System

Abstract

An object of a system according to an embodiment is to provide effective measures against nuisance calls and fraudulent calls.SOLUTION: A system includes an automatic response part, a database operation part, and a risk evaluation part. The automatic response unit automatically responds to the nuisance call by using the generated AI. The database operation unit collects information on the nuisance call responded by the automatic response unit and registers the information in the database. The risk evaluation unit displays a risk ranking on an incoming call screen on the basis of the information on the nuisance call registered by the database operation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient countermeasures against nuisance calls and fraudulent calls, and users may be harmed.

[0005] The system according to the embodiment aims to provide an effective measure against nuisance calls and fraudulent calls. [Means for solving the problem]

[0006] The system according to the embodiment includes an automatic response unit, a database operation unit, and a risk assessment unit. The automatic response unit uses a generation AI to automatically respond to nuisance calls. The database operation unit collects information about nuisance calls answered by the automatic response unit and registers it in a database. The risk assessment unit displays a risk ranking on the incoming call screen based on the information about nuisance calls registered by the database operation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an effective measure against nuisance calls and fraudulent calls. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The spam and fraud call prevention system according to an embodiment of the present invention is a system that uses a generative AI to automatically respond to nuisance calls, responds using fixed phrases, operates a database for eliminating nuisance calls, and displays a risk ranking on the incoming call screen. This allows the spam and fraud call prevention system to automatically respond to nuisance calls, collect information, and evaluate and display the risk level.

[0029] The spam and fraud call prevention system according to the embodiment includes an automatic response unit, a database operation unit, and a risk assessment unit. The automatic response unit uses a generation AI to automatically respond to nuisance calls. For example, the generation AI uses a fixed phrase such as, "This is an automatic response system. How can I help you?" The automatic response unit can also analyze the caller's speech and select an appropriate fixed phrase to respond. The database operation unit collects information about nuisance calls answered by the automatic response unit and registers it in a database. For example, the generation AI collects information such as the source, content, and frequency of nuisance calls and registers it in the database. The database operation unit can also analyze the collected information and identify new nuisance call patterns. The risk assessment unit displays a risk ranking on the incoming call screen based on the nuisance call information registered by the database operation unit. For example, the generation AI displays a risk ranking such as "high," "medium," or "low" based on the number of times and content of calls reported as nuisance in the past. The risk assessment unit can also use the generation AI to evaluate the risk level in real time when a call comes in and display it on the incoming call screen. As a result, the spam and fraud call prevention system according to the embodiment can perform automatic responses to nuisance calls, information collection, and risk assessment in an integrated manner. For example, users can save time by automatically responding to nuisance calls. In addition, by operating a database for eliminating nuisance calls, new nuisance call patterns can be quickly identified and countermeasures can be taken. Furthermore, by displaying a risk ranking on the incoming call screen, users can understand the risk of nuisance calls in advance and take appropriate measures.

[0030] The automated response unit can analyze the content of the other party's conversation in real time and automatically insert a warning message if there is a high possibility of fraud. For example, if the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Please tell me your bank account information," it can insert a warning message saying, "This may be a scam. Please be careful." If the other party says, "Please transfer money now," the automated response unit can insert a warning message saying, "This may be a scam. We will report you to the police." If the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Your personal information has been leaked," it can insert a warning message saying, "This may be a scam. Do not give out your personal information." This ensures user safety by automatically inserting warning messages when there is a high possibility of fraud.

[0031] The auto-response unit can develop an algorithm that learns past response history and dynamically selects the most effective fixed phrase. For example, the generation AI can learn past response history and, if a particular phrase was found to be effective, develop an algorithm that preferentially selects that phrase. The auto-response unit can also develop an algorithm that analyzes past response history and dynamically selects an effective phrase for a specific situation. For example, in response to a fraudulent call, it selects a phrase such as "I will call the police." The auto-response unit can also develop an algorithm that dynamically selects an effective phrase for a specific caller based on past response history. For example, in response to a nuisance call from a specific phone number, it selects a phrase such as "This number has been blocked." This allows the optimal response to be provided based on past response history.

[0032] The automatic response unit can add a function that records the other party's voice and allows the user to check it later. For example, the automatic response unit can add a function that allows the generation AI to record the other party's voice when automatically responding and allows the user to check the recording later. For example, the recorded data can be stored in the cloud and made accessible to the user. The automatic response unit can also record the other party's voice during an automatic response and allow the user to play back the recording later to check the content. For example, the recorded data can be played back using a smartphone app. The automatic response unit can also add a function that allows the generation AI to record the other party's voice during an automatic response and allow the user to download and save the recording later. For example, the recorded data can be sent by email. This allows the user to check the recording later and use it as evidence.

[0033] The automatic response unit can add a function to convert the other party's voice into text and display it to the user in real time. For example, the automatic response unit can add a function to convert the other party's voice into text in real time while the generation AI is automatically responding and display it to the user. For example, the text can be displayed on a smartphone screen. The automatic response unit can also convert the other party's voice into text during an automatic response and allow the user to check the text in real time. For example, the text data can be displayed in chat format. The automatic response unit can also add a function to convert the other party's voice into text during an automatic response and allow the user to save the text. For example, the text data can be saved in the cloud. This allows the user to check what the other party is saying in real time.

[0034] The generation AI comprises a voice analysis unit that analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls; a geographic information collection unit that collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls; and a report generation unit that analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. In the voice analysis unit, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on voice patterns. For example, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on specific voice patterns. For example, it detects specific phrases and tones. In the geographic information collection unit, the generation AI collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls. For example, the generation AI collects geographic information on the source of nuisance calls and builds a system that analyzes regional trends in nuisance calls. For example, it identifies patterns of nuisance calls that occur frequently in specific regions. In the report generation unit, the generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, we will build a system in which the AI ​​analyzes the content of nuisance calls and automatically generates reports to identify fraud methods and trends. For example, the AI ​​can classify fraud methods and compile them into reports. This will allow us to identify the characteristics of nuisance calls, analyze trends by region, and identify fraud methods and trends.

