system

Abstract

The system according to the embodiment aims to extract characteristics of fraudulent and nuisance calls and respond quickly to them. [Solution] The system according to the embodiment comprises an analysis unit, a collection unit, a determination unit, a notification unit, and a blocking unit. The analysis unit analyzes past report data and extracts characteristics of fraudulent and nuisance calls. The collection unit patrols the Internet and collects information related to fraudulent and nuisance calls. The analysis unit analyzes the information collected by the collection unit and extracts characteristics of new fraudulent and nuisance calls. The determination unit determines numbers suspected of being fraudulent or nuisance calls. The notification unit issues notifications to determined numbers. The blocking unit automatically blocks fraudulent and nuisance call numbers.

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 making it difficult to efficiently extract the characteristics of fraudulent and nuisance calls and respond quickly.

[0005] The system according to the embodiment aims to extract characteristics of fraudulent and nuisance calls and respond quickly to them. [Means for solving the problem]

[0006] The system according to the embodiment comprises an analysis unit, a collection unit, a determination unit, a notification unit, and a blocking unit. The analysis unit analyzes past report data and extracts characteristics of fraudulent and nuisance calls. The collection unit patrols the Internet and collects information related to fraudulent and nuisance calls. The analysis unit analyzes the information collected by the collection unit and extracts characteristics of new fraudulent and nuisance calls. The determination unit determines numbers that are suspected of being fraudulent or nuisance calls. The notification unit issues notifications to determined numbers. The blocking unit automatically blocks fraudulent and nuisance call numbers. [Effects of the Invention]

[0007] The system according to the embodiment can extract the characteristics of fraudulent and nuisance calls and respond quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The fraud and nuisance call prevention system according to an embodiment of the present invention utilizes generation AI and RPA to check whether a phone number has been reported or registered as a fraud or nuisance call in the past. This system analyzes past reported data and extracts characteristics of fraud and nuisance calls. Next, RPA periodically patrols the Internet and scrapes and collects information on fraud and nuisance calls. The collected data is analyzed to extract, predict, and detect new characteristics of fraud and nuisance calls. Feedback is provided to users based on information from blocked numbers and callers to prevent potential damage. AI also automatically blocks and warns users of such numbers, reducing the risk of being harmed by accidentally answering the call and protecting users from fraud and nuisance calls. For example, generation AI analyzes past reported data and extracts characteristics of fraud and nuisance calls. Based on the reported data, generation AI learns patterns of fraud and nuisance calls and extracts their characteristics. For example, it analyzes characteristics such as specific phone numbers, caller regions, and call content. Next, RPA periodically patrols the Internet and scrapes and collects information on fraud and nuisance calls. For example, information about scams and nuisance calls is collected from bulletin boards, social media posts, and reporting sites. The collected information is provided to the generation AI for analysis. The generation AI analyzes the collected information and extracts characteristics of new scams and nuisance calls. For example, it predicts and detects new scam methods and nuisance call patterns. This allows the system to constantly maintain up-to-date information and issue appropriate warnings to users. Furthermore, it identifies numbers that may be scams or nuisance calls. Instead of ringing the phone, the system notifies the caller in the app, encouraging them to block the call. Furthermore, an AI chatbot automatically answers suspicious calls, reducing the risk of the recipient answering the phone directly. This mechanism protects users from scams and nuisance calls and prevents them from becoming victims. Furthermore, the AI ​​has the ability to automatically block and warn the number, reducing the risk of being harmed by answering the call by mistake. This fraud and nuisance call prevention system ensures user safety and prevents scams and nuisance calls from occurring.

[0029] The fraud and nuisance call prevention system according to the embodiment includes an analysis unit, a collection unit, a determination unit, a notification unit, and a block unit. The analysis unit analyzes past report data and extracts characteristics of fraud and nuisance calls. The analysis unit, for example, uses a generation AI to learn fraud and nuisance call patterns based on the report data and extracts the characteristics. For example, the generation AI analyzes characteristics such as specific phone numbers, caller regions, and call content. The analysis unit also analyzes information collected by the collection unit and extracts characteristics of new fraud and nuisance calls. The collection unit patrols the Internet to collect information related to fraud and nuisance calls. For example, the collection unit uses RPA to collect information from bulletin boards, social media posts, and reporting sites related to fraud and nuisance calls. For example, the collection unit regularly patrols specific websites, bulletin boards, and social media to scrape and collect information related to fraud and nuisance calls. The determination unit identifies numbers that may be fraud or nuisance calls. For example, the determination unit uses a generation AI to identify numbers that may be fraud or nuisance calls based on the collected information. For example, the determination unit analyzes fraudulent or nuisance call patterns based on past report data and collected information to determine numbers that may be fraudulent or nuisance calls. The notification unit issues a notification to the determined number and prompts the user to set up call blocking. The notification unit may, for example, use a generation AI to issue a notification to the determined number on an app, prompting the user to set up call blocking. The notification unit may also notify the user via SMS, email, in-app notification, or other methods. The blocking unit automatically blocks fraudulent or nuisance call numbers. The blocking unit may, for example, use a generation AI to automatically block determined numbers. For example, the blocking unit may create a phone number blacklist and block fraudulent or nuisance call numbers in real time. As a result, the fraud and nuisance call prevention system according to the embodiment can ensure the safety of users and prevent fraud and nuisance call damage.

[0030] The analysis unit can learn patterns of fraud and nuisance calls based on the reported data and extract their features. For example, the analysis unit uses generation AI to learn patterns of fraud and nuisance calls based on the reported data and extract their features. For example, the generation AI analyzes features such as specific phone numbers, the region of origin, and the content of the calls. The analysis unit can also use machine learning models and data mining technology to learn patterns of fraud and nuisance calls based on the reported data. For example, the analysis unit can extract common patterns of fraud and nuisance calls based on past reported data and predict the features of new fraud and nuisance calls. This allows the analysis unit to learn patterns of fraud and nuisance calls based on the reported data and extract their features, enabling more accurate analysis.

[0031] The collection unit can collect information from bulletin boards, social media posts, and reporting sites related to scams and nuisance calls. For example, the collection unit uses RPA to collect information from bulletin boards, social media posts, and reporting sites related to scams and nuisance calls. For example, the collection unit periodically patrols specific websites, bulletin boards, and social media to scrape and collect information related to scams and nuisance calls. The collection unit can also use an API to collect information related to scams and nuisance calls. For example, the collection unit uses an API that provides information about scams and nuisance calls to collect information in real time. This allows information about scams and nuisance calls to be collected from a wide range of sources, making more data available for analysis.

[0032] The analysis unit can analyze the collected information and extract characteristics of new scams and nuisance calls. The analysis unit can, for example, use generation AI to analyze the collected information and extract characteristics of new scams and nuisance calls. For example, generation AI predicts and detects new fraud methods and nuisance call patterns. The analysis unit can also use machine learning models and data mining technology to analyze the collected information and extract characteristics of new scams and nuisance calls. For example, the analysis unit can extract common patterns of new scams and nuisance calls based on the collected information and build a predictive model. This allows the system to respond to the latest fraud methods by extracting characteristics of new scams and nuisance calls.

[0033] The determination unit can determine numbers that are likely to be fraudulent or nuisance calls. The determination unit, for example, uses generative AI to determine numbers that are likely to be fraudulent or nuisance calls based on collected information. For example, the determination unit analyzes fraudulent or nuisance call patterns based on past report data and collected information to determine numbers that are likely to be fraudulent or nuisance calls. The determination unit can also use machine learning algorithms or rule-based determination methods to determine numbers that are likely to be fraudulent or nuisance calls. For example, the determination unit uses a machine learning model to predict numbers that are likely to be fraudulent or nuisance calls and makes a determination in real time. This allows the determination of numbers that are likely to be fraudulent or nuisance calls to issue a warning to the user.

