System

Abstract

An object of the system according to the embodiment is to effectively prevent a phone call or a mail of a special fraud.SOLUTION: A system includes an information collection part, a fraud determination part, a shutout part, and a warning part. The information collection unit collects information from a user. The fraud determination unit determines the possibility of fraud based on the information collected by the information collection unit. The shutout unit shuts out a telephone call or a mail that is determined to be highly likely to be a fraud by the fraud determination unit. The warning unit gives a warning to the person who is shut out by the shutout unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty effectively preventing special fraud phone calls and emails, leaving users at risk of being victimized.

[0005] The system according to the embodiment aims to effectively prevent special fraud phone calls and emails. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a fraud determination unit, a shutout unit, and a warning unit. The information collection unit collects information from users. The fraud determination unit determines the possibility of fraud based on the information collected by the information collection unit. The shutout unit blocks calls and emails that the fraud determination unit determines to be highly likely to be fraudulent. The warning unit issues a warning to parties that have been shut out by the shutout unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively prevent special fraud phone calls and emails. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The fraud prevention system according to an embodiment of the present invention collects information provided by users, analyzes it using a generation AI to determine the possibility of fraud, blocks calls and emails that are likely to be fraudulent, and issues a warning to the other party. This allows the fraud prevention system to quickly block calls and emails that are likely to be fraudulent and issue a warning to the other party.

[0029] The fraud prevention system according to the embodiment includes an information collection unit, a fraud determination unit, a shutout unit, and a warning unit. The information collection unit collects information from users. For example, the information collection unit collects information such as phone numbers, email addresses, call contents, and email text provided by the users. The information collection unit also uses a generation AI to store the information provided by the users in a database. For example, the generation AI analyzes the information provided by the users and stores it in a database. The fraud determination unit determines the possibility of fraud based on the information collected by the information collection unit. For example, the generation AI analyzes patterns and characteristics of previously reported frauds and determines whether a newly received phone call or email falls into those categories. If the fraud determination unit determines using the generation AI that a call or email is likely to be fraudulent, it immediately shuts out the call or email. The shutout unit shuts out calls or emails determined by the fraud determination unit to be likely to be fraudulent. For example, the shutout unit automatically disconnects calls that are likely to be fraudulent. The shutout unit automatically deletes emails that are likely to be fraudulent. The warning unit issues a warning to parties shut out by the shutout unit. For example, in the case of a phone call, the warning unit plays a voice message such as "This call may be fraudulent, so the connection will be terminated." In the case of an email, the warning unit replies with a message such as "This email may be fraudulent, so we have rejected it." This allows the fraud prevention system to quickly shut out calls and emails that are likely to be fraudulent and warn the other party.

[0030] The information collection unit allows the generation AI to automatically add geographical information and time zones to information provided by the user. For example, the information collection unit allows the generation AI to automatically add geographical information of the source to phone numbers and email addresses provided by the user. For example, the generation AI identifies the region of origin from the phone number's country code and area code and analyzes fraud patterns. The information collection unit also allows the generation AI to automatically add the time zone of receipt to the call content and email body provided by the user. For example, if a call is received late at night or early in the morning, it determines that there is a high possibility of fraud. The information collection unit also allows the generation AI to automatically add related past fraud cases to the information provided by the user. For example, it checks whether the same phone number or email address matches previously reported fraud cases. This allows for more detailed analysis of fraud patterns.

[0031] The information collection unit allows the user to include images and videos in the information provided by the user, thereby collecting visual evidence. The information collection unit, for example, allows the user to include images and videos in the information provided by the user. For example, screenshots of fraudulent emails and recordings of phone calls are uploaded. The information collection unit also collects visual evidence by enabling the user to provide information that includes images and videos. For example, it analyzes images and videos that show fraudulent methods and stores them in a database. The information collection unit also analyzes fraud patterns in more detail by including images and videos in the information provided by the user. For example, it visually checks the format of fraudulent emails and the content of phone calls. In this way, by collecting visual evidence, it is possible to analyze fraud patterns in more detail.

[0032] The information collection unit can integrate information provided from different devices and collect more diverse data. For example, the information collection unit builds a system that integrates information provided from different devices. For example, it centrally manages information provided from smartphones, tablets, and PCs. The information collection unit also collects more diverse data by integrating information provided from different devices. For example, it integrates call recordings from smartphones and email text from PCs. The information collection unit also integrates information provided from different devices and analyzes fraud patterns. For example, it combines location information from smartphones and IP addresses from PCs and analyzes them. This allows more diverse data to be collected by integrating information from different devices.

