system

Abstract

The system according to this embodiment aims to integrate information from different data sources and to immediately determine incident risk. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a decision unit. The collection unit collects information such as document data, image data, and sensor data. The analysis unit analyzes the information collected by the collection unit. The decision unit determines the incident risk based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to integrate information from different data sources and immediately determine an incident risk, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate information from different data sources and immediately determine an incident risk.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a determination unit. The collection unit collects information such as document data, image data, and sensor data. The analysis unit analyzes the information collected by the collection unit. The determination unit determines an incident risk based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate information from different data sources and immediately determine incident risk. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The incident risk assessment system according to an embodiment of the present invention is a system that integrates different data sources (documents, images, sensor data, etc.) in real time to immediately determine incident risk. This incident risk assessment system collects information from different data sources such as document data, image data, and sensor data, analyzes it using a domestically produced LLM (Large-Scale Language Model), and immediately determines incident risk. For example, it collects video data from security cameras within a company, employee access logs, and environmental data from sensors. Next, it analyzes the collected data using the domestically produced LLM. The domestically produced LLM has natural language processing capabilities adapted to the business flow and corporate culture of Japanese companies and can be customized to match the corporate culture. For example, it analyzes video data from security cameras to detect suspicious activity. It also analyzes employee access logs to detect abnormal access patterns. Furthermore, it analyzes environmental data from sensors to detect abnormal environmental changes. Based on the analyzed data, it immediately determines incident risk. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the system comprehensively evaluates this information to determine incident risk. By conducting a comprehensive risk assessment from multiple perspectives, it is possible to prevent incidents that could be fatal to a company. This system will first be built in-house for effectiveness verification and demonstration, and then expanded to domestic corporate clients, who are likely to be the main target of domestic LLMs. This will provide a secure environment for companies that handle large amounts of personal and confidential information, minimizing incident risks. As a result, the incident risk assessment system will be able to instantly determine a company's incident risks and enable a rapid response.

[0029] The incident risk assessment system according to this embodiment comprises a collection unit, an analysis unit, and a decision unit. The collection unit collects information such as document data, image data, and sensor data. The collection unit can, for example, collect video data from security cameras within a company. The collection unit can also collect employee access logs. Furthermore, the collection unit can collect environmental data from sensors. For example, the collection unit can collect video data from security cameras within a company in real time and provide it to the analysis unit. The collection unit can also periodically collect employee access logs and provide them to the analysis unit. The collection unit can also continuously collect environmental data from sensors and provide it to the analysis unit. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze video data from security cameras and detect suspicious activity. Furthermore, the analysis unit can analyze employee access logs and detect abnormal access patterns. Furthermore, the analysis unit can analyze environmental data from sensors and detect abnormal environmental changes. For example, the analysis unit can analyze video data from security cameras and detect suspicious activity. The analysis unit can analyze employee access logs and detect abnormal access patterns. The analysis unit can also analyze environmental data from sensors and detect abnormal environmental changes. The decision unit determines incident risk based on the information analyzed by the analysis unit. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the decision unit can comprehensively evaluate this information and determine incident risk. By conducting a comprehensive risk assessment from multiple perspectives, the decision unit can prevent the occurrence of incidents that could be fatal to the company. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the decision unit comprehensively evaluates this information and determines incident risk. As a result, the incident risk determination system according to this embodiment can integrate information from different data sources and make an immediate determination of incident risk.

[0030] The data collection unit collects information such as document data, image data, and sensor data. Specifically, it collects video data from security cameras within the company in real time and provides it to the analysis unit. Security cameras are installed in important areas and entrances of the company and record video 24 hours a day. This allows the data collection unit to always obtain the latest video data and detect abnormal movements or suspicious behavior early. The data collection unit also periodically collects employee access logs and provides them to the analysis unit. The access logs include the date and time when an employee accessed the system, the IP address from which the access originated, and information about the files or databases accessed. This allows the data collection unit to understand the employee's behavior history in detail and provide basic data for detecting abnormal access patterns. Furthermore, the data collection unit continuously collects environmental data from sensors and provides it to the analysis unit. Environmental data includes information such as temperature, humidity, illuminance, and vibration, allowing for real-time monitoring of environmental changes within the company. For example, if the temperature in the server room rises sharply or abnormal vibrations are detected in a critical area, the data collection unit immediately sends the data to the analysis unit, enabling a rapid response. This allows the data collection unit to gather a wide range of information from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and decision-making units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes video data from security cameras to detect suspicious activity. It utilizes AI-based image recognition technology to analyze the movement of people and objects in the video in real time. For example, if human movement is detected in a specific area outside of normal business hours, the analysis unit can determine that movement is suspicious and issue an alert. The analysis unit also analyzes employee access logs to detect abnormal access patterns. It uses an AI-based anomaly detection algorithm to identify abnormal behavior by comparing it to normal access patterns. For example, if a specific employee downloads a large amount of data during a time when they do not normally access the system, the analysis unit can determine that this behavior is abnormal and immediately notify the administrator. Furthermore, the analysis unit analyzes environmental data from sensors to detect abnormal environmental changes. For example, if the temperature in a server room rises sharply, the analysis unit can detect an anomaly in the cooling system based on that data and take early countermeasures. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Additionally, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past access logs, the system can predict fluctuations in risk for specific employees or departments and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The decision-making unit assesses incident risk based on information analyzed by the analysis unit. Specifically, when suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the unit comprehensively evaluates this information to determine incident risk. The decision-making unit utilizes AI-based risk assessment algorithms to perform comprehensive risk assessments from multiple perspectives. For example, if suspicious activity detected from security camera video data and abnormal access patterns detected from employee access logs occur simultaneously, the decision-making unit can integrate this information and determine that the incident risk is high. Furthermore, when abnormal environmental changes are detected, the decision-making unit evaluates whether the change is related to other data and determines the degree of risk. For example, if the temperature in a server room rises sharply, the unit can evaluate whether the cause is an external attack or an internal system failure and take appropriate countermeasures. In addition, the decision-making unit can utilize past incident data and statistical information to predict future risks and plan countermeasures in advance. For example, based on past data, it can predict fluctuations in risk at specific times or situations and take preventative measures. Furthermore, the decision-making unit can continuously revise its risk assessment based on real-time updated data, enabling it to respond to the latest situation. This allows the decision-making unit to always provide highly accurate risk assessments based on the latest information, supporting a quick and appropriate response. As a result, the incident risk judgment system according to this embodiment can integrate information from different data sources and make an immediate judgment on incident risk.