[0034] The notification unit can send a notification to the determined number and prompt the user to set up call blocking. For example, the notification unit can use a generation AI to send a notification to the determined number on the app, urging the user to set up call blocking. The notification unit can also notify the user by SMS, email, in-app notification, or other methods. For example, the notification unit can send a warning message to the determined number and prompt the user to set up call blocking. The notification unit can also customize the content and method of the notification according to the user's settings. For example, the notification unit can send notifications at times or days of the week specified by the user. This allows the user to be notified of numbers that may be fraudulent or nuisance calls and prompt the user to set up call blocking, thereby preventing damage before it occurs.

[0035] The blocking unit can automatically block fraudulent or nuisance call numbers. For example, the blocking unit uses generative AI to automatically block numbers that have been determined to be fraudulent or nuisance. For example, the blocking unit creates a blacklist of phone numbers and blocks fraudulent or nuisance call numbers in real time. The blocking unit can also customize blocking criteria according to user settings. For example, the blocking unit can execute blocking during user-specified time periods or days of the week. This reduces the risk of users accidentally answering calls by automatically blocking fraudulent or nuisance call numbers.

[0036] The response unit can have the AI ​​chatbot automatically respond to suspicious phone calls. The response unit, for example, uses generation AI to have the AI ​​chatbot automatically respond to suspicious phone calls. For example, the response unit uses natural language processing technology to provide an appropriate response to suspicious phone calls. The response unit can also use a dialogue system algorithm to respond to suspicious phone calls in real time. For example, the response unit has the AI ​​chatbot ask questions in response to suspicious phone calls to confirm the caller's intentions. The response unit can also customize the content of the response according to user settings. For example, the response unit responds to suspicious phone calls using a response message specified by the user. In this way, having the AI ​​chatbot automatically respond to suspicious phone calls reduces the risk of the user having to answer the phone directly.

[0037] The analysis unit can evaluate the reliability of past report data and prioritize analyzing highly reliable data. The analysis unit, for example, uses a generation AI to evaluate the reliability of past report data and prioritize analyzing highly reliable data. For example, the analysis unit evaluates the reliability of report data providers and prioritizes analyzing data from providers with high ratings. The analysis unit can also prioritize analyzing report data if the content of the report data is detailed. Furthermore, the analysis unit can also prioritize analyzing report data if the report data is provided frequently. This improves the accuracy of the analysis by prioritizing analysis of highly reliable data.

[0038] The analysis unit can take into account patterns that occur during specific time periods or days of the week when extracting the characteristics of fraudulent or nuisance calls. For example, when using generative AI to extract the characteristics of fraudulent or nuisance calls, the analysis unit can take into account patterns that occur during specific time periods or days of the week. For example, the analysis unit can extract patterns of fraudulent calls that occur frequently during the daytime on weekdays. The analysis unit can also extract patterns of nuisance calls that occur frequently on weekend nights. Furthermore, the analysis unit can extract patterns of fraudulent calls that occur frequently on specific holidays. By taking into account patterns that occur during specific time periods or days of the week, more accurate feature extraction is possible.

[0039] When analyzing the reported data, the analysis unit can extract regional characteristics of fraud and nuisance calls. When analyzing the reported data, the analysis unit, for example, uses generative AI to extract regional characteristics of fraud and nuisance calls. For example, the analysis unit extracts characteristics of fraudulent calls that occur frequently in urban areas. The analysis unit can also extract characteristics of nuisance calls that occur frequently in rural areas. Furthermore, the analysis unit can analyze patterns of fraudulent calls that occur frequently in specific areas. By extracting regional characteristics, it is possible to respond to regionally specific fraud and nuisance calls.

[0040] When analyzing the report data, the analysis unit performs audio analysis of the call content to extract characteristics of fraudulent and nuisance calls. When analyzing the report data, the analysis unit performs audio analysis of the call content, for example, using generative AI, to extract characteristics of fraudulent and nuisance calls. For example, the analysis unit performs audio analysis of the call content to extract common phrases in fraudulent calls. The analysis unit can also perform audio analysis of the call content to extract characteristic voice patterns of nuisance calls. Furthermore, the analysis unit can also perform audio analysis of the call content to extract characteristics of the voice of the caller making the fraudulent call. This makes it possible to extract more detailed characteristics by performing audio analysis of the call content.

[0041] The collection unit can prioritize collecting specific keywords and phrases when browsing the Internet. The collection unit, for example, uses RPA to prioritize collecting specific keywords and phrases when browsing the Internet. For example, the collection unit prioritizes collecting keywords such as "fraudulent calls" and "nuisance calls." The collection unit can also prioritize collecting phrases such as "fraudulent methods" and "characteristics of nuisance calls." Furthermore, the collection unit can prioritize collecting keywords such as "fraud victim" and "report nuisance calls." In this way, by prioritizing the collection of specific keywords and phrases, more relevant information can be collected.

[0042] The collection unit can evaluate the reliability of the collected information and prioritize analysis of highly reliable information. The collection unit, for example, uses RPA to evaluate the reliability of the collected information and prioritize analysis of highly reliable information. For example, the collection unit evaluates the reliability of information providers and prioritizes analysis of information from providers with high ratings. The collection unit can also prioritize analysis of information with detailed content if the information is provided in detail. Furthermore, the collection unit can also prioritize analysis of information with a high frequency of provision. This improves the accuracy of the analysis by prioritizing analysis of highly reliable information.

[0043] The collection unit can prioritize collecting information on specific regions or countries when patrolling the Internet. The collection unit, for example, uses RPA to prioritize collecting information on specific regions or countries when patrolling the Internet. For example, the collection unit can prioritize collecting information on fraudulent calls in urban areas. The collection unit can also prioritize collecting information on nuisance calls in rural areas. Furthermore, the collection unit can prioritize collecting information on fraudulent calls that occur frequently in specific countries or regions. In this way, by prioritizing the collection of information on specific regions or countries, it is possible to respond to scams and nuisance calls that are specific to the region.

[0044] The collection unit can prioritize collecting information posted during specific time periods or days of the week when browsing the Internet. The collection unit, for example, uses RPA to prioritize collecting information posted during specific time periods or days of the week when browsing the Internet. For example, the collection unit prioritizes collecting information about scam calls posted during the daytime on weekdays. The collection unit can also prioritize collecting information about nuisance calls posted during the night on weekends. Furthermore, the collection unit can prioritize collecting information about scam calls posted on specific holidays. In this way, by prioritizing the collection of information posted during specific time periods or days of the week, more relevant information can be collected.

[0045] The judgment unit can improve the accuracy of the judgment by referring to past judgment results when making a judgment. The judgment unit can improve the accuracy of the judgment by referring to past judgment results when making a judgment, for example, using a generation AI. For example, the judgment unit can prioritize numbers that are likely to be fraudulent calls based on past judgment results. The judgment unit can also prioritize numbers that are likely to be nuisance calls based on past judgment results. Furthermore, the judgment unit can adjust the algorithm for improving the accuracy of the judgment based on past judgment results. In this way, the accuracy of the judgment is improved by referring to past judgment results.

[0046] The determination unit can take into account patterns that occur during specific time periods or days of the week when making a determination. The determination unit can use, for example, a generation AI to take into account patterns that occur during specific time periods or days of the week when making a determination. For example, the determination unit can take into account patterns of fraudulent calls that occur frequently during the daytime on weekdays when making a determination. The determination unit can also take into account patterns of nuisance calls that occur frequently on weekend nights when making a determination. Furthermore, the determination unit can take into account patterns of fraudulent calls that occur frequently on specific holidays when making a determination. In this way, by taking into account patterns that occur during specific time periods or days of the week, more accurate determinations can be made.