[0033] The fraud detection unit allows the generation AI to automatically learn new fraud patterns based on past fraud data, thereby improving the accuracy of detection. For example, the generation AI automatically learns new fraud patterns based on past fraud data. For example, it analyzes past fraud cases and extracts common features. The fraud detection unit also allows the generation AI to learn new fraud patterns based on past fraud data, thereby improving the accuracy of detection. For example, it learns newly reported fraud cases in real time. The fraud detection unit also allows the generation AI to automatically learn new fraud patterns based on past fraud data, thereby improving the accuracy of detection. For example, it tracks and learns changes in fraud methods and techniques. This allows it to learn new fraud patterns and improve the accuracy of detection.

[0034] When determining the possibility of fraud, the fraud determination unit allows the generation AI to analyze not only the content of calls and emails, but also the sender's IP address and communication route. For example, the fraud determination unit analyzes not only the content of calls and emails, but also the sender's IP address. For example, it checks whether the IP address matches previously reported fraud cases. The fraud determination unit also analyzes not only the content of calls and emails, but also the communication route. For example, if the communication route passes through a suspicious route, it will determine that there is a high possibility of fraud. The fraud determination unit also analyzes not only the content of calls and emails, but also the sender's IP address and communication route. For example, if IP addresses and communication routes are concentrated in a specific area, it will determine that there is a high possibility of fraud. This allows for more accurate determination of the possibility of fraud by analyzing not only the content of calls and emails, but also the sender's IP address and communication route.

[0035] The fraud determination unit enables fraud detection in different languages, strengthening international fraud prevention measures. The fraud determination unit, for example, enables the generation AI to detect fraud in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The fraud determination unit also strengthens international fraud prevention measures by enabling fraud detection in different languages. For example, it collects fraud cases from each country and stores them in a database. The fraud determination unit also strengthens international fraud prevention measures by enabling the generation AI to detect fraud in different languages. For example, it analyzes the content of phone calls and email texts in different languages ​​to determine the possibility of fraud. This enables fraud detection in different languages, strengthening international fraud prevention measures.

[0036] The fraud determination unit can compare the results of the fraud determination with the user's past behavioral history and provide an individually customized warning. The fraud determination unit, for example, compares the results of the fraud determination with the user's past behavioral history and provides an individually customized warning. For example, a strong warning is sent to a user who has been a victim of fraud in the past. The fraud determination unit also customizes the results of the fraud determination based on the user's past behavioral history. For example, a detailed warning is provided to a user who has received calls or emails in the past that are likely to be fraudulent. The fraud determination unit also compares the results of the fraud determination with the user's past behavioral history and provides an individually customized warning. For example, specific countermeasures are suggested to a user who has received calls or emails in the past that are likely to be fraudulent. This makes it possible to compare the results with the user's past behavioral history and provide an individually customized warning.

[0037] If the shutout unit determines that there is a high possibility of fraud, the generation AI can automatically record the contents of calls and emails and save them as evidence. For example, the shutout unit automatically records the contents of calls and emails that are determined to be highly fraudulent and saves them as evidence. For example, it saves the recording of calls and the text of emails in a database. Furthermore, if the generation AI determines that there is a high possibility of fraud, the shutout unit records the contents of calls and emails in real time and saves them as evidence. For example, it automatically starts recording calls and saves them. Furthermore, the shutout unit automatically records the contents of calls and emails that are determined to be highly fraudulent and saves them as evidence. For example, it analyzes the text of emails and extracts and saves the important parts. This allows the contents of calls and emails to be recorded and saved as evidence if there is a high possibility of fraud.

[0038] When shutting out a call, the generation AI analyzes the other party's reaction in real time and changes the warning message as necessary. When shutting out a call, the generation AI analyzes the other party's reaction in real time and changes the warning message as necessary. For example, if the other party persistently tries to continue the call, a strong warning message is sent. When shutting out a call, the generation AI analyzes the other party's reaction in real time and dynamically changes the warning message. For example, if the other party denies fraud, a message presenting concrete evidence is sent. When shutting out a call, the generation AI analyzes the other party's reaction in real time and changes the warning message as necessary. For example, if the other party uses threatening language or behavior, a message suggesting legal action is sent. This allows the generation AI to analyze the other party's reaction in real time and change the warning message as necessary when shutting out a call.

