System

Abstract

An object of a system according to an embodiment is to support introduction and operation of a generated AI and to provide an appropriate use method while ensuring security.SOLUTION: A system according to an embodiment includes a generated AI introduction supporting unit, a security ensuring unit, and a lecture unit. The generated AI introduction supporting unit supports introduction of the generated AI. The security ensuring unit ensures security through a dedicated line and cloud management. The lecture section lectures an input method and an output utilization method.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there were challenges in introducing and operating generative AI, such as difficulty in ensuring security and understanding how to use it appropriately.

[0005] The system according to the embodiment aims to support the introduction and operation of generative AI and provide appropriate usage methods while ensuring security. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI introduction support unit, a security assurance unit, and a lecture unit. The generation AI introduction support unit supports the introduction of the generation AI. The security assurance unit ensures security through a dedicated line and cloud management. The lecture unit provides lectures on input methods and output utilization methods. [Effects of the Invention]

[0007] The system according to the embodiment can support the introduction and operation of generative AI and provide appropriate usage methods while ensuring security. [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) The generative AI introduction support system according to an embodiment of the present invention is a system that implements the introduction, management, and line rental of generative AI for corporations. This system provides a comprehensive service from the introduction of generative AI to its management and network sales, allowing corporations to use generative AI with peace of mind. As a result, the generative AI introduction support system allows corporations to introduce and operate generative AI with peace of mind.

[0029] The generative AI implementation support system according to the embodiment includes a generative AI implementation support unit, a security assurance unit, and a lecture unit. The generative AI implementation support unit supports the implementation of generative AI. For example, it provides lectures on basic usage and configuration methods for generative AI. It can also provide lectures on how to use generative AI to analyze internal data. It can also provide lectures on the procedure for creating reports using generative AI. The security assurance unit ensures security through dedicated lines and cloud management. For example, it securely transmits and receives data using dedicated lines. It can also protect and manage data through cloud management. It can also encrypt data and control access to prevent unauthorized access and data leaks. The lecture unit provides lectures on input methods and output utilization methods. For example, it provides lectures on input methods and output utilization methods for generative AI. It can also perform regular maintenance, updates, and troubleshooting of generative AI. It can also use an emotion estimation function to analyze user emotions in real time and automatically select the explanation method that is easiest for users to understand. This allows the generative AI implementation support system to enable businesses to implement and operate generative AI with confidence.

[0030] The Generative AI Implementation Support Department can provide lectures on the basic usage and configuration of Generative AI. For example, when lecturing on the basic usage of Generative AI, the Generative AI Implementation Support Department will demonstrate specific operating procedures. For example, they will explain how to use the Generative AI interface and how to input basic commands. Furthermore, when lecturing on how to configure Generative AI, they will provide detailed explanations of the steps from initial setup to customized configuration. For example, they will show how to create a user account and how to adjust security settings. Furthermore, when lecturing on how to create prompts for Generative AI, they will provide specific explanations of effective prompt creation techniques. For example, they will show the structure of the prompt and how to select keywords. This will enable them to provide corporations with the basic usage and configuration of Generative AI.

[0031] The Generative AI Implementation Support Department can provide lectures on how to analyze internal data using generative AI. For example, when providing a lecture on how to analyze internal data using generative AI, the Generative AI Implementation Support Department will explain the specific steps for importing data. For example, they will show how to read CSV files and the steps for preprocessing data. They will also explain specific analysis methods for analyzing internal data using generative AI. For example, they will show techniques for data clustering and trend analysis. They will also lecture on how to interpret the results of analyzing internal data using generative AI. For example, they will show how to visualize the analysis results and the steps for creating reports. This will allow them to provide corporations with a method for analyzing internal data using generative AI.

[0032] The Generative AI Implementation Support Department can provide lectures on the procedures for creating reports using generative AI. For example, when providing lectures on the procedures for creating reports using generative AI, the Generative AI Implementation Support Department will explain how to use specific report templates. For example, they will show how to select a standard report format and how to customize a template. In addition, when creating a report using generative AI, they will explain the entire process from importing data to generating the report. For example, they will show how to import data, analysis methods, and the procedure for generating a report. In addition, when creating a report using generative AI, they will provide lectures on how to structure an effective report. For example, they will show how to highlight important data points and how to create visually easy-to-understand graphs. In this way, they can provide corporations with the procedures for creating reports using generative AI.

[0033] The security assurance department can send and receive data safely using a dedicated line. The security assurance department, for example, builds a system that can send and receive data safely using a dedicated line. For example, it can implement data encryption and authentication protocols to ensure the security of communications. It can also introduce methods to minimize security risks that arise when sending and receiving data by using a dedicated line. For example, it can install a monitoring system for the dedicated line to detect unauthorized access. It can also formulate security policies to ensure the security of communications when sending and receiving data using a dedicated line. For example, it can encrypt data before sending it and define procedures for verifying data after receiving it. This allows it to send and receive data safely using a dedicated line.

