System

Abstract

An object of a system according to an embodiment is to enable a user to take the initiative of data and appropriately communicate with a company.SOLUTION: A system according to an embodiment comprises a data agent. The DataAgent uses the generated AI to understand the user's data-related requests and instructions. The data agent suggests appropriate actions to the enterprise.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not allow users to take the initiative in managing their data, and companies do not adequately manage their data, which can lead to incidents.

[0005] The system according to the embodiment aims to enable users to take the initiative in data and communicate appropriately with companies. [Means for solving the problem]

[0006] The system according to the embodiment includes a data agent that uses generative AI to understand a user's data requests and instructions, and then suggests appropriate actions to the company. [Effects of the Invention]

[0007] The system according to the embodiment allows users to take control of their data and communicate appropriately with companies. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data agent system according to an embodiment of the present invention is a system for users and companies to manage and communicate data. This system uses generative AI to understand users' requests and instructions regarding data and propose appropriate actions to companies. This enables the data agent system to efficiently manage and communicate data between users and companies.

[0029] A data agent system according to an embodiment includes a data agent, a generation AI, a user, and a company. The data agent uses the generation AI to understand the user's requests and instructions regarding data. For example, if a user makes a request such as "I want to check the usage status of my data," the generation AI analyzes the request and instructs the company to provide the data usage status. The data agent also uses the generation AI to suggest appropriate actions to the company. For example, if a user makes a request such as "I want to delete some of my data," the generation AI analyzes the request and instructs the company to delete the data. The data agent also uses the generation AI to manage data and communicate between the user and the company. For example, if a user makes a request such as "I want to know how my data is being used," the generation AI analyzes the request and provides the user with the data usage status. This allows the data agent system according to an embodiment to efficiently manage data and communicate between the user and the company.

[0030] Generative AI can learn a user's past data usage history, predict their data management trends, and suggest optimal management methods. For example, generative AI can analyze a user's past data usage history and identify frequently accessed data and usage patterns. For example, if a user frequently uses specific data during specific time periods, generative AI can learn that pattern and suggest optimal management methods. Generative AI can also predict a user's data management trends and suggest methods for organizing data and controlling access. For example, if a user is struggling with how to classify data, generative AI can suggest an appropriate classification method. This makes it possible to predict a user's data management trends and suggest optimal management methods.

[0031] The generative AI can analyze a user's natural language instructions in real time and instantly instruct a company to take appropriate action. For example, the generative AI analyzes a user's natural language instructions and instructs a company to provide or update data. For example, if a user instructs, "Show me the latest sales data," the generative AI analyzes the instruction and instructs the company to provide the latest sales data. The generative AI can also analyze a user's natural language instructions in real time and instantly instruct a company to take appropriate action. For example, if a user instructs, "I want some of my data to be deleted," the generative AI analyzes the instruction and instructs the company to delete the data. This allows the generative AI to analyze a user's natural language instructions in real time and instantly instruct a company to take appropriate action.

[0032] The data agent can analyze the user's voice commands and realize data management through voice input. The data agent, for example, analyzes the user's voice commands and searches for and displays data. For example, if the user gives a voice command such as "Show me the latest sales data," the data agent analyzes the command and displays the latest sales data. The data agent also uses voice recognition technology to realize data management through voice input. For example, if the user gives a voice command such as "Update my data," the data agent analyzes the command and updates the data. This makes it possible to realize data management through voice input.

[0033] A data agent can automatically synchronize data between different devices, allowing users to manage data from any device. For example, a data agent can automatically synchronize data between a user's devices, allowing them to access the latest data from any device. For example, data updated by a user on a smartphone is immediately updated on a PC. The data agent also adjusts the frequency and method of data synchronization to maintain data consistency. For example, if a user updates data on multiple devices simultaneously, the data agent selects an appropriate synchronization method to maintain data consistency. This allows data synchronization between different devices to be automatically performed, allowing users to manage data from any device.

