system

Abstract

The system according to the embodiment aims to detect a corporate crisis early and respond to it quickly and effectively. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a discovery unit, an alert unit, a proposal unit, and a generation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The discovery unit detects crises early based on the data analyzed by the analysis unit. The alert unit issues an alert for a crisis detected by the discovery unit. The proposal unit proposes countermeasures based on the alert issued by the alert unit. The generation unit generates a draft for effective communication based on the countermeasures proposed by the proposal unit.

Description

[Technical Field]

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

[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 a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to detect corporate crises early and respond quickly and effectively.

[0005] The system according to the embodiment aims to detect a corporate crisis early and respond to it quickly and effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a discovery unit, an alert unit, a proposal unit, and a generation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The discovery unit detects crises early based on the data analyzed by the analysis unit. The alert unit issues an alert for a crisis discovered by the discovery unit. The proposal unit proposes countermeasures based on the alert issued by the alert unit. The generation unit generates a draft for effective communication based on the countermeasures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect a corporate crisis early and respond to it quickly and effectively. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A crisis management support system according to an embodiment of the present invention uses generative AI to support corporate crisis management. This crisis management support system utilizes a wide range of data sources and features real-time monitoring and alerting to support early detection and rapid response to crises. It simulates various scenarios, from proposing countermeasures to determining the timing of their implementation and even generating drafts for effective communication, to fully support companies in making informed and wise decisions. For example, the crisis management support system utilizes a wide range of data sources to monitor the company's situation in real time. The generative AI analyzes the collected data to detect crises early. When a crisis is detected, an alert function is activated and relevant parties are notified. The generative AI then proposes countermeasures and indicates the timing of their implementation. For example, if a specific risk increases, the generative AI proposes the optimal countermeasure for that risk and indicates the timing of its implementation. The generative AI also generates drafts for effective communication and provides them to relevant parties. This allows companies to make informed and wise decisions. As a specific example, if a company is subjected to a cyberattack, the generative AI detects abnormal network activity in real time and issues an alert. The generative AI then analyzes the details of the attack and proposes optimal countermeasures. For example, it provides specific instructions such as isolating specific servers or changing passwords. The generative AI also generates drafts for effective communication to employees and customers, supporting a rapid response. This system enables companies to detect crises early and respond quickly, minimizing damage. Simulations performed by the generative AI also prepare for various scenarios, improving a company's crisis management capabilities. This allows the crisis management support system to efficiently support corporate crisis management, enabling early detection and rapid response.

[0029] A crisis management support system according to an embodiment includes a collection unit, an analysis unit, a detection unit, an alert unit, a proposal unit, and a generation unit. The collection unit collects data to monitor the situation of a company in real time. The collection unit can collect data by utilizing a wide range of data sources, such as an internal database, an external API, and social media data. The collection unit can also adjust the frequency and means of data collection. For example, the collection unit can increase or decrease the frequency of data collection depending on the situation of the company. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using an analysis method such as outlier detection, pattern recognition, or predictive modeling. The analysis unit analyzes the data using a generative AI to detect crises early. The detection unit detects crises early based on the data analyzed by the analysis unit. The detection unit can detect crises using, for example, outlier detection or pattern recognition. The alert unit issues an alert for a crisis detected by the detection unit. The alert unit can issue an alert in the form of, for example, an email notification, an app notification, or a voice alert. The proposal unit proposes countermeasures based on the alert issued by the alert unit. The proposal unit can propose countermeasures such as emergency response procedures, risk avoidance measures, and recovery plans. The generation unit generates a draft for effective communication based on the countermeasures proposed by the proposal unit. The generation unit can generate the draft in the form of, for example, a report, presentation materials, or email template. As a result, the crisis management support system according to the embodiment efficiently supports corporate crisis management, enabling early detection and rapid response.

[0030] The collection unit can monitor the company's situation in real time by utilizing a wide range of data sources. The collection unit can collect data by utilizing a wide range of data sources, such as an internal database, external APIs, and social media data. For example, the collection unit can collect the company's financial and operational data from an internal database. The collection unit can also collect market and competitive data using external APIs. Furthermore, the collection unit can collect social media data to understand customer feedback and market trends. This enables real-time monitoring of the company's situation and enables rapid response. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input social media data into a generation AI, which then analyzes the data and extracts important information.

[0031] The analysis unit can analyze the collected data and detect crises early. The analysis unit can analyze the data using analytical methods such as outlier detection, pattern recognition, and predictive models. For example, the analysis unit can detect outliers and identify data that exceeds a normal range. The analysis unit can also find specific patterns in the data using pattern recognition. Furthermore, the analysis unit can predict future risks using predictive models. This enables early detection of crises through data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which then analyzes the data and detects outliers.

[0032] The discovery unit can detect crises early based on the analyzed data. The discovery unit can detect crises using, for example, outlier detection or pattern recognition. For example, the discovery unit can detect outliers and identify data that exceeds a normal range. The discovery unit can also find specific patterns in the data using pattern recognition. This enables early detection of crises based on the analyzed data. Some or all of the above-mentioned processing in the discovery unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the discovery unit can input the analyzed data into a generation AI, which then analyzes the data and detects crises.

[0033] The alert unit can issue an alert for a detected crisis. The alert unit can issue the alert in the form of, for example, an email notification, an app notification, or a voice alert. For example, when a crisis is detected, the alert unit can send a notification to relevant parties by email. The alert unit can also send a notification in real time via an app. Furthermore, the alert unit can notify relevant parties of the crisis by issuing a voice alert. This enables prompt issuance of an alert when a crisis is detected. Some or all of the above-described processing in the alert unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the alert unit can input information about the detected crisis into the generation AI, which can then suggest an optimal method for issuing an alert.

[0034] The suggestion unit can propose countermeasures based on the alert. The suggestion unit can propose countermeasures such as emergency response procedures, risk avoidance measures, and recovery plans. For example, the suggestion unit can propose emergency response procedures when a specific risk increases. The suggestion unit can also propose specific measures to avoid risks. Furthermore, the suggestion unit can propose a recovery plan after a crisis occurs. This makes it possible to propose appropriate countermeasures based on the alert. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input alert information into the generation AI, which then proposes optimal countermeasures.

[0035] The generation unit can generate a draft manuscript for effective communication based on the proposed response measures. The generation unit can generate the draft manuscript in the form of, for example, a report, presentation materials, email template, etc. For example, the generation unit can generate a crisis report and provide it to relevant parties. The generation unit can also generate presentation materials to use for explanations at meetings. Furthermore, the generation unit can generate email templates to support rapid communication. This makes it possible to generate a draft manuscript for effective communication based on the response measures. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information about the proposed response measures into the generation AI, which can then generate an optimal draft manuscript.

