system

Abstract

The system according to the embodiment aims to ensure that the created work does not use AI. [Solution] A system according to an embodiment includes a monitoring unit, an analysis unit, and a certification unit. The monitoring unit monitors the creation process in real time. The analysis unit analyzes the data collected by the monitoring unit and checks whether AI is involved. The certification unit issues a certificate based on the verification results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that there are no well-established means to ensure that the works created are not made using AI, making them unreliable.

[0005] The system according to the embodiment aims to ensure that the created work does not use AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, and a certification unit. The monitoring unit monitors the creation process in real time. The analysis unit analyzes the data collected by the monitoring unit and checks whether AI is involved. The certification unit issues a certificate based on the verification results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can ensure that the created work does not use AI. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) An art guarantee system according to an embodiment of the present invention is a system that guarantees that a work of art has not been created using AI. This system monitors the creation process in real time, analyzes the monitored data to confirm that AI is not involved, and issues a certificate of the confirmation result. For example, when a work of art is created, cameras and sensors are used to record the artist's movements and the tools used. When painting a painting, a camera can capture the artist's hand movements and record the paints and brushes used. Next, the collected data is analyzed to confirm that the artist's movements were human. It also confirms that the tools used were not created by AI. This proves that the work has not been created using AI. Finally, the confirmation result is issued as a certificate and attached to the work. The certificate includes detailed data on the creation process and analysis results. For example, attaching a certificate to the back of a painting can demonstrate the authenticity of the work to buyers and viewers. This system can increase the authenticity of works that do not use AI. For example, by proving that AI is not involved in works such as text, paintings, and videos, the originality of the artist can be protected and the value of the work can be increased. It also demonstrates the authenticity of the work to buyers and viewers. This will increase the market value of works that do not use AI.The work guarantee system will guarantee that the created works do not use AI, increasing their reliability.

[0029] The artwork guarantee system according to the embodiment includes a monitoring unit, an analysis unit, and a certification unit. The monitoring unit monitors the creation process in real time. The monitoring unit records the creator's movements and the tools used, for example, using a camera or sensors. For example, when painting a painting, the camera can capture the creator's hand movements and record the type of paint and brush used. The monitoring unit can also use motion capture technology for motion analysis. For example, optical motion capture technology can be used to record the creator's movements in detail. The monitoring unit can also use RFID technology for tool identification. For example, RFID tags can be attached to tools to accurately identify the tools used. The analysis unit analyzes the data collected by the monitoring unit to verify that AI is not involved. For example, the analysis unit can verify that the creator's movements are human using a motion analysis algorithm. For example, machine learning algorithms can be used to analyze the motion data and distinguish between AI-generated movements and human-generated movements. The analysis unit can also verify that the tools used are not AI-generated using a tool identification algorithm. For example, an image recognition algorithm can be used to identify the type of tool and eliminate the use of AI-generated tools. The certification unit issues the verification result obtained by the analysis unit as a certificate. The certification unit can, for example, issue a digital certificate and use blockchain technology to prevent tampering. For example, the contents of the certificate can be recorded on a blockchain to make it impossible to tamper with. This allows the work assurance system according to the embodiment to guarantee that the created work has not used AI, thereby increasing reliability.

[0030] The monitoring unit may use motion capture technology for motion analysis. Examples of motion capture technology include optical motion capture technology and inertial motion capture technology. Optical motion capture technology uses a camera and markers to record the creator's movements in detail. For example, markers can be attached to the creator's hands or arms and their movements can be tracked with a camera to collect motion data. Inertial motion capture technology uses acceleration sensors and gyro sensors to record the creator's movements. For example, sensors can be attached to the creator's hands or arms to collect their movements as data. This allows for a detailed analysis of the creator's movements and eliminates the involvement of AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit may input motion data acquired by motion capture technology into a generation AI and have the generation AI analyze the motion data.

[0031] The monitoring unit can use RFID technology for tool identification. Examples of RFID technology include passive RFID technology and active RFID technology. Passive RFID technology uses an RFID tag that operates by receiving radio waves. For example, a tool can be identified by attaching a passive RFID tag to the tool and reading the tag with an RFID reader. Active RFID technology uses an RFID tag with a built-in battery. For example, a tool can be identified by attaching an active RFID tag to the tool and reading the tag with an RFID reader. This allows the tool to be accurately identified and eliminates the involvement of AI. Some or all of the above-described processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input tool data obtained by an RFID reader into the generation AI and have the generation AI identify the tool.

[0032] The analysis unit can use a motion analysis algorithm. Examples of the motion analysis algorithm include machine learning algorithms and image analysis algorithms. Machine learning algorithms learn large amounts of motion data and distinguish between AI-generated motions and human-generated motions. For example, the motion data can be analyzed using algorithms such as support vector machines (SVMs) and random forests. Image analysis algorithms analyze video footage of motions captured by a camera and distinguish between AI-generated motions and human-generated motions. For example, a convolutional neural network (CNN) can be used to extract and analyze motion characteristics. This allows for detailed analysis of the creator's motions and eliminates the involvement of AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input motion data into a generation AI and have the generation AI perform motion analysis.

[0033] The analysis unit can use a tool identification algorithm. Examples of the tool identification algorithm include an image recognition algorithm and an RFID-based identification algorithm. An image recognition algorithm analyzes an image of a tool captured by a camera to identify the type of tool. For example, a convolutional neural network (CNN) can be used to extract and identify the tool's features. An RFID-based identification algorithm analyzes data from an RFID tag read by an RFID reader to identify the type of tool. For example, the RFID tag ID can be matched with a database to identify the type of tool. This allows the tool to be accurately identified and eliminates the need for AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input tool data into a generation AI and have the generation AI perform tool identification.