[0034] The generation AI can analyze data usage history in detail and provide it to users in a format that is visually easy to understand. For example, the generation AI can analyze data usage history and visually display it using graphs and charts. For example, it can show the frequency of data use and access time in graphs, allowing users to understand it at a glance. The generation AI can also create a dashboard based on the data usage history, allowing users to grasp the data usage status in a unified manner. For example, if a user uses multiple datasets, the generation AI can consolidate and display the usage status of those datasets. This makes it possible to provide the data usage history in a format that is visually easy to understand.

[0035] Generative AI can monitor data usage in real time and immediately notify users if abnormal usage is detected. Generative AI can, for example, monitor data usage in real time and notify users if abnormal access or unauthorized usage is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generative AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it can use algorithms to detect abnormal access frequency or unauthorized manipulation of data. This allows it to monitor data usage in real time and immediately notify users if abnormal usage is detected.

[0036] A data agent can automatically manage data sharing between different companies, allowing users to grasp data usage status in a unified manner. For example, a data agent can automatically manage data sharing between different companies, allowing users to grasp data usage status in a unified manner. For example, data from multiple companies can be integrated and displayed to users on a single dashboard. A data agent can also encrypt data and set access permissions to ensure data security. For example, data can be encrypted when shared and access permissions can be set to prevent unauthorized access. This allows data sharing between different companies to be automatically managed, allowing users to grasp data usage status in a unified manner.

[0037] The data agent can collect feedback on users' data usage and improve data management methods based on that feedback. For example, if a user is dissatisfied with the speed of data access, the generative AI will suggest improvements. The data agent also diversifies the methods of collecting feedback to incorporate a wide range of user opinions. For example, feedback can be collected through surveys and user interviews. This allows the data agent to collect feedback on users' data usage and improve data management methods based on that feedback.

[0038] The generative AI can learn the user's data management history and suggest a data management method that suits the user's preferences. For example, the generative AI can analyze the user's data management history and suggest a data organization method that suits the user's preferences. For example, if the user prefers a specific folder structure, the generative AI can organize data based on that structure. The generative AI can also learn the user's data management history and suggest a data management method that suits the user's preferences. For example, it can suggest the optimal data management method based on the settings and operation history selected by the user in the past. This makes it possible to learn the user's data management history and suggest a data management method that suits the user's preferences.

[0039] The generating AI can analyze a user's instructions regarding data management in real time and instantly instruct companies to take appropriate actions. For example, the generating AI can analyze a user's instructions regarding data management in real time and instruct companies to provide or update data. For example, if a user instructs, "Show me the latest sales data," the generating AI analyzes the instruction and instructs the company to provide the latest sales data. The generating AI can also analyze a user's instructions regarding data management in real time and instantly instruct companies to take appropriate actions. For example, if a user instructs, "I want some of my data to be deleted," the generating AI analyzes the instruction and instructs the company to delete the data. This allows the generating AI to analyze a user's instructions regarding data management in real time and instantly instruct companies to take appropriate actions.

[0040] The data agent can analyze the user's voice commands and realize data management through voice input. The data agent, for example, analyzes the user's voice commands and searches for and displays data. For example, if the user gives a voice command such as "Show me the latest sales data," the data agent analyzes the command and displays the latest sales data. The data agent also uses voice recognition technology to realize data management through voice input. For example, if the user gives a voice command such as "Update my data," the data agent analyzes the command and updates the data. This makes it possible to realize data management through voice input.

[0041] A data agent can automatically synchronize data between different devices, allowing users to manage data from any device. For example, a data agent can automatically synchronize data between a user's devices, allowing them to access the latest data from any device. For example, data updated by a user on a smartphone is immediately updated on a PC. The data agent also adjusts the frequency and method of data synchronization to maintain data consistency. For example, if a user updates data on multiple devices simultaneously, the data agent selects an appropriate synchronization method to maintain data consistency. This allows data synchronization between different devices to be automatically performed, allowing users to manage data from any device.

[0042] Generator AI can monitor data usage and issue a warning if abnormal access or unauthorized use is detected. For example, generator AI can monitor data usage in real time and notify users if abnormal access or unauthorized use is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generator AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it can use algorithms to detect abnormal access frequency or unauthorized data manipulation. This allows it to monitor data usage and issue a warning if abnormal access or unauthorized use is detected.

