System

Abstract

An object of the system according to the embodiment is to prevent the risk of manufacturing equipment being used for military purposes.SOLUTION: A system includes a monitoring unit, a prediction unit, and a stop unit. The monitoring unit monitors a motion of the manufacturing equipment. The prediction unit performs prediction and risk assessment of the product based on the operation data collected by the monitoring unit. The stop unit stops activation of the device when the prediction unit determines that the risk is high.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of lacking effective means to prevent the risk of manufacturing equipment being used for military purposes.

[0005] The system according to the embodiment aims to prevent the risk of manufacturing equipment being used for military purposes. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a prediction unit, and a shutdown unit. The monitoring unit monitors the operation of manufacturing equipment. The prediction unit predicts and assesses risk for the product based on operational data collected by the monitoring unit. The shutdown unit halts the operation of the equipment when the prediction unit determines that the risk is high. [Effects of the Invention]

[0007] The system according to the embodiment can prevent the risk of manufacturing equipment being used for military purposes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) An AI system according to an embodiment of the present invention is a system for preventing the diversion of manufacturing equipment to military purposes. This system monitors the operation of manufacturing equipment and halts the operation of the equipment if there is a possibility that a product for military purposes will be produced. In this way, the AI ​​system can prevent the diversion of manufacturing equipment to military purposes and promote peaceful uses.

[0029] The AI ​​system according to the embodiment includes a monitoring unit, a prediction unit, and a stopping unit. The monitoring unit monitors the operation of manufacturing equipment. For example, the monitoring unit collects operational data of the manufacturing equipment in real time. The monitoring unit can also collect information on materials used in the manufacturing equipment. Furthermore, the monitoring unit can analyze the operational patterns of the manufacturing equipment and detect abnormal operation. For example, the monitoring unit uses sensors to collect operational data of the manufacturing equipment and detect abnormal operation patterns. The prediction unit performs product prediction and risk assessment based on the operational data collected by the monitoring unit. For example, the prediction unit analyzes the collected operational data to predict what type of product the manufacturing equipment is manufacturing. The prediction unit can also learn from past data and the characteristics of known weapons and perform risk assessment based on that data. Furthermore, the prediction unit can determine whether a product has the potential to be diverted into weapons based on the product's shape and material combination. For example, the prediction unit performs product prediction and risk assessment using a machine learning algorithm. The stopping unit stops the operation of the equipment if the prediction unit determines that the risk is high. For example, if the stopping unit detects a specific operational pattern and determines that it is manufacturing weapons parts, it immediately stops the operation of the equipment. The stopping unit can also stop the activation of the equipment using an emergency stop button. Furthermore, the stopping unit can also stop the activation of the equipment using software control. For example, the stopping unit stops the operation of the equipment using software control. This allows the AI ​​system according to the embodiment to prevent the diversion of manufacturing equipment to military purposes. For example, the AI ​​system can analyze the operation data of the manufacturing equipment and prevent the risk of manufacturing products for military purposes. Furthermore, the AI ​​system can notify the manager or supervisory agency of the manufacturing equipment that a risk has been detected. This allows for a prompt response.

[0030] The monitoring unit can detect abnormal behavior by collecting not only operation data but also vibration and sound wave data of the equipment. For example, the monitoring unit collects not only operation data but also vibration data of the equipment. For example, a vibration sensor can be installed to detect abnormal vibration patterns, thereby enabling early detection of abnormal behavior of the equipment. The monitoring unit can also collect sound wave data and detect abnormal sound wave patterns. For example, a microphone can be installed to analyze sound waves that differ from normal operating sounds, thereby detecting abnormalities. The monitoring unit can also analyze the operation data, vibration data, and sound wave data in an integrated manner. For example, abnormal operation patterns can be detected from multiple data sources, achieving more accurate anomaly detection. This allows for highly accurate detection of abnormal behavior.

[0031] The monitoring unit can analyze the chemical components of the materials used in real time and determine whether materials suitable for military applications are being used. The monitoring unit, for example, analyzes the chemical components of the materials used in real time. For example, a chemical sensor is installed and the components of the materials are analyzed to determine whether materials suitable for military applications are being used. The monitoring unit also compares the chemical components of the materials used with a database to determine whether materials suitable for military applications are being used. For example, the monitoring unit compares the chemical components with a database of known materials for military applications. The monitoring unit also analyzes the chemical components of the materials used and issues a warning if an abnormal component is found. For example, an alert is issued if a component not used in a normal manufacturing process is detected. This makes it possible to detect the use of materials suitable for military applications.

[0032] The monitoring unit can share the operation data of the manufacturing equipment with other production lines and factories, and build a network for early detection of abnormal operation patterns. The monitoring unit, for example, shares the operation data of the manufacturing equipment with other production lines, and detects abnormal operation patterns early. For example, if an abnormal operation pattern is detected, it sends a warning to other production lines. The monitoring unit also shares the operation data of the manufacturing equipment with other factories, and builds a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, it sends a warning to other factories. The monitoring unit also shares the operation data of the manufacturing equipment on the cloud, and builds a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, it sends a warning on the cloud. This allows early detection of abnormal operation patterns.

[0033] The monitoring unit also collects environmental data such as temperature and humidity, and can evaluate the impact of environmental conditions on the manufacturing process. The monitoring unit, for example, collects temperature data and evaluates the impact of environmental conditions on the manufacturing process. For example, a temperature sensor is installed and the impact of abnormal temperature changes on the manufacturing process is analyzed. The monitoring unit also collects humidity data and evaluates the impact of environmental conditions on the manufacturing process. For example, a humidity sensor is installed and the impact of abnormal humidity changes on the manufacturing process is analyzed. The monitoring unit also integrates and analyzes environmental data such as temperature and humidity to evaluate the impact of environmental conditions on the manufacturing process. For example, the impact of abnormal environmental conditions on the manufacturing process is comprehensively analyzed. This makes it possible to evaluate the impact of environmental conditions on the manufacturing process.

[0034] When making a product prediction, the prediction unit can generate a 3D model and analyze the shape and structure of the product in detail. When making a product prediction, the prediction unit, for example, generates a 3D model and analyzes the shape and structure of the product in detail. For example, the prediction unit generates a 3D model based on CAD data and analyzes the shape and structure of the product. The prediction unit also analyzes the shape and structure of the product in detail using a 3D scanner. For example, the prediction unit scans the actual product and generates and analyzes a 3D model. The prediction unit also creates a product prototype using a 3D printer and analyzes the shape and structure in detail. For example, the prediction unit creates a product prototype using a 3D printer and analyzes it. This allows the shape and structure of the product to be analyzed in detail.