[0034] The Security Assurance Department can protect and manage data through cloud management. For example, the Security Assurance Department builds a system that protects and manages data through cloud management. For example, it provides data backup and restore functions to ensure data safety. In addition, by using cloud management, it introduces methods for efficiently protecting and managing data. For example, it performs access control and records audit logs to prevent unauthorized access. In addition, it formulates security policies when protecting and managing data through cloud management. For example, it defines procedures for encrypting data and setting access permissions. This makes it possible to protect and manage data through cloud management.

[0035] The security assurance unit can perform data encryption and access control to prevent unauthorized access and data leakage. The security assurance unit, for example, encrypts data and builds a system that prevents unauthorized access and data leakage. For example, the security assurance unit encrypts data using the AES encryption algorithm to ensure communication security. The security assurance unit also implements access control and techniques to prevent unauthorized access. For example, the security assurance unit performs user authentication and sets access permissions to prevent unauthorized access to data. The security assurance unit also combines data encryption and access control to build a system that prevents unauthorized access and data leakage. For example, the security assurance unit encrypts data before transmission and controls access after reception. This enables the security assurance unit to encrypt data and control access to prevent unauthorized access and data leakage.

[0036] The lecture department can give lectures on how to input data into generative AI and how to utilize its output. For example, when giving lectures on how to input data into generative AI and how to utilize its output, the lecture department will explain the specific steps. For example, they will demonstrate methods such as text input, voice input, and image input. They will also explain how to utilize the data output by generative AI. For example, they will demonstrate how to utilize it for report creation, data analysis, presentations, etc. This will enable them to provide corporations with information on how to input data into generative AI and how to utilize its output.

[0037] The lecture department can perform regular maintenance, updates, and troubleshooting of generative AI. For example, the lecture department will build a system for regular maintenance, updates, and troubleshooting of generative AI. For example, they will perform software updates, hardware checks, and performance tuning. In addition, when troubleshooting generative AI, they will analyze error logs and demonstrate how to identify and resolve problems. This will enable them to provide regular maintenance, updates, and troubleshooting of generative AI to corporations.

[0038] The lecture department can automatically analyze a user's business process and propose optimal generation AI settings and prompts. The lecture department will, for example, build a system that automatically analyzes a user's business process and proposes optimal generation AI settings. For example, it will analyze the business flow and automatically select the appropriate AI model and parameters. It will also develop an algorithm that proposes optimal prompts based on the results of the business process analysis. For example, it will present specific prompt examples according to the business content. It will also build a system that monitors a user's business process in real time and dynamically adjusts the generation AI settings and prompts. For example, it will automatically update the prompts according to the progress of the business. This will make it possible to provide optimal generation AI settings and prompts according to the user's business process.

[0039] The security assurance unit can automatically evaluate security risks when data is sent and received, and implement necessary measures in real time. The security assurance unit, for example, builds a system that automatically evaluates security risks when data is sent and received, and implements necessary measures in real time. For example, it performs risk assessment before data is sent, and applies necessary encryption and authentication measures. It also develops a security risk assessment algorithm and evaluates risks in real time when data is sent and received. For example, it calculates a risk score based on the content of the sent data and the reliability of the destination. It also builds a system that evaluates security risks when data is sent and received, and automatically implements necessary measures. For example, it applies additional security measures based on the risk assessment results. This makes it possible to evaluate security risks when data is sent and received, and implement necessary measures in real time.

[0040] The security assurance unit can detect anomalies based on user access history and prevent unauthorized access before it happens. The security assurance unit, for example, analyzes user access history and builds a system to detect anomalies. For example, it detects behavior that differs from normal access patterns and warns of the possibility of unauthorized access. It also develops anomaly detection algorithms based on access history data. For example, it analyzes access frequency and the IP address of the access source to identify abnormal access. It also builds a system that monitors user access history in real time and detects anomalies. For example, it immediately issues an alert if abnormal access is detected. This makes it possible to detect anomalies based on user access history and prevent unauthorized access before it happens.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The Generative AI Implementation Support Department provides support for the implementation of generative AI. For example, it provides lectures on the basic usage and configuration of generative AI. It can also provide lectures on how to use generative AI to analyze internal data and the procedure for creating reports using generative AI. The Security Assurance Department ensures security through dedicated lines and cloud management. For example, it securely transmits and receives data using dedicated lines. It can also protect and manage data through cloud management. It can also encrypt data and control access to prevent unauthorized access and data leaks. The Lecture Department provides lectures on input methods and output utilization. For example, it provides lectures on how to use generative AI input and output. It can also perform regular maintenance, updates, and troubleshooting of generative AI. It can also use emotion estimation to analyze user emotions in real time and automatically select the explanation method that is easiest for users to understand. This allows the Generative AI Implementation Support System to enable companies to implement and operate generative AI with confidence.