[0033] The data collection unit can collect video data from security cameras within the company, employee access logs, and environmental data from sensors. For example, the data collection unit can collect video data from security cameras within the company in real time. It can also collect employee access logs periodically. Furthermore, it can continuously collect environmental data from sensors. This enables comprehensive data collection by gathering information from multiple data sources within the company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input video data from security cameras within the company into an AI and have the AI ​​perform the video data collection.

[0034] The analysis unit can analyze video data from security cameras and detect suspicious movements. For example, the analysis unit can analyze video data from security cameras and detect suspicious movements. The analysis unit can also extract specific movements from the video data and determine whether those movements are suspicious. Furthermore, the analysis unit can detect abnormal movements from the video data and determine whether those movements could cause an incident risk. In this way, suspicious movements can be detected by analyzing video data from security cameras. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input video data from security cameras into the generation AI and have the generation AI perform the detection of suspicious movements.

[0035] The analysis unit can analyze employee access logs and detect abnormal access patterns. For example, the analysis unit can analyze employee access logs and detect abnormal access patterns. The analysis unit can also extract specific patterns from the access logs and determine whether those patterns are abnormal. Furthermore, the analysis unit can detect abnormal access patterns from the access logs and determine whether those patterns have the potential to cause incident risks. In this way, abnormal access patterns can be detected by analyzing employee access logs. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input employee access logs into the generation AI and have the generation AI perform the detection of abnormal access patterns.

[0036] The analysis unit can analyze environmental data from sensors and detect abnormal environmental changes. For example, the analysis unit can analyze environmental data from sensors and detect abnormal environmental changes. The analysis unit can also extract specific changes from the environmental data and determine whether those changes are abnormal. Furthermore, the analysis unit can detect abnormal changes from the environmental data and determine whether those changes could cause incident risks. In this way, abnormal environmental changes can be detected by analyzing environmental data from sensors. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input environmental data from sensors into the generation AI and have the generation AI perform the detection of abnormal environmental changes.

[0037] The decision-making unit can perform a comprehensive risk assessment from multiple perspectives and determine the incident risk. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the decision-making unit comprehensively evaluates this information and determines the incident risk. The decision-making unit can also perform a risk assessment from multiple perspectives and make a comprehensive judgment on the incident risk. Furthermore, the decision-making unit can make an immediate judgment on the incident risk based on the results of the risk assessment. This allows for an accurate determination of the incident risk by performing a comprehensive risk assessment from multiple perspectives. Some or all of the above processing in the decision-making unit is performed using a generation AI. For example, the decision-making unit can input risk assessments from multiple perspectives into the generation AI and have the generation AI perform the incident risk judgment.

[0038] The data collection unit can dynamically change the types of data it collects according to the company's business workflow. For example, during peak business periods, the unit can prioritize efficiency by collecting only essential data. Conversely, during off-peak periods, it can collect detailed data to prepare for future analysis. Furthermore, if a specific project is underway, the unit can prioritize collecting data related to that project. This enables efficient data collection by dynamically changing the types of data collected according to the company's business workflow. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on the company's business workflow into the AI, and have the AI ​​execute the dynamic changes in data collection.

[0039] The data collection unit can adjust the accuracy of the data it collects according to the importance of the data being collected. For example, if the data is important, the collection unit will use high-precision sensors for collection. If the data is general, the collection unit can use standard sensors for collection. Furthermore, if the data is of low importance, the collection unit can use simple sensors for collection. This allows for efficient data collection by adjusting the data accuracy according to the importance of the data being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the data accuracy based on the importance of the data being collected into the AI ​​and have the AI ​​perform the data collection accuracy adjustment.