[0035] When performing a risk assessment, the prediction unit can simulate a product usage scenario and evaluate the possibility of the product being diverted to military uses. For example, when performing a risk assessment, the prediction unit simulates a product usage scenario and evaluates the possibility of the product being diverted to military uses. For example, the prediction unit uses simulation software to analyze the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario and evaluates the possibility of the product being diverted to military uses. For example, the prediction unit uses virtual reality (VR) technology to simulate the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario and evaluates the possibility of the product being diverted to military uses. For example, the prediction unit simulates a plurality of product usage scenarios and evaluates the risk. This makes it possible to evaluate the risk of the product being diverted to military uses.

[0036] The prediction unit can link with product databases from different industries and refer to risk assessments of similar products. For example, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for the aerospace industry and the automotive industry. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for medical devices and electronic devices. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for construction machinery and agricultural machinery. This allows risk assessments to be made by referring to databases from different industries.

[0037] The prediction unit can evaluate long-term risks by taking into account the entire life cycle of the product. For example, when performing a risk assessment, the prediction unit evaluates long-term risks by taking into account the entire life cycle of the product. For example, the prediction unit analyzes the entire process from product design, manufacturing, use, and disposal. Furthermore, when performing a risk assessment, the prediction unit evaluates long-term risks by taking into account the entire life cycle of the product. For example, the prediction unit analyzes the process of product maintenance and repair as well. Furthermore, when performing a risk assessment, the prediction unit evaluates long-term risks by taking into account the entire life cycle of the product. For example, the prediction unit analyzes the process of product recycling and reuse as well. This makes it possible to perform risk assessment by taking into account the entire life cycle of the product.

[0038] The stopping unit can record the reason for stopping the startup of equipment in detail so that it can be analyzed later. For example, when stopping the startup of equipment, the stopping unit records the reason for stopping in detail. For example, the operation pattern that triggered the shutdown and information about the materials used are saved in a log. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for stopping in detail so that it can be analyzed later. For example, it records environmental data at the time of shutdown and the emotional state of the operator. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for stopping in detail so that it can be analyzed later. For example, it records the progress of the manufacturing process at the time of shutdown and information about the tools used. In this way, the reason for stopping is recorded in detail so that it can be analyzed later.

[0039] The stopping unit can cross-check data from multiple sensors to prevent false detection before stopping the activation of the device. The stopping unit, for example, cross-checks data from multiple sensors before stopping the activation of the device. For example, data from a motion sensor, a vibration sensor, and an acoustic sensor is integrated and analyzed. The stopping unit also cross-checks data from multiple sensors before stopping the activation of the device to prevent false detection. For example, data from a temperature sensor and a humidity sensor is also taken into consideration for analysis. The stopping unit also cross-checks data from multiple sensors before stopping the activation of the device to prevent false detection. For example, data from a chemical sensor and a biological sensor is also integrated and analyzed. This allows the data from multiple sensors to be cross-checked to prevent false detection.

[0040] When stopping the startup of equipment, the stopping unit issues a stop command in conjunction with other related equipment, thereby ensuring overall safety. For example, when stopping the startup of equipment, the stopping unit also issues a stop command in conjunction with other related equipment. For example, all equipment on an entire production line can be stopped simultaneously to ensure safety. Furthermore, when stopping the startup of equipment, the stopping unit also issues a stop command in conjunction with other related equipment to ensure overall safety. For example, equipment that handles hazardous chemicals can be stopped preferentially. Furthermore, when stopping the startup of equipment, the stopping unit also issues a stop command in conjunction with other related equipment to ensure overall safety. For example, related equipment can be stopped sequentially to prevent secondary risks associated with stopping the equipment. In this way, stop commands can be issued in conjunction with other related equipment to ensure overall safety.

[0041] The stopping unit may also remotely stop the device when stopping its activation, thereby enabling a rapid response in an emergency. The stopping unit may, for example, remotely stop the device when stopping its activation. For example, a smartphone or a tablet may be used to stop the activation of the device from a remote location. The stopping unit may also remotely stop the device when stopping its activation, thereby enabling a rapid response in an emergency. For example, the activation of the device may be stopped from a remote location via the Internet. The stopping unit may also remotely stop the device when stopping its activation, thereby enabling a rapid response in an emergency. For example, a dedicated remote control device may be used to stop the activation of the device from a remote location. This allows a rapid response by remote control in an emergency.

[0042] When comparing data with a database, the prediction unit can refer to not only past weapons manufacturing data but also the latest research papers and patent data. For example, when comparing data with a database, the prediction unit refers to not only past weapons manufacturing data but also the latest research papers. For example, the prediction unit grasps the latest technological trends and reflects them in the risk assessment. Furthermore, when comparing data with a database, the prediction unit refers to not only past weapons manufacturing data but also patent data. For example, it identifies new risks based on patent information. Furthermore, when comparing data with a database, the prediction unit integrates and analyzes not only past weapons manufacturing data but also the latest research papers and patent data. For example, it integrates multiple data sources to perform more accurate risk assessment. This allows risk assessment to be performed by referring to the latest research papers and patent data.

[0043] When comparing data with a database, the prediction unit integrates data between different databases, thereby achieving more accurate matching. For example, when comparing data with a database, the prediction unit integrates data between different databases. For example, a weapons manufacturing database and a materials database are integrated to achieve more accurate matching. Furthermore, when comparing data with a database, the prediction unit integrates data between different databases to achieve more accurate matching. For example, a manufacturing process database and a patent database are integrated. Furthermore, when comparing data with a database, the prediction unit integrates data between different databases to achieve more accurate matching. For example, a research paper database and a market database are integrated. In this way, data between different databases is integrated to achieve more accurate matching.

[0044] When comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, when comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, it links with an international weapons manufacturing database. Furthermore, when comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, it links with patent databases of each country. Furthermore, when comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, it links with an international research paper database. This makes it possible to assess risk from a global perspective.