[0043] Generative AI can monitor data usage in real time and immediately notify users if abnormal usage is detected. Generative AI can, for example, monitor data usage in real time and notify users if abnormal access or unauthorized usage is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generative AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it can use algorithms to detect abnormal access frequency or unauthorized manipulation of data. This allows it to monitor data usage in real time and immediately notify users if abnormal usage is detected.

[0044] Generative AI can analyze complex legal documents and privacy policies and provide easy-to-understand explanations to users. For example, generative AI can analyze complex legal documents and provide users with concise, easy-to-understand explanations. For example, it can convert long privacy policies into short summaries and present them to users. Generative AI can also analyze the contents of legal documents and privacy policies and provide them in a visually easy-to-understand format for users. For example, it can highlight important points and explain them using diagrams and charts. This allows generative AI to analyze complex legal documents and privacy policies and provide users with easy-to-understand explanations.

[0045] The generative AI can answer users' questions about privacy policies in real time and help them understand them. For example, the generative AI can analyze users' questions about privacy policies in real time and provide instant answers. For example, if a user asks, "How will this data be used?", the generative AI will explain how that data will be used. The generative AI can also provide detailed explanations to users' questions and help them understand. For example, if a user asks, "Please tell me the contents of this privacy policy," the generative AI will analyze the contents and provide a concise explanation. This allows the generative AI to answer users' questions about privacy policies in real time and help them understand them.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data agent can collect user feedback on data management and improve data management methods based on that feedback. For example, if a user is dissatisfied with the speed of data access, the data agent can suggest improvements. The data agent also diversifies the methods of collecting feedback to incorporate a wide range of user opinions. For example, feedback can be collected through surveys and user interviews. This allows the agent to collect feedback on user data usage and improve data management methods based on that feedback.

[0048] Data agents can automatically manage data sharing between different companies, allowing users to understand data usage status in a unified manner. For example, they can integrate data from multiple companies and display it to users on a single dashboard. Data agents also ensure data security by encrypting data and setting access permissions. For example, they can encrypt data when it is shared and set access permissions to prevent unauthorized access. This automatically manages data sharing between different companies, allowing users to understand data usage status in a unified manner.

[0049] Generative AI can analyze complex legal documents and privacy policies and provide easy-to-understand explanations to users. For example, it can analyze complex legal documents and provide users with concise, easy-to-understand explanations. For example, it can convert long privacy policies into short summaries and present them to users. Generative AI can also analyze the contents of legal documents and privacy policies and provide them in a visually easy-to-understand format for users. For example, it can highlight important points and explain them using diagrams and charts. This allows it to analyze complex legal documents and privacy policies and provide easy-to-understand explanations to users.

[0050] The generative AI can answer users' questions about privacy policies in real time and help them understand them. For example, it can analyze users' questions about privacy policies in real time and provide instant answers. For example, if a user asks, "How will this data be used?", the generative AI will explain how that data will be used. The generative AI can also provide detailed explanations to users' questions and help them understand. For example, if a user asks, "Please tell me the contents of this privacy policy," the generative AI will analyze the contents and provide a concise explanation. This allows the generative AI to answer users' questions about privacy policies in real time and help them understand.

[0051] Generative AI can monitor data usage and issue warnings if abnormal access or unauthorized use is detected. For example, it can monitor data usage in real time and notify users if abnormal access or unauthorized use is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generative AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it uses algorithms to detect abnormal access frequency and unauthorized data manipulation. This allows it to monitor data usage and issue warnings if abnormal access or unauthorized use is detected.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data agent uses generative AI to understand the user's data requests and instructions. For example, if a user requests, "I want to check my data usage status," the generative AI analyzes the request and instructs the company to provide the data usage status. Step 2: The data agent uses generative AI to suggest appropriate actions to the company. For example, if a user requests "I want to delete some of my data," the generative AI analyzes the request and instructs the company to delete the data. Step 3: The data agent uses generative AI to manage and communicate data between the user and the company. For example, if a user makes a request such as "I want to know how my data is being used," the generative AI will analyze the request and provide the user with information about how the data is being used.