[0036] The collection unit can dynamically change the type of data to be collected based on the company's current situation and industry trends. For example, if the company's financial situation is deteriorating, the collection unit can prioritize collecting financial data. Furthermore, if industry trends are changing, the collection unit can also collect relevant market data. Furthermore, if a new product is released by the company, the collection unit can collect customer feedback related to the product. This enables data collection according to the company's situation and industry trends. Some or all of the above-described processing in the collection unit may be performed, for example, using or without the generation AI. For example, the collection unit can input data related to the company's situation and industry trends into the generation AI and dynamically change the type of data the generation AI collects.

[0037] During data collection, the collection unit can integrate and collect internal data and external data of the company. For example, the collection unit can integrate and collect internal data (sales data, inventory data, etc.) of the company with external data (market data, competitor data, etc.). The collection unit can also integrate and collect internal data (employee data, customer data, etc.) of the company with external data (social media data, news data, etc.). Furthermore, the collection unit can integrate and collect internal data (financial data, operational data, etc.) of the company with external data (economic data, policy data, etc.). This enables the integrated collection of internal data and external data. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input internal data and external data into a generation AI, which then integrates and collects the data.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. For example, the collection unit can prioritize collecting economic data for a region related to the company's location. The collection unit can also prioritize collecting competitive data for a region related to the company's location. Furthermore, the collection unit can prioritize collecting customer feedback for a region related to the company's location. This enables highly relevant data to be collected based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the geographical location information of the company into the generation AI, which can then prioritize collecting highly relevant data.

[0039] During data collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit can collect customer feedback related to the company's social media activities. The collection unit can also collect competitive data related to the company's social media activities. Furthermore, the collection unit can collect market data related to the company's social media activities. This enables the collection of related data based on social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the collection unit can input the company's social media activity data into the generation AI, which can collect related data.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also perform a detailed analysis on a company's financial data and a simplified analysis on other data. Furthermore, the analysis unit can perform a detailed analysis on data related to a company's new products and a simplified analysis on other data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a financial analysis algorithm to financial data and a customer analysis algorithm to customer data. The analysis unit can also apply a market analysis algorithm to market data and a competitor analysis algorithm to competitor data. Furthermore, the analysis unit can apply a social media analysis algorithm to social media data and a news analysis algorithm to news data. This makes it possible to apply an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI, which then applies an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the most recent data and postpone analyzing older data. The analysis unit can also prioritize analyzing data immediately after an important event occurs. Furthermore, the analysis unit can prioritize analyzing financial data based on the company's financial reporting period. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on data relevance. For example, the analysis unit can adjust the order of analysis taking into account the relevance between a company's financial data and market data. The analysis unit can also adjust the order of analysis taking into account the relevance between a company's customer data and social media data. Furthermore, the analysis unit can adjust the order of analysis taking into account the relevance between a company's competitor data and news data. This makes it possible to adjust the order of analysis based on data relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data relevance into the generation AI, which can then adjust the order of analysis.

[0044] The discovery unit can improve the accuracy of crisis detection by taking into account the interrelationships between data during discovery. The discovery unit can improve the accuracy of crisis detection by taking into account, for example, the interrelationships between financial data and market data. The discovery unit can also improve the accuracy of crisis detection by taking into account the interrelationships between customer data and social media data. Furthermore, the discovery unit can improve the accuracy of crisis detection by taking into account the interrelationships between competitor data and news data. In this way, the accuracy of crisis detection is improved by taking into account the interrelationships between data. Some or all of the above-mentioned processing in the discovery unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the discovery unit can input the interrelationships between data into the generation AI, which can improve the accuracy of crisis detection.

[0045] The discovery unit can discover crises by taking into account the attribute information of the company during discovery. The discovery unit can, for example, prioritize the discovery of specific crises based on the company's industry. The discovery unit can also prioritize the discovery of specific crises based on the size of the company. Furthermore, the discovery unit can prioritize the discovery of specific crises based on the company's location. This makes it possible to discover crises based on the attribute information of the company. Some or all of the above-mentioned processing in the discovery unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the discovery unit can input the attribute information of the company into the generation AI, which can then discover crises.

[0046] During discovery, the discovery unit can discover crises by taking the geographical distribution of data into consideration. For example, the discovery unit can prioritize discovering crises in areas related to the company's location. The discovery unit can also prioritize discovering crises related to the company's business operations area. Furthermore, the discovery unit can prioritize discovering crises in areas related to the company's supply chain. This enables crisis detection based on geographical distribution. Some or all of the above-mentioned processing in the discovery unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the discovery unit can input the geographical distribution of data into the generation AI, which can then discover crises.

[0047] The discovery unit can improve the accuracy of crisis detection by referring to related literature during discovery. The discovery unit can, for example, improve the accuracy of crisis detection by referring to literature related to the company's industry. The discovery unit can also improve the accuracy of crisis detection by referring to literature related to the company's size. Furthermore, the discovery unit can improve the accuracy of crisis detection by referring to literature related to the company's location. This improves the accuracy of crisis detection by referring to related literature. Some or all of the above-mentioned processing in the discovery unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the discovery unit can input related literature into the generation AI, which can improve the accuracy of crisis detection.

[0048] When issuing an alert, the alert unit can adjust the level of detail of the alert based on the severity of the crisis. For example, the alert unit can issue a detailed alert for a major crisis and a simple alert for a minor crisis. The alert unit can also issue a detailed alert for a company's financial crisis and a simple alert for other crises. Furthermore, the alert unit can issue a detailed alert for a crisis related to a company's new product and a simple alert for other crises. This makes it possible to adjust the level of detail of the alert depending on the severity of the crisis. Some or all of the above-mentioned processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input the severity of the crisis into the generation AI, and the generation AI can adjust the level of detail of the alert.

[0049] When issuing an alert, the alert unit can apply different alert methods depending on the category of the crisis. For example, the alert unit can issue an alert by email for a cybersecurity crisis and by SMS for a physical crisis. The alert unit can also issue an alert with a detailed report attached for a financial crisis and a simple alert for other crises. Furthermore, the alert unit can issue an alert by push notification for a crisis related to a company's new product and by email for other crises. This makes it possible to apply an appropriate alert method depending on the category of the crisis. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert unit can input the category of the crisis into the generation AI, which can then apply an appropriate alert method.

