system

Abstract

The system according to the embodiment aims to safely train a generation AI to learn from a company's big data. [Solution] A system according to an embodiment includes an encryption unit, an access control unit, a learning unit, a monitoring unit, and an alert unit. The encryption unit encrypts a company's big data. The access control unit makes the data encrypted by the encryption unit accessible only to the generating AI. The learning unit learns the data accessed by the access control unit. The monitoring unit monitors the usage history of the data learned by the learning unit. The alert unit issues an alert if unauthorized access is detected by the monitoring 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] With conventional technology, it was difficult to safely generate a company's big data and have AI learn it, posing the risk of data leaks to other companies.

[0005] The system according to the embodiment aims to safely train a generation AI to learn from a company's big data. [Means for solving the problem]

[0006] The system according to the embodiment includes an encryption unit, an access control unit, a learning unit, a monitoring unit, and an alert unit. The encryption unit encrypts a company's big data. The access control unit makes the data encrypted by the encryption unit accessible only to the generating AI. The learning unit learns the data accessed by the access control unit. The monitoring unit monitors the usage history of the data learned by the learning unit. The alert unit issues an alert when unauthorized access is detected by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can safely train a generation AI to learn from a company's big data. [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) A data protection system according to an embodiment of the present invention is a system that prevents other companies from learning its own big data when the data is trained by a generation AI. When a company provides its big data to a generation AI, this data protection system encrypts the data and makes it accessible only to a specific generation AI. The generation AI then learns the provided data and builds a generation AI specialized in specialized content. Finally, the generation AI is controlled so that the trained data is not provided to other companies. This mechanism allows a company to safely train its own big data with a generation AI. For example, when a company provides its big data to a generation AI, the data is encrypted using public key cryptography and made accessible only to a specific generation AI. The generation AI then learns the provided data and builds a generation AI specialized in specialized content. The generation AI implements an authentication system to allow only specific generation AIs to access the data. Furthermore, to prevent the generation AI from providing the learned data to other companies, a system is implemented that monitors the data usage history and issues an alert if unauthorized access occurs. This mechanism allows a company to safely train its own big data with a generation AI. This data protection system allows a company to safely train its own big data with a generation AI.

[0029] A data protection system according to an embodiment includes an encryption unit, an access control unit, a learning unit, a monitoring unit, and an alert unit. The encryption unit encrypts a company's big data. For example, the encryption unit encrypts the data using public key cryptography. The public key cryptography can use algorithms such as Rivest-Shamir-Adleman (RSA) or Elliptic Curve Cryptography (ECC). For example, RSA encrypts and decrypts data using a public and private key pair. ECC utilizes the mathematical properties of elliptic curves to provide high security with a smaller key size. The access control unit makes the data encrypted by the encryption unit accessible only to a specific generation AI. For example, the access control unit implements an authentication system to grant access only to a specific generation AI. The authentication system can use password authentication or biometric authentication (such as fingerprint authentication or facial authentication). The learning unit learns from the data accessed by the access control unit. For example, the learning unit learns from the provided data and builds a generation AI specialized in specialized content. The generation AI can learn content specialized in a specific field, such as medical data or financial data. The monitoring unit monitors the usage history of the data learned by the learning unit. For example, the monitoring unit monitors the usage history of the data and issues an alert if unauthorized access occurs. The monitoring unit can monitor abnormal access patterns, the number of authentication failures, etc. The alert unit issues an alert if unauthorized access occurs by the monitoring unit. For example, the alert unit sets trigger conditions for the alert and determines the method of alert notification. Alert notification methods include pop-up display and email notification. As a result, the data protection system according to the embodiment allows the generation AI to safely learn corporate big data.

[0030] The encryption unit can encrypt data using a public key cryptography. Public key cryptography includes, for example, RSA (Rivest-Shamir-Adleman) and ECC (Elliptic Curve Cryptography). RSA encrypts and decrypts data using a pair of a public key and a private key. For example, RSA encrypts data using a public key and decrypts the data using a private key. ECC utilizes the mathematical properties of elliptic curves to provide high security with a smaller key size. For example, ECC encrypts and decrypts data using operations on points on elliptic curves. This makes data encryption more secure. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit may input data to a generation AI and have the generation AI select an encryption algorithm.

[0031] The access control unit can introduce an authentication system and allow access only to specific generation AIs. Authentication systems include, for example, password authentication and biometric authentication (fingerprint authentication, facial authentication, etc.). Password authentication uses a password set by the user. For example, the user enters a password, and the system verifies it. Biometric authentication uses the user's biometric information to perform authentication. For example, fingerprint authentication scans the user's fingerprint and compares it with registered fingerprint data. Facial authentication photographs the user's face with a camera and compares it with registered facial data. This improves data security by allowing access only to specific generation AIs. Some or all of the above-mentioned processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can input the user's authentication data into the generation AI and have the generation AI determine the authentication result.

[0032] The learning unit can learn from the provided data and build a generative AI specialized in specialized content. The learning unit learns content specialized in a specific field, such as medical data or financial data. For example, the learning unit learns medical data and builds a generative AI specialized in the medical field. Medical data includes patient medical records and test results. The learning unit analyzes this data and builds a generative AI specialized in the medical field. The learning unit also learns financial data and builds a generative AI specialized in the financial field. Financial data includes trading history and market data. The learning unit analyzes this data and builds a generative AI specialized in the financial field. In this way, a generative AI specialized in specialized content can be built. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input provided data into the generative AI and have the generative AI analyze and learn the data.

[0033] The monitoring unit monitors data usage history and can issue an alert if unauthorized access occurs. The monitoring unit monitors, for example, abnormal access patterns and the number of authentication failures. For example, the monitoring unit issues an alert if access that differs from a normal access pattern is detected. The monitoring unit can also issue an alert if a certain number of authentication failures occur. In this way, unauthorized access is detected and the security of data is ensured by issuing an alert. Some or all of the above-mentioned 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 access logs into a generation AI and have the generation AI detect abnormal access patterns.

[0034] The encryption unit can apply different encryption algorithms depending on the type of data during encryption. For example, the encryption unit applies a strong encryption algorithm such as AES-256 to highly confidential data. For example, AES-256 encrypts data using a 256-bit key and provides high security. For general data, the encryption unit applies a standard encryption algorithm such as AES-128. For example, AES-128 encrypts data using a 128-bit key and provides balanced security and performance. For less confidential data, the encryption unit applies a lightweight encryption algorithm such as RC4. For example, RC4 is a stream cipher that provides a simple structure and fast encryption. This enables optimal encryption depending on the type of data. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit inputs the type of data into a generation AI and has the generation AI select the optimal encryption algorithm.