[0039] The shut-out unit can add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. The shut-out unit can add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. For example, it can automatically generate a report email including detailed information about the fraud. The shut-out unit can also add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. For example, it can automatically send a report with a recorded call or the body of the email attached. The shut-out unit can also add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. For example, it can generate the report content in real time and send it immediately. This can add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud.

[0040] When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. For example, it analyzes the other party's speaking style and vocabulary to learn new fraudulent patterns. When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. For example, it analyzes the other party's speaking style and vocabulary and saves it in a database. When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. For example, it analyzes the other party's reactions and behavioral patterns to identify new fraudulent methods. This allows the generation AI to analyze the other party's voice and text to learn fraudulent methods in real time when shutting out a call.

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

[0042] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral patterns. The behavioral analysis unit may, for example, collect a user's website browsing history or app usage history and use this information as additional data to determine the possibility of fraud. For example, if a specific fraudulent website is frequently accessed, it may determine that there is a high possibility of fraud. The behavioral analysis unit may also analyze a user's online shopping history and issue a warning if a suspicious transaction is occurring. Furthermore, the behavioral analysis unit may monitor a user's social media activity and detect posts or messages that pose a high risk of fraud. This allows for more accurate determination of the possibility of fraud by analyzing a user's behavioral patterns.

[0043] The fraud prevention system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and stress level to detect situations where there is a high risk of fraud. For example, if the user's heart rate rises sharply, it may determine that the user is likely to be experiencing fraud. The health monitoring unit may also analyze the user's sleep patterns and evaluate whether lack of sleep is a factor that increases the risk of fraud. Furthermore, the health monitoring unit may monitor the user's exercise habits and provide advice for stress reduction. In this way, the risk of fraud can be reduced by monitoring the user's health condition.

[0044] The fraud prevention system may further include a financial monitoring unit that monitors the user's financial transactions. The financial monitoring unit may, for example, analyze the user's bank account or credit card transaction history to detect suspicious transactions. For example, if a large amount of cash is withdrawn or a suspicious transaction is made from overseas, it may determine that there is a high possibility of fraud. The financial monitoring unit may also monitor the user's investment activities and detect high-risk investment fraud. Furthermore, the financial monitoring unit may monitor the user's insurance policies and detect fraudulent insurance claims. Thus, by monitoring the user's financial transactions, the risk of fraud can be reduced.

[0045] The fraud prevention system may further include a location information analysis unit that utilizes the user's location information. The location information analysis unit, for example, analyzes the user's current location and movement history to identify areas where there is a high risk of fraud. For example, if the user frequently visits an area where fraud is prevalent, it may determine that there is a high possibility of fraud. The location information analysis unit may also analyze the user's movement patterns and issue a warning if suspicious movement is detected. Furthermore, the location information analysis unit may identify time periods when there is a high risk of fraud based on the user's location information. In this way, the risk of fraud can be reduced by utilizing the user's location information.

[0046] The fraud prevention system may further include a security enhancement unit that enhances the security of a user's device. The security enhancement unit, for example, monitors applications installed on the user's device and detects unauthorized applications. For example, if malware or spyware is installed, it may determine that there is a high risk of fraud. The security enhancement unit may also monitor the network connection of the user's device and detect suspicious connections. Furthermore, the security enhancement unit may monitor the security settings of the user's device and provide advice on enhancing security as necessary. This enhances the user's device security, thereby reducing the risk of fraud.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The information collection unit collects information from the user. For example, it collects information such as the phone number, email address, call content, and email body provided by the user. The information collection unit also uses the generation AI to store the information provided by the user in a database. The generation AI analyzes the information provided by the user and stores it in the database. Step 2: The fraud determination unit determines the possibility of fraud based on the information collected by the information collection unit. The generation AI analyzes the patterns and characteristics of previously reported frauds and determines whether a newly received phone call or email fits the criteria. If the fraud determination unit uses the generation AI to determine that there is a high possibility of fraud, it immediately blocks the call or email. Step 3: The shutout unit shuts out calls and emails that are determined by the fraud determination unit to be highly likely to be fraudulent. For example, the shutout unit automatically disconnects calls that are highly likely to be fraudulent and automatically deletes emails that are highly likely to be fraudulent. Step 4: The warning unit issues a warning to the party shut out by the shutout unit. For example, in the case of a phone call, the warning unit plays a voice message such as "This call may be fraudulent, so the connection will be terminated." In the case of an email, the warning unit sends a reply with a sentence such as "This email may be fraudulent, so we have refused to receive it."

