system

Abstract

The system according to this embodiment aims to automatically generate and update passwords in response to different password requirements. [Solution] The system according to the embodiment comprises a reading unit, a generation unit, an update unit, and a notification unit. The reading unit reads the password requirements. The generation unit generates a password based on the requirements read by the reading unit. The update unit updates the password generated by the generation unit. The notification unit notifies the user of the password updated by the update unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in order to meet different password requirements, it is necessary for the user to manually generate and update passwords, which is time-consuming and difficult to ensure security.

[0005] The system according to the embodiment is intended to meet different password requirements and automatically generate and update passwords.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, a generation unit, an update unit, and a notification unit. The reading unit reads the password requirements. The generation unit generates a password based on the requirements read by the reading unit. The update unit updates the password generated by the generation unit. The notification unit notifies the user of the updated password by the update unit. [Effects of the Invention]

[0007] The system according to this embodiment can accommodate different password requirements and automatically generate and update passwords. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The password update agent system according to an embodiment of the present invention is a system that reads the different password requirements (such as uppercase and lowercase letters and symbols) for each website, automatically generates and updates an appropriate password according to the expiration date (the interval at which renewal is required), and notifies the user of the updated password via a linked messaging application. The password update agent system reads the different password requirements for each website, automatically generates and updates an appropriate password according to the expiration date, and notifies the user of the updated password via a linked messaging application. For example, the password update agent system reads the password requirements for each website. For example, if one website requires a password that includes uppercase and lowercase letters and symbols, and another website requires a password that includes numbers, the password update agent system accurately grasps the requirements for each. Next, the password update agent system stores the password expiration date for each website. For example, if one website requires a password to be updated every 30 days, and another website requires a password to be updated every 90 days, the password update agent system manages the respective expiration dates. When the password expiration date approaches, the password update agent system automatically generates a new password. At this time, the password update agent system generates a highly secure password based on the password requirements for each website. For example, if a password requires uppercase and lowercase letters and symbols, the password update agent system will generate a password that meets these requirements. The generated new password will be notified to a linked messaging app. For example, the new password will be sent to a messaging app specified by the user. This allows the user to easily verify their new password. This mechanism frees users from the hassle of updating passwords. For example, users no longer need to remember the password requirements for each website or worry about password expiration dates. In addition, because the password update agent system automatically generates highly secure passwords, users can minimize the risk of leaking personal information digitally.Furthermore, the password refresh agent system functions as an Identity as a Service (IDaaS) for individuals, even when enterprise-level single sign-on or end-user social login is unavailable. This is expected to lead to increased adoption in industries requiring identity verification processes, such as financial institutions. Thus, the password refresh agent system is a groundbreaking solution for reducing the burden of password management for users and providing a highly secure digital environment.

[0029] The password update agent system according to the embodiment comprises a reading unit, a generation unit, an update unit, and a notification unit. The reading unit reads the password requirements. The reading unit reads, for example, the password requirements for each website. For example, if one website requires a password containing uppercase letters, lowercase letters, and symbols, and another website requires a password containing numbers, the reading unit accurately understands the requirements for each. The generation unit generates a password based on the requirements read by the reading unit. The generation unit generates a highly secure password based on the requirements read. For example, if a password containing uppercase letters, lowercase letters, and symbols is required, the generation unit generates a password that satisfies these requirements. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the read requirements into the generation AI, and the generation AI generates a password based on the requirements. The update unit updates the password generated by the generation unit. The update unit manages the password expiration date and automatically updates the password when the expiration date approaches. For example, the update unit manages the expiration dates for different websites, such as one website requiring password updates every 30 days and another every 90 days. Some or all of the above processes in the update unit may be performed using AI, or not. For example, the update unit inputs the password expiration date into the AI, and the AI ​​updates the password based on the expiration date. The notification unit notifies the user of the password updated by the update unit. The notification unit notifies a linked messaging app of the updated password, for example. For example, the notification unit sends the new password to a messaging app specified by the user. Some or all of the above processes in the notification unit may be performed using AI, or not. For example, the notification unit inputs the updated password into the AI, and the AI ​​notifies the user of the password.As a result, the password update agent system according to the embodiment automatically reads, generates, updates, and notifies users of password requirements, reducing the effort required from the user and enabling highly secure password management.

[0030] The reading unit reads password requirements. For example, it reads the password requirements for each website. Specifically, the reading unit analyzes the HTML structure and JavaScript® code of a website and extracts information about the password requirements. For example, if a website requires passwords to include uppercase letters, lowercase letters, numbers, and symbols, the reading unit accurately identifies these requirements. Similarly, if another website requires passwords to be at least 8 characters long, the reading unit also identifies that requirement. The reading unit stores these requirements in a database for use in subsequent processing. Furthermore, the reading unit has the ability to periodically scan websites and detect changes in password requirements. For example, if a website's password policy changes, the reading unit detects the change and updates the database with the latest requirements. This ensures that the reading unit always has the most up-to-date password requirements, improving the overall accuracy and reliability of the system.

[0031] The generation unit generates a password based on the requirements read by the reading unit. For example, the generation unit generates a highly secure password based on the read requirements. Specifically, the generation unit uses an algorithm to generate random strings and generates a password that satisfies the read requirements. For example, if a password is required that includes uppercase letters, lowercase letters, numbers, and symbols, the generation unit generates a random string that satisfies these requirements. Some or all of the above processing in the generation unit may be performed using a generation AI. The generation AI uses natural language processing technology to analyze the read requirements and generate a password based on them. For example, the generation AI is given prompts such as "include uppercase letters," "include lowercase letters," "include numbers," and "include symbols," and generates a highly secure password based on them. The generated password is stored in a database and used for subsequent processing by the update unit. This allows the generation unit to automatically provide highly secure passwords, saving the user the trouble of manually generating them.