[0045] When performing matching against a database, the prediction unit can acquire database update information in real time and perform matching based on the latest information. For example, when performing matching against a database, the prediction unit acquires database update information in real time and performs matching based on the latest information. For example, the prediction unit acquires the latest data using a database API. When performing matching against a database, the prediction unit also acquires database update information in real time and performs matching based on the latest information. For example, the prediction unit receives a database update notification and performs matching immediately. When performing matching against a database, the prediction unit also acquires database update information in real time and performs matching based on the latest information. For example, the prediction unit analyzes the database update history and reflects the latest data. This allows matching to be performed in real time based on the latest information.

[0046] When issuing an alert, the alert unit can record the details of the alert so that it can be analyzed later. For example, when issuing an alert, the alert unit records the details of the alert. For example, the reason for issuing the alert and the circumstances at the time of issuance are saved in a log. Furthermore, when issuing an alert, the alert unit records the details of the alert so that it can be analyzed later. For example, environmental data and the emotional state of the operator at the time of issuing the alert are recorded. Furthermore, when issuing an alert, the alert unit records the details of the alert so that it can be analyzed later. For example, the progress of the manufacturing process and information on the tools used at the time of issuing the alert are recorded. In this way, the details of the alert can be recorded so that it can be analyzed later.

[0047] The alert unit can reliably notify using multiple communication means when sending an alert. The alert unit, for example, reliably notifies using multiple communication means when sending an alert. For example, it simultaneously sends email, SMS, and app notification. The alert unit also reliably notifies using multiple communication means when sending an alert. For example, it selects the most appropriate communication means to notify depending on the level of urgency. The alert unit also reliably notifies using multiple communication means when sending an alert. For example, it periodically sends reminders until the notification is confirmed. This allows for reliably notifying using multiple communication means.

[0048] When issuing an alert, the alert unit can cooperate with other related systems to ensure overall safety. When issuing an alert, the alert unit, for example, cooperates with other related systems to ensure overall safety. For example, it cooperates with systems for the entire production line and automatically implements safety measures when an alert is issued. When issuing an alert, the alert unit also cooperates with other related systems to ensure overall safety. For example, it cooperates with systems that handle hazardous chemical substances and automatically implements safety measures when an alert is issued. When issuing an alert, the alert unit also cooperates with other related systems to ensure overall safety. For example, in order to prevent secondary risks associated with equipment shutdown, it cooperates with related systems to implement safety measures. In this way, it is possible to cooperate with other related systems to ensure overall safety.

[0049] The alert unit can be configured to quickly notify relevant parties in remote locations when issuing an alert. For example, the alert unit can quickly notify relevant parties in remote locations when issuing an alert. For example, the alert can be sent to relevant parties in remote locations via the Internet. Furthermore, the alert unit can quickly notify relevant parties in remote locations when issuing an alert. For example, the alert can be sent to relevant parties in remote locations using a dedicated remote notification system. Furthermore, the alert unit can quickly notify relevant parties in remote locations when issuing an alert. For example, the alert can be sent to relevant parties in remote locations using a cloud-based notification system. This allows relevant parties in remote locations to be quickly notified.

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

[0051] The monitoring unit can share the operation data of the manufacturing equipment with other production lines and factories, and build a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, a warning is sent to other production lines. The monitoring unit can also share the operation data of the manufacturing equipment with other factories, and build a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, a warning is sent to other factories. The monitoring unit can also share the operation data of the manufacturing equipment on the cloud, and build a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, a warning is sent to the cloud. This allows early detection of abnormal operation patterns.

[0052] The monitoring unit also collects environmental data such as temperature and humidity to evaluate the impact of environmental conditions on the manufacturing process. For example, temperature data is collected to evaluate the impact of environmental conditions on the manufacturing process. For example, a temperature sensor is installed to analyze the impact of abnormal temperature changes on the manufacturing process. The monitoring unit also collects humidity data to evaluate the impact of environmental conditions on the manufacturing process. For example, a humidity sensor is installed to analyze the impact of abnormal humidity changes on the manufacturing process. The monitoring unit also integrates and analyzes environmental data such as temperature and humidity to evaluate the impact of environmental conditions on the manufacturing process. For example, the impact of abnormal environmental conditions on the manufacturing process is comprehensively analyzed. This makes it possible to evaluate the impact of environmental conditions on the manufacturing process.

[0053] When making product predictions, the prediction unit can generate a 3D model and analyze the shape and structure of the product in detail. For example, when making product predictions, a 3D model is generated and the shape and structure of the product is analyzed in detail. For example, a 3D model is generated based on CAD data, and the shape and structure of the product is analyzed. The prediction unit also analyzes the shape and structure of the product in detail using a 3D scanner. For example, the actual product is scanned, and a 3D model is generated and analyzed. The prediction unit also creates a product prototype using a 3D printer and analyzes the shape and structure in detail. For example, a product prototype is created using a 3D printer and analyzed. This allows the shape and structure of the product to be analyzed in detail.

[0054] When performing a risk assessment, the prediction unit can simulate a product usage scenario to assess the possibility of the product being diverted to military uses. For example, when performing a risk assessment, the prediction unit simulates a product usage scenario to assess the possibility of the product being diverted to military uses. For example, simulation software is used to analyze the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario to assess the possibility of the product being diverted to military uses. For example, virtual reality (VR) technology is used to simulate the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario to assess the possibility of the product being diverted to military uses. For example, multiple product usage scenarios are simulated to assess the risk. This makes it possible to assess the risk of the product being diverted to military uses.

[0055] The prediction unit can link with product databases from different industries and refer to risk assessments of similar products. For example, when making a product prediction, it links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for the aerospace industry and the automotive industry. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for medical devices and electronic devices. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for construction machinery and agricultural machinery. This allows risk assessments to be made by referring to databases from different industries.

