system

Abstract

The system according to this embodiment aims to efficiently analyze the content of received SMS messages, identify fraud and spam, automatically copy authentication codes, and classify them according to their content. [Solution] The system according to the embodiment comprises an analysis unit, a discrimination unit, a copy unit, and a classification unit. The analysis unit analyzes the content of the received SMS. The discrimination unit identifies fraud and spam based on the content analyzed by the analysis unit. The copy unit automatically copies the authentication code based on the content analyzed by the analysis unit. The classification unit classifies the SMS into conversation, authentication, spam, etc., based on the content analyzed by the analysis 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, there is a problem that the content of received SMS has not been sufficiently analyzed efficiently to discriminate fraud or nuisance mails, automatically copy authentication codes, and perform classification according to the content.

[0005] The system according to the embodiment aims to efficiently analyze the content of received SMS, discriminate fraud or nuisance mails, automatically copy authentication codes, and perform classification according to the content.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a discrimination unit, a copying unit, and a classification unit. The analysis unit analyzes the content of the received SMS. The discrimination unit determines whether the SMS is fraudulent or spam based on the content analyzed by the analysis unit. The copying unit automatically copies the authentication code based on the content analyzed by the analysis unit. The classification unit classifies the SMS into conversation, authentication, spam, etc., based on the content analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze the content of received SMS messages, identify fraudulent or spam messages, automatically copy authentication codes, and classify them according to their content. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 A only, B only, 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 receiving 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 receiving 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 SMS analysis system according to an embodiment of the present invention is a system that enhances its functionality by asking questions to a generating AI regarding the content of received SMS messages. This SMS analysis system has the generating AI analyze the content of received SMS messages. The generating AI analyzes the content of the SMS and automatically identifies fraud and spam. For example, a generating AI that has learned characteristic phrases and patterns of fraudulent emails analyzes the content of a received SMS and determines whether or not it is a fraudulent email. Next, it provides an automatic copy function when an SMS authentication message is received. The generating AI analyzes the content of the SMS and, if an authentication code is included, automatically copies the authentication code. This eliminates the need for the user to manually copy the authentication code. Furthermore, it provides an automatic classification function according to the content of the received SMS. The generating AI analyzes the content of the SMS and automatically classifies it into categories such as conversation, authentication, and spam. For example, an SMS containing conversation content is classified into the "Conversation" category, an SMS containing an authentication code is classified into the "Authentication" category, and an SMS that may be spam is classified into the "Spam" category. In this way, by using generation AI, it is possible to perform advanced analysis of the content of received SMS messages and provide functions such as automatic detection of fraud and spam, automatic copying of SMS authentication messages, and automatic classification based on content. As a result, the SMS analysis system can perform advanced analysis of the content of received SMS messages, automatically detect fraud and spam, automatically copy of SMS authentication messages, and automatically classify them based on content.

[0029] The SMS analysis system according to this embodiment comprises an analysis unit, a discrimination unit, a copying unit, and a classification unit. The analysis unit analyzes the content of received SMS messages. The analysis unit analyzes the content of SMS messages using, for example, text analysis technology. The analysis unit can also analyze the content of SMS messages using pattern recognition technology. Furthermore, the analysis unit can also analyze the content of SMS messages using machine learning algorithms. For example, the analysis unit analyzes the content of SMS messages using natural language processing technology and extracts important information. Pattern recognition technology detects specific patterns contained in the content of SMS messages and provides analysis results. Machine learning algorithms learn from large amounts of SMS data and analyze the content of newly received SMS messages. The discrimination unit distinguishes between fraud and spam based on the content analyzed by the analysis unit. The discrimination unit learns characteristic phrases and patterns of fraudulent emails and analyzes the content of received SMS messages to determine whether or not they are fraudulent. The discrimination unit can also learn the characteristics of spam emails and analyze the content of received SMS messages to determine whether or not they are spam. Furthermore, the discrimination unit can also use a generation AI to distinguish between fraud and spam. For example, the discrimination unit analyzes the content of a received SMS based on the characteristics of fraudulent emails learned by the generation AI and determines whether or not it is a fraudulent email. A generation AI that has learned the characteristics of spam analyzes the content of a received SMS and determines whether or not it is spam. The copy unit automatically copies the authentication code based on the content analyzed by the analysis unit. For example, if the content of the SMS contains an authentication code, the copy unit automatically copies that authentication code. The copy unit can also analyze the format of the authentication code and copy it in the appropriate format. Furthermore, the copy unit can also automatically copy the authentication code using a generation AI. For example, the copy unit automatically copies the authentication code analyzed by the generation AI, saving the user time. A generation AI that has analyzed the format of the authentication code copies the authentication code in the appropriate format. The classification unit classifies the SMS into conversation, authentication, spam, etc., based on the content analyzed by the analysis unit. The classification unit, for example, analyzes the content of SMS messages and classifies SMS messages containing conversation content into the "Conversation" category. The classification unit can also classify SMS messages containing authentication codes into the "Authentication" category.Furthermore, the classification unit can also categorize potentially spam SMS messages into the "spam" category. For example, the classification unit uses a generation AI to analyze the content of SMS messages and automatically categorize them into categories such as conversation, authentication, and spam. The generation AI analyzes the content of SMS messages and categorizes SMS messages containing conversation content into the "conversation" category. The generation AI categorizes SMS messages containing authentication codes into the "authentication" category and categorizes potentially spam SMS messages into the "spam" category. As a result, the SMS analysis system according to the embodiment can highly analyze the content of received SMS messages, enabling automatic detection of fraud and spam, automatic copying upon receipt of SMS authentication, and automatic classification according to content.

[0030] The analysis unit analyzes the content of received SMS messages. For example, the analysis unit uses text analysis technology to analyze the content of SMS messages. Specifically, text analysis technology analyzes the grammatical structure and word meanings of SMS messages and extracts important information. For example, natural language processing technology is used to tokenize the content of SMS messages and tag the part of speech of each token. This allows for a more detailed analysis of the SMS content and the identification of important keywords and phrases. The analysis unit can also analyze the content of SMS messages using pattern recognition technology. Pattern recognition technology detects specific patterns in the content of SMS messages and provides analysis results. For example, it can analyze the frequency of occurrence of specific phrases or words to identify characteristics of fraud or spam messages. Furthermore, the analysis unit can also analyze the content of SMS messages using machine learning algorithms. Machine learning algorithms learn from large amounts of SMS data and analyze the content of newly received SMS messages. For example, supervised learning can be used to learn the characteristics of fraudulent and spam messages based on past SMS data, and then analyze the content of newly received SMS messages to identify fraudulent or spam messages. This allows the analysis unit to perform a sophisticated analysis of the received SMS content and extract important information.

[0031] The discrimination unit identifies fraud and spam based on the content analyzed by the analysis unit. For example, the discrimination unit learns characteristic phrases and patterns of fraudulent emails and analyzes the content of received SMS messages to determine whether or not they are fraudulent. Specifically, the discrimination unit identifies keywords and phrases common to fraudulent emails and analyzes their frequency and context. The discrimination unit can also learn the characteristics of spam and analyze the content of received SMS messages to determine whether or not they are spam. Characteristics of spam include the appearance of specific links or phone numbers and excessive advertising text. Furthermore, the discrimination unit can also use generative AI to distinguish between fraud and spam. For example, the generative AI learns from a large amount of SMS data using a deep learning model and extracts characteristics of fraudulent and spam emails. Based on the characteristics of fraudulent emails learned by the generative AI, it analyzes the content of received SMS messages to determine whether or not they are fraudulent. A generative AI that has learned the characteristics of spam analyzes the content of received SMS messages to determine whether or not they are spam. This allows the discrimination unit to highly analyze the content of received SMS messages and accurately identify fraud and spam.

