System

Abstract

An object of a system according to an embodiment is to find a line having a possibility of potential unauthorized use at an early stage and to minimize damage.SOLUTION: A system includes an illegal case analysis part, a contract information monitoring part, and a communication log monitoring part. The fraud case analysis unit analyzes past fraud cases or communication logs. The contract information monitoring unit monitors information at the time of a new contract and contract change information. The communication log monitoring unit monitors domestic and overseas call and communication logs.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of not being able to detect lines that may be subject to potential fraudulent use early on and minimize damage.

[0005] The system according to the embodiment aims to detect lines that may be potentially subject to fraudulent use at an early stage and minimize damage. [Means for solving the problem]

[0006] The system according to the embodiment includes a fraud case analysis unit, a contract information monitoring unit, and a communication log monitoring unit. The fraud case analysis unit analyzes past fraud cases or communication logs. The contract information monitoring unit monitors information at the time of new contracts and contract change information. The communication log monitoring unit monitors domestic and international call and communication logs. [Effects of the Invention]

[0007] The system according to the embodiment can detect lines that are potentially subject to fraudulent use at an early stage and minimize damage. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The fraudulent usage monitoring system according to an embodiment of the present invention is a system that sets conditions based on past fraudulent cases and communication logs, and analyzes information at the time of new contracts, contract amendment information, and domestic and international call and communication logs to monitor and detect lines that may be potentially subject to fraudulent usage and suspend the lines at an early stage. This enables the fraudulent usage monitoring system to quickly detect lines that may be potentially subject to fraudulent usage and respond quickly.

[0029] The fraud monitoring system according to the embodiment includes a fraud case analysis unit, a contract information monitoring unit, and a communication log monitoring unit. The fraud case analysis unit analyzes past fraud cases or communication logs. For example, the generation AI analyzes past fraud cases and communication logs to extract fraudulent usage patterns and conditions. The generation AI can also detect fraudulent usage concentrated in specific time periods and abnormal communication volume from specific regions. The generation AI also learns fraudulent usage patterns based on data from past fraud cases and communication logs. The contract information monitoring unit monitors information at the time of a new contract and contract change information. For example, the generation AI monitors information at the time of a new contract and contract change information to determine whether there is a possibility of fraudulent usage. The generation AI can also detect multiple contract changes in a short period of time or abnormally high contract terms. The generation AI also evaluates the possibility of fraudulent usage based on information at the time of a new contract and contract change information. The communication log monitoring unit monitors domestic and international call and communication logs. For example, the generation AI monitors domestic and international call and communication logs to detect signs of fraudulent usage. The generation AI can also detect abnormal communication volumes that deviate from normal usage patterns and suspicious communications from specific countries or regions. The generation AI also analyzes signs of fraudulent use based on call and communication log data. This allows the fraudulent use monitoring system according to the embodiment to quickly detect lines with potential fraudulent use and respond to them promptly. For example, the output unit minimizes damage by quickly suspending lines that are suspected of fraudulent use that have been detected. The output unit can also notify the administrator of the monitoring results and prompt them to take appropriate action. Furthermore, the output unit can store the monitoring results in a database to help prevent future fraudulent use.

[0030] The fraud case analysis unit can perform simulations to predict future fraudulent activities based on patterns extracted from past fraudulent activities and communication logs. The fraud case analysis unit, for example, uses a generative AI to perform simulations to predict future fraudulent activities based on patterns extracted from past fraudulent activities and communication logs. For example, it calculates the probability of fraudulent activities occurring in a specific time period or area and proposes preventive measures. The fraud case analysis unit can also build a predictive model for future fraudulent activities based on data from past fraudulent activities and communication logs. For example, the generative AI uses a predictive model based on past data to calculate the probability of future fraudulent activities occurring and proposes preventive measures. This makes it possible to predict future fraudulent activities and propose preventive measures.

[0031] The fraud case analysis unit can include not only communication logs but also related social media posts and news articles in its analysis. For example, when analyzing fraud cases, the fraud case analysis unit can include not only communication logs but also related social media posts and news articles in its analysis. For example, it can detect posts containing specific keywords and identify signs of fraud. The fraud case analysis unit can also analyze signs of fraud based on data from social media posts and news articles. For example, the generative AI can identify signs of fraud based on data from social media posts and news articles and issue a warning. By including social media and news articles in its analysis, signs of fraud can be detected more widely.

[0032] The fraud case analysis unit can compare the analysis results of a fraud case with fraud patterns in other industries to find commonalities. For example, the fraud case analysis unit compares the analysis results of a fraud case with fraud patterns in other industries (such as the financial or retail industries) to find commonalities. For example, it issues a warning if similar methods are used. The fraud case analysis unit can also identify commonalities in fraudulent activities based on data on fraud patterns in other industries. For example, the generation AI can identify commonalities in fraudulent activities based on data on fraud patterns in the financial and retail industries and issue a warning. This makes it possible to detect signs of common fraudulent activities by comparing them with fraud patterns in other industries.

[0033] The fraud case analysis unit can convert call contents into text using speech recognition technology when analyzing communication logs and detect abnormalities in the content. For example, the fraud case analysis unit can convert call contents into text using speech recognition technology when analyzing communication logs and detect abnormalities in the content. For example, it can analyze calls containing specific keywords and identify signs of fraudulent activity. The fraud case analysis unit can also convert call contents into text using speech recognition technology and identify abnormalities in the content. For example, the generation AI can convert call contents into text using speech recognition technology, analyze calls containing specific keywords, and issue a warning. In this way, by converting call contents into text, it is possible to detect abnormalities in the content.