[0056] When stopping the startup of equipment, the stopping unit can record the reason for the shutdown in detail so that it can be analyzed later. For example, when stopping the startup of equipment, the stopping unit records the reason for the shutdown in detail. For example, the operation pattern that triggered the shutdown and information about the materials used are saved in a log. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for the shutdown in detail so that it can be analyzed later. For example, environmental data at the time of shutdown and the emotional state of the operator are recorded. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for the shutdown in detail so that it can be analyzed later. For example, the progress of the manufacturing process at the time of shutdown and information about the tools used are recorded. In this way, the reason for the shutdown is recorded in detail so that it can be analyzed later.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The monitoring unit monitors the operation of the manufacturing equipment. For example, the monitoring unit can collect operational data of the manufacturing equipment in real time and also collect information on the materials used. The monitoring unit can also analyze the operational patterns of the manufacturing equipment and detect abnormal operation. For example, a sensor can be used to collect operational data of the manufacturing equipment and detect abnormal operation patterns. Step 2: The prediction unit performs product prediction and risk assessment based on the operational data collected by the monitoring unit. For example, the prediction unit analyzes the collected operational data and predicts what kind of product the manufacturing equipment is producing. It can also learn from past data and the characteristics of known weapons and perform risk assessment based on that. It can also determine whether a product has the potential to be diverted into weapons based on its shape and material combination. For example, machine learning algorithms are used to perform product prediction and risk assessment. Step 3: The shutdown unit stops the activation of the equipment if the prediction unit determines that the risk is high. For example, if it detects a specific movement pattern and determines that it is manufacturing weapons parts, it will immediately stop the operation of the equipment. It is also possible to stop the activation of the equipment using an emergency stop button or software control.

[0059] (Example 2) An AI system according to an embodiment of the present invention is a system for preventing the diversion of manufacturing equipment to military purposes. This system monitors the operation of manufacturing equipment and halts the operation of the equipment if there is a possibility that a product for military purposes will be produced. In this way, the AI ​​system can prevent the diversion of manufacturing equipment to military purposes and promote peaceful uses.

[0060] The AI ​​system according to the embodiment includes a monitoring unit, a prediction unit, and a stopping unit. The monitoring unit monitors the operation of manufacturing equipment. For example, the monitoring unit collects operational data of the manufacturing equipment in real time. The monitoring unit can also collect information on materials used in the manufacturing equipment. Furthermore, the monitoring unit can analyze the operational patterns of the manufacturing equipment and detect abnormal operation. For example, the monitoring unit uses sensors to collect operational data of the manufacturing equipment and detect abnormal operation patterns. The prediction unit performs product prediction and risk assessment based on the operational data collected by the monitoring unit. For example, the prediction unit analyzes the collected operational data to predict what type of product the manufacturing equipment is manufacturing. The prediction unit can also learn from past data and the characteristics of known weapons and perform risk assessment based on that data. Furthermore, the prediction unit can determine whether a product has the potential to be diverted into weapons based on the product's shape and material combination. For example, the prediction unit performs product prediction and risk assessment using a machine learning algorithm. The stopping unit stops the operation of the equipment if the prediction unit determines that the risk is high. For example, if the stopping unit detects a specific operational pattern and determines that it is manufacturing weapons parts, it immediately stops the operation of the equipment. The stopping unit can also stop the activation of the equipment using an emergency stop button. Furthermore, the stopping unit can also stop the activation of the equipment using software control. For example, the stopping unit stops the operation of the equipment using software control. This allows the AI ​​system according to the embodiment to prevent the diversion of manufacturing equipment to military purposes. For example, the AI ​​system can analyze the operation data of the manufacturing equipment and prevent the risk of manufacturing products for military purposes. Furthermore, the AI ​​system can notify the manager or supervisory agency of the manufacturing equipment that a risk has been detected. This allows for a prompt response.

[0061] The monitoring unit can detect abnormal behavior by collecting not only operation data but also vibration and sound wave data of the equipment. For example, the monitoring unit collects not only operation data but also vibration data of the equipment. For example, a vibration sensor can be installed to detect abnormal vibration patterns, thereby enabling early detection of abnormal behavior of the equipment. The monitoring unit can also collect sound wave data and detect abnormal sound wave patterns. For example, a microphone can be installed to analyze sound waves that differ from normal operating sounds, thereby detecting abnormalities. The monitoring unit can also analyze the operation data, vibration data, and sound wave data in an integrated manner. For example, abnormal operation patterns can be detected from multiple data sources, achieving more accurate anomaly detection. This allows for highly accurate detection of abnormal behavior.

[0062] The monitoring unit can analyze the chemical components of the materials used in real time and determine whether materials suitable for military applications are being used. The monitoring unit, for example, analyzes the chemical components of the materials used in real time. For example, a chemical sensor is installed and the components of the materials are analyzed to determine whether materials suitable for military applications are being used. The monitoring unit also compares the chemical components of the materials used with a database to determine whether materials suitable for military applications are being used. For example, the monitoring unit compares the chemical components with a database of known materials for military applications. The monitoring unit also analyzes the chemical components of the materials used and issues a warning if an abnormal component is found. For example, an alert is issued if a component not used in a normal manufacturing process is detected. This makes it possible to detect the use of materials suitable for military applications.

[0063] The monitoring unit monitors the emotions of the operator and can issue a warning if abnormal stress or tension is detected. The monitoring unit, for example, analyzes the emotions of the operator in real time. For example, it uses facial expression recognition technology to detect the stress or tension of the operator and issues a warning if an abnormality is detected. The monitoring unit also analyzes the voice data of the operator to infer the emotional state. For example, it analyzes the tone and speed of the voice and issues a warning if abnormal stress or tension is detected. The monitoring unit also analyzes the biometric data of the operator (heart rate, electrodermal activity, etc.) to infer the emotional state. For example, it issues a warning if abnormal stress or tension is detected. In this way, abnormal stress or tension in the operator can be detected and a warning can be issued.

[0064] The monitoring unit can share the operation data of the manufacturing equipment with other production lines and factories, and build a network for early detection of abnormal operation patterns. The monitoring unit, for example, shares the operation data of the manufacturing equipment with other production lines, and detects abnormal operation patterns early. For example, if an abnormal operation pattern is detected, it sends a warning to other production lines. The monitoring unit also shares the operation data of the manufacturing equipment with other factories, and builds a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, it sends a warning to other factories. The monitoring unit also shares the operation data of the manufacturing equipment on the cloud, and builds a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, it sends a warning on the cloud. This allows early detection of abnormal operation patterns.

[0065] The monitoring unit also collects environmental data such as temperature and humidity, and can evaluate the impact of environmental conditions on the manufacturing process. The monitoring unit, for example, collects temperature data and evaluates the impact of environmental conditions on the manufacturing process. For example, a temperature sensor is installed and the impact of abnormal temperature changes on the manufacturing process is analyzed. The monitoring unit also collects humidity data and evaluates the impact of environmental conditions on the manufacturing process. For example, a humidity sensor is installed and the impact of abnormal humidity changes on the manufacturing process is analyzed. The monitoring unit also integrates and analyzes environmental data such as temperature and humidity to evaluate the impact of environmental conditions on the manufacturing process. For example, the impact of abnormal environmental conditions on the manufacturing process is comprehensively analyzed. This makes it possible to evaluate the impact of environmental conditions on the manufacturing process.