[0034] The certification unit issues digital certificates and can prevent tampering using blockchain technology. Blockchain technology includes, for example, public blockchains and private blockchains. A public blockchain is a decentralized blockchain that anyone can participate in and is extremely difficult to tamper with. For example, public blockchains such as Bitcoin and Ethereum can be used. A private blockchain is a blockchain used only within a specific organization or group and allows access control. For example, a private blockchain can be used for data management within a company. This increases the reliability of certificates and prevents tampering. Some or all of the above-mentioned processing in the certification unit may be performed using, for example, AI, or may be performed without AI. For example, the certification unit can input the contents of the certificate into a generation AI and have the generation AI issue the certificate.

[0035] The monitoring unit can select the optimal monitoring method by referring to the creator's past creation process data. The monitoring unit selects the optimal monitoring method based on, for example, tools and techniques used by the creator in the past. For example, the monitoring unit analyzes the past creation process data to detect specific behavior patterns and adjusts the monitoring method based on those patterns. The monitoring unit can also select the most efficient monitoring method by referring to the creator's past creation process data. For example, the monitoring unit optimizes the monitoring method for specific tools and techniques based on the past creation process data. This allows the optimal monitoring method to be selected based on past data and the accuracy of monitoring to be improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the past creation process data into the generation AI and have the generation AI select the optimal monitoring method.

[0036] The monitoring unit can automatically recognize the type and placement of tools used by the creator and dynamically adjust the monitoring range. For example, the monitoring unit recognizes the type of tool used by the creator using a camera and automatically adjusts the monitoring range. For example, the monitoring unit analyzes images of the tool captured by a camera to identify the type of tool and adjusts the monitoring range based on its placement. The monitoring unit can also detect the position where the creator places the tool using a sensor and dynamically adjust the monitoring range. For example, the monitoring unit can detect the position of the tool using a sensor and adjust the monitoring range based on the position. The monitoring unit can also automatically recognize a new tool when the creator uses it and update the monitoring range. For example, when a new tool is used, the monitoring unit automatically recognizes the tool and adjusts the monitoring range appropriately. This makes it possible to automatically recognize the type and placement of the tool and optimize the monitoring range. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input image data of the tool to the generation AI and cause the generation AI to recognize the tool and adjust the monitoring range.

[0037] The monitoring unit can prioritize collecting highly relevant data by taking into account the creator's geographical location information. For example, if the creator is working in a specific location, the monitoring unit prioritizes collecting data related to that location. For example, if the creator is creating a painting in an art museum, the monitoring unit collects environmental data about the museum and reflects it in the monitoring data. Furthermore, if the creator is working while traveling, the monitoring unit can prioritize collecting data related to the creator's route. For example, if the creator is sketching while traveling, the monitoring unit collects data about the route and reflects it in the monitoring data. Furthermore, if the creator is working in a specific area, the monitoring unit can prioritize collecting environmental data about that area. For example, if the creator is working in a natural environment, the monitoring unit collects weather data and environmental data about that area and reflects it in the monitoring data. This allows for the collection of highly relevant data by taking into account the geographical location information, thereby improving the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0038] The monitoring unit can analyze the creator's social media activities and collect related data. For example, the monitoring unit collects related data based on information shared by the creator on social media. For example, if the creator posts about the progress of a painting on social media, the monitoring unit analyzes the content of the post and collects related data. The monitoring unit can also analyze specific trends and interests from the creator's social media activities and collect related data. For example, if the creator posts using a specific hashtag, the monitoring unit collects data related to that hashtag. The monitoring unit can also collect related data based on hashtags used by the creator on social media. For example, if the creator posts using hashtags such as "#art" or "#painting," the monitoring unit collects data related to those hashtags. This allows for analyzing social media activities and collecting related data, thereby improving the accuracy of monitoring. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input social media data into the generation AI and have the generation AI collect related data.

[0039] The analysis unit can optimize the analysis algorithm by referring to past analysis data. The analysis unit, for example, adjusts parameters of the analysis algorithm based on the past analysis data to improve accuracy. For example, the analysis unit analyzes past analysis data, extracts specific patterns, and optimizes the analysis algorithm based on the patterns. The analysis unit can also detect specific movement patterns from the past analysis data and adjust the analysis algorithm based on the patterns. For example, the analysis unit optimizes parameters of the analysis algorithm for specific movement patterns based on the past analysis data. This allows the analysis algorithm to be optimized based on the past data and improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to a generation AI and cause the generation AI to optimize the analysis algorithm.

[0040] The analysis unit can generate a detailed timeline of the creation process and detect abnormal behavior. The analysis unit, for example, generates a timeline of the creation process and detects abnormal behavior or patterns. For example, it records each step of the creation process along a timeline and identifies abnormal behavior. The analysis unit can also detect abnormalities that occur during specific time periods based on the timeline of the creation process. For example, it can analyze changes in behavior during specific time periods and identify abnormalities. The analysis unit can also analyze the timeline of the creation process and identify the cause of abnormal behavior. For example, it can analyze abnormal behavior on the timeline and identify its cause. By generating a detailed timeline and detecting abnormal behavior, it is possible to eliminate the involvement of AI. Some or all of the above-described processing by the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input timeline data to a generation AI and have the generation AI detect abnormal behavior.