[0035] The voice analysis unit can analyze the voice data of nuisance calls and identify the characteristics of new nuisance calls based on the voice patterns. For example, the voice analysis unit uses a generation AI to analyze the voice data of nuisance calls and identify the characteristics of new nuisance calls based on specific voice patterns. For example, it can detect specific phrases or tones. The voice analysis unit can also analyze the voice data of nuisance calls and develop an algorithm to identify the characteristics of new nuisance calls based on the voice patterns. For example, it can extract common patterns of fraudulent calls. The voice analysis unit can also build a system in which a generation AI analyzes the voice data of nuisance calls and identify the characteristics of new nuisance calls based on the voice patterns. For example, it can extract features using voice recognition technology. This makes it possible to identify the characteristics of new nuisance calls.

[0036] The geographic information collection unit can collect geographic information on the sources of nuisance calls and analyze trends in nuisance calls by region. For example, the geographic information collection unit can build a system in which a generation AI collects geographic information on the sources of nuisance calls and analyzes trends in nuisance calls by region. For example, it can identify patterns of nuisance calls that occur frequently in specific regions. The geographic information collection unit can also collect geographic information on the sources of nuisance calls and develop an algorithm that analyzes trends in nuisance calls by region. For example, it can analyze the frequency of nuisance calls in specific regions. The geographic information collection unit can also operate a database in which a generation AI collects geographic information on the sources of nuisance calls and analyzes trends in nuisance calls by region. For example, it can display the sources of nuisance calls on a map. This makes it possible to analyze trends in nuisance calls by region.

[0037] The report generation unit can analyze the content of nuisance calls and automatically generate reports to identify fraudulent methods and trends. For example, the report generation unit builds a system in which a generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, it classifies fraudulent methods and summarizes them in a report. The report generation unit can also develop an algorithm that analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, it analyzes fraudulent methods in chronological order. The report generation unit can also operate a database in which a generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, it displays fraudulent methods in graphs and charts. This makes it possible to automatically generate reports to identify fraudulent methods and trends.

[0038] The generation AI includes a security linking unit that links the nuisance call database with other security systems, a cloud operation unit that operates the nuisance call database on the cloud and makes it accessible from multiple devices, and an emotional response collection unit that collects users' emotional responses to the content of nuisance calls and reflects them in the database. The security linking unit links the nuisance call database with other security systems to provide comprehensive security measures. For example, the generation AI links the nuisance call database with other security systems to build a system that provides comprehensive security measures. For example, it links with antivirus software. The cloud operation unit allows the generation AI to operate the nuisance call database on the cloud and make it accessible from multiple devices. For example, it builds a system where the generation AI operates the nuisance call database on the cloud and allows users to access it from multiple devices. For example, it makes it accessible from smartphones and tablets. The emotional response collection unit allows the generation AI to collect users' emotional responses to the content of nuisance calls and reflects them in the database. For example, it uses an emotion estimation function to collect users' emotional responses to the content of nuisance calls and builds a system that reflects that data in the database. For example, it records the user's emotional score. This allows the nuisance call database to be linked to other security systems, run on the cloud, and collect users' emotional responses which can be reflected in the database.

[0039] The security collaboration unit can link the nuisance call database with other security systems to provide comprehensive security measures. For example, the generation AI in the security collaboration unit can link the nuisance call database with other security systems to build a system that provides comprehensive security measures. For example, it can link with antivirus software. The security collaboration unit can also link the nuisance call database with other security systems to develop an algorithm that provides comprehensive security measures. For example, it can link with firewalls. The security collaboration unit can also operate a database that links the nuisance call database with other security systems to provide comprehensive security measures. For example, it can link with network security systems. This makes it possible to provide comprehensive security measures.

[0040] The cloud operations department can operate the nuisance call database on the cloud, making it accessible from multiple devices. For example, the cloud operations department can build a system in which the generation AI operates the nuisance call database on the cloud and allows users to access it from multiple devices. For example, it can make it accessible from smartphones and tablets. The cloud operations department can also develop an algorithm to operate the nuisance call database on the cloud and make it accessible from multiple devices. For example, it can synchronize data between devices. The cloud operations department can also operate a database in which the generation AI operates the nuisance call database on the cloud and makes it accessible from multiple devices. For example, it can use cloud storage. This can improve convenience by making it accessible from multiple devices.

[0041] The generation AI includes a history analysis unit that analyzes past call history and improves the accuracy of the risk ranking to display a risk ranking on the incoming call screen; an information collection unit that collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking; and an individual evaluation unit that learns the user's past response history and provides an individual risk ranking. The history analysis unit develops an algorithm for the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, the generation AI analyzes past call history and develops an algorithm for improving the accuracy of the risk ranking. For example, the generation AI learns past nuisance call patterns to more accurately evaluate the risk. The information collection unit collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a system is constructed in which the generation AI collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a nuisance call report database is referenced. The individual evaluation unit learns the user's past response history and provides an individual risk ranking. For example, we will build a system in which the generation AI learns a user's past response history and provides an individual risk ranking. For example, it will perform the optimal risk assessment for a specific user. This will enable us to provide a more accurate risk ranking based on past history and real-time information.

[0042] The history analysis unit can analyze past call history and develop an algorithm to improve the accuracy of the risk ranking. For example, the history analysis unit develops an algorithm that allows the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, it learns past nuisance call patterns and more accurately evaluates the risk level. The history analysis unit can also develop an algorithm that improves the accuracy of the risk ranking based on past call history. For example, it analyzes the frequency and content of specific phone numbers and evaluates the risk level. The history analysis unit can also operate a database that allows the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, it dynamically updates the risk level based on past data. This allows the accuracy of the risk ranking to be improved based on past call history.

[0043] The information collection unit can collect additional information from the Internet in real time when a call is received and dynamically update the risk ranking. For example, the information collection unit builds a system in which the generation AI collects additional information from the Internet in real time when a call is received and dynamically updates the risk ranking. For example, it references a nuisance call report database. The information collection unit can also develop an algorithm that collects additional information from the Internet when a call is received and dynamically updates the risk ranking. For example, it reflects the latest nuisance call information in real time. The information collection unit can also operate a database in which the generation AI collects additional information from the Internet when a call is received and dynamically updates the risk ranking. For example, it uses a database on the cloud. This allows additional information to be collected in real time and the risk ranking to be dynamically updated.

[0044] The individual evaluation unit can learn the user's past response history and provide an individual risk ranking. For example, the individual evaluation unit constructs a system in which a generation AI learns the user's past response history and provides an individual risk ranking. For example, it performs an optimal risk assessment for a specific user. The individual evaluation unit can also develop an algorithm that provides an individual risk ranking based on the user's past response history. For example, it analyzes the user's response patterns and evaluates the risk. The individual evaluation unit can also operate a database in which the generation AI learns the user's past response history and provides an individual risk ranking. For example, it dynamically updates the risk based on data for each user. This makes it possible to provide an individual risk ranking based on the user's past response history.