[0035] During encryption, the encryption unit can adjust the encryption level based on the importance of the data. For example, the encryption unit applies multiple encryption layers to highly important data to increase the level of detail. For example, the encryption unit applies double encryption using AES-256 and RSA to highly important data. For medium-importance data, the encryption unit applies a single encryption layer to maintain standard level of detail. For example, the encryption unit encrypts data using AES-128. For low-importance data, the encryption unit applies simple encryption to reduce the level of detail. For example, the encryption unit encrypts data using RC4. This enables encryption according to the importance of the data. Some or all of the above-described processing in the encryption unit may be performed using AI, or may be performed without AI. For example, the encryption unit may input the importance of the data to the generation AI and cause the generation AI to adjust the encryption level.

[0036] During encryption, the encryption unit can determine encryption priorities based on the geographic distribution of data. For example, the encryption unit gives the highest priority to international data. For example, the encryption unit encrypts international data using AES-256. For domestic data, encryption is given a second priority. For example, the encryption unit encrypts domestic data using AES-128. For local data, encryption is given last. For example, the encryption unit encrypts local data using RC4. This enables encryption according to the geographic distribution of data. Some or all of the above-described processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit may input the geographic distribution of data to the generation AI and have the generation AI determine the encryption priorities.

[0037] The encryption unit can improve the accuracy of encryption by referring to literature related to the data during encryption. For example, the encryption unit selects an optimal encryption algorithm based on literature related to the data. For example, the encryption unit selects an encryption algorithm such as AES-256 or RSA by referring to literature related to the data. The encryption unit adjusts the level of encryption detail by referring to literature related to the data. For example, the encryption unit applies multiple encryption layers based on literature related to the data. The encryption timing is optimized based on literature related to the data. For example, the encryption unit performs encryption before transmitting or after receiving data based on literature related to the data. In this way, the accuracy of encryption is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, for example, or may be performed without using AI. For example, the encryption unit inputs literature related to the data into a generation AI and causes the generation AI to select an algorithm for improving the accuracy of encryption.

[0038] The access control unit can improve the accuracy of access control based on the interrelationships between data during access control. The access control unit, for example, analyzes the interrelationships between data and performs strict access control on highly related data. For example, the access control unit analyzes data dependencies and sets strict access control standards for highly related data. The access control unit determines the priority of access control based on the interrelationships between data. For example, the access control unit sets the priority of access control based on the interrelationships between data. The access control unit adjusts the level of detail of access control taking into account the interrelationships between data. For example, the access control unit adjusts the level of detail of access control based on the interrelationships between data. This enables access control based on the interrelationships between data. Some or all of the above-mentioned processing in the access control unit may be performed using, for example, AI, or may be performed without using AI. For example, the access control unit inputs the interrelationships between data into a generation AI and has the generation AI select an algorithm for improving the accuracy of access control.

[0039] The access control unit can perform access control based on the attribute information of the data submitter during access control. For example, if the data submitter is an administrator, the access control unit relaxes access control. For example, the access control unit relaxes the access control standards for administrators based on the submitter's job title information. For example, the access control unit tightens access control if the data submitter is a general user. For example, the access control unit sets strict access control standards for general users based on the submitter's job title information. The access control unit adjusts the access control standards according to the data submitter's job title. For example, the access control unit sets access control standards according to the job title based on the submitter's job title information. This enables access control based on the attribute information of the data submitter. Some or all of the above-described processing in the access control unit may be performed using, for example, AI, or may be performed without using AI. For example, the access control unit inputs the submitter's attribute information into a generation AI and causes the generation AI to adjust the access control standards.

[0040] The access control unit can perform access control based on the geographical distribution of data during access control. The access control unit applies strict access control to international data, for example. For example, the access control unit sets strict access control standards for international data. Apply standard access control to domestic data. For example, the access control unit sets standard access control standards for domestic data. Apply relaxed access control to local data. For example, the access control unit sets relaxed access control standards for local data. This enables access control according to the geographical distribution of data. Some or all of the above-mentioned processing in the access control unit may be performed using AI, for example, or may be performed without using AI. For example, the access control unit inputs the geographical distribution of data to a generation AI and has the generation AI set the access control standards.

[0041] The access control unit can improve the accuracy of access control by referring to literature related to the data during access control. The access control unit, for example, sets optimal access control criteria based on literature related to the data. For example, the access control unit refers to literature related to the data and sets access control criteria. The access control unit adjusts the level of detail of access control by referring to literature related to the data. For example, the access control unit adjusts the level of detail of access control based on literature related to the data. The timing of access control is optimized based on literature related to the data. For example, the access control unit sets the timing of access control based on literature related to the data. In this way, the accuracy of access control is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the access control unit may be performed using AI, for example, or may be performed without using AI. For example, the access control unit inputs literature related to the data into a generation AI and has the generation AI select an algorithm for improving the accuracy of access control.

[0042] During learning, the learning unit can optimize the learning algorithm based on past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. For example, the learning unit refers to past training data sets and selects an optimal learning algorithm. The learning unit adjusts parameters of the learning algorithm by referring to past learning data. For example, the learning unit adjusts parameters of the learning algorithm based on past model parameters. The accuracy of the learning algorithm is improved based on past learning data. For example, the learning unit improves the accuracy of the learning algorithm based on past learning data. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and have the generation AI optimize the learning algorithm.

[0043] During learning, the learning unit can adjust the learning order based on the relevance of the data. The learning unit, for example, prioritizes highly relevant data in learning. For example, the learning unit analyzes the correlation of the data and prioritizes learning of highly relevant data. Less relevant data is put off for learning. For example, the learning unit analyzes the dependency of the data and puts less relevant data off for learning. The learning order is optimized based on the relevance of the data. For example, the learning unit optimizes the learning order based on the relevance of the data. This makes it possible to optimize the learning order based on the relevance of the data. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, AI. For example, the learning unit inputs the relevance of the data to the generation AI and causes the generation AI to optimize the learning order.