[0043] The Generative AI Implementation Support Department can provide lectures on the basic usage and configuration of Generative AI. For example, when lecturing on the basic usage of Generative AI, they will show specific operating procedures. For example, they will explain how to use the Generative AI interface and how to input basic commands. Furthermore, when lecturing on how to configure Generative AI, they will provide detailed explanations of the steps from initial setup to customized configuration. For example, they will show how to create a user account and how to adjust security settings. Furthermore, when lecturing on how to create prompts for Generative AI, they will provide specific explanations on how to create effective prompts. For example, they will show the structure of the prompt and how to select keywords. This will enable them to provide corporations with the basic usage and configuration of Generative AI.

[0044] The Generative AI Implementation Support Department can provide lectures on how to analyze internal data using generative AI. For example, when lecturing on how to analyze internal data using generative AI, they will explain the specific steps for importing data. For example, they will show how to read CSV files and the steps for preprocessing data. They will also explain specific analysis methods for analyzing internal data using generative AI. For example, they will show techniques for data clustering and trend analysis. They will also lecture on how to interpret the results of analyzing internal data using generative AI. For example, they will show how to visualize the analysis results and the steps for creating reports. This will allow them to provide companies with a method for analyzing internal data using generative AI.

[0045] The Generative AI Implementation Support Department can provide lectures on the procedures for creating reports using generative AI. For example, when lecturing on the procedures for creating reports using generative AI, they will explain how to use specific report templates. For example, they will show how to select a standard report format and how to customize a template. They will also explain the entire process from data import to report generation when creating a report using generative AI. For example, they will show how to import data, analysis methods, and the steps for generating a report. They will also provide lectures on how to effectively structure reports when creating reports using generative AI. For example, they will show how to highlight important data points and how to create visually easy-to-understand graphs. This will allow them to provide corporations with the procedures for creating reports using generative AI.

[0046] The Security Assurance Department can send and receive data safely using dedicated lines. For example, it builds a system that can send and receive data safely using dedicated lines. For example, it introduces data encryption and authentication protocols to ensure the security of communications. It also introduces methods to minimize security risks that arise when sending and receiving data by using dedicated lines. For example, it installs a monitoring system for dedicated lines to detect unauthorized access. It also formulates security policies to ensure the security of communications when sending and receiving data using dedicated lines. For example, it defines procedures for encrypting data before sending it and verifying data after it is received. This allows it to send and receive data safely using dedicated lines.

[0047] The Security Assurance Department can protect and manage data through cloud management. For example, it builds a system that protects and manages data through cloud management. For example, it provides data backup and restore functions to ensure data safety. It also introduces methods for efficient data protection and management by using cloud management. For example, it performs access control and records audit logs to prevent unauthorized access. It also formulates security policies when protecting and managing data through cloud management. For example, it defines procedures for encrypting data and setting access permissions. This makes it possible to protect and manage data through cloud management.

[0048] The security assurance department can prevent unauthorized access and data leaks by encrypting data and controlling access. For example, it can encrypt data and build a system that prevents unauthorized access and data leaks. For example, it can encrypt data using the AES encryption algorithm to ensure communication security. It can also implement access control and methods to prevent unauthorized access. For example, it can perform user authentication and set access permissions to prevent unauthorized access to data. It can also combine data encryption and access control to build a system that prevents unauthorized access and data leaks. For example, it can encrypt data before sending it and control access after receiving it. This can prevent unauthorized access and data leaks by encrypting data and controlling access.

[0049] The lecture department can give lectures on how to input data into generative AI and how to utilize its output. For example, when giving lectures on how to input data into generative AI and how to utilize its output, they will explain the specific steps. For example, they will show methods such as text input, voice input, and image input. They will also explain how to utilize the data output by generative AI. For example, they will show how to use it for report creation, data analysis, presentations, etc. This will enable them to provide corporations with information on how to input data into generative AI and how to utilize its output.

[0050] The lecture team will be able to perform regular maintenance, updates, and troubleshooting of generative AI. For example, they will build a system for regular maintenance, updates, and troubleshooting of generative AI. For example, they will perform software updates, hardware checks, and performance tuning. In addition, when troubleshooting generative AI, they will demonstrate how to analyze error logs and identify and resolve problems. This will enable them to provide regular maintenance, updates, and troubleshooting of generative AI to corporations.

