System

Abstract

An object of a system according to an embodiment is to effectively prevent telephone fraud for elderly people.SOLUTION: A system includes a context analysis unit, a black list filter unit, and an in-terminal AI processing unit. The context analysis unit analyzes a context. The blacklist filtering unit performs filtering using an existing telephone number blacklist. The in-terminal AI processing unit performs AI processing in the terminals.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 techniques have had the problem of making it difficult to effectively prevent telephone fraud targeting elderly people.

[0005] The system according to the embodiment aims to effectively prevent telephone fraud targeting the elderly. [Means for solving the problem]

[0006] The system according to the embodiment includes a context analysis unit, a blacklist filter unit, and an in-terminal AI processing unit. The context analysis unit analyzes context. The blacklist filter unit performs filtering using an existing phone number blacklist. The in-terminal AI processing unit performs AI processing within the terminal. [Effects of the Invention]

[0007] The system according to the embodiment can effectively prevent telephone fraud targeting the elderly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Telephone Samurai System, an embodiment of the present invention, is a basic telephone application service for protecting the elderly. It is a system that prevents telephone fraud before it occurs by using LLM technology, a generative AI with the characteristic of understanding context. As a result, the Telephone Samurai System can reduce fraud victims among the elderly and realize a safer society.

[0029] The Telephone Samurai system according to the present embodiment includes a context analysis unit, a blacklist filter unit, and an in-device AI processing unit. The context analysis unit analyzes the context of a call. For example, the generation AI receives voice and text data from a call, analyzes the content, and infers the possibility of fraud. Furthermore, when multiple consecutive calls are received, the context analysis unit can analyze the context and determine whether the calls are likely to be fraudulent. The blacklist filter unit performs filtering using an existing phone number blacklist. For example, if a phone number previously used in a fraudulent transaction is registered on the blacklist, calls from that number are automatically rejected. The blacklist filter unit also periodically updates the blacklist and can add new fraudulent phone numbers. The in-device AI processing unit performs AI processing within the device. For example, the generation AI operates within the device and analyzes the voice and text data from the call. This prevents data from being transmitted externally, protecting privacy. The generation AI can analyze the content of the call and infer the possibility of fraud using text generation AI (e.g., LLM) or multimodal generation AI. As a result, the Telephone Samurai system according to the embodiment enables effective telephone fraud prevention to protect the elderly. For example, if the content of a call is judged to be potentially fraudulent, the generation AI automatically issues a warning to warn the elderly. It also has a function to contact family members or the police if necessary.

[0030] The context analysis unit analyzes the tone and speed of a speaker's voice from audio data and can infer the possibility of fraud. For example, the context analysis unit uses a generation AI to analyze telephone audio data in real time and detect the speaker's tone and speed. For example, if the tone of voice suddenly changes or the speaking speed increases, it determines that there is a high possibility of fraud. The context analysis unit also analyzes changes in the speaker's tone and speed and develops an algorithm to infer the possibility of fraud. For example, if the speaker appears nervous or in a hurry, it determines that there is a high possibility of fraud. The context analysis unit also uses a generation AI to analyze the tone and speed of a speaker's voice and infer the possibility of fraud based on that data. For example, if the speaker appears emotional or their voice is trembling, it determines that there is a high possibility of fraud. This allows for highly accurate inference of the possibility of fraud.

[0031] The context analysis unit can analyze context based on call history and message history. For example, the generation AI analyzes past call history and message history to understand the context of the current call. For example, if there has been a call with the same content in the past, the possibility of that call being fraudulent increases. The context analysis unit also refers to the call history and message history and develops an algorithm for the generation AI to analyze the context. For example, it compares the content of past calls with the content of the current call to infer the possibility of fraud. The context analysis unit also allows the generation AI to analyze context based on past call history and message history to infer the possibility of fraud. For example, if there has been a call suspected of being fraudulent in the past, the call history can be referenced to determine the possibility of fraud in the current call. This enables more detailed context analysis.

[0032] The context analysis unit can translate the contents of phone calls in real time, enabling it to respond to scam calls in different languages. The context analysis unit, for example, allows the generation AI to translate the contents of phone calls in real time, enabling it to respond to scam calls in different languages. For example, it translates a phone call in Japanese into English and