[0041] The analysis unit can perform analysis taking into account the geographic distribution of the creation process. For example, the analysis unit analyzes behavior patterns in a specific region based on the geographic distribution of the creation process. For example, the analysis unit collects creation process data in a specific region and analyzes behavior patterns specific to that region. The analysis unit can also perform analysis that reflects the characteristics of each region by taking into account the geographic distribution of the creation process. For example, the analysis unit collects environmental data for each region and performs analysis based on that data. The analysis unit can also analyze the geographic distribution of the creation process and detect abnormal behavior in each region. For example, the analysis unit analyzes abnormal behavior in a specific region and identifies its cause. In this way, analysis that takes into account the geographic distribution enables analysis that reflects the characteristics of each region. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data to a generation AI and have the generation AI perform the analysis.

[0042] The analysis unit can improve the accuracy of the analysis by referring to related literature and databases. For example, the analysis unit can refer to related literature and improve the accuracy of the analysis algorithm. For example, the analysis unit can refer to a database of academic papers and improve the analysis algorithm based on the latest research results. The analysis unit can also refer to a database to obtain information that complements the analysis results. For example, the analysis unit can refer to a patent database to obtain related technical information and reflect it in the analysis results. The analysis unit can also identify areas for improvement in the analysis algorithm based on related literature and databases and improve its accuracy. For example, the analysis unit can adjust the parameters of the analysis algorithm based on past research results to improve its accuracy. In this way, the analysis accuracy can be improved by referring to related literature and databases. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input information from literature and databases into the generation AI and have the generation AI improve the analysis algorithm.

[0043] The certification unit can optimize the certificate format by referencing past certification data. The certification unit, for example, adjusts the certificate format based on the past certification data to improve visibility. For example, the certification unit analyzes past certification data to confirm that a specific format is effective and adopts it. The certification unit can also confirm that a specific format is effective from the past certification data and adopt it. For example, the certification unit evaluates the visibility of a specific format based on the past certification data and optimizes the format based on the results. This makes it possible to optimize the certificate format based on past data and improve visibility. Some or all of the above-mentioned processing in the certification unit may be performed using, for example, AI, or may be performed without using AI. For example, the certification unit can input past certification data into a generation AI and cause the generation AI to optimize the certificate format.

[0044] The certification unit can automatically update the contents of the certificate to reflect the latest information. For example, the certification unit reflects the latest creation process data when issuing a certificate. For example, the certification unit updates the progress status of the creation process and information about the tools used to the latest information. The certification unit can also reflect the latest analysis results when issuing a certificate. For example, the certification unit reflects the latest results of an analysis algorithm in the certificate. The certification unit can also reflect information about the latest tools and techniques when issuing a certificate. For example, information about newly used tools and techniques is added to the certificate. This automatically updates the contents of the certificate to reflect the latest information, thereby increasing reliability. Some or all of the above-mentioned processing in the certification unit may be performed using, or without, AI. For example, the certification unit can input the latest creation process data and analysis results into the generation AI and cause the generation AI to update the contents of the certificate.

[0045] The certification unit can customize the certificate taking into account geographical information of the creation process. The certification unit, for example, customizes the contents of the certificate based on the geographical information of the creation process. For example, if the creation process was performed in a specific region, information reflecting the characteristics of that region is added to the certificate. The certification unit can also issue a certificate that reflects the characteristics of each region by taking into account the geographical information of the creation process. For example, it collects data about the creation process in a specific region and customizes the certificate based on that data. The certification unit can also analyze the geographical information of the creation process and issue a certificate that reflects abnormal behavior in each region. For example, it analyzes abnormal behavior in a specific region and reflects that information in the certificate. In this way, by customizing the certificate taking into account geographical information, it is possible to issue a certificate that reflects the characteristics of each region. Some or all of the above-mentioned processing in the certification unit may be performed using, for example, AI, or may be performed without using AI. For example, the certification unit can input geographical information into a generation AI and cause the generation AI to customize the certificate.

[0046] The certification unit can improve the reliability of the certificate by referring to relevant legal documents and regulations. For example, the certification unit can adjust the content of the certificate by referring to relevant legal documents. For example, the certification unit can refer to legal provisions and regulatory guidelines to make the content of the certificate legally compliant. The certification unit can also optimize the format and content of the certificate by referring to regulations. For example, the certification unit can adjust the format of the certificate based on specific regulations to improve readability. The certification unit can also improve the reliability of the certificate based on relevant legal documents and regulations. For example, the certification unit can verify the content of the certificate based on legal documents and regulations to increase its reliability. In this way, the reliability of the certificate can be improved by referring to relevant legal documents and regulations. Some or all of the above-mentioned processing in the certification unit can be performed using AI, for example, or without AI. For example, the certification unit can input information from legal documents and regulations into a generation AI and have the generation AI adjust the content of the certificate.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The monitoring unit can monitor the creator's biometric data in real time and monitor the creator's health condition. For example, it can collect data such as heart rate, blood pressure, and oxygen saturation, and issue an alert if an abnormality is detected. The monitoring unit can also analyze the creator's posture and movements and provide advice to reduce fatigue and stress caused by long hours of work. Furthermore, the monitoring unit can record the creator's health data over the long term and generate reports for health management. This can help maintain the creator's health and improve work efficiency.

[0049] The analysis unit can refer to the creator's past work data to learn the creator's style and techniques. For example, it can analyze the color usage, composition, brush strokes, etc. of past works to extract the creator's characteristics. The analysis unit can also evaluate whether the creator's current work maintains the creator's originality based on the creator's style. Furthermore, the analysis unit can provide advice to the creator when trying new techniques or styles based on the creator's style. This can support the creator's growth and improve the quality of their work.

[0050] The monitoring unit can monitor the creator's work environment and provide work advice in response to changes in the environment. For example, it can monitor room temperature, humidity, and lighting conditions and provide advice for maintaining an optimal work environment. The monitoring unit can also suggest improvements to the work environment if the creator's work environment is affected by external factors such as noise and vibration. Furthermore, the monitoring unit can record the creator's work environment data over the long term and generate reports for improving the work environment. This allows the creator's work environment to be optimized and work efficiency to be improved.