[0045] The generation AI includes a detailed information display unit that displays detailed information about past nuisance calls along with a risk ranking on the incoming call screen, a countermeasure suggestion unit that suggests specific countermeasures that the user should take, and an emotion reduction unit that displays a message to reduce the anxiety and vigilance the user feels when receiving a call. The detailed information display unit adds a function to the incoming call screen where the generation AI displays detailed information about past nuisance calls along with a risk ranking. For example, the generation AI adds a function to the incoming call screen where the generation AI displays detailed information about past nuisance calls along with a risk ranking. For example, the content and caller of past nuisance calls are displayed. The countermeasure suggestion unit adds a function to the incoming call screen where the generation AI suggests specific countermeasures that the user should take along with a risk ranking. For example, the generation AI displays a suggestion such as "Please ignore this call." The emotion reduction unit uses the emotion estimation function to display a message to reduce the anxiety and vigilance the user feels when receiving a call. For example, a system is constructed where the emotion estimation function is used to display a message to reduce the anxiety and vigilance the user feels when receiving a call. For example, a message such as "This call is safe" is displayed. This allows users to receive detailed information about past nuisance calls and specific countermeasures, reducing their anxiety and vigilance.

[0046] The detailed information display unit can display detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, the detailed information display unit adds a function that allows the generation AI to display detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, it displays the content and caller of past nuisance calls. The detailed information display unit can also develop an algorithm that displays detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, it references the history of past nuisance calls. The detailed information display unit can also operate a database that allows the generation AI to display detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, it displays detailed information based on past data. In this way, by displaying detailed information about past nuisance calls, the user can obtain more specific information.

[0047] The countermeasure suggestion unit can suggest specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, the countermeasure suggestion unit adds a function in which the generation AI suggests specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, it displays a suggestion such as "Please ignore this call." The countermeasure suggestion unit can also develop an algorithm that suggests specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, it displays a suggestion such as "This call may be fraudulent. Please report it to the police." The countermeasure suggestion unit can also operate a database in which the generation AI suggests specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, it can suggest optimal countermeasures based on past data. This allows the user to take appropriate action against nuisance calls by suggesting specific countermeasures.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The automated response unit can analyze the content of the other party's conversation in real time and automatically insert a warning message if there is a high possibility of fraud. For example, if the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Please tell me your bank account information," it can insert a warning message saying, "This may be a scam. Please be careful." If the other party says, "Please transfer money now," the automated response unit can insert a warning message saying, "This may be a scam. We will report you to the police." If the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Your personal information has been leaked," it can insert a warning message saying, "This may be a scam. Do not give out your personal information." This ensures user safety by automatically inserting warning messages when there is a high possibility of fraud.

[0050] The auto-response unit can develop an algorithm that learns from past response history and dynamically selects the most effective fixed phrase. For example, if the generation AI learns from past response history and finds that a particular phrase is effective, it can develop an algorithm that preferentially selects that phrase. The auto-response unit can also develop an algorithm that analyzes past response history and dynamically selects effective phrases for specific situations. For example, it can select a phrase such as "I will call the police" in response to a scam call. The auto-response unit can also develop an algorithm that dynamically selects effective phrases for specific callers based on past response history. For example, it can select a phrase such as "This number has been blocked" in response to a nuisance call from a specific phone number. This allows the system to provide the optimal response based on past response history.

[0051] The automatic response unit can add a function that records the other party's voice and allows the user to check it later. For example, a function can be added in which the generation AI records the other party's voice when automatically responding and allows the user to check the recording later. For example, the recording data can be saved in the cloud and made accessible to the user. The automatic response unit can also record the other party's voice during an automatic response and allow the user to play back the recording later to check the content. For example, the recording data can be played back using a smartphone app. The automatic response unit can also add a function in which the generation AI records the other party's voice during an automatic response and allows the user to download and save the recording later. For example, the recording data can be sent by email. This allows the user to check the recording later and use it as evidence.

[0052] The automatic response unit can add a function to convert the other party's voice into text and display it to the user in real time. For example, a function can be added in which the generation AI converts the other party's voice into text in real time during an automatic response and displays it to the user. For example, the text can be displayed on a smartphone screen. The automatic response unit can also convert the other party's voice into text during an automatic response and allow the user to check the text in real time. For example, the text data can be displayed in chat format. The automatic response unit can also add a function to convert the other party's voice into text during an automatic response and allow the user to save the text. For example, the text data can be saved in the cloud. This allows the user to check what the other party is saying in real time.

[0053] The generation AI comprises a voice analysis unit that analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls; a geographic information collection unit that collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls; and a report generation unit that analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. In the voice analysis unit, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on voice patterns. For example, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on specific voice patterns. For example, it detects specific phrases and tones. In the geographic information collection unit, the generation AI collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls. For example, the generation AI collects geographic information on the source of nuisance calls and builds a system that analyzes regional trends in nuisance calls. For example, it identifies patterns of nuisance calls that occur frequently in specific regions. In the report generation unit, the generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, we will build a system in which the AI ​​analyzes the content of nuisance calls and automatically generates reports to identify fraud methods and trends. For example, the AI ​​can classify fraud methods and compile them into reports. This will allow us to identify the characteristics of nuisance calls, analyze trends by region, and identify fraud methods and trends.

[0054] The generation AI includes a history analysis unit that analyzes past call history and improves the accuracy of the risk ranking to display a risk ranking on the incoming call screen; an information collection unit that collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking; and an individual evaluation unit that learns the user's past response history and provides an individual risk ranking. The history analysis unit develops an algorithm for the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, the generation AI analyzes past call history and develops an algorithm for improving the accuracy of the risk ranking. For example, the generation AI learns past nuisance call patterns to more accurately evaluate the risk. The information collection unit collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a system is constructed in which the generation AI collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a nuisance call report database is referenced. The individual evaluation unit learns the user's past response history and provides an individual risk ranking. For example, we will build a system in which the generation AI learns a user's past response history and provides an individual risk ranking. For example, it will perform the optimal risk assessment for a specific user. This will enable us to provide a more accurate risk ranking based on past history and real-time information.