[0032] The copy unit automatically copies the authentication code based on the content analyzed by the analysis unit. For example, if the content of an SMS contains an authentication code, the copy unit will automatically copy that code. Specifically, the copy unit can analyze the format of the authentication code and copy it in the appropriate format. For example, if the authentication code consists only of numbers, it will automatically extract and copy that sequence of numbers. Also, if the authentication code contains letters or symbols, it can copy them appropriately according to their format. Furthermore, the copy unit can also automatically copy the authentication code using a generation AI. The generation AI analyzes the content of the SMS and learns an algorithm to identify the authentication code. For example, the generation AI learns the format and location of the authentication code based on past SMS data, and then analyzes the content of a newly received SMS to identify the authentication code. The authentication code analyzed by the generation AI is automatically copied, saving the user time. The generation AI, having analyzed the format of the authentication code, copies the authentication code in the appropriate format. As a result, the copy unit can highly analyze the content of received SMS messages and automatically copy the authentication code.

[0033] The classification unit categorizes SMS messages into categories such as conversation, authentication, and spam based on the content analyzed by the analysis unit. For example, the classification unit analyzes the content of an SMS message and classifies SMS messages containing conversational content into the "Conversation" category. Specifically, the classification unit uses natural language processing technology to analyze the content of the SMS message and identify the content of the conversation. For example, conversational content includes everyday exchanges, questions, and answers. The classification unit can also classify SMS messages containing authentication codes into the "Authentication" category. Authentication codes are usually expressed in a specific format or wording, so they can be classified based on these characteristics. Furthermore, the classification unit can classify SMS messages that may be spam into the "Spam" category. Characteristics of spam include the appearance of specific links or phone numbers and excessive advertising text. For example, the classification unit uses generative AI to analyze the content of an SMS message and automatically classify it into categories such as conversation, authentication, and spam. The generative AI analyzes the content of an SMS message and classifies SMS messages containing conversational content into the "Conversation" category. The generation AI, which categorizes SMS messages containing authentication codes into the "authentication" category, categorizes potentially spam SMS messages into the "spam" category. This allows the classification unit to highly analyze the content of received SMS messages and automatically categorize them appropriately.

[0034] The analysis unit can analyze the content of received SMS messages. For example, the analysis unit can analyze the content of SMS messages using text analysis technology. The analysis unit can also analyze the content of SMS messages using pattern recognition technology. For example, the analysis unit can analyze the content of SMS messages using pattern recognition technology and detect specific patterns. Furthermore, the analysis unit can also analyze the content of SMS messages using machine learning algorithms. For example, the analysis unit can analyze the content of SMS messages using machine learning algorithms and extract important information. This allows for subsequent processing by analyzing the content of received SMS messages. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the content of the received SMS message into a generative AI, and the generative AI outputs the analysis results.

[0035] The discrimination unit can learn characteristic phrases and patterns of fraudulent emails and analyze the content of received SMS messages to determine whether or not they are fraudulent. For example, the discrimination unit can learn characteristic phrases and patterns of fraudulent emails and analyze the content of received SMS messages to determine whether or not they are fraudulent. The discrimination unit can also learn the characteristics of spam emails and analyze the content of received SMS messages to determine whether or not they are spam. Furthermore, the discrimination unit can use a generation AI to distinguish between fraudulent and spam emails. For example, the discrimination unit analyzes the content of received SMS messages based on the characteristics of fraudulent emails learned by the generation AI and determines whether or not they are fraudulent. A generation AI that has learned the characteristics of spam emails analyzes the content of received SMS messages and determines whether or not they are spam. In this way, learning the characteristics of fraudulent emails improves the accuracy of fraudulent email detection. Some or all of the above processing in the discrimination unit may be performed using a generation AI or not. For example, the discrimination unit inputs the content of a received SMS message into a generation AI, and the generation AI determines whether or not it is a fraudulent email.

[0036] The copy unit can automatically copy authentication codes if they are included. For example, if an authentication code is included in the content of an SMS, the copy unit will automatically copy it. The copy unit can also analyze the format of the authentication code and copy it in the appropriate format. Furthermore, the copy unit can use a generation AI to automatically copy authentication codes. For example, the copy unit can automatically copy the authentication code analyzed by the generation AI, saving the user time. The generation AI, having analyzed the format of the authentication code, copies the authentication code in the appropriate format. This allows for automatic copying of authentication codes, saving the user time. Some or all of the above-described processes in the copy unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the copy unit inputs the content of the received SMS into the generation AI, and the generation AI automatically copies the authentication code.

[0037] The classification unit can analyze the content of received SMS messages and automatically classify them into categories such as conversation, authentication, and spam. For example, the classification unit can analyze the content of an SMS and classify SMS messages containing conversation content into the "Conversation" category. It can also classify SMS messages containing authentication codes into the "Authentication" category. Furthermore, it can classify potentially spam SMS messages into the "Spam" category. For example, the classification unit uses a generation AI to analyze the content of an SMS and automatically classify it into categories such as conversation, authentication, and spam. The generation AI analyzes the content of an SMS and classifies SMS messages containing conversation content into the "Conversation" category. The generation AI, which classifies SMS messages containing authentication codes into the "Authentication" category, also classifies potentially spam SMS messages into the "Spam" category. This allows users to efficiently manage their SMS messages by automatically classifying their content. Some or all of the above-described processes in the classification unit may be performed using the generation AI or not. For example, the classification unit inputs the content of a received SMS into the generation AI, and the generation AI classifies it into categories.

[0038] The analysis unit can optimize its analysis algorithm by referring to past SMS data. For example, the analysis unit can extract frequently occurring keywords from past SMS data and reflect them in the analysis algorithm. The analysis unit can also build a feedback loop to reduce false positives based on the analysis results of past SMS data. Furthermore, the analysis unit can analyze trends in past SMS data and update the algorithm to respond to the latest fraud techniques. For example, the analysis unit optimizes the analysis algorithm by referring to past SMS data. This improves the accuracy of the analysis algorithm by referring to past data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs past SMS data into a generative AI, and the generative AI optimizes the analysis algorithm.

[0039] The analysis unit can improve the accuracy of its analysis by considering the sender information of the SMS. For example, if the sender of the SMS is a trustworthy company, the analysis unit will lower the priority of the analysis. The analysis unit can also perform a detailed analysis and assess the possibility of fraud if the sender of the SMS is unknown or suspicious. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to past sending history based on the sender information of the SMS. For example, the analysis unit improves the accuracy of its analysis by considering the sender information of the SMS. This improves the accuracy of the analysis by considering the sender information. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs the sender information of the SMS into a generating AI, and the generating AI improves the accuracy of the analysis.

[0040] The analysis unit can determine the priority of analysis by considering the sending time of SMS messages. For example, the analysis unit may determine that SMS messages received late at night are of low urgency and lower their analysis priority. Conversely, the analysis unit may determine that SMS messages received during business hours are of high urgency and raise their analysis priority. Furthermore, the analysis unit can learn patterns of SMS messages frequently received during specific time periods and adjust the analysis priority accordingly. For example, the analysis unit may determine the analysis priority by considering the sending time of SMS messages. This allows for the appropriate determination of analysis priority by considering the sending time. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the sending time of SMS messages into a generative AI, and the generative AI determines the analysis priority.

[0041] The analysis unit can improve the accuracy of its analysis by referring to external data related to the content of the SMS. For example, the analysis unit can refer to news articles related to the content of the SMS to assess the likelihood of fraud. The analysis unit can also refer to the official website of a company related to the content of the SMS to verify its reliability. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to past fraud cases related to the content of the SMS. For example, the analysis unit improves the accuracy of its analysis by referring to external data related to the content of the SMS. Thus, the accuracy of the analysis is improved by referring to external data. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs external data related to the content of the SMS into a generating AI, and the generating AI improves the accuracy of the analysis.

[0042] The discrimination unit can optimize its discrimination algorithm by referring to past fraud email data. For example, the discrimination unit can extract characteristic keywords from past fraud email data and reflect them in the discrimination algorithm. The discrimination unit can also build a feedback loop to reduce false positives based on the analysis results of past fraud email data. Furthermore, the discrimination unit can analyze trends in past fraud email data and update its algorithm to respond to the latest fraud methods. For example, the discrimination unit optimizes its discrimination algorithm by referring to past fraud email data. This improves the accuracy of the discrimination algorithm by referring to past data. Some or all of the above processing in the discrimination unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the discrimination unit inputs past fraud email data into a generative AI, and the generative AI optimizes the discrimination algorithm.