[0049] (Example 2) The fraud prevention system according to an embodiment of the present invention collects information provided by users, analyzes it using a generation AI to determine the possibility of fraud, blocks calls and emails that are likely to be fraudulent, and issues a warning to the other party. This allows the fraud prevention system to quickly block calls and emails that are likely to be fraudulent and issue a warning to the other party.

[0050] The fraud prevention system according to the embodiment includes an information collection unit, a fraud determination unit, a shutout unit, and a warning unit. The information collection unit collects information from users. For example, the information collection unit collects information such as phone numbers, email addresses, call contents, and email text provided by the users. The information collection unit also uses a generation AI to store the information provided by the users in a database. For example, the generation AI analyzes the information provided by the users and stores it in a database. The fraud determination unit determines the possibility of fraud based on the information collected by the information collection unit. For example, the generation AI analyzes patterns and characteristics of previously reported frauds and determines whether a newly received phone call or email falls into those categories. If the fraud determination unit determines using the generation AI that a call or email is likely to be fraudulent, it immediately shuts out the call or email. The shutout unit shuts out calls or emails determined by the fraud determination unit to be likely to be fraudulent. For example, the shutout unit automatically disconnects calls that are likely to be fraudulent. The shutout unit automatically deletes emails that are likely to be fraudulent. The warning unit issues a warning to parties shut out by the shutout unit. For example, in the case of a phone call, the warning unit plays a voice message such as "This call may be fraudulent, so the connection will be terminated." In the case of an email, the warning unit replies with a message such as "This email may be fraudulent, so we have rejected it." This allows the fraud prevention system to quickly shut out calls and emails that are likely to be fraudulent and warn the other party.

[0051] The information collection unit allows the generation AI to automatically add geographical information and time zones to information provided by the user. For example, the information collection unit allows the generation AI to automatically add geographical information of the source to phone numbers and email addresses provided by the user. For example, the generation AI identifies the region of origin from the phone number's country code and area code and analyzes fraud patterns. The information collection unit also allows the generation AI to automatically add the time zone of receipt to the call content and email body provided by the user. For example, if a call is received late at night or early in the morning, it determines that there is a high possibility of fraud. The information collection unit also allows the generation AI to automatically add related past fraud cases to the information provided by the user. For example, it checks whether the same phone number or email address matches previously reported fraud cases. This allows for more detailed analysis of fraud patterns.

[0052] When a user provides information by voice, the information collection unit uses speech recognition technology to automatically convert the information into text, and can then perform emotion analysis. For example, the information collection unit uses speech recognition technology to automatically convert the contents of a call provided by the user into text. For example, the call contents are transcribed in real time and stored in a database. The information collection unit also uses speech recognition technology to have the generation AI perform emotion analysis on the call contents converted into text. For example, the information collection unit analyzes emotions such as anxiety and fear from the user's tone of voice and choice of words. The information collection unit also has the generation AI automatically assign an emotion score to the information provided by the user by voice. For example, a high emotion score determines that there is a high possibility of fraud. This allows for more accurate determination of the possibility of fraud by converting the voice information into text and performing emotion analysis.

[0053] The information collection unit uses the emotion estimation function to analyze the degree of anxiety or fear felt by the user when providing information, and can utilize this information to determine fraud. The information collection unit, for example, uses the emotion estimation function to analyze the degree of anxiety or fear felt by the user when providing information. For example, an emotion score is calculated from the user's facial expression and tone of voice. The information collection unit also uses the emotion estimation function to analyze the degree of anxiety or fear felt by the user when providing information in real time. For example, the information collection unit analyzes the keyboard typing speed and frequency of typos when the user types. The information collection unit also uses the emotion estimation function to analyze the degree of anxiety or fear felt by the user when providing information, and utilizes this information to determine fraud. For example, a high emotion score is determined to indicate a high possibility of fraud. This makes it possible to determine the possibility of fraud by taking the user's emotions into consideration.