[0051] The monitoring unit can learn the creator's work patterns and provide advice to improve work efficiency. For example, it can learn that the creator's concentration increases during certain times of the day and suggest that the creator perform important tasks during those times. The monitoring unit can also optimize the timing of breaks and the order of tasks based on the creator's work patterns. Furthermore, the monitoring unit can record the creator's work patterns over the long term and generate reports to improve work efficiency. This can optimize the creator's work patterns and improve work efficiency.

[0052] The monitoring unit can refer to the creator's work history and track the progress of work in real time. For example, it can evaluate the progress of the current work based on data on work performed by the creator in the past and provide feedback to the creator. The monitoring unit can also detect work delays and problems early based on the creator's work history and propose appropriate countermeasures. Furthermore, the monitoring unit can record the creator's work history over the long term and generate reports to improve work efficiency. This makes it possible to manage the progress of work by utilizing the creator's work history and improve work efficiency.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The monitoring unit monitors the creation process in real time. The monitoring unit uses cameras and sensors to record the creator's movements and the tools they use. For example, when painting a picture, a camera can capture the creator's hand movements and record the type of paint and brushes they use. Motion capture technology for movement analysis and RFID technology for tool identification can also be used. Step 2: The analysis unit analyzes the data collected by the monitoring unit and verifies that AI is not involved. The analysis unit uses motion analysis algorithms and machine learning algorithms to verify that the creator's actions are human. It also uses tool identification algorithms and image recognition algorithms to verify that the tools used are not AI-generated. Step 3: The verification unit issues a certificate based on the verification results obtained by the analysis unit. The verification unit issues a digital certificate and can prevent tampering using blockchain technology. The contents of the certificate are recorded on the blockchain, making them impossible to tamper with.

[0055] (Example 2) An art guarantee system according to an embodiment of the present invention is a system that guarantees that a work of art has not been created using AI. This system monitors the creation process in real time, analyzes the monitored data to confirm that AI is not involved, and issues a certificate of the confirmation result. For example, when a work of art is created, cameras and sensors are used to record the artist's movements and the tools used. When painting a painting, a camera can capture the artist's hand movements and record the paints and brushes used. Next, the collected data is analyzed to confirm that the artist's movements were human. It also confirms that the tools used were not created by AI. This proves that the work has not been created using AI. Finally, the confirmation result is issued as a certificate and attached to the work. The certificate includes detailed data on the creation process and analysis results. For example, attaching a certificate to the back of a painting can demonstrate the authenticity of the work to buyers and viewers. This system can increase the authenticity of works that do not use AI. For example, by proving that AI is not involved in works such as text, paintings, and videos, the originality of the artist can be protected and the value of the work can be increased. It also demonstrates the authenticity of the work to buyers and viewers. This will increase the market value of works that do not use AI.The work guarantee system will guarantee that the created works do not use AI, increasing their reliability.

[0056] The artwork guarantee system according to the embodiment includes a monitoring unit, an analysis unit, and a certification unit. The monitoring unit monitors the creation process in real time. The monitoring unit records the creator's movements and the tools used, for example, using a camera or sensors. For example, when painting a painting, the camera can capture the creator's hand movements and record the type of paint and brush used. The monitoring unit can also use motion capture technology for motion analysis. For example, optical motion capture technology can be used to record the creator's movements in detail. The monitoring unit can also use RFID technology for tool identification. For example, RFID tags can be attached to tools to accurately identify the tools used. The analysis unit analyzes the data collected by the monitoring unit to verify that AI is not involved. For example, the analysis unit can verify that the creator's movements are human using a motion analysis algorithm. For example, machine learning algorithms can be used to analyze the motion data and distinguish between AI-generated movements and human-generated movements. The analysis unit can also verify that the tools used are not AI-generated using a tool identification algorithm. For example, an image recognition algorithm can be used to identify the type of tool and eliminate the use of AI-generated tools. The certification unit issues the verification result obtained by the analysis unit as a certificate. The certification unit can, for example, issue a digital certificate and use blockchain technology to prevent tampering. For example, the contents of the certificate can be recorded on a blockchain to make it impossible to tamper with. This allows the work assurance system according to the embodiment to guarantee that the created work has not used AI, thereby increasing reliability.

[0057] The monitoring unit may use motion capture technology for motion analysis. Examples of motion capture technology include optical motion capture technology and inertial motion capture technology. Optical motion capture technology uses a camera and markers to record the creator's movements in detail. For example, markers can be attached to the creator's hands or arms and their movements can be tracked with a camera to collect motion data. Inertial motion capture technology uses acceleration sensors and gyro sensors to record the creator's movements. For example, sensors can be attached to the creator's hands or arms to collect their movements as data. This allows for a detailed analysis of the creator's movements and eliminates the involvement of AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit may input motion data acquired by motion capture technology into a generation AI and have the generation AI analyze the motion data.

[0058] The monitoring unit can use RFID technology for tool identification. Examples of RFID technology include passive RFID technology and active RFID technology. Passive RFID technology uses an RFID tag that operates by receiving radio waves. For example, a tool can be identified by attaching a passive RFID tag to the tool and reading the tag with an RFID reader. Active RFID technology uses an RFID tag with a built-in battery. For example, a tool can be identified by attaching an active RFID tag to the tool and reading the tag with an RFID reader. This allows the tool to be accurately identified and eliminates the involvement of AI. Some or all of the above-described processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input tool data obtained by an RFID reader into the generation AI and have the generation AI identify the tool.