[0051] The lecture department can automatically analyze a user's business process and propose optimal generation AI settings and prompts. For example, we will build a system that automatically analyzes a user's business process and proposes optimal generation AI settings. For example, we will analyze the business flow and automatically select the appropriate AI model and parameters. We will also develop an algorithm that proposes optimal prompts based on the results of the business process analysis. For example, we will present specific prompt examples according to the business content. We will also build a system that monitors a user's business process in real time and dynamically adjusts the generation AI settings and prompts. For example, we will automatically update the prompts according to the progress of the business. This will make it possible to provide optimal generation AI settings and prompts according to the user's business process.

[0052] The security assurance unit can automatically evaluate security risks when data is sent and received, and implement necessary measures in real time. For example, a system can be constructed that automatically evaluates security risks when data is sent and received, and implements necessary measures in real time. For example, a risk assessment can be performed before data is sent, and necessary encryption and authentication measures can be applied. A security risk assessment algorithm can also be developed to evaluate risks in real time when data is sent and received. For example, a risk score can be calculated based on the content of the sent data and the reliability of the destination. A system can also be constructed that evaluates security risks when data is sent and received, and automatically implements necessary measures. For example, additional security measures can be applied based on the risk assessment results. This makes it possible to evaluate security risks when data is sent and received, and implement necessary measures in real time.

[0053] The security assurance department can detect anomalies based on user access history and prevent unauthorized access before it happens. For example, a system can be built that analyzes user access history and detects anomalies. For example, it can detect behavior that differs from normal access patterns and warn of the possibility of unauthorized access. Anomaly detection algorithms can also be developed based on access history data. For example, it can analyze access frequency and the IP address from which the access originates to identify abnormal access. A system can also be built that monitors user access history in real time and detects anomalies. For example, it can immediately issue an alert if abnormal access is detected. This makes it possible to detect anomalies based on user access history and prevent unauthorized access before it happens.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The Generative AI Implementation Support Department will provide support for the implementation of Generative AI. For example, they will provide lectures on the basic usage and configuration of Generative AI. They can also provide lectures on how to use Generative AI to analyze internal data and the steps for creating reports. Step 2: The Security Department ensures security through dedicated lines and cloud management. For example, data is sent and received securely using dedicated lines, and data is protected and managed through cloud management. Data encryption and access control are also performed to prevent unauthorized access and data leaks. Step 3: The lecture department provides lectures on input methods and output utilization. For example, they provide lectures on input methods and output utilization for the generative AI. They can also perform regular maintenance, updates, and troubleshooting of the generative AI. Furthermore, they can use the emotion estimation function to analyze the user's emotions in real time and automatically select the explanation method that is easiest for the user to understand.

[0056] (Example 2) The generative AI introduction support system according to an embodiment of the present invention is a system that implements the introduction, management, and line rental of generative AI for corporations. This system provides a comprehensive service from the introduction of generative AI to its management and network sales, allowing corporations to use generative AI with peace of mind. As a result, the generative AI introduction support system allows corporations to introduce and operate generative AI with peace of mind.

[0057] The generative AI implementation support system according to the embodiment includes a generative AI implementation support unit, a security assurance unit, and a lecture unit. The generative AI implementation support unit supports the implementation of generative AI. For example, it provides lectures on basic usage and configuration methods for generative AI. It can also provide lectures on how to use generative AI to analyze internal data. It can also provide lectures on the procedure for creating reports using generative AI. The security assurance unit ensures security through dedicated lines and cloud management. For example, it securely transmits and receives data using dedicated lines. It can also protect and manage data through cloud management. It can also encrypt data and control access to prevent unauthorized access and data leaks. The lecture unit provides lectures on input methods and output utilization methods. For example, it provides lectures on input methods and output utilization methods for generative AI. It can also perform regular maintenance, updates, and troubleshooting of generative AI. It can also use an emotion estimation function to analyze user emotions in real time and automatically select the explanation method that is easiest for users to understand. This allows the generative AI implementation support system to enable businesses to implement and operate generative AI with confidence.

[0058] The Generative AI Implementation Support Department can provide lectures on the basic usage and configuration of Generative AI. For example, when lecturing on the basic usage of Generative AI, the Generative AI Implementation Support Department will demonstrate specific operating procedures. For example, they will explain how to use the Generative AI interface and how to input basic commands. Furthermore, when lecturing on how to configure Generative AI, they will provide detailed explanations of the steps from initial setup to customized configuration. For example, they will show how to create a user account and how to adjust security settings. Furthermore, when lecturing on how to create prompts for Generative AI, they will provide specific explanations of effective prompt creation techniques. For example, they will show the structure of the prompt and how to select keywords. This will enable them to provide corporations with the basic usage and configuration of Generative AI.