[0032] The update unit updates the passwords generated by the generation unit. For example, the update unit manages password expiration dates and automatically updates passwords when they are about to expire. Specifically, the update unit stores the password expiration dates for each website in a database and automatically requests a new password from the generation unit when the expiration date approaches, then updates the password. For example, if one website requires a password update every 30 days and another website requires an update every 90 days, the update unit manages the respective expiration dates and automatically generates and updates a new password when the expiration date approaches. Some or all of the above processes in the update unit may be performed using AI. The AI ​​manages password expiration dates and automatically generates and updates a new password when the expiration date approaches. For example, the AI ​​stores the password expiration dates for each website in a database and automatically generates and updates a new password when the expiration date approaches. This allows the update unit to save users the trouble of manually updating their passwords and always maintain the latest password.

[0033] The notification unit notifies the user of the password updated by the update unit. The notification unit notifies the user of the updated password, for example, via a linked messaging app. Specifically, the notification unit sends the new password to the messaging app specified by the user. Some or all of the above processing in the notification unit may be performed using AI. The AI ​​notifies the user of the updated password. For example, the AI ​​sends the updated password to the messaging app specified by the user. This allows the notification unit to eliminate the need for the user to manually check their password and to always provide the user with the latest password. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification unit reviews and improves the notification content based on user feedback. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using voice calls, SMS, email, etc., in addition to messaging app notifications. This allows the notification unit to quickly and reliably notify the user of their password and improve user convenience.

[0034] The reading unit can read the password requirements for each website. For example, the reading unit accurately reads the password requirements for each website. For example, if one website requires a password containing uppercase letters, lowercase letters, and symbols, and another website requires a password containing numbers, the reading unit accurately understands each requirement. This allows the reading unit to accurately read the password requirements for each website. Some or all of the above processing in the reading unit may be performed using AI, for example, or not using AI. For example, the reading unit inputs the password requirements for each website into the AI, and the AI ​​reads the requirements.

[0035] The generation unit can generate a highly secure password based on the read requirements. For example, the generation unit can automatically generate a highly secure password based on the read requirements. For example, if the generation unit requires a password containing uppercase letters, lowercase letters, and symbols, it will generate a password that satisfies these requirements. This enables the automatic generation of highly secure passwords. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit inputs the read requirements into the generation AI, and the generation AI generates a password based on the requirements.

[0036] The update unit manages password expiration dates and can automatically update passwords as they approach their expiration date. For example, if a password needs to be updated every 30 days for one website and every 90 days for another, the update unit manages the expiration dates for each. This ensures security by managing and automatically updating password expiration dates. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit inputs the password expiration date into the AI, and the AI ​​updates the password based on the expiration date.

[0037] The notification unit can notify a linked messaging app of the updated password. For example, the notification unit notifies a linked messaging app of the updated password. For example, the notification unit sends the new password to a messaging app specified by the user. This allows the user to be quickly notified of the updated password. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit inputs the updated password into the AI, and the AI ​​notifies the user of the password.

[0038] The notification unit can send a new password to a messaging app specified by the user. For example, the notification unit can send a new password to a messaging app specified by the user. This allows the user to easily confirm their new password by sending it to the messaging app specified by the user. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit inputs the updated password into the AI, and the AI ​​notifies the user of the password.

[0039] The reading unit can predict changes in requirements by referring to past requirement change history when reading password requirements for each website. For example, the reading unit can analyze past requirement change history and predict when the next change will occur. For example, the reading unit can learn patterns of requirement changes and predict the content of the next change. For example, the reading unit can analyze the frequency of requirement changes and predict the timing of the next change. This allows for prediction of requirement changes and appropriate responses by referring to past requirement change history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input past requirement change history into AI, and the AI ​​can predict changes in requirements.

[0040] The reading unit can evaluate the importance of password requirements by considering the website's security policy when reading them. For example, the reading unit can analyze the website's security policy and evaluate the importance of the requirements. For example, the reading unit can rank the importance of the requirements according to the strictness of the security policy. For example, the reading unit can refer to the security policy change history and re-evaluate the importance of the requirements. This allows for a proper evaluation of the importance of requirements by considering the website's security policy. Some or all of the above processing in the reading unit may be performed using AI, for example, or not using AI. For example, the reading unit inputs the website's security policy into AI, and the AI ​​evaluates the importance of the requirements.

[0041] The reading unit can filter password requirements by considering the geographical location of the website when reading them. For example, the reading unit may prioritize reading requirements for geographically close websites, or postpone requirements for geographically distant websites. For example, the reading unit may evaluate the importance of the requirements based on their geographical location. This allows for filtering of requirements by considering geographical location. Some or all of the above processing in the reading unit may be performed using AI, or not. For example, the reading unit may input the geographical location of the website into the AI, which then filters the requirements.

[0042] The reading unit can evaluate the importance of password requirements by considering the frequency of website usage when reading them. For example, the reading unit may prioritize reading requirements for frequently used websites. For example, the reading unit may postpone reading requirements for less frequently used websites. For example, the reading unit may rank the importance of requirements based on usage frequency. This allows for a proper evaluation of requirement importance by considering website usage frequency. Some or all of the above processing in the reading unit may be performed using AI, or not using AI. For example, the reading unit may input website usage frequency data into the AI, and the AI ​​may evaluate the importance of the requirements.

[0043] The generation unit can select the optimal generation algorithm by referring to past password generation history when generating a password. For example, the generation unit can analyze past generation history and select the optimal algorithm. For example, the generation unit can select an algorithm that suits the user's preferences from the generation history. For example, the generation unit can select an algorithm with a high security level based on the generation history. In this way, the optimal generation algorithm can be selected by referring to past generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs past generation history data into a generation AI, and the generation AI selects the optimal generation algorithm.