[0055] The security collaboration unit can link the nuisance call database with other security systems to provide comprehensive security measures. For example, the generation AI can link the nuisance call database with other security systems to build a system that provides comprehensive security measures. For example, it can link with antivirus software. The security collaboration unit can also link the nuisance call database with other security systems to develop an algorithm that provides comprehensive security measures. For example, it can link with firewalls. The security collaboration unit can also link the nuisance call database with other security systems to operate a database that provides comprehensive security measures. For example, it can link with a network security system. This makes it possible to provide comprehensive security measures.

[0056] The cloud operations department can operate the nuisance call database on the cloud, making it accessible from multiple devices. For example, it can build a system in which the generation AI operates the nuisance call database on the cloud and allows users to access it from multiple devices. For example, it can make it accessible from smartphones and tablets. The cloud operations department can also develop algorithms that operate the nuisance call database on the cloud and make it accessible from multiple devices. For example, it can synchronize data between devices. The cloud operations department can also operate a database in which the generation AI operates the nuisance call database on the cloud and makes it accessible from multiple devices. For example, it can use cloud storage. This can improve convenience by making it accessible from multiple devices.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The automatic response unit uses the generation AI to automatically respond to nuisance calls. For example, the generation AI uses a fixed phrase such as "This is the automatic response system. How can I help you?" The automatic response unit can also analyze what the caller is saying and select an appropriate fixed phrase to respond to the call. Step 2: The database operation department collects information about nuisance calls answered by the automated response department and registers it in a database. For example, the generation AI collects information such as the source, content, and frequency of nuisance calls and registers it in a database. The database operation department can also analyze the collected information and identify new nuisance call patterns. Step 3: The risk assessment unit displays a risk ranking on the incoming call screen based on the nuisance call information registered by the database operation unit. For example, the generation AI displays a risk ranking such as "high," "medium," or "low" based on the number of times and content of calls reported as nuisance calls in the past. The risk assessment unit can also have the generation AI evaluate the risk in real time when a call is received and display it on the incoming call screen.

[0059] (Example 2) The spam and fraud call prevention system according to an embodiment of the present invention is a system that uses a generative AI to automatically respond to nuisance calls, responds using fixed phrases, operates a database for eliminating nuisance calls, and displays a risk ranking on the incoming call screen. This allows the spam and fraud call prevention system to automatically respond to nuisance calls, collect information, and evaluate and display the risk level.

[0060] The spam and fraud call prevention system according to the embodiment includes an automatic response unit, a database operation unit, and a risk assessment unit. The automatic response unit uses a generation AI to automatically respond to nuisance calls. For example, the generation AI uses a fixed phrase such as, "This is an automatic response system. How can I help you?" The automatic response unit can also analyze the caller's speech and select an appropriate fixed phrase to respond. The database operation unit collects information about nuisance calls answered by the automatic response unit and registers it in a database. For example, the generation AI collects information such as the source, content, and frequency of nuisance calls and registers it in the database. The database operation unit can also analyze the collected information and identify new nuisance call patterns. The risk assessment unit displays a risk ranking on the incoming call screen based on the nuisance call information registered by the database operation unit. For example, the generation AI displays a risk ranking such as "high," "medium," or "low" based on the number of times and content of calls reported as nuisance in the past. The risk assessment unit can also use the generation AI to evaluate the risk level in real time when a call comes in and display it on the incoming call screen. As a result, the spam and fraud call prevention system according to the embodiment can perform automatic responses to nuisance calls, information collection, and risk assessment in an integrated manner. For example, users can save time by automatically responding to nuisance calls. In addition, by operating a database for eliminating nuisance calls, new nuisance call patterns can be quickly identified and countermeasures can be taken. Furthermore, by displaying a risk ranking on the incoming call screen, users can understand the risk of nuisance calls in advance and take appropriate measures.

[0061] The automatic response unit can analyze the tone of the other person's voice and speaking style, and use the emotion estimation function to select a fixed phrase that corresponds to the other person's emotions. For example, the automatic response unit's generation AI can analyze the other person's tone of voice and speaking style in real time, and use the emotion estimation function to select a phrase such as "Please speak calmly" if the other person is angry. In addition, if the other person's tone of voice is unstable, the generation AI can select a phrase such as "Don't worry, we'll handle it" to ease the other person's anxiety. In addition, the automatic response unit can estimate the other person's emotions from the way they speak, and if the other person seems impatient, it can select a phrase such as "Please speak calmly." This makes it possible to provide an appropriate response according to the other person's emotions.

[0062] The automated response unit can analyze the content of the other party's conversation in real time and automatically insert a warning message if there is a high possibility of fraud. For example, if the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Please tell me your bank account information," it can insert a warning message saying, "This may be a scam. Please be careful." If the other party says, "Please transfer money now," the automated response unit can insert a warning message saying, "This may be a scam. We will report you to the police." If the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Your personal information has been leaked," it can insert a warning message saying, "This may be a scam. Do not give out your personal information." This ensures user safety by automatically inserting warning messages when there is a high possibility of fraud.

[0063] The auto-response unit can develop an algorithm that learns past response history and dynamically selects the most effective fixed phrase. For example, the generation AI can learn past response history and, if a particular phrase was found to be effective, develop an algorithm that preferentially selects that phrase. The auto-response unit can also develop an algorithm that analyzes past response history and dynamically selects an effective phrase for a specific situation. For example, in response to a fraudulent call, it selects a phrase such as "I will call the police." The auto-response unit can also develop an algorithm that dynamically selects an effective phrase for a specific caller based on past response history. For example, in response to a nuisance call from a specific phone number, it selects a phrase such as "This number has been blocked." This allows the optimal response to be provided based on past response history.

[0064] The automatic response unit can add a function that records the other party's voice and allows the user to check it later. For example, the automatic response unit can add a function that allows the generation AI to record the other party's voice when automatically responding and allows the user to check the recording later. For example, the recorded data can be stored in the cloud and made accessible to the user. The automatic response unit can also record the other party's voice during an automatic response and allow the user to play back the recording later to check the content. For example, the recorded data can be played back using a smartphone app. The automatic response unit can also add a function that allows the generation AI to record the other party's voice during an automatic response and allow the user to download and save the recording later. For example, the recorded data can be sent by email. This allows the user to check the recording later and use it as evidence.