[0054] The information collection unit allows the user to include images and videos in the information provided by the user, thereby collecting visual evidence. The information collection unit, for example, allows the user to include images and videos in the information provided by the user. For example, screenshots of fraudulent emails and recordings of phone calls are uploaded. The information collection unit also collects visual evidence by enabling the user to provide information that includes images and videos. For example, it analyzes images and videos that show fraudulent methods and stores them in a database. The information collection unit also analyzes fraud patterns in more detail by including images and videos in the information provided by the user. For example, it visually checks the format of fraudulent emails and the content of phone calls. In this way, by collecting visual evidence, it is possible to analyze fraud patterns in more detail.

[0055] The information collection unit can integrate information provided from different devices and collect more diverse data. For example, the information collection unit builds a system that integrates information provided from different devices. For example, it centrally manages information provided from smartphones, tablets, and PCs. The information collection unit also collects more diverse data by integrating information provided from different devices. For example, it integrates call recordings from smartphones and email text from PCs. The information collection unit also integrates information provided from different devices and analyzes fraud patterns. For example, it combines location information from smartphones and IP addresses from PCs and analyzes them. This allows more diverse data to be collected by integrating information from different devices.

[0056] The information collection unit introduces a chatbot equipped with an emotion estimation function, and can provide emotion-based support when a user provides information in real time. The information collection unit, for example, introduces a chatbot equipped with an emotion estimation function, and provides emotion-based support when a user provides information in real time. For example, if a user is feeling anxious, the chatbot sends a reassuring message. The information collection unit also analyzes the user's emotions in real time and provides appropriate support. For example, if a user is feeling fear, the chatbot suggests specific measures. The information collection unit also uses the chatbot equipped with the emotion estimation function to provide emotion-based support when a user provides information. For example, if a user is feeling angry, the chatbot sends a message urging the user to stay calm. This makes it possible to provide emotion-based support when a user provides information in real time.

[0057] The fraud detection unit allows the generation AI to automatically learn new fraud patterns based on past fraud data, thereby improving the accuracy of detection. For example, the generation AI automatically learns new fraud patterns based on past fraud data. For example, it analyzes past fraud cases and extracts common features. The fraud detection unit also allows the generation AI to learn new fraud patterns based on past fraud data, thereby improving the accuracy of detection. For example, it learns newly reported fraud cases in real time. The fraud detection unit also allows the generation AI to automatically learn new fraud patterns based on past fraud data, thereby improving the accuracy of detection. For example, it tracks and learns changes in fraud methods and techniques. This allows it to learn new fraud patterns and improve the accuracy of detection.

[0058] When determining the possibility of fraud, the fraud determination unit allows the generation AI to analyze not only the content of calls and emails, but also the sender's IP address and communication route. For example, the fraud determination unit analyzes not only the content of calls and emails, but also the sender's IP address. For example, it checks whether the IP address matches previously reported fraud cases. The fraud determination unit also analyzes not only the content of calls and emails, but also the communication route. For example, if the communication route passes through a suspicious route, it will determine that there is a high possibility of fraud. The fraud determination unit also analyzes not only the content of calls and emails, but also the sender's IP address and communication route. For example, if IP addresses and communication routes are concentrated in a specific area, it will determine that there is a high possibility of fraud. This allows for more accurate determination of the possibility of fraud by analyzing not only the content of calls and emails, but also the sender's IP address and communication route.

[0059] When the fraud determination unit determines using the emotion estimation function that there is a high possibility of fraud, it can select an appropriate warning method taking into account the user's emotional state. For example, when the fraud determination unit uses the emotion estimation function to determine that there is a high possibility of fraud, it selects an appropriate warning method taking into account the user's emotional state. For example, if the user is feeling anxious, it sends a reassuring message. Furthermore, when the fraud determination unit uses the emotion estimation function to determine that there is a high possibility of fraud, it analyzes the user's emotional state in real time and selects an appropriate warning method. For example, if the user is feeling fear, it suggests specific countermeasures. Furthermore, when the fraud determination unit uses the emotion estimation function to determine that there is a high possibility of fraud, it selects an appropriate warning method taking into account the user's emotional state. For example, if the user is feeling angry, it sends a message urging the user to stay calm. In this way, it is possible to select an appropriate warning method taking into account the user's emotional state.