[0044] The generation unit can apply different generation algorithms depending on the security level of the website when generating passwords. For example, the generation unit can apply a complex algorithm to a website with a high security level. For example, the generation unit can apply a simple algorithm to a website with a low security level. For example, the generation unit can adjust the complexity of the algorithm according to the security level. This allows the appropriate generation algorithm to be applied according to the security level of the website. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit inputs the website's security level data into the generation AI, and the generation AI applies an appropriate generation algorithm.

[0045] The generation unit can determine the priority of passwords to generate based on the frequency of website usage. For example, the generation unit may prioritize generating passwords for frequently used websites. For example, the generation unit may postpone generating passwords for less frequently used websites. For example, the generation unit determines the priority of passwords to generate based on usage frequency. This enables efficient password generation by determining password priority based on website usage frequency. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs website usage frequency data into the generation AI, and the generation AI determines the password priority.

[0046] The generation unit can apply different generation algorithms depending on the website category when generating passwords. For example, the generation unit may apply a complex algorithm to financial institution websites, or a simpler algorithm to social media websites. For example, the generation unit adjusts the complexity of the algorithm according to the category. This allows for the application of an appropriate generation algorithm depending on the website category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit inputs website category data into the generation AI, and the generation AI applies an appropriate generation algorithm.

[0047] The update unit can set the optimal update interval by referring to past update history when managing password expiration dates. For example, the update unit analyzes past update history and sets the optimal update interval. For example, the update unit sets an update interval that suits the user's preferences from the update history. For example, the update unit sets an update interval with a high level of security based on the update history. In this way, the optimal update interval can be set by referring to past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit inputs past update history data into AI, and the AI ​​sets the optimal update interval.

[0048] The update unit can evaluate the importance of a password update, taking into account the website's security policy. For example, the update unit can analyze the website's security policy and evaluate the importance of the update. For example, the update unit can rank the importance of the update according to the strictness of the security policy. For example, the update unit can refer to the security policy change history and re-evaluate the importance of the update. This allows for an appropriate evaluation of the importance of the update by taking the website's security policy into consideration. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input website security policy data into AI, and the AI ​​can evaluate the importance of the update.

[0049] The update unit can perform password updates while considering the geographical location of the website. For example, the update unit may prioritize updating passwords for geographically close websites. For example, the update unit may postpone updating passwords for geographically distant websites. For example, the update unit may evaluate the importance of updates based on geographical location information. This allows passwords to be updated at the appropriate time by considering geographical location information. Some or all of the above processes in the update unit may be performed using AI, or not using AI. For example, the update unit may input the geographical location information of the website into the AI, and the AI ​​will perform the update.

[0050] The update unit can evaluate the importance of password updates by considering the frequency of website usage. For example, the update unit may prioritize updating passwords for frequently used websites. For example, the update unit may postpone updating passwords for less frequently used websites. For example, the update unit may rank the importance of updates based on usage frequency. This allows for an appropriate evaluation of update importance by considering website usage frequency. Some or all of the above processing in the update unit may be performed using AI, or not. For example, the update unit may input website usage frequency data into the AI, and the AI ​​may evaluate the importance of updates.

[0051] The notification unit can select the optimal notification method by referring to past notification history when notifying users of updated passwords. For example, the notification unit can analyze past notification history and select the notification method to which the user responded most effectively. For example, the notification unit can select a notification method that suits the user's preferences from the notification history. For example, the notification unit can select the most effective notification method based on the notification history. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into AI, and the AI ​​can select the optimal notification method.

[0052] The notification unit can apply different notification methods depending on the user's security level when notifying them of updated passwords. For example, the notification unit may apply an encrypted notification method to users with a high security level, or a simpler notification method to users with a low security level. For example, the notification unit may adjust the complexity of the notification method according to the security level. This ensures that an appropriate notification method is applied according to the user's security level. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit may input the user's security level data into the AI, and the AI ​​may apply an appropriate notification method.

[0053] The notification unit can consider the user's geographical location when notifying them of an updated password. For example, the notification unit can provide a detailed notification if the user is at home, or a concise notification if the user is out. For example, the notification unit can select the optimal notification method based on the user's location. This allows for timely notifications by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's geographical location into the AI, which then issues the notification.

[0054] The notification unit can evaluate the importance of a notification by considering the user's frequency of use when notifying users of updated passwords. For example, the notification unit may prioritize notifications for frequently used websites. For example, the notification unit may postpone notifications for less frequently used websites. For example, the notification unit may rank the importance of notifications based on usage frequency. This allows for an appropriate evaluation of notification importance by considering the user's frequency of use. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input user usage frequency data into the AI, and the AI ​​may evaluate the importance of the notification.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reading unit can predict changes in requirements by referring to past requirement change history when reading password requirements for each website. For example, it can analyze past requirement change history and predict when the next change will occur. It can also learn patterns of requirement changes and predict the content of the next change. It can also analyze the frequency of requirement changes and predict the timing of the next change. This allows for prediction of requirement changes and appropriate responses by referring to past requirement change history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input past requirement change history into AI, which can then predict changes in requirements.

[0057] The generation unit can select the optimal generation algorithm by referring to past password generation history when generating a password. For example, it can analyze past generation history and select the optimal algorithm. It can also select an algorithm that suits the user's preferences from the generation history. It can also select an algorithm with a high security level based on the generation history. In this way, the optimal generation algorithm can be selected by referring to past generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past generation history data into a generation AI, and the generation AI can select the optimal generation algorithm.