[0043] The discrimination unit can improve the accuracy of its discrimination by considering the reliability information of the SMS sender. For example, if the SMS sender is a trustworthy company, the discrimination unit will rate the likelihood of fraud lower. Also, if the SMS sender is unknown or suspicious, the discrimination unit can perform a detailed analysis and rate the likelihood of fraud higher. Furthermore, the discrimination unit can improve the accuracy of its discrimination by referring to past sending history based on the SMS sender information. For example, the discrimination unit improves the accuracy of its discrimination by considering the reliability information of the SMS sender. This improves the accuracy of discrimination by considering the reliability information of the sender. Some or all of the above processing in the discrimination unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the discrimination unit inputs the reliability information of the SMS sender into the generating AI, and the generating AI improves the accuracy of the discrimination.

[0044] The discrimination unit can evaluate the likelihood of a phishing email by considering the frequency of SMS transmissions. For example, if a large number of SMS messages are sent from the same sender in a short period of time, the discrimination unit will rate the likelihood of fraud higher. The discrimination unit can also rate the likelihood of fraud lower for SMS messages that are sent regularly over a long period of time. Furthermore, the discrimination unit can also rate the likelihood of fraud higher for SMS messages that are sent in a concentrated period of time. For example, the discrimination unit evaluates the likelihood of a phishing email by considering the frequency of SMS transmissions. This allows for an appropriate evaluation of the likelihood of a phishing email by considering the transmission frequency. Some or all of the above processing in the discrimination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the discrimination unit inputs the frequency of SMS transmissions into a generation AI, and the generation AI evaluates the likelihood of a phishing email.

[0045] The discrimination unit can improve the accuracy of its discrimination by referring to external data related to the content of the SMS. For example, the discrimination unit can refer to news articles related to the content of the SMS to assess the likelihood of fraud. The discrimination unit can also refer to the official website of a company related to the content of the SMS to verify its reliability. Furthermore, the discrimination unit can improve the accuracy of its discrimination by referring to past fraud cases related to the content of the SMS. For example, the discrimination unit improves the accuracy of its discrimination by referring to external data related to the content of the SMS. Thus, the accuracy of discrimination is improved by referring to external data. Some or all of the above processing in the discrimination unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the discrimination unit inputs external data related to the content of the SMS into a generating AI, and the generating AI improves the accuracy of the discrimination.

[0046] The copy unit can optimize its copy algorithm by referring to the past usage history of authentication codes. For example, the copy unit can extract frequently occurring patterns from the past usage history of authentication codes and reflect them in the copy algorithm. The copy unit can also build a feedback loop to reduce erroneous copies based on the past usage history of authentication codes. Furthermore, the copy unit can analyze the past usage history of authentication codes and propose the most efficient copy method. For example, the copy unit optimizes the copy algorithm by referring to the past usage history of authentication codes. This improves the accuracy of the copy algorithm by referring to past data. Some or all of the above processing in the copy unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the copy unit inputs the past usage history of authentication codes into a generative AI, and the generative AI optimizes the copy algorithm.

[0047] The copying unit can improve the accuracy of copying by considering the sender information of the SMS. For example, if the sender of the SMS is a trusted company, the copying unit will lower the priority of copying. The copying unit can also improve the accuracy of copying by performing a detailed analysis if the sender of the SMS is unknown or suspicious. Furthermore, the copying unit can improve the accuracy of copying by referring to past sending history based on the sender information of the SMS. For example, the copying unit improves the accuracy of copying by considering the sender information of the SMS. This improves the accuracy of copying by considering the sender information. Some or all of the above processing in the copying unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the copying unit inputs the sender information of the SMS into a generation AI, and the generation AI improves the accuracy of copying.

[0048] The copying unit can evaluate the validity of authentication codes by considering the SMS transmission time. For example, the copying unit may determine that authentication codes received late at night are of low urgency and lower their copying priority. Conversely, the copying unit may determine that authentication codes received during business hours are of high urgency and raise their copying priority. Furthermore, the copying unit can learn patterns of authentication codes frequently received during specific time periods and adjust the copying priority accordingly. For example, the copying unit evaluates the validity of authentication codes by considering the SMS transmission time. This allows for a proper evaluation of the validity of authentication codes by considering the transmission time. Some or all of the above processing in the copying unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the copying unit inputs the SMS transmission time into the generation AI, and the generation AI evaluates the validity of the authentication code.

[0049] The copying unit can improve the accuracy of the copy by referring to external data related to the content of the SMS. For example, the copying unit can refer to news articles related to the content of the SMS to evaluate the validity of the authentication code. The copying unit can also refer to the official website of a company related to the content of the SMS to verify the reliability of the authentication code. Furthermore, the copying unit can improve the accuracy of the copy by referring to past authentication code cases related to the content of the SMS. For example, the copying unit improves the accuracy of the copy by referring to external data related to the content of the SMS. Thus, the accuracy of the copy is improved by referring to external data. Some or all of the above processing in the copying unit may be performed using a generating AI, or not using a generating AI. For example, the copying unit inputs external data related to the content of the SMS into a generating AI, and the generating AI improves the accuracy of the copy.

[0050] The classification unit can optimize its classification algorithm by referring to past SMS data. For example, the classification unit can extract frequently occurring keywords from past SMS data and incorporate them into the classification algorithm. The classification unit can also build a feedback loop to reduce misclassification based on the classification results of past SMS data. Furthermore, the classification unit can analyze trends in past SMS data and update its algorithm to accommodate the latest classification criteria. For example, the classification unit optimizes its classification algorithm by referring to past SMS data. This improves the accuracy of the classification algorithm by referring to past data. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit inputs past SMS data into a generative AI, and the generative AI optimizes the classification algorithm.

[0051] The classification unit can improve the accuracy of classification by considering the sender information of SMS messages. For example, if the sender of an SMS message is a trusted company, the classification unit will automatically classify it into a specific category. Furthermore, if the sender of an SMS message is unknown or suspicious, the classification unit can perform a detailed analysis and classify it into an appropriate category. In addition, the classification unit can improve the accuracy of classification by referring to past sending history based on the sender information of the SMS message. For example, the classification unit improves the accuracy of classification by considering the sender information of the SMS message. This improves the accuracy of classification by considering the sender information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs the sender information of the SMS message into a generative AI, and the generative AI improves the accuracy of classification.

[0052] The classification unit can determine the classification priority by considering the time the SMS was sent. For example, the classification unit may determine that an SMS received late at night is of low urgency and lower its classification priority. Conversely, the classification unit may determine that an SMS received during business hours is of high urgency and raise its classification priority. Furthermore, the classification unit can learn patterns of SMS frequently received during specific time periods and adjust the classification priority accordingly. For example, the classification unit may determine the classification priority by considering the time the SMS was sent. This allows for an appropriate determination of classification priority by considering the sending time. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs the SMS sending time into the generative AI, and the generative AI determines the classification priority.

[0053] The classification unit can improve the accuracy of its classification by referring to external data related to the content of the SMS messages. For example, the classification unit can refer to news articles related to the content of the SMS messages and classify them into the appropriate category. The classification unit can also refer to the official websites of companies related to the content of the SMS messages to verify their reliability. Furthermore, the classification unit can improve the accuracy of its classification by referring to past classification examples related to the content of the SMS messages. For example, the classification unit improves the accuracy of its classification by referring to external data related to the content of the SMS messages. This improves the accuracy of the classification by referring to external data. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs external data related to the content of the SMS messages into a generative AI, and the generative AI improves the accuracy of the classification.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history. For example, the analysis unit can learn what kind of SMS the user has received in the past and how they responded to it, and use this as a reference when analyzing new SMS with similar patterns. The analysis unit can also adjust the priority of analysis based on how the user has handled SMS from a particular sender in the past. Furthermore, the analysis unit can improve the accuracy of the analysis results by adjusting the sensitivity to specific keywords and phrases based on the user's past behavior history. In this way, the accuracy of the analysis is improved by referring to the user's past behavior history. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's past behavior history into a generative AI, and the generative AI can improve the accuracy of the analysis.

[0056] The analysis unit can optimize its analysis algorithm by referring to past SMS data. For example, it can extract frequently occurring keywords from past SMS data and incorporate them into the analysis algorithm. The analysis unit can also build a feedback loop to reduce false positives based on the analysis results of past SMS data. Furthermore, the analysis unit can analyze trends in past SMS data and update the algorithm to address the latest fraud techniques. This improves the accuracy of the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit inputs past SMS data into a generative AI, and the generative AI optimizes the analysis algorithm.

