to predict future risks. For example, the analysis unit can learn from patterns in past economic crises. For example, it can analyze data from bubble collapses and financial crises and extract those patterns. The analysis unit can also learn from data on past technological failures and apply those patterns to the current technological environment. For example, it can analyze the causes of technological failures and extract patterns to prevent similar failures. Furthermore, the analysis unit can learn from past management failures and apply those patterns to the current management environment. For example, it can analyze the causes of management failures and extract patterns to prevent similar failures. This makes it possible to predict risks applied to the current business environment by learning from past failures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on past failures into a generating AI and have the generating AI extract failure patterns.

[0036] The prediction unit can predict the risk of future economic crises by learning patterns from past economic crises and comparing them with the current economic situation. For example, the prediction unit can learn data from past bubble collapses and compare it with the current economic situation. The prediction unit can also learn data from past financial crises and compare it with the current financial market. For example, the prediction unit can assess the risk of the current financial market based on patterns from past financial crises. Furthermore, the prediction unit can learn data from past recessions and compare it with current economic indicators. For example, the prediction unit can assess the risk of current economic indicators based on patterns from past recessions. This makes it possible to predict the risk of future economic crises by learning patterns from past economic crises. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from past economic crises into a generating AI and have the generating AI perform a risk prediction for future economic crises.

[0037] The proposal department can propose strategies and risk management methods to help companies avoid repeating past mistakes. For example, the proposal department can conduct risk assessments. For example, the proposal department can conduct risk assessments based on past failure cases and identify potential risks that companies may face. The proposal department can also propose risk response plans. For example, the proposal department can develop and propose response plans to companies in the event of a risk occurring. Furthermore, the proposal department can propose risk monitoring methods. For example, the proposal department can propose a monitoring system to detect the occurrence of risks early. This strengthens companies' risk management by proposing specific strategies and risk management methods to avoid repeating past mistakes. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input data on past failure cases into a generating AI and have the generating AI execute risk management methods.

[0038] The proposal department can propose specific measures and strategies tailored to the needs of target companies. For example, the proposal department can conduct surveys. For example, the proposal department can conduct surveys to understand the needs of target companies and propose measures and strategies based on the results. The proposal department can also conduct interviews. For example, the proposal department can interview management and strategic planning personnel of target companies to understand their needs. Furthermore, the proposal department can conduct industry analysis. For example, the proposal department can analyze trends in the industry to which the target company belongs and propose measures and strategies based on the results. In this way, by proposing specific measures and strategies tailored to the needs of target companies, it can support the strategic planning of companies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the results of the survey into a generating AI and have the generating AI execute proposals for measures and strategies.

[0039] The data collection unit can focus on specific eras or regions when collecting data on world history, Japanese history, corporate history, and startup history. For example, it could focus on collecting data from the 19th-century Industrial Revolution and apply it to modern technological innovation. It could also collect data from the post-World War II economic recovery period and use it to inform modern economic policy. Furthermore, it could collect success stories of Silicon Valley startups and apply them to modern corporate strategies. By focusing on specific eras or regions, it becomes possible to collect more relevant data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit could input data on a specific era or region into a generating AI and have the generating AI collect the relevant data.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the user's industry and business model during data collection. For example, for a manufacturing user, the data collection unit can prioritize the collection of past manufacturing failures. For a service industry user, the data collection unit can also prioritize the collection of past service industry failures. Furthermore, for a technology company user, the data collection unit can also prioritize the collection of past technology company failures. This makes it possible to provide more useful data by prioritizing the collection of highly relevant data based on the user's industry and business model. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data about the user's industry and business model into a generating AI and have the generating AI perform the collection of relevant data.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in Asia, the data collection unit can prioritize the collection of historical data from Asia. If the user is in Europe, the data collection unit can also prioritize the collection of historical data from Europe. Furthermore, if the user is in America, the data collection unit can also prioritize the collection of historical data from America. This makes it possible to provide more useful data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. This makes it possible to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] 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 can perform a detailed analysis on important data. For example, the analysis unit can also perform a simplified analysis on general data. Furthermore, the analysis unit can choose not to analyze unnecessary 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, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can perform analysis using an economic model for economic data. For example, the analysis unit can also perform analysis using a sociological model for social data. Furthermore, the analysis unit can also perform analysis using a technical model for technical data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, it may prioritize the most recent data while also considering past data. Furthermore, the analysis unit may analyze older data only for reference. This allows for efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of important data. For example, the analysis unit may also prioritize the analysis of highly relevant data. Furthermore, the analysis unit may postpone the analysis of less relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The prediction unit can improve the accuracy of its predictions based on the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between economic data and social data. It can also make predictions by considering the interrelationships between technical data and market data. Furthermore, the prediction unit can make predictions by considering the interrelationships between historical data and contemporary data. This improves the accuracy of predictions by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0048] The prediction unit can make predictions based on the attribute information of the data submitter. For example, if the submitter is an economist, the prediction unit will prioritize economic data in its predictions. If the submitter is an engineer, the prediction unit can also prioritize technical data in its predictions. Furthermore, if the submitter is a historian, the prediction unit can also prioritize historical data in its predictions. This allows for more accurate predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the submitter's attribute information into a generating AI and have the generating AI perform the prediction.