[0047] The determination unit can take into account the characteristics of fraud and nuisance calls in each region when making a determination. The determination unit can use, for example, generative AI to take into account the characteristics of fraud and nuisance calls in each region when making a determination. For example, the determination unit can make a determination by taking into account the characteristics of fraudulent calls that occur frequently in urban areas. The determination unit can also make a determination by taking into account the characteristics of nuisance calls that occur frequently in rural areas. Furthermore, the determination unit can make a determination by taking into account the patterns of fraudulent calls that occur frequently in specific regions. In this way, by taking into account the characteristics of each region, it is possible to respond to fraud and nuisance calls that are unique to each region.

[0048] When making a judgment, the judgment unit can perform audio analysis of the call content to determine the possibility of a fraudulent or nuisance call. When making a judgment, the judgment unit can perform audio analysis of the call content, for example, using a generation AI, to determine the possibility of a fraudulent or nuisance call. For example, the judgment unit can perform audio analysis of the call content to make a judgment based on common phrases used in fraudulent calls. The judgment unit can also perform audio analysis of the call content to make a judgment based on characteristic voice patterns of nuisance calls. Furthermore, the judgment unit can perform audio analysis of the call content to make a judgment based on the voice characteristics of the caller making the fraudulent call. This makes it possible to make a more detailed judgment by performing audio analysis of the call content.

[0049] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. The notification unit can, for example, use a generation AI to select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit can select the notification method to which the user is most likely to respond based on the past notification history. The notification unit can also select notification content that is easiest for the user to understand based on the past notification history. Furthermore, the notification unit can select the notification method that the user can respond to most quickly based on the past notification history. In this way, the optimal notification method can be selected by referring to the past notification history.

[0050] The notification unit can send notifications during specific time periods or days of the week at the time of notification. The notification unit can use, for example, a generation AI to send notifications during specific time periods or days of the week at the time of notification. For example, the notification unit can send notifications during the daytime on weekdays so that the user can respond quickly. The notification unit can also send notifications during the evening on weekends so that the user can respond when they are relaxed. Furthermore, the notification unit can send notifications on specific holidays so that the user can respond with ample time. As a result, by sending notifications during specific time periods or days of the week, it is possible to provide information at more appropriate times.

[0051] The notification unit can take into account the characteristics of scams and nuisance calls in each region when sending a notification. The notification unit can, for example, use generative AI to take into account the characteristics of scams and nuisance calls in each region when sending a notification. For example, the notification unit can send a notification taking into account the characteristics of scam calls that occur frequently in urban areas. The notification unit can also send a notification taking into account the characteristics of nuisance calls that occur frequently in rural areas. Furthermore, the notification unit can send a notification taking into account the patterns of scam calls that occur frequently in specific regions. In this way, by taking into account the characteristics of each region, it is possible to respond to scams and nuisance calls that are unique to each region.

[0052] The notification unit can adjust the notification content based on the results of voice analysis of the call content when sending a notification. The notification unit can adjust the notification content based on the results of voice analysis of the call content when sending a notification, for example, using a generation AI. For example, the notification unit sends a warning notification when there is a high possibility of a fraudulent call based on the results of voice analysis of the call content. The notification unit can also send a warning notification when there is a high possibility of a nuisance call based on the results of voice analysis of the call content. Furthermore, the notification unit can include characteristics of the caller of the fraudulent call in the notification content based on the results of voice analysis of the call content. This makes it possible to provide more appropriate information by adjusting the notification content based on the results of voice analysis of the call content.

[0053] The blocking unit can refer to past block history to select the optimal blocking method when blocking. For example, the blocking unit uses a generation AI to refer to past block history when blocking. For example, the blocking unit selects the method that allows the user to block most effectively based on past block history. The blocking unit can also select the method that allows the user to block most quickly based on past block history. Furthermore, the blocking unit can also select the blocking method that gives the user the most peace of mind based on past block history. In this way, the optimal blocking method can be selected by referring to past block history.

[0054] The blocking unit can execute the block at a specific time period or day of the week when blocking. The blocking unit can execute the block at a specific time period or day of the week when blocking, for example, using a generation AI. For example, the blocking unit can execute the block during the daytime on weekdays, allowing the user to respond quickly. The blocking unit can also execute the block at night on weekends, allowing the user to respond at a time when they are relaxed. Furthermore, the blocking unit can execute the block on specific holidays, allowing the user to respond with ample time. This allows the block to be executed at a more appropriate time by executing the block at a specific time period or day of the week.

[0055] The blocking unit can take into account the characteristics of fraudulent and nuisance calls in each region when blocking. For example, the blocking unit can use generative AI to take into account the characteristics of fraudulent and nuisance calls in each region when blocking. For example, the blocking unit can take into account the characteristics of fraudulent calls that occur frequently in urban areas when blocking. The blocking unit can also take into account the characteristics of nuisance calls that occur frequently in rural areas when blocking. Furthermore, the blocking unit can take into account the patterns of fraudulent calls that occur frequently in specific regions when blocking. This makes it possible to respond to fraudulent and nuisance calls that are unique to each region by taking into account the characteristics of each region.

[0056] When blocking, the blocking unit can adjust the blocking criteria based on the results of the voice analysis of the call content. When blocking, the blocking unit can adjust the blocking criteria based on the results of the voice analysis of the call content, for example, using a generation AI. For example, the blocking unit can execute a block if there is a high possibility that the call is a fraudulent call based on the results of the voice analysis of the call content. The blocking unit can also execute a block if there is a high possibility that the call is a nuisance call based on the results of the voice analysis of the call content. Furthermore, the blocking unit can include characteristics of fraudulent callers in the blocking criteria based on the results of the voice analysis of the call content. This allows for more appropriate blocking by adjusting the blocking criteria based on the results of the voice analysis of the call content.

[0057] The response unit can refer to past response history to select the optimal response method when responding. The response unit, for example, uses a generation AI to refer to past response history when responding. For example, the response unit selects a method that allows the user to respond most effectively based on the past response history. The response unit can also select a method that allows the user to respond most quickly based on the past response history. Furthermore, the response unit can also select a response method that makes the user feel most at ease based on the past response history. In this way, the optimal response method can be selected by referring to the past response history.

[0058] The response unit can execute a response during a specific time period or day of the week when responding. The response unit executes a response during a specific time period or day of the week when responding, for example, using a generation AI. For example, the response unit executes a response during the daytime on weekdays, allowing the user to respond quickly. The response unit can also execute a response during the evening on weekends, allowing the user to respond at a time when they are relaxed. Furthermore, the response unit can execute a response on a specific public holiday, allowing the user to respond with ample time to spare. As a result, by executing a response during a specific time period or day of the week, it is possible to respond at a more appropriate time.

[0059] The response unit can take into account the characteristics of scams and nuisance calls in each region when responding. The response unit can, for example, use generative AI to take into account the characteristics of scams and nuisance calls in each region when responding. For example, the response unit can take into account the characteristics of scam calls that are more common in urban areas when responding. The response unit can also take into account the characteristics of nuisance calls that are more common in rural areas when responding. Furthermore, the response unit can take into account the patterns of scam calls that are more common in specific regions when responding. In this way, by taking into account the characteristics of each region, it is possible to respond to scams and nuisance calls that are specific to each region.

[0060] The response unit can adjust the response content based on the results of voice analysis of the call content when answering the call. The response unit can adjust the response content based on the results of voice analysis of the call content when answering the call, for example, using a generation AI. For example, the response unit executes a warning response when there is a high possibility of the call being a fraudulent call based on the results of voice analysis of the call content. The response unit can also execute a warning response when there is a high possibility of the call being a nuisance call based on the results of voice analysis of the call content. Furthermore, the response unit can include characteristics of the caller of the fraudulent call in the response content based on the results of voice analysis of the call content. This makes it possible to provide a more appropriate response by adjusting the response content based on the results of voice analysis of the call content.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The analysis unit can evaluate the reliability of past report data and prioritize analysis of highly reliable data. For example, the analysis unit evaluates the reliability of the provider of the report data and prioritizes analysis of data from providers with high ratings. In addition, if the content of the report data is detailed, the analysis unit can also prioritize analysis of that data. Furthermore, if report data is provided frequently, the analysis unit can also prioritize analysis of that data. In this way, by prioritizing analysis of highly reliable data, the accuracy of the analysis is improved.