[0054] (Example 2) A data agent system according to an embodiment of the present invention is a system for users and companies to manage and communicate data. This system uses generative AI to understand users' requests and instructions regarding data and propose appropriate actions to companies. This enables the data agent system to efficiently manage and communicate data between users and companies.

[0055] A data agent system according to an embodiment includes a data agent, a generation AI, a user, and a company. The data agent uses the generation AI to understand the user's requests and instructions regarding data. For example, if a user makes a request such as "I want to check the usage status of my data," the generation AI analyzes the request and instructs the company to provide the data usage status. The data agent also uses the generation AI to suggest appropriate actions to the company. For example, if a user makes a request such as "I want to delete some of my data," the generation AI analyzes the request and instructs the company to delete the data. The data agent also uses the generation AI to manage data and communicate between the user and the company. For example, if a user makes a request such as "I want to know how my data is being used," the generation AI analyzes the request and provides the user with the data usage status. This allows the data agent system according to an embodiment to efficiently manage data and communicate between the user and the company.

[0056] Generative AI can learn a user's past data usage history, predict their data management trends, and suggest optimal management methods. For example, generative AI can analyze a user's past data usage history and identify frequently accessed data and usage patterns. For example, if a user frequently uses specific data during specific time periods, generative AI can learn that pattern and suggest optimal management methods. Generative AI can also predict a user's data management trends and suggest methods for organizing data and controlling access. For example, if a user is struggling with how to classify data, generative AI can suggest an appropriate classification method. This makes it possible to predict a user's data management trends and suggest optimal management methods.

[0057] The generative AI can analyze a user's natural language instructions in real time and instantly instruct a company to take appropriate action. For example, the generative AI analyzes a user's natural language instructions and instructs a company to provide or update data. For example, if a user instructs, "Show me the latest sales data," the generative AI analyzes the instruction and instructs the company to provide the latest sales data. The generative AI can also analyze a user's natural language instructions in real time and instantly instruct a company to take appropriate action. For example, if a user instructs, "I want some of my data to be deleted," the generative AI analyzes the instruction and instructs the company to delete the data. This allows the generative AI to analyze a user's natural language instructions in real time and instantly instruct a company to take appropriate action.

[0058] The generative AI can use its emotion estimation function to analyze the user's emotional state and suggest data management methods to reduce stress and anxiety. For example, the generative AI can use its emotion estimation function to analyze the user's emotional state in real time and suggest data management methods to reduce stress and anxiety. For example, if the user is feeling stressed, the generative AI can suggest organizing and simplifying the data. The generative AI can also analyze the user's emotional state and adjust the frequency of notifications and the display method of data. For example, if the user is feeling anxious, the generative AI can reduce the frequency of notifications and simplify the display method. This allows the generative AI to analyze the user's emotional state and suggest data management methods to reduce stress and anxiety.

[0059] The data agent can analyze the user's voice commands and realize data management through voice input. The data agent, for example, analyzes the user's voice commands and searches for and displays data. For example, if the user gives a voice command such as "Show me the latest sales data," the data agent analyzes the command and displays the latest sales data. The data agent also uses voice recognition technology to realize data management through voice input. For example, if the user gives a voice command such as "Update my data," the data agent analyzes the command and updates the data. This makes it possible to realize data management through voice input.

[0060] A data agent can automatically synchronize data between different devices, allowing users to manage data from any device. For example, a data agent can automatically synchronize data between a user's devices, allowing them to access the latest data from any device. For example, data updated by a user on a smartphone is immediately updated on a PC. The data agent also adjusts the frequency and method of data synchronization to maintain data consistency. For example, if a user updates data on multiple devices simultaneously, the data agent selects an appropriate synchronization method to maintain data consistency. This allows data synchronization between different devices to be automatically performed, allowing users to manage data from any device.