[0049] The prediction unit can make predictions based on the geographical distribution of the data. For example, the prediction unit may prioritize using geographically close data for predictions. It may also prioritize using geographically relevant data for predictions. Furthermore, it may use geographically distant data for predictions only as a reference. This allows for more accurate predictions by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the prediction.

[0050] The prediction unit can improve the accuracy of its predictions based on relevant literature during the prediction process. For example, the prediction unit can improve the accuracy of its predictions based on relevant literature. The prediction unit can also make predictions by referring to data from relevant literature. Furthermore, the prediction unit can improve the accuracy of its predictions based on the analysis results of the relevant literature. By improving the accuracy of predictions based on relevant literature, more reliable predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from relevant literature into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0051] The proposal unit can adjust the level of detail in its proposals based on the importance of the risks. For example, it can provide detailed proposals for significant risks, and simplified proposals for common risks. Furthermore, it can choose not to provide proposals for unnecessary risks. This allows for efficient proposals by adjusting the level of detail based on the importance of the risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the risks into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0052] The proposal unit can apply different proposal algorithms depending on the risk category when making a proposal. For example, for economic risks, the proposal unit can make proposals using economic models. For social risks, the proposal unit can also make proposals using sociological models. Furthermore, for technological risks, the proposal unit can also make proposals using technological models. By applying different proposal algorithms depending on the risk category, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the risk category into a generating AI and have the generating AI execute the application of an appropriate proposal algorithm.

[0053] The proposal unit can determine the priority of proposals based on the timing of risk occurrence. For example, the proposal unit can prioritize proposals for immediate risks. For example, the proposal unit can also make proposals with a moderate priority for medium-term risks. Furthermore, the proposal unit can postpone proposals for long-term risks. This allows for efficient proposals by determining the priority of proposals based on the timing of risk occurrence. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of risk occurrence into a generating AI and have the generating AI determine the priority of proposals.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the risks. For example, the proposal unit may prioritize proposing important risks. It may also prioritize proposing highly relevant risks. Furthermore, it may postpone proposing less relevant risks. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the risks into a generating AI and have the generating AI adjust the order of proposals.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The predictive model provision system can further customize predictive models based on the user's industry and business model. For example, it can provide a manufacturing-specific predictive model to a manufacturing user, a service-specific predictive model to a service-oriented user, and a technology-specific predictive model to a technology company user. By providing a predictive model customized based on the user's industry and business model, it becomes possible to provide a more useful predictive model. Some or all of the above-described processes in the predictive model provision system may be performed using AI, for example, or not. For example, the predictive model provision system can input data about the user's industry and business model into a generating AI and have the generating AI perform the customization of the predictive model.

[0057] The predictive model provision system can further customize the predictive model based on the user's geographical location information. For example, if the user is in Asia, it can provide a predictive model that emphasizes Asian economic data. If the user is in Europe, it can provide a predictive model that emphasizes European economic data. Furthermore, if the user is in the United States, it can provide a predictive model that emphasizes American economic data. By providing a predictive model customized based on the user's geographical location information, it becomes possible to provide a more relevant predictive model. Some or all of the above processing in the predictive model provision system may be performed using AI, for example, or without AI. For example, the predictive model provision system can input the user's geographical location information into a generating AI and have the generating AI perform the customization of the predictive model.

[0058] The predictive model provisioning system can further analyze users' social media activity and collect relevant data. This makes it possible to efficiently collect relevant data by analyzing users' social media activity. Some or all of the above-described processes in the predictive model provisioning system may be performed using AI, for example, or without AI. For example, the predictive model provisioning system can input users' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0059] The predictive model provisioning system can further adjust the accuracy of the predictive model based on the data collection timing. For example, it can update the predictive model by prioritizing the use of the latest data. Alternatively, it can prioritize the latest data while also referring to past data. Furthermore, older data can be used only as a reference. This makes it possible to provide a more reliable predictive model by adjusting the accuracy of the predictive model based on the data collection timing. Some or all of the above processing in the predictive model provisioning system may be performed using AI, for example, or without AI. For example, the predictive model provisioning system can input the data collection timing into a generating AI and have the generating AI perform the adjustment of the predictive model's accuracy.