[0057] The discrimination unit can improve the accuracy of its discrimination by considering the reliability information of the SMS sender. For example, if the SMS sender is a trustworthy company, it will rate the likelihood of fraud lower. Conversely, if the SMS sender is unknown or suspicious, it can perform a detailed analysis and rate the likelihood of fraud higher. Furthermore, the discrimination unit can improve the accuracy of its discrimination by referring to past sending history based on the SMS sender information. This improves the accuracy of discrimination by considering the reliability information of the sender. Some or all of the above processing in the discrimination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the discrimination unit inputs the reliability information of the SMS sender into the generation AI, and the generation AI improves the accuracy of the discrimination.

[0058] The copy unit can optimize its copy algorithm by referring to the past usage history of authentication codes. For example, it can extract frequently occurring patterns from the past usage history of authentication codes and reflect them in the copy algorithm. The copy unit can also build a feedback loop to reduce erroneous copies based on the past usage history of authentication codes. Furthermore, the copy unit can analyze the past usage history of authentication codes and propose the most efficient copy method. This improves the accuracy of the copy algorithm by referring to past data. Some or all of the above processes in the copy unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the copy unit inputs the past usage history of authentication codes into a generative AI, and the generative AI optimizes the copy algorithm.

[0059] The classification unit can improve the accuracy of classification by considering the sender information of SMS messages. For example, if the sender of an SMS message is a trusted company, it can be automatically classified into a specific category. Furthermore, if the sender of an SMS message is unknown or suspicious, it can perform a detailed analysis and classify it into an appropriate category. In addition, the classification unit can improve the accuracy of classification by referring to past sending history based on the sender information of the SMS message. Thus, considering the sender information improves the accuracy of classification. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs the sender information of the SMS message into a generative AI, which then improves the accuracy of the classification.

[0060] The analysis unit can determine the priority of analysis by considering the sending time of SMS messages. For example, SMS messages received late at night may be judged as less urgent and given a lower priority for analysis. Conversely, SMS messages received during business hours may be judged as more urgent and given a higher priority for analysis. Furthermore, the analysis unit can learn patterns of SMS messages frequently received during specific time periods and adjust the analysis priority accordingly. This allows for the appropriate determination of analysis priority by considering the sending time. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the sending time of the SMS messages into the generative AI, and the generative AI determines the analysis priority.

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

[0062] Step 1: The analysis unit analyzes the content of the received SMS. The analysis unit uses text analysis technology, pattern recognition technology, machine learning algorithms, and natural language processing technology to analyze the content of the SMS and extract important information. Step 2: The discrimination unit identifies fraudulent and spam emails based on the information analyzed by the analysis unit. The discrimination unit learns characteristic phrases and patterns of fraudulent and spam emails and uses a generative AI to distinguish between fraudulent and spam emails. Step 3: The copy unit automatically copies the authentication code based on the analysis performed by the analysis unit. The copy unit can analyze the format of the authentication code and copy it in the appropriate format. Automatic copying of the authentication code can also be performed using a generation AI. Step 4: The classification unit categorizes SMS messages into conversations, authentication, spam, etc., based on the content analyzed by the analysis unit. The classification unit uses a generation AI to analyze the content of SMS messages and automatically categorizes them into conversations, authentication, spam, etc.

[0063] (Example of form 2) The SMS analysis system according to an embodiment of the present invention is a system that enhances its functionality by asking questions to a generating AI regarding the content of received SMS messages. This SMS analysis system has the generating AI analyze the content of received SMS messages. The generating AI analyzes the content of the SMS and automatically identifies fraud and spam. For example, a generating AI that has learned characteristic phrases and patterns of fraudulent emails analyzes the content of a received SMS and determines whether or not it is a fraudulent email. Next, it provides an automatic copy function when an SMS authentication message is received. The generating AI analyzes the content of the SMS and, if an authentication code is included, automatically copies the authentication code. This eliminates the need for the user to manually copy the authentication code. Furthermore, it provides an automatic classification function according to the content of the received SMS. The generating AI analyzes the content of the SMS and automatically classifies it into categories such as conversation, authentication, and spam. For example, an SMS containing conversation content is classified into the "Conversation" category, an SMS containing an authentication code is classified into the "Authentication" category, and an SMS that may be spam is classified into the "Spam" category. In this way, by using generation AI, it is possible to perform advanced analysis of the content of received SMS messages and provide functions such as automatic detection of fraud and spam, automatic copying of SMS authentication messages, and automatic classification based on content. As a result, the SMS analysis system can perform advanced analysis of the content of received SMS messages, automatically detect fraud and spam, automatically copy of SMS authentication messages, and automatically classify them based on content.

[0064] The SMS analysis system according to this embodiment comprises an analysis unit, a discrimination unit, a copying unit, and a classification unit. The analysis unit analyzes the content of received SMS messages. The analysis unit analyzes the content of SMS messages using, for example, text analysis technology. The analysis unit can also analyze the content of SMS messages using pattern recognition technology. Furthermore, the analysis unit can also analyze the content of SMS messages using machine learning algorithms. For example, the analysis unit analyzes the content of SMS messages using natural language processing technology and extracts important information. Pattern recognition technology detects specific patterns contained in the content of SMS messages and provides analysis results. Machine learning algorithms learn from large amounts of SMS data and analyze the content of newly received SMS messages. The discrimination unit distinguishes between fraud and spam based on the content analyzed by the analysis unit. The discrimination unit learns characteristic phrases and patterns of fraudulent emails and analyzes the content of received SMS messages to determine whether or not they are fraudulent. The discrimination unit can also learn the characteristics of spam emails and analyze the content of received SMS messages to determine whether or not they are spam. Furthermore, the discrimination unit can also use a generation AI to distinguish between fraud and spam. For example, the discrimination unit analyzes the content of a received SMS based on the characteristics of fraudulent emails learned by the generation AI and determines whether or not it is a fraudulent email. A generation AI that has learned the characteristics of spam analyzes the content of a received SMS and determines whether or not it is spam. The copy unit automatically copies the authentication code based on the content analyzed by the analysis unit. For example, if the content of the SMS contains an authentication code, the copy unit automatically copies that authentication code. The copy unit can also analyze the format of the authentication code and copy it in the appropriate format. Furthermore, the copy unit can also automatically copy the authentication code using a generation AI. For example, the copy unit automatically copies the authentication code analyzed by the generation AI, saving the user time. A generation AI that has analyzed the format of the authentication code copies the authentication code in the appropriate format. The classification unit classifies the SMS into conversation, authentication, spam, etc., based on the content analyzed by the analysis unit. The classification unit, for example, analyzes the content of SMS messages and classifies SMS messages containing conversation content into the "Conversation" category. The classification unit can also classify SMS messages containing authentication codes into the "Authentication" category.Furthermore, the classification unit can also categorize potentially spam SMS messages into the "spam" category. For example, the classification unit uses a generation AI to analyze the content of SMS messages and automatically categorize them into categories such as conversation, authentication, and spam. The generation AI analyzes the content of SMS messages and categorizes SMS messages containing conversation content into the "conversation" category. The generation AI categorizes SMS messages containing authentication codes into the "authentication" category and categorizes potentially spam SMS messages into the "spam" category. As a result, the SMS analysis system according to the embodiment can highly analyze the content of received SMS messages, enabling automatic detection of fraud and spam, automatic copying upon receipt of SMS authentication, and automatic classification according to content.

[0065] The analysis unit analyzes the content of received SMS messages. For example, the analysis unit uses text analysis technology to analyze the content of SMS messages. Specifically, text analysis technology analyzes the grammatical structure and word meanings of SMS messages and extracts important information. For example, natural language processing technology is used to tokenize the content of SMS messages and tag the part of speech of each token. This allows for a more detailed analysis of the SMS content and the identification of important keywords and phrases. The analysis unit can also analyze the content of SMS messages using pattern recognition technology. Pattern recognition technology detects specific patterns in the content of SMS messages and provides analysis results. For example, it can analyze the frequency of occurrence of specific phrases or words to identify characteristics of fraud or spam messages. Furthermore, the analysis unit can also analyze the content of SMS messages using machine learning algorithms. Machine learning algorithms learn from large amounts of SMS data and analyze the content of newly received SMS messages. For example, supervised learning can be used to learn the characteristics of fraudulent and spam messages based on past SMS data, and then analyze the content of newly received SMS messages to identify fraudulent or spam messages. This allows the analysis unit to perform a sophisticated analysis of the received SMS content and extract important information.