[0034] The contract information monitoring unit uses generation AI to analyze information at the time of new contracts and contract change information in real time, and can immediately detect abnormal patterns. The contract information monitoring unit, for example, uses generation AI to analyze information at the time of new contracts and contract change information in real time, and can immediately detect abnormal patterns. For example, it identifies multiple contract changes in a short period of time and issues a warning. The contract information monitoring unit can also use generation AI to identify abnormal patterns based on information at the time of new contracts and contract change information. For example, the generation AI analyzes information at the time of new contracts and contract change information in real time, and can immediately detect abnormal patterns and issue a warning. This makes it possible to detect abnormal patterns at the time of new contracts and contract changes in real time.

[0035] When monitoring contract information, the contract information monitoring unit can also analyze the contract holder's past purchase history and credit score. For example, when monitoring contract information, the contract information monitoring unit can also analyze the contract holder's past purchase history and credit score. For example, it can identify abnormal purchase patterns and low credit scores and perform risk assessments. The contract information monitoring unit can also identify abnormal contract patterns based on data on the contract holder's purchase history and credit score. For example, the generation AI can analyze the contract holder's purchase history and credit score, identify abnormal contract patterns, and issue a warning. In this way, abnormal contract patterns can be detected by analyzing the contract holder's purchase history and credit score.

[0036] The contract information monitoring unit can link information at the time of a new contract with the databases of other telecommunications carriers and share abnormal contract patterns. The contract information monitoring unit, for example, links information at the time of a new contract with the databases of other telecommunications carriers and shares abnormal contract patterns. For example, it issues a warning if contracts are made with multiple carriers at the same time. The contract information monitoring unit can also link with the databases of other telecommunications carriers to identify abnormal contract patterns. For example, the generation AI links information at the time of a new contract with the databases of other telecommunications carriers, shares abnormal contract patterns, and issues a warning. In this way, by linking with other telecommunications carriers, abnormal contract patterns can be shared and risks can be reduced.

[0037] The contract information monitoring unit can also analyze the geographical movement patterns of the contract holder when monitoring contract change information. For example, when monitoring contract change information, the contract information monitoring unit can also analyze the geographical movement patterns of the contract holder. For example, if an abnormal movement pattern is detected, a risk assessment is performed. The contract information monitoring unit can also identify abnormal contract changes based on data on the geographical movement patterns of the contract holder. For example, the generation AI can analyze the geographical movement patterns of the contract holder, identify abnormal contract changes, and issue a warning. In this way, abnormal contract changes can be detected by analyzing the geographical movement patterns of the contract holder.

[0038] The communication log monitoring unit can use the generation AI to extract specific keywords and phrases from the call and communication logs and detect abnormal patterns. The communication log monitoring unit can, for example, use the generation AI to extract specific keywords and phrases from the call and communication logs and detect abnormal patterns. For example, it can identify calls that contain keywords related to specific fraudulent activity and issue a warning. The communication log monitoring unit can also use the generation AI to identify abnormal patterns based on the call and communication log data. For example, the generation AI can extract specific keywords and phrases from the call and communication logs, detect abnormal patterns, and issue a warning. This makes it possible to extract specific keywords and phrases from the call and communication logs and detect abnormal patterns.

[0039] The communication log monitoring unit can analyze the relationship between the sender and receiver of a communication when monitoring the communication log and identify abnormal communication patterns. For example, when monitoring the communication log, the communication log monitoring unit analyzes the relationship between the sender and receiver of a communication and identify abnormal communication patterns. For example, it identifies abnormal communication that differs from normal communication patterns and issues a warning. The communication log monitoring unit can also identify abnormal communication patterns based on data on the relationship between the sender and receiver of a communication. For example, when monitoring the communication log, the generation AI analyzes the relationship between the sender and receiver of a communication, identifies abnormal communication patterns, and issues a warning. In this way, it is possible to identify abnormal communication patterns by analyzing the relationship between the sender and receiver of a communication.

[0040] The communication log monitoring unit can perform an integrated analysis of different communication protocols when monitoring communication logs. For example, the communication log monitoring unit performs an integrated analysis of different communication protocols (e.g., VoIP and SMS) when monitoring communication logs. For example, it issues a warning when an abnormal communication pattern is detected in multiple protocols. The communication log monitoring unit can also identify abnormal communication patterns based on data from different communication protocols. For example, the generation AI performs an integrated analysis of different communication protocols when monitoring communication logs, identifies abnormal communication patterns, and issues a warning. This makes it possible to detect abnormal communication patterns by performing an integrated analysis of different communication protocols.

[0041] The communication log monitoring unit can integrate the analysis results of the communication log with other data sets to identify abnormal patterns. The communication log monitoring unit, for example, integrates the analysis results of the communication log with other data sets (e.g., location information or purchase history) to identify abnormal patterns. For example, it identifies an abnormal communication pattern associated with specific location information and issues a warning. The communication log monitoring unit can also identify abnormal patterns based on data from other data sets. For example, the generation AI integrates the analysis results of the communication log with other data sets, identifies abnormal patterns, and issues a warning. In this way, by integrating the analysis results of the communication log with other data sets, it is possible to identify abnormal patterns.