[0061] The data agent can use its emotion estimation function to customize the interface design and operation methods so that users will have positive feelings about data management. For example, the data agent can use its emotion estimation function to analyze the user's emotional state and suggest an interface design that will elicit positive emotions. For example, if the user is feeling stressed, the generation AI will suggest a simple and intuitive design. The data agent also customizes the operation methods according to the user's emotional state. For example, if the user is feeling anxious, the generation AI will simplify the operation procedures, allowing the user to manage their data with peace of mind. This allows the interface design and operation methods to be customized so that users will have positive feelings about data management.

[0062] The generation AI can analyze data usage history in detail and provide it to users in a format that is visually easy to understand. For example, the generation AI can analyze data usage history and visually display it using graphs and charts. For example, it can show the frequency of data use and access time in graphs, allowing users to understand it at a glance. The generation AI can also create a dashboard based on the data usage history, allowing users to grasp the data usage status in a unified manner. For example, if a user uses multiple datasets, the generation AI can consolidate and display the usage status of those datasets. This makes it possible to provide the data usage history in a format that is visually easy to understand.

[0063] Generative AI can monitor data usage in real time and immediately notify users if abnormal usage is detected. Generative AI can, for example, monitor data usage in real time and notify users if abnormal access or unauthorized usage is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generative AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it can use algorithms to detect abnormal access frequency or unauthorized manipulation of data. This allows it to monitor data usage in real time and immediately notify users if abnormal usage is detected.

[0064] The generation AI can use its emotion estimation function to customize the explanation of data usage status so that the user feels reassured about the transparency of the data. For example, the generation AI uses the emotion estimation function to analyze the user's emotional state and customize the explanation of data usage status. For example, if the user feels anxious, the generation AI provides a detailed explanation to reassure the user. The generation AI also adjusts the content and format of the explanation according to the user's emotional state. For example, it visually displays the data usage status to reassure the user. This allows the explanation of data usage status to be customized so that the user feels reassured about the transparency of the data.

[0065] A data agent can automatically manage data sharing between different companies, allowing users to grasp data usage status in a unified manner. For example, a data agent can automatically manage data sharing between different companies, allowing users to grasp data usage status in a unified manner. For example, data from multiple companies can be integrated and displayed to users on a single dashboard. A data agent can also encrypt data and set access permissions to ensure data security. For example, data can be encrypted when shared and access permissions can be set to prevent unauthorized access. This allows data sharing between different companies to be automatically managed, allowing users to grasp data usage status in a unified manner.

[0066] The data agent can collect feedback on users' data usage and improve data management methods based on that feedback. For example, if a user is dissatisfied with the speed of data access, the generative AI will suggest improvements. The data agent also diversifies the methods of collecting feedback to incorporate a wide range of user opinions. For example, feedback can be collected through surveys and user interviews. This allows the data agent to collect feedback on users' data usage and improve data management methods based on that feedback.

[0067] The generation AI can use its emotion estimation function to customize the explanation of data usage so that the user feels positive about data transparency. For example, the generation AI uses the emotion estimation function to analyze the user's emotional state and customize the explanation of data usage. For example, if the user feels anxious, the generation AI provides a detailed explanation to reassure the user. The generation AI also adjusts the content and format of the explanation according to the user's emotional state. For example, it visually displays the data usage so that the user feels reassured. This makes it possible to customize the explanation of data usage so that the user feels positive about data transparency.

[0068] The generative AI can learn the user's data management history and suggest a data management method that suits the user's preferences. For example, the generative AI can analyze the user's data management history and suggest a data organization method that suits the user's preferences. For example, if the user prefers a specific folder structure, the generative AI can organize data based on that structure. The generative AI can also learn the user's data management history and suggest a data management method that suits the user's preferences. For example, it can suggest the optimal data management method based on the settings and operation history selected by the user in the past. This makes it possible to learn the user's data management history and suggest a data management method that suits the user's preferences.