[0065] The automatic response unit can add a function to convert the other party's voice into text and display it to the user in real time. For example, the automatic response unit can add a function to convert the other party's voice into text in real time while the generation AI is automatically responding and display it to the user. For example, the text can be displayed on a smartphone screen. The automatic response unit can also convert the other party's voice into text during an automatic response and allow the user to check the text in real time. For example, the text data can be displayed in chat format. The automatic response unit can also add a function to convert the other party's voice into text during an automatic response and allow the user to save the text. For example, the text data can be saved in the cloud. This allows the user to check what the other party is saying in real time.

[0066] The automatic response unit can use the emotion estimation function to avoid specific fixed phrases if the other party is feeling angry or impatient. For example, the automatic response unit uses the generation AI to estimate the other party's emotions and avoid phrases such as "Please speak calmly" if the other party is feeling angry. The automatic response unit can also use the emotion estimation function to avoid phrases such as "Please speak calmly" if the other party is feeling impatient. The automatic response unit can also analyze the other party's emotions and avoid specific fixed phrases if the other party is feeling angry or impatient. For example, phrases such as "Don't worry" can be avoided. This allows for an appropriate response to be given according to the other party's emotions.

[0067] The generation AI comprises a voice analysis unit that analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls; a geographic information collection unit that collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls; and a report generation unit that analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. In the voice analysis unit, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on voice patterns. For example, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on specific voice patterns. For example, it detects specific phrases and tones. In the geographic information collection unit, the generation AI collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls. For example, the generation AI collects geographic information on the source of nuisance calls and builds a system that analyzes regional trends in nuisance calls. For example, it identifies patterns of nuisance calls that occur frequently in specific regions. In the report generation unit, the generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, we will build a system in which the AI ​​analyzes the content of nuisance calls and automatically generates reports to identify fraud methods and trends. For example, the AI ​​can classify fraud methods and compile them into reports. This will allow us to identify the characteristics of nuisance calls, analyze trends by region, and identify fraud methods and trends.

[0068] The voice analysis unit can analyze the voice data of nuisance calls and identify the characteristics of new nuisance calls based on the voice patterns. For example, the voice analysis unit uses a generation AI to analyze the voice data of nuisance calls and identify the characteristics of new nuisance calls based on specific voice patterns. For example, it can detect specific phrases or tones. The voice analysis unit can also analyze the voice data of nuisance calls and develop an algorithm to identify the characteristics of new nuisance calls based on the voice patterns. For example, it can extract common patterns of fraudulent calls. The voice analysis unit can also build a system in which a generation AI analyzes the voice data of nuisance calls and identify the characteristics of new nuisance calls based on the voice patterns. For example, it can extract features using voice recognition technology. This makes it possible to identify the characteristics of new nuisance calls.

[0069] The geographic information collection unit can collect geographic information on the sources of nuisance calls and analyze trends in nuisance calls by region. For example, the geographic information collection unit can build a system in which a generation AI collects geographic information on the sources of nuisance calls and analyzes trends in nuisance calls by region. For example, it can identify patterns of nuisance calls that occur frequently in specific regions. The geographic information collection unit can also collect geographic information on the sources of nuisance calls and develop an algorithm that analyzes trends in nuisance calls by region. For example, it can analyze the frequency of nuisance calls in specific regions. The geographic information collection unit can also operate a database in which a generation AI collects geographic information on the sources of nuisance calls and analyzes trends in nuisance calls by region. For example, it can display the sources of nuisance calls on a map. This makes it possible to analyze trends in nuisance calls by region.

[0070] The report generation unit can analyze the content of nuisance calls and automatically generate reports to identify fraudulent methods and trends. For example, the report generation unit builds a system in which a generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, it classifies fraudulent methods and summarizes them in a report. The report generation unit can also develop an algorithm that analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, it analyzes fraudulent methods in chronological order. The report generation unit can also operate a database in which a generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, it displays fraudulent methods in graphs and charts. This makes it possible to automatically generate reports to identify fraudulent methods and trends.

[0071] The generation AI includes a security linking unit that links the nuisance call database with other security systems, a cloud operation unit that operates the nuisance call database on the cloud and makes it accessible from multiple devices, and an emotional response collection unit that collects users' emotional responses to the content of nuisance calls and reflects them in the database. The security linking unit links the nuisance call database with other security systems to provide comprehensive security measures. For example, the generation AI links the nuisance call database with other security systems to build a system that provides comprehensive security measures. For example, it links with antivirus software. The cloud operation unit allows the generation AI to operate the nuisance call database on the cloud and make it accessible from multiple devices. For example, it builds a system where the generation AI operates the nuisance call database on the cloud and allows users to access it from multiple devices. For example, it makes it accessible from smartphones and tablets. The emotional response collection unit allows the generation AI to collect users' emotional responses to the content of nuisance calls and reflects them in the database. For example, it uses an emotion estimation function to collect users' emotional responses to the content of nuisance calls and builds a system that reflects that data in the database. For example, it records the user's emotional score. This allows the nuisance call database to be linked to other security systems, run on the cloud, and collect users' emotional responses which can be reflected in the database.

[0072] The security collaboration unit can link the nuisance call database with other security systems to provide comprehensive security measures. For example, the generation AI in the security collaboration unit can link the nuisance call database with other security systems to build a system that provides comprehensive security measures. For example, it can link with antivirus software. The security collaboration unit can also link the nuisance call database with other security systems to develop an algorithm that provides comprehensive security measures. For example, it can link with firewalls. The security collaboration unit can also operate a database that links the nuisance call database with other security systems to provide comprehensive security measures. For example, it can link with network security systems. This makes it possible to provide comprehensive security measures.

[0073] The cloud operations department can operate the nuisance call database on the cloud, making it accessible from multiple devices. For example, the cloud operations department can build a system in which the generation AI operates the nuisance call database on the cloud and allows users to access it from multiple devices. For example, it can make it accessible from smartphones and tablets. The cloud operations department can also develop an algorithm to operate the nuisance call database on the cloud and make it accessible from multiple devices. For example, it can synchronize data between devices. The cloud operations department can also operate a database in which the generation AI operates the nuisance call database on the cloud and makes it accessible from multiple devices. For example, it can use cloud storage. This can improve convenience by making it accessible from multiple devices.