[0042] When a line with potential fraudulent use is discovered, not only can the line be immediately suspended, but alternative means can be suggested. When a line with potential fraudulent use is discovered, for example, generation AI can be used to not only immediately suspend the line, but also suggest alternative means. For example, it can provide the user with alternative means of communication. Furthermore, generation AI can also be used to build a system that suggests alternative means when a line with potential fraudulent use is discovered. For example, generation AI can guide the user to alternative means of communication when the line is suspended. This makes it possible to maintain user convenience by suggesting alternative means when the line is suspended.

[0043] When deciding whether to suspend a line, the optimal timing for suspension can be determined by taking into account the past success rate of fraudulent use and the amount of damage. When deciding whether to suspend a line, the optimal timing for suspension can be determined by taking into account, for example, the past success rate of fraudulent use and the amount of damage. For example, the line can be suspended before the amount of damage reaches a certain level. In addition, when deciding whether to suspend a line, the generation AI can also determine the optimal timing for suspension based on data on the past success rate of fraudulent use and the amount of damage. For example, the generation AI can determine the optimal timing for suspension and suspend the line by taking into account the past success rate of fraudulent use and the amount of damage. In this way, the optimal timing for suspension can be determined by taking into account the past success rate of fraudulent use and the amount of damage.

[0044] When a line with potential fraudulent use is discovered, it can cooperate with other telecommunications carriers and share information. When a line with potential fraudulent use is discovered, it can cooperate with other telecommunications carriers and share information. For example, it can issue a warning if similar fraudulent activity is occurring at other carriers. Furthermore, when a line with potential fraudulent use is discovered, the generation AI can cooperate with other telecommunications carriers and build a system to share information. For example, the generation AI can cooperate with other telecommunications carriers, share information, and issue a warning. In this way, by collaborating with other telecommunications carriers, information on fraudulent use can be shared and risks can be reduced.

[0045] It is possible to build a system that automatically provides an alternative means to a user when a line is suspended. For example, a system can be built that automatically provides an alternative means to a user when a line is suspended. For example, the generation AI can guide the user to other means of communication when a line is suspended. In this way, convenience for users can be maintained by automatically providing an alternative means when a line is suspended.

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

[0047] The fraud case analysis unit can not only refer to fraud patterns in other industries, but also include fraud cases from different cultures and countries in its analysis. For example, it can analyze patterns of fraud that occur frequently in a particular country or region and find commonalities. The fraud case analysis unit can also identify common signs of fraud based on data on fraud from different cultures. For example, generative AI can analyze data on fraud from different cultures, identify common signs of fraud, and issue a warning. This makes it possible to detect signs of a wider range of fraud by analyzing data on fraud from different cultures.

[0048] The contract information monitoring unit can also analyze the contract holder's past purchase history and credit score. For example, it can identify abnormal purchase patterns and low credit scores and perform risk assessments. The contract information monitoring unit can also identify abnormal contract patterns based on the contract holder's purchase history and credit score data. For example, the generation AI can analyze the contract holder's purchase history and credit score, identify abnormal contract patterns, and issue a warning. This makes it possible to detect abnormal contract patterns by analyzing the contract holder's purchase history and credit score.

[0049] The communication log monitoring unit can perform an integrated analysis of different communication protocols when monitoring communication logs. For example, it can perform an integrated analysis of different communication protocols (e.g., VoIP and SMS) and issue a warning if an abnormal communication pattern is detected in multiple protocols. The communication log monitoring unit can also identify abnormal communication patterns based on data from different communication protocols. For example, when monitoring communication logs, the generation AI can perform an integrated analysis of different communication protocols, identify abnormal communication patterns, and issue a warning. This makes it possible to detect abnormal communication patterns by performing an integrated analysis of different communication protocols.

[0050] When a line with potential fraudulent use is discovered, it can collaborate with other telecommunications carriers and share information. For example, it can issue a warning if similar fraudulent activity is occurring at other carriers. Furthermore, when a line with potential fraudulent use is discovered, the generation AI can collaborate with other telecommunications carriers to build a system for sharing information. For example, the generation AI can collaborate with other telecommunications carriers, share information, and issue a warning. By collaborating with other telecommunications carriers, it is possible to share information about fraudulent use and reduce risks.

[0051] When deciding whether to suspend a line, the generation AI can determine the optimal timing for suspension by taking into account the success rate of past fraudulent use and the amount of damage. For example, the line can be suspended before the amount of damage reaches a certain level. In addition, when deciding whether to suspend a line, the generation AI can also determine the optimal timing for suspension based on data on the success rate of past fraudulent use and the amount of damage. For example, the generation AI can determine the optimal timing for suspension and suspend the line by taking into account the success rate of past fraudulent use and the amount of damage. In this way, the optimal timing for suspension can be determined by taking into account the success rate of past fraudulent use and the amount of damage.