[0060] The predictive model provisioning system can further improve the accuracy of its predictive models based on the interrelationships of data. For example, it can update the predictive model by considering the interrelationships between economic and social data. It can also update the predictive model by considering the interrelationships between technical and market data. Furthermore, it can update the predictive model by considering the interrelationships between historical and contemporary data. In this way, the accuracy of the predictive model is improved by considering the interrelationships of data. Some or all of the above processing in the predictive model provisioning system may be performed using AI, for example, or without AI. For example, the predictive model provisioning system can input the interrelationships of data into a generating AI and have the generating AI perform the improvement of the accuracy of the predictive model.

[0061] The predictive model provision system can further customize the predictive model based on the attribute information of the data submitter. For example, if the submitter is an economist, the system can provide a predictive model that emphasizes economic data. Similarly, if the submitter is an engineer, it can provide a predictive model that emphasizes technical data. Furthermore, if the submitter is a historian, it can provide a predictive model that emphasizes historical data. This makes it possible to provide a more accurate predictive model by considering the attribute information of the data submitter. Some or all of the above processing in the predictive model provision system may be performed using AI, for example, or without AI. For example, the predictive model provision system can input the submitter's attribute information into a generating AI and have the generating AI perform the customization of the predictive model.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects historical data. For example, the data collection unit collects data from world history, Japanese history, corporate history, and startup history. The data collection unit can collect data from publicly available databases on the internet or from internal company databases. The data collection unit can also use crawling techniques for data collection. For example, the data collection unit crawls websites based on specific keywords and collects relevant data. Furthermore, the data collection unit can utilize APIs for data collection. For example, the data collection unit retrieves data from databases through public APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. The analysis unit extracts patterns from the data using machine learning algorithms. The analysis unit can also analyze text data using natural language processing techniques. For example, the analysis unit extracts keywords from text data and evaluates the relationships between the data. Furthermore, the analysis unit can perform data clustering. For example, the analysis unit clusters the data and groups similar data together. Step 3: The prediction unit predicts future risks based on the data analyzed by the analysis unit. The prediction unit can predict risks using, for example, AI. The prediction unit uses algorithms that predict future risks based on past data. The prediction unit can also predict risks using simulation technology. For example, the prediction unit performs simulations to evaluate future risks. Furthermore, the prediction unit can also perform data trend analysis. For example, the prediction unit analyzes data trends to predict future risks. Step 4: The proposal department proposes specific countermeasures based on the risks predicted by the forecasting department. For example, the proposal department may use AI to propose countermeasures. The proposal department may propose risk avoidance measures or risk mitigation measures. The proposal department may also propose risk transfer measures. For example, the proposal department may propose methods for transferring risks to other companies or insurance companies. Furthermore, the proposal department may also propose risk response plans. For example, the proposal department may propose response plans in the event that a risk occurs.

[0064] (Example of form 2) The predictive model provision system according to an embodiment of the present invention is a system that uses AI to analyze historical failure cases and provides predictive models to prevent similar failures in the future. The predictive model provision system collects vast amounts of data from world history, Japanese history, corporate history, and startup history, and the AI ​​analyzes this data to learn from past failure cases, applying these patterns to the modern business environment to predict future risks. Furthermore, the predictive model provision system proposes specific countermeasures based on the predicted risks. This system is particularly targeted at management, strategic planning departments, and risk management departments of medium- and large-sized enterprises, and supports corporate strategic planning and risk management. For example, the predictive model provision system utilizes IDC Frontier Co., Ltd.'s hyperscale data center and cloud services to safely and efficiently manage and analyze historical and modern data. This enables high-speed and accurate data processing of the AI ​​model and improves the accuracy of future predictions. Furthermore, the predictive model provision system is estimated to have a global corporate strategy and risk management market of approximately 30 trillion yen, and an AI-powered corporate strategy and risk management market of approximately 3 trillion yen, with a target of 10% market share and 2 billion yen in sales in Japan by 2026. This allows the predictive model provision system to support companies in strategic planning and risk management.

[0065] The predictive model provision system according to this embodiment comprises a data collection unit, an analysis unit, a prediction unit, and a proposal unit. The data collection unit collects historical data. For example, the data collection unit collects data from world history, Japanese history, corporate history, and startup history. The data collection unit can collect data from publicly available databases on the internet or internal corporate databases. The data collection unit can also use crawling technology for data collection. For example, the data collection unit crawls websites based on specific keywords and collects relevant data. Furthermore, the data collection unit can utilize APIs for data collection. For example, the data collection unit obtains data from databases through public APIs. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the data using AI. For example, the analysis unit extracts data patterns using machine learning algorithms. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit extracts keywords from text data and evaluates the relevance of the data. Furthermore, the analysis unit can perform data clustering. For example, the analysis unit clusters the data and groups similar data. The prediction unit predicts future risks based on data analyzed by the analysis unit. The prediction unit may use AI to predict risks, for example. The prediction unit may use algorithms that predict future risks based on historical data, for example. The prediction unit can also predict risks using simulation technology, for example. The prediction unit performs simulations to evaluate future risks. Furthermore, the prediction unit can perform data trend analysis, for example. The prediction unit analyzes data trends to predict future risks. The proposal unit proposes specific countermeasures based on the risks predicted by the prediction unit. The proposal unit may use AI to propose countermeasures, for example. The proposal unit may propose risk avoidance or risk mitigation measures, for example. The proposal unit can also propose risk transfer measures, for example. The proposal unit may propose methods for transferring risks to other companies or insurance companies. Furthermore, the proposal unit can propose risk response plans, for example. The proposal unit may propose response plans in the event of a risk occurring.As a result, the predictive model provisioning system according to this embodiment can support corporate strategic planning and risk management.