[0066] The discrimination unit identifies fraud and spam based on the content analyzed by the analysis unit. For example, the discrimination unit learns characteristic phrases and patterns of fraudulent emails and analyzes the content of received SMS messages to determine whether or not they are fraudulent. Specifically, the discrimination unit identifies keywords and phrases common to fraudulent emails and analyzes their frequency and context. The discrimination unit can also learn the characteristics of spam and analyze the content of received SMS messages to determine whether or not they are spam. Characteristics of spam include the appearance of specific links or phone numbers and excessive advertising text. Furthermore, the discrimination unit can also use generative AI to distinguish between fraud and spam. For example, the generative AI learns from a large amount of SMS data using a deep learning model and extracts characteristics of fraudulent and spam emails. Based on the characteristics of fraudulent emails learned by the generative AI, it analyzes the content of received SMS messages to determine whether or not they are fraudulent. A generative AI that has learned the characteristics of spam analyzes the content of received SMS messages to determine whether or not they are spam. This allows the discrimination unit to highly analyze the content of received SMS messages and accurately identify fraud and spam.

[0067] The copy unit automatically copies the authentication code based on the content analyzed by the analysis unit. For example, if the content of an SMS contains an authentication code, the copy unit will automatically copy that code. Specifically, the copy unit can analyze the format of the authentication code and copy it in the appropriate format. For example, if the authentication code consists only of numbers, it will automatically extract and copy that sequence of numbers. Also, if the authentication code contains letters or symbols, it can copy them appropriately according to their format. Furthermore, the copy unit can also automatically copy the authentication code using a generation AI. The generation AI analyzes the content of the SMS and learns an algorithm to identify the authentication code. For example, the generation AI learns the format and location of the authentication code based on past SMS data, and then analyzes the content of a newly received SMS to identify the authentication code. The authentication code analyzed by the generation AI is automatically copied, saving the user time. The generation AI, having analyzed the format of the authentication code, copies the authentication code in the appropriate format. As a result, the copy unit can highly analyze the content of received SMS messages and automatically copy the authentication code.

[0068] The classification unit categorizes SMS messages into categories such as conversation, authentication, and spam based on the content analyzed by the analysis unit. For example, the classification unit analyzes the content of an SMS message and classifies SMS messages containing conversational content into the "Conversation" category. Specifically, the classification unit uses natural language processing technology to analyze the content of the SMS message and identify the content of the conversation. For example, conversational content includes everyday exchanges, questions, and answers. The classification unit can also classify SMS messages containing authentication codes into the "Authentication" category. Authentication codes are usually expressed in a specific format or wording, so they can be classified based on these characteristics. Furthermore, the classification unit can classify SMS messages that may be spam into the "Spam" category. Characteristics of spam include the appearance of specific links or phone numbers and excessive advertising text. For example, the classification unit uses generative AI to analyze the content of an SMS message and automatically classify it into categories such as conversation, authentication, and spam. The generative AI analyzes the content of an SMS message and classifies SMS messages containing conversational content into the "Conversation" category. The generation AI, which categorizes SMS messages containing authentication codes into the "authentication" category, categorizes potentially spam SMS messages into the "spam" category. This allows the classification unit to highly analyze the content of received SMS messages and automatically categorize them appropriately.

[0069] The analysis unit can analyze the content of received SMS messages. For example, the analysis unit can analyze the content of SMS messages using text analysis technology. The analysis unit can also analyze the content of SMS messages using pattern recognition technology. For example, the analysis unit can analyze the content of SMS messages using pattern recognition technology and detect specific patterns. Furthermore, the analysis unit can also analyze the content of SMS messages using machine learning algorithms. For example, the analysis unit can analyze the content of SMS messages using machine learning algorithms and extract important information. This allows for subsequent processing by analyzing the content of received SMS messages. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the content of the received SMS message into a generative AI, and the generative AI outputs the analysis results.

[0070] The discrimination unit can learn characteristic phrases and patterns of fraudulent emails and analyze the content of received SMS messages to determine whether or not they are fraudulent. For example, the discrimination unit can learn characteristic phrases and patterns of fraudulent emails and analyze the content of received SMS messages to determine whether or not they are fraudulent. The discrimination unit can also learn the characteristics of spam emails and analyze the content of received SMS messages to determine whether or not they are spam. Furthermore, the discrimination unit can use a generation AI to distinguish between fraudulent and spam emails. For example, the discrimination unit analyzes the content of received SMS messages based on the characteristics of fraudulent emails learned by the generation AI and determines whether or not they are fraudulent. A generation AI that has learned the characteristics of spam emails analyzes the content of received SMS messages and determines whether or not they are spam. In this way, learning the characteristics of fraudulent emails improves the accuracy of fraudulent email detection. Some or all of the above processing in the discrimination unit may be performed using a generation AI or not. For example, the discrimination unit inputs the content of a received SMS message into a generation AI, and the generation AI determines whether or not it is a fraudulent email.

[0071] The copy unit can automatically copy authentication codes if they are included. For example, if an authentication code is included in the content of an SMS, the copy unit will automatically copy it. The copy unit can also analyze the format of the authentication code and copy it in the appropriate format. Furthermore, the copy unit can use a generation AI to automatically copy authentication codes. For example, the copy unit can automatically copy the authentication code analyzed by the generation AI, saving the user time. The generation AI, having analyzed the format of the authentication code, copies the authentication code in the appropriate format. This allows for automatic copying of authentication codes, saving the user time. Some or all of the above-described processes in the copy unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the copy unit inputs the content of the received SMS into the generation AI, and the generation AI automatically copies the authentication code.

[0072] The classification unit can analyze the content of received SMS messages and automatically classify them into categories such as conversation, authentication, and spam. For example, the classification unit can analyze the content of an SMS and classify SMS messages containing conversation content into the "Conversation" category. It can also classify SMS messages containing authentication codes into the "Authentication" category. Furthermore, it can classify potentially spam SMS messages into the "Spam" category. For example, the classification unit uses a generation AI to analyze the content of an SMS and automatically classify it into categories such as conversation, authentication, and spam. The generation AI analyzes the content of an SMS and classifies SMS messages containing conversation content into the "Conversation" category. The generation AI, which classifies SMS messages containing authentication codes into the "Authentication" category, also classifies potentially spam SMS messages into the "Spam" category. This allows users to efficiently manage their SMS messages by automatically classifying their content. Some or all of the above-described processes in the classification unit may be performed using the generation AI or not. For example, the classification unit inputs the content of a received SMS into the generation AI, and the generation AI classifies it into categories.

[0073] The analysis unit can estimate the user's emotions and adjust the SMS analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can summarize the analysis results concisely and display only the most important information. If the user is relaxed, the analysis unit can provide detailed analysis results and display additional information. Furthermore, if the user is in a hurry, the analysis unit can perform the analysis quickly and prioritize displaying the most important information. For example, the analysis unit estimates the user's emotions and adjusts the SMS analysis method based on the estimated emotions. This allows for more appropriate analysis results by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit inputs user emotion data into a generative AI, and the generative AI adjusts the analysis method.

[0074] The analysis unit can optimize its analysis algorithm by referring to past SMS data. For example, the analysis unit can extract frequently occurring keywords from past SMS data and reflect them in the analysis algorithm. The analysis unit can also build a feedback loop to reduce false positives based on the analysis results of past SMS data. Furthermore, the analysis unit can analyze trends in past SMS data and update the algorithm to respond to the latest fraud techniques. For example, the analysis unit optimizes the analysis algorithm by referring to past SMS data. This improves the accuracy of the analysis algorithm by referring to past data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs past SMS data into a generative AI, and the generative AI optimizes the analysis algorithm.