[0059] The Generative AI Implementation Support Department can provide lectures on how to analyze internal data using generative AI. For example, when providing a lecture on how to analyze internal data using generative AI, the Generative AI Implementation Support Department will explain the specific steps for importing data. For example, they will show how to read CSV files and the steps for preprocessing data. They will also explain specific analysis methods for analyzing internal data using generative AI. For example, they will show techniques for data clustering and trend analysis. They will also lecture on how to interpret the results of analyzing internal data using generative AI. For example, they will show how to visualize the analysis results and the steps for creating reports. This will allow them to provide corporations with a method for analyzing internal data using generative AI.

[0060] The Generative AI Implementation Support Department can provide lectures on the procedures for creating reports using generative AI. For example, when providing lectures on the procedures for creating reports using generative AI, the Generative AI Implementation Support Department will explain how to use specific report templates. For example, they will show how to select a standard report format and how to customize a template. In addition, when creating a report using generative AI, they will explain the entire process from importing data to generating the report. For example, they will show how to import data, analysis methods, and the procedure for generating a report. In addition, when creating a report using generative AI, they will provide lectures on how to structure an effective report. For example, they will show how to highlight important data points and how to create visually easy-to-understand graphs. In this way, they can provide corporations with the procedures for creating reports using generative AI.

[0061] The security assurance department can send and receive data safely using a dedicated line. The security assurance department, for example, builds a system that can send and receive data safely using a dedicated line. For example, it can implement data encryption and authentication protocols to ensure the security of communications. It can also introduce methods to minimize security risks that arise when sending and receiving data by using a dedicated line. For example, it can install a monitoring system for the dedicated line to detect unauthorized access. It can also formulate security policies to ensure the security of communications when sending and receiving data using a dedicated line. For example, it can encrypt data before sending it and define procedures for verifying data after receiving it. This allows it to send and receive data safely using a dedicated line.

[0062] The Security Assurance Department can protect and manage data through cloud management. For example, the Security Assurance Department builds a system that protects and manages data through cloud management. For example, it provides data backup and restore functions to ensure data safety. In addition, by using cloud management, it introduces methods for efficiently protecting and managing data. For example, it performs access control and records audit logs to prevent unauthorized access. In addition, it formulates security policies when protecting and managing data through cloud management. For example, it defines procedures for encrypting data and setting access permissions. This makes it possible to protect and manage data through cloud management.

[0063] The security assurance unit can perform data encryption and access control to prevent unauthorized access and data leakage. The security assurance unit, for example, encrypts data and builds a system that prevents unauthorized access and data leakage. For example, the security assurance unit encrypts data using the AES encryption algorithm to ensure communication security. The security assurance unit also implements access control and techniques to prevent unauthorized access. For example, the security assurance unit performs user authentication and sets access permissions to prevent unauthorized access to data. The security assurance unit also combines data encryption and access control to build a system that prevents unauthorized access and data leakage. For example, the security assurance unit encrypts data before transmission and controls access after reception. This enables the security assurance unit to encrypt data and control access to prevent unauthorized access and data leakage.

[0064] The lecture department can give lectures on how to input data into generative AI and how to utilize its output. For example, when giving lectures on how to input data into generative AI and how to utilize its output, the lecture department will explain the specific steps. For example, they will demonstrate methods such as text input, voice input, and image input. They will also explain how to utilize the data output by generative AI. For example, they will demonstrate how to utilize it for report creation, data analysis, presentations, etc. This will enable them to provide corporations with information on how to input data into generative AI and how to utilize its output.

[0065] The lecture department can perform regular maintenance, updates, and troubleshooting of generative AI. For example, the lecture department will build a system for regular maintenance, updates, and troubleshooting of generative AI. For example, they will perform software updates, hardware checks, and performance tuning. In addition, when troubleshooting generative AI, they will analyze error logs and demonstrate how to identify and resolve problems. This will enable them to provide regular maintenance, updates, and troubleshooting of generative AI to corporations.

[0066] The lecture department can use the emotion estimation function to analyze the user's emotions in real time and automatically select the explanation method that is easiest for the user to understand. The lecture department, for example, builds a system that uses the emotion estimation function to analyze the user's emotions in real time and selects the explanation method that is easiest for the user to understand. For example, it analyzes the user's facial expressions and tone of voice and presents an appropriate explanation style. It also develops an algorithm that automatically selects the optimal explanation method based on the user's emotional data. For example, it provides a detailed explanation if the user is relaxed and a concise explanation if the user is nervous. It also builds a system that uses the emotion estimation function to monitor the user's level of understanding in real time and adjusts the explanation method as needed. For example, if the user is confused, it provides additional explanation or examples. This makes it possible to provide the optimal explanation method according to the user's emotions.