[0069] The generating AI can analyze a user's instructions regarding data management in real time and instantly instruct companies to take appropriate actions. For example, the generating AI can analyze a user's instructions regarding data management in real time and instruct companies to provide or update data. For example, if a user instructs, "Show me the latest sales data," the generating AI analyzes the instruction and instructs the company to provide the latest sales data. The generating AI can also analyze a user's instructions regarding data management in real time and instantly instruct companies to take appropriate actions. For example, if a user instructs, "I want some of my data to be deleted," the generating AI analyzes the instruction and instructs the company to delete the data. This allows the generating AI to analyze a user's instructions regarding data management in real time and instantly instruct companies to take appropriate actions.

[0070] The generative AI can use its emotion estimation function to analyze the user's emotional state and suggest data management methods to reduce stress and anxiety. For example, the generative AI can use its emotion estimation function to analyze the user's emotional state in real time and suggest data management methods to reduce stress and anxiety. For example, if the user is feeling stressed, the generative AI can suggest organizing and simplifying the data. The generative AI can also analyze the user's emotional state and adjust the frequency of notifications and the display method of data. For example, if the user is feeling anxious, the generative AI can reduce the frequency of notifications and simplify the display method. This allows the generative AI to analyze the user's emotional state and suggest data management methods to reduce stress and anxiety.

[0071] The data agent can analyze the user's voice commands and realize data management through voice input. The data agent, for example, analyzes the user's voice commands and searches for and displays data. For example, if the user gives a voice command such as "Show me the latest sales data," the data agent analyzes the command and displays the latest sales data. The data agent also uses voice recognition technology to realize data management through voice input. For example, if the user gives a voice command such as "Update my data," the data agent analyzes the command and updates the data. This makes it possible to realize data management through voice input.

[0072] A data agent can automatically synchronize data between different devices, allowing users to manage data from any device. For example, a data agent can automatically synchronize data between a user's devices, allowing them to access the latest data from any device. For example, data updated by a user on a smartphone is immediately updated on a PC. The data agent also adjusts the frequency and method of data synchronization to maintain data consistency. For example, if a user updates data on multiple devices simultaneously, the data agent selects an appropriate synchronization method to maintain data consistency. This allows data synchronization between different devices to be automatically performed, allowing users to manage data from any device.

[0073] The data agent can use its emotion estimation function to customize the interface design and operation methods so that users will have positive feelings about data management. For example, the data agent can use its emotion estimation function to analyze the user's emotional state and suggest an interface design that will elicit positive emotions. For example, if the user is feeling stressed, the generation AI will suggest a simple and intuitive design. The data agent also customizes the operation methods according to the user's emotional state. For example, if the user is feeling anxious, the generation AI will simplify the operation procedures, allowing the user to manage their data with peace of mind. This allows the interface design and operation methods to be customized so that users will have positive feelings about data management.

[0074] Generator AI can monitor data usage and issue a warning if abnormal access or unauthorized use is detected. For example, generator AI can monitor data usage in real time and notify users if abnormal access or unauthorized use is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generator AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it can use algorithms to detect abnormal access frequency or unauthorized data manipulation. This allows it to monitor data usage and issue a warning if abnormal access or unauthorized use is detected.

[0075] Generative AI can monitor data usage in real time and immediately notify users if abnormal usage is detected. Generative AI can, for example, monitor data usage in real time and notify users if abnormal access or unauthorized usage is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generative AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it can use algorithms to detect abnormal access frequency or unauthorized manipulation of data. This allows it to monitor data usage in real time and immediately notify users if abnormal usage is detected.

[0076] The generation AI can use its emotion estimation function to customize the explanation of data usage status so that the user feels reassured about the transparency of the data. For example, the generation AI uses the emotion estimation function to analyze the user's emotional state and customize the explanation of data usage status. For example, if the user feels anxious, the generation AI provides a detailed explanation to reassure the user. The generation AI also adjusts the content and format of the explanation according to the user's emotional state. For example, it visually displays the data usage status to reassure the user. This allows the explanation of data usage status to be customized so that the user feels reassured about the transparency of the data.