[0063] The collection unit can prioritize collecting specific keywords and phrases when browsing the Internet. For example, it can prioritize collecting keywords such as "scam calls" and "nuisance calls." It can also prioritize collecting phrases such as "fraud methods" and "characteristics of nuisance calls." It can also prioritize collecting keywords such as "victim of fraud" and "report nuisance calls." By prioritizing the collection of specific keywords and phrases, more relevant information can be collected.

[0064] When making a judgment, the judgment unit can improve the accuracy of the judgment by referring to past judgment results. For example, based on past judgment results, it can prioritize numbers that are likely to be fraudulent calls. It can also prioritize numbers that are likely to be nuisance calls based on past judgment results. Furthermore, it can adjust the algorithm for improving the accuracy of the judgment based on past judgment results. In this way, by referring to past judgment results, the accuracy of the judgment can be improved.

[0065] When sending a notification, the notification unit can select the optimal notification method by referring to past notification history. For example, the notification method that the user is most likely to respond to can be selected based on the past notification history. The notification content that the user can most easily understand can also be selected based on the past notification history. Furthermore, the notification method that the user can respond to most quickly can also be selected based on the past notification history. In this way, the optimal notification method can be selected by referring to the past notification history.

[0066] When blocking, the blocking unit can refer to past block history to select the optimal blocking method. For example, based on past block history, the blocking unit can select the method that allows the user to block most effectively. Also, based on past block history, the blocking unit can select the method that allows the user to block most quickly. Furthermore, based on past block history, the blocking unit can select the blocking method that gives the user the most peace of mind. In this way, by referring to past block history, the optimal blocking method can be selected.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The analysis unit analyzes past reported data and extracts characteristics of fraudulent and nuisance calls. For example, using generative AI, it learns patterns of fraudulent and nuisance calls based on reported data and analyzes characteristics such as specific phone numbers, originating regions, and call content. It also analyzes information collected by the collection unit to extract characteristics of new fraudulent and nuisance calls. Step 2: The collection unit crawls the internet to collect information about scams and nuisance calls. For example, using RPA, it collects information from bulletin boards, social media posts, and reporting sites related to scams and nuisance calls. It periodically crawls specific websites, bulletin boards, and social media, scraping and collecting information about scams and nuisance calls. Step 3: The judgment unit determines which numbers are likely to be fraudulent or nuisance calls. For example, using generation AI, it determines which numbers are likely to be fraudulent or nuisance calls based on collected information. Based on past reported data and collected information, it analyzes patterns of fraudulent and nuisance calls and determines which numbers are likely to be fraudulent or nuisance calls. Step 4: The notification unit sends a notification to the identified number, instructing them to set up call blocking. For example, using the generation AI, a notification can be sent to the identified number in the app, urging them to set up call blocking. Notifications can also be sent to the user via SMS, email, in-app notifications, etc. Step 5: The blocking unit automatically blocks fraudulent and nuisance numbers. For example, it uses generative AI to automatically block numbers that have been identified. It creates a phone number blacklist and blocks fraudulent and nuisance numbers in real time.

[0069] (Example 2) The fraud and nuisance call prevention system according to an embodiment of the present invention utilizes generation AI and RPA to check whether a phone number has been reported or registered as a fraud or nuisance call in the past. This system analyzes past reported data and extracts characteristics of fraud and nuisance calls. Next, RPA periodically patrols the Internet and scrapes and collects information on fraud and nuisance calls. The collected data is analyzed to extract, predict, and detect new characteristics of fraud and nuisance calls. Feedback is provided to users based on information from blocked numbers and callers to prevent potential damage. AI also automatically blocks and warns users of such numbers, reducing the risk of being harmed by accidentally answering the call and protecting users from fraud and nuisance calls. For example, generation AI analyzes past reported data and extracts characteristics of fraud and nuisance calls. Based on the reported data, generation AI learns patterns of fraud and nuisance calls and extracts their characteristics. For example, it analyzes characteristics such as specific phone numbers, caller regions, and call content. Next, RPA periodically patrols the Internet and scrapes and collects information on fraud and nuisance calls. For example, information about scams and nuisance calls is collected from bulletin boards, social media posts, and reporting sites. The collected information is provided to the generation AI for analysis. The generation AI analyzes the collected information and extracts characteristics of new scams and nuisance calls. For example, it predicts and detects new scam methods and nuisance call patterns. This allows the system to constantly maintain up-to-date information and issue appropriate warnings to users. Furthermore, it identifies numbers that may be scams or nuisance calls. Instead of ringing the phone, the system notifies the caller in the app, encouraging them to block the call. Furthermore, an AI chatbot automatically answers suspicious calls, reducing the risk of the recipient answering the phone directly. This mechanism protects users from scams and nuisance calls and prevents them from becoming victims. Furthermore, the AI ​​has the ability to automatically block and warn the number, reducing the risk of being harmed by answering the call by mistake. This fraud and nuisance call prevention system ensures user safety and prevents scams and nuisance calls from occurring.

[0070] The fraud and nuisance call prevention system according to the embodiment includes an analysis unit, a collection unit, a determination unit, a notification unit, and a block unit. The analysis unit analyzes past report data and extracts characteristics of fraud and nuisance calls. The analysis unit, for example, uses a generation AI to learn fraud and nuisance call patterns based on the report data and extracts the characteristics. For example, the generation AI analyzes characteristics such as specific phone numbers, caller regions, and call content. The analysis unit also analyzes information collected by the collection unit and extracts characteristics of new fraud and nuisance calls. The collection unit patrols the Internet to collect information related to fraud and nuisance calls. For example, the collection unit uses RPA to collect information from bulletin boards, social media posts, and reporting sites related to fraud and nuisance calls. For example, the collection unit regularly patrols specific websites, bulletin boards, and social media to scrape and collect information related to fraud and nuisance calls. The determination unit identifies numbers that may be fraud or nuisance calls. For example, the determination unit uses a generation AI to identify numbers that may be fraud or nuisance calls based on the collected information. For example, the determination unit analyzes fraudulent or nuisance call patterns based on past report data and collected information to determine numbers that may be fraudulent or nuisance calls. The notification unit issues a notification to the determined number and prompts the user to set up call blocking. The notification unit may, for example, use a generation AI to issue a notification to the determined number on an app, prompting the user to set up call blocking. The notification unit may also notify the user via SMS, email, in-app notification, or other methods. The blocking unit automatically blocks fraudulent or nuisance call numbers. The blocking unit may, for example, use a generation AI to automatically block determined numbers. For example, the blocking unit may create a phone number blacklist and block fraudulent or nuisance call numbers in real time. As a result, the fraud and nuisance call prevention system according to the embodiment can ensure the safety of users and prevent fraud and nuisance call damage.

[0071] The analysis unit can learn patterns of fraud and nuisance calls based on the reported data and extract their features. For example, the analysis unit uses generation AI to learn patterns of fraud and nuisance calls based on the reported data and extract their features. For example, the generation AI analyzes features such as specific phone numbers, the region of origin, and the content of the calls. The analysis unit can also use machine learning models and data mining technology to learn patterns of fraud and nuisance calls based on the reported data. For example, the analysis unit can extract common patterns of fraud and nuisance calls based on past reported data and predict the features of new fraud and nuisance calls. This allows the analysis unit to learn patterns of fraud and nuisance calls based on the reported data and extract their features, enabling more accurate analysis.