[0044] During monitoring, the monitoring unit can optimize current monitoring based on past monitoring data. The monitoring unit, for example, selects an optimal monitoring method based on past monitoring data. For example, the monitoring unit refers to past monitoring logs and selects an optimal monitoring method. The monitoring unit adjusts monitoring parameters by referring to past monitoring data. For example, the monitoring unit adjusts monitoring parameters based on past alert data. The accuracy of monitoring is improved based on past monitoring data. For example, the monitoring unit improves monitoring accuracy based on past monitoring data. In this way, the accuracy of monitoring is improved by referring to past monitoring data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit inputs past monitoring data into a generation AI and has the generation AI optimize current monitoring.

[0045] During monitoring, the monitoring unit can apply different monitoring methods to different data categories. For example, the monitoring unit applies a keyword monitoring method to text data. For example, the monitoring unit monitors specific keywords in the text data to detect anomalies. For image data, the monitoring unit applies an image analysis method. For example, the monitoring unit analyzes specific patterns in the image data to detect anomalies. For audio data, the monitoring unit applies an audio analysis method. For example, the monitoring unit analyzes specific tones or patterns in the audio data to detect anomalies. This makes it possible to apply the optimal monitoring method depending on the data category. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit inputs the data category into a generation AI and has the generation AI select the optimal monitoring method.

[0046] During monitoring, the monitoring unit can analyze changes in monitoring based on the time of data submission. For example, the monitoring unit monitors the latest data in real time and immediately analyzes changes. For example, the monitoring unit monitors the latest data in real time and detects anomalies. For past data, the monitoring unit periodically monitors and analyzes changes. For example, the monitoring unit periodically monitors past data and detects anomalies. For data over a specific period, the monitoring unit monitors data over a specific period and analyzes changes. For example, the monitoring unit monitors data over a specific period and detects anomalies. This makes it possible to analyze changes in monitoring based on the time of data submission. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit may input the time of data submission to a generation AI and have the generation AI analyze changes in monitoring.

[0047] During monitoring, the monitoring unit can analyze the monitoring by referring to market data related to the data. The monitoring unit, for example, analyzes the data to be monitored based on the related market data and detects anomalies. For example, the monitoring unit refers to the related market data and analyzes trends in the data to be monitored. For example, the monitoring unit predicts fluctuations in the data to be monitored based on the related market data. For example, the monitoring unit predicts fluctuations in the data to be monitored based on the related market data. This improves the accuracy of monitoring by referring to the market data related to the data. Some or all of the above-mentioned 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 related market data to a generation AI and have the generation AI perform monitoring analysis.

[0048] At the time of alerting, the alert unit can optimize the current alert based on past alert data. The alert unit, for example, selects the optimal alert method based on past alert data. For example, the alert unit refers to past alert logs and selects the optimal alert method. The alert unit adjusts alert parameters by referring to past alert data. For example, the alert unit adjusts alert parameters based on past alert history. The alert accuracy is improved based on past alert data. For example, the alert unit improves alert accuracy based on past alert data. In this way, the accuracy of alerts is improved by referring to past alert data. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit inputs past alert data into a generation AI and causes the generation AI to optimize the current alert.

[0049] The alert unit can apply different alert methods to different data categories when issuing an alert. For example, the alert unit applies a keyword alert method to text data. For example, the alert unit monitors specific keywords in the text data to detect anomalies. For image data, the alert unit applies an alert method using image analysis. For example, the alert unit analyzes specific patterns in the image data to detect anomalies. For audio data, the alert unit applies an alert method using audio analysis. For example, the alert unit analyzes specific tones or patterns in the audio data to detect anomalies. This makes it possible to apply the optimal alert method depending on the data category. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit inputs the data category into the generation AI and has the generation AI select the optimal alert method.

[0050] At the time of alerting, the alert unit can analyze changes in the alert based on the time of data submission. For example, the alert unit issues alerts in real time for the latest data and immediately analyzes changes. For example, the alert unit monitors the latest data in real time and detects anomalies. For past data, the alert unit issues alerts periodically and analyzes changes. For example, the alert unit periodically monitors past data and detects anomalies. For data from a specific period, the alert unit issues alerts for each period and analyzes changes. For example, the alert unit monitors data from a specific period for each period and detects anomalies. This makes it possible to analyze changes in alerts based on the time of data submission. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the time of data submission to the generation AI and have the generation AI analyze changes in the alert.

[0051] At the time of issuing an alert, the alert unit can analyze the alert by referring to market data related to the data. The alert unit, for example, analyzes the data that is the target of the alert based on the related market data and detects anomalies. For example, the alert unit refers to the related market data and analyzes trends in the data that is the target of the alert. For example, the alert unit predicts fluctuations in the data that is the target of the alert based on the related market data. For example, the alert unit predicts fluctuations in the data that is the target of the alert based on the related market data. In this way, by referring to the market data related to the data, the accuracy of the alert is improved. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the related market data to a generation AI and have the generation AI analyze the alert.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The data protection system may further include a data classifier. The data classifier may automatically classify provided data and distinguish between highly confidential and less confidential data. For example, the data classifier may use natural language processing technology to analyze text data and identify highly confidential information (e.g., personal information or financial data). The data classifier may also use image analysis technology to analyze image data and identify highly confidential images (e.g., medical images or scans of confidential documents). Furthermore, the data protection system may use voice analysis technology to analyze voice data and identify highly confidential conversation content. This allows the data protection system to perform appropriate encryption and access control depending on the confidentiality of the provided data.

[0054] The encryption unit can also include a data compression function. The data compression function compresses data before encryption to reduce its size. For example, the encryption unit can compress data using compression algorithms such as LZ77 or Huffman coding. This improves the efficiency of the encryption process and increases the data transfer speed. Furthermore, compressed data can maintain the same security level as the original data even after encryption. Furthermore, the data compression function can also reduce data storage capacity. This allows the data protection system to manage data efficiently and securely.

[0055] The access control unit can further adjust the access control criteria based on the user's behavioral history. For example, the access control unit can analyze the user's past access history and detect access that differs from normal behavioral patterns. If a user attempts access during a time when the user normally does not access data, the access control criteria can be set to be stricter. Similarly, if a user attempts to access data that the user normally does not access, the access control criteria can be adjusted. This enables flexible access control based on the user's behavioral patterns, improving data security.

[0056] The learning unit can further include a function to evaluate the reliability of data. For example, the learning unit can check the origin and creator of the provided data and prioritize highly reliable data for learning. To evaluate the reliability of data, the learning unit can analyze data metadata (creation date and time, creator, data change history, etc.). It can also cross-check the content of the data and determine that consistent data is highly reliable. This allows the learning unit to build generative AI using highly reliable data and provide more accurate results.