[0050] When issuing an alert, the alert unit can prioritize issuing highly relevant alerts taking into account the geographical location information of the company. For example, the alert unit can prioritize issuing alerts for crises in an area related to the company's location. The alert unit can also prioritize issuing alerts for crises related to the company's business operations area. Furthermore, the alert unit can prioritize issuing alerts for crises in an area related to the company's supply chain. This makes it possible to issue highly relevant alerts based on geographical location information. Some or all of the above-mentioned processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input the company's geographical location information into the generation AI, which can then prioritize issuing highly relevant alerts.

[0051] When issuing an alert, the alert unit can analyze the company's social media activity and issue a related alert. For example, the alert unit can issue an alert based on customer feedback related to the company's social media activity. The alert unit can also issue an alert based on competitive data related to the company's social media activity. Furthermore, the alert unit can issue an alert based on market data related to the company's social media activity. This makes it possible to issue related alerts based on social media activity. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert unit can input the company's social media activity data into the generation AI, which can then issue a related alert.

[0052] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the crisis. For example, the suggestion unit can provide a detailed proposal for a major crisis and a simplified proposal for a minor crisis. The suggestion unit can also provide a detailed proposal for a company's financial crisis and a simplified proposal for other crises. Furthermore, the suggestion unit can provide a detailed proposal for a crisis related to a company's new product and a simplified proposal for other crises. This makes it possible to adjust the level of detail of the proposal depending on the importance of the crisis. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can input the importance of the crisis to the generation AI, which can then adjust the level of detail of the proposal.

[0053] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the crisis. For example, the proposal unit can apply a cybersecurity proposal algorithm to a cybersecurity crisis and a physical crisis proposal algorithm to a physical crisis. The proposal unit can also apply a financial proposal algorithm to a financial crisis and a simplified proposal algorithm to other crises. Furthermore, the proposal unit can apply a new product proposal algorithm to a crisis related to a company's new product and a simplified proposal algorithm to other crises. This makes it possible to apply an appropriate proposal algorithm depending on the category of the crisis. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the category of the crisis into the generation AI, which can then apply an appropriate proposal algorithm.

[0054] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of the crisis. For example, the proposal unit can prioritize providing proposals for the most recent crisis and postpone providing proposals for older crises. The proposal unit can also prioritize providing proposals for crises that occur immediately after an important event occurs. Furthermore, the proposal unit can prioritize providing proposals for financial crises based on the company's financial reporting period. This makes it possible to determine the priority of proposals based on the timing of the crisis. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the timing of the crisis into the generation AI, which can then determine the priority of the proposals.

[0055] The proposal unit can adjust the order of proposals based on the relevance of the crisis when making a proposal. The proposal unit can adjust the order of proposals, for example, by taking into account the relevance of a company's financial crisis and market data. The proposal unit can also adjust the order of proposals by taking into account the relevance of a company's customer data and social media data. Furthermore, the proposal unit can adjust the order of proposals by taking into account the relevance of a company's competitor data and news data. This makes it possible to adjust the order of proposals based on the relevance of the crisis. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the relevance of the crisis into the generation AI, which can then adjust the order of proposals.

[0056] When generating a draft manuscript, the generation unit can adjust the level of detail of the draft manuscript based on the importance of the crisis. For example, the generation unit can generate a detailed draft manuscript for a major crisis and a simplified draft manuscript for a minor crisis. The generation unit can also generate a detailed draft manuscript for a company's financial crisis and a simplified draft manuscript for other crises. Furthermore, the generation unit can generate a detailed draft manuscript for a crisis related to a company's new product and a simplified draft manuscript for other crises. This makes it possible to adjust the level of detail of the draft manuscript according to the importance of the crisis. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the crisis into the generation AI, which can adjust the level of detail of the draft manuscript.

[0057] When generating a manuscript draft, the generation unit can apply different manuscript draft generation algorithms depending on the category of the crisis. For example, the generation unit can apply a cybersecurity manuscript draft generation algorithm to a cybersecurity crisis and a physical crisis manuscript draft generation algorithm to a physical crisis. The generation unit can also apply a financial manuscript draft generation algorithm to a financial crisis and a simplified manuscript draft generation algorithm to other crises. Furthermore, the generation unit can apply a new product manuscript draft generation algorithm to a crisis related to a company's new product and a simplified manuscript draft generation algorithm to other crises. This makes it possible to apply an appropriate manuscript draft generation algorithm depending on the category of the crisis. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the crisis into the generation AI, which can then apply an appropriate manuscript draft generation algorithm.

[0058] When generating draft manuscripts, the generation unit can determine the priority of the draft manuscripts based on the timing of the occurrence of a crisis. For example, the generation unit can prioritize generating draft manuscripts for the most recent crisis and postpone generating draft manuscripts for older crises. The generation unit can also prioritize generating draft manuscripts for crises that occur immediately after an important event occurs. Furthermore, the generation unit can prioritize generating draft manuscripts for financial crises based on the company's financial reporting period. This makes it possible to determine the priority of draft manuscripts based on the timing of the occurrence of a crisis. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the timing of the crisis occurrence into the generation AI, which can then determine the priority of the draft manuscripts.

[0059] The generation unit can adjust the order of the draft manuscripts based on the relevance of the crisis when generating the draft manuscripts. For example, the generation unit can adjust the order of the draft manuscripts by taking into account the relevance between a company's financial crisis and market data. The generation unit can also adjust the order of the draft manuscripts by taking into account the relevance between the company's customer data and social media data. Furthermore, the generation unit can adjust the order of the draft manuscripts by taking into account the relevance between the company's competitor data and news data. This makes it possible to adjust the order of the draft manuscripts based on the relevance of the crisis. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of the crisis into the generation AI, which can then adjust the order of the draft manuscripts.

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

[0061] The crisis management support system may further include a prediction unit. The prediction unit can predict future crises based on data from the collection unit and the analysis unit. For example, the prediction unit can predict the possibility of future cyber attacks using past data. The prediction unit can also analyze a company's financial data to predict future financial risks. Furthermore, the prediction unit can analyze social media data to predict future customer reactions. This allows a company to prepare for future risks in advance.

[0062] The collection unit can further collect environmental data. For example, the collection unit can collect weather data and evaluate the risk of natural disasters. The collection unit can also collect earthquake data and evaluate the risk of earthquake occurrence. The collection unit can also collect climate change data and evaluate long-term environmental risks. This allows companies to take measures against environmental risks.

[0063] The discovery unit may further include an abnormal behavior detection function. The discovery unit may detect abnormal behavior from collected data and detect potential crises early. For example, the discovery unit may analyze employee behavior data and detect abnormal behavior. The discovery unit may also analyze customer purchase data and detect abnormal purchasing patterns. Furthermore, the discovery unit may analyze network traffic data and detect abnormal communication patterns. This allows companies to detect potential crises early and take measures.