[0066] The data collection unit collects historical data. For example, it collects data from world history, Japanese history, corporate history, and startup history. Specifically, the data collection unit can collect data from publicly available databases on the internet and internal corporate databases. For example, data on world history includes historical events, economic conditions, and political changes in various countries. Data on Japanese history includes domestic historical events, cultural changes, and economic development. Corporate history includes management strategies, market fluctuations, and relationships with competitors from the company's founding to the present. Startup history includes the growth process of emerging companies, funding history, and technological innovation. The data collection unit can use crawling technology to efficiently collect this data. For example, it can crawl websites based on specific keywords and automatically collect relevant data. The data collection unit can also utilize APIs for data collection. For example, it can retrieve data from databases through public APIs. This allows the data collection unit to collect a wide range of data from diverse sources and enrich the system's overall database. Furthermore, the data collection unit centrally manages the collected data, making it easily accessible to the analysis and prediction units. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. Specifically, it extracts data patterns using machine learning algorithms. For example, it can analyze historical economic data to identify fluctuation patterns in specific economic indicators. It can also analyze text data using natural language processing techniques. For example, it can extract keywords from historical documents and news articles and evaluate the relevance of the data. Furthermore, the analysis unit can perform data clustering. For example, it can cluster the growth patterns of companies and group companies with similar growth patterns. This allows the analysis unit to analyze the collected data from multiple perspectives and reveal the patterns and trends behind the data. Additionally, the analysis unit can compare historical and current data to detect unusual patterns and outliers. For example, it can detect unusual fluctuations in economic indicators during specific periods and identify their causes. Through this, the analysis unit can improve the overall accuracy and reliability of the system through data analysis.

[0068] The forecasting unit predicts future risks based on data analyzed by the analysis unit. For example, the forecasting unit uses AI to predict risks. Specifically, it employs algorithms that predict future risks based on historical data. For instance, it can predict future economic risks based on historical economic data. The forecasting unit can also predict risks using simulation technology. For example, it can perform simulations to evaluate future market fluctuations and corporate growth patterns. Furthermore, the forecasting unit can perform data trend analysis. For example, it can analyze trends in historical data to predict future risks. This allows the forecasting unit to predict future risks with high accuracy and provide information useful for corporate strategy planning and risk management. Moreover, the forecasting unit can continuously revise its prediction results based on real-time updated data to respond to the latest situations. For example, if economic indicators or market fluctuations change rapidly, the forecasting unit immediately incorporates new data and updates its prediction results. This allows the forecasting unit to always provide highly accurate risk predictions based on the latest information, supporting quick and appropriate responses.

[0069] The proposal department proposes specific countermeasures based on the risks predicted by the forecasting department. For example, the proposal department uses AI to propose countermeasures. Specifically, it proposes risk avoidance and risk mitigation measures. For example, if a particular market risk increases, it may propose withdrawal from that market or reduction of investment. The proposal department can also propose risk transfer measures. For example, it may propose methods for transferring risk to other companies or insurance companies. Furthermore, the proposal department can propose risk response plans. For example, it may propose a response plan in the event of a risk occurring. This allows the proposal department to help companies respond to risks quickly and appropriately. In addition, the proposal department can continuously evaluate the effectiveness of the proposed measures and revise them as needed. For example, after the proposed measures are implemented, it monitors their effectiveness and proposes new measures as needed. The proposal department can also present multiple countermeasures and help companies select the optimal one. In this way, the proposal department can support companies in strategic planning and risk management, enabling sustainable growth and stability for companies.

[0070] The data collection unit can collect data from world history, Japanese history, corporate history, and startup history. For example, the unit can collect data from world history. The unit can also focus on collecting data from specific eras or regions. For example, the unit can focus on collecting data from the 19th-century Industrial Revolution and apply it to modern technological innovation. It can also collect data from the post-World War II economic recovery period and use it to inform modern economic policy. Furthermore, the unit can collect success stories of Silicon Valley startups and apply them to modern corporate strategies. By collecting diverse historical data, a broader range of risk predictions becomes possible. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on specific eras or regions into a generating AI and have the generating AI collect the relevant data.