[0057] The monitoring unit can also be equipped with a real-time anomaly detection function. For example, the monitoring unit can monitor the usage status of the provided data in real time and immediately detect abnormal access or unauthorized operations. For real-time anomaly detection, a machine learning algorithm can be used to learn normal access patterns and detect abnormal patterns. Furthermore, if an abnormality is detected, an alert can be immediately issued to notify the administrator. This makes it possible to ensure the safety of data in real time.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The encryption unit encrypts the company's big data. For example, the encryption unit encrypts the data using public key cryptography. Public key cryptography can use algorithms such as Rivest-Shamir-Adleman (RSA) or Elliptic Curve Cryptography (ECC). RSA uses a public and private key pair to encrypt and decrypt data. ECC takes advantage of the mathematical properties of elliptic curves to provide high security with a smaller key size. Step 2: The access control unit makes the data encrypted by the encryption unit accessible only to the specific generation AI. For example, the access control unit introduces an authentication system to allow access only to the specific generation AI. The authentication system can use password authentication or biometric authentication (fingerprint authentication, face authentication, etc.). Step 3: The learning unit learns the data accessed by the access control unit. For example, the learning unit learns from the provided data and builds a generative AI specialized in specialized content. The generative AI can learn content specialized in a specific field, such as medical data or financial data. Step 4: The monitoring unit monitors the usage history of the data learned by the learning unit. For example, the monitoring unit monitors the usage history of the data and issues an alert if there is unauthorized access. The monitoring unit can monitor abnormal access patterns, the number of authentication failures, etc. Step 5: The alert unit issues an alert if unauthorized access is detected by the monitoring unit. For example, the alert unit sets the trigger conditions for the alert and determines the method of notification for the alert. Alert notification methods include pop-up display and email notification.

[0060] (Example 2) A data protection system according to an embodiment of the present invention is a system that prevents other companies from learning its own big data when the data is trained by a generation AI. When a company provides its big data to a generation AI, this data protection system encrypts the data and makes it accessible only to a specific generation AI. The generation AI then learns the provided data and builds a generation AI specialized in specialized content. Finally, the generation AI is controlled so that the trained data is not provided to other companies. This mechanism allows a company to safely train its own big data with a generation AI. For example, when a company provides its big data to a generation AI, the data is encrypted using public key cryptography and made accessible only to a specific generation AI. The generation AI then learns the provided data and builds a generation AI specialized in specialized content. The generation AI implements an authentication system to allow only specific generation AIs to access the data. Furthermore, to prevent the generation AI from providing the learned data to other companies, a system is implemented that monitors the data usage history and issues an alert if unauthorized access occurs. This mechanism allows a company to safely train its own big data with a generation AI. This data protection system allows a company to safely train its own big data with a generation AI.

[0061] A data protection system according to an embodiment includes an encryption unit, an access control unit, a learning unit, a monitoring unit, and an alert unit. The encryption unit encrypts a company's big data. For example, the encryption unit encrypts the data using public key cryptography. The public key cryptography can use algorithms such as Rivest-Shamir-Adleman (RSA) or Elliptic Curve Cryptography (ECC). For example, RSA encrypts and decrypts data using a public and private key pair. ECC utilizes the mathematical properties of elliptic curves to provide high security with a smaller key size. The access control unit makes the data encrypted by the encryption unit accessible only to a specific generation AI. For example, the access control unit implements an authentication system to grant access only to a specific generation AI. The authentication system can use password authentication or biometric authentication (such as fingerprint authentication or facial authentication). The learning unit learns from the data accessed by the access control unit. For example, the learning unit learns from the provided data and builds a generation AI specialized in specialized content. The generation AI can learn content specialized in a specific field, such as medical data or financial data. The monitoring unit monitors the usage history of the data learned by the learning unit. For example, the monitoring unit monitors the usage history of the data and issues an alert if unauthorized access occurs. The monitoring unit can monitor abnormal access patterns, the number of authentication failures, etc. The alert unit issues an alert if unauthorized access occurs by the monitoring unit. For example, the alert unit sets trigger conditions for the alert and determines the method of alert notification. Alert notification methods include pop-up display and email notification. As a result, the data protection system according to the embodiment allows the generation AI to safely learn corporate big data.

[0062] The encryption unit can encrypt data using a public key cryptography. Public key cryptography includes, for example, RSA (Rivest-Shamir-Adleman) and ECC (Elliptic Curve Cryptography). RSA encrypts and decrypts data using a pair of a public key and a private key. For example, RSA encrypts data using a public key and decrypts the data using a private key. ECC utilizes the mathematical properties of elliptic curves to provide high security with a smaller key size. For example, ECC encrypts and decrypts data using operations on points on elliptic curves. This makes data encryption more secure. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit may input data to a generation AI and have the generation AI select an encryption algorithm.

[0063] The access control unit can introduce an authentication system and allow access only to specific generation AIs. Authentication systems include, for example, password authentication and biometric authentication (fingerprint authentication, facial authentication, etc.). Password authentication uses a password set by the user. For example, the user enters a password, and the system verifies it. Biometric authentication uses the user's biometric information to perform authentication. For example, fingerprint authentication scans the user's fingerprint and compares it with registered fingerprint data. Facial authentication photographs the user's face with a camera and compares it with registered facial data. This improves data security by allowing access only to specific generation AIs. Some or all of the above-mentioned processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can input the user's authentication data into the generation AI and have the generation AI determine the authentication result.

[0064] The learning unit can learn from the provided data and build a generative AI specialized in specialized content. The learning unit learns content specialized in a specific field, such as medical data or financial data. For example, the learning unit learns medical data and builds a generative AI specialized in the medical field. Medical data includes patient medical records and test results. The learning unit analyzes this data and builds a generative AI specialized in the medical field. The learning unit also learns financial data and builds a generative AI specialized in the financial field. Financial data includes trading history and market data. The learning unit analyzes this data and builds a generative AI specialized in the financial field. In this way, a generative AI specialized in specialized content can be built. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input provided data into the generative AI and have the generative AI analyze and learn the data.