[0058] The update unit can set the optimal update interval by referring to past update history when managing password expiration dates. For example, it can analyze past update history and set the optimal update interval. It can also set an update interval that suits the user's preferences based on the update history. It can also set an update interval with a high level of security based on the update history. In this way, the optimal update interval can be set by referring to past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input past update history data into AI, and the AI ​​can set the optimal update interval.

[0059] The notification unit can select the optimal notification method by referring to past notification history when notifying users of updated passwords. For example, it can analyze past notification history and select the notification method to which users responded most effectively. It can also select a notification method that suits the user's preferences from the notification history. It can also select the most effective notification method based on the notification history. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into AI, and the AI ​​can select the optimal notification method.

[0060] The notification unit can apply different notification methods depending on the user's security level when notifying them of updated passwords. For example, encrypted notifications can be applied to users with high security levels, while simpler notifications can be applied to users with low security levels. The complexity of the notification method can also be adjusted according to the security level. This ensures that an appropriate notification method is applied according to the user's security level. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user security level data into the AI, which can then apply an appropriate notification method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reading unit reads the password requirements. For example, it reads the password requirements for each website and accurately identifies requirements such as uppercase and lowercase letters, symbols, and numbers. Step 2: The generation unit generates a password based on the requirements read by the reading unit. For example, if a password containing uppercase letters, lowercase letters, and symbols is required, it generates a highly secure password that meets these requirements. The processing in the generation unit may also be performed using a generation AI. Step 3: The update unit updates the password generated by the generation unit. For example, it manages the password expiration date and automatically updates the password when the expiration date approaches. The processing in the update unit may also be performed using AI. Step 4: The notification unit notifies the user of the password updated by the update unit. For example, it notifies the linked messaging app of the updated password and sends the new password to the messaging app specified by the user. Processing in the notification unit may also be performed using AI.

[0063] (Example of form 2) The password update agent system according to an embodiment of the present invention is a system that reads the different password requirements (such as uppercase and lowercase letters and symbols) for each website, automatically generates and updates an appropriate password according to the expiration date (the interval at which renewal is required), and notifies the user of the updated password via a linked messaging application. The password update agent system reads the different password requirements for each website, automatically generates and updates an appropriate password according to the expiration date, and notifies the user of the updated password via a linked messaging application. For example, the password update agent system reads the password requirements for each website. For example, if one website requires a password that includes uppercase and lowercase letters and symbols, and another website requires a password that includes numbers, the password update agent system accurately grasps the requirements for each. Next, the password update agent system stores the password expiration date for each website. For example, if one website requires a password to be updated every 30 days, and another website requires a password to be updated every 90 days, the password update agent system manages the respective expiration dates. When the password expiration date approaches, the password update agent system automatically generates a new password. At this time, the password update agent system generates a highly secure password based on the password requirements for each website. For example, if a password requires uppercase and lowercase letters and symbols, the password update agent system will generate a password that meets these requirements. The generated new password will be notified to a linked messaging app. For example, the new password will be sent to a messaging app specified by the user. This allows the user to easily verify their new password. This mechanism frees users from the hassle of updating passwords. For example, users no longer need to remember the password requirements for each website or worry about password expiration dates. In addition, because the password update agent system automatically generates highly secure passwords, users can minimize the risk of leaking personal information digitally.Furthermore, the password refresh agent system functions as an Identity as a Service (IDaaS) for individuals, even when enterprise-level single sign-on or end-user social login is unavailable. This is expected to lead to increased adoption in industries requiring identity verification processes, such as financial institutions. Thus, the password refresh agent system is a groundbreaking solution for reducing the burden of password management for users and providing a highly secure digital environment.

[0064] The password update agent system according to the embodiment comprises a reading unit, a generation unit, an update unit, and a notification unit. The reading unit reads the password requirements. The reading unit reads, for example, the password requirements for each website. For example, if one website requires a password containing uppercase letters, lowercase letters, and symbols, and another website requires a password containing numbers, the reading unit accurately understands the requirements for each. The generation unit generates a password based on the requirements read by the reading unit. The generation unit generates a highly secure password based on the requirements read. For example, if a password containing uppercase letters, lowercase letters, and symbols is required, the generation unit generates a password that satisfies these requirements. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the read requirements into the generation AI, and the generation AI generates a password based on the requirements. The update unit updates the password generated by the generation unit. The update unit manages the password expiration date and automatically updates the password when the expiration date approaches. For example, the update unit manages the expiration dates for different websites, such as one website requiring password updates every 30 days and another every 90 days. Some or all of the above processes in the update unit may be performed using AI, or not. For example, the update unit inputs the password expiration date into the AI, and the AI ​​updates the password based on the expiration date. The notification unit notifies the user of the password updated by the update unit. The notification unit notifies a linked messaging app of the updated password, for example. For example, the notification unit sends the new password to a messaging app specified by the user. Some or all of the above processes in the notification unit may be performed using AI, or not. For example, the notification unit inputs the updated password into the AI, and the AI ​​notifies the user of the password.As a result, the password update agent system according to the embodiment automatically reads, generates, updates, and notifies users of password requirements, reducing the effort required from the user and enabling highly secure password management.

[0065] The reading unit reads password requirements. For example, it reads the password requirements for each website. Specifically, the reading unit analyzes the HTML structure and JavaScript code of a website and extracts information about the password requirements. For example, if a website requires passwords to include uppercase letters, lowercase letters, numbers, and symbols, the reading unit accurately identifies these requirements. Similarly, if another website requires passwords to be at least 8 characters long, the reading unit also identifies that requirement. The reading unit stores these requirements in a database and uses them for subsequent processing. Furthermore, the reading unit has the ability to periodically scan websites and detect changes in password requirements. For example, if a website's password policy changes, the reading unit detects the change and updates the database with the latest requirements. This ensures that the reading unit always has the most up-to-date password requirements, improving the overall accuracy and reliability of the system.