[0075] The analysis unit can improve the accuracy of its analysis by considering the sender information of the SMS. For example, if the sender of the SMS is a trustworthy company, the analysis unit will lower the priority of the analysis. The analysis unit can also perform a detailed analysis and assess the possibility of fraud if the sender of the SMS is unknown or suspicious. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to past sending history based on the sender information of the SMS. For example, the analysis unit improves the accuracy of its analysis by considering the sender information of the SMS. This improves the accuracy of the analysis by considering the sender information. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs the sender information of the SMS into a generating AI, and the generating AI improves the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the display method.

[0077] The analysis unit can determine the priority of analysis by considering the sending time of SMS messages. For example, the analysis unit may determine that SMS messages received late at night are of low urgency and lower their analysis priority. Conversely, the analysis unit may determine that SMS messages received during business hours are of high urgency and raise their analysis priority. Furthermore, the analysis unit can learn patterns of SMS messages frequently received during specific time periods and adjust the analysis priority accordingly. For example, the analysis unit may determine the analysis priority by considering the sending time of SMS messages. This allows for the appropriate determination of analysis priority by considering the sending time. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the sending time of SMS messages into a generative AI, and the generative AI determines the analysis priority.

[0078] The analysis unit can improve the accuracy of its analysis by referring to external data related to the content of the SMS. For example, the analysis unit can refer to news articles related to the content of the SMS to assess the likelihood of fraud. The analysis unit can also refer to the official website of a company related to the content of the SMS to verify its reliability. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to past fraud cases related to the content of the SMS. For example, the analysis unit improves the accuracy of its analysis by referring to external data related to the content of the SMS. Thus, the accuracy of the analysis is improved by referring to external data. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs external data related to the content of the SMS into a generating AI, and the generating AI improves the accuracy of the analysis.

[0079] The discrimination unit can estimate the user's emotions and adjust the criteria for identifying fraudulent emails based on the estimated emotions. For example, if the user is feeling anxious, the discrimination unit can apply strict criteria and rate the likelihood of the email being fraudulent highly. The discrimination unit can also apply normal criteria if the user is relaxed. Furthermore, if the user is in a hurry, the discrimination unit can perform a quick determination and notify only the essential information. For example, the discrimination unit estimates the user's emotions and adjusts the criteria for identifying fraudulent emails based on the estimated emotions. This allows for more appropriate determination results by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the discrimination unit may be performed using or without the generative AI. For example, the discrimination unit inputs user emotion data into the generative AI, which then adjusts the criteria.

[0080] The discrimination unit can optimize its discrimination algorithm by referring to past fraud email data. For example, the discrimination unit can extract characteristic keywords from past fraud email data and reflect them in the discrimination algorithm. The discrimination unit can also build a feedback loop to reduce false positives based on the analysis results of past fraud email data. Furthermore, the discrimination unit can analyze trends in past fraud email data and update its algorithm to respond to the latest fraud methods. For example, the discrimination unit optimizes its discrimination algorithm by referring to past fraud email data. This improves the accuracy of the discrimination algorithm by referring to past data. Some or all of the above processing in the discrimination unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the discrimination unit inputs past fraud email data into a generative AI, and the generative AI optimizes the discrimination algorithm.

[0081] The discrimination unit can improve the accuracy of its discrimination by considering the reliability information of the SMS sender. For example, if the SMS sender is a trustworthy company, the discrimination unit will rate the likelihood of fraud lower. Also, if the SMS sender is unknown or suspicious, the discrimination unit can perform a detailed analysis and rate the likelihood of fraud higher. Furthermore, the discrimination unit can improve the accuracy of its discrimination by referring to past sending history based on the SMS sender information. For example, the discrimination unit improves the accuracy of its discrimination by considering the reliability information of the SMS sender. This improves the accuracy of discrimination by considering the reliability information of the sender. Some or all of the above processing in the discrimination unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the discrimination unit inputs the reliability information of the SMS sender into the generating AI, and the generating AI improves the accuracy of the discrimination.

[0082] The discrimination unit can estimate the user's emotions and adjust the notification method of the discrimination result based on the estimated user emotions. For example, if the user is nervous, the discrimination unit can provide a simple and highly visible notification method. If the user is relaxed, the discrimination unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the discrimination unit can provide a concise notification method. For example, the discrimination unit estimates the user's emotions and adjusts the notification method of the discrimination result based on the estimated user emotions. This allows for the provision of more appropriate information by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the discrimination unit may be performed using the generative AI or not. For example, the discrimination unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the notification method.

[0083] The discrimination unit can evaluate the likelihood of a phishing email by considering the frequency of SMS transmissions. For example, if a large number of SMS messages are sent from the same sender in a short period of time, the discrimination unit will rate the likelihood of fraud higher. The discrimination unit can also rate the likelihood of fraud lower for SMS messages that are sent regularly over a long period of time. Furthermore, the discrimination unit can also rate the likelihood of fraud higher for SMS messages that are sent in a concentrated period of time. For example, the discrimination unit evaluates the likelihood of a phishing email by considering the frequency of SMS transmissions. This allows for an appropriate evaluation of the likelihood of a phishing email by considering the transmission frequency. Some or all of the above processing in the discrimination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the discrimination unit inputs the frequency of SMS transmissions into a generation AI, and the generation AI evaluates the likelihood of a phishing email.

[0084] The discrimination unit can improve the accuracy of its discrimination by referring to external data related to the content of the SMS. For example, the discrimination unit can refer to news articles related to the content of the SMS to assess the likelihood of fraud. The discrimination unit can also refer to the official website of a company related to the content of the SMS to verify its reliability. Furthermore, the discrimination unit can improve the accuracy of its discrimination by referring to past fraud cases related to the content of the SMS. For example, the discrimination unit improves the accuracy of its discrimination by referring to external data related to the content of the SMS. Thus, the accuracy of discrimination is improved by referring to external data. Some or all of the above processing in the discrimination unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the discrimination unit inputs external data related to the content of the SMS into a generating AI, and the generating AI improves the accuracy of the discrimination.

[0085] The copy unit can estimate the user's emotions and adjust the authentication code copying method based on the estimated emotions. For example, if the user is stressed, the copy unit can provide a simple interface and minimize the copying procedure. If the user is relaxed, the copy unit can also provide detailed copying options and suggest a customizable copying method. Furthermore, if the user is in a hurry, the copy unit can prioritize voice input to allow for quick copying of the authentication code. For example, the copy unit estimates the user's emotions and adjusts the authentication code copying method based on the estimated emotions. This allows for more appropriate copying results by adjusting the copying method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the copy unit may be performed using or without a generative AI. For example, the copy unit inputs user emotion data into a generative AI, which then adjusts the copying method.

[0086] The copy unit can optimize its copy algorithm by referring to the past usage history of authentication codes. For example, the copy unit can extract frequently occurring patterns from the past usage history of authentication codes and reflect them in the copy algorithm. The copy unit can also build a feedback loop to reduce erroneous copies based on the past usage history of authentication codes. Furthermore, the copy unit can analyze the past usage history of authentication codes and propose the most efficient copy method. For example, the copy unit optimizes the copy algorithm by referring to the past usage history of authentication codes. This improves the accuracy of the copy algorithm by referring to past data. Some or all of the above processing in the copy unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the copy unit inputs the past usage history of authentication codes into a generative AI, and the generative AI optimizes the copy algorithm.

[0087] The copying unit can improve the accuracy of copying by considering the sender information of the SMS. For example, if the sender of the SMS is a trusted company, the copying unit will lower the priority of copying. The copying unit can also improve the accuracy of copying by performing a detailed analysis if the sender of the SMS is unknown or suspicious. Furthermore, the copying unit can improve the accuracy of copying by referring to past sending history based on the sender information of the SMS. For example, the copying unit improves the accuracy of copying by considering the sender information of the SMS. This improves the accuracy of copying by considering the sender information. Some or all of the above processing in the copying unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the copying unit inputs the sender information of the SMS into a generation AI, and the generation AI improves the accuracy of copying.