[0040] The data collection unit can dynamically change the scope of data collected according to the geographical location of the company. For example, the data collection unit can collect detailed data at the company's main locations. It can also collect basic data at the company's satellite offices. Furthermore, it can collect data at the company's overseas locations in accordance with local regulations. This enables efficient data collection by dynamically changing the scope of data collected according to the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the scope of data to be collected based on the company's geographical location into the AI ​​and have the AI ​​execute the dynamic changes in data collection.

[0041] The data collection unit can customize the format of the data it collects according to the company's business operations. For example, in the manufacturing industry, the data collection unit can collect machine operation data. In the service industry, the data collection unit can also collect customer feedback data. Furthermore, in the IT industry, the data collection unit can collect system log data. By customizing the format of the data collected according to the company's business operations, efficient data collection becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the format of the data to be collected based on the company's business operations into the AI ​​and have the AI ​​perform the data collection customization.

[0042] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the interrelationships between document data and image data to derive more accurate results. It can also analyze the interrelationships between sensor data and access logs to detect anomalies. Furthermore, the analysis unit can analyze the interrelationships between video data and environmental data to detect suspicious movements. In this way, considering the interrelationships between data improves the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the interrelationships between data into an AI and have the AI ​​perform the analysis of those interrelationships.

[0043] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to document data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a time series analysis algorithm to sensor data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different analysis algorithms for each data category into the AI ​​and have the AI ​​perform the analysis.

[0044] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can integrate geographically dispersed data and analyze the overall trend. The analysis unit can also analyze data concentrated in a specific region and detect region-specific risks. Furthermore, the analysis unit can detect anomalous patterns based on the geographical distribution. In this way, region-specific risks can be detected by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the geographical distribution of the data into AI and have AI perform the analysis of the geographical distribution.

[0045] The analysis unit can improve the accuracy of its analysis by referencing relevant external data during the analysis process. For example, the analysis unit can reference external weather data to improve the accuracy of its environmental data analysis. It can also reference external crime data to improve the accuracy of its security risk analysis. Furthermore, it can reference external market data to improve the accuracy of its business risk analysis. In this way, referencing relevant external data improves the accuracy of the analysis. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input relevant external data into the generating AI and have the generating AI perform the referencing of the external data.

[0046] The decision-making unit can improve the accuracy of its risk assessment by referring to past incident data when making a decision. For example, the decision-making unit can identify similar risks based on past incident data. It can also analyze past incident data to identify risk occurrence patterns. Furthermore, the decision-making unit can refer to past incident data to evaluate the impact of risks. As a result, the accuracy of risk assessment is improved by referring to past incident data. Some or all of the above processing in the decision-making unit is performed using a generative AI. For example, the decision-making unit can input past incident data into the generative AI and have the generative AI perform the task of improving the accuracy of risk assessment.

[0047] The decision-making unit can adjust the level of detail in the risk assessment according to the importance of the data when making a decision. For example, the decision-making unit performs a detailed risk assessment for important data. It can also perform a standard risk assessment for general data. Furthermore, it can perform a simplified risk assessment for low-importance data. By adjusting the level of detail in the risk assessment according to the importance of the data, efficient risk assessment becomes possible. Some or all of the above processing in the decision-making unit is performed using a generative AI. For example, the decision-making unit can input the level of detail in the risk assessment based on the importance of the data into the generative AI and have the generative AI perform the adjustment of the risk assessment.

[0048] The decision-making unit can perform risk assessments while considering the geographical location of the company. For example, the decision-making unit can perform detailed risk assessments at major locations. It can also perform basic risk assessments at satellite offices. Furthermore, the decision-making unit can perform risk assessments at overseas locations in accordance with local regulations. This allows for the assessment of region-specific risks by considering the geographical location of the company. Some or all of the above processes in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input risk assessments based on the geographical location of the company into the AI ​​and have the AI ​​perform the risk assessment.

[0049] The decision-making unit can perform risk assessments by referring to relevant market data when making decisions. For example, the decision-making unit can refer to market trend data to assess business risk. It can also refer to competitor data to assess competitive risk. Furthermore, it can refer to economic indicator data to assess economic risk. This improves the assessment of business risk by referring to relevant market data. Some or all of the above processing in the decision-making unit is performed using generative AI. For example, the decision-making unit can input relevant market data into the generative AI and have the generative AI perform the market data referencing.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can dynamically change the types of data it collects according to the company's business workflow. For example, during peak business periods, the unit can prioritize efficiency by collecting only essential data. Conversely, during off-peak periods, it can collect detailed data to prepare for future analysis. Furthermore, if a specific project is underway, the unit can prioritize collecting data related to that project. This enables efficient data collection by dynamically changing the types of data collected according to the company's business workflow. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on the company's business workflow into the AI, and have the AI ​​execute the dynamic changes in data collection.