[0066] The monitoring unit provides an interface that causes the operator to feel positive emotions, thereby reducing operational errors. The monitoring unit, for example, analyzes the operator's emotions in real time and provides an interface that causes positive emotions. For example, the design or color of the interface is changed depending on the operator's emotional state. The monitoring unit also analyzes the operator's emotions and provides feedback to elicit positive emotions. For example, the monitoring unit provides messages or audio that cause the operator to feel positive emotions. The monitoring unit also analyzes the operator's emotions and provides an interface that causes positive emotions. For example, the monitoring unit incorporates game elements into the interface that cause the operator to feel positive emotions. This makes it possible to reduce operational errors.

[0067] When making a product prediction, the prediction unit can generate a 3D model and analyze the shape and structure of the product in detail. When making a product prediction, the prediction unit, for example, generates a 3D model and analyzes the shape and structure of the product in detail. For example, the prediction unit generates a 3D model based on CAD data and analyzes the shape and structure of the product. The prediction unit also analyzes the shape and structure of the product in detail using a 3D scanner. For example, the prediction unit scans the actual product and generates and analyzes a 3D model. The prediction unit also creates a product prototype using a 3D printer and analyzes the shape and structure in detail. For example, the prediction unit creates a product prototype using a 3D printer and analyzes it. This allows the shape and structure of the product to be analyzed in detail.

[0068] When performing a risk assessment, the prediction unit can simulate a product usage scenario and evaluate the possibility of the product being diverted to military uses. For example, when performing a risk assessment, the prediction unit simulates a product usage scenario and evaluates the possibility of the product being diverted to military uses. For example, the prediction unit uses simulation software to analyze the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario and evaluates the possibility of the product being diverted to military uses. For example, the prediction unit uses virtual reality (VR) technology to simulate the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario and evaluates the possibility of the product being diverted to military uses. For example, the prediction unit simulates a plurality of product usage scenarios and evaluates the risk. This makes it possible to evaluate the risk of the product being diverted to military uses.

[0069] The prediction unit uses the emotion estimation function to estimate the intentions of the product's designers and engineers and determine whether military purposes are included. For example, when performing product prediction and risk assessment, the prediction unit uses the emotion estimation function to estimate the intentions of the product's designers and engineers. For example, the emotional state of the designer is analyzed to determine whether military purposes are included. The prediction unit also uses the emotion estimation function to estimate the intentions of the product's designers and engineers when performing product prediction and risk assessment. For example, voice data of the designer is analyzed to determine whether military purposes are included. The prediction unit also uses the emotion estimation function to estimate the intentions of the product's designers and engineers when performing product prediction and risk assessment. For example, biometric data of the designer is analyzed to determine whether military purposes are included. In this way, the intentions of the designers and engineers can be estimated and military purposes can be determined.

[0070] The prediction unit can link with product databases from different industries and refer to risk assessments of similar products. For example, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for the aerospace industry and the automotive industry. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for medical devices and electronic devices. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for construction machinery and agricultural machinery. This allows risk assessments to be made by referring to databases from different industries.

[0071] The prediction unit can evaluate long-term risks by taking into account the entire life cycle of the product. For example, when performing a risk assessment, the prediction unit evaluates long-term risks by taking into account the entire life cycle of the product. For example, the prediction unit analyzes the entire process from product design, manufacturing, use, and disposal. Furthermore, when performing a risk assessment, the prediction unit evaluates long-term risks by taking into account the entire life cycle of the product. For example, the prediction unit analyzes the process of product maintenance and repair as well. Furthermore, when performing a risk assessment, the prediction unit evaluates long-term risks by taking into account the entire life cycle of the product. For example, the prediction unit analyzes the process of product recycling and reuse as well. This makes it possible to perform risk assessment by taking into account the entire life cycle of the product.

[0072] The prediction unit can use the emotion estimation function to predict what emotions a product user will have and reflect the prediction in risk assessment. For example, when performing product prediction and risk assessment, the prediction unit uses the emotion estimation function to predict what emotions a product user will have. For example, it analyzes the user's past feedback and reviews to estimate emotions. Furthermore, when performing product prediction and risk assessment, the prediction unit also uses the emotion estimation function to predict what emotions a product user will have. For example, it analyzes the user's social media posts to estimate emotions. Furthermore, when performing product prediction and risk assessment, the prediction unit also uses the emotion estimation function to predict what emotions a product user will have. For example, it analyzes the results of a user survey to estimate emotions. This enables risk assessment that takes user emotions into consideration.

[0073] The stopping unit can record the reason for stopping the startup of equipment in detail so that it can be analyzed later. For example, when stopping the startup of equipment, the stopping unit records the reason for stopping in detail. For example, the operation pattern that triggered the shutdown and information about the materials used are saved in a log. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for stopping in detail so that it can be analyzed later. For example, it records environmental data at the time of shutdown and the emotional state of the operator. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for stopping in detail so that it can be analyzed later. For example, it records the progress of the manufacturing process at the time of shutdown and information about the tools used. In this way, the reason for stopping is recorded in detail so that it can be analyzed later.

[0074] The stopping unit can cross-check data from multiple sensors to prevent false detection before stopping the activation of the device. The stopping unit, for example, cross-checks data from multiple sensors before stopping the activation of the device. For example, data from a motion sensor, a vibration sensor, and an acoustic sensor is integrated and analyzed. The stopping unit also cross-checks data from multiple sensors before stopping the activation of the device to prevent false detection. For example, data from a temperature sensor and a humidity sensor is also taken into consideration for analysis. The stopping unit also cross-checks data from multiple sensors before stopping the activation of the device to prevent false detection. For example, data from a chemical sensor and a biological sensor is also integrated and analyzed. This allows the data from multiple sensors to be cross-checked to prevent false detection.