[0074] The emotional response collection unit can collect users' emotional responses to the content of nuisance calls and reflect them in a database. The emotional response collection unit can, for example, use an emotion estimation function to collect users' emotional responses to the content of nuisance calls and build a system that reflects the data in a database. For example, it records users' emotional scores. The emotional response collection unit can also develop an algorithm that collects users' emotional responses to the content of nuisance calls and reflects them in a database. For example, it analyzes users' emotional changes. The emotional response collection unit can also use the emotion estimation function to collect users' emotional responses to the content of nuisance calls and operate a database that reflects them in a database. For example, it stores emotional scores in a database. In this way, by collecting users' emotional responses and reflecting them in a database, more effective measures against nuisance calls are possible.

[0075] The generation AI includes a history analysis unit that analyzes past call history and improves the accuracy of the risk ranking to display a risk ranking on the incoming call screen; an information collection unit that collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking; and an individual evaluation unit that learns the user's past response history and provides an individual risk ranking. The history analysis unit develops an algorithm for the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, the generation AI analyzes past call history and develops an algorithm for improving the accuracy of the risk ranking. For example, the generation AI learns past nuisance call patterns to more accurately evaluate the risk. The information collection unit collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a system is constructed in which the generation AI collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a nuisance call report database is referenced. The individual evaluation unit learns the user's past response history and provides an individual risk ranking. For example, we will build a system in which the generation AI learns a user's past response history and provides an individual risk ranking. For example, it will perform the optimal risk assessment for a specific user. This will enable us to provide a more accurate risk ranking based on past history and real-time information.

[0076] The history analysis unit can analyze past call history and develop an algorithm to improve the accuracy of the risk ranking. For example, the history analysis unit develops an algorithm that allows the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, it learns past nuisance call patterns and more accurately evaluates the risk level. The history analysis unit can also develop an algorithm that improves the accuracy of the risk ranking based on past call history. For example, it analyzes the frequency and content of specific phone numbers and evaluates the risk level. The history analysis unit can also operate a database that allows the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, it dynamically updates the risk level based on past data. This allows the accuracy of the risk ranking to be improved based on past call history.

[0077] The information collection unit can collect additional information from the Internet in real time when a call is received and dynamically update the risk ranking. For example, the information collection unit builds a system in which the generation AI collects additional information from the Internet in real time when a call is received and dynamically updates the risk ranking. For example, it references a nuisance call report database. The information collection unit can also develop an algorithm that collects additional information from the Internet when a call is received and dynamically updates the risk ranking. For example, it reflects the latest nuisance call information in real time. The information collection unit can also operate a database in which the generation AI collects additional information from the Internet when a call is received and dynamically updates the risk ranking. For example, it uses a database on the cloud. This allows additional information to be collected in real time and the risk ranking to be dynamically updated.

[0078] The individual evaluation unit can learn the user's past response history and provide an individual risk ranking. For example, the individual evaluation unit constructs a system in which a generation AI learns the user's past response history and provides an individual risk ranking. For example, it performs an optimal risk assessment for a specific user. The individual evaluation unit can also develop an algorithm that provides an individual risk ranking based on the user's past response history. For example, it analyzes the user's response patterns and evaluates the risk. The individual evaluation unit can also operate a database in which the generation AI learns the user's past response history and provides an individual risk ranking. For example, it dynamically updates the risk based on data for each user. This makes it possible to provide an individual risk ranking based on the user's past response history.

[0079] The generation AI includes a detailed information display unit that displays detailed information about past nuisance calls along with a risk ranking on the incoming call screen, a countermeasure suggestion unit that suggests specific countermeasures that the user should take, and an emotion reduction unit that displays a message to reduce the anxiety and vigilance the user feels when receiving a call. The detailed information display unit adds a function to the incoming call screen where the generation AI displays detailed information about past nuisance calls along with a risk ranking. For example, the generation AI adds a function to the incoming call screen where the generation AI displays detailed information about past nuisance calls along with a risk ranking. For example, the content and caller of past nuisance calls are displayed. The countermeasure suggestion unit adds a function to the incoming call screen where the generation AI suggests specific countermeasures that the user should take along with a risk ranking. For example, the generation AI displays a suggestion such as "Please ignore this call." The emotion reduction unit uses the emotion estimation function to display a message to reduce the anxiety and vigilance the user feels when receiving a call. For example, a system is constructed where the emotion estimation function is used to display a message to reduce the anxiety and vigilance the user feels when receiving a call. For example, a message such as "This call is safe" is displayed. This allows users to receive detailed information about past nuisance calls and specific countermeasures, reducing their anxiety and vigilance.

[0080] The detailed information display unit can display detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, the detailed information display unit adds a function that allows the generation AI to display detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, it displays the content and caller of past nuisance calls. The detailed information display unit can also develop an algorithm that displays detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, it references the history of past nuisance calls. The detailed information display unit can also operate a database that allows the generation AI to display detailed information about past nuisance calls along with a danger ranking on the incoming call screen. For example, it displays detailed information based on past data. In this way, by displaying detailed information about past nuisance calls, the user can obtain more specific information.

[0081] The countermeasure suggestion unit can suggest specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, the countermeasure suggestion unit adds a function in which the generation AI suggests specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, it displays a suggestion such as "Please ignore this call." The countermeasure suggestion unit can also develop an algorithm that suggests specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, it displays a suggestion such as "This call may be fraudulent. Please report it to the police." The countermeasure suggestion unit can also operate a database in which the generation AI suggests specific countermeasures that the user should take along with a danger ranking on the incoming call screen. For example, it can suggest optimal countermeasures based on past data. This allows the user to take appropriate action against nuisance calls by suggesting specific countermeasures.

[0082] The emotion alleviation unit can use the emotion estimation function to display a message to alleviate the anxiety and vigilance a user feels when receiving a call. For example, the emotion alleviation unit uses the emotion estimation function to build a system that displays a message to alleviate the anxiety and vigilance a user feels when receiving a call. For example, it displays a message such as "This call is safe." The emotion alleviation unit can also develop an algorithm that displays a message to alleviate the anxiety and vigilance a user feels when receiving a call. For example, it displays a message such as "This call may be a nuisance call, please ignore it." The emotion alleviation unit can also use the emotion estimation function to operate a database that displays a message to alleviate the anxiety and vigilance a user feels when receiving a call. For example, it displays an optimal message based on the user's emotion data. In this way, a sense of security can be provided by displaying a message to alleviate the user's anxiety and vigilance.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The automatic response unit can analyze the other person's tone of voice and speaking style, and use the emotion estimation function to select fixed phrases that correspond to the other person's emotions. For example, if the generation AI analyzes the other person's tone of voice and speaking style in real time, and uses the emotion estimation function to select a phrase such as "Please speak calmly" if the other person is angry. In addition, if the other person's tone of voice is unstable, the generation AI can select a phrase such as "Don't worry, we'll handle it" to ease the other person's anxiety. In addition, the generation AI can estimate the other person's emotions from the way they speak, and if the other person seems impatient, it can select a phrase such as "Please speak calmly." This makes it possible to provide an appropriate response according to the other person's emotions.