[0077] Generative AI can analyze complex legal documents and privacy policies and provide easy-to-understand explanations to users. For example, generative AI can analyze complex legal documents and provide users with concise, easy-to-understand explanations. For example, it can convert long privacy policies into short summaries and present them to users. Generative AI can also analyze the contents of legal documents and privacy policies and provide them in a visually easy-to-understand format for users. For example, it can highlight important points and explain them using diagrams and charts. This allows generative AI to analyze complex legal documents and privacy policies and provide users with easy-to-understand explanations.

[0078] The generative AI can answer users' questions about privacy policies in real time and help them understand them. For example, the generative AI can analyze users' questions about privacy policies in real time and provide instant answers. For example, if a user asks, "How will this data be used?", the generative AI will explain how that data will be used. The generative AI can also provide detailed explanations to users' questions and help them understand. For example, if a user asks, "Please tell me the contents of this privacy policy," the generative AI will analyze the contents and provide a concise explanation. This allows the generative AI to answer users' questions about privacy policies in real time and help them understand them.

[0079] The generative AI can use its emotion estimation function to customize explanations so that users have positive feelings toward legal documents and privacy policies. For example, the generative AI uses its emotion estimation function to analyze the user's emotional state and customize the explanations for legal documents and privacy policies. For example, if the user is feeling anxious, the generative AI can provide a detailed explanation to reassure them. The generative AI can also adjust the content and format of the explanation according to the user's emotional state. For example, it can visually display the contents of legal documents and privacy policies to reassure the user. This allows the explanations to be customized so that users have positive feelings toward legal documents and privacy policies.

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

[0081] The data agent can collect user feedback on data management and improve data management methods based on that feedback. For example, if a user is dissatisfied with the speed of data access, the data agent can suggest improvements. The data agent also diversifies the methods of collecting feedback to incorporate a wide range of user opinions. For example, feedback can be collected through surveys and user interviews. This allows the agent to collect feedback on user data usage and improve data management methods based on that feedback.

[0082] Data agents can automatically manage data sharing between different companies, allowing users to understand data usage status in a unified manner. For example, they can integrate data from multiple companies and display it to users on a single dashboard. Data agents also ensure data security by encrypting data and setting access permissions. For example, they can encrypt data when it is shared and set access permissions to prevent unauthorized access. This automatically manages data sharing between different companies, allowing users to understand data usage status in a unified manner.

[0083] Generative AI can analyze complex legal documents and privacy policies and provide easy-to-understand explanations to users. For example, it can analyze complex legal documents and provide users with concise, easy-to-understand explanations. For example, it can convert long privacy policies into short summaries and present them to users. Generative AI can also analyze the contents of legal documents and privacy policies and provide them in a visually easy-to-understand format for users. For example, it can highlight important points and explain them using diagrams and charts. This allows it to analyze complex legal documents and privacy policies and provide easy-to-understand explanations to users.

[0084] The generative AI can answer users' questions about privacy policies in real time and help them understand them. For example, it can analyze users' questions about privacy policies in real time and provide instant answers. For example, if a user asks, "How will this data be used?", the generative AI will explain how that data will be used. The generative AI can also provide detailed explanations to users' questions and help them understand. For example, if a user asks, "Please tell me the contents of this privacy policy," the generative AI will analyze the contents and provide a concise explanation. This allows the generative AI to answer users' questions about privacy policies in real time and help them understand.

[0085] Generative AI can monitor data usage and issue warnings if abnormal access or unauthorized use is detected. For example, it can monitor data usage in real time and notify users if abnormal access or unauthorized use is detected. For example, it can issue a warning if a large amount of data is accessed at an unusual time of day. Generative AI can also set criteria for abnormal usage and detect abnormal usage based on those criteria. For example, it uses algorithms to detect abnormal access frequency and unauthorized data manipulation. This allows it to monitor data usage and issue warnings if abnormal access or unauthorized use is detected.