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

[0053] Step 1: The fraud case analysis unit analyzes past fraud cases or communication logs. For example, the generation AI analyzes past fraud cases and communication logs to extract fraudulent usage patterns and conditions. It can also detect fraudulent usage that occurs concentrated in specific time periods or abnormal communication volume from specific regions. Furthermore, the generation AI learns fraudulent usage patterns based on data from past fraud cases and communication logs. Step 2: The contract information monitoring unit monitors information at the time of new contracts and contract change information. For example, the generation AI monitors information at the time of new contracts and contract change information to determine whether there is a possibility of fraudulent use. It can also detect multiple contract changes in a short period of time and contract terms that are abnormally expensive. Furthermore, the generation AI evaluates the possibility of fraudulent use based on information at the time of new contracts and contract change information. Step 3: The communication log monitoring unit monitors domestic and international call and communication logs. For example, the generation AI monitors domestic and international call and communication logs to detect signs of fraudulent use. It can also detect abnormal communication volumes that differ from normal usage patterns and suspicious communications from specific countries or regions. Furthermore, the generation AI analyzes signs of fraudulent use based on the call and communication log data.

[0054] (Example 2) The fraudulent usage monitoring system according to an embodiment of the present invention is a system that sets conditions based on past fraudulent cases and communication logs, and analyzes information at the time of new contracts, contract amendment information, and domestic and international call and communication logs to monitor and detect lines that may be potentially subject to fraudulent usage and suspend the lines at an early stage. This enables the fraudulent usage monitoring system to quickly detect lines that may be potentially subject to fraudulent usage and respond quickly.

[0055] The fraud monitoring system according to the embodiment includes a fraud case analysis unit, a contract information monitoring unit, and a communication log monitoring unit. The fraud case analysis unit analyzes past fraud cases or communication logs. For example, the generation AI analyzes past fraud cases and communication logs to extract fraudulent usage patterns and conditions. The generation AI can also detect fraudulent usage concentrated in specific time periods and abnormal communication volume from specific regions. The generation AI also learns fraudulent usage patterns based on data from past fraud cases and communication logs. The contract information monitoring unit monitors information at the time of a new contract and contract change information. For example, the generation AI monitors information at the time of a new contract and contract change information to determine whether there is a possibility of fraudulent usage. The generation AI can also detect multiple contract changes in a short period of time or abnormally high contract terms. The generation AI also evaluates the possibility of fraudulent usage based on information at the time of a new contract and contract change information. The communication log monitoring unit monitors domestic and international call and communication logs. For example, the generation AI monitors domestic and international call and communication logs to detect signs of fraudulent usage. The generation AI can also detect abnormal communication volumes that deviate from normal usage patterns and suspicious communications from specific countries or regions. The generation AI also analyzes signs of fraudulent use based on call and communication log data. This allows the fraudulent use monitoring system according to the embodiment to quickly detect lines with potential fraudulent use and respond to them promptly. For example, the output unit minimizes damage by quickly suspending lines that are suspected of fraudulent use that have been detected. The output unit can also notify the administrator of the monitoring results and prompt them to take appropriate action. Furthermore, the output unit can store the monitoring results in a database to help prevent future fraudulent use.

[0056] The fraud case analysis unit can perform simulations to predict future fraudulent activities based on patterns extracted from past fraudulent activities and communication logs. The fraud case analysis unit, for example, uses a generative AI to perform simulations to predict future fraudulent activities based on patterns extracted from past fraudulent activities and communication logs. For example, it calculates the probability of fraudulent activities occurring in a specific time period or area and proposes preventive measures. The fraud case analysis unit can also build a predictive model for future fraudulent activities based on data from past fraudulent activities and communication logs. For example, the generative AI uses a predictive model based on past data to calculate the probability of future fraudulent activities occurring and proposes preventive measures. This makes it possible to predict future fraudulent activities and propose preventive measures.

[0057] The fraud case analysis unit can include not only communication logs but also related social media posts and news articles in its analysis. For example, when analyzing fraud cases, the fraud case analysis unit can include not only communication logs but also related social media posts and news articles in its analysis. For example, it can detect posts containing specific keywords and identify signs of fraud. The fraud case analysis unit can also analyze signs of fraud based on data from social media posts and news articles. For example, the generative AI can identify signs of fraud based on data from social media posts and news articles and issue a warning. By including social media and news articles in its analysis, signs of fraud can be detected more widely.

[0058] The fraud case analysis unit can use the emotion estimation function to analyze the emotional patterns of people involved in past fraud cases and detect communications with similar emotional patterns. For example, the fraud case analysis unit can use the emotion estimation function to analyze the emotional patterns of people involved in past fraud cases and detect communications with similar emotional patterns. For example, it can identify communications with strong emotions such as anger or impatience and issue a warning. The fraud case analysis unit can also use the emotion estimation function to identify communications with similar emotional patterns based on the emotional patterns of people involved in past fraud cases. For example, the generation AI can use the emotion estimation function to analyze the emotional patterns of people involved in past fraud cases, identify communications with similar emotional patterns, and issue a warning. In this way, signs of emotional fraud can be detected by analyzing emotional patterns.

[0059] The fraud case analysis unit can compare the analysis results of a fraud case with fraud patterns in other industries to find commonalities. For example, the fraud case analysis unit compares the analysis results of a fraud case with fraud patterns in other industries (such as the financial or retail industries) to find commonalities. For example, it issues a warning if similar methods are used. The fraud case analysis unit can also identify commonalities in fraudulent activities based on data on fraud patterns in other industries. For example, the generation AI can identify commonalities in fraudulent activities based on data on fraud patterns in the financial and retail industries and issue a warning. This makes it possible to detect signs of common fraudulent activities by comparing them with fraud patterns in other industries.