[0065] The monitoring unit monitors data usage history and can issue an alert if unauthorized access occurs. The monitoring unit monitors, for example, abnormal access patterns and the number of authentication failures. For example, the monitoring unit issues an alert if access that differs from a normal access pattern is detected. The monitoring unit can also issue an alert if a certain number of authentication failures occur. In this way, unauthorized access is detected and the security of data is ensured by issuing an alert. Some or all of the above-mentioned 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 access logs into a generation AI and have the generation AI detect abnormal access patterns.

[0066] The encryption unit can estimate the user's emotions and adjust the encryption strength based on the estimated user emotions. For example, if the user is feeling anxious, the encryption unit sets the encryption strength to maximum to ensure data security. For example, the encryption unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. For example, if the user is relaxed, the encryption strength is set to medium to prioritize processing speed. The encryption unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, the encryption strength is set to minimum to enable rapid data processing. The encryption unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This allows the encryption strength to be adjusted according to the user's emotions, optimizing data security and processing speed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 encryption unit may be performed using AI, or may be performed without using AI. For example, the encryption unit may input user emotion data into the generation AI and have the generation AI adjust the encryption strength.

[0067] The encryption unit can apply different encryption algorithms depending on the type of data during encryption. For example, the encryption unit applies a strong encryption algorithm such as AES-256 to highly confidential data. For example, AES-256 encrypts data using a 256-bit key and provides high security. For general data, the encryption unit applies a standard encryption algorithm such as AES-128. For example, AES-128 encrypts data using a 128-bit key and provides balanced security and performance. For less confidential data, the encryption unit applies a lightweight encryption algorithm such as RC4. For example, RC4 is a stream cipher that provides a simple structure and fast encryption. This enables optimal encryption depending on the type of data. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, for example, or without AI. For example, the encryption unit inputs the type of data into a generation AI and has the generation AI select the optimal encryption algorithm.

[0068] During encryption, the encryption unit can adjust the encryption level based on the importance of the data. For example, the encryption unit applies multiple encryption layers to highly important data to increase the level of detail. For example, the encryption unit applies double encryption using AES-256 and RSA to highly important data. For medium-importance data, the encryption unit applies a single encryption layer to maintain standard level of detail. For example, the encryption unit encrypts data using AES-128. For low-importance data, the encryption unit applies simple encryption to reduce the level of detail. For example, the encryption unit encrypts data using RC4. This enables encryption according to the importance of the data. Some or all of the above-described processing in the encryption unit may be performed using AI, or may be performed without AI. For example, the encryption unit may input the importance of the data to the generation AI and cause the generation AI to adjust the encryption level.

[0069] The encryption unit can estimate the user's emotions and adjust the timing of encryption based on the estimated user emotions. For example, if the user is feeling stressed, the encryption unit immediately starts encryption to ensure data security. For example, the encryption unit captures the user's facial expression with a camera and estimates their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. If the user is relaxed, encryption is performed before sending the data. The encryption unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, encryption is performed after the data is received. The encryption unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This adjusts the timing of encryption according to the user's emotions to ensure data security. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 encryption unit may be performed using AI, or may be performed without using AI. For example, the encryption unit may input user emotion data into the generation AI and have the generation AI adjust the timing of encryption.

[0070] During encryption, the encryption unit can determine encryption priorities based on the geographic distribution of data. For example, the encryption unit gives the highest priority to international data. For example, the encryption unit encrypts international data using AES-256. For domestic data, encryption is given a second priority. For example, the encryption unit encrypts domestic data using AES-128. For local data, encryption is given last. For example, the encryption unit encrypts local data using RC4. This enables encryption according to the geographic distribution of data. Some or all of the above-described processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit may input the geographic distribution of data to the generation AI and have the generation AI determine the encryption priorities.

[0071] The encryption unit can improve the accuracy of encryption by referring to literature related to the data during encryption. For example, the encryption unit selects an optimal encryption algorithm based on literature related to the data. For example, the encryption unit selects an encryption algorithm such as AES-256 or RSA by referring to literature related to the data. The encryption unit adjusts the level of encryption detail by referring to literature related to the data. For example, the encryption unit applies multiple encryption layers based on literature related to the data. The encryption timing is optimized based on literature related to the data. For example, the encryption unit performs encryption before transmitting or after receiving data based on literature related to the data. In this way, the accuracy of encryption is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, for example, or may be performed without using AI. For example, the encryption unit inputs literature related to the data into a generation AI and causes the generation AI to select an algorithm for improving the accuracy of encryption.

[0072] The access control unit can estimate the user's emotions and adjust the access control criteria based on the estimated user emotions. For example, if the user is feeling anxious, the access control unit sets stricter access control criteria. For example, the access control unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. If the user is relaxed, the access control unit relaxes the access control criteria. The access control unit can also record the user's voice and estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, the access control unit can quickly set the access control criteria. The access control unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This ensures data security by adjusting the access control criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 access control unit may be performed using AI, or may be performed without using AI. For example, the access control unit may input user emotion data into the generation AI and cause the generation AI to adjust the access control criteria.

[0073] The access control unit can improve the accuracy of access control based on the interrelationships between data during access control. The access control unit, for example, analyzes the interrelationships between data and performs strict access control on highly related data. For example, the access control unit analyzes data dependencies and sets strict access control standards for highly related data. The access control unit determines the priority of access control based on the interrelationships between data. For example, the access control unit sets the priority of access control based on the interrelationships between data. The access control unit adjusts the level of detail of access control taking into account the interrelationships between data. For example, the access control unit adjusts the level of detail of access control based on the interrelationships between data. This enables access control based on the interrelationships between data. Some or all of the above-mentioned processing in the access control unit may be performed using, for example, AI, or may be performed without using AI. For example, the access control unit inputs the interrelationships between data into a generation AI and has the generation AI select an algorithm for improving the accuracy of access control.

[0074] The access control unit can perform access control based on the attribute information of the data submitter during access control. For example, if the data submitter is an administrator, the access control unit relaxes access control. For example, the access control unit relaxes the access control standards for administrators based on the submitter's job title information. For example, the access control unit tightens access control if the data submitter is a general user. For example, the access control unit sets strict access control standards for general users based on the submitter's job title information. The access control unit adjusts the access control standards according to the data submitter's job title. For example, the access control unit sets access control standards according to the job title based on the submitter's job title information. This enables access control based on the attribute information of the data submitter. Some or all of the above-described processing in the access control unit may be performed using, for example, AI, or may be performed without using AI. For example, the access control unit inputs the submitter's attribute information into a generation AI and causes the generation AI to adjust the access control standards.