[0086] The generative AI can use its emotion estimation function to customize explanations so that users have positive feelings toward legal documents and privacy policies. For example, it can use the emotion estimation function to analyze the user's emotional state and customize the explanations for legal documents and privacy policies. For example, if the user is feeling anxious, the generative AI can provide a detailed explanation to reassure them. The generative AI can also adjust the content and format of the explanation according to the user's emotional state. For example, it can visually display the contents of legal documents and privacy policies to reassure the user. This allows the explanations to be customized so that users have positive feelings toward legal documents and privacy policies.

[0087] The generation AI can use its emotion estimation function to customize the explanation of data usage so that the user feels reassured about the transparency of the data. For example, the emotion estimation function can be used to analyze the user's emotional state and customize the explanation of data usage. For example, if the user is feeling anxious, the generation AI can provide a detailed explanation to reassure the user. The generation AI can also adjust the content and format of the explanation depending on the user's emotional state. For example, it can visually display the data usage status to reassure the user. This allows the explanation of data usage to be customized so that the user feels reassured about the transparency of the data.

[0088] The generative AI can use its emotion estimation function to analyze the user's emotional state and suggest data management methods to reduce stress and anxiety. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest data management methods to reduce stress and anxiety. For example, if the user is feeling stressed, the generative AI can suggest organizing and simplifying the data. The generative AI can also analyze the user's emotional state and adjust the frequency of notifications and the display method of data. For example, if the user is feeling anxious, the generative AI can reduce the frequency of notifications and simplify the display method. This makes it possible to analyze the user's emotional state and suggest data management methods to reduce stress and anxiety.

[0089] The data agent can use its emotion estimation function to customize the interface design and operation methods so that users will have positive feelings toward data management. For example, the emotion estimation function can be used to analyze the user's emotional state and suggest an interface design that will elicit positive emotions. For example, if the user is feeling stressed, the generative AI can suggest a simple and intuitive design. The data agent also customizes the operation methods according to the user's emotional state. For example, if the user is feeling anxious, the generative AI can simplify the operation procedures, allowing the user to manage their data with peace of mind. This makes it possible to customize the interface design and operation methods so that users will have positive feelings toward data management.

[0090] The generation AI can use its emotion estimation function to customize the explanation of data usage so that the user feels positive about data transparency. For example, the emotion estimation function can be used to analyze the user's emotional state and customize the explanation of data usage. For example, if the user is feeling anxious, the generation AI can provide a detailed explanation to reassure the user. The generation AI can also adjust the content and format of the explanation depending on the user's emotional state. For example, it can visually display the data usage status to reassure the user. This makes it possible to customize the explanation of data usage so that the user feels positive about data transparency.

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

[0092] Step 1: The data agent uses generative AI to understand the user's data requests and instructions. For example, if a user requests, "I want to check my data usage status," the generative AI analyzes the request and instructs the company to provide the data usage status. Step 2: The data agent uses generative AI to suggest appropriate actions to the company. For example, if a user requests "I want to delete some of my data," the generative AI analyzes the request and instructs the company to delete the data. Step 3: The data agent uses generative AI to manage and communicate data between the user and the company. For example, if a user makes a request such as "I want to know how my data is being used," the generative AI will analyze the request and provide the user with information about how the data is being used.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system in which users and companies manage and communicate data through data agents, The data agent uses a generating AI to Understand the user's requests and instructions regarding their data; Propose appropriate actions to the company A system characterized by:

2. The generated AI is The user's natural language instructions are analyzed in real time, and appropriate actions are immediately instructed to the company.

2. The system of claim 1.

3. The data agent: Analyzing the user's voice commands and realizing data management through voice input 2. The system of claim 1.

4. The generated AI is The usage history of the data is analyzed in detail and provided to the user in a visually easy-to-understand format.

2. The system of claim 1.

5. The data agent: Automatically manage data sharing between different companies, allowing users to centrally understand data usage 2. The system of claim 1.

6. The generated AI is Learning the data management history of the user and proposing a data management method that matches the user's preferences 2. The system of claim 1.

7. The data agent: Analyzing the user's voice commands and realizing data management through voice input 2. The system of claim 1.

8. The generated AI is Using sentiment estimation, customize the description to encourage the user to have positive sentiment toward the legal document or privacy policy.

2. The system of claim 1.

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