[0060] The fraud case analysis unit can convert call contents into text using speech recognition technology when analyzing communication logs and detect abnormalities in the content. For example, the fraud case analysis unit can convert call contents into text using speech recognition technology when analyzing communication logs and detect abnormalities in the content. For example, it can analyze calls containing specific keywords and identify signs of fraudulent activity. The fraud case analysis unit can also convert call contents into text using speech recognition technology and identify abnormalities in the content. For example, the generation AI can convert call contents into text using speech recognition technology, analyze calls containing specific keywords, and issue a warning. In this way, by converting call contents into text, it is possible to detect abnormalities in the content.

[0061] The fraud case analysis unit can use the emotion estimation function to extract portions of the communication log that contain particularly emotional interactions and analyze them in detail. The fraud case analysis unit can, for example, use the emotion estimation function to extract portions of the communication log that contain particularly emotional interactions and analyze them in detail. For example, it can identify calls with strong emotions of anger or impatience and detect signs of fraudulent activity. The fraud case analysis unit can also use the emotion estimation function to identify portions of the communication log that contain particularly emotional interactions and analyze them in detail. For example, the generation AI can use the emotion estimation function to extract portions of the communication log that contain particularly emotional interactions, analyze them in detail, and issue a warning. In this way, by analyzing portions of the communication log that contain particularly emotional interactions in detail, it is possible to detect signs of fraudulent activity.

[0062] The contract information monitoring unit uses generation AI to analyze information at the time of new contracts and contract change information in real time, and can immediately detect abnormal patterns. The contract information monitoring unit, for example, uses generation AI to analyze information at the time of new contracts and contract change information in real time, and can immediately detect abnormal patterns. For example, it identifies multiple contract changes in a short period of time and issues a warning. The contract information monitoring unit can also use generation AI to identify abnormal patterns based on information at the time of new contracts and contract change information. For example, the generation AI analyzes information at the time of new contracts and contract change information in real time, and can immediately detect abnormal patterns and issue a warning. This makes it possible to detect abnormal patterns at the time of new contracts and contract changes in real time.

[0063] When monitoring contract information, the contract information monitoring unit can also analyze the contract holder's past purchase history and credit score. For example, when monitoring contract information, the contract information monitoring unit can also analyze the contract holder's past purchase history and credit score. For example, it can identify abnormal purchase patterns and low credit scores and perform risk assessments. The contract information monitoring unit can also identify abnormal contract patterns based on data on the contract holder's purchase history and credit score. For example, the generation AI can analyze the contract holder's purchase history and credit score, identify abnormal contract patterns, and issue a warning. In this way, abnormal contract patterns can be detected by analyzing the contract holder's purchase history and credit score.

[0064] The contract information monitoring unit can use the emotion estimation function to analyze the emotional state of the contractor and issue a warning if there is an abnormal emotional fluctuation. The contract information monitoring unit, for example, uses the emotion estimation function to analyze the emotional state of the contractor and issue a warning if there is an abnormal emotional fluctuation. For example, it issues a warning if strong feelings of anxiety or impatience are detected at the time of signing the contract. The contract information monitoring unit can also use the emotion estimation function to identify abnormal emotional fluctuations based on the contractor's emotional state. For example, the generation AI uses the emotion estimation function to analyze the contractor's emotional state and issue a warning if there is an abnormal emotional fluctuation. In this way, by analyzing the contractor's emotional state, it is possible to detect abnormal emotional fluctuations and issue a warning.

[0065] The contract information monitoring unit can link information at the time of a new contract with the databases of other telecommunications carriers and share abnormal contract patterns. The contract information monitoring unit, for example, links information at the time of a new contract with the databases of other telecommunications carriers and shares abnormal contract patterns. For example, it issues a warning if contracts are made with multiple carriers at the same time. The contract information monitoring unit can also link with the databases of other telecommunications carriers to identify abnormal contract patterns. For example, the generation AI links information at the time of a new contract with the databases of other telecommunications carriers, shares abnormal contract patterns, and issues a warning. In this way, by linking with other telecommunications carriers, abnormal contract patterns can be shared and risks can be reduced.

[0066] The contract information monitoring unit can also analyze the geographical movement patterns of the contract holder when monitoring contract change information. For example, when monitoring contract change information, the contract information monitoring unit can also analyze the geographical movement patterns of the contract holder. For example, if an abnormal movement pattern is detected, a risk assessment is performed. The contract information monitoring unit can also identify abnormal contract changes based on data on the geographical movement patterns of the contract holder. For example, the generation AI can analyze the geographical movement patterns of the contract holder, identify abnormal contract changes, and issue a warning. In this way, abnormal contract changes can be detected by analyzing the geographical movement patterns of the contract holder.

[0067] The contract information monitoring unit can use the emotion estimation function to monitor the emotional state of the contractor when making contract changes in real time and issue a warning if an abnormality is detected. The contract information monitoring unit can, for example, use the emotion estimation function to monitor the emotional state of the contractor when making contract changes in real time and issue a warning if an abnormality is detected. For example, a warning is issued if strong feelings of anxiety or impatience are detected when making contract changes. The contract information monitoring unit can also use the emotion estimation function to identify abnormal emotional fluctuations based on the contractor's emotional state. For example, the generation AI can use the emotion estimation function to monitor the emotional state of the contractor when making contract changes in real time and issue a warning if an abnormality is detected. In this way, by monitoring the contractor's emotional state in real time, abnormal emotional fluctuations can be detected and a warning can be issued.