[0052] The data collection unit can adjust the accuracy of the data it collects according to the importance of the data being collected. For example, if the data is important, the collection unit will use high-precision sensors for collection. If the data is general, the collection unit can use standard sensors for collection. Furthermore, if the data is of low importance, the collection unit can use simple sensors for collection. This allows for efficient data collection by adjusting the data accuracy according to the importance of the data being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the data accuracy based on the importance of the data being collected into the AI ​​and have the AI ​​perform the data collection accuracy adjustment.

[0053] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the interrelationships between document data and image data to derive more accurate results. It can also analyze the interrelationships between sensor data and access logs to detect anomalies. Furthermore, the analysis unit can analyze the interrelationships between video data and environmental data to detect suspicious movements. In this way, considering the interrelationships between data improves the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the interrelationships between data into an AI and have the AI ​​perform the analysis of those interrelationships.

[0054] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to document data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a time series analysis algorithm to sensor data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different analysis algorithms for each data category into the AI ​​and have the AI ​​perform the analysis.

[0055] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can integrate geographically dispersed data and analyze the overall trend. The analysis unit can also analyze data concentrated in a specific region and detect region-specific risks. Furthermore, the analysis unit can detect anomalous patterns based on the geographical distribution. In this way, region-specific risks can be detected by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the geographical distribution of the data into AI and have AI perform the analysis of the geographical distribution.

[0056] The analysis unit can improve the accuracy of its analysis by referencing relevant external data during the analysis process. For example, the analysis unit can reference external weather data to improve the accuracy of its environmental data analysis. It can also reference external crime data to improve the accuracy of its security risk analysis. Furthermore, it can reference external market data to improve the accuracy of its business risk analysis. In this way, referencing relevant external data improves the accuracy of the analysis. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input relevant external data into the generating AI and have the generating AI perform the referencing of the external data.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects information such as document data, image data, and sensor data. For example, it collects video data from security cameras within the company, employee access logs, and environmental data from sensors. The collection unit collects this data in real time or periodically and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes video data from security cameras to detect suspicious activity, analyzes employee access logs to detect abnormal access patterns, and analyzes environmental data from sensors to detect abnormal environmental changes. Step 3: The judgment unit determines the incident risk based on the information analyzed by the analysis unit. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the unit comprehensively evaluates this information and determines the incident risk.

[0059] (Example of form 2) The incident risk assessment system according to an embodiment of the present invention is a system that integrates different data sources (documents, images, sensor data, etc.) in real time to immediately determine incident risk. This incident risk assessment system collects information from different data sources such as document data, image data, and sensor data, analyzes it using a domestically produced LLM (Large-Scale Language Model), and immediately determines incident risk. For example, it collects video data from security cameras within a company, employee access logs, and environmental data from sensors. Next, it analyzes the collected data using the domestically produced LLM. The domestically produced LLM has natural language processing capabilities adapted to the business flow and corporate culture of Japanese companies and can be customized to match the corporate culture. For example, it analyzes video data from security cameras to detect suspicious activity. It also analyzes employee access logs to detect abnormal access patterns. Furthermore, it analyzes environmental data from sensors to detect abnormal environmental changes. Based on the analyzed data, it immediately determines incident risk. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the system comprehensively evaluates this information to determine incident risk. By conducting a comprehensive risk assessment from multiple perspectives, it is possible to prevent incidents that could be fatal to a company. This system will first be built in-house for effectiveness verification and demonstration, and then expanded to domestic corporate clients, who are likely to be the main target of domestic LLMs. This will provide a secure environment for companies that handle large amounts of personal and confidential information, minimizing incident risks. As a result, the incident risk assessment system will be able to instantly determine a company's incident risks and enable a rapid response.

[0060] The incident risk assessment system according to this embodiment comprises a collection unit, an analysis unit, and a decision unit. The collection unit collects information such as document data, image data, and sensor data. The collection unit can, for example, collect video data from security cameras within a company. The collection unit can also collect employee access logs. Furthermore, the collection unit can collect environmental data from sensors. For example, the collection unit can collect video data from security cameras within a company in real time and provide it to the analysis unit. The collection unit can also periodically collect employee access logs and provide them to the analysis unit. The collection unit can also continuously collect environmental data from sensors and provide it to the analysis unit. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze video data from security cameras and detect suspicious activity. Furthermore, the analysis unit can analyze employee access logs and detect abnormal access patterns. Furthermore, the analysis unit can analyze environmental data from sensors and detect abnormal environmental changes. For example, the analysis unit can analyze video data from security cameras and detect suspicious activity. The analysis unit can analyze employee access logs and detect abnormal access patterns. The analysis unit can also analyze environmental data from sensors and detect abnormal environmental changes. The decision unit determines incident risk based on the information analyzed by the analysis unit. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the decision unit can comprehensively evaluate this information and determine incident risk. By conducting a comprehensive risk assessment from multiple perspectives, the decision unit can prevent the occurrence of incidents that could be fatal to the company. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the decision unit comprehensively evaluates this information and determines incident risk. As a result, the incident risk determination system according to this embodiment can integrate information from different data sources and make an immediate determination of incident risk.