[0067] The lecture department can automatically analyze a user's business process and propose optimal generation AI settings and prompts. The lecture department will, for example, build a system that automatically analyzes a user's business process and proposes optimal generation AI settings. For example, it will analyze the business flow and automatically select the appropriate AI model and parameters. It will also develop an algorithm that proposes optimal prompts based on the results of the business process analysis. For example, it will present specific prompt examples according to the business content. It will also build a system that monitors a user's business process in real time and dynamically adjusts the generation AI settings and prompts. For example, it will automatically update the prompts according to the progress of the business. This will make it possible to provide optimal generation AI settings and prompts according to the user's business process.

[0068] The security assurance unit can use the emotion estimation function to detect user anxiety in real time and propose appropriate security measures. The security assurance unit, for example, builds a system that uses the emotion estimation function to detect user anxiety in real time and proposes appropriate security measures. For example, it analyzes the user's facial expressions and tone of voice and suggests security enhancement measures if the user feels anxious. It also develops an algorithm that automatically proposes optimal security measures based on the user's emotion data. For example, it suggests additional authentication measures if the user feels anxious. It also builds a system that uses the emotion estimation function to monitor user anxiety in real time and adjust security measures as needed. For example, it strengthens security settings if the user feels anxious. This makes it possible to provide appropriate security measures according to the user's anxiety.

[0069] The security assurance unit can automatically evaluate security risks when data is sent and received, and implement necessary measures in real time. The security assurance unit, for example, builds a system that automatically evaluates security risks when data is sent and received, and implements necessary measures in real time. For example, it performs risk assessment before data is sent, and applies necessary encryption and authentication measures. It also develops a security risk assessment algorithm and evaluates risks in real time when data is sent and received. For example, it calculates a risk score based on the content of the sent data and the reliability of the destination. It also builds a system that evaluates security risks when data is sent and received, and automatically implements necessary measures. For example, it applies additional security measures based on the risk assessment results. This makes it possible to evaluate security risks when data is sent and received, and implement necessary measures in real time.

[0070] The security assurance unit can detect anomalies based on user access history and prevent unauthorized access before it happens. The security assurance unit, for example, analyzes user access history and builds a system to detect anomalies. For example, it detects behavior that differs from normal access patterns and warns of the possibility of unauthorized access. It also develops anomaly detection algorithms based on access history data. For example, it analyzes access frequency and the IP address of the access source to identify abnormal access. It also builds a system that monitors user access history in real time and detects anomalies. For example, it immediately issues an alert if abnormal access is detected. This makes it possible to detect anomalies based on user access history and prevent unauthorized access before it happens.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The Generative AI Implementation Support Department provides support for the implementation of generative AI. For example, it provides lectures on the basic usage and configuration of generative AI. It can also provide lectures on how to use generative AI to analyze internal data and the procedure for creating reports using generative AI. The Security Assurance Department ensures security through dedicated lines and cloud management. For example, it securely transmits and receives data using dedicated lines. It can also protect and manage data through cloud management. It can also encrypt data and control access to prevent unauthorized access and data leaks. The Lecture Department provides lectures on input methods and output utilization. For example, it provides lectures on how to use generative AI input and output. It can also perform regular maintenance, updates, and troubleshooting of generative AI. It can also use emotion estimation to analyze user emotions in real time and automatically select the explanation method that is easiest for users to understand. This allows the Generative AI Implementation Support System to enable companies to implement and operate generative AI with confidence.

[0073] The Generative AI Implementation Support Department can provide lectures on the basic usage and configuration of Generative AI. For example, when lecturing on the basic usage of Generative AI, they will show specific operating procedures. For example, they will explain how to use the Generative AI interface and how to input basic commands. Furthermore, when lecturing on how to configure Generative AI, they will provide detailed explanations of the steps from initial setup to customized configuration. For example, they will show how to create a user account and how to adjust security settings. Furthermore, when lecturing on how to create prompts for Generative AI, they will provide specific explanations on how to create effective prompts. For example, they will show the structure of the prompt and how to select keywords. This will enable them to provide corporations with the basic usage and configuration of Generative AI.

[0074] The Generative AI Implementation Support Department can provide lectures on how to analyze internal data using generative AI. For example, when lecturing on how to analyze internal data using generative AI, they will explain the specific steps for importing data. For example, they will show how to read CSV files and the steps for preprocessing data. They will also explain specific analysis methods for analyzing internal data using generative AI. For example, they will show techniques for data clustering and trend analysis. They will also lecture on how to interpret the results of analyzing internal data using generative AI. For example, they will show how to visualize the analysis results and the steps for creating reports. This will allow them to provide companies with a method for analyzing internal data using generative AI.

[0075] The Generative AI Implementation Support Department can provide lectures on the procedures for creating reports using generative AI. For example, when lecturing on the procedures for creating reports using generative AI, they will explain how to use specific report templates. For example, they will show how to select a standard report format and how to customize a template. They will also explain the entire process from data import to report generation when creating a report using generative AI. For example, they will show how to import data, analysis methods, and the steps for generating a report. They will also provide lectures on how to effectively structure reports when creating reports using generative AI. For example, they will show how to highlight important data points and how to create visually easy-to-understand graphs. This will allow them to provide corporations with the procedures for creating reports using generative AI.