[0068] The communication log monitoring unit can use the generation AI to extract specific keywords and phrases from the call and communication logs and detect abnormal patterns. The communication log monitoring unit can, for example, use the generation AI to extract specific keywords and phrases from the call and communication logs and detect abnormal patterns. For example, it can identify calls that contain keywords related to specific fraudulent activity and issue a warning. The communication log monitoring unit can also use the generation AI to identify abnormal patterns based on the call and communication log data. For example, the generation AI can extract specific keywords and phrases from the call and communication logs, detect abnormal patterns, and issue a warning. This makes it possible to extract specific keywords and phrases from the call and communication logs and detect abnormal patterns.

[0069] The communication log monitoring unit can analyze the relationship between the sender and receiver of a communication when monitoring the communication log and identify abnormal communication patterns. For example, when monitoring the communication log, the communication log monitoring unit analyzes the relationship between the sender and receiver of a communication and identify abnormal communication patterns. For example, it identifies abnormal communication that differs from normal communication patterns and issues a warning. The communication log monitoring unit can also identify abnormal communication patterns based on data on the relationship between the sender and receiver of a communication. For example, when monitoring the communication log, the generation AI analyzes the relationship between the sender and receiver of a communication, identifies abnormal communication patterns, and issues a warning. In this way, it is possible to identify abnormal communication patterns by analyzing the relationship between the sender and receiver of a communication.

[0070] The communication log monitoring unit can use the emotion estimation function to analyze the emotional tone of the call content and issue a warning if there is an abnormal emotional change. The communication log monitoring unit, for example, uses the emotion estimation function to analyze the emotional tone of the call content and issue a warning if there is an abnormal emotional change. For example, a warning is issued if strong emotions of anger or impatience are detected during a call. The communication log monitoring unit can also use the emotion estimation function to identify abnormal emotional changes based on the emotional tone of the call content. For example, the generation AI uses the emotion estimation function to analyze the emotional tone of the call content and issue a warning if there is an abnormal emotional change. In this way, by analyzing the emotional tone of the call content, abnormal emotional changes can be detected and a warning can be issued.

[0071] The communication log monitoring unit can perform an integrated analysis of different communication protocols when monitoring communication logs. For example, the communication log monitoring unit performs an integrated analysis of different communication protocols (e.g., VoIP and SMS) when monitoring communication logs. For example, it issues a warning when an abnormal communication pattern is detected in multiple protocols. The communication log monitoring unit can also identify abnormal communication patterns based on data from different communication protocols. For example, the generation AI performs an integrated analysis of different communication protocols when monitoring communication logs, identifies abnormal communication patterns, and issues a warning. This makes it possible to detect abnormal communication patterns by performing an integrated analysis of different communication protocols.

[0072] The communication log monitoring unit can integrate the analysis results of the communication log with other data sets to identify abnormal patterns. The communication log monitoring unit, for example, integrates the analysis results of the communication log with other data sets (e.g., location information or purchase history) to identify abnormal patterns. For example, it identifies an abnormal communication pattern associated with specific location information and issues a warning. The communication log monitoring unit can also identify abnormal patterns based on data from other data sets. For example, the generation AI integrates the analysis results of the communication log with other data sets, identifies abnormal patterns, and issues a warning. In this way, by integrating the analysis results of the communication log with other data sets, it is possible to identify abnormal patterns.

[0073] The communication log monitoring unit can use the emotion estimation function to analyze emotion trends in specific regions and time periods and issue a warning if there are abnormal emotion fluctuations. The communication log monitoring unit, for example, uses the emotion estimation function to analyze emotion trends in specific regions and time periods and issue a warning if there are abnormal emotion fluctuations. For example, a warning is issued if strong emotions of anger or impatience are detected in a specific region. The communication log monitoring unit can also use the emotion estimation function to identify abnormal emotion fluctuations based on emotion trends in specific regions and time periods. For example, the generation AI uses the emotion estimation function to analyze emotion trends in specific regions and time periods and issue a warning if there are abnormal emotion fluctuations. In this way, by analyzing emotion trends in specific regions and time periods, abnormal emotion fluctuations can be detected and a warning can be issued.

[0074] When a line with potential fraudulent use is discovered, not only can the line be immediately suspended, but alternative means can be suggested. When a line with potential fraudulent use is discovered, for example, generation AI can be used to not only immediately suspend the line, but also suggest alternative means. For example, it can provide the user with alternative means of communication. Furthermore, generation AI can also be used to build a system that suggests alternative means when a line with potential fraudulent use is discovered. For example, generation AI can guide the user to alternative means of communication when the line is suspended. This makes it possible to maintain user convenience by suggesting alternative means when the line is suspended.