[0066] The generation unit generates a password based on the requirements read by the reading unit. For example, the generation unit generates a highly secure password based on the read requirements. Specifically, the generation unit uses an algorithm to generate random strings and generates a password that satisfies the read requirements. For example, if a password is required that includes uppercase letters, lowercase letters, numbers, and symbols, the generation unit generates a random string that satisfies these requirements. Some or all of the above processing in the generation unit may be performed using a generation AI. The generation AI uses natural language processing technology to analyze the read requirements and generate a password based on them. For example, the generation AI is given prompts such as "include uppercase letters," "include lowercase letters," "include numbers," and "include symbols," and generates a highly secure password based on them. The generated password is stored in a database and used for subsequent processing by the update unit. This allows the generation unit to automatically provide highly secure passwords, saving the user the trouble of manually generating them.

[0067] The update unit updates the passwords generated by the generation unit. For example, the update unit manages password expiration dates and automatically updates passwords when they are about to expire. Specifically, the update unit stores the password expiration dates for each website in a database and automatically requests a new password from the generation unit when the expiration date approaches, then updates the password. For example, if one website requires a password update every 30 days and another website requires an update every 90 days, the update unit manages the respective expiration dates and automatically generates and updates a new password when the expiration date approaches. Some or all of the above processes in the update unit may be performed using AI. The AI ​​manages password expiration dates and automatically generates and updates a new password when the expiration date approaches. For example, the AI ​​stores the password expiration dates for each website in a database and automatically generates and updates a new password when the expiration date approaches. This allows the update unit to save users the trouble of manually updating their passwords and always maintain the latest password.

[0068] The notification unit notifies the user of the password updated by the update unit. The notification unit notifies the user of the updated password, for example, via a linked messaging app. Specifically, the notification unit sends the new password to the messaging app specified by the user. Some or all of the above processing in the notification unit may be performed using AI. The AI ​​notifies the user of the updated password. For example, the AI ​​sends the updated password to the messaging app specified by the user. This allows the notification unit to eliminate the need for the user to manually check their password and to always provide the user with the latest password. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of the notification content. For example, the notification unit reviews and improves the notification content based on user feedback. In addition, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using voice calls, SMS, email, etc., in addition to messaging app notifications. This allows the notification unit to quickly and reliably notify the user of their password and improve user convenience.

[0069] The reading unit can read the password requirements for each website. For example, the reading unit accurately reads the password requirements for each website. For example, if one website requires a password containing uppercase letters, lowercase letters, and symbols, and another website requires a password containing numbers, the reading unit accurately understands each requirement. This allows the reading unit to accurately read the password requirements for each website. Some or all of the above processing in the reading unit may be performed using AI, for example, or not using AI. For example, the reading unit inputs the password requirements for each website into the AI, and the AI ​​reads the requirements.

[0070] The generation unit can generate a highly secure password based on the read requirements. For example, the generation unit can automatically generate a highly secure password based on the read requirements. For example, if the generation unit requires a password containing uppercase letters, lowercase letters, and symbols, it will generate a password that satisfies these requirements. This enables the automatic generation of highly secure passwords. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit inputs the read requirements into the generation AI, and the generation AI generates a password based on the requirements.

[0071] The update unit manages password expiration dates and can automatically update passwords as they approach their expiration date. For example, if a password needs to be updated every 30 days for one website and every 90 days for another, the update unit manages the expiration dates for each. This ensures security by managing and automatically updating password expiration dates. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit inputs the password expiration date into the AI, and the AI ​​updates the password based on the expiration date.

[0072] The notification unit can notify a linked messaging app of the updated password. For example, the notification unit notifies a linked messaging app of the updated password. For example, the notification unit sends the new password to a messaging app specified by the user. This allows the user to be quickly notified of the updated password. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit inputs the updated password into the AI, and the AI ​​notifies the user of the password.

[0073] The notification unit can send a new password to a messaging app specified by the user. For example, the notification unit can send a new password to a messaging app specified by the user. This allows the user to easily confirm their new password by sending it to the messaging app specified by the user. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit inputs the updated password into the AI, and the AI ​​notifies the user of the password.

[0074] The reading unit can estimate the user's emotions and adjust the timing of reading password requirements based on the estimated emotions. For example, if the user is stressed, the reading unit can delay the reading timing and read the password requirements when the user is relaxed. For example, if the user is in a hurry, the reading unit can speed up the reading timing to quickly obtain the password requirements. For example, if the user is focused, the reading unit can read the password requirements at that time. This reduces the user's burden by adjusting the timing of reading password requirements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not using AI. For example, the reading unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the reading timing based on the result.

[0075] The reading unit can predict changes in requirements by referring to past requirement change history when reading password requirements for each website. For example, the reading unit can analyze past requirement change history and predict when the next change will occur. For example, the reading unit can learn patterns of requirement changes and predict the content of the next change. For example, the reading unit can analyze the frequency of requirement changes and predict the timing of the next change. This allows for prediction of requirement changes and appropriate responses by referring to past requirement change history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input past requirement change history into AI, and the AI ​​can predict changes in requirements.

[0076] The reading unit can evaluate the importance of password requirements by considering the website's security policy when reading them. For example, the reading unit can analyze the website's security policy and evaluate the importance of the requirements. For example, the reading unit can rank the importance of the requirements according to the strictness of the security policy. For example, the reading unit can refer to the security policy change history and re-evaluate the importance of the requirements. This allows for a proper evaluation of the importance of requirements by considering the website's security policy. Some or all of the above processing in the reading unit may be performed using AI, for example, or not using AI. For example, the reading unit inputs the website's security policy into AI, and the AI ​​evaluates the importance of the requirements.