[0060] The fraud determination unit enables fraud detection in different languages, strengthening international fraud prevention measures. The fraud determination unit, for example, enables the generation AI to detect fraud in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The fraud determination unit also strengthens international fraud prevention measures by enabling fraud detection in different languages. For example, it collects fraud cases from each country and stores them in a database. The fraud determination unit also strengthens international fraud prevention measures by enabling the generation AI to detect fraud in different languages. For example, it analyzes the content of phone calls and email texts in different languages ​​to determine the possibility of fraud. This enables fraud detection in different languages, strengthening international fraud prevention measures.

[0061] The fraud determination unit can compare the results of the fraud determination with the user's past behavioral history and provide an individually customized warning. The fraud determination unit, for example, compares the results of the fraud determination with the user's past behavioral history and provides an individually customized warning. For example, a strong warning is sent to a user who has been a victim of fraud in the past. The fraud determination unit also customizes the results of the fraud determination based on the user's past behavioral history. For example, a detailed warning is provided to a user who has received calls or emails in the past that are likely to be fraudulent. The fraud determination unit also compares the results of the fraud determination with the user's past behavioral history and provides an individually customized warning. For example, specific countermeasures are suggested to a user who has received calls or emails in the past that are likely to be fraudulent. This makes it possible to compare the results with the user's past behavioral history and provide an individually customized warning.

[0062] The fraud determination unit can use the emotion estimation function to automatically generate a follow-up message to give the user a sense of security when it determines that there is a high possibility of fraud. For example, the fraud determination unit uses the emotion estimation function to automatically generate a follow-up message to give the user a sense of security when it determines that there is a high possibility of fraud. For example, if the user is feeling anxious, the fraud determination unit sends a reassuring message. Furthermore, the fraud determination unit uses the emotion estimation function to analyze the user's emotional state in real time when it determines that there is a high possibility of fraud and automatically generates a follow-up message to give the user a sense of security. For example, if the user is feeling fear, the fraud determination unit suggests specific countermeasures. Furthermore, the fraud determination unit uses the emotion estimation function to automatically generate a follow-up message to give the user a sense of security when it determines that there is a high possibility of fraud. For example, if the user is feeling angry, the fraud determination unit sends a message urging the user to stay calm. In this way, a follow-up message to give the user a sense of security can be automatically generated when it determines that there is a high possibility of fraud.

[0063] If the shutout unit determines that there is a high possibility of fraud, the generation AI can automatically record the contents of calls and emails and save them as evidence. For example, the shutout unit automatically records the contents of calls and emails that are determined to be highly fraudulent and saves them as evidence. For example, it saves the recording of calls and the text of emails in a database. Furthermore, if the generation AI determines that there is a high possibility of fraud, the shutout unit records the contents of calls and emails in real time and saves them as evidence. For example, it automatically starts recording calls and saves them. Furthermore, the shutout unit automatically records the contents of calls and emails that are determined to be highly fraudulent and saves them as evidence. For example, it analyzes the text of emails and extracts and saves the important parts. This allows the contents of calls and emails to be recorded and saved as evidence if there is a high possibility of fraud.

[0064] When shutting out a call, the generation AI analyzes the other party's reaction in real time and changes the warning message as necessary. When shutting out a call, the generation AI analyzes the other party's reaction in real time and changes the warning message as necessary. For example, if the other party persistently tries to continue the call, a strong warning message is sent. When shutting out a call, the generation AI analyzes the other party's reaction in real time and dynamically changes the warning message. For example, if the other party denies fraud, a message presenting concrete evidence is sent. When shutting out a call, the generation AI analyzes the other party's reaction in real time and changes the warning message as necessary. For example, if the other party uses threatening language or behavior, a message suggesting legal action is sent. This allows the generation AI to analyze the other party's reaction in real time and change the warning message as necessary when shutting out a call.

[0065] The shut-out unit can use the emotion estimation function to monitor the emotional state of the user when it is determined that there is a high possibility of fraud and provide appropriate support. The shut-out unit, for example, uses the emotion estimation function to monitor the emotional state of the user when it is determined that there is a high possibility of fraud and provide appropriate support. For example, if the user is feeling anxious, it sends a message to reassure the user. The shut-out unit also uses the emotion estimation function to monitor the emotional state of the user in real time and provide appropriate support. For example, if the user is feeling fear, it suggests specific measures. The shut-out unit also uses the emotion estimation function to monitor the emotional state of the user when it is determined that there is a high possibility of fraud and provide appropriate support. For example, if the user is feeling angry, it sends a message urging the user to stay calm. In this way, the shut-out unit can monitor the emotional state of the user when it is determined that there is a high possibility of fraud and provide appropriate support.