[0088] The copy unit can estimate the user's emotions and adjust the notification method of the copy result based on the estimated emotions. For example, if the user is nervous, the copy unit can provide a simple and highly visible notification method. If the user is relaxed, the copy unit can also provide a notification method that includes detailed information. Furthermore, if the user is in a hurry, the copy unit can provide a notification method that gets straight to the point. For example, the copy unit estimates the user's emotions and adjusts the notification method of the copy result based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the notification method according to the user's 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 copy unit may be performed using or without a generative AI. For example, the copy unit inputs user emotion data into a generative AI, and the generative AI adjusts the notification method.

[0089] The copying unit can evaluate the validity of authentication codes by considering the SMS transmission time. For example, the copying unit may determine that authentication codes received late at night are of low urgency and lower their copying priority. Conversely, the copying unit may determine that authentication codes received during business hours are of high urgency and raise their copying priority. Furthermore, the copying unit can learn patterns of authentication codes frequently received during specific time periods and adjust the copying priority accordingly. For example, the copying unit evaluates the validity of authentication codes by considering the SMS transmission time. This allows for a proper evaluation of the validity of authentication codes by considering the transmission time. Some or all of the above processing in the copying unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the copying unit inputs the SMS transmission time into the generation AI, and the generation AI evaluates the validity of the authentication code.

[0090] The copying unit can improve the accuracy of the copy by referring to external data related to the content of the SMS. For example, the copying unit can refer to news articles related to the content of the SMS to evaluate the validity of the authentication code. The copying unit can also refer to the official website of a company related to the content of the SMS to verify the reliability of the authentication code. Furthermore, the copying unit can improve the accuracy of the copy by referring to past authentication code cases related to the content of the SMS. For example, the copying unit improves the accuracy of the copy by referring to external data related to the content of the SMS. Thus, the accuracy of the copy is improved by referring to external data. Some or all of the above processing in the copying unit may be performed using a generating AI, or not using a generating AI. For example, the copying unit inputs external data related to the content of the SMS into a generating AI, and the generating AI improves the accuracy of the copy.

[0091] The classification unit can estimate the user's emotions and adjust the classification criteria for SMS messages based on the estimated emotions. For example, if the user is stressed, the classification unit can apply simple classification criteria and display only important categories. Alternatively, if the user is relaxed, the classification unit can apply detailed classification criteria and display all categories. Furthermore, if the user is in a hurry, the classification unit can perform rapid classification and prioritize displaying the most important categories. For example, the classification unit estimates the user's emotions and adjusts the classification criteria for SMS messages based on the estimated emotions. This allows for more appropriate classification results by adjusting the classification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using or without generative AI. For example, the classification unit inputs user emotion data into the generative AI, and the generative AI adjusts the classification criteria.

[0092] The classification unit can optimize its classification algorithm by referring to past SMS data. For example, the classification unit can extract frequently occurring keywords from past SMS data and incorporate them into the classification algorithm. The classification unit can also build a feedback loop to reduce misclassification based on the classification results of past SMS data. Furthermore, the classification unit can analyze trends in past SMS data and update its algorithm to accommodate the latest classification criteria. For example, the classification unit optimizes its classification algorithm by referring to past SMS data. This improves the accuracy of the classification algorithm by referring to past data. Some or all of the above processes in the classification unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the classification unit inputs past SMS data into a generative AI, and the generative AI optimizes the classification algorithm.

[0093] The classification unit can improve the accuracy of classification by considering the sender information of SMS messages. For example, if the sender of an SMS message is a trusted company, the classification unit will automatically classify it into a specific category. Furthermore, if the sender of an SMS message is unknown or suspicious, the classification unit can perform a detailed analysis and classify it into an appropriate category. In addition, the classification unit can improve the accuracy of classification by referring to past sending history based on the sender information of the SMS message. For example, the classification unit improves the accuracy of classification by considering the sender information of the SMS message. This improves the accuracy of classification by considering the sender information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs the sender information of the SMS message into a generative AI, and the generative AI improves the accuracy of classification.

[0094] The classification unit can estimate the user's emotions and adjust the display method of the classification results based on the estimated emotions. For example, if the user is tense, the classification unit can provide a simple and highly visible display method. If the user is relaxed, the classification unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the classification unit can provide a concise display method. For example, the classification unit estimates the user's emotions and adjusts the display method of the classification results based on the estimated emotions. This allows for the provision of more appropriate information by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the classification unit may be performed using the generative AI or not. For example, the classification unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the display method.

[0095] The classification unit can determine the classification priority by considering the time the SMS was sent. For example, the classification unit may determine that an SMS received late at night is of low urgency and lower its classification priority. Conversely, the classification unit may determine that an SMS received during business hours is of high urgency and raise its classification priority. Furthermore, the classification unit can learn patterns of SMS frequently received during specific time periods and adjust the classification priority accordingly. For example, the classification unit may determine the classification priority by considering the time the SMS was sent. This allows for an appropriate determination of classification priority by considering the sending time. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs the SMS sending time into the generative AI, and the generative AI determines the classification priority.

[0096] The classification unit can improve the accuracy of its classification by referring to external data related to the content of the SMS messages. For example, the classification unit can refer to news articles related to the content of the SMS messages and classify them into the appropriate category. The classification unit can also refer to the official websites of companies related to the content of the SMS messages to verify their reliability. Furthermore, the classification unit can improve the accuracy of its classification by referring to past classification examples related to the content of the SMS messages. For example, the classification unit improves the accuracy of its classification by referring to external data related to the content of the SMS messages. This improves the accuracy of the classification by referring to external data. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs external data related to the content of the SMS messages into a generative AI, and the generative AI improves the accuracy of the classification.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history. For example, the analysis unit can learn what kind of SMS the user has received in the past and how they responded to it, and use this as a reference when analyzing new SMS with similar patterns. The analysis unit can also adjust the priority of analysis based on how the user has handled SMS from a particular sender in the past. Furthermore, the analysis unit can improve the accuracy of the analysis results by adjusting the sensitivity to specific keywords and phrases based on the user's past behavior history. In this way, the accuracy of the analysis is improved by referring to the user's past behavior history. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the user's past behavior history into a generative AI, and the generative AI can improve the accuracy of the analysis.

[0099] The discrimination unit can estimate the user's emotions and adjust the criteria for identifying fraudulent emails based on the estimated emotions. For example, if the user is feeling anxious, a strict discrimination criterion can be applied, and the likelihood of the email being fraudulent can be highly evaluated. If the user is relaxed, a normal discrimination criterion can be applied. Furthermore, if the user is in a hurry, a quick discrimination can be performed, and only important information can be notified. In this way, by adjusting the discrimination criteria according to the user's emotions, more appropriate discrimination results can be provided. 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 discrimination unit may be performed using or without a generative AI. For example, the discrimination unit inputs user emotion data into a generative AI, and the generative AI adjusts the discrimination criteria.

[0100] The copy unit can estimate the user's emotions and adjust the authentication code copying method based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the copying steps. If the user is relaxed, it can provide detailed copying options and suggest a customizable copying method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick copying of the authentication code. This allows for more appropriate copying results by adjusting the copying method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the copy unit may be performed using or without a generative AI. For example, the copy unit inputs user emotion data into a generative AI, which then adjusts the copying method.

[0101] The classification unit can estimate the user's emotions and adjust the classification criteria for SMS messages based on the estimated emotions. For example, if the user is stressed, a simple classification criterion can be applied, displaying only the most important categories. If the user is relaxed, a more detailed classification criterion can be applied, displaying all categories. Furthermore, if the user is in a hurry, the classification can be performed quickly, prioritizing the display of the most important categories. This allows for more appropriate classification results by adjusting the classification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using or without generative AI. For example, the classification unit inputs user emotion data into the generative AI, which then adjusts the classification criteria.

[0102] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the display method.

[0103] The analysis unit can optimize its analysis algorithm by referring to past SMS data. For example, it can extract frequently occurring keywords from past SMS data and incorporate them into the analysis algorithm. The analysis unit can also build a feedback loop to reduce false positives based on the analysis results of past SMS data. Furthermore, the analysis unit can analyze trends in past SMS data and update the algorithm to address the latest fraud techniques. This improves the accuracy of the analysis algorithm by referring to past data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit inputs past SMS data into a generative AI, and the generative AI optimizes the analysis algorithm.