[0061] The data collection unit collects information such as document data, image data, and sensor data. Specifically, it collects video data from security cameras within the company in real time and provides it to the analysis unit. Security cameras are installed in important areas and entrances of the company and record video 24 hours a day. This allows the data collection unit to always obtain the latest video data and detect abnormal movements or suspicious behavior early. The data collection unit also periodically collects employee access logs and provides them to the analysis unit. The access logs include the date and time when an employee accessed the system, the IP address from which the access originated, and information about the files or databases accessed. This allows the data collection unit to understand the employee's behavior history in detail and provide basic data for detecting abnormal access patterns. Furthermore, the data collection unit continuously collects environmental data from sensors and provides it to the analysis unit. Environmental data includes information such as temperature, humidity, illuminance, and vibration, allowing for real-time monitoring of environmental changes within the company. For example, if the temperature in the server room rises sharply or abnormal vibrations are detected in a critical area, the data collection unit immediately sends the data to the analysis unit, enabling a rapid response. This allows the data collection unit to gather a wide range of information from diverse data sources and understand the situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and decision-making units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0062] The analysis unit analyzes the information collected by the collection unit. Specifically, it analyzes video data from security cameras to detect suspicious activity. It utilizes AI-based image recognition technology to analyze the movement of people and objects in the video in real time. For example, if human movement is detected in a specific area outside of normal business hours, the analysis unit can determine that movement is suspicious and issue an alert. The analysis unit also analyzes employee access logs to detect abnormal access patterns. It uses an AI-based anomaly detection algorithm to identify abnormal behavior by comparing it to normal access patterns. For example, if a specific employee downloads a large amount of data during a time when they do not normally access the system, the analysis unit can determine that this behavior is abnormal and immediately notify the administrator. Furthermore, the analysis unit analyzes environmental data from sensors to detect abnormal environmental changes. For example, if the temperature in a server room rises sharply, the analysis unit can detect an anomaly in the cooling system based on that data and take early countermeasures. This allows the analysis unit to quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Additionally, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past access logs, the system can predict fluctuations in risk for specific employees or departments and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0063] The decision-making unit assesses incident risk based on information analyzed by the analysis unit. Specifically, when suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the unit comprehensively evaluates this information to determine incident risk. The decision-making unit utilizes AI-based risk assessment algorithms to perform comprehensive risk assessments from multiple perspectives. For example, if suspicious activity detected from security camera video data and abnormal access patterns detected from employee access logs occur simultaneously, the decision-making unit can integrate this information and determine that the incident risk is high. Furthermore, when abnormal environmental changes are detected, the decision-making unit evaluates whether the change is related to other data and determines the degree of risk. For example, if the temperature in a server room rises sharply, the unit can evaluate whether the cause is an external attack or an internal system failure and take appropriate countermeasures. In addition, the decision-making unit can utilize past incident data and statistical information to predict future risks and plan countermeasures in advance. For example, based on past data, it can predict fluctuations in risk at specific times or situations and take preventative measures. Furthermore, the decision-making unit can continuously revise its risk assessment based on real-time updated data, enabling it to respond to the latest situation. This allows the decision-making unit to always provide highly accurate risk assessments based on the latest information, supporting a quick and appropriate response. As a result, the incident risk judgment system according to this embodiment can integrate information from different data sources and make an immediate judgment on incident risk.

[0064] The data collection unit can collect video data from security cameras within the company, employee access logs, and environmental data from sensors. For example, the data collection unit can collect video data from security cameras within the company in real time. It can also collect employee access logs periodically. Furthermore, it can continuously collect environmental data from sensors. This enables comprehensive data collection by gathering information from multiple data sources within the company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input video data from security cameras within the company into an AI and have the AI ​​perform the video data collection.

[0065] The analysis unit can analyze video data from security cameras and detect suspicious movements. For example, the analysis unit can analyze video data from security cameras and detect suspicious movements. The analysis unit can also extract specific movements from the video data and determine whether those movements are suspicious. Furthermore, the analysis unit can detect abnormal movements from the video data and determine whether those movements could cause an incident risk. In this way, suspicious movements can be detected by analyzing video data from security cameras. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input video data from security cameras into the generation AI and have the generation AI perform the detection of suspicious movements.

[0066] The analysis unit can analyze employee access logs and detect abnormal access patterns. For example, the analysis unit can analyze employee access logs and detect abnormal access patterns. The analysis unit can also extract specific patterns from the access logs and determine whether those patterns are abnormal. Furthermore, the analysis unit can detect abnormal access patterns from the access logs and determine whether those patterns have the potential to cause incident risks. In this way, abnormal access patterns can be detected by analyzing employee access logs. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input employee access logs into the generation AI and have the generation AI perform the detection of abnormal access patterns.