[0075] The access control unit can estimate the user's emotions and adjust the display order of access control results based on the estimated user emotions. For example, if the user is feeling anxious, the access control unit displays important access control results first. For example, the access control unit captures the user's facial expression with a camera and estimates their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. If the user is relaxed, detailed access control results are displayed sequentially. The access control unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, it can display access control results that highlight the main points first. The access control unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations. This ensures data security by adjusting the display order of access control results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may 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 access control unit may be performed using AI, or may be performed without using AI. For example, the access control unit may input user emotion data into the generation AI and cause the generation AI to adjust the order in which the results of access control are displayed.

[0076] The access control unit can perform access control based on the geographical distribution of data during access control. The access control unit applies strict access control to international data, for example. For example, the access control unit sets strict access control standards for international data. Apply standard access control to domestic data. For example, the access control unit sets standard access control standards for domestic data. Apply relaxed access control to local data. For example, the access control unit sets relaxed access control standards for local data. This enables access control according to the geographical distribution of data. Some or all of the above-mentioned processing in the access control unit may be performed using AI, for example, or may be performed without using AI. For example, the access control unit inputs the geographical distribution of data to a generation AI and has the generation AI set the access control standards.

[0077] The access control unit can improve the accuracy of access control by referring to literature related to the data during access control. The access control unit, for example, sets optimal access control criteria based on literature related to the data. For example, the access control unit refers to literature related to the data and sets access control criteria. The access control unit adjusts the level of detail of access control by referring to literature related to the data. For example, the access control unit adjusts the level of detail of access control based on literature related to the data. The timing of access control is optimized based on literature related to the data. For example, the access control unit sets the timing of access control based on literature related to the data. In this way, the accuracy of access control is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the access control unit may be performed using AI, for example, or may be performed without using AI. For example, the access control unit inputs literature related to the data into a generation AI and has the generation AI select an algorithm for improving the accuracy of access control.

[0078] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling anxious, the learning unit prioritizes highly reliable data for learning. For example, the learning unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. If the user is relaxed, the learning unit learns from a variety of data. The learning unit can also record the user's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, it selects data that can be quickly learned. The learning unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This enables the selection of training data according to the user's emotions. 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 learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input user emotion data to the generation AI and have the generation AI select learning data.

[0079] During learning, the learning unit can optimize the learning algorithm based on past learning data. The learning unit, for example, selects an optimal learning algorithm based on past learning data. For example, the learning unit refers to past training data sets and selects an optimal learning algorithm. The learning unit adjusts parameters of the learning algorithm by referring to past learning data. For example, the learning unit adjusts parameters of the learning algorithm based on past model parameters. The accuracy of the learning algorithm is improved based on past learning data. For example, the learning unit improves the accuracy of the learning algorithm based on past learning data. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and have the generation AI optimize the learning algorithm.

[0080] During learning, the learning unit can adjust the learning order based on the relevance of the data. The learning unit, for example, prioritizes highly relevant data in learning. For example, the learning unit analyzes the correlation of the data and prioritizes learning of highly relevant data. Less relevant data is put off for learning. For example, the learning unit analyzes the dependency of the data and puts less relevant data off for learning. The learning order is optimized based on the relevance of the data. For example, the learning unit optimizes the learning order based on the relevance of the data. This makes it possible to optimize the learning order based on the relevance of the data. Some or all of the above-mentioned processing in the learning unit may be performed using, or without, AI. For example, the learning unit inputs the relevance of the data to the generation AI and causes the generation AI to optimize the learning order.

[0081] The monitoring unit can estimate the user's emotions and adjust the monitoring display method based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit provides a simple, highly visible display method. For example, the monitoring unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. If the user is relaxed, the monitoring unit provides a display method that includes detailed information. The monitoring unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, the monitoring unit provides a display method that focuses on the key points. The monitoring unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide a monitoring display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data into the generation AI and have the generation AI adjust the display method of the monitoring.

[0082] During monitoring, the monitoring unit can optimize current monitoring based on past monitoring data. The monitoring unit, for example, selects an optimal monitoring method based on past monitoring data. For example, the monitoring unit refers to past monitoring logs and selects an optimal monitoring method. The monitoring unit adjusts monitoring parameters by referring to past monitoring data. For example, the monitoring unit adjusts monitoring parameters based on past alert data. The accuracy of monitoring is improved based on past monitoring data. For example, the monitoring unit improves monitoring accuracy based on past monitoring data. In this way, the accuracy of monitoring is improved by referring to past monitoring data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit inputs past monitoring data into a generation AI and has the generation AI optimize current monitoring.

[0083] During monitoring, the monitoring unit can apply different monitoring methods to different data categories. For example, the monitoring unit applies a keyword monitoring method to text data. For example, the monitoring unit monitors specific keywords in the text data to detect anomalies. For image data, the monitoring unit applies an image analysis method. For example, the monitoring unit analyzes specific patterns in the image data to detect anomalies. For audio data, the monitoring unit applies an audio analysis method. For example, the monitoring unit analyzes specific tones or patterns in the audio data to detect anomalies. This makes it possible to apply the optimal monitoring method depending on the data category. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit inputs the data category into a generation AI and has the generation AI select the optimal monitoring method.

[0084] The monitoring unit can estimate the user's emotions and adjust the importance of monitoring based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit prioritizes and displays important monitoring items. For example, the monitoring unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. If the user is relaxed, detailed monitoring items are displayed sequentially. The monitoring unit can also record the user's voice and estimate emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, it can display monitoring items that highlight the key points first. The monitoring unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide a monitoring importance level that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data into the generation AI and have the generation AI adjust the importance of monitoring.

[0085] During monitoring, the monitoring unit can analyze changes in monitoring based on the time of data submission. For example, the monitoring unit monitors the latest data in real time and immediately analyzes changes. For example, the monitoring unit monitors the latest data in real time and detects anomalies. For past data, the monitoring unit periodically monitors and analyzes changes. For example, the monitoring unit periodically monitors past data and detects anomalies. For data over a specific period, the monitoring unit monitors data over a specific period and analyzes changes. For example, the monitoring unit monitors data over a specific period and detects anomalies. This makes it possible to analyze changes in monitoring based on the time of data submission. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit may input the time of data submission to a generation AI and have the generation AI analyze changes in monitoring.