[0077] The reading unit can estimate the user's emotions and determine the priority of password requirements to read based on the estimated emotions. For example, if the user is stressed, the reading unit will postpone less important requirements. For example, if the user is relaxed, the reading unit will prioritize reading more important requirements. For example, if the user is in a hurry, the reading unit will prioritize requirements that need to be read quickly. This reduces the user's burden by prioritizing password requirements according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and determines priorities based on the result.

[0078] The reading unit can filter password requirements by considering the geographical location of the website when reading them. For example, the reading unit may prioritize reading requirements for geographically close websites, or postpone requirements for geographically distant websites. For example, the reading unit may evaluate the importance of the requirements based on their geographical location. This allows for filtering of requirements by considering geographical location. Some or all of the above processing in the reading unit may be performed using AI, or not. For example, the reading unit may input the geographical location of the website into the AI, which then filters the requirements.

[0079] The reading unit can evaluate the importance of password requirements by considering the frequency of website usage when reading them. For example, the reading unit may prioritize reading requirements for frequently used websites. For example, the reading unit may postpone reading requirements for less frequently used websites. For example, the reading unit may rank the importance of requirements based on usage frequency. This allows for a proper evaluation of requirement importance by considering website usage frequency. Some or all of the above processing in the reading unit may be performed using AI, or not using AI. For example, the reading unit may input website usage frequency data into the AI, and the AI ​​may evaluate the importance of the requirements.

[0080] The generation unit can estimate the user's emotions and adjust the complexity of the generated password based on the estimated emotions. For example, if the user is stressed, the generation unit will generate a simple password. For example, if the user is relaxed, the generation unit will generate a complex password. For example, if the user is in a hurry, the generation unit will generate a password that can be generated quickly. This reduces the burden on the user by adjusting the password complexity according to their emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs user emotion data into a generation AI, the generation AI estimates the emotions, and adjusts the password complexity based on the result.

[0081] The generation unit can select the optimal generation algorithm by referring to past password generation history when generating a password. For example, the generation unit can analyze past generation history and select the optimal algorithm. For example, the generation unit can select an algorithm that suits the user's preferences from the generation history. For example, the generation unit can select an algorithm with a high security level based on the generation history. In this way, the optimal generation algorithm can be selected by referring to past generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs past generation history data into a generation AI, and the generation AI selects the optimal generation algorithm.

[0082] The generation unit can apply different generation algorithms depending on the security level of the website when generating passwords. For example, the generation unit can apply a complex algorithm to a website with a high security level. For example, the generation unit can apply a simple algorithm to a website with a low security level. For example, the generation unit can adjust the complexity of the algorithm according to the security level. This allows the appropriate generation algorithm to be applied according to the security level of the website. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit inputs the website's security level data into the generation AI, and the generation AI applies an appropriate generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the generated password based on the estimated emotions. For example, if the user is stressed, the generation unit generates a short password. For example, if the user is relaxed, the generation unit generates a long password. For example, if the user is in a hurry, the generation unit generates a short password that can be generated quickly. This reduces the burden on the user by adjusting the password length according to the user's emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's emotion data into a generation AI, the generation AI estimates the emotions, and adjusts the password length based on the result.

[0084] The generation unit can determine the priority of passwords to generate based on the frequency of website usage. For example, the generation unit may prioritize generating passwords for frequently used websites. For example, the generation unit may postpone generating passwords for less frequently used websites. For example, the generation unit determines the priority of passwords to generate based on usage frequency. This enables efficient password generation by determining password priority based on website usage frequency. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs website usage frequency data into the generation AI, and the generation AI determines the password priority.

[0085] The generation unit can apply different generation algorithms depending on the website category when generating passwords. For example, the generation unit may apply a complex algorithm to financial institution websites, or a simpler algorithm to social media websites. For example, the generation unit adjusts the complexity of the algorithm according to the category. This allows for the application of an appropriate generation algorithm depending on the website category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit inputs website category data into the generation AI, and the generation AI applies an appropriate generation algorithm.

[0086] The update unit can estimate the user's emotions and adjust the password update timing based on the estimated emotions. For example, if the user is stressed, the update unit may delay the update. For example, if the user is relaxed, the update unit may speed up the update. For example, if the user is in a hurry, the update unit may update quickly. This reduces the user's burden by adjusting the password update timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit inputs user emotion data into the generative AI, the generative AI estimates the emotions, and adjusts the update timing based on the result.

[0087] The update unit can set the optimal update interval by referring to past update history when managing password expiration dates. For example, the update unit analyzes past update history and sets the optimal update interval. For example, the update unit sets an update interval that suits the user's preferences from the update history. For example, the update unit sets an update interval with a high level of security based on the update history. In this way, the optimal update interval can be set by referring to past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit inputs past update history data into AI, and the AI ​​sets the optimal update interval.

[0088] The update unit can evaluate the importance of a password update, taking into account the website's security policy. For example, the update unit can analyze the website's security policy and evaluate the importance of the update. For example, the update unit can rank the importance of the update according to the strictness of the security policy. For example, the update unit can refer to the security policy change history and re-evaluate the importance of the update. This allows for an appropriate evaluation of the importance of the update by taking the website's security policy into consideration. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input website security policy data into AI, and the AI ​​can evaluate the importance of the update.

[0089] The update unit can estimate the user's emotions and determine the priority of passwords to update based on the estimated emotions. For example, if the user is stressed, the update unit will postpone updating less important passwords. For example, if the user is relaxed, the update unit will prioritize updating more important passwords. For example, if the user is in a hurry, the update unit will prioritize passwords that need to be updated quickly. This reduces the user's burden by prioritizing passwords according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not using AI. For example, the update unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and determines the priority based on the result.