[0067] The analysis unit can analyze environmental data from sensors and detect abnormal environmental changes. For example, the analysis unit can analyze environmental data from sensors and detect abnormal environmental changes. The analysis unit can also extract specific changes from the environmental data and determine whether those changes are abnormal. Furthermore, the analysis unit can detect abnormal changes from the environmental data and determine whether those changes could cause incident risks. In this way, abnormal environmental changes can be detected by analyzing environmental data from sensors. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit can input environmental data from sensors into the generation AI and have the generation AI perform the detection of abnormal environmental changes.

[0068] The decision-making unit can perform a comprehensive risk assessment from multiple perspectives and determine the incident risk. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the decision-making unit comprehensively evaluates this information and determines the incident risk. The decision-making unit can also perform a risk assessment from multiple perspectives and make a comprehensive judgment on the incident risk. Furthermore, the decision-making unit can make an immediate judgment on the incident risk based on the results of the risk assessment. This allows for an accurate determination of the incident risk by performing a comprehensive risk assessment from multiple perspectives. Some or all of the above processing in the decision-making unit is performed using a generation AI. For example, the decision-making unit can input risk assessments from multiple perspectives into the generation AI and have the generation AI perform the incident risk judgment.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection and collect more detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only the most important data. This reduces the user's burden by adjusting the timing of data collection based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can dynamically change the types of data it collects according to the company's business workflow. For example, during peak business periods, the unit can prioritize efficiency by collecting only essential data. Conversely, during off-peak periods, it can collect detailed data to prepare for future analysis. Furthermore, if a specific project is underway, the unit can prioritize collecting data related to that project. This enables efficient data collection by dynamically changing the types of data collected according to the company's business workflow. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on the company's business workflow into the AI, and have the AI ​​execute the dynamic changes in data collection.

[0071] The data collection unit can adjust the accuracy of the data it collects according to the importance of the data being collected. For example, if the data is important, the collection unit will use high-precision sensors for collection. If the data is general, the collection unit can use standard sensors for collection. Furthermore, if the data is of low importance, the collection unit can use simple sensors for collection. This allows for efficient data collection by adjusting the data accuracy according to the importance of the data being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the data accuracy based on the importance of the data being collected into the AI ​​and have the AI ​​perform the data collection accuracy adjustment.

[0072] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit can also prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, by prioritizing the data to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can dynamically change the scope of data collected according to the geographical location of the company. For example, the data collection unit can collect detailed data at the company's main locations. It can also collect basic data at the company's satellite offices. Furthermore, it can collect data at the company's overseas locations in accordance with local regulations. This enables efficient data collection by dynamically changing the scope of data collected according to the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the scope of data to be collected based on the company's geographical location into the AI ​​and have the AI ​​execute the dynamic changes in data collection.

[0074] The data collection unit can customize the format of the data it collects according to the company's business operations. For example, in the manufacturing industry, the data collection unit can collect machine operation data. In the service industry, the data collection unit can also collect customer feedback data. Furthermore, in the IT industry, the data collection unit can collect system log data. By customizing the format of the data collected according to the company's business operations, efficient data collection becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the format of the data to be collected based on the company's business operations into the AI ​​and have the AI ​​perform the data collection customization.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easily understandable presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. By adjusting the presentation of the analysis results based on the user's emotions, the analysis results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the interrelationships between document data and image data to derive more accurate results. It can also analyze the interrelationships between sensor data and access logs to detect anomalies. Furthermore, the analysis unit can analyze the interrelationships between video data and environmental data to detect suspicious movements. In this way, considering the interrelationships between data improves the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the interrelationships between data into an AI and have the AI ​​perform the analysis of those interrelationships.

[0077] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to document data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a time series analysis algorithm to sensor data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different analysis algorithms for each data category into the AI ​​and have the AI ​​perform the analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can display important information first. If the user is relaxed, the analysis unit can also display detailed information sequentially. Furthermore, if the user is in a hurry, the analysis unit can display the main points first. This allows for prioritizing the display of information important to the user by adjusting the display order of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can integrate geographically dispersed data and analyze the overall trend. The analysis unit can also analyze data concentrated in a specific region and detect region-specific risks. Furthermore, the analysis unit can detect anomalous patterns based on the geographical distribution. In this way, region-specific risks can be detected by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the geographical distribution of the data into AI and have AI perform the analysis of the geographical distribution.

[0080] The analysis unit can improve the accuracy of its analysis by referencing relevant external data during the analysis process. For example, the analysis unit can reference external weather data to improve the accuracy of its environmental data analysis. It can also reference external crime data to improve the accuracy of its security risk analysis. Furthermore, it can reference external market data to improve the accuracy of its business risk analysis. In this way, referencing relevant external data improves the accuracy of the analysis. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input relevant external data into the generating AI and have the generating AI perform the referencing of the external data.