[0075] When deciding whether to suspend a line, the optimal timing for suspension can be determined by taking into account the past success rate of fraudulent use and the amount of damage. When deciding whether to suspend a line, the optimal timing for suspension can be determined by taking into account, for example, the past success rate of fraudulent use and the amount of damage. For example, the line can be suspended before the amount of damage reaches a certain level. In addition, when deciding whether to suspend a line, the generation AI can also determine the optimal timing for suspension based on data on the past success rate of fraudulent use and the amount of damage. For example, the generation AI can determine the optimal timing for suspension and suspend the line by taking into account the past success rate of fraudulent use and the amount of damage. In this way, the optimal timing for suspension can be determined by taking into account the past success rate of fraudulent use and the amount of damage.

[0076] The emotion estimation function can be used to analyze the user's emotional state when the line is suspended and take appropriate action. The emotion estimation function can be used to analyze the user's emotional state when the line is suspended and take appropriate action. For example, customer support can be provided if the user is feeling strong anxiety or anger. The emotion estimation function can also be used by the generation AI to take appropriate action based on the user's emotional state when the line is suspended. For example, the generation AI can analyze the user's emotional state when the line is suspended and take appropriate action. This makes it possible to analyze the user's emotional state when the line is suspended and take appropriate action, thereby reducing the user's anxiety and anger.

[0077] When a line with potential fraudulent use is discovered, it can cooperate with other telecommunications carriers and share information. When a line with potential fraudulent use is discovered, it can cooperate with other telecommunications carriers and share information. For example, it can issue a warning if similar fraudulent activity is occurring at other carriers. Furthermore, when a line with potential fraudulent use is discovered, the generation AI can cooperate with other telecommunications carriers and build a system to share information. For example, the generation AI can cooperate with other telecommunications carriers, share information, and issue a warning. In this way, by collaborating with other telecommunications carriers, information on fraudulent use can be shared and risks can be reduced.

[0078] It is possible to build a system that automatically provides an alternative means to a user when a line is suspended. For example, a system can be built that automatically provides an alternative means to a user when a line is suspended. For example, the generation AI can guide the user to other means of communication when a line is suspended. In this way, convenience for users can be maintained by automatically providing an alternative means when a line is suspended.

[0079] The emotion estimation function can be used to monitor the user's emotional state after a line is terminated and provide appropriate follow-up. The emotion estimation function can be used to monitor the user's emotional state after a line is terminated and provide appropriate follow-up. For example, customer support can be provided if the user is feeling strong anxiety or anger. The emotion estimation function can also be used by the generation AI to provide appropriate follow-up based on the user's emotional state after a line is terminated. For example, the generation AI can monitor the user's emotional state after a line is terminated and provide appropriate follow-up. This makes it possible to monitor the user's emotional state after a line is terminated and provide appropriate follow-up, thereby reducing the user's anxiety and anger.

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

[0081] The fraud case analysis unit can not only refer to fraud patterns in other industries, but also include fraud cases from different cultures and countries in its analysis. For example, it can analyze patterns of fraud that occur frequently in a particular country or region and find commonalities. The fraud case analysis unit can also identify common signs of fraud based on data on fraud from different cultures. For example, generative AI can analyze data on fraud from different cultures, identify common signs of fraud, and issue a warning. This makes it possible to detect signs of a wider range of fraud by analyzing data on fraud from different cultures.

[0082] The fraud case analysis unit can use the emotion estimation function to analyze the emotional patterns of people involved in past fraud cases and detect communications with similar emotional patterns. For example, it can identify communications with strong emotions such as anger or impatience and issue a warning. The fraud case analysis unit can also use the emotion estimation function to identify communications with similar emotional patterns based on the emotional patterns of people involved in past fraud cases. For example, the generation AI can use the emotion estimation function to analyze the emotional patterns of people involved in past fraud cases, identify communications with similar emotional patterns, and issue a warning. In this way, by analyzing emotional patterns, it is possible to detect signs of emotional fraud.

[0083] The contract information monitoring unit can also analyze the contract holder's past purchase history and credit score. For example, it can identify abnormal purchase patterns and low credit scores and perform risk assessments. The contract information monitoring unit can also identify abnormal contract patterns based on the contract holder's purchase history and credit score data. For example, the generation AI can analyze the contract holder's purchase history and credit score, identify abnormal contract patterns, and issue a warning. This makes it possible to detect abnormal contract patterns by analyzing the contract holder's purchase history and credit score.

[0084] The communication log monitoring unit can use the emotion estimation function to analyze the emotional tone of the call content and issue a warning if there is an abnormal emotional change. For example, it can issue a warning if strong emotions such as anger or impatience are detected during a call. The communication log monitoring unit can also use the emotion estimation function to identify abnormal emotional changes based on the emotional tone of the call content. For example, the generation AI can use the emotion estimation function to analyze the emotional tone of the call content and issue a warning if there is an abnormal emotional change. In this way, by analyzing the emotional tone of the call content, it is possible to detect abnormal emotional changes and issue a warning.

[0085] The communication log monitoring unit can perform an integrated analysis of different communication protocols when monitoring communication logs. For example, it can perform an integrated analysis of different communication protocols (e.g., VoIP and SMS) and issue a warning if an abnormal communication pattern is detected in multiple protocols. The communication log monitoring unit can also identify abnormal communication patterns based on data from different communication protocols. For example, when monitoring communication logs, the generation AI can perform an integrated analysis of different communication protocols, identify abnormal communication patterns, and issue a warning. This makes it possible to detect abnormal communication patterns by performing an integrated analysis of different communication protocols.