[0071] The analysis unit can learn from past failures and apply those patterns to the current business environment to predict future risks. For example, the analysis unit can learn from patterns in past economic crises. For example, it can analyze data from bubble collapses and financial crises and extract those patterns. The analysis unit can also learn from data on past technological failures and apply those patterns to the current technological environment. For example, it can analyze the causes of technological failures and extract patterns to prevent similar failures. Furthermore, the analysis unit can learn from past management failures and apply those patterns to the current management environment. For example, it can analyze the causes of management failures and extract patterns to prevent similar failures. This makes it possible to predict risks applied to the current business environment by learning from past failures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on past failures into a generating AI and have the generating AI extract failure patterns.

[0072] The prediction unit can predict the risk of future economic crises by learning patterns from past economic crises and comparing them with the current economic situation. For example, the prediction unit can learn data from past bubble collapses and compare it with the current economic situation. The prediction unit can also learn data from past financial crises and compare it with the current financial market. For example, the prediction unit can assess the risk of the current financial market based on patterns from past financial crises. Furthermore, the prediction unit can learn data from past recessions and compare it with current economic indicators. For example, the prediction unit can assess the risk of current economic indicators based on patterns from past recessions. This makes it possible to predict the risk of future economic crises by learning patterns from past economic crises. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from past economic crises into a generating AI and have the generating AI perform a risk prediction for future economic crises.

[0073] The proposal department can propose strategies and risk management methods to help companies avoid repeating past mistakes. For example, the proposal department can conduct risk assessments. For example, the proposal department can conduct risk assessments based on past failure cases and identify potential risks that companies may face. The proposal department can also propose risk response plans. For example, the proposal department can develop and propose response plans to companies in the event of a risk occurring. Furthermore, the proposal department can propose risk monitoring methods. For example, the proposal department can propose a monitoring system to detect the occurrence of risks early. This strengthens companies' risk management by proposing specific strategies and risk management methods to avoid repeating past mistakes. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input data on past failure cases into a generating AI and have the generating AI execute risk management methods.

[0074] The proposal department can propose specific measures and strategies tailored to the needs of target companies. For example, the proposal department can conduct surveys. For example, the proposal department can conduct surveys to understand the needs of target companies and propose measures and strategies based on the results. The proposal department can also conduct interviews. For example, the proposal department can interview management and strategic planning personnel of target companies to understand their needs. Furthermore, the proposal department can conduct industry analysis. For example, the proposal department can analyze trends in the industry to which the target company belongs and propose measures and strategies based on the results. In this way, by proposing specific measures and strategies tailored to the needs of target companies, it can support the strategic planning of companies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the results of the survey into a generating AI and have the generating AI execute proposals for measures and strategies.

[0075] 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 stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only important data. This reduces the user's burden by adjusting the timing of data collection 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 can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0076] The data collection unit can focus on specific eras or regions when collecting data on world history, Japanese history, corporate history, and startup history. For example, it could focus on collecting data from the 19th-century Industrial Revolution and apply it to modern technological innovation. It could also collect data from the post-World War II economic recovery period and use it to inform modern economic policy. Furthermore, it could collect success stories of Silicon Valley startups and apply them to modern corporate strategies. By focusing on specific eras or regions, it becomes possible to collect more relevant data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit could input data on a specific era or region into a generating AI and have the generating AI collect the relevant data.

[0077] The data collection unit can prioritize the collection of highly relevant data based on the user's industry and business model during data collection. For example, for a manufacturing user, the data collection unit can prioritize the collection of past manufacturing failures. For a service industry user, the data collection unit can also prioritize the collection of past service industry failures. Furthermore, for a technology company user, the data collection unit can also prioritize the collection of past technology company failures. This makes it possible to provide more useful data by prioritizing the collection of highly relevant data based on the user's industry and business model. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data about the user's industry and business model into a generating AI and have the generating AI perform the collection of relevant data.

[0078] 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 stressed, the data collection unit may prioritize collecting only important data. If the user is relaxed, the data collection unit may also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit may prioritize collecting data that can be collected quickly. This reduces the user's burden by prioritizing the data to be collected 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 can input the user's emotion data into a generative AI and have the generative AI determine the priority of data to be collected.