[0066] The shut-out unit can add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. The shut-out unit can add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. For example, it can automatically generate a report email including detailed information about the fraud. The shut-out unit can also add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. For example, it can automatically send a report with a recorded call or the body of the email attached. The shut-out unit can also add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud. For example, it can generate the report content in real time and send it immediately. This can add a function that allows the generation AI to automatically report to the police or related agencies if it determines that there is a high possibility of fraud.

[0067] When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. For example, it analyzes the other party's speaking style and vocabulary to learn new fraudulent patterns. When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. For example, it analyzes the other party's speaking style and vocabulary and saves it in a database. When shutting out a call, the generation AI analyzes the other party's voice and text to learn fraudulent methods in real time. For example, it analyzes the other party's reactions and behavioral patterns to identify new fraudulent methods. This allows the generation AI to analyze the other party's voice and text to learn fraudulent methods in real time when shutting out a call.

[0068] The shutout unit can use the emotion estimation function to introduce a counseling service for providing psychological support to the user when it is determined that there is a high possibility of fraud. For example, the shutout unit uses the emotion estimation function to introduce a counseling service for providing psychological support to the user when it is determined that there is a high possibility of fraud. For example, the shutout unit provides a method for contacting a professional counselor. The shutout unit also uses the emotion estimation function to analyze the user's emotional state in real time and introduces a counseling service for providing psychological support. For example, the shutout unit sends a link to book online counseling. The shutout unit also uses the emotion estimation function to introduce a counseling service for providing psychological support to the user when it is determined that there is a high possibility of fraud. For example, the shutout unit provides contact information for the counseling service. This makes it possible to introduce a counseling service for providing psychological support to the user when it is determined that there is a high possibility of fraud.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The fraud prevention system may further include a behavioral analysis unit that analyzes a user's behavioral patterns. The behavioral analysis unit may, for example, collect a user's website browsing history or app usage history and use this information as additional data to determine the possibility of fraud. For example, if a specific fraudulent website is frequently accessed, it may determine that there is a high possibility of fraud. The behavioral analysis unit may also analyze a user's online shopping history and issue a warning if a suspicious transaction is occurring. Furthermore, the behavioral analysis unit may monitor a user's social media activity and detect posts or messages that pose a high risk of fraud. This allows for more accurate determination of the possibility of fraud by analyzing a user's behavioral patterns.

[0071] The fraud prevention system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and stress level to detect situations where there is a high risk of fraud. For example, if the user's heart rate rises sharply, it may determine that the user is likely to be experiencing fraud. The health monitoring unit may also analyze the user's sleep patterns and evaluate whether lack of sleep is a factor that increases the risk of fraud. Furthermore, the health monitoring unit may monitor the user's exercise habits and provide advice for stress reduction. In this way, the risk of fraud can be reduced by monitoring the user's health condition.

[0072] The fraud prevention system may further include a financial monitoring unit that monitors the user's financial transactions. The financial monitoring unit may, for example, analyze the user's bank account or credit card transaction history to detect suspicious transactions. For example, if a large amount of cash is withdrawn or a suspicious transaction is made from overseas, it may determine that there is a high possibility of fraud. The financial monitoring unit may also monitor the user's investment activities and detect high-risk investment fraud. Furthermore, the financial monitoring unit may monitor the user's insurance policies and detect fraudulent insurance claims. Thus, by monitoring the user's financial transactions, the risk of fraud can be reduced.

[0073] The fraud prevention system may further include a location information analysis unit that utilizes the user's location information. The location information analysis unit, for example, analyzes the user's current location and movement history to identify areas where there is a high risk of fraud. For example, if the user frequently visits an area where fraud is prevalent, it may determine that there is a high possibility of fraud. The location information analysis unit may also analyze the user's movement patterns and issue a warning if suspicious movement is detected. Furthermore, the location information analysis unit may identify time periods when there is a high risk of fraud based on the user's location information. In this way, the risk of fraud can be reduced by utilizing the user's location information.

[0074] The fraud prevention system may further include a security enhancement unit that enhances the security of a user's device. The security enhancement unit, for example, monitors applications installed on the user's device and detects unauthorized applications. For example, if malware or spyware is installed, it may determine that there is a high risk of fraud. The security enhancement unit may also monitor the network connection of the user's device and detect suspicious connections. Furthermore, the security enhancement unit may monitor the security settings of the user's device and provide advice on enhancing security as necessary. This enhances the user's device security, thereby reducing the risk of fraud.