[0064] The proposal unit may further include a scenario-based proposal function. The proposal unit may generate multiple scenarios based on the collected data and propose countermeasures for each scenario. For example, the proposal unit may generate a scenario in the event of a cyber attack and propose countermeasures. The proposal unit may also generate a scenario in the event of a natural disaster and propose countermeasures. Furthermore, the proposal unit may generate a scenario in the event of an increased financial risk and propose countermeasures. This allows a company to prepare for various scenarios.

[0065] The collection unit can further collect voice data. For example, the collection unit can collect voice data of meetings and analyze the contents of the meetings. The collection unit can also collect voice data of telephone conversations with customers and analyze customer feedback. Furthermore, the collection unit can collect voice data of employees and analyze their opinions. This allows companies to use voice data to collect more detailed information.

[0066] The analysis unit may further include an image analysis function. The analysis unit may analyze collected image data to evaluate the company's situation. For example, the analysis unit may analyze surveillance camera footage to detect abnormal behavior. The analysis unit may also analyze images posted on social media to evaluate customer reactions. Furthermore, the analysis unit may analyze product images to detect quality issues. This allows companies to utilize image data to obtain more detailed information.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection department collects data to monitor the company's situation in real time. The collection department can collect data from a variety of data sources, including internal databases, external APIs, and social media data. The collection department can also adjust the frequency and means of data collection. For example, the frequency of data collection can be increased or decreased depending on the company's situation. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using analytical methods such as outlier detection, pattern recognition, and predictive models. Generative AI is used to analyze the data and detect crises early. Step 3: The detection unit detects crises early based on the data analyzed by the analysis unit. The detection unit can detect crises using outlier detection and pattern recognition. Step 4: The alert unit issues an alert for the crisis detected by the detection unit. The alert unit can issue an alert in the form of an email notification, an app notification, a voice alert, etc. Step 5: The suggestion unit proposes countermeasures based on the alert issued by the alert unit. The suggestion unit can propose countermeasures such as emergency response procedures, risk avoidance measures, and recovery plans. Step 6: The generation unit generates a draft for effective communication based on the countermeasures proposed by the proposal unit. The generation unit can generate the draft in the form of a report, presentation material, email template, etc.

[0069] (Example 2) A crisis management support system according to an embodiment of the present invention uses generative AI to support corporate crisis management. This crisis management support system utilizes a wide range of data sources and features real-time monitoring and alerting to support early detection and rapid response to crises. It simulates various scenarios, from proposing countermeasures to determining the timing of their implementation and even generating drafts for effective communication, to fully support companies in making informed and wise decisions. For example, the crisis management support system utilizes a wide range of data sources to monitor the company's situation in real time. The generative AI analyzes the collected data to detect crises early. When a crisis is detected, an alert function is activated and relevant parties are notified. The generative AI then proposes countermeasures and indicates the timing of their implementation. For example, if a specific risk increases, the generative AI proposes the optimal countermeasure for that risk and indicates the timing of its implementation. The generative AI also generates drafts for effective communication and provides them to relevant parties. This allows companies to make informed and wise decisions. As a specific example, if a company is subjected to a cyberattack, the generative AI detects abnormal network activity in real time and issues an alert. The generative AI then analyzes the details of the attack and proposes optimal countermeasures. For example, it provides specific instructions such as isolating specific servers or changing passwords. The generative AI also generates drafts for effective communication to employees and customers, supporting a rapid response. This system enables companies to detect crises early and respond quickly, minimizing damage. Simulations performed by the generative AI also prepare for various scenarios, improving a company's crisis management capabilities. This allows the crisis management support system to efficiently support corporate crisis management, enabling early detection and rapid response.

[0070] A crisis management support system according to an embodiment includes a collection unit, an analysis unit, a detection unit, an alert unit, a proposal unit, and a generation unit. The collection unit collects data to monitor the situation of a company in real time. The collection unit can collect data by utilizing a wide range of data sources, such as an internal database, an external API, and social media data. The collection unit can also adjust the frequency and means of data collection. For example, the collection unit can increase or decrease the frequency of data collection depending on the situation of the company. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using an analysis method such as outlier detection, pattern recognition, or predictive modeling. The analysis unit analyzes the data using a generative AI to detect crises early. The detection unit detects crises early based on the data analyzed by the analysis unit. The detection unit can detect crises using, for example, outlier detection or pattern recognition. The alert unit issues an alert for a crisis detected by the detection unit. The alert unit can issue an alert in the form of, for example, an email notification, an app notification, or a voice alert. The proposal unit proposes countermeasures based on the alert issued by the alert unit. The proposal unit can propose countermeasures such as emergency response procedures, risk avoidance measures, and recovery plans. The generation unit generates a draft for effective communication based on the countermeasures proposed by the proposal unit. The generation unit can generate the draft in the form of, for example, a report, presentation materials, or email template. As a result, the crisis management support system according to the embodiment efficiently supports corporate crisis management, enabling early detection and rapid response.

[0071] The collection unit can monitor the company's situation in real time by utilizing a wide range of data sources. The collection unit can collect data by utilizing a wide range of data sources, such as an internal database, external APIs, and social media data. For example, the collection unit can collect the company's financial and operational data from an internal database. The collection unit can also collect market and competitive data using external APIs. Furthermore, the collection unit can collect social media data to understand customer feedback and market trends. This enables real-time monitoring of the company's situation and enables rapid response. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input social media data into a generation AI, which then analyzes the data and extracts important information.

[0072] The analysis unit can analyze the collected data and detect crises early. The analysis unit can analyze the data using analytical methods such as outlier detection, pattern recognition, and predictive models. For example, the analysis unit can detect outliers and identify data that exceeds a normal range. The analysis unit can also find specific patterns in the data using pattern recognition. Furthermore, the analysis unit can predict future risks using predictive models. This enables early detection of crises through data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which then analyzes the data and detects outliers.

[0073] The discovery unit can detect crises early based on the analyzed data. The discovery unit can detect crises using, for example, outlier detection or pattern recognition. For example, the discovery unit can detect outliers and identify data that exceeds a normal range. The discovery unit can also find specific patterns in the data using pattern recognition. This enables early detection of crises based on the analyzed data. Some or all of the above-mentioned processing in the discovery unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the discovery unit can input the analyzed data into a generation AI, which then analyzes the data and detects crises.