[0079] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in Asia, the data collection unit can prioritize the collection of historical data from Asia. If the user is in Europe, the data collection unit can also prioritize the collection of historical data from Europe. Furthermore, if the user is in America, the data collection unit can also prioritize the collection of historical data from America. This makes it possible to provide more useful data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. This makes it possible to efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] 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 stressed, the analysis unit can provide a simple, visual analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. This makes it possible to provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0082] 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 can perform a detailed analysis on important data. For example, the analysis unit can also perform a simplified analysis on general data. Furthermore, the analysis unit can choose not to analyze unnecessary 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, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can perform analysis using an economic model for economic data. For example, the analysis unit can also perform analysis using a sociological model for social data. Furthermore, the analysis unit can also perform analysis using a technical model for technical data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0084] 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 stressed, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can provide an analysis that can be quickly understood. By adjusting the length of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, it may prioritize the most recent data while also considering past data. Furthermore, the analysis unit may analyze older data only for reference. This allows for efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of important data. For example, the analysis unit may also prioritize the analysis of highly relevant data. Furthermore, the analysis unit may postpone the analysis of less relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0087] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize providing high-risk predictions. For example, if the user is relaxed, the prediction unit can also provide detailed predictions. Furthermore, if the user is in a hurry, the prediction unit can provide predictions that can be quickly understood. This makes it possible to provide prediction results that are easy for the user to understand by adjusting the prediction criteria 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the prediction criteria.

[0088] The prediction unit can improve the accuracy of its predictions based on the interrelationships between data. For example, the prediction unit can make predictions by considering the interrelationships between economic data and social data. It can also make predictions by considering the interrelationships between technical data and market data. Furthermore, the prediction unit can make predictions by considering the interrelationships between historical data and contemporary data. This improves the accuracy of predictions by considering the interrelationships between data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the predictions.

[0089] The prediction unit can make predictions based on the attribute information of the data submitter. For example, if the submitter is an economist, the prediction unit will prioritize economic data in its predictions. If the submitter is an engineer, the prediction unit can also prioritize technical data in its predictions. Furthermore, if the submitter is a historian, the prediction unit can also prioritize historical data in its predictions. This allows for more accurate predictions by considering the attribute information of the data submitter. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the submitter's attribute information into a generating AI and have the generating AI perform the prediction.

[0090] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated emotions. For example, if the user is stressed, the prediction unit can prioritize displaying important prediction results. For example, if the user is relaxed, the prediction unit can also display detailed prediction results. Furthermore, if the user is in a hurry, the prediction unit can display prediction results that can be quickly understood. This makes it possible to provide prediction results that are easy for the user to understand by adjusting the display order of prediction results 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 prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display order of prediction results.

[0091] The prediction unit can make predictions based on the geographical distribution of the data. For example, the prediction unit may prioritize using geographically close data for predictions. It may also prioritize using geographically relevant data for predictions. Furthermore, it may use geographically distant data for predictions only as a reference. This allows for more accurate predictions by considering the geographical distribution of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the prediction.

[0092] The prediction unit can improve the accuracy of its predictions based on relevant literature during the prediction process. For example, the prediction unit can improve the accuracy of its predictions based on relevant literature. The prediction unit can also make predictions by referring to data from relevant literature. Furthermore, the prediction unit can improve the accuracy of its predictions based on the analysis results of the relevant literature. By improving the accuracy of predictions based on relevant literature, more reliable predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from relevant literature into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0093] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple, visual suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions. This allows for suggestions that are easy for the user to understand by adjusting the presentation of suggestions 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of suggestions.

[0094] The proposal unit can adjust the level of detail in its proposals based on the importance of the risks. For example, it can provide detailed proposals for significant risks, and simplified proposals for common risks. Furthermore, it can choose not to provide proposals for unnecessary risks. This allows for efficient proposals by adjusting the level of detail based on the importance of the risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the risks into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0095] The proposal unit can apply different proposal algorithms depending on the risk category when making a proposal. For example, for economic risks, the proposal unit can make proposals using economic models. For social risks, the proposal unit can also make proposals using sociological models. Furthermore, for technological risks, the proposal unit can also make proposals using technological models. By applying different proposal algorithms depending on the risk category, more accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the risk category into a generating AI and have the generating AI execute the application of an appropriate proposal algorithm.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, for example, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that can be quickly understood. By adjusting the length of suggestions according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0097] The proposal unit can determine the priority of proposals based on the timing of risk occurrence. For example, the proposal unit can prioritize proposals for immediate risks. For example, the proposal unit can also make proposals with a moderate priority for medium-term risks. Furthermore, the proposal unit can postpone proposals for long-term risks. This allows for efficient proposals by determining the priority of proposals based on the timing of risk occurrence. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the timing of risk occurrence into a generating AI and have the generating AI determine the priority of proposals.

[0098] The proposal unit can adjust the order of proposals based on the relevance of the risks. For example, the proposal unit may prioritize proposing important risks. It may also prioritize proposing highly relevant risks. Furthermore, it may postpone proposing less relevant risks. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the risks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the risks into a generating AI and have the generating AI adjust the order of proposals.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The predictive model provisioning system can further estimate the user's emotions and adjust how the predictive model is provided based on the estimated emotions. For example, if the user is stressed, the system can provide a concise and to-the-point predictive model. If the user is relaxed, it can provide a detailed predictive model. Furthermore, if the user is in a hurry, it can provide a predictive model in a format that can be quickly understood. This makes it possible to provide a predictive model that is easy for the user to understand by adjusting how the predictive model is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the predictive model provisioning system may be performed using AI or not using AI. For example, the predictive model provisioning system can input user emotion data into a generative AI and have the generative AI adjust how the predictive model is provided.