[0104] The discrimination unit can improve the accuracy of its discrimination by considering the reliability information of the SMS sender. For example, if the SMS sender is a trustworthy company, it will rate the likelihood of fraud lower. Conversely, if the SMS sender is unknown or suspicious, it can perform a detailed analysis and rate the likelihood of fraud higher. Furthermore, the discrimination unit can improve the accuracy of its discrimination by referring to past sending history based on the SMS sender information. This improves the accuracy of discrimination by considering the reliability information of the sender. Some or all of the above processing in the discrimination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the discrimination unit inputs the reliability information of the SMS sender into the generation AI, and the generation AI improves the accuracy of the discrimination.

[0105] The copy unit can optimize its copy algorithm by referring to the past usage history of authentication codes. For example, it can extract frequently occurring patterns from the past usage history of authentication codes and reflect them in the copy algorithm. The copy unit can also build a feedback loop to reduce erroneous copies based on the past usage history of authentication codes. Furthermore, the copy unit can analyze the past usage history of authentication codes and propose the most efficient copy method. This improves the accuracy of the copy algorithm by referring to past data. Some or all of the above processes in the copy unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the copy unit inputs the past usage history of authentication codes into a generative AI, and the generative AI optimizes the copy algorithm.

[0106] The classification unit can improve the accuracy of classification by considering the sender information of SMS messages. For example, if the sender of an SMS message is a trusted company, it can be automatically classified into a specific category. Furthermore, if the sender of an SMS message is unknown or suspicious, it can perform a detailed analysis and classify it into an appropriate category. In addition, the classification unit can improve the accuracy of classification by referring to past sending history based on the sender information of the SMS message. Thus, considering the sender information improves the accuracy of classification. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit inputs the sender information of the SMS message into a generative AI, which then improves the accuracy of the classification.

[0107] The analysis unit can determine the priority of analysis by considering the sending time of SMS messages. For example, SMS messages received late at night may be judged as less urgent and given a lower priority for analysis. Conversely, SMS messages received during business hours may be judged as more urgent and given a higher priority for analysis. Furthermore, the analysis unit can learn patterns of SMS messages frequently received during specific time periods and adjust the analysis priority accordingly. This allows for the appropriate determination of analysis priority by considering the sending time. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the sending time of the SMS messages into the generative AI, and the generative AI determines the analysis priority.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The analysis unit analyzes the content of the received SMS. The analysis unit uses text analysis technology, pattern recognition technology, machine learning algorithms, and natural language processing technology to analyze the content of the SMS and extract important information. Step 2: The discrimination unit identifies fraudulent and spam emails based on the information analyzed by the analysis unit. The discrimination unit learns characteristic phrases and patterns of fraudulent and spam emails and uses a generative AI to distinguish between fraudulent and spam emails. Step 3: The copy unit automatically copies the authentication code based on the analysis performed by the analysis unit. The copy unit can analyze the format of the authentication code and copy it in the appropriate format. Automatic copying of the authentication code can also be performed using a generation AI. Step 4: The classification unit categorizes SMS messages into conversations, authentication, spam, etc., based on the content analyzed by the analysis unit. The classification unit uses a generation AI to analyze the content of SMS messages and automatically categorizes them into conversations, authentication, spam, etc.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the analysis unit, discrimination unit, copying unit, and classification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the content of the received SMS. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies fraud and spam based on the analyzed content. The copying unit is implemented by the control unit 46A of the smart device 14 and automatically copies the authentication code. The classification unit is implemented by the identification processing unit 290 of the data processing device 12 and classifies the SMS into conversation, authentication, spam, etc. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the analysis unit, discrimination unit, copying unit, and classification unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the content of the received SMS. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies fraud and spam based on the analyzed content. The copying unit is implemented by the control unit 46A of the smart glasses 214 and automatically copies the authentication code. The classification unit is implemented by the identification processing unit 290 of the data processing device 12 and classifies the SMS into conversation, authentication, spam, etc. 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.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the analysis unit, discrimination unit, copying unit, and classification unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the content of the received SMS. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies fraud and spam based on the analyzed content. The copying unit is implemented by the control unit 46A of the headset terminal 314 and automatically copies the authentication code. The classification unit is implemented by the identification processing unit 290 of the data processing device 12 and classifies the SMS into conversation, authentication, spam, etc. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the analysis unit, discrimination unit, copying unit, and classification unit, is implemented in at least one of the robot 414 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the content of the received SMS. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines whether an SMS is fraudulent or spam based on the analyzed content. The copying unit is implemented by the control unit 46A of the robot 414 and automatically copies the authentication code. The classification unit is implemented by the identification processing unit 290 of the data processing device 12 and classifies the SMS into conversation, authentication, spam, etc. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) An analysis unit that analyzes the content of received SMS messages, A discrimination unit that identifies fraud and spam based on the content analyzed by the aforementioned analysis unit, A copy unit that automatically copies the authentication code based on the content analyzed by the analysis unit, The system includes a classification unit that classifies SMS messages into categories such as conversation, authentication, and spam based on the content analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the content of the received SMS. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned discrimination unit is It learns the characteristic wording and patterns of phishing emails and analyzes the content of received SMS messages to determine whether or not they are phishing emails. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned copying unit is If an authentication code is included, it will be copied automatically. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned classification unit is The system analyzes the content of received SMS messages and automatically categorizes them into conversations, authentication messages, spam, and other categories. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We estimate the user's emotions and adjust the SMS analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Optimize the analysis algorithm by referring to past SMS data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Improve the accuracy of the analysis by considering the sender information of the SMS. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Prioritize analysis by considering the SMS sending time. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, Improve the accuracy of analysis by referencing external data related to the content of SMS messages. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned discrimination unit is We estimate the user's emotions and adjust the criteria for identifying fraudulent emails based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned discrimination unit is The detection algorithm is optimized by referring to past fraud email data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned discrimination unit is Improve the accuracy of identification by considering the reliability information of the SMS sender. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the notification method for the determination results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned discrimination unit is Evaluate the likelihood of a phishing email by considering the frequency of SMS messages sent. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned discrimination unit is Improve the accuracy of identification by referencing external data related to the content of SMS messages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned copying unit is It estimates the user's emotions and adjusts how the authentication code is copied based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned copying unit is The copy algorithm is optimized by referring to the usage history of past authentication codes. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned copying unit is Improve copy accuracy by considering the sender information of SMS messages. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned copying unit is It estimates the user's emotions and adjusts how the copy results are notified based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned copying unit is The validity of the verification code is evaluated taking into account the SMS transmission time. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned copying unit is Referencing external data related to the content of SMS messages improves the accuracy of the copy. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned classification unit is It estimates the user's sentiment and adjusts the SMS classification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned classification unit is Optimize the classification algorithm by referring to past SMS data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned classification unit is Improve classification accuracy by considering the sender information of SMS messages. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned classification unit is It estimates the user's emotions and adjusts how the classification results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned classification unit is Prioritize classification by considering the SMS sending time. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned classification unit is Improve classification accuracy by referencing external data related to the content of SMS messages. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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. An analysis unit that analyzes the content of received SMS messages, A discrimination unit that identifies fraud and spam based on the content analyzed by the aforementioned analysis unit, A copy unit that automatically copies the authentication code based on the content analyzed by the analysis unit, The system includes a classification unit that classifies SMS messages into categories such as conversation, authentication, and spam based on the content analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the content of the received SMS. The system according to feature 1.

3. The aforementioned discrimination unit is It learns the characteristic wording and patterns of phishing emails and analyzes the content of received SMS messages to determine whether or not they are phishing emails. The system according to feature 1.

4. The aforementioned copying unit is If an authentication code is included, it will be copied automatically. The system according to feature 1.

5. The aforementioned classification unit is The system analyzes the content of received SMS messages and automatically categorizes them into conversations, authentication messages, spam, and other categories. The system according to feature 1.

6. The aforementioned analysis unit, We estimate the user's emotions and adjust the SMS analysis method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, Optimize the analysis algorithm by referring to past SMS data. The system according to feature 1.

8. The aforementioned analysis unit, Improve the accuracy of the analysis by considering the sender information of the SMS. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, Prioritize analysis by considering the SMS sending time. The system according to feature 1.

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