[0085] The automated response unit can analyze the content of the other party's conversation in real time and automatically insert a warning message if there is a high possibility of fraud. For example, if the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Please tell me your bank account information," it can insert a warning message saying, "This may be a scam. Please be careful." If the other party says, "Please transfer money now," the automated response unit can insert a warning message saying, "This may be a scam. We will report you to the police." If the generation AI analyzes the content of the other party's conversation and includes a phrase such as "Your personal information has been leaked," it can insert a warning message saying, "This may be a scam. Do not give out your personal information." This ensures user safety by automatically inserting warning messages when there is a high possibility of fraud.

[0086] The auto-response unit can develop an algorithm that learns from past response history and dynamically selects the most effective fixed phrase. For example, if the generation AI learns from past response history and finds that a particular phrase is effective, it can develop an algorithm that preferentially selects that phrase. The auto-response unit can also develop an algorithm that analyzes past response history and dynamically selects effective phrases for specific situations. For example, it can select a phrase such as "I will call the police" in response to a scam call. The auto-response unit can also develop an algorithm that dynamically selects effective phrases for specific callers based on past response history. For example, it can select a phrase such as "This number has been blocked" in response to a nuisance call from a specific phone number. This allows the system to provide the optimal response based on past response history.

[0087] The automatic response unit can add a function that records the other party's voice and allows the user to check it later. For example, a function can be added in which the generation AI records the other party's voice when automatically responding and allows the user to check the recording later. For example, the recording data can be saved in the cloud and made accessible to the user. The automatic response unit can also record the other party's voice during an automatic response and allow the user to play back the recording later to check the content. For example, the recording data can be played back using a smartphone app. The automatic response unit can also add a function in which the generation AI records the other party's voice during an automatic response and allows the user to download and save the recording later. For example, the recording data can be sent by email. This allows the user to check the recording later and use it as evidence.

[0088] The automatic response unit can add a function to convert the other party's voice into text and display it to the user in real time. For example, a function can be added in which the generation AI converts the other party's voice into text in real time during an automatic response and displays it to the user. For example, the text can be displayed on a smartphone screen. The automatic response unit can also convert the other party's voice into text during an automatic response and allow the user to check the text in real time. For example, the text data can be displayed in chat format. The automatic response unit can also add a function to convert the other party's voice into text during an automatic response and allow the user to save the text. For example, the text data can be saved in the cloud. This allows the user to check what the other party is saying in real time.

[0089] The automatic response unit can use the emotion estimation function to avoid certain fixed phrases if the other party is feeling angry or impatient. For example, the generation AI can estimate the other party's emotions and avoid phrases such as "Please speak calmly" if the other party is feeling angry. The automatic response unit can also use the emotion estimation function to avoid phrases such as "Please speak calmly" if the other party is feeling impatient. The automatic response unit can also analyze the other party's emotions and avoid certain fixed phrases if the other party is feeling angry or impatient. For example, phrases such as "Don't worry" can be avoided. This allows for an appropriate response to be given according to the other party's emotions.

[0090] The generation AI comprises a voice analysis unit that analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls; a geographic information collection unit that collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls; and a report generation unit that analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. In the voice analysis unit, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on voice patterns. For example, the generation AI analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls based on specific voice patterns. For example, it detects specific phrases and tones. In the geographic information collection unit, the generation AI collects geographic information on the source of nuisance calls and analyzes regional trends in nuisance calls. For example, the generation AI collects geographic information on the source of nuisance calls and builds a system that analyzes regional trends in nuisance calls. For example, it identifies patterns of nuisance calls that occur frequently in specific regions. In the report generation unit, the generation AI analyzes the content of nuisance calls and automatically generates reports to identify fraudulent methods and trends. For example, we will build a system in which the AI ​​analyzes the content of nuisance calls and automatically generates reports to identify fraud methods and trends. For example, the AI ​​can classify fraud methods and compile them into reports. This will allow us to identify the characteristics of nuisance calls, analyze trends by region, and identify fraud methods and trends.

[0091] The generation AI includes a history analysis unit that analyzes past call history and improves the accuracy of the risk ranking to display a risk ranking on the incoming call screen; an information collection unit that collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking; and an individual evaluation unit that learns the user's past response history and provides an individual risk ranking. The history analysis unit develops an algorithm for the generation AI to analyze past call history and improve the accuracy of the risk ranking. For example, the generation AI analyzes past call history and develops an algorithm for improving the accuracy of the risk ranking. For example, the generation AI learns past nuisance call patterns to more accurately evaluate the risk. The information collection unit collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a system is constructed in which the generation AI collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking. For example, a nuisance call report database is referenced. The individual evaluation unit learns the user's past response history and provides an individual risk ranking. For example, we will build a system in which the generation AI learns a user's past response history and provides an individual risk ranking. For example, it will perform the optimal risk assessment for a specific user. This will enable us to provide a more accurate risk ranking based on past history and real-time information.

[0092] The generation AI includes a detailed information display unit that displays detailed information about past nuisance calls along with a risk ranking on the incoming call screen, a countermeasure suggestion unit that suggests specific countermeasures that the user should take, and an emotion reduction unit that displays a message to reduce the anxiety and vigilance the user feels when receiving a call. The detailed information display unit adds a function to the incoming call screen where the generation AI displays detailed information about past nuisance calls along with a risk ranking. For example, the generation AI adds a function to the incoming call screen where the generation AI displays detailed information about past nuisance calls along with a risk ranking. For example, the content and caller of past nuisance calls are displayed. The countermeasure suggestion unit adds a function to the incoming call screen where the generation AI suggests specific countermeasures that the user should take along with a risk ranking. For example, the generation AI displays a suggestion such as "Please ignore this call." The emotion reduction unit uses the emotion estimation function to display a message to reduce the anxiety and vigilance the user feels when receiving a call. For example, a system is constructed where the emotion estimation function is used to display a message to reduce the anxiety and vigilance the user feels when receiving a call. For example, a message such as "This call is safe" is displayed. This allows users to receive detailed information about past nuisance calls and specific countermeasures, reducing their anxiety and vigilance.