[0072] The collection unit can collect information from bulletin boards, social media posts, and reporting sites related to scams and nuisance calls. For example, the collection unit uses RPA to collect information from bulletin boards, social media posts, and reporting sites related to scams and nuisance calls. For example, the collection unit periodically patrols specific websites, bulletin boards, and social media to scrape and collect information related to scams and nuisance calls. The collection unit can also use an API to collect information related to scams and nuisance calls. For example, the collection unit uses an API that provides information about scams and nuisance calls to collect information in real time. This allows information about scams and nuisance calls to be collected from a wide range of sources, making more data available for analysis.

[0073] The analysis unit can analyze the collected information and extract characteristics of new scams and nuisance calls. The analysis unit can, for example, use generation AI to analyze the collected information and extract characteristics of new scams and nuisance calls. For example, generation AI predicts and detects new fraud methods and nuisance call patterns. The analysis unit can also use machine learning models and data mining technology to analyze the collected information and extract characteristics of new scams and nuisance calls. For example, the analysis unit can extract common patterns of new scams and nuisance calls based on the collected information and build a predictive model. This allows the system to respond to the latest fraud methods by extracting characteristics of new scams and nuisance calls.

[0074] The determination unit can determine numbers that are likely to be fraudulent or nuisance calls. The determination unit, for example, uses generative AI to determine numbers that are likely to be fraudulent or nuisance calls based on collected information. For example, the determination unit analyzes fraudulent or nuisance call patterns based on past report data and collected information to determine numbers that are likely to be fraudulent or nuisance calls. The determination unit can also use machine learning algorithms or rule-based determination methods to determine numbers that are likely to be fraudulent or nuisance calls. For example, the determination unit uses a machine learning model to predict numbers that are likely to be fraudulent or nuisance calls and makes a determination in real time. This allows the determination of numbers that are likely to be fraudulent or nuisance calls to issue a warning to the user.

[0075] The notification unit can send a notification to the determined number and prompt the user to set up call blocking. For example, the notification unit can use a generation AI to send a notification to the determined number on the app, urging the user to set up call blocking. The notification unit can also notify the user by SMS, email, in-app notification, or other methods. For example, the notification unit can send a warning message to the determined number and prompt the user to set up call blocking. The notification unit can also customize the content and method of the notification according to the user's settings. For example, the notification unit can send notifications at times or days of the week specified by the user. This allows the user to be notified of numbers that may be fraudulent or nuisance calls and prompt the user to set up call blocking, thereby preventing damage before it occurs.

[0076] The blocking unit can automatically block fraudulent or nuisance call numbers. For example, the blocking unit uses generative AI to automatically block numbers that have been determined to be fraudulent or nuisance. For example, the blocking unit creates a blacklist of phone numbers and blocks fraudulent or nuisance call numbers in real time. The blocking unit can also customize blocking criteria according to user settings. For example, the blocking unit can execute blocking during user-specified time periods or days of the week. This reduces the risk of users accidentally answering calls by automatically blocking fraudulent or nuisance call numbers.

[0077] The response unit can have the AI ​​chatbot automatically respond to suspicious phone calls. The response unit, for example, uses generation AI to have the AI ​​chatbot automatically respond to suspicious phone calls. For example, the response unit uses natural language processing technology to provide an appropriate response to suspicious phone calls. The response unit can also use a dialogue system algorithm to respond to suspicious phone calls in real time. For example, the response unit has the AI ​​chatbot ask questions in response to suspicious phone calls to confirm the caller's intentions. The response unit can also customize the content of the response according to user settings. For example, the response unit responds to suspicious phone calls using a response message specified by the user. In this way, having the AI ​​chatbot automatically respond to suspicious phone calls reduces the risk of the user having to answer the phone directly.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, a generative AI and adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can prioritize analyzing report data with high urgency. In addition, if the user is relaxed, the analysis unit can also perform analysis with normal priority. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing the most reliable data from past report data. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions.

[0079] The analysis unit can evaluate the reliability of past report data and prioritize analyzing highly reliable data. The analysis unit, for example, uses a generation AI to evaluate the reliability of past report data and prioritize analyzing highly reliable data. For example, the analysis unit evaluates the reliability of report data providers and prioritizes analyzing data from providers with high ratings. The analysis unit can also prioritize analyzing report data if the content of the report data is detailed. Furthermore, the analysis unit can also prioritize analyzing report data if the report data is provided frequently. This improves the accuracy of the analysis by prioritizing analysis of highly reliable data.

[0080] The analysis unit can take into account patterns that occur during specific time periods or days of the week when extracting the characteristics of fraudulent or nuisance calls. For example, when using generative AI to extract the characteristics of fraudulent or nuisance calls, the analysis unit can take into account patterns that occur during specific time periods or days of the week. For example, the analysis unit can extract patterns of fraudulent calls that occur frequently during the daytime on weekdays. The analysis unit can also extract patterns of nuisance calls that occur frequently on weekend nights. Furthermore, the analysis unit can extract patterns of fraudulent calls that occur frequently on specific holidays. By taking into account patterns that occur during specific time periods or days of the week, more accurate feature extraction is possible.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, a generative AI, and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a concise display method that gives a sense of security. In addition, if the user is relaxed, the analysis unit can also display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display analysis results that focus on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results according to the user's emotions.

[0082] When analyzing the reported data, the analysis unit can extract regional characteristics of fraud and nuisance calls. When analyzing the reported data, the analysis unit, for example, uses generative AI to extract regional characteristics of fraud and nuisance calls. For example, the analysis unit extracts characteristics of fraudulent calls that occur frequently in urban areas. The analysis unit can also extract characteristics of nuisance calls that occur frequently in rural areas. Furthermore, the analysis unit can analyze patterns of fraudulent calls that occur frequently in specific areas. By extracting regional characteristics, it is possible to respond to regionally specific fraud and nuisance calls.

[0083] When analyzing the report data, the analysis unit performs audio analysis of the call content to extract characteristics of fraudulent and nuisance calls. When analyzing the report data, the analysis unit performs audio analysis of the call content, for example, using generative AI, to extract characteristics of fraudulent and nuisance calls. For example, the analysis unit performs audio analysis of the call content to extract common phrases in fraudulent calls. The analysis unit can also perform audio analysis of the call content to extract characteristic voice patterns of nuisance calls. Furthermore, the analysis unit can also perform audio analysis of the call content to extract characteristics of the voice of the caller making the fraudulent call. This makes it possible to extract more detailed characteristics by performing audio analysis of the call content.

[0084] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. The collection unit can estimate the user's emotions using, for example, RPA, and determine the priority of information to be collected based on the estimated user's emotions. For example, when the user is feeling anxious, the collection unit can prioritize collecting information with high urgency. Furthermore, when the user is relaxed, the collection unit can also collect information with normal priority. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting highly reliable information. This enables more appropriate information collection by prioritizing information according to the user's emotions.

[0085] The collection unit can prioritize collecting specific keywords and phrases when browsing the Internet. The collection unit, for example, uses RPA to prioritize collecting specific keywords and phrases when browsing the Internet. For example, the collection unit prioritizes collecting keywords such as "fraudulent calls" and "nuisance calls." The collection unit can also prioritize collecting phrases such as "fraudulent methods" and "characteristics of nuisance calls." Furthermore, the collection unit can prioritize collecting keywords such as "fraud victim" and "report nuisance calls." In this way, by prioritizing the collection of specific keywords and phrases, more relevant information can be collected.

[0086] The collection unit can evaluate the reliability of the collected information and prioritize analysis of highly reliable information. The collection unit, for example, uses RPA to evaluate the reliability of the collected information and prioritize analysis of highly reliable information. For example, the collection unit evaluates the reliability of information providers and prioritizes analysis of information from providers with high ratings. The collection unit can also prioritize analysis of information with detailed content if the information is provided in detail. Furthermore, the collection unit can also prioritize analysis of information with a high frequency of provision. This improves the accuracy of the analysis by prioritizing analysis of highly reliable information.