[0076] The Security Assurance Department can send and receive data safely using dedicated lines. For example, it builds a system that can send and receive data safely using dedicated lines. For example, it introduces data encryption and authentication protocols to ensure the security of communications. It also introduces methods to minimize security risks that arise when sending and receiving data by using dedicated lines. For example, it installs a monitoring system for dedicated lines to detect unauthorized access. It also formulates security policies to ensure the security of communications when sending and receiving data using dedicated lines. For example, it defines procedures for encrypting data before sending it and verifying data after it is received. This allows it to send and receive data safely using dedicated lines.

[0077] The Security Assurance Department can protect and manage data through cloud management. For example, it builds a system that protects and manages data through cloud management. For example, it provides data backup and restore functions to ensure data safety. It also introduces methods for efficient data protection and management by using cloud management. For example, it performs access control and records audit logs to prevent unauthorized access. It also formulates security policies when protecting and managing data through cloud management. For example, it defines procedures for encrypting data and setting access permissions. This makes it possible to protect and manage data through cloud management.

[0078] The security assurance department can prevent unauthorized access and data leaks by encrypting data and controlling access. For example, it can encrypt data and build a system that prevents unauthorized access and data leaks. For example, it can encrypt data using the AES encryption algorithm to ensure communication security. It can also implement access control and methods to prevent unauthorized access. For example, it can perform user authentication and set access permissions to prevent unauthorized access to data. It can also combine data encryption and access control to build a system that prevents unauthorized access and data leaks. For example, it can encrypt data before sending it and control access after receiving it. This can prevent unauthorized access and data leaks by encrypting data and controlling access.

[0079] The lecture department can give lectures on how to input data into generative AI and how to utilize its output. For example, when giving lectures on how to input data into generative AI and how to utilize its output, they will explain the specific steps. For example, they will show methods such as text input, voice input, and image input. They will also explain how to utilize the data output by generative AI. For example, they will show how to use it for report creation, data analysis, presentations, etc. This will enable them to provide corporations with information on how to input data into generative AI and how to utilize its output.

[0080] The lecture team will be able to perform regular maintenance, updates, and troubleshooting of generative AI. For example, they will build a system for regular maintenance, updates, and troubleshooting of generative AI. For example, they will perform software updates, hardware checks, and performance tuning. In addition, when troubleshooting generative AI, they will demonstrate how to analyze error logs and identify and resolve problems. This will enable them to provide regular maintenance, updates, and troubleshooting of generative AI to corporations.

[0081] The lecture department can use the emotion estimation function to analyze the user's emotions in real time and automatically select the explanation method that is easiest for the user to understand. For example, we will build a system that uses the emotion estimation function to analyze the user's emotions in real time and select the explanation method that is easiest for the user to understand. For example, we will analyze the user's facial expressions and tone of voice and present an appropriate explanation style. We will also develop an algorithm that automatically selects the optimal explanation method based on the user's emotional data. For example, if the user is relaxed, we will provide a detailed explanation, and if they are nervous, we will provide a concise explanation. We will also build a system that uses the emotion estimation function to monitor the user's level of understanding in real time and adjust the explanation method as needed. For example, if the user is confused, we will provide additional explanation or examples. This will allow us to provide the optimal explanation method according to the user's emotions.

[0082] The lecture department can automatically analyze a user's business process and propose optimal generation AI settings and prompts. For example, we will build a system that automatically analyzes a user's business process and proposes optimal generation AI settings. For example, we will analyze the business flow and automatically select the appropriate AI model and parameters. We will also develop an algorithm that proposes optimal prompts based on the results of the business process analysis. For example, we will present specific prompt examples according to the business content. We will also build a system that monitors a user's business process in real time and dynamically adjusts the generation AI settings and prompts. For example, we will automatically update the prompts according to the progress of the business. This will make it possible to provide optimal generation AI settings and prompts according to the user's business process.

[0083] The security assurance unit can use the emotion estimation function to detect user anxiety in real time and suggest appropriate security measures. For example, we will build a system that uses the emotion estimation function to detect user anxiety in real time and suggest appropriate security measures. For example, we will analyze the user's facial expressions and tone of voice and suggest security enhancement measures if the user feels anxious. We will also develop an algorithm that automatically suggests optimal security measures based on the user's emotion data. For example, if the user feels anxious, we will suggest additional authentication measures. We will also build a system that uses the emotion estimation function to monitor user anxiety in real time and adjust security measures as needed. For example, if the user feels anxious, we will strengthen security settings. This will make it possible to provide appropriate security measures according to the user's anxiety.