[0093] The emotion reduction unit can use the emotion estimation function to display a message to reduce the anxiety and vigilance a user feels when receiving a call. For example, the emotion estimation function can be used to build a system that displays a message to reduce the anxiety and vigilance a user feels when receiving a call. For example, a message such as "This call is safe" can be displayed. The emotion reduction unit can also develop an algorithm that displays a message to reduce the anxiety and vigilance a user feels when receiving a call. For example, a message such as "This call may be a nuisance call, please ignore it" can be displayed. The emotion reduction unit can also use the emotion estimation function to operate a database that displays a message to reduce the anxiety and vigilance a user feels when receiving a call. For example, an optimal message can be displayed based on the user's emotion data. This can provide a sense of security by displaying a message to reduce the user's anxiety and vigilance.

[0094] The generation AI includes a security linking unit that links the nuisance call database with other security systems, a cloud operation unit that operates the nuisance call database on the cloud and makes it accessible from multiple devices, and an emotional response collection unit that collects users' emotional responses to the content of nuisance calls and reflects them in the database. The security linking unit links the nuisance call database with other security systems to provide comprehensive security measures. For example, the generation AI links the nuisance call database with other security systems to build a system that provides comprehensive security measures. For example, it links with antivirus software. The cloud operation unit allows the generation AI to operate the nuisance call database on the cloud and make it accessible from multiple devices. For example, it builds a system where the generation AI operates the nuisance call database on the cloud and allows users to access it from multiple devices. For example, it makes it accessible from smartphones and tablets. The emotional response collection unit allows the generation AI to collect users' emotional responses to the content of nuisance calls and reflects them in the database. For example, it uses an emotion estimation function to collect users' emotional responses to the content of nuisance calls and builds a system that reflects that data in the database. For example, it records the user's emotional score. This allows the nuisance call database to be linked to other security systems, run on the cloud, and collect users' emotional responses which can be reflected in the database.

[0095] The security collaboration unit can link the nuisance call database with other security systems to provide comprehensive security measures. For example, the generation AI can link the nuisance call database with other security systems to build a system that provides comprehensive security measures. For example, it can link with antivirus software. The security collaboration unit can also link the nuisance call database with other security systems to develop an algorithm that provides comprehensive security measures. For example, it can link with firewalls. The security collaboration unit can also link the nuisance call database with other security systems to operate a database that provides comprehensive security measures. For example, it can link with a network security system. This makes it possible to provide comprehensive security measures.

[0096] The cloud operations department can operate the nuisance call database on the cloud, making it accessible from multiple devices. For example, it can build a system in which the generation AI operates the nuisance call database on the cloud and allows users to access it from multiple devices. For example, it can make it accessible from smartphones and tablets. The cloud operations department can also develop algorithms that operate the nuisance call database on the cloud and make it accessible from multiple devices. For example, it can synchronize data between devices. The cloud operations department can also operate a database in which the generation AI operates the nuisance call database on the cloud and makes it accessible from multiple devices. For example, it can use cloud storage. This can improve convenience by making it accessible from multiple devices.

[0097] The emotional response collection unit can collect users' emotional responses to the content of nuisance calls and reflect them in a database. For example, a system can be constructed that uses an emotion estimation function to collect users' emotional responses to the content of nuisance calls and reflect the data in a database. For example, the user's emotional score can be recorded. The emotional response collection unit can also develop an algorithm that collects users' emotional responses to the content of nuisance calls and reflects the data in a database. For example, the user's emotional changes can be analyzed. The emotional response collection unit can also use the emotion estimation function to collect users' emotional responses to the content of nuisance calls and operate a database that reflects the data in a database. For example, the emotional score can be stored in a database. In this way, by collecting users' emotional responses and reflecting them in a database, more effective measures against nuisance calls can be taken.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The automatic response unit uses the generation AI to automatically respond to nuisance calls. For example, the generation AI uses a fixed phrase such as "This is the automatic response system. How can I help you?" The automatic response unit can also analyze what the caller is saying and select an appropriate fixed phrase to respond to the call. Step 2: The database operation department collects information about nuisance calls answered by the automated response department and registers it in a database. For example, the generation AI collects information such as the source, content, and frequency of nuisance calls and registers it in a database. The database operation department can also analyze the collected information and identify new nuisance call patterns. Step 3: The risk assessment unit displays a risk ranking on the incoming call screen based on the nuisance call information registered by the database operation unit. For example, the generation AI displays a risk ranking such as "high," "medium," or "low" based on the number of times and content of calls reported as nuisance calls in the past. The risk assessment unit can also have the generation AI evaluate the risk in real time when a call is received and display it on the incoming call screen.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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, in order to avoid confusion and to 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system characterized by comprising an automatic response unit that uses a generation AI to automatically respond to nuisance calls, a database operation unit that collects information about the nuisance calls responded to by the automatic response unit and registers it in a database, and a risk assessment unit that displays a risk ranking on the incoming call screen based on the information about the nuisance calls registered by the database operation unit.

2. 2. The system according to claim 1, wherein the automatic answering unit adds a function of recording the voice of the other party so that the user can check it later.

3. The generation AI is a system characterized by comprising: a voice analysis unit that analyzes the voice data of nuisance calls and identifies the characteristics of new nuisance calls; a geographic information collection unit that collects geographical information on the source of the nuisance calls and analyzes trends in nuisance calls by region; and a report generation unit that analyzes the content of the nuisance calls and automatically generates reports to identify fraud methods and trends.

4. The generation AI is a system characterized by comprising: a history analysis unit that analyzes past call history and improves the accuracy of the risk ranking in order to display a risk ranking on the incoming call screen; an information collection unit that collects additional information from the Internet in real time when a call comes in and dynamically updates the risk ranking; and an individual evaluation unit that learns the user's past response history and provides an individual risk ranking.

5. 2. The system according to claim 1, wherein the automatic response unit analyzes the tone of voice and speaking style of the other party and selects a fixed phrase according to the other party's emotion.

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