[0087] The collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, RPA, and adjust the display method of the collected information based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can provide a concise display method that gives a sense of security. Furthermore, if the user is relaxed, the collection unit can also display detailed information. Furthermore, if the user is in a hurry, the collection unit can display information that focuses on the main points. In this way, by adjusting the display method of information according to the user's emotions, more appropriate information can be provided.

[0088] The collection unit can prioritize collecting information on specific regions or countries when patrolling the Internet. The collection unit, for example, uses RPA to prioritize collecting information on specific regions or countries when patrolling the Internet. For example, the collection unit can prioritize collecting information on fraudulent calls in urban areas. The collection unit can also prioritize collecting information on nuisance calls in rural areas. Furthermore, the collection unit can prioritize collecting information on fraudulent calls that occur frequently in specific countries or regions. In this way, by prioritizing the collection of information on specific regions or countries, it is possible to respond to scams and nuisance calls that are specific to the region.

[0089] The collection unit can prioritize collecting information posted during specific time periods or days of the week when browsing the Internet. The collection unit, for example, uses RPA to prioritize collecting information posted during specific time periods or days of the week when browsing the Internet. For example, the collection unit prioritizes collecting information about scam calls posted during the daytime on weekdays. The collection unit can also prioritize collecting information about nuisance calls posted during the night on weekends. Furthermore, the collection unit can prioritize collecting information about scam calls posted on specific holidays. In this way, by prioritizing the collection of information posted during specific time periods or days of the week, more relevant information can be collected.

[0090] The determination unit can estimate the user's emotions and adjust the determination criteria based on the estimated user emotions. The determination unit can estimate the user's emotions using, for example, a generative AI and adjust the determination criteria based on the estimated user emotions. For example, the determination unit can make a determination using strict criteria when the user is feeling anxious. The determination unit can also make a determination using normal criteria when the user is relaxed. Furthermore, the determination unit can make a quick determination based on highly reliable data when the user is in a hurry. This allows for more appropriate determination by adjusting the determination criteria according to the user's emotions.

[0091] The judgment unit can improve the accuracy of the judgment by referring to past judgment results when making a judgment. The judgment unit can improve the accuracy of the judgment by referring to past judgment results when making a judgment, for example, using a generation AI. For example, the judgment unit can prioritize numbers that are likely to be fraudulent calls based on past judgment results. The judgment unit can also prioritize numbers that are likely to be nuisance calls based on past judgment results. Furthermore, the judgment unit can adjust the algorithm for improving the accuracy of the judgment based on past judgment results. In this way, the accuracy of the judgment is improved by referring to past judgment results.

[0092] The determination unit can take into account patterns that occur during specific time periods or days of the week when making a determination. The determination unit can use, for example, a generation AI to take into account patterns that occur during specific time periods or days of the week when making a determination. For example, the determination unit can take into account patterns of fraudulent calls that occur frequently during the daytime on weekdays when making a determination. The determination unit can also take into account patterns of nuisance calls that occur frequently on weekend nights when making a determination. Furthermore, the determination unit can take into account patterns of fraudulent calls that occur frequently on specific holidays when making a determination. In this way, by taking into account patterns that occur during specific time periods or days of the week, more accurate determinations can be made.

[0093] The determination unit can estimate the user's emotions and adjust the display method of the determination result based on the estimated user's emotions. The determination unit can, for example, use a generation AI to estimate the user's emotions and adjust the display method of the determination result based on the estimated user's emotions. For example, if the user is feeling anxious, the determination unit can provide a concise display method that gives a sense of security. Furthermore, if the user is relaxed, the determination unit can also display a detailed determination result. Furthermore, if the user is in a hurry, the determination unit can display a determination result that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method of the determination result according to the user's emotions.

[0094] The determination unit can take into account the characteristics of fraud and nuisance calls in each region when making a determination. The determination unit can use, for example, generative AI to take into account the characteristics of fraud and nuisance calls in each region when making a determination. For example, the determination unit can make a determination by taking into account the characteristics of fraudulent calls that occur frequently in urban areas. The determination unit can also make a determination by taking into account the characteristics of nuisance calls that occur frequently in rural areas. Furthermore, the determination unit can make a determination by taking into account the patterns of fraudulent calls that occur frequently in specific regions. In this way, by taking into account the characteristics of each region, it is possible to respond to fraud and nuisance calls that are unique to each region.

[0095] When making a judgment, the judgment unit can perform audio analysis of the call content to determine the possibility of a fraudulent or nuisance call. When making a judgment, the judgment unit can perform audio analysis of the call content, for example, using a generation AI, to determine the possibility of a fraudulent or nuisance call. For example, the judgment unit can perform audio analysis of the call content to make a judgment based on common phrases used in fraudulent calls. The judgment unit can also perform audio analysis of the call content to make a judgment based on characteristic voice patterns of nuisance calls. Furthermore, the judgment unit can perform audio analysis of the call content to make a judgment based on the voice characteristics of the caller making the fraudulent call. This makes it possible to make a more detailed judgment by performing audio analysis of the call content.

[0096] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. The notification unit can estimate the user's emotions using, for example, a generation AI, and adjust the content of the notification based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can provide notification content that gives a sense of security. Furthermore, if the user is relaxed, the notification unit can provide detailed notification content. Furthermore, if the user is in a hurry, the notification unit can provide notification content that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the content of the notification according to the user's emotions.

[0097] The notification unit can select the optimal notification method by referring to past notification history when sending a notification. The notification unit can, for example, use a generation AI to select the optimal notification method by referring to past notification history when sending a notification. For example, the notification unit can select the notification method to which the user is most likely to respond based on the past notification history. The notification unit can also select notification content that is easiest for the user to understand based on the past notification history. Furthermore, the notification unit can select the notification method that the user can respond to most quickly based on the past notification history. In this way, the optimal notification method can be selected by referring to the past notification history.

[0098] The notification unit can send notifications during specific time periods or days of the week at the time of notification. The notification unit can use, for example, a generation AI to send notifications during specific time periods or days of the week at the time of notification. For example, the notification unit can send notifications during the daytime on weekdays so that the user can respond quickly. The notification unit can also send notifications during the evening on weekends so that the user can respond when they are relaxed. Furthermore, the notification unit can send notifications on specific holidays so that the user can respond with ample time. As a result, by sending notifications during specific time periods or days of the week, it is possible to provide information at more appropriate times.

[0099] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user emotions. The notification unit can estimate the user's emotions using, for example, a generation AI and determine the priority of notifications based on the estimated user emotions. For example, if the user is feeling anxious, the notification unit can prioritize sending notifications with high urgency. Also, if the user is relaxed, the notification unit can send notifications with normal priority. Furthermore, if the user is in a hurry, the notification unit can prioritize sending important notifications. This makes it possible to provide more appropriate information by determining the priority of notifications according to the user's emotions.

[0100] The notification unit can take into account the characteristics of scams and nuisance calls in each region when sending a notification. The notification unit can, for example, use generative AI to take into account the characteristics of scams and nuisance calls in each region when sending a notification. For example, the notification unit can send a notification taking into account the characteristics of scam calls that occur frequently in urban areas. The notification unit can also send a notification taking into account the characteristics of nuisance calls that occur frequently in rural areas. Furthermore, the notification unit can send a notification taking into account the patterns of scam calls that occur frequently in specific regions. In this way, by taking into account the characteristics of each region, it is possible to respond to scams and nuisance calls that are unique to each region.