[0086] During monitoring, the monitoring unit can analyze the monitoring by referring to market data related to the data. The monitoring unit, for example, analyzes the data to be monitored based on the related market data and detects anomalies. For example, the monitoring unit refers to the related market data and analyzes trends in the data to be monitored. For example, the monitoring unit predicts fluctuations in the data to be monitored based on the related market data. For example, the monitoring unit predicts fluctuations in the data to be monitored based on the related market data. This improves the accuracy of monitoring by referring to the market data related to the data. Some or all of the above-mentioned 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 related market data to a generation AI and have the generation AI perform monitoring analysis.

[0087] The alert unit can estimate the user's emotions and adjust the alert display method based on the estimated user emotions. For example, if the user is feeling anxious, the alert unit displays a simple, highly visible alert. For example, the alert unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. If the user is relaxed, an alert containing detailed information is displayed. The alert unit can also record the user's voice and estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, an alert that focuses on the main points is displayed. The alert unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide a method for displaying alerts according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 alert unit may be performed using AI, or may be performed without using AI. For example, the alert unit may input user emotion data into the generation AI and cause the generation AI to adjust the way the alert is displayed.

[0088] At the time of alerting, the alert unit can optimize the current alert based on past alert data. The alert unit, for example, selects the optimal alert method based on past alert data. For example, the alert unit refers to past alert logs and selects the optimal alert method. The alert unit adjusts alert parameters by referring to past alert data. For example, the alert unit adjusts alert parameters based on past alert history. The alert accuracy is improved based on past alert data. For example, the alert unit improves alert accuracy based on past alert data. In this way, the accuracy of alerts is improved by referring to past alert data. Some or all of the above-described processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit inputs past alert data into a generation AI and causes the generation AI to optimize the current alert.

[0089] The alert unit can apply different alert methods to different data categories when issuing an alert. For example, the alert unit applies a keyword alert method to text data. For example, the alert unit monitors specific keywords in the text data to detect anomalies. For image data, the alert unit applies an alert method using image analysis. For example, the alert unit analyzes specific patterns in the image data to detect anomalies. For audio data, the alert unit applies an alert method using audio analysis. For example, the alert unit analyzes specific tones or patterns in the audio data to detect anomalies. This makes it possible to apply the optimal alert method depending on the data category. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit inputs the data category into the generation AI and has the generation AI select the optimal alert method.

[0090] The alert unit can estimate the user's emotions and adjust the importance of alerts based on the estimated user emotions. For example, if the user is feeling anxious, the alert unit prioritizes displaying important alerts. For example, the alert unit captures the user's facial expression with a camera and estimates their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. If the user is relaxed, detailed alerts are displayed sequentially. The alert unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to calculate an emotion score. If the user is in a hurry, it can display an alert that focuses on the main points first. The alert unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations. This makes it possible to provide alert importance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 alert unit may be performed using AI, or may be performed without using AI. For example, the alert unit may input user emotion data into the generation AI and have the generation AI adjust the importance of the alert.

[0091] At the time of alerting, the alert unit can analyze changes in the alert based on the time of data submission. For example, the alert unit issues alerts in real time for the latest data and immediately analyzes changes. For example, the alert unit monitors the latest data in real time and detects anomalies. For past data, the alert unit issues alerts periodically and analyzes changes. For example, the alert unit periodically monitors past data and detects anomalies. For data from a specific period, the alert unit issues alerts for each period and analyzes changes. For example, the alert unit monitors data from a specific period for each period and detects anomalies. This makes it possible to analyze changes in alerts based on the time of data submission. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the time of data submission to the generation AI and have the generation AI analyze changes in the alert.

[0092] At the time of issuing an alert, the alert unit can analyze the alert by referring to market data related to the data. The alert unit, for example, analyzes the data that is the target of the alert based on the related market data and detects anomalies. For example, the alert unit refers to the related market data and analyzes trends in the data that is the target of the alert. For example, the alert unit predicts fluctuations in the data that is the target of the alert based on the related market data. For example, the alert unit predicts fluctuations in the data that is the target of the alert based on the related market data. In this way, by referring to the market data related to the data, the accuracy of the alert is improved. Some or all of the above-mentioned processing in the alert unit may be performed using, for example, AI, or may be performed without using AI. For example, the alert unit can input the related market data to a generation AI and have the generation AI analyze the alert. === Hard Collateral 1-1 === Each of the multiple elements, including the encryption unit, access control unit, learning unit, monitoring unit, and alert 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 encryption unit is realized by the processor 46 of the smart device 14 and encrypts data. The access control unit is realized by the specific processing unit 290 of the data processing device 12 and allows access only to a specific generation AI. The learning unit is realized by the control unit 46A of the smart device 14 and learns the provided data. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the data usage history. The alert unit is realized by the control unit 46A of the smart device 14 and issues an alert in the event of unauthorized access. === Hard Collateral 1-2 === Each of the multiple elements including the encryption unit, access control unit, learning unit, monitoring unit, and alert 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 encryption unit is realized by the processor 46 of the smart glasses 214 and encrypts data. The access control unit is realized by the specific processing unit 290 of the data processing device 12 and allows access only to a specific generation AI. The learning unit is realized by the control unit 46A of the smart glasses 214 and learns the provided data. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the data usage history. The alert unit is realized by the control unit 46A of the smart glasses 214 and issues an alert in the event of unauthorized access. === Hard Collateral 1-3 === Each of the multiple elements including the encryption unit, access control unit, learning unit, monitoring unit, and alert 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 encryption unit is realized by the processor 46 of the headset type terminal 314 and encrypts data. The access control unit is realized by the specific processing unit 290 of the data processing device 12 and allows access only to specific generation AIs. The learning unit is realized by the control unit 46A of the headset type terminal 314 and learns the provided data. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the data usage history. The alert unit is realized by the control unit 46A of the headset type terminal 314 and issues an alert in the event of unauthorized access. === Hard Collateral 1-4 === Each of the multiple elements including the encryption unit, access control unit, learning unit, monitoring unit, and alert unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the encryption unit is realized by the processor 46 of the robot 414 and encrypts data. The access control unit is realized by the specific processing unit 290 of the data processing device 12 and allows access only to a specific generation AI. The learning unit is realized by the control unit 46A of the robot 414 and learns the provided data. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and monitors the data usage history. The alert unit is realized by the control unit 46A of the robot 414 and issues an alert in the event of unauthorized access.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The data protection system may further include a data classifier. The data classifier may automatically classify provided data and distinguish between highly confidential and less confidential data. For example, the data classifier may use natural language processing technology to analyze text data and identify highly confidential information (e.g., personal information or financial data). The data classifier may also use image analysis technology to analyze image data and identify highly confidential images (e.g., medical images or scans of confidential documents). Furthermore, the data protection system may use voice analysis technology to analyze voice data and identify highly confidential conversation content. This allows the data protection system to perform appropriate encryption and access control depending on the confidentiality of the provided data.