[0074] The alert unit can issue an alert for a detected crisis. The alert unit can issue the alert in the form of, for example, an email notification, an app notification, or a voice alert. For example, when a crisis is detected, the alert unit can send a notification to relevant parties by email. The alert unit can also send a notification in real time via an app. Furthermore, the alert unit can notify relevant parties of the crisis by issuing a voice alert. This enables prompt issuance of an alert when a crisis is detected. Some or all of the above-described processing in the alert unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the alert unit can input information about the detected crisis into the generation AI, which can then suggest an optimal method for issuing an alert.

[0075] The suggestion unit can propose countermeasures based on the alert. The suggestion unit can propose countermeasures such as emergency response procedures, risk avoidance measures, and recovery plans. For example, the suggestion unit can propose emergency response procedures when a specific risk increases. The suggestion unit can also propose specific measures to avoid risks. Furthermore, the suggestion unit can propose a recovery plan after a crisis occurs. This makes it possible to propose appropriate countermeasures based on the alert. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input alert information into the generation AI, which then proposes optimal countermeasures.

[0076] The generation unit can generate a draft manuscript for effective communication based on the proposed response measures. The generation unit can generate the draft manuscript in the form of, for example, a report, presentation materials, email template, etc. For example, the generation unit can generate a crisis report and provide it to relevant parties. The generation unit can also generate presentation materials to use for explanations at meetings. Furthermore, the generation unit can generate email templates to support rapid communication. This makes it possible to generate a draft manuscript for effective communication based on the response measures. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input information about the proposed response measures into the generation AI, which can then generate an optimal draft manuscript.

[0077] The crisis management support system further includes a collection unit that estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect only important data. Furthermore, when the user is relaxed, the collection unit can collect detailed data and provide comprehensive information. Furthermore, when the user is in a hurry, the collection unit can prioritize the collection of important data in real time. This enables the timing of data collection to be adjusted according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then adjust the timing of data collection.

[0078] The collection unit can dynamically change the type of data to be collected based on the company's current situation and industry trends. For example, if the company's financial situation is deteriorating, the collection unit can prioritize collecting financial data. Furthermore, if industry trends are changing, the collection unit can also collect relevant market data. Furthermore, if a new product is released by the company, the collection unit can collect customer feedback related to the product. This enables data collection according to the company's situation and industry trends. Some or all of the above-described processing in the collection unit may be performed, for example, using or without the generation AI. For example, the collection unit can input data related to the company's situation and industry trends into the generation AI and dynamically change the type of data the generation AI collects.

[0079] During data collection, the collection unit can integrate and collect internal data and external data of the company. For example, the collection unit can integrate and collect internal data (sales data, inventory data, etc.) of the company with external data (market data, competitor data, etc.). The collection unit can also integrate and collect internal data (employee data, customer data, etc.) of the company with external data (social media data, news data, etc.). Furthermore, the collection unit can integrate and collect internal data (financial data, operational data, etc.) of the company with external data (economic data, policy data, etc.). This enables the integrated collection of internal data and external data. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input internal data and external data into a generation AI, which then integrates and collects the data.

[0080] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only important data. The collection unit can also prioritize collecting detailed data when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting important data in real time. This makes it possible to prioritize data according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and determine the priority of data to be collected by the generation AI.

[0081] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the company. For example, the collection unit can prioritize collecting economic data for a region related to the company's location. The collection unit can also prioritize collecting competitive data for a region related to the company's location. Furthermore, the collection unit can prioritize collecting customer feedback for a region related to the company's location. This enables highly relevant data to be collected based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the geographical location information of the company into the generation AI, which can then prioritize collecting highly relevant data.

[0082] During data collection, the collection unit can analyze the company's social media activities and collect related data. For example, the collection unit can collect customer feedback related to the company's social media activities. The collection unit can also collect competitive data related to the company's social media activities. Furthermore, the collection unit can collect market data related to the company's social media activities. This enables the collection of related data based on social media activities. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI or can be performed without using the generation AI. For example, the collection unit can input the company's social media activity data into the generation AI, which can collect related data.

[0083] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple analysis method and analyze only important data. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis method and perform comprehensive data analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize analysis of important data in real time. This enables adjustment of the data analysis method according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the data analysis method.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also perform a detailed analysis on a company's financial data and a simplified analysis on other data. Furthermore, the analysis unit can perform a detailed analysis on data related to a company's new products and a simplified analysis on other data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input the importance of the data to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a financial analysis algorithm to financial data and a customer analysis algorithm to customer data. The analysis unit can also apply a market analysis algorithm to market data and a competitor analysis algorithm to competitor data. Furthermore, the analysis unit can apply a social media analysis algorithm to social media data and a news analysis algorithm to news data. This makes it possible to apply an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the data category into the generation AI, which then applies an appropriate analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can then adjust the display method of the analysis results.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the most recent data and postpone analyzing older data. The analysis unit can also prioritize analyzing data immediately after an important event occurs. Furthermore, the analysis unit can prioritize analyzing financial data based on the company's financial reporting period. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI, and the generation AI can determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on data relevance. For example, the analysis unit can adjust the order of analysis taking into account the relevance between a company's financial data and market data. The analysis unit can also adjust the order of analysis taking into account the relevance between a company's customer data and social media data. Furthermore, the analysis unit can adjust the order of analysis taking into account the relevance between a company's competitor data and news data. This makes it possible to adjust the order of analysis based on data relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data relevance into the generation AI, which can then adjust the order of analysis.

[0089] The discovery unit can estimate the user's emotions and adjust the criteria for crisis detection based on the estimated user's emotions. For example, when the user is feeling stressed, the discovery unit can prioritize detecting only important crises. The discovery unit can also apply detailed crisis detection criteria when the user is relaxed. Furthermore, when the user is in a hurry, the discovery unit can prioritize detecting important crises in real time. This makes it possible to adjust the crisis detection criteria according to the user's emotions. Some or all of the above-mentioned processing in the discovery unit may be performed using, or without, a generation AI. For example, the discovery unit can input user emotion data into the generation AI, which can then adjust the criteria for crisis detection.

[0090] The discovery unit can improve the accuracy of crisis detection by taking into account the interrelationships between data during discovery. The discovery unit can improve the accuracy of crisis detection by taking into account, for example, the interrelationships between financial data and market data. The discovery unit can also improve the accuracy of crisis detection by taking into account the interrelationships between customer data and social media data. Furthermore, the discovery unit can improve the accuracy of crisis detection by taking into account the interrelationships between competitor data and news data. In this way, the accuracy of crisis detection is improved by taking into account the interrelationships between data. Some or all of the above-mentioned processing in the discovery unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the discovery unit can input the interrelationships between data into the generation AI, which can improve the accuracy of crisis detection.