[0101] The notification unit can adjust the notification content based on the results of voice analysis of the call content when sending a notification. The notification unit can adjust the notification content based on the results of voice analysis of the call content when sending a notification, for example, using a generation AI. For example, the notification unit sends a warning notification when there is a high possibility of a fraudulent call based on the results of voice analysis of the call content. The notification unit can also send a warning notification when there is a high possibility of a nuisance call based on the results of voice analysis of the call content. Furthermore, the notification unit can include characteristics of the caller of the fraudulent call in the notification content based on the results of voice analysis of the call content. This makes it possible to provide more appropriate information by adjusting the notification content based on the results of voice analysis of the call content.

[0102] The blocking unit can estimate the user's emotions and adjust the blocking criteria based on the estimated user emotions. The blocking unit can estimate the user's emotions using, for example, a generative AI and adjust the blocking criteria based on the estimated user emotions. For example, if the user is feeling anxious, the blocking unit can block the user using strict criteria. Alternatively, if the user is relaxed, the blocking unit can block the user using normal criteria. Furthermore, if the user is in a hurry, the blocking unit can block the user quickly based on reliable data. This allows for more appropriate blocking by adjusting the blocking criteria according to the user's emotions.

[0103] The blocking unit can refer to past block history to select the optimal blocking method when blocking. For example, the blocking unit uses a generation AI to refer to past block history when blocking. For example, the blocking unit selects the method that allows the user to block most effectively based on past block history. The blocking unit can also select the method that allows the user to block most quickly based on past block history. Furthermore, the blocking unit can also select the blocking method that gives the user the most peace of mind based on past block history. In this way, the optimal blocking method can be selected by referring to past block history.

[0104] The blocking unit can execute the block at a specific time period or day of the week when blocking. The blocking unit can execute the block at a specific time period or day of the week when blocking, for example, using a generation AI. For example, the blocking unit can execute the block during the daytime on weekdays, allowing the user to respond quickly. The blocking unit can also execute the block at night on weekends, allowing the user to respond at a time when they are relaxed. Furthermore, the blocking unit can execute the block on specific holidays, allowing the user to respond with ample time. This allows the block to be executed at a more appropriate time by executing the block at a specific time period or day of the week.

[0105] The blocking unit can estimate the user's emotions and determine the priority of blocks based on the estimated user emotions. The blocking unit can estimate the user's emotions using, for example, a generation AI and determine the priority of blocks based on the estimated user emotions. For example, if the user is feeling anxious, the blocking unit can prioritize blocks with high urgency. Also, if the user is relaxed, the blocking unit can execute blocks with normal priority. Furthermore, if the user is in a hurry, the blocking unit can prioritize important blocks. This enables more appropriate blocking by determining the priority of blocks according to the user's emotions.

[0106] The blocking unit can take into account the characteristics of fraudulent and nuisance calls in each region when blocking. For example, the blocking unit can use generative AI to take into account the characteristics of fraudulent and nuisance calls in each region when blocking. For example, the blocking unit can take into account the characteristics of fraudulent calls that occur frequently in urban areas when blocking. The blocking unit can also take into account the characteristics of nuisance calls that occur frequently in rural areas when blocking. Furthermore, the blocking unit can take into account the patterns of fraudulent calls that occur frequently in specific regions when blocking. This makes it possible to respond to fraudulent and nuisance calls that are unique to each region by taking into account the characteristics of each region.

[0107] When blocking, the blocking unit can adjust the blocking criteria based on the results of the voice analysis of the call content. When blocking, the blocking unit can adjust the blocking criteria based on the results of the voice analysis of the call content, for example, using a generation AI. For example, the blocking unit can execute a block if there is a high possibility that the call is a fraudulent call based on the results of the voice analysis of the call content. The blocking unit can also execute a block if there is a high possibility that the call is a nuisance call based on the results of the voice analysis of the call content. Furthermore, the blocking unit can include characteristics of fraudulent callers in the blocking criteria based on the results of the voice analysis of the call content. This allows for more appropriate blocking by adjusting the blocking criteria based on the results of the voice analysis of the call content.

[0108] The response unit can estimate the user's emotions and adjust the content of the response based on the estimated user emotions. The response unit can estimate the user's emotions using, for example, a generation AI, and adjust the content of the response based on the estimated user emotions. For example, if the user is feeling anxious, the response unit can provide a response that gives a sense of security. Furthermore, if the user is relaxed, the response unit can provide a detailed response. Furthermore, if the user is in a hurry, the response unit can provide a response that gets to the point. This allows for a more appropriate response by adjusting the content of the response according to the user's emotions.

[0109] The response unit can refer to past response history to select the optimal response method when responding. The response unit, for example, uses a generation AI to refer to past response history when responding. For example, the response unit selects a method that allows the user to respond most effectively based on the past response history. The response unit can also select a method that allows the user to respond most quickly based on the past response history. Furthermore, the response unit can also select a response method that makes the user feel most at ease based on the past response history. In this way, the optimal response method can be selected by referring to the past response history.

[0110] The response unit can execute a response during a specific time period or day of the week when responding. The response unit executes a response during a specific time period or day of the week when responding, for example, using a generation AI. For example, the response unit executes a response during the daytime on weekdays, allowing the user to respond quickly. The response unit can also execute a response during the evening on weekends, allowing the user to respond at a time when they are relaxed. Furthermore, the response unit can execute a response on a specific public holiday, allowing the user to respond with ample time to spare. As a result, by executing a response during a specific time period or day of the week, it is possible to respond at a more appropriate time.

[0111] The response unit can estimate the user's emotions and determine the priority of responses based on the estimated user's emotions. The response unit can estimate the user's emotions using, for example, a generation AI and determine the priority of responses based on the estimated user's emotions. For example, if the user is feeling anxious, the response unit can prioritize responses with a high level of urgency. Also, if the user is relaxed, the response unit can also prioritize responses with a normal level of priority. Furthermore, if the user is in a hurry, the response unit can prioritize responses with an important level of priority. This enables more appropriate responses by determining the priority of responses according to the user's emotions.

[0112] The response unit can take into account the characteristics of scams and nuisance calls in each region when responding. The response unit can, for example, use generative AI to take into account the characteristics of scams and nuisance calls in each region when responding. For example, the response unit can take into account the characteristics of scam calls that are more common in urban areas when responding. The response unit can also take into account the characteristics of nuisance calls that are more common in rural areas when responding. Furthermore, the response unit can take into account the patterns of scam calls that are more common in specific regions when responding. In this way, by taking into account the characteristics of each region, it is possible to respond to scams and nuisance calls that are specific to each region.