[0059] The analysis unit can use a motion analysis algorithm. Examples of the motion analysis algorithm include machine learning algorithms and image analysis algorithms. Machine learning algorithms learn large amounts of motion data and distinguish between AI-generated motions and human-generated motions. For example, the motion data can be analyzed using algorithms such as support vector machines (SVMs) and random forests. Image analysis algorithms analyze video footage of motions captured by a camera and distinguish between AI-generated motions and human-generated motions. For example, a convolutional neural network (CNN) can be used to extract and analyze motion characteristics. This allows for detailed analysis of the creator's motions and eliminates the involvement of AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input motion data into a generation AI and have the generation AI perform motion analysis.

[0060] The analysis unit can use a tool identification algorithm. Examples of the tool identification algorithm include an image recognition algorithm and an RFID-based identification algorithm. An image recognition algorithm analyzes an image of a tool captured by a camera to identify the type of tool. For example, a convolutional neural network (CNN) can be used to extract and identify the tool's features. An RFID-based identification algorithm analyzes data from an RFID tag read by an RFID reader to identify the type of tool. For example, the RFID tag ID can be matched with a database to identify the type of tool. This allows the tool to be accurately identified and eliminates the need for AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input tool data into a generation AI and have the generation AI perform tool identification.

[0061] The certification unit issues digital certificates and can prevent tampering using blockchain technology. Blockchain technology includes, for example, public blockchains and private blockchains. A public blockchain is a decentralized blockchain that anyone can participate in and is extremely difficult to tamper with. For example, public blockchains such as Bitcoin and Ethereum can be used. A private blockchain is a blockchain used only within a specific organization or group and allows access control. For example, a private blockchain can be used for data management within a company. This increases the reliability of certificates and prevents tampering. Some or all of the above-mentioned processing in the certification unit may be performed using, for example, AI, or may be performed without AI. For example, the certification unit can input the contents of the certificate into a generation AI and have the generation AI issue the certificate.

[0062] The monitoring unit can estimate the creator's emotions and adjust the monitoring frequency based on the estimated creator's emotions. For example, the monitoring unit captures the creator's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on changes in facial expressions and adjusts the monitoring frequency. The monitoring unit can also record the creator's voice and estimate the emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the monitoring frequency. The monitoring unit can also collect the creator's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on heart rate fluctuations and adjust the monitoring frequency. This allows the monitoring frequency to be adjusted according to the creator's emotions and optimize the creation process. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input image data of the creator captured by a camera into the generation AI and have the generation AI estimate the creator's emotions.

[0063] The monitoring unit can select the optimal monitoring method by referring to the creator's past creation process data. The monitoring unit selects the optimal monitoring method based on, for example, tools and techniques used by the creator in the past. For example, the monitoring unit analyzes the past creation process data to detect specific behavior patterns and adjusts the monitoring method based on those patterns. The monitoring unit can also select the most efficient monitoring method by referring to the creator's past creation process data. For example, the monitoring unit optimizes the monitoring method for specific tools and techniques based on the past creation process data. This allows the optimal monitoring method to be selected based on past data and the accuracy of monitoring to be improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the past creation process data into the generation AI and have the generation AI select the optimal monitoring method.

[0064] The monitoring unit can automatically recognize the type and placement of tools used by the creator and dynamically adjust the monitoring range. For example, the monitoring unit recognizes the type of tool used by the creator using a camera and automatically adjusts the monitoring range. For example, the monitoring unit analyzes images of the tool captured by a camera to identify the type of tool and adjusts the monitoring range based on its placement. The monitoring unit can also detect the position where the creator places the tool using a sensor and dynamically adjust the monitoring range. For example, the monitoring unit can detect the position of the tool using a sensor and adjust the monitoring range based on the position. The monitoring unit can also automatically recognize a new tool when the creator uses it and update the monitoring range. For example, when a new tool is used, the monitoring unit automatically recognizes the tool and adjusts the monitoring range appropriately. This makes it possible to automatically recognize the type and placement of the tool and optimize the monitoring range. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input image data of the tool to the generation AI and cause the generation AI to recognize the tool and adjust the monitoring range.

[0065] The monitoring unit can estimate the creator's emotions and prioritize the monitoring data based on the estimated emotions. For example, the monitoring unit captures the creator's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on changes in facial expressions and prioritizes the monitoring data. The monitoring unit can also record the creator's voice and estimate the emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice to calculate an emotion score and prioritize the monitoring data. The monitoring unit can also collect the creator's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on heart rate fluctuations and prioritize the monitoring data. This allows the monitoring data to be prioritized according to the creator's emotions and important data to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input image data of the creator captured by a camera into the generation AI and have the generation AI estimate the creator's emotions.

[0066] The monitoring unit can prioritize collecting highly relevant data by taking into account the creator's geographical location information. For example, if the creator is working in a specific location, the monitoring unit prioritizes collecting data related to that location. For example, if the creator is creating a painting in an art museum, the monitoring unit collects environmental data about the museum and reflects it in the monitoring data. Furthermore, if the creator is working while traveling, the monitoring unit can prioritize collecting data related to the creator's route. For example, if the creator is sketching while traveling, the monitoring unit collects data about the route and reflects it in the monitoring data. Furthermore, if the creator is working in a specific area, the monitoring unit can prioritize collecting environmental data about that area. For example, if the creator is working in a natural environment, the monitoring unit collects weather data and environmental data about that area and reflects it in the monitoring data. This allows for the collection of highly relevant data by taking into account the geographical location information, thereby improving the accuracy of monitoring. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0067] The monitoring unit can analyze the creator's social media activities and collect related data. For example, the monitoring unit collects related data based on information shared by the creator on social media. For example, if the creator posts about the progress of a painting on social media, the monitoring unit analyzes the content of the post and collects related data. The monitoring unit can also analyze specific trends and interests from the creator's social media activities and collect related data. For example, if the creator posts using a specific hashtag, the monitoring unit collects data related to that hashtag. The monitoring unit can also collect related data based on hashtags used by the creator on social media. For example, if the creator posts using hashtags such as "#art" or "#painting," the monitoring unit collects data related to those hashtags. This allows for analyzing social media activities and collecting related data, thereby improving the accuracy of monitoring. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input social media data into the generation AI and have the generation AI collect related data.