[0101] The predictive model provision system can further customize predictive models based on the user's industry and business model. For example, it can provide a manufacturing-specific predictive model to a manufacturing user, a service-specific predictive model to a service-oriented user, and a technology-specific predictive model to a technology company user. By providing a predictive model customized based on the user's industry and business model, it becomes possible to provide a more useful predictive model. Some or all of the above-described processes in the predictive model provision system may be performed using AI, for example, or not. For example, the predictive model provision system can input data about the user's industry and business model into a generating AI and have the generating AI perform the customization of the predictive model.

[0102] The predictive model provisioning system can further estimate the user's emotions and adjust the accuracy of the predictive model based on the estimated emotions. For example, if the user is stressed, the system can prioritize providing high-risk predictions. If the user is relaxed, it can provide more detailed predictions. Furthermore, if the user is in a hurry, it can provide predictions that can be quickly understood. This makes it possible to provide a predictive model that is easy for the user to understand by adjusting the accuracy of the predictive model 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 predictive model provisioning system may be performed using AI or not using AI. For example, the predictive model provisioning system can input user emotion data into a generative AI and have the generative AI perform the adjustment of the accuracy of the predictive model.

[0103] The predictive model provision system can further customize the predictive model based on the user's geographical location information. For example, if the user is in Asia, it can provide a predictive model that emphasizes Asian economic data. If the user is in Europe, it can provide a predictive model that emphasizes European economic data. Furthermore, if the user is in the United States, it can provide a predictive model that emphasizes American economic data. By providing a predictive model customized based on the user's geographical location information, it becomes possible to provide a more relevant predictive model. Some or all of the above processing in the predictive model provision system may be performed using AI, for example, or without AI. For example, the predictive model provision system can input the user's geographical location information into a generating AI and have the generating AI perform the customization of the predictive model.

[0104] The predictive model provisioning system can further estimate the user's emotions and adjust how the predictive model is displayed based on the estimated emotions. For example, if the user is stressed, the system can provide a simple, visual predictive model. If the user is relaxed, it can provide a more detailed predictive model. Furthermore, if the user is in a hurry, it can provide a concise predictive model. This allows the system to provide a predictive model that is easy for the user to understand by adjusting how the predictive model is displayed 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 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 predictive model provisioning system may be performed using AI or not using AI. For example, the predictive model provisioning system can input user emotion data into a generative AI and have the generative AI adjust how the predictive model is displayed.

[0105] The predictive model provisioning system can further analyze users' social media activity and collect relevant data. This makes it possible to efficiently collect relevant data by analyzing users' social media activity. Some or all of the above-described processes in the predictive model provisioning system may be performed using AI, for example, or without AI. For example, the predictive model provisioning system can input users' social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0106] The predictive model provisioning system can further estimate the user's emotions and adjust the update frequency of the predictive model based on the estimated emotions. For example, if the user is stressed, the system can reduce the update frequency of the predictive model to alleviate the user's burden. Conversely, if the user is relaxed, the system can increase the update frequency of the predictive model to provide more detailed data. Furthermore, if the user is in a hurry, the system can prioritize updating only the most important data. This makes it possible to reduce the user's burden by adjusting the update frequency of the predictive model 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 predictive model provisioning system may be performed using AI or not using AI. For example, the predictive model provisioning system can input user emotion data into a generative AI and have the generative AI adjust the update frequency of the predictive model.

[0107] The predictive model provisioning system can further adjust the accuracy of the predictive model based on the data collection timing. For example, it can update the predictive model by prioritizing the use of the latest data. Alternatively, it can prioritize the latest data while also referring to past data. Furthermore, older data can be used only as a reference. This makes it possible to provide a more reliable predictive model by adjusting the accuracy of the predictive model based on the data collection timing. Some or all of the above processing in the predictive model provisioning system may be performed using AI, for example, or without AI. For example, the predictive model provisioning system can input the data collection timing into a generating AI and have the generating AI perform the adjustment of the predictive model's accuracy.

[0108] The predictive model provisioning system can further improve the accuracy of its predictive models based on the interrelationships of data. For example, it can update the predictive model by considering the interrelationships between economic and social data. It can also update the predictive model by considering the interrelationships between technical and market data. Furthermore, it can update the predictive model by considering the interrelationships between historical and contemporary data. In this way, the accuracy of the predictive model is improved by considering the interrelationships of data. Some or all of the above processing in the predictive model provisioning system may be performed using AI, for example, or without AI. For example, the predictive model provisioning system can input the interrelationships of data into a generating AI and have the generating AI perform the improvement of the accuracy of the predictive model.