[0081] The decision-making unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated emotions. For example, if the user is tense, the decision-making unit can tighten the risk assessment criteria. Conversely, if the user is relaxed, the decision-making unit can loosen the risk assessment criteria. Furthermore, if the user is in a hurry, the decision-making unit can make a risk assessment quickly. This allows for more appropriate risk assessment by adjusting the risk assessment criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit is performed using generative AI. For example, the decision-making unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0082] The decision-making unit can improve the accuracy of its risk assessment by referring to past incident data when making a decision. For example, the decision-making unit can identify similar risks based on past incident data. It can also analyze past incident data to identify risk occurrence patterns. Furthermore, the decision-making unit can refer to past incident data to evaluate the impact of risks. As a result, the accuracy of risk assessment is improved by referring to past incident data. Some or all of the above processing in the decision-making unit is performed using a generative AI. For example, the decision-making unit can input past incident data into the generative AI and have the generative AI perform the task of improving the accuracy of risk assessment.

[0083] The decision-making unit can adjust the level of detail in the risk assessment according to the importance of the data when making a decision. For example, the decision-making unit performs a detailed risk assessment for important data. It can also perform a standard risk assessment for general data. Furthermore, it can perform a simplified risk assessment for low-importance data. By adjusting the level of detail in the risk assessment according to the importance of the data, efficient risk assessment becomes possible. Some or all of the above processing in the decision-making unit is performed using a generative AI. For example, the decision-making unit can input the level of detail in the risk assessment based on the importance of the data into the generative AI and have the generative AI perform the adjustment of the risk assessment.

[0084] The decision-making unit can estimate the user's emotions and determine the priority of risk assessments based on the estimated emotions. For example, if the user is tense, the decision-making unit will prioritize important risks. If the user is relaxed, the decision-making unit can also perform a detailed risk assessment. Furthermore, if the user is in a hurry, the decision-making unit can make a rapid risk assessment. This allows for prioritizing important risks by determining the priority of risk assessments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision-making unit are performed using generative AI. For example, the decision-making unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0085] The decision-making unit can perform risk assessments while considering the geographical location of the company. For example, the decision-making unit can perform detailed risk assessments at major locations. It can also perform basic risk assessments at satellite offices. Furthermore, the decision-making unit can perform risk assessments at overseas locations in accordance with local regulations. This allows for the assessment of region-specific risks by considering the geographical location of the company. Some or all of the above processes in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input risk assessments based on the geographical location of the company into the AI ​​and have the AI ​​perform the risk assessment.

[0086] The decision-making unit can perform risk assessments by referring to relevant market data when making decisions. For example, the decision-making unit can refer to market trend data to assess business risk. It can also refer to competitor data to assess competitive risk. Furthermore, it can refer to economic indicator data to assess economic risk. This improves the assessment of business risk by referring to relevant market data. Some or all of the above processing in the decision-making unit is performed using generative AI. For example, the decision-making unit can input relevant market data into the generative AI and have the generative AI perform the market data referencing.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection and collect more detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of only the most important data. This reduces the user's burden by adjusting the timing of data collection based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The data collection unit can dynamically change the types of data it collects according to the company's business workflow. For example, during peak business periods, the unit can prioritize efficiency by collecting only essential data. Conversely, during off-peak periods, it can collect detailed data to prepare for future analysis. Furthermore, if a specific project is underway, the unit can prioritize collecting data related to that project. This enables efficient data collection by dynamically changing the types of data collected according to the company's business workflow. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the types of data to be collected based on the company's business workflow into the AI, and have the AI ​​execute the dynamic changes in data collection.

[0090] The data collection unit can adjust the accuracy of the data it collects according to the importance of the data being collected. For example, if the data is important, the collection unit will use high-precision sensors for collection. If the data is general, the collection unit can use standard sensors for collection. Furthermore, if the data is of low importance, the collection unit can use simple sensors for collection. This allows for efficient data collection by adjusting the data accuracy according to the importance of the data being collected. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the data accuracy based on the importance of the data being collected into the AI ​​and have the AI ​​perform the data collection accuracy adjustment.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easily understandable presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. By adjusting the presentation of the analysis results based on the user's emotions, the analysis results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0092] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis process. For example, the analysis unit can analyze the interrelationships between document data and image data to derive more accurate results. It can also analyze the interrelationships between sensor data and access logs to detect anomalies. Furthermore, the analysis unit can analyze the interrelationships between video data and environmental data to detect suspicious movements. In this way, considering the interrelationships between data improves the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the interrelationships between data into an AI and have the AI ​​perform the analysis of those interrelationships.

[0093] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to document data. It can also apply an image recognition algorithm to image data. Furthermore, it can apply a time series analysis algorithm to sensor data. By applying different analysis algorithms to each data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different analysis algorithms for each data category into the AI ​​and have the AI ​​perform the analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can display important information first. If the user is relaxed, the analysis unit can also display detailed information sequentially. Furthermore, if the user is in a hurry, the analysis unit can display the main points first. This allows for prioritizing the display of information important to the user by adjusting the display order of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using generative AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can integrate geographically dispersed data and analyze the overall trend. The analysis unit can also analyze data concentrated in a specific region and detect region-specific risks. Furthermore, the analysis unit can detect anomalous patterns based on the geographical distribution. In this way, region-specific risks can be detected by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the geographical distribution of the data into AI and have AI perform the analysis of the geographical distribution.