[0095] The encryption unit can also include a data compression function. The data compression function compresses data before encryption to reduce its size. For example, the encryption unit can compress data using compression algorithms such as LZ77 or Huffman coding. This improves the efficiency of the encryption process and increases the data transfer speed. Furthermore, compressed data can maintain the same security level as the original data even after encryption. Furthermore, the data compression function can also reduce data storage capacity. This allows the data protection system to manage data efficiently and securely.

[0096] The access control unit can further adjust the access control criteria based on the user's behavioral history. For example, the access control unit can analyze the user's past access history and detect access that differs from normal behavioral patterns. If a user attempts access during a time when the user normally does not access data, the access control criteria can be set to be stricter. Similarly, if a user attempts to access data that the user normally does not access, the access control criteria can be adjusted. This enables flexible access control based on the user's behavioral patterns, improving data security.

[0097] The learning unit can further include a function to evaluate the reliability of data. For example, the learning unit can check the origin and creator of the provided data and prioritize highly reliable data for learning. To evaluate the reliability of data, the learning unit can analyze data metadata (creation date and time, creator, data change history, etc.). It can also cross-check the content of the data and determine that consistent data is highly reliable. This allows the learning unit to build generative AI using highly reliable data and provide more accurate results.

[0098] The monitoring unit can also be equipped with a real-time anomaly detection function. For example, the monitoring unit can monitor the usage status of the provided data in real time and immediately detect abnormal access or unauthorized operations. For real-time anomaly detection, a machine learning algorithm can be used to learn normal access patterns and detect abnormal patterns. Furthermore, if an abnormality is detected, an alert can be immediately issued to notify the administrator. This makes it possible to ensure the safety of data in real time.

[0099] The encryption unit can estimate the user's emotions and select an encryption algorithm based on the estimated user emotions. For example, if the user feels anxious, a stronger encryption algorithm (e.g., AES-256) can be selected to ensure data security. If the user feels relaxed, a standard encryption algorithm (e.g., AES-128) can be selected to prioritize processing speed. The encryption unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. This makes it possible to select the optimal encryption algorithm according to the user's emotions.

[0100] The access control unit can estimate the user's emotions and select an access control method based on the estimated user emotions. For example, if the user feels anxious, a stricter access control method (e.g., two-factor authentication) can be selected to ensure data security. If the user feels relaxed, a standard access control method (e.g., password authentication) can be selected. The access control unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to select the optimal access control method according to the user's emotions.

[0101] The learning unit can estimate the user's emotions and adjust the learning progress speed based on the estimated user emotions. For example, if the user feels anxious, the learning progress speed can be slowed down and detailed explanations can be provided to deepen understanding. If the user feels relaxed, the learning progress speed can be set to normal, allowing for efficient learning. The learning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, it can also record the user's voice and estimate their emotions using voice analysis technology. This makes it possible to provide an optimal learning progress speed according to the user's emotions.

[0102] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user feels anxious, the monitoring frequency can be increased and real-time monitoring can be performed. If the user feels relaxed, the monitoring frequency can be set to normal and regular monitoring can be performed. The monitoring unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Furthermore, the monitoring unit can also record the user's voice and estimate the emotions using voice analysis technology. This makes it possible to provide an optimal monitoring frequency according to the user's emotions.

[0103] The alert unit can estimate the user's emotions and adjust the alert notification method based on the estimated user emotions. For example, if the user is feeling anxious, the alert can be notified in a way that immediately catches the user's attention, such as a pop-up display or a voice notification. If the user is relaxed, a regular notification method, such as an email notification or a dashboard display, can be used. The alert unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. It can also record the user's voice and estimate the emotion using voice analysis technology. This makes it possible to provide the optimal alert notification method according to the user's emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The encryption unit encrypts the company's big data. For example, the encryption unit encrypts the data using public key cryptography. Public key cryptography can use algorithms such as Rivest-Shamir-Adleman (RSA) or Elliptic Curve Cryptography (ECC). RSA uses a public and private key pair to encrypt and decrypt data. ECC takes advantage of the mathematical properties of elliptic curves to provide high security with a smaller key size. Step 2: The access control unit makes the data encrypted by the encryption unit accessible only to the specific generation AI. For example, the access control unit introduces an authentication system to allow access only to the specific generation AI. The authentication system can use password authentication or biometric authentication (fingerprint authentication, face authentication, etc.). Step 3: The learning unit learns the data accessed by the access control unit. For example, the learning unit learns from the provided data and builds a generative AI specialized in specialized content. The generative AI can learn content specialized in a specific field, such as medical data or financial data. Step 4: The monitoring unit monitors the usage history of the data learned by the learning unit. For example, the monitoring unit monitors the usage history of the data and issues an alert if there is unauthorized access. The monitoring unit can monitor abnormal access patterns, the number of authentication failures, etc. Step 5: The alert unit issues an alert if unauthorized access is detected by the monitoring unit. For example, the alert unit sets the trigger conditions for the alert and determines the method of notification for the alert. Alert notification methods include pop-up display and email notification.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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. An encryption department that encrypts the company's big data, an access control unit that makes the data encrypted by the encryption unit accessible only to the generating AI; a learning unit that learns data accessed by the access control unit; a monitoring unit that monitors the usage history of the data learned by the learning unit; an alert unit that issues an alert when unauthorized access is detected by the monitoring unit. A system characterized by:

2. The encryption unit Encrypting data using public key cryptography The system of claim 1 .

3. The access control unit Introducing an authentication system to allow access only to specific generated AI The system of claim 1 .

4. The learning unit Learn from the data provided and build content-specific generative AI The system of claim 1 .

5. The monitoring unit Monitor data usage history and alert you to any unauthorized access The system of claim 1 .

6. The encryption unit Estimate user emotions and adjust encryption strength based on the estimated user emotions The system of claim 1 .

7. The encryption unit When encrypting, different encryption algorithms are applied depending on the type of data. The system of claim 1 .

8. The encryption unit When encrypting, adjust the encryption granularity based on the importance of the data The system of claim 1 .

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