[0109] The predictive model provision system can further customize the predictive model based on the attribute information of the data submitter. For example, if the submitter is an economist, the system can provide a predictive model that emphasizes economic data. Similarly, if the submitter is an engineer, it can provide a predictive model that emphasizes technical data. Furthermore, if the submitter is a historian, it can provide a predictive model that emphasizes historical data. This makes it possible to provide a more accurate predictive model by considering the attribute information of the data submitter. Some or all of the above processing in the predictive model provision system may be performed using AI, for example, or without AI. For example, the predictive model provision system can input the submitter's attribute information into a generating AI and have the generating AI perform the customization of the predictive model.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects historical data. For example, the data collection unit collects data from world history, Japanese history, corporate history, and startup history. The data collection unit can collect data from publicly available databases on the internet or from internal company databases. The data collection unit can also use crawling techniques for data collection. For example, the data collection unit crawls websites based on specific keywords and collects relevant data. Furthermore, the data collection unit can utilize APIs for data collection. For example, the data collection unit retrieves data from databases through public APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, AI. The analysis unit extracts patterns from the data using machine learning algorithms. The analysis unit can also analyze text data using natural language processing techniques. For example, the analysis unit extracts keywords from text data and evaluates the relationships between the data. Furthermore, the analysis unit can perform data clustering. For example, the analysis unit clusters the data and groups similar data together. Step 3: The prediction unit predicts future risks based on the data analyzed by the analysis unit. The prediction unit can predict risks using, for example, AI. The prediction unit uses algorithms that predict future risks based on past data. The prediction unit can also predict risks using simulation technology. For example, the prediction unit performs simulations to evaluate future risks. Furthermore, the prediction unit can also perform data trend analysis. For example, the prediction unit analyzes data trends to predict future risks. Step 4: The proposal department proposes specific countermeasures based on the risks predicted by the forecasting department. For example, the proposal department may use AI to propose countermeasures. The proposal department may propose risk avoidance measures or risk mitigation measures. The proposal department may also propose risk transfer measures. For example, the proposal department may propose methods for transferring risks to other companies or insurance companies. Furthermore, the proposal department may also propose risk response plans. For example, the proposal department may propose response plans in the event that a risk occurs.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and communication I / F 44 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future risks based on the analyzed data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes specific countermeasures based on the predicted risks. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and communication I / F 44 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. 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 prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts future risks based on the analyzed data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes specific countermeasures based on the predicted risks. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and communication I / F 44 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future risks based on the analyzed data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes specific countermeasures based on the predicted risks. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and communication I / F 44 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future risks based on the analyzed data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes specific countermeasures based on the predicted risks. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The collection department collects historical data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit that predicts future risks based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes specific countermeasures based on the risks predicted by the prediction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from world history, Japanese history, corporate history, and startup history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By learning from past failures and applying those patterns to the current business environment, we can predict future risks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, By learning patterns from past economic crises and comparing them to the current economic situation, we can predict the risk of future economic crises. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose strategies and risk management methods to help companies avoid repeating past mistakes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose specific measures and strategies tailored to the needs of target companies. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is When collecting data on world history, Japanese history, corporate history, and startup history, focus on specific eras or regions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, the system prioritizes collecting data that is highly relevant based on the user's industry and business model. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During 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 18) 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 19) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, improve the accuracy of predictions based on the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the predictions are based on the attribute information of the data submitters. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the predictions are based on the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, improve the accuracy of the predictions based on relevant literature for the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the risks. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the risk category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on when the risk will occur. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the risks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects historical data, An analysis unit analyzes the data collected by the aforementioned collection unit, A prediction unit that predicts future risks based on the data analyzed by the aforementioned analysis unit, The system includes a proposal unit that proposes specific countermeasures based on the risks predicted by the prediction unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data from world history, Japanese history, corporate history, and startup history. The system according to feature 1.

3. The aforementioned analysis unit, By learning from past failures and applying those patterns to the current business environment, we can predict future risks. The system according to feature 1.

4. The prediction unit, By learning patterns from past economic crises and comparing them to the current economic situation, we can predict the risk of future economic crises. The system according to feature 1.

5. The aforementioned proposal section is, We propose strategies and risk management methods to help companies avoid repeating past mistakes. The system according to feature 1.

6. The aforementioned proposal section is, We propose specific measures and strategies tailored to the needs of target companies. The system according to feature 1.

7. 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.

8. The aforementioned collection unit is When collecting data on world history, Japanese history, corporate history, and startup history, focus on specific eras or regions. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, the system prioritizes collecting data that is highly relevant based on the user's industry and business model. The system according to feature 1.

10. 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.

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