[0068] The analysis unit can estimate the creator's emotions and adjust the parameters of the analysis algorithm based on the estimated emotions. For example, the analysis unit captures the creator's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions and adjusts the parameters of the analysis algorithm. The analysis unit can also record the creator's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the parameters of the analysis algorithm. The analysis unit can also collect the creator's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the parameters of the analysis algorithm. This allows the parameters of the analysis algorithm to be adjusted according to the creator's emotions, improving the accuracy of the analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input emotion data to the generation AI and cause the generation AI to adjust the parameters of the analysis algorithm.

[0069] The analysis unit can optimize the analysis algorithm by referring to past analysis data. The analysis unit, for example, adjusts parameters of the analysis algorithm based on the past analysis data to improve accuracy. For example, the analysis unit analyzes past analysis data, extracts specific patterns, and optimizes the analysis algorithm based on the patterns. The analysis unit can also detect specific movement patterns from the past analysis data and adjust the analysis algorithm based on the patterns. For example, the analysis unit optimizes parameters of the analysis algorithm for specific movement patterns based on the past analysis data. This allows the analysis algorithm to be optimized based on the past data and improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data to a generation AI and cause the generation AI to optimize the analysis algorithm.

[0070] The analysis unit can generate a detailed timeline of the creation process and detect abnormal behavior. The analysis unit, for example, generates a timeline of the creation process and detects abnormal behavior or patterns. For example, it records each step of the creation process along a timeline and identifies abnormal behavior. The analysis unit can also detect abnormalities that occur during specific time periods based on the timeline of the creation process. For example, it can analyze changes in behavior during specific time periods and identify abnormalities. The analysis unit can also analyze the timeline of the creation process and identify the cause of abnormal behavior. For example, it can analyze abnormal behavior on the timeline and identify its cause. By generating a detailed timeline and detecting abnormal behavior, it is possible to eliminate the involvement of AI. Some or all of the above-described processing by the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input timeline data to a generation AI and have the generation AI detect abnormal behavior.

[0071] The analysis unit can estimate the creator's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit captures the creator's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the display method of the analysis results. The analysis unit can also record the creator's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the analysis results. The analysis unit can also collect the creator's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the analysis results. This allows the display method of the analysis results to be adjusted according to the creator's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input emotion data to the generation AI and have the generation AI adjust the display method of the analysis results.

[0072] The analysis unit can perform analysis taking into account the geographic distribution of the creation process. For example, the analysis unit analyzes behavior patterns in a specific region based on the geographic distribution of the creation process. For example, the analysis unit collects creation process data in a specific region and analyzes behavior patterns specific to that region. The analysis unit can also perform analysis that reflects the characteristics of each region by taking into account the geographic distribution of the creation process. For example, the analysis unit collects environmental data for each region and performs analysis based on that data. The analysis unit can also analyze the geographic distribution of the creation process and detect abnormal behavior in each region. For example, the analysis unit analyzes abnormal behavior in a specific region and identifies its cause. In this way, analysis that takes into account the geographic distribution enables analysis that reflects the characteristics of each region. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographic distribution data to a generation AI and have the generation AI perform the analysis.

[0073] The analysis unit can improve the accuracy of the analysis by referring to related literature and databases. For example, the analysis unit can refer to related literature and improve the accuracy of the analysis algorithm. For example, the analysis unit can refer to a database of academic papers and improve the analysis algorithm based on the latest research results. The analysis unit can also refer to a database to obtain information that complements the analysis results. For example, the analysis unit can refer to a patent database to obtain related technical information and reflect it in the analysis results. The analysis unit can also identify areas for improvement in the analysis algorithm based on related literature and databases and improve its accuracy. For example, the analysis unit can adjust the parameters of the analysis algorithm based on past research results to improve its accuracy. In this way, the analysis accuracy can be improved by referring to related literature and databases. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input information from literature and databases into the generation AI and have the generation AI improve the analysis algorithm.

[0074] The certification unit can estimate the creator's emotions and adjust the timing of certificate issuance based on the estimated emotions. For example, the certification unit captures the creator's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the certification unit calculates an emotion score based on changes in facial expression and adjusts the timing of certificate issuance. The certification unit can also record the creator's voice and estimate the emotions using voice analysis technology. For example, the certification unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of certificate issuance. The certification unit can also collect the creator's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the certification unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of certificate issuance. This allows the timing of certificate issuance to be adjusted according to the creator's emotions and optimize the certificate creation process. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the certification unit may be performed using, for example, AI, or may be performed without using AI. For example, the certification unit may input emotion data to the generation AI and have the generation AI adjust the timing of certificate issuance.

[0075] The certification unit can optimize the certificate format by referencing past certification data. The certification unit, for example, adjusts the certificate format based on the past certification data to improve visibility. For example, the certification unit analyzes past certification data to confirm that a specific format is effective and adopts it. The certification unit can also confirm that a specific format is effective from the past certification data and adopt it. For example, the certification unit evaluates the visibility of a specific format based on the past certification data and optimizes the format based on the results. This makes it possible to optimize the certificate format based on past data and improve visibility. Some or all of the above-mentioned processing in the certification unit may be performed using, for example, AI, or may be performed without using AI. For example, the certification unit can input past certification data into a generation AI and cause the generation AI to optimize the certificate format.