[0075] The stopping unit can use the emotion estimation function to stop the activation of the equipment at an appropriate timing, taking into account the emotional state of the operator. For example, when stopping the activation of the equipment, the stopping unit analyzes the emotional state of the operator in real time and stops the equipment at an appropriate timing. For example, if the operator is in a high stress state, the stopping unit stops the activation of the equipment. Furthermore, when stopping the activation of the equipment, the stopping unit stops the equipment at an appropriate timing, taking into account the emotional state of the operator. For example, if the operator is tense, the stopping unit temporarily suspends the activation of the equipment to provide time for the operator to relax. Furthermore, when stopping the activation of the equipment, the stopping unit stops the equipment at an appropriate timing, taking into account the emotional state of the operator. For example, if the operator is tired, the stopping unit stops the activation of the equipment and encourages the operator to take a break. In this way, the activation of the equipment can be stopped at an appropriate timing, taking into account the emotional state of the operator.

[0076] When stopping the startup of equipment, the stopping unit issues a stop command in conjunction with other related equipment, thereby ensuring overall safety. For example, when stopping the startup of equipment, the stopping unit also issues a stop command in conjunction with other related equipment. For example, all equipment on an entire production line can be stopped simultaneously to ensure safety. Furthermore, when stopping the startup of equipment, the stopping unit also issues a stop command in conjunction with other related equipment to ensure overall safety. For example, equipment that handles hazardous chemicals can be stopped preferentially. Furthermore, when stopping the startup of equipment, the stopping unit also issues a stop command in conjunction with other related equipment to ensure overall safety. For example, related equipment can be stopped sequentially to prevent secondary risks associated with stopping the equipment. In this way, stop commands can be issued in conjunction with other related equipment to ensure overall safety.

[0077] The stopping unit may also remotely stop the device when stopping its activation, thereby enabling a rapid response in an emergency. The stopping unit may, for example, remotely stop the device when stopping its activation. For example, a smartphone or a tablet may be used to stop the activation of the device from a remote location. The stopping unit may also remotely stop the device when stopping its activation, thereby enabling a rapid response in an emergency. For example, the activation of the device may be stopped from a remote location via the Internet. The stopping unit may also remotely stop the device when stopping its activation, thereby enabling a rapid response in an emergency. For example, a dedicated remote control device may be used to stop the activation of the device from a remote location. This allows a rapid response by remote control in an emergency.

[0078] The stopping unit can use the emotion estimation function to predict how the operator will feel about the shutdown of the equipment and provide appropriate feedback. For example, when stopping the startup of the equipment, the stopping unit analyzes the operator's emotions in real time and provides appropriate feedback. For example, if the operator is feeling anxious, it displays a message to reassure him / her. Furthermore, when stopping the startup of the equipment, the stopping unit predicts the operator's emotions and provides appropriate feedback. For example, if the operator is feeling angry, it displays a message urging the operator to stay calm. Furthermore, when stopping the startup of the equipment, the stopping unit predicts the operator's emotions and provides appropriate feedback. For example, if the operator is confused, it displays a message that clearly instructs the operator on what action to take next. In this way, the operator's emotions can be predicted and appropriate feedback can be provided.

[0079] When comparing data with a database, the prediction unit can refer to not only past weapons manufacturing data but also the latest research papers and patent data. For example, when comparing data with a database, the prediction unit refers to not only past weapons manufacturing data but also the latest research papers. For example, the prediction unit grasps the latest technological trends and reflects them in the risk assessment. Furthermore, when comparing data with a database, the prediction unit refers to not only past weapons manufacturing data but also patent data. For example, it identifies new risks based on patent information. Furthermore, when comparing data with a database, the prediction unit integrates and analyzes not only past weapons manufacturing data but also the latest research papers and patent data. For example, it integrates multiple data sources to perform more accurate risk assessment. This allows risk assessment to be performed by referring to the latest research papers and patent data.

[0080] When comparing data with a database, the prediction unit integrates data between different databases, thereby achieving more accurate matching. For example, when comparing data with a database, the prediction unit integrates data between different databases. For example, a weapons manufacturing database and a materials database are integrated to achieve more accurate matching. Furthermore, when comparing data with a database, the prediction unit integrates data between different databases to achieve more accurate matching. For example, a manufacturing process database and a patent database are integrated. Furthermore, when comparing data with a database, the prediction unit integrates data between different databases to achieve more accurate matching. For example, a research paper database and a market database are integrated. In this way, data between different databases is integrated to achieve more accurate matching.

[0081] The prediction unit can use the emotion estimation function to estimate the intention of the creator or provider of the database and evaluate the reliability of the data. For example, when comparing against a database, the prediction unit uses the emotion estimation function to estimate the intention of the creator or provider of the database. For example, the creator's emotional state is analyzed to evaluate the reliability of the data. Furthermore, when comparing against a database, the prediction unit uses the emotion estimation function to estimate the intention of the creator or provider of the database. For example, the provider's voice data is analyzed to evaluate the reliability of the data. Furthermore, when comparing against a database, the prediction unit uses the emotion estimation function to estimate the intention of the creator or provider of the database. For example, the provider's biometric data is analyzed to evaluate the reliability of the data. In this way, the intention of the database creator or provider can be estimated and the reliability of the data can be evaluated.

[0082] When comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, when comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, it links with an international weapons manufacturing database. Furthermore, when comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, it links with patent databases of each country. Furthermore, when comparing against a database, the prediction unit also links with databases of different countries and regions, allowing for risk assessment from a global perspective. For example, it links with an international research paper database. This makes it possible to assess risk from a global perspective.

[0083] When performing matching against a database, the prediction unit can acquire database update information in real time and perform matching based on the latest information. For example, when performing matching against a database, the prediction unit acquires database update information in real time and performs matching based on the latest information. For example, the prediction unit acquires the latest data using a database API. When performing matching against a database, the prediction unit also acquires database update information in real time and performs matching based on the latest information. For example, the prediction unit receives a database update notification and performs matching immediately. When performing matching against a database, the prediction unit also acquires database update information in real time and performs matching based on the latest information. For example, the prediction unit analyzes the database update history and reflects the latest data. This allows matching to be performed in real time based on the latest information.

[0084] The prediction unit uses the emotion estimation function to predict what emotions the database user will have, thereby optimizing how the data is used. For example, when comparing data with the database, the prediction unit uses the emotion estimation function to predict what emotions the database user will have. For example, it analyzes the user's past search history and feedback to estimate emotions. The prediction unit also uses the emotion estimation function to predict what emotions the database user will have when comparing data with the database. For example, it analyzes the user's social media posts to estimate emotions. The prediction unit also uses the emotion estimation function to predict what emotions the database user will have when comparing data with the database. For example, it analyzes the results of a user survey to estimate emotions. This makes it possible to predict the emotions of the database user and optimize how the data is used.