[0091] The discovery unit can discover crises by taking into account the attribute information of the company during discovery. The discovery unit can, for example, prioritize the discovery of specific crises based on the company's industry. The discovery unit can also prioritize the discovery of specific crises based on the size of the company. Furthermore, the discovery unit can prioritize the discovery of specific crises based on the company's location. This makes it possible to discover crises based on the attribute information of the company. Some or all of the above-mentioned processing in the discovery unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the discovery unit can input the attribute information of the company into the generation AI, which can then discover crises.

[0092] The discovery unit can estimate the user's emotions and adjust the order in which crisis detection results are displayed based on the estimated user's emotions. For example, when the user is feeling stressed, the discovery unit can prioritize displaying important crises. Furthermore, when the user is relaxed, the discovery unit can also display detailed crisis information in an orderly manner. Furthermore, when the user is in a hurry, the discovery unit can prioritize displaying important crises in real time. This makes it possible to adjust the display order of crisis detection results according to the user's emotions. Some or all of the above-mentioned processing in the discovery unit may be performed using, or without, a generation AI. For example, the discovery unit can input user emotion data into the generation AI and adjust the order in which the generation AI displays crisis detection results.

[0093] During discovery, the discovery unit can discover crises by taking the geographical distribution of data into consideration. For example, the discovery unit can prioritize discovering crises in areas related to the company's location. The discovery unit can also prioritize discovering crises related to the company's business operations area. Furthermore, the discovery unit can prioritize discovering crises in areas related to the company's supply chain. This enables crisis detection based on geographical distribution. Some or all of the above-mentioned processing in the discovery unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the discovery unit can input the geographical distribution of data into the generation AI, which can then discover crises.

[0094] The discovery unit can improve the accuracy of crisis detection by referring to related literature during discovery. The discovery unit can, for example, improve the accuracy of crisis detection by referring to literature related to the company's industry. The discovery unit can also improve the accuracy of crisis detection by referring to literature related to the company's size. Furthermore, the discovery unit can improve the accuracy of crisis detection by referring to literature related to the company's location. This improves the accuracy of crisis detection by referring to related literature. Some or all of the above-mentioned processing in the discovery unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the discovery unit can input related literature into the generation AI, which can improve the accuracy of crisis detection.

[0095] The alert unit can estimate the user's emotions and adjust the alert transmission method based on the estimated user's emotions. For example, if the user is feeling stressed, the alert unit can issue a simple, highly visible alert. Furthermore, if the user is relaxed, the alert unit can issue an alert containing detailed information. Furthermore, if the user is in a hurry, the alert unit can issue an alert that focuses on the main points. This makes it possible to adjust the alert transmission method according to the user's emotions. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the alert unit can input the user's emotion data into the generation AI, which can then adjust the alert transmission method.

[0096] When issuing an alert, the alert unit can adjust the level of detail of the alert based on the severity of the crisis. For example, the alert unit can issue a detailed alert for a major crisis and a simple alert for a minor crisis. The alert unit can also issue a detailed alert for a company's financial crisis and a simple alert for other crises. Furthermore, the alert unit can issue a detailed alert for a crisis related to a company's new product and a simple alert for other crises. This makes it possible to adjust the level of detail of the alert depending on the severity of the crisis. Some or all of the above-mentioned processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input the severity of the crisis into the generation AI, and the generation AI can adjust the level of detail of the alert.

[0097] When issuing an alert, the alert unit can apply different alert methods depending on the category of the crisis. For example, the alert unit can issue an alert by email for a cybersecurity crisis and by SMS for a physical crisis. The alert unit can also issue an alert with a detailed report attached for a financial crisis and a simple alert for other crises. Furthermore, the alert unit can issue an alert by push notification for a crisis related to a company's new product and by email for other crises. This makes it possible to apply an appropriate alert method depending on the category of the crisis. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert unit can input the category of the crisis into the generation AI, which can then apply an appropriate alert method.

[0098] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated user emotions. For example, if the user is feeling stressed, the alert unit can prioritize issuing important alerts. Furthermore, if the user is relaxed, the alert unit can prioritize issuing detailed alerts. Furthermore, if the user is in a hurry, the alert unit can prioritize issuing important alerts in real time. This makes it possible to determine the priority of alerts according to the user's emotions. Some or all of the above-mentioned processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input user emotion data into the generation AI, which can then determine the priority of alerts.

[0099] When issuing an alert, the alert unit can prioritize issuing highly relevant alerts taking into account the geographical location information of the company. For example, the alert unit can prioritize issuing alerts for crises in an area related to the company's location. The alert unit can also prioritize issuing alerts for crises related to the company's business operations area. Furthermore, the alert unit can prioritize issuing alerts for crises in an area related to the company's supply chain. This makes it possible to issue highly relevant alerts based on geographical location information. Some or all of the above-mentioned processing in the alert unit may be performed using, or without, a generation AI. For example, the alert unit can input the company's geographical location information into the generation AI, which can then prioritize issuing highly relevant alerts.

[0100] When issuing an alert, the alert unit can analyze the company's social media activity and issue a related alert. For example, the alert unit can issue an alert based on customer feedback related to the company's social media activity. The alert unit can also issue an alert based on competitive data related to the company's social media activity. Furthermore, the alert unit can issue an alert based on market data related to the company's social media activity. This makes it possible to issue related alerts based on social media activity. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the alert unit can input the company's social media activity data into the generation AI, which can then issue a related alert.

[0101] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This makes it possible to adjust the way suggestions are expressed according to the user's emotions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input user emotion data into the generation AI, which can then adjust the way suggestions are expressed.

[0102] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the crisis. For example, the suggestion unit can provide a detailed proposal for a major crisis and a simplified proposal for a minor crisis. The suggestion unit can also provide a detailed proposal for a company's financial crisis and a simplified proposal for other crises. Furthermore, the suggestion unit can provide a detailed proposal for a crisis related to a company's new product and a simplified proposal for other crises. This makes it possible to adjust the level of detail of the proposal depending on the importance of the crisis. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI, for example. For example, the suggestion unit can input the importance of the crisis to the generation AI, which can then adjust the level of detail of the proposal.

[0103] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the crisis. For example, the proposal unit can apply a cybersecurity proposal algorithm to a cybersecurity crisis and a physical crisis proposal algorithm to a physical crisis. The proposal unit can also apply a financial proposal algorithm to a financial crisis and a simplified proposal algorithm to other crises. Furthermore, the proposal unit can apply a new product proposal algorithm to a crisis related to a company's new product and a simplified proposal algorithm to other crises. This makes it possible to apply an appropriate proposal algorithm depending on the category of the crisis. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can input the category of the crisis into the generation AI, which can then apply an appropriate proposal algorithm.