[0113] The response unit can adjust the response content based on the results of voice analysis of the call content when answering the call. The response unit can adjust the response content based on the results of voice analysis of the call content when answering the call, for example, using a generation AI. For example, the response unit executes a warning response when there is a high possibility of the call being a fraudulent call based on the results of voice analysis of the call content. The response unit can also execute a warning response when there is a high possibility of the call being a nuisance call based on the results of voice analysis of the call content. Furthermore, the response unit can include characteristics of the caller of the fraudulent call in the response content based on the results of voice analysis of the call content. This makes it possible to provide a more appropriate response by adjusting the response content based on the results of voice analysis of the call content. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, collection unit, determination unit, notification unit, and blocking unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes past report data to extract characteristics of fraudulent and nuisance calls. The collection unit is implemented, for example, by the control unit 46A of the smart device 14 and patrols the Internet to collect information related to fraudulent and nuisance calls. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and determines numbers that may be fraudulent or nuisance calls based on the collected information. The notification unit is implemented, for example, by the control unit 46A of the smart device 14 and issues a notification to the determined number and prompts the user to set up call rejection. The blocking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically blocks fraudulent and nuisance call numbers. === Hard Collateral 1-2 === Each of the multiple elements, including the analysis unit, collection unit, determination unit, notification unit, and blocking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes past report data to extract characteristics of fraudulent and nuisance calls. The collection unit is realized, for example, by the control unit 46A of the smart glasses 214 and patrols the Internet to collect information related to fraudulent and nuisance calls. The determination unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and determines numbers that may be fraudulent or nuisance calls based on the collected information. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and issues a notification to the determined number and prompts the user to set up call rejection. The blocking unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and automatically blocks fraudulent and nuisance call numbers. === Hard Collateral 1-3 === Each of the multiple elements, including the analysis unit, collection unit, determination unit, notification unit, and block unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes past report data to extract characteristics of fraudulent and nuisance calls. The collection unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and patrols the Internet to collect information related to fraudulent and nuisance calls. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and determines numbers that may be fraudulent or nuisance calls based on the collected information. The notification unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and issues a notification to the determined number and prompts the user to set up call rejection. The block unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and automatically blocks fraudulent and nuisance call numbers. === Hard Collateral 1-4 === Each of the multiple elements, including the analysis unit, collection unit, determination unit, notification unit, and block unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes past report data to extract characteristics of fraudulent and nuisance calls. The collection unit is realized, for example, by the control unit 46A of the robot 414 and patrols the Internet to collect information related to fraudulent and nuisance calls. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines numbers that may be fraudulent or nuisance calls based on the collected information. The notification unit is realized, for example, by the control unit 46A of the robot 414 and issues a notification to the determined number and prompts the user to set up call rejection. The block unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically blocks fraudulent and nuisance call numbers.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is feeling anxious, it can prioritize analysis of urgent report data. If the user is relaxed, it can also perform analysis with normal priority. Furthermore, if the user is in a hurry, it can prioritize analysis of the most reliable data from past report data. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions.

[0116] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. For example, if the user is feeling anxious, it can prioritize collection of information with high urgency. If the user is relaxed, it can also collect information with normal priority. Furthermore, if the user is in a hurry, it can also collect information with high reliability. In this way, by determining the priority of information according to the user's emotions, more appropriate information collection is possible.

[0117] The determination unit can estimate the user's emotions and adjust the criteria for determination based on the estimated emotions. For example, if the user is feeling anxious, the determination can be made using strict criteria. If the user is relaxed, the determination can be made using normal criteria. Furthermore, if the user is in a hurry, the determination can be made quickly based on highly reliable data. This allows for more appropriate determination by adjusting the criteria for determination according to the user's emotions.

[0118] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is feeling anxious, notification content that gives a sense of security can be provided. If the user is relaxed, detailed notification content can be provided. Furthermore, if the user is in a hurry, notification content that focuses on the main points can be provided. In this way, by adjusting the content of the notification according to the user's emotions, more appropriate information can be provided.

[0119] The blocking unit can estimate the user's emotions and adjust the blocking criteria based on the estimated emotions. For example, if the user is feeling anxious, the blocking unit can block the user using strict criteria. If the user is relaxed, the blocking unit can block the user using normal criteria. Furthermore, if the user is in a hurry, the blocking unit can block the user quickly based on reliable data. This allows the blocking unit to adjust the blocking criteria according to the user's emotions, making it possible to block the user more appropriately.

[0120] The analysis unit can evaluate the reliability of past report data and prioritize analysis of highly reliable data. For example, the analysis unit evaluates the reliability of the provider of the report data and prioritizes analysis of data from providers with high ratings. In addition, if the content of the report data is detailed, the analysis unit can also prioritize analysis of that data. Furthermore, if report data is provided frequently, the analysis unit can also prioritize analysis of that data. In this way, by prioritizing analysis of highly reliable data, the accuracy of the analysis is improved.

[0121] The collection unit can prioritize collecting specific keywords and phrases when browsing the Internet. For example, it can prioritize collecting keywords such as "scam calls" and "nuisance calls." It can also prioritize collecting phrases such as "fraud methods" and "characteristics of nuisance calls." It can also prioritize collecting keywords such as "victim of fraud" and "report nuisance calls." By prioritizing the collection of specific keywords and phrases, more relevant information can be collected.

[0122] When making a judgment, the judgment unit can improve the accuracy of the judgment by referring to past judgment results. For example, based on past judgment results, it can prioritize numbers that are likely to be fraudulent calls. It can also prioritize numbers that are likely to be nuisance calls based on past judgment results. Furthermore, it can adjust the algorithm for improving the accuracy of the judgment based on past judgment results. In this way, by referring to past judgment results, the accuracy of the judgment can be improved.

[0123] When sending a notification, the notification unit can select the optimal notification method by referring to past notification history. For example, the notification method that the user is most likely to respond to can be selected based on the past notification history. The notification content that the user can most easily understand can also be selected based on the past notification history. Furthermore, the notification method that the user can respond to most quickly can also be selected based on the past notification history. In this way, the optimal notification method can be selected by referring to the past notification history.

[0124] When blocking, the blocking unit can refer to past block history to select the optimal blocking method. For example, based on past block history, the blocking unit can select the method that allows the user to block most effectively. Also, based on past block history, the blocking unit can select the method that allows the user to block most quickly. Furthermore, based on past block history, the blocking unit can select the blocking method that gives the user the most peace of mind. In this way, by referring to past block history, the optimal blocking method can be selected.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The analysis unit analyzes past reported data and extracts characteristics of fraudulent and nuisance calls. For example, using generative AI, it learns patterns of fraudulent and nuisance calls based on reported data and analyzes characteristics such as specific phone numbers, originating regions, and call content. It also analyzes information collected by the collection unit to extract characteristics of new fraudulent and nuisance calls. Step 2: The collection unit crawls the internet to collect information about scams and nuisance calls. For example, using RPA, it collects information from bulletin boards, social media posts, and reporting sites related to scams and nuisance calls. It periodically crawls specific websites, bulletin boards, and social media, scraping and collecting information about scams and nuisance calls. Step 3: The judgment unit determines which numbers are likely to be fraudulent or nuisance calls. For example, using generation AI, it determines which numbers are likely to be fraudulent or nuisance calls based on collected information. Based on past reported data and collected information, it analyzes patterns of fraudulent and nuisance calls and determines which numbers are likely to be fraudulent or nuisance calls. Step 4: The notification unit sends a notification to the identified number, instructing them to set up call blocking. For example, using the generation AI, a notification can be sent to the identified number in the app, urging them to set up call blocking. Notifications can also be sent to the user via SMS, email, in-app notifications, etc. Step 5: The blocking unit automatically blocks fraudulent and nuisance numbers. For example, it uses generative AI to automatically block numbers that have been identified. It creates a phone number blacklist and blocks fraudulent and nuisance numbers in real time.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0129] 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.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 7, a 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] 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.

[0176] 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.

[0177] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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).

[0184] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis section that analyzes past report data and extracts characteristics of fraud and nuisance calls; A collection department that crawls the internet and collects information about scams and nuisance calls; an analysis unit that analyzes the information collected by the collection unit and extracts characteristics of new frauds and nuisance calls; A determination unit that determines numbers that are suspected of being fraudulent or nuisance calls; a notification unit that issues a notification to the determined number; A blocking unit that automatically blocks fraudulent and nuisance call numbers. A system characterized by:

2. The analysis unit Learn patterns of fraud and nuisance calls based on reported data and extract features 2. The system of claim 1.

3. The collecting unit Collect information about scams and nuisance calls from message boards, social media posts, and reporting sites.

2. The system of claim 1.

4. The analysis unit Analyze the collected information to extract the characteristics of new scams and nuisance calls 2. The system of claim 1.

5. The determination unit Identify numbers that may be fraudulent or spam calls 2. The system of claim 1.

6. The notification unit The detected number will be notified and asked to set up call blocking.

2. The system of claim 1.

7. The block portion is Automatically block scam and spam numbers 2. The system of claim 1.

8. Equipped with a response section where an AI chatbot automatically responds to suspicious phone calls 2. The system of claim 1.

9. The analysis unit Estimate user emotions and adjust analysis priorities based on the estimated user emotions 2. The system of claim 1.

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