[0086] Using the emotion estimation function, the user's emotional state can be analyzed when the line is suspended, and appropriate responses can be taken. For example, customer support can be provided if the user is feeling strong anxiety or anger. Furthermore, using the emotion estimation function, the generation AI can also take appropriate responses based on the user's emotional state when the line is suspended. For example, the generation AI can analyze the user's emotional state when the line is suspended and take appropriate responses. This allows the user's anxiety and anger to be alleviated by analyzing the user's emotional state when the line is suspended and taking appropriate responses.

[0087] When a line with potential fraudulent use is discovered, it can collaborate with other telecommunications carriers and share information. For example, it can issue a warning if similar fraudulent activity is occurring at other carriers. Furthermore, when a line with potential fraudulent use is discovered, the generation AI can collaborate with other telecommunications carriers to build a system for sharing information. For example, the generation AI can collaborate with other telecommunications carriers, share information, and issue a warning. By collaborating with other telecommunications carriers, it is possible to share information about fraudulent use and reduce risks.

[0088] The contract information monitoring unit uses the emotion estimation function to monitor the emotional state of the contract holder when making contract changes in real time, and can issue a warning if there is an abnormality. For example, a warning is issued if strong feelings of anxiety or impatience are detected when making contract changes. The contract information monitoring unit can also use the emotion estimation function to identify abnormal emotional fluctuations based on the contract holder's emotional state. For example, the generation AI uses the emotion estimation function to monitor the emotional state of the contract holder when making contract changes in real time, and can issue a warning if there is an abnormality. In this way, by monitoring the contract holder's emotional state in real time, abnormal emotional fluctuations can be detected and a warning can be issued.

[0089] When deciding whether to suspend a line, the generation AI can determine the optimal timing for suspension by taking into account the success rate of past fraudulent use and the amount of damage. For example, the line can be suspended before the amount of damage reaches a certain level. In addition, when deciding whether to suspend a line, the generation AI can also determine the optimal timing for suspension based on data on the success rate of past fraudulent use and the amount of damage. For example, the generation AI can determine the optimal timing for suspension and suspend the line by taking into account the success rate of past fraudulent use and the amount of damage. In this way, the optimal timing for suspension can be determined by taking into account the success rate of past fraudulent use and the amount of damage.

[0090] Using the emotion estimation function, it is possible to monitor the user's emotional state after a line is suspended and provide appropriate follow-up. For example, if the user is feeling strong anxiety or anger, customer support can be provided. In addition, using the emotion estimation function, the generation AI can also provide appropriate follow-up based on the user's emotional state after a line is suspended. For example, the generation AI can monitor the user's emotional state after a line is suspended and provide appropriate follow-up. In this way, by monitoring the user's emotional state after a line is suspended and providing appropriate follow-up, it is possible to alleviate the user's anxiety and anger.

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

[0092] Step 1: The fraud case analysis unit analyzes past fraud cases or communication logs. For example, the generation AI analyzes past fraud cases and communication logs to extract fraudulent usage patterns and conditions. It can also detect fraudulent usage that occurs concentrated in specific time periods or abnormal communication volume from specific regions. Furthermore, the generation AI learns fraudulent usage patterns based on data from past fraud cases and communication logs. Step 2: The contract information monitoring unit monitors information at the time of new contracts and contract change information. For example, the generation AI monitors information at the time of new contracts and contract change information to determine whether there is a possibility of fraudulent use. It can also detect multiple contract changes in a short period of time and contract terms that are abnormally expensive. Furthermore, the generation AI evaluates the possibility of fraudulent use based on information at the time of new contracts and contract change information. Step 3: The communication log monitoring unit monitors domestic and international call and communication logs. For example, the generation AI monitors domestic and international call and communication logs to detect signs of fraudulent use. It can also detect abnormal communication volumes that differ from normal usage patterns and suspicious communications from specific countries or regions. Furthermore, the generation AI analyzes signs of fraudulent use based on the call and communication log data.

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

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following 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.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

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

Claims

1. a fraud case analysis unit that analyzes past fraud cases or communication logs; a contract information monitoring unit that monitors information at the time of a new contract and information on contract changes; A communication log monitoring unit that monitors domestic and international call and communication logs. A system characterized by:

2. The fraud case analysis unit In addition to the communication logs, analysis will also include related social media posts and news articles.

2. The system of claim 1.

3. The contract information monitoring unit Generative AI is used to analyze the information at the time of the new contract and the contract change information in real time, and to immediately detect abnormal patterns.

2. The system of claim 1.

4. The communication log monitoring unit In monitoring the communication logs, the relationship between the sender and receiver of the communication is analyzed to identify abnormal communication patterns.

2. The system of claim 1.

5. The fraud case analysis unit Analyzes the emotional patterns of individuals involved in past fraud cases and detects communications with similar emotional patterns 2. The system of claim 1.

6. The contract information monitoring unit Analyzes the emotional state of the subscriber and issues a warning if there are abnormal emotional fluctuations 2. The system of claim 1.

7. The communication log monitoring unit Analyzes the emotional tone of calls and issues alerts if there are abnormal emotional fluctuations 2. The system of claim 1.

8. Analyze the user's emotional state when the line is interrupted and take appropriate action 2. The system of claim 1.

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