[0104] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can also provide a longer suggestion with detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide a short suggestion that can be quickly implemented. This makes it possible to adjust the length of the suggestion according to the user's emotions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI, which can then adjust the length of the suggestion.

[0105] When making a proposal, the proposal unit can determine the priority of the proposal based on the timing of the crisis. For example, the proposal unit can prioritize providing proposals for the most recent crisis and postpone providing proposals for older crises. The proposal unit can also prioritize providing proposals for crises that occur immediately after an important event occurs. Furthermore, the proposal unit can prioritize providing proposals for financial crises based on the company's financial reporting period. This makes it possible to determine the priority of proposals based on the timing of the crisis. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI. For example, the proposal unit can input the timing of the crisis into the generation AI, which can then determine the priority of the proposals.

[0106] The proposal unit can adjust the order of proposals based on the relevance of the crisis when making a proposal. The proposal unit can adjust the order of proposals, for example, by taking into account the relevance of a company's financial crisis and market data. The proposal unit can also adjust the order of proposals by taking into account the relevance of a company's customer data and social media data. Furthermore, the proposal unit can adjust the order of proposals by taking into account the relevance of a company's competitor data and news data. This makes it possible to adjust the order of proposals based on the relevance of the crisis. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, a generation AI. For example, the proposal unit can input the relevance of the crisis into the generation AI, which can then adjust the order of proposals.

[0107] The generation unit can estimate the user's emotions and adjust the expression method of the manuscript draft based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate a simple, highly visible manuscript draft. Furthermore, if the user is relaxed, the generation unit can generate a manuscript draft including detailed information. Furthermore, if the user is in a hurry, the generation unit can generate a manuscript draft that focuses on the main points. This makes it possible to adjust the expression method of the manuscript draft according to the user's emotions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user emotion data into the generation AI, which can adjust the expression method of the manuscript draft.

[0108] When generating a draft manuscript, the generation unit can adjust the level of detail of the draft manuscript based on the importance of the crisis. For example, the generation unit can generate a detailed draft manuscript for a major crisis and a simplified draft manuscript for a minor crisis. The generation unit can also generate a detailed draft manuscript for a company's financial crisis and a simplified draft manuscript for other crises. Furthermore, the generation unit can generate a detailed draft manuscript for a crisis related to a company's new product and a simplified draft manuscript for other crises. This makes it possible to adjust the level of detail of the draft manuscript according to the importance of the crisis. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the crisis into the generation AI, which can adjust the level of detail of the draft manuscript.

[0109] When generating a manuscript draft, the generation unit can apply different manuscript draft generation algorithms depending on the category of the crisis. For example, the generation unit can apply a cybersecurity manuscript draft generation algorithm to a cybersecurity crisis and a physical crisis manuscript draft generation algorithm to a physical crisis. The generation unit can also apply a financial manuscript draft generation algorithm to a financial crisis and a simplified manuscript draft generation algorithm to other crises. Furthermore, the generation unit can apply a new product manuscript draft generation algorithm to a crisis related to a company's new product and a simplified manuscript draft generation algorithm to other crises. This makes it possible to apply an appropriate manuscript draft generation algorithm depending on the category of the crisis. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the category of the crisis into the generation AI, which can then apply an appropriate manuscript draft generation algorithm.

[0110] The generation unit can estimate the user's emotions and adjust the length of the draft based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate a short, concise draft. If the user is relaxed, the generation unit can also generate a longer draft with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a short draft that can be quickly executed. This makes it possible to adjust the length of the draft according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the length of the draft.

[0111] When generating draft manuscripts, the generation unit can determine the priority of the draft manuscripts based on the timing of the occurrence of a crisis. For example, the generation unit can prioritize generating draft manuscripts for the most recent crisis and postpone generating draft manuscripts for older crises. The generation unit can also prioritize generating draft manuscripts for crises that occur immediately after an important event occurs. Furthermore, the generation unit can prioritize generating draft manuscripts for financial crises based on the company's financial reporting period. This makes it possible to determine the priority of draft manuscripts based on the timing of the occurrence of a crisis. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the timing of the crisis occurrence into the generation AI, which can then determine the priority of the draft manuscripts.

[0112] The generation unit can adjust the order of the draft manuscripts based on the relevance of the crisis when generating the draft manuscripts. For example, the generation unit can adjust the order of the draft manuscripts by taking into account the relevance between a company's financial crisis and market data. The generation unit can also adjust the order of the draft manuscripts by taking into account the relevance between the company's customer data and social media data. Furthermore, the generation unit can adjust the order of the draft manuscripts by taking into account the relevance between the company's competitor data and news data. This makes it possible to adjust the order of the draft manuscripts based on the relevance of the crisis. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of the crisis into the generation AI, which can then adjust the order of the draft manuscripts. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, discovery unit, alert unit, suggestion unit, generation unit, and emotion estimation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and monitors the data in real time using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The discovery unit is realized by the specific processing unit 290 of the data processing device 12 and detects crises early based on the analyzed data. The alert unit issues an alert using the output device 40 of the smart device 14. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and proposes countermeasures. The generation unit is realized by the control unit 46A of the smart device 14 and generates a draft for effective communication. The emotion estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the timing of data collection by the collection unit. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, discovery unit, alert unit, suggestion unit, generation unit, and emotion estimation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and monitors the data in real time using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The discovery unit is realized by the specific processing unit 290 of the data processing device 12 and detects a crisis early based on the analyzed data. The alert unit issues an alert using the speaker 240 of the smart glasses 214. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests countermeasures. The generation unit is realized by the control unit 46A of the smart glasses 214 and generates a draft for effective communication. The emotion estimation unit is realized by the specific processing unit 290 of the data processing device 12, estimates the user's emotion, and adjusts the timing of data collection by the collection unit. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, discovery unit, alert unit, suggestion unit, generation unit, and emotion estimation unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset type terminal 314 and monitors the data in real time using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The discovery unit is realized by the specific processing unit 290 of the data processing device 12 and detects a crisis early based on the analyzed data. The alert unit issues an alert using the speaker 240 of the headset type terminal 314. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests countermeasures. The generation unit is realized by the control unit 46A of the headset type terminal 314 and generates a draft for effective communication. The emotion estimation unit is realized by the specific processing unit 290 of the data processing device 12, estimates the user's emotion, and adjusts the timing of data collection by the collection unit. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, discovery unit, alert unit, suggestion unit, generation unit, and emotion estimation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and monitors the data in real time using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The discovery unit is realized by the specific processing unit 290 of the data processing device 12 and detects crises early based on the analyzed data. The alert unit issues an alert using the speaker 240 of the robot 414. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests countermeasures. The generation unit is realized by the control unit 46A of the robot 414 and generates a draft for effective communication. The emotion estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the timing of data collection by the collection unit.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] The crisis management support system may further include a prediction unit. The prediction unit can predict future crises based on data from the collection unit and the analysis unit. For example, the prediction unit can predict the possibility of future cyber attacks using past data. The prediction unit can also analyze a company's financial data to predict future financial risks. Furthermore, the prediction unit can analyze social media data to predict future customer reactions. This allows a company to prepare for future risks in advance.