[0076] The certification unit can automatically update the contents of the certificate to reflect the latest information. For example, the certification unit reflects the latest creation process data when issuing a certificate. For example, the certification unit updates the progress status of the creation process and information about the tools used to the latest information. The certification unit can also reflect the latest analysis results when issuing a certificate. For example, the certification unit reflects the latest results of an analysis algorithm in the certificate. The certification unit can also reflect information about the latest tools and techniques when issuing a certificate. For example, information about newly used tools and techniques is added to the certificate. This automatically updates the contents of the certificate to reflect the latest information, thereby increasing reliability. Some or all of the above-mentioned processing in the certification unit may be performed using, or without, AI. For example, the certification unit can input the latest creation process data and analysis results into the generation AI and cause the generation AI to update the contents of the certificate.

[0077] The certification unit can estimate the creator's emotions and adjust the display method of the certificate based on the estimated emotions. For example, the certification unit captures the creator's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the certification unit calculates an emotion score based on changes in facial expression and adjusts the display method of the certificate. The certification unit can also record the creator's voice and estimate the emotions using voice analysis technology. For example, the certification unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the display method of the certificate. The certification unit can also collect the creator's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the certification unit can calculate an emotion score based on heart rate fluctuations and adjust the display method of the certificate. This allows the display method of the certificate to be adjusted according to the creator's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the certification unit may be performed using, for example, AI, or may be performed without using AI. For example, the certification unit may input emotion data to the generation AI and have the generation AI adjust the display method of the certificate.

[0078] The certification unit can customize the certificate taking into account geographical information of the creation process. The certification unit, for example, customizes the contents of the certificate based on the geographical information of the creation process. For example, if the creation process was performed in a specific region, information reflecting the characteristics of that region is added to the certificate. The certification unit can also issue a certificate that reflects the characteristics of each region by taking into account the geographical information of the creation process. For example, it collects data about the creation process in a specific region and customizes the certificate based on that data. The certification unit can also analyze the geographical information of the creation process and issue a certificate that reflects abnormal behavior in each region. For example, it analyzes abnormal behavior in a specific region and reflects that information in the certificate. In this way, by customizing the certificate taking into account geographical information, it is possible to issue a certificate that reflects the characteristics of each region. Some or all of the above-mentioned processing in the certification unit may be performed using, for example, AI, or may be performed without using AI. For example, the certification unit can input geographical information into a generation AI and cause the generation AI to customize the certificate.

[0079] The certification unit can improve the reliability of the certificate by referring to relevant legal documents and regulations. For example, the certification unit can adjust the content of the certificate by referring to relevant legal documents. For example, the certification unit can refer to legal provisions and regulatory guidelines to make the content of the certificate legally compliant. The certification unit can also optimize the format and content of the certificate by referring to regulations. For example, the certification unit can adjust the format of the certificate based on specific regulations to improve readability. The certification unit can also improve the reliability of the certificate based on relevant legal documents and regulations. For example, the certification unit can verify the content of the certificate based on legal documents and regulations to increase its reliability. In this way, the reliability of the certificate can be improved by referring to relevant legal documents and regulations. Some or all of the above-mentioned processing in the certification unit can be performed using AI, for example, or without AI. For example, the certification unit can input information from legal documents and regulations into a generation AI and have the generation AI adjust the content of the certificate. === Hard Collateral 1-1 === Each of the multiple elements including the monitoring unit, analysis unit, and certifying unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit records the creator's actions and the tools used using the camera 42 or sensors of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to confirm that AI is not involved. The certifying unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues the confirmation result as a certificate. The certifying unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the monitoring unit, analysis unit, and certifying unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit records the creator's actions and the tools used using the camera 42 or sensors of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to confirm that AI is not involved. The certifying unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues the confirmation result as a certificate. The certifying unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, analysis unit, and certifying unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit uses the camera 42 or sensors of the headset type terminal 314 to record the creator's actions and the tools used. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to confirm that AI is not involved. The certifying unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues the confirmation result as a certificate. The certifying unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, analysis unit, and certifying unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit uses the camera 42 or sensors of the robot 414 to record the creator's movements and the tools used. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to confirm that AI is not involved. The certifying unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues the confirmation result as a certificate. The certifying unit may be realized, for example, by the control unit 46A of the robot 414.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The monitoring unit can monitor the creator's biometric data in real time and monitor the creator's health condition. For example, it can collect data such as heart rate, blood pressure, and oxygen saturation, and issue an alert if an abnormality is detected. The monitoring unit can also analyze the creator's posture and movements and provide advice to reduce fatigue and stress caused by long hours of work. Furthermore, the monitoring unit can record the creator's health data over the long term and generate reports for health management. This can help maintain the creator's health and improve work efficiency.

[0082] The analysis unit can refer to the creator's past work data to learn the creator's style and techniques. For example, it can analyze the color usage, composition, brush strokes, etc. of past works to extract the creator's characteristics. The analysis unit can also evaluate whether the creator's current work maintains the creator's originality based on the creator's style. Furthermore, the analysis unit can provide advice to the creator when trying new techniques or styles based on the creator's style. This can support the creator's growth and improve the quality of their work.