[0085] When issuing an alert, the alert unit can record the details of the alert so that it can be analyzed later. For example, when issuing an alert, the alert unit records the details of the alert. For example, the reason for issuing the alert and the circumstances at the time of issuance are saved in a log. Furthermore, when issuing an alert, the alert unit records the details of the alert so that it can be analyzed later. For example, environmental data and the emotional state of the operator at the time of issuing the alert are recorded. Furthermore, when issuing an alert, the alert unit records the details of the alert so that it can be analyzed later. For example, the progress of the manufacturing process and information on the tools used at the time of issuing the alert are recorded. In this way, the details of the alert can be recorded so that it can be analyzed later.

[0086] The alert unit can reliably notify using multiple communication means when sending an alert. The alert unit, for example, reliably notifies using multiple communication means when sending an alert. For example, it simultaneously sends email, SMS, and app notification. The alert unit also reliably notifies using multiple communication means when sending an alert. For example, it selects the most appropriate communication means to notify depending on the level of urgency. The alert unit also reliably notifies using multiple communication means when sending an alert. For example, it periodically sends reminders until the notification is confirmed. This allows for reliably notifying using multiple communication means.

[0087] The alert unit can use the emotion estimation function to consider the emotional state of the person receiving the alert and issue the alert at an appropriate timing. When issuing an alert, the alert unit, for example, analyzes the emotional state of the person in real time and issues the alert at an appropriate timing. For example, if the person in question is in a high stress state, the alert unit delays the issuance of the alert. Furthermore, when issuing an alert, the alert unit considers the emotional state of the person in question and issues the alert at an appropriate timing. For example, the alert is issued when the person in question is relaxed. Furthermore, when issuing an alert, the alert unit considers the emotional state of the person in question and issues the alert at an appropriate timing. For example, the alert is issued when the person in question is concentrating. In this way, the alert can be issued at an appropriate timing taking into account the emotional state of the person in question.

[0088] When issuing an alert, the alert unit can cooperate with other related systems to ensure overall safety. When issuing an alert, the alert unit, for example, cooperates with other related systems to ensure overall safety. For example, it cooperates with systems for the entire production line and automatically implements safety measures when an alert is issued. When issuing an alert, the alert unit also cooperates with other related systems to ensure overall safety. For example, it cooperates with systems that handle hazardous chemical substances and automatically implements safety measures when an alert is issued. When issuing an alert, the alert unit also cooperates with other related systems to ensure overall safety. For example, in order to prevent secondary risks associated with equipment shutdown, it cooperates with related systems to implement safety measures. In this way, it is possible to cooperate with other related systems to ensure overall safety.

[0089] The alert unit can be configured to quickly notify relevant parties in remote locations when issuing an alert. For example, the alert unit can quickly notify relevant parties in remote locations when issuing an alert. For example, the alert can be sent to relevant parties in remote locations via the Internet. Furthermore, the alert unit can quickly notify relevant parties in remote locations when issuing an alert. For example, the alert can be sent to relevant parties in remote locations using a dedicated remote notification system. Furthermore, the alert unit can quickly notify relevant parties in remote locations when issuing an alert. For example, the alert can be sent to relevant parties in remote locations using a cloud-based notification system. This allows relevant parties in remote locations to be quickly notified.

[0090] The alert unit can use the emotion estimation function to predict the emotions of the relevant person receiving the alert and provide appropriate feedback. For example, when issuing an alert, the alert unit analyzes the emotions of the relevant person in real time and provides appropriate feedback. For example, if the relevant person is feeling anxious, a reassuring message is displayed. The alert unit also predicts the emotions of the relevant person when issuing an alert and provides appropriate feedback. For example, if the relevant person is feeling angry, a message urging the relevant person to remain calm is displayed. The alert unit also predicts the emotions of the relevant person when issuing an alert and provides appropriate feedback. For example, if the relevant person is confused, a message that clearly instructs the relevant person on what action to take next is displayed. In this way, the alert unit can predict the emotions of the relevant person and provide appropriate feedback.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The monitoring unit can share the operation data of the manufacturing equipment with other production lines and factories, and build a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, a warning is sent to other production lines. The monitoring unit can also share the operation data of the manufacturing equipment with other factories, and build a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, a warning is sent to other factories. The monitoring unit can also share the operation data of the manufacturing equipment on the cloud, and build a network for early detection of abnormal operation patterns. For example, if an abnormal operation pattern is detected, a warning is sent to the cloud. This allows early detection of abnormal operation patterns.

[0093] The monitoring unit also collects environmental data such as temperature and humidity to evaluate the impact of environmental conditions on the manufacturing process. For example, temperature data is collected to evaluate the impact of environmental conditions on the manufacturing process. For example, a temperature sensor is installed to analyze the impact of abnormal temperature changes on the manufacturing process. The monitoring unit also collects humidity data to evaluate the impact of environmental conditions on the manufacturing process. For example, a humidity sensor is installed to analyze the impact of abnormal humidity changes on the manufacturing process. The monitoring unit also integrates and analyzes environmental data such as temperature and humidity to evaluate the impact of environmental conditions on the manufacturing process. For example, the impact of abnormal environmental conditions on the manufacturing process is comprehensively analyzed. This makes it possible to evaluate the impact of environmental conditions on the manufacturing process.

[0094] The monitoring unit monitors the emotions of the operator and can issue a warning if abnormal stress or tension is detected. For example, the monitoring unit analyzes the operator's emotions in real time. For example, facial expression recognition technology is used to detect the operator's stress or tension and issue a warning if an abnormality is detected. The monitoring unit also analyzes the operator's voice data to infer their emotional state. For example, the monitoring unit analyzes the tone and speed of the voice and issues a warning if abnormal stress or tension is detected. The monitoring unit also analyzes the operator's biometric data (heart rate, electrodermal activity, etc.) to infer their emotional state. For example, a warning is issued if abnormal stress or tension is detected. In this way, abnormal stress or tension in the operator can be detected and a warning can be issued.

[0095] The monitoring unit can provide an interface that causes the operator to feel positive emotions and reduce operation errors. For example, the monitoring unit can analyze the operator's emotions in real time and provide an interface that causes positive emotions. For example, the design or color of the interface can be changed depending on the operator's emotional state. The monitoring unit can also analyze the operator's emotions and provide feedback to elicit positive emotions. For example, the monitoring unit can provide messages or sounds that cause the operator to feel positive emotions. The monitoring unit can also analyze the operator's emotions and provide an interface that causes positive emotions. For example, the monitoring unit can incorporate game elements into the interface that cause the operator to feel positive emotions. This can reduce operation errors.