[0115] The collection unit can further collect environmental data. For example, the collection unit can collect weather data and evaluate the risk of natural disasters. The collection unit can also collect earthquake data and evaluate the risk of earthquake occurrence. The collection unit can also collect climate change data and evaluate long-term environmental risks. This allows companies to take measures against environmental risks.

[0116] The analysis unit may further include a sentiment analysis function. The analysis unit may analyze user sentiment from the collected data and evaluate emotional reactions to the company's situation. For example, the analysis unit may analyze social media data and evaluate customer sentiment. The analysis unit may also analyze employee feedback data and evaluate employee sentiment. Furthermore, the analysis unit may analyze customer survey data and evaluate customer satisfaction. This allows the company to take measures against emotional risks.

[0117] The discovery unit may further include an abnormal behavior detection function. The discovery unit may detect abnormal behavior from collected data and detect potential crises early. For example, the discovery unit may analyze employee behavior data and detect abnormal behavior. The discovery unit may also analyze customer purchase data and detect abnormal purchasing patterns. Furthermore, the discovery unit may analyze network traffic data and detect abnormal communication patterns. This allows companies to detect potential crises early and take measures.

[0118] The alert unit can further include an emotion-based alert priority determination function. The alert unit can estimate the user's emotion and determine the priority of alerts based on the estimated emotion. For example, if the user is feeling stressed, it can prioritize sending important alerts. Also, if the user is relaxed, it can prioritize sending detailed alerts. Furthermore, if the user is in a hurry, it can prioritize sending important alerts in real time. This makes it possible to determine the priority of alerts according to the user's emotion.

[0119] The proposal unit may further include a scenario-based proposal function. The proposal unit may generate multiple scenarios based on the collected data and propose countermeasures for each scenario. For example, the proposal unit may generate a scenario in the event of a cyber attack and propose countermeasures. The proposal unit may also generate a scenario in the event of a natural disaster and propose countermeasures. Furthermore, the proposal unit may generate a scenario in the event of an increased financial risk and propose countermeasures. This allows a company to prepare for various scenarios.

[0120] The generation unit can further include a function for generating a manuscript draft based on emotions. The generation unit can estimate the user's emotions and adjust the way the manuscript draft is expressed based on the estimated emotions. For example, if the user is feeling stressed, a simple and highly visible manuscript draft can be generated. If the user is relaxed, a manuscript draft including detailed information can be generated. Furthermore, if the user is in a hurry, a manuscript draft that focuses on the main points can be generated. This makes it possible to adjust the way the manuscript draft is expressed based on the user's emotions.

[0121] The collection unit can further collect voice data. For example, the collection unit can collect voice data of meetings and analyze the contents of the meetings. The collection unit can also collect voice data of telephone conversations with customers and analyze customer feedback. Furthermore, the collection unit can collect voice data of employees and analyze their opinions. This allows companies to use voice data to collect more detailed information.

[0122] The analysis unit may further include an image analysis function. The analysis unit may analyze collected image data to evaluate the company's situation. For example, the analysis unit may analyze surveillance camera footage to detect abnormal behavior. The analysis unit may also analyze images posted on social media to evaluate customer reactions. Furthermore, the analysis unit may analyze product images to detect quality issues. This allows companies to utilize image data to obtain more detailed information.

[0123] The detection unit may further include a function for adjusting crisis detection criteria based on emotions. The detection unit may estimate the user's emotions and adjust the crisis detection criteria based on the estimated emotions. For example, if the user is feeling stressed, it may be possible to prioritize detection of only important crises. Also, if the user is relaxed, it may be possible to apply detailed crisis detection criteria. Furthermore, if the user is in a hurry, it may be possible to prioritize detection of important crises in real time. This makes it possible to adjust the crisis detection criteria according to the user's emotions.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The collection department collects data to monitor the company's situation in real time. The collection department can collect data from a variety of data sources, including internal databases, external APIs, and social media data. The collection department can also adjust the frequency and means of data collection. For example, the frequency of data collection can be increased or decreased depending on the company's situation. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using analytical methods such as outlier detection, pattern recognition, and predictive models. Generative AI is used to analyze the data and detect crises early. Step 3: The detection unit detects crises early based on the data analyzed by the analysis unit. The detection unit can detect crises using outlier detection and pattern recognition. Step 4: The alert unit issues an alert for the crisis detected by the detection unit. The alert unit can issue an alert in the form of an email notification, an app notification, a voice alert, etc. Step 5: The suggestion unit proposes countermeasures based on the alert issued by the alert unit. The suggestion unit can propose countermeasures such as emergency response procedures, risk avoidance measures, and recovery plans. Step 6: The generation unit generates a draft for effective communication based on the countermeasures proposed by the proposal unit. The generation unit can generate the draft in the form of a report, presentation material, email template, etc.

[0126] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0128] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 7, a 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.

[0164] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0166] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0168] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0169] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0170] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0176] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0181] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0182] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0183] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0187] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0188] 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.

[0189] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0190] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0191] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0192] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0194] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0195] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0196] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0197] [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a detection unit that detects a crisis early based on the data analyzed by the analysis unit; an alert unit that issues an alert in response to a crisis detected by the detection unit; a suggestion unit that proposes a countermeasure based on the alert issued by the alert unit; a generation unit that generates a draft for effective communication based on the countermeasures proposed by the proposal unit. A system characterized by:

2. The collecting unit Monitor your business in real time using a wide range of data sources 2. The system of claim 1.

3. The analysis unit Analyzing collected data and detecting crises early 2. The system of claim 1.

4. The discovery unit Early detection of crises based on analyzed data 2. The system of claim 1.

5. The alert unit Issue alerts for discovered crises 2. The system of claim 1.

6. The proposal unit Suggest actions based on alerts 2. The system of claim 1.

7. The generation unit Generate a draft for effective communication based on the proposed response 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

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