[0084] The security assurance unit can automatically evaluate security risks when data is sent and received, and implement necessary measures in real time. For example, a system can be constructed that automatically evaluates security risks when data is sent and received, and implements necessary measures in real time. For example, a risk assessment can be performed before data is sent, and necessary encryption and authentication measures can be applied. A security risk assessment algorithm can also be developed to evaluate risks in real time when data is sent and received. For example, a risk score can be calculated based on the content of the sent data and the reliability of the destination. A system can also be constructed that evaluates security risks when data is sent and received, and automatically implements necessary measures. For example, additional security measures can be applied based on the risk assessment results. This makes it possible to evaluate security risks when data is sent and received, and implement necessary measures in real time.

[0085] The security assurance department can detect anomalies based on user access history and prevent unauthorized access before it happens. For example, a system can be built that analyzes user access history and detects anomalies. For example, it can detect behavior that differs from normal access patterns and warn of the possibility of unauthorized access. Anomaly detection algorithms can also be developed based on access history data. For example, it can analyze access frequency and the IP address from which the access originates to identify abnormal access. A system can also be built that monitors user access history in real time and detects anomalies. For example, it can immediately issue an alert if abnormal access is detected. This makes it possible to detect anomalies based on user access history and prevent unauthorized access before it happens.

[0086] The lecture department can use the emotion estimation function to monitor the user's stress level in real time and suggest appropriate break times. For example, we will build a system that uses the emotion estimation function to monitor the user's stress level in real time and suggest appropriate break times. For example, we will analyze the user's facial expressions and voice tone and suggest a break if stress is rising. We will also develop an algorithm that automatically suggests the optimal break timing based on the user's emotion data. For example, if the user is losing concentration, we will suggest a short break. We will also build a system that uses the emotion estimation function to monitor the user's stress level in real time and adjust the break timing as needed. For example, if the user is feeling tired, we will suggest a longer break. This will allow us to provide appropriate break times according to the user's stress level.

[0087] The lecture department can use the emotion estimation function to analyze a user's motivation in real time and suggest optimal task allocations. For example, we will build a system that uses the emotion estimation function to analyze a user's motivation in real time and suggest optimal task allocations. For example, we will analyze a user's facial expressions and voice tone and suggest more difficult tasks when motivation is high. We will also develop an algorithm that automatically suggests optimal task allocations based on user emotion data. For example, we will suggest easy tasks when a user is tired. We will also build a system that uses the emotion estimation function to monitor a user's motivation in real time and adjust task allocation as needed. For example, if a user is losing concentration, we will suggest a refreshing task. This will make it possible to provide optimal task allocations according to the user's motivation.

[0088] The security assurance unit can use the emotion estimation function to monitor the user's sense of security in real time and dynamically adjust security settings. For example, we will build a system that uses the emotion estimation function to monitor the user's sense of security in real time and dynamically adjust security settings. For example, we will analyze the user's facial expressions and tone of voice and strengthen security settings if the sense of security is declining. We will also develop an algorithm that automatically adjusts optimal security settings based on the user's emotional data. For example, if the user feels anxious, we will apply additional authentication measures. We will also build a system that uses the emotion estimation function to monitor the user's sense of security in real time and adjust security settings as needed. For example, if the user feels secure, we will relax security settings. This will make it possible to provide optimal security settings according to the user's sense of security.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The Generative AI Implementation Support Department will provide support for the implementation of Generative AI. For example, they will provide lectures on the basic usage and configuration of Generative AI. They can also provide lectures on how to use Generative AI to analyze internal data and the steps for creating reports. Step 2: The Security Department ensures security through dedicated lines and cloud management. For example, data is sent and received securely using dedicated lines, and data is protected and managed through cloud management. Data encryption and access control are also performed to prevent unauthorized access and data leaks. Step 3: The lecture department provides lectures on input methods and output utilization. For example, they provide lectures on input methods and output utilization for the generative AI. They can also perform regular maintenance, updates, and troubleshooting of the generative AI. Furthermore, they can use the emotion estimation function to analyze the user's emotions in real time and automatically select the explanation method that is easiest for the user to understand.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 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. The Generative AI Implementation Support Department provides support for the introduction of generative AI, The Security Department ensures security through dedicated lines and cloud management, A lecture department that provides lectures on input methods and output utilization methods. A system characterized by:

2. The Generative AI Introduction Support Department: Lecture on the basic usage and settings of generative AI 2. The system of claim 1.

3. The Generative AI Introduction Support Department: Lecture on how to analyze internal data using generative AI 2. The system of claim 1.

4. The Generative AI Introduction Support Department: Lecture on the steps to create reports using generative AI 2. The system of claim 1.

5. The security assurance unit Securely transmit and receive data using a dedicated line 2. The system of claim 1.

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