[0096] When making product predictions, the prediction unit can generate a 3D model and analyze the shape and structure of the product in detail. For example, when making product predictions, a 3D model is generated and the shape and structure of the product is analyzed in detail. For example, a 3D model is generated based on CAD data, and the shape and structure of the product is analyzed. The prediction unit also analyzes the shape and structure of the product in detail using a 3D scanner. For example, the actual product is scanned, and a 3D model is generated and analyzed. The prediction unit also creates a product prototype using a 3D printer and analyzes the shape and structure in detail. For example, a product prototype is created using a 3D printer and analyzed. This allows the shape and structure of the product to be analyzed in detail.

[0097] When performing a risk assessment, the prediction unit can simulate a product usage scenario to assess the possibility of the product being diverted to military uses. For example, when performing a risk assessment, the prediction unit simulates a product usage scenario to assess the possibility of the product being diverted to military uses. For example, simulation software is used to analyze the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario to assess the possibility of the product being diverted to military uses. For example, virtual reality (VR) technology is used to simulate the product usage scenario. When performing a risk assessment, the prediction unit also simulates a product usage scenario to assess the possibility of the product being diverted to military uses. For example, multiple product usage scenarios are simulated to assess the risk. This makes it possible to assess the risk of the product being diverted to military uses.

[0098] The prediction unit can use the emotion estimation function to estimate the intentions of the product's designers and engineers and determine whether military purposes are included. For example, when performing product prediction and risk assessment, the emotion estimation function is used to estimate the intentions of the product's designers and engineers. For example, the emotional state of the designer is analyzed to determine whether military purposes are included. The prediction unit also uses the emotion estimation function to estimate the intentions of the product's designers and engineers when performing product prediction and risk assessment. For example, voice data of the designer is analyzed to determine whether military purposes are included. The prediction unit also uses the emotion estimation function to estimate the intentions of the product's designers and engineers when performing product prediction and risk assessment. For example, biometric data of the designer is analyzed to determine whether military purposes are included. In this way, the intentions of the designers and engineers can be estimated and military purposes can be determined.

[0099] The prediction unit can link with product databases from different industries and refer to risk assessments of similar products. For example, when making a product prediction, it links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for the aerospace industry and the automotive industry. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for medical devices and electronic devices. Furthermore, when making a product prediction, the prediction unit links with product databases from different industries and refers to risk assessments of similar products. For example, it links with databases for construction machinery and agricultural machinery. This allows risk assessments to be made by referring to databases from different industries.

[0100] The prediction unit can use the emotion estimation function to predict what emotions the product user will have and reflect this in risk assessment. For example, when performing product prediction and risk assessment, the emotion estimation function is used to predict what emotions the product user will have. For example, emotions are estimated by analyzing the user's past feedback and reviews. The prediction unit also uses the emotion estimation function to predict what emotions the product user will have when performing product prediction and risk assessment. For example, emotions are estimated by analyzing the user's social media posts. The prediction unit also uses the emotion estimation function to predict what emotions the product user will have when performing product prediction and risk assessment. For example, emotions are estimated by analyzing the results of a user survey. This makes it possible to perform risk assessment that takes user emotions into consideration.

[0101] When stopping the startup of equipment, the stopping unit can record the reason for the shutdown in detail so that it can be analyzed later. For example, when stopping the startup of equipment, the stopping unit records the reason for the shutdown in detail. For example, the operation pattern that triggered the shutdown and information about the materials used are saved in a log. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for the shutdown in detail so that it can be analyzed later. For example, environmental data at the time of shutdown and the emotional state of the operator are recorded. Furthermore, when stopping the startup of equipment, the stopping unit records the reason for the shutdown in detail so that it can be analyzed later. For example, the progress of the manufacturing process at the time of shutdown and information about the tools used are recorded. In this way, the reason for the shutdown is recorded in detail so that it can be analyzed later.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The monitoring unit monitors the operation of the manufacturing equipment. For example, the monitoring unit can collect operational data of the manufacturing equipment in real time and also collect information on the materials used. The monitoring unit can also analyze the operational patterns of the manufacturing equipment and detect abnormal operation. For example, a sensor can be used to collect operational data of the manufacturing equipment and detect abnormal operation patterns. Step 2: The prediction unit performs product prediction and risk assessment based on the operational data collected by the monitoring unit. For example, the prediction unit analyzes the collected operational data and predicts what kind of product the manufacturing equipment is producing. It can also learn from past data and the characteristics of known weapons and perform risk assessment based on that. It can also determine whether a product has the potential to be diverted into weapons based on its shape and material combination. For example, machine learning algorithms are used to perform product prediction and risk assessment. Step 3: The shutdown unit stops the activation of the equipment if the prediction unit determines that the risk is high. For example, if it detects a specific movement pattern and determines that it is manufacturing weapons parts, it will immediately stop the operation of the equipment. It is also possible to stop the activation of the equipment using an emergency stop button or software control.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

[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] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a monitoring unit that monitors the operation of the manufacturing equipment; a prediction unit that performs prediction and risk assessment of the product based on the operational data collected by the monitoring unit; a stop unit that stops the activation of the device when the prediction unit determines that the risk is high. A system characterized by:

2. The monitoring unit In addition to the operational data, vibration and sound wave data of the device are also collected to detect abnormal operation.

2. The system of claim 1.

3. The prediction unit When making predictions about the product, the 3D model is generated and the shape and structure of the product are analyzed in detail.

2. The system of claim 1.

4. The stop portion is When the device is stopped, the reason for the stop is recorded in detail so that it can be analyzed later.

2. The system of claim 1.

5. The alert section is Sending alerts at appropriate times, taking into account the emotional state of the people receiving the alerts 2. The system of claim 1.

6. The monitoring unit Monitor operator emotions and issue alerts when unusual stress or tension is detected 2. The system of claim 1.

7. The prediction unit Inferring the intent of the designers and engineers of said products and determining whether they have military purposes 2. The system of claim 1.

8. The stop portion is Consider the emotional state of the operator and stop the activation of the equipment at an appropriate time.

2. The system of claim 1.

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