[0090] The update unit can perform password updates while considering the geographical location of the website. For example, the update unit may prioritize updating passwords for geographically close websites. For example, the update unit may postpone updating passwords for geographically distant websites. For example, the update unit may evaluate the importance of updates based on geographical location information. This allows passwords to be updated at the appropriate time by considering geographical location information. Some or all of the above processes in the update unit may be performed using AI, or not using AI. For example, the update unit may input the geographical location information of the website into the AI, and the AI ​​will perform the update.

[0091] The update unit can evaluate the importance of password updates by considering the frequency of website usage. For example, the update unit may prioritize updating passwords for frequently used websites. For example, the update unit may postpone updating passwords for less frequently used websites. For example, the update unit may rank the importance of updates based on usage frequency. This allows for an appropriate evaluation of update importance by considering website usage frequency. Some or all of the above processing in the update unit may be performed using AI, or not. For example, the update unit may input website usage frequency data into the AI, and the AI ​​may evaluate the importance of updates.

[0092] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the user is stressed, the notification unit will provide a simple and highly visible notification. For example, if the user is relaxed, the notification unit will provide a notification containing detailed information. For example, if the user is in a hurry, the notification unit will provide a concise notification that gets straight to the point. This reduces the user's burden by adjusting the way notifications are presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit inputs user emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the way notifications are presented based on the result.

[0093] The notification unit can select the optimal notification method by referring to past notification history when notifying users of updated passwords. For example, the notification unit can analyze past notification history and select the notification method to which the user responded most effectively. For example, the notification unit can select a notification method that suits the user's preferences from the notification history. For example, the notification unit can select the most effective notification method based on the notification history. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into AI, and the AI ​​can select the optimal notification method.

[0094] The notification unit can apply different notification methods depending on the user's security level when notifying them of updated passwords. For example, the notification unit may apply an encrypted notification method to users with a high security level, or a simpler notification method to users with a low security level. For example, the notification unit may adjust the complexity of the notification method according to the security level. This ensures that an appropriate notification method is applied according to the user's security level. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit may input the user's security level data into the AI, and the AI ​​may apply an appropriate notification method.

[0095] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may delay the notification timing. For example, if the user is relaxed, the notification unit may speed up the notification timing. For example, if the user is in a hurry, the notification unit may send a notification quickly. This reduces the burden on the user by adjusting the timing of notifications according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit inputs user emotion data into a generative AI, the generative AI estimates the emotion, and adjusts the timing of notifications based on the result.

[0096] The notification unit can consider the user's geographical location when notifying them of an updated password. For example, the notification unit can provide a detailed notification if the user is at home, or a concise notification if the user is out. For example, the notification unit can select the optimal notification method based on the user's location. This allows for timely notifications by considering the user's geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the user's geographical location into the AI, which then issues the notification.

[0097] The notification unit can evaluate the importance of a notification by considering the user's frequency of use when notifying users of updated passwords. For example, the notification unit may prioritize notifications for frequently used websites. For example, the notification unit may postpone notifications for less frequently used websites. For example, the notification unit may rank the importance of notifications based on usage frequency. This allows for an appropriate evaluation of notification importance by considering the user's frequency of use. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit may input user usage frequency data into the AI, and the AI ​​may evaluate the importance of the notification.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reading unit can estimate the user's emotions and adjust the timing of reading password requirements based on the estimated emotions. For example, if the user is stressed, the reading timing can be delayed so that the password is read when the user is relaxed. If the user is in a hurry, the reading timing can be advanced to quickly obtain the password requirements. If the user is focused, the password requirements can be read at that time. This reduces the user's burden by adjusting the timing of reading password requirements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not using AI. For example, the reading unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the reading timing can be adjusted based on the result.

[0100] The generation unit can estimate the user's emotions and adjust the complexity of the generated password based on the estimated emotions. For example, if the user is stressed, a simple password can be generated. If the user is relaxed, a complex password can be generated. If the user is in a hurry, a password that can be generated quickly can also be generated. This reduces the burden on the user by adjusting the password complexity according to their emotions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into a generation AI, the generation AI can estimate the emotions, and the password complexity can be adjusted based on the result.

[0101] The update unit can estimate the user's emotions and adjust the password update timing based on the estimated emotions. For example, if the user is stressed, the update timing can be delayed. If the user is relaxed, the update timing can be accelerated. If the user is in a hurry, the update can be performed quickly. This reduces the burden on the user by adjusting the password update timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the update timing can be adjusted based on the result.

[0102] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the user is stressed, a simple and highly visible notification can be displayed. If the user is relaxed, a notification containing detailed information can be displayed. If the user is in a hurry, a concise notification that gets straight to the point can be displayed. This reduces the user's burden by adjusting the way notifications are presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI, the generative AI can estimate the emotion, and the way notifications are presented can be adjusted based on the result.

[0103] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification timing can be delayed. If the user is relaxed, the notification timing can be advanced. If the user is in a hurry, notifications can be sent quickly. This reduces the burden on the user by adjusting the timing of notifications according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user emotion data into a generative AI, the generative AI can estimate the emotion, and the timing of notifications can be adjusted based on the result.

[0104] The reading unit can predict changes in requirements by referring to past requirement change history when reading password requirements for each website. For example, it can analyze past requirement change history and predict when the next change will occur. It can also learn patterns of requirement changes and predict the content of the next change. It can also analyze the frequency of requirement changes and predict the timing of the next change. This allows for prediction of requirement changes and appropriate responses by referring to past requirement change history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input past requirement change history into AI, which can then predict changes in requirements.

[0105] The generation unit can select the optimal generation algorithm by referring to past password generation history when generating a password. For example, it can analyze past generation history and select the optimal algorithm. It can also select an algorithm that suits the user's preferences from the generation history. It can also select an algorithm with a high security level based on the generation history. In this way, the optimal generation algorithm can be selected by referring to past generation history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past generation history data into a generation AI, and the generation AI can select the optimal generation algorithm.