[0075] The fraud prevention system can further estimate the user's emotions and suggest appropriate actions to the user if it determines that there is a high risk of fraud. For example, if the user feels anxious, it can determine that there is a high risk of fraud and send the user a message urging them to stay calm. If the user feels fear, it can suggest specific countermeasures. Furthermore, if the user feels anger, it can send a message urging them to stay calm. In this way, by taking the user's emotions into consideration and suggesting appropriate actions, it is possible to reduce the risk of fraud.

[0076] The fraud prevention system can also estimate the user's emotions and provide psychological support to the user if it determines that there is a high risk of fraud. For example, if the user feels anxious, it can send a reassuring message. If the user feels fear, it can suggest specific measures. Furthermore, if the user feels angry, it can send a message encouraging the user to stay calm. In this way, by providing psychological support that takes the user's emotions into consideration, it is possible to reduce the risk of fraud.

[0077] The fraud prevention system can further estimate the user's emotions and automatically generate follow-up messages for the user if it determines that there is a high risk of fraud. For example, if the user feels anxious, it can send a reassuring message. If the user feels fear, it can suggest specific countermeasures. Furthermore, if the user feels angry, it can send a message encouraging the user to stay calm. In this way, the risk of fraud can be reduced by automatically generating follow-up messages that take the user's emotions into consideration.

[0078] The fraud prevention system can further estimate the user's emotions and, if it determines that there is a high risk of fraud, can refer the user to counseling services. For example, if the user feels anxious, it can provide a way to contact a professional counselor. If the user feels fear, it can send a link to book an online counseling session. Furthermore, if the user feels anger, it can provide contact information for counseling services. In this way, the risk of fraud can be reduced by referring the user to counseling services taking into account the user's emotions.

[0079] The fraud prevention system can also estimate the user's emotions and select an appropriate warning method for the user if it determines that there is a high risk of fraud. For example, if the user feels anxious, it can send a reassuring message. If the user feels fear, it can suggest specific measures. Furthermore, if the user feels angry, it can send a message encouraging the user to stay calm. In this way, by selecting an appropriate warning method taking the user's emotions into consideration, it is possible to reduce the risk of fraud.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The information collection unit collects information from the user. For example, it collects information such as the phone number, email address, call content, and email body provided by the user. The information collection unit also uses the generation AI to store the information provided by the user in a database. The generation AI analyzes the information provided by the user and stores it in the database. Step 2: The fraud determination unit determines the possibility of fraud based on the information collected by the information collection unit. The generation AI analyzes the patterns and characteristics of previously reported frauds and determines whether a newly received phone call or email fits the criteria. If the fraud determination unit uses the generation AI to determine that there is a high possibility of fraud, it immediately blocks the call or email. Step 3: The shutout unit shuts out calls and emails that are determined by the fraud determination unit to be highly likely to be fraudulent. For example, the shutout unit automatically disconnects calls that are highly likely to be fraudulent and automatically deletes emails that are highly likely to be fraudulent. Step 4: The warning unit issues a warning to the party shut out by the shutout unit. For example, in the case of a phone call, the warning unit plays a voice message such as "This call may be fraudulent, so the connection will be terminated." In the case of an email, the warning unit sends a reply with a sentence such as "This email may be fraudulent, so we have refused to receive it."

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

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

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information from users; a fraud determination unit that determines the possibility of fraud based on the information collected by the information collection unit; a shut-out unit that shuts out calls and emails that are determined by the fraud determination unit to have a high possibility of being fraudulent; a warning unit that issues a warning to the other party shut out by the shut out unit. A system characterized by:

2. The information collecting unit The AI ​​automatically adds geographical information and time zones to the information provided by the user.

2. The system of claim 1.

3. The information collecting unit When a user provides information by voice, the generative AI automatically converts it into text using speech recognition technology and then performs sentiment analysis.

2. The system of claim 1.

4. The information collecting unit Analyze the degree of anxiety and fear felt by users when providing information and use that information to determine whether it is a fraud.

2. The system of claim 1.

5. The information collecting unit Allow users to include images and videos in the information they provide to gather visual evidence 2. The system of claim 1.

Images