[0096] The analysis unit can improve the accuracy of its analysis by referencing relevant external data during the analysis process. For example, the analysis unit can reference external weather data to improve the accuracy of its environmental data analysis. It can also reference external crime data to improve the accuracy of its security risk analysis. Furthermore, it can reference external market data to improve the accuracy of its business risk analysis. In this way, referencing relevant external data improves the accuracy of the analysis. Some or all of the above processing in the analysis unit is performed using a generating AI. For example, the analysis unit can input relevant external data into the generating AI and have the generating AI perform the referencing of the external data.

[0097] The decision-making unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated emotions. For example, if the user is tense, the decision-making unit can tighten the risk assessment criteria. Conversely, if the user is relaxed, the decision-making unit can loosen the risk assessment criteria. Furthermore, if the user is in a hurry, the decision-making unit can make a risk assessment quickly. This allows for more appropriate risk assessment by adjusting the risk assessment criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit is performed using generative AI. For example, the decision-making unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The collection unit collects information such as document data, image data, and sensor data. For example, it collects video data from security cameras within the company, employee access logs, and environmental data from sensors. The collection unit collects this data in real time or periodically and provides it to the analysis unit. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes video data from security cameras to detect suspicious activity, analyzes employee access logs to detect abnormal access patterns, and analyzes environmental data from sensors to detect abnormal environmental changes. Step 3: The judgment unit determines the incident risk based on the information analyzed by the analysis unit. For example, if suspicious activity, abnormal access patterns, or abnormal environmental changes are detected, the unit comprehensively evaluates this information and determines the incident risk.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and decision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect video data from security cameras within the company, employee access logs, and environmental data from sensors. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using domestically produced LLM to detect suspicious activity, abnormal access patterns, and abnormal environmental changes. The decision unit is implemented in the specific processing unit 290 of the data processing unit 12, and immediately determines the incident risk based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and decision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect video data from security cameras within the company, employee access logs, and environmental data from sensors. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using domestically produced LLM to detect suspicious movements, abnormal access patterns, and abnormal environmental changes. The decision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which immediately determines the incident risk based on the analyzed information. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and decision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect video data from security cameras within the company, employee access logs, and environmental data from sensors. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using domestically produced LLM to detect suspicious activity, abnormal access patterns, and abnormal environmental changes. The decision unit is implemented in the specific processing unit 290 of the data processing unit 12, and immediately determines the incident risk based on the analyzed information. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the data collection unit, analysis unit, and decision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect video data from security cameras within the company, employee access logs, and environmental data from sensors. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using domestically produced LLM to detect suspicious movements, abnormal access patterns, and abnormal environmental changes. The decision unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, which immediately determines the incident risk based on the analyzed information. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) A collection unit that collects information such as document data, image data, and sensor data, An analysis unit analyzes the information collected by the aforementioned collection unit, The system comprises a determination unit that determines incident risk based on information analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect video data from security cameras within the company, employee access logs, and environmental data from sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze security camera footage to detect suspicious activity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze employee access logs to detect abnormal access patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze environmental data from sensors to detect abnormal environmental changes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The unit that makes the determination said, A comprehensive risk assessment is conducted from multiple perspectives to determine the risk of an incident. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The types of data collected are dynamically changed according to the company's business workflow. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The accuracy of the collected data is adjusted according to the importance of the subject being collected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is The scope of data collected is dynamically changed according to the geographical location of the company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Customize the format of the data collected according to the company's business operations. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant external data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The unit that makes the determination said, We estimate user sentiment and adjust risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The unit that makes the determination said, When making decisions, we refer to past incident data to improve the accuracy of risk assessments. The system described in Appendix 1, characterized by the features described herein. (Note 21) The unit that makes the determination said, When making a decision, adjust the level of detail in the risk assessment according to the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The unit that makes the determination said, It estimates user sentiment and determines the priority of risk assessments based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The unit that makes the determination said, When making a decision, the risk assessment should take into account the geographical location of the company. The system described in Appendix 1, characterized by the features described herein. (Note 24) The unit that makes the determination said, When making a decision, we will perform a risk assessment by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information such as document data, image data, and sensor data, An analysis unit analyzes the information collected by the aforementioned collection unit, The system comprises a determination unit that determines incident risk based on information analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect video data from security cameras within the company, employee access logs, and environmental data from sensors. The system according to feature 1.

3. The aforementioned analysis unit, Analyze security camera footage to detect suspicious activity. The system according to feature 1.

4. The aforementioned analysis unit, Analyze employee access logs to detect abnormal access patterns. The system according to feature 1.

5. The aforementioned analysis unit, Analyze environmental data from sensors to detect abnormal environmental changes. The system according to feature 1.

6. The unit that makes the determination said, A comprehensive risk assessment is conducted from multiple perspectives to determine the risk of an incident. The system according to feature 1.

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

8. The aforementioned collection unit is The types of data collected are dynamically changed according to the company's business workflow. The system according to feature 1.

9. The aforementioned collection unit is The accuracy of the collected data is adjusted according to the importance of the subject being collected. The system according to feature 1.

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

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