[0106] The update unit can set the optimal update interval by referring to past update history when managing password expiration dates. For example, it can analyze past update history and set the optimal update interval. It can also set an update interval that suits the user's preferences based on the update history. It can also set an update interval with a high level of security based on the update history. In this way, the optimal update interval can be set by referring to past update history. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input past update history data into AI, and the AI ​​can set the optimal update interval.

[0107] The notification unit can select the optimal notification method by referring to past notification history when notifying users of updated passwords. For example, it can analyze past notification history and select the notification method to which users responded most effectively. It can also select a notification method that suits the user's preferences from the notification history. It can also select the most effective notification method based on the notification history. In this way, the optimal notification method can be selected by referring to past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input past notification history data into AI, and the AI ​​can select the optimal notification method.

[0108] The notification unit can apply different notification methods depending on the user's security level when notifying them of updated passwords. For example, encrypted notifications can be applied to users with high security levels, while simpler notifications can be applied to users with low security levels. The complexity of the notification method can also be adjusted according to the security level. This ensures that an appropriate notification method is applied according to the user's security level. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input user security level data into the AI, which can then apply an appropriate notification method.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reading unit reads the password requirements. For example, it reads the password requirements for each website and accurately identifies requirements such as uppercase and lowercase letters, symbols, and numbers. Step 2: The generation unit generates a password based on the requirements read by the reading unit. For example, if a password containing uppercase letters, lowercase letters, and symbols is required, it generates a highly secure password that meets these requirements. The processing in the generation unit may also be performed using a generation AI. Step 3: The update unit updates the password generated by the generation unit. For example, it manages the password expiration date and automatically updates the password when the expiration date approaches. The processing in the update unit may also be performed using AI. Step 4: The notification unit notifies the user of the password updated by the update unit. For example, it notifies the linked messaging app of the updated password and sends the new password to the messaging app specified by the user. Processing in the notification unit may also be performed using AI.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the reading unit, generation unit, update unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reading unit is implemented by the control unit 46A of the smart device 14 and reads the password requirements for each website. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a password based on the read requirements. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and updates the generated password. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the updated password. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reading unit, generation unit, update unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit is implemented by the control unit 46A of the smart glasses 214 and reads the password requirements for each website. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a password based on the read requirements. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and updates the generated password. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of the updated password. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reading unit, generation unit, update unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the headset terminal 314 and reads the password requirements for each website. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a password based on the read requirements. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the generated password. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the updated password. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reading unit, generation unit, update unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the robot 414 and reads the password requirements for each website. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a password based on the read requirements. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the generated password. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the user of the updated password. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A reading unit that reads the password requirements, A generation unit generates a password based on the requirements read by the aforementioned reading unit, An update unit updates the password generated by the generation unit, The system includes a notification unit that notifies the user of the password updated by the update unit. A system characterized by the following features. (Note 2) The reading unit is Read the password requirements for each website. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate a highly secure password based on the requirements read. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned update unit is Manage password expiration dates and automatically renew passwords as they approach their expiration date. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, The updated password will be notified to the linked messaging app. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Send the new password to the messaging app specified by the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The reading unit is It estimates the user's sentiment and adjusts the timing of password requirement reading based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The reading unit is When reading the password requirements for each website, we refer to past requirement change history to predict changes in requirements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The reading unit is When reading password requirements, assess the importance of the requirements by considering the website's security policy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The reading unit is It estimates the user's emotions and prioritizes password requirements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The reading unit is When reading password requirements, filter the requirements considering the website's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The reading unit is When reviewing password requirements, assess the importance of the requirements by considering how often the website is used. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the complexity of the password generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a password, the system selects the optimal generation algorithm by referring to past password generation history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating passwords, different generation algorithms are applied depending on the security level of the website. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generated password based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating passwords, the system prioritizes the passwords to be generated based on how often the website is used. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating passwords, different generation algorithms are applied depending on the website category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is It estimates the user's emotions and adjusts the password update timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update unit is When managing password expiration dates, refer to past update history to set the optimal update interval. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is When updating passwords, the importance of the update is evaluated considering the website's security policy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is It estimates the user's emotions and determines the priority of passwords to update based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is When updating passwords, the website's geographical location information should be taken into consideration during the update process. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is When updating passwords, the importance of the update is evaluated based on how often the website is used. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When notifying users of updated passwords, the system will refer to past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When notifying users of updated passwords, different notification methods are applied depending on the user's security level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When notifying users of updated passwords, the system takes the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When notifying users of updated passwords, the importance of the notification is evaluated based on the user's frequency of use. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reading unit that reads the password requirements, A generation unit generates a password based on the requirements read by the aforementioned reading unit, An update unit updates the password generated by the generation unit, The system includes a notification unit that notifies the user of the password updated by the update unit. A system characterized by the following features.

2. The reading unit is Read the password requirements for each website. The system according to feature 1.

3. The generating unit is Generate a highly secure password based on the requirements read. The system according to feature 1.

4. The aforementioned update unit is, Manage password expiration dates and automatically renew passwords as they approach their expiration date. The system according to feature 1.

5. The aforementioned notification unit, The updated password will be notified to the linked messaging app. The system according to feature 1.

6. The aforementioned notification unit, Send the new password to the messaging app specified by the user. The system according to feature 1.

7. The reading unit is It estimates the user's sentiment and adjusts the timing of password requirement reading based on the estimated user sentiment. The system according to feature 1.

8. The reading unit is When reading the password requirements for each website, we refer to past requirement change history to predict changes in requirements. The system according to feature 1.

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