[0083] The certification unit can estimate the creator's emotions and customize the certificate design based on the estimated creator's emotions. For example, if the creator is feeling joyful or satisfied, it can add bright colors and a positive message to the certificate. Alternatively, if the creator is feeling nervous or anxious, it can add calm colors and an encouraging message to the certificate. Furthermore, the certification unit can adjust the font and layout of the certificate based on the creator's emotions. This allows the certificate to be more in tune with the creator's emotions and provide a more personalized certificate.

[0084] The monitoring unit can monitor the creator's work environment and provide work advice in response to changes in the environment. For example, it can monitor room temperature, humidity, and lighting conditions and provide advice for maintaining an optimal work environment. The monitoring unit can also suggest improvements to the work environment if the creator's work environment is affected by external factors such as noise and vibration. Furthermore, the monitoring unit can record the creator's work environment data over the long term and generate reports for improving the work environment. This allows the creator's work environment to be optimized and work efficiency to be improved.

[0085] The analysis unit can estimate the creator's emotions and adjust the feedback of the analysis results based on the estimated emotions of the creator. For example, if the creator has positive emotions, the analysis results can be explained in detail to deepen the creator's understanding. Alternatively, if the creator has negative emotions, the analysis results can be summarized concisely to reduce the creator's burden. Furthermore, the analysis unit can adjust the presentation method of the analysis results based on the creator's emotions and provide feedback in a format that is most easily understood by the creator. This makes it possible to provide analysis result feedback that takes the creator's emotions into consideration and promote the creator's understanding.

[0086] The monitoring unit can learn the creator's work patterns and provide advice to improve work efficiency. For example, it can learn that the creator's concentration increases during certain times of the day and suggest that the creator perform important tasks during those times. The monitoring unit can also optimize the timing of breaks and the order of tasks based on the creator's work patterns. Furthermore, the monitoring unit can record the creator's work patterns over the long term and generate reports to improve work efficiency. This can optimize the creator's work patterns and improve work efficiency.

[0087] The analysis unit can estimate the creator's emotions and adjust the analysis process based on the estimated creator's emotions. For example, if the creator is feeling stressed, the analysis process can be simplified to reduce the creator's burden. Alternatively, if the creator is relaxed, a more detailed analysis can be performed to provide deeper insights. Furthermore, the analysis unit can adjust the speed of the analysis process based on the creator's emotions and proceed with the analysis at a pace that the creator feels most comfortable with. This provides an analysis process that takes the creator's emotions into consideration, thereby improving the creator's satisfaction.

[0088] The certification unit can estimate the creator's emotions and adjust the content of the certificate based on the estimated creator's emotions. For example, if the creator feels a sense of accomplishment, a message emphasizing the creator's efforts and achievements can be added to the certificate. Alternatively, if the creator feels anxious, an encouraging message can be added to the certificate to boost the creator's confidence. Furthermore, the certification unit can adjust the layout and design of the certificate based on the creator's emotions and provide the certificate in a format that is most satisfactory to the creator. This makes it possible to provide a certificate that is in line with the creator's emotions and improve the creator's satisfaction.

[0089] The monitoring unit can refer to the creator's work history and track the progress of work in real time. For example, it can evaluate the progress of the current work based on data on work performed by the creator in the past and provide feedback to the creator. The monitoring unit can also detect work delays and problems early based on the creator's work history and propose appropriate countermeasures. Furthermore, the monitoring unit can record the creator's work history over the long term and generate reports to improve work efficiency. This makes it possible to manage the progress of work by utilizing the creator's work history and improve work efficiency.

[0090] The analysis unit can estimate the creator's emotions and evaluate the importance of the analysis results based on the estimated creator's emotions. For example, if the creator is highly motivated, detailed analysis results can be provided to deepen the creator's understanding. Alternatively, if the creator is less motivated, analysis results that focus on important points can be provided to reduce the creator's burden. Furthermore, the analysis unit can adjust the presentation method of the analysis results based on the creator's emotions and provide feedback in a format that is most easily understood by the creator. This allows for the provision of analysis result feedback that takes the creator's emotions into consideration, thereby promoting the creator's understanding.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The monitoring unit monitors the creation process in real time. The monitoring unit uses cameras and sensors to record the creator's movements and the tools they use. For example, when painting a picture, a camera can capture the creator's hand movements and record the type of paint and brushes they use. Motion capture technology for movement analysis and RFID technology for tool identification can also be used. Step 2: The analysis unit analyzes the data collected by the monitoring unit and verifies that AI is not involved. The analysis unit uses motion analysis algorithms and machine learning algorithms to verify that the creator's actions are human. It also uses tool identification algorithms and image recognition algorithms to verify that the tools used are not AI-generated. Step 3: The verification unit issues a certificate based on the verification results obtained by the analysis unit. The verification unit issues a digital certificate and can prevent tampering using blockchain technology. The contents of the certificate are recorded on the blockchain, making them impossible to tamper with.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0144] The data processing system 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.

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

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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, to avoid confusion and 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 creation process in real time; an analysis unit that analyzes the data collected by the monitoring unit and checks whether AI is involved; a certification unit that issues a certificate based on the verification result obtained by the analysis unit; Equipped with A system characterized by:

2. The monitoring unit Use motion capture technology for movement analysis 2. The system of claim 1.

3. The monitoring unit Using RFID technology for tool identification 2. The system of claim 1.

4. The analysis unit Using motion analysis algorithms 2. The system of claim 1.

5. The analysis unit Use a tool identification algorithm 2. The system of claim 1.

6. The certification unit Issue digital certificates and use blockchain technology to prevent tampering 2. The system of claim 1.

7. The monitoring unit A method for estimating creator sentiment and adjusting monitoring frequency based on the estimated creator sentiment.

2. The system of claim 1.

8. The monitoring unit Select the optimal monitoring method by referring to the creator's past creation process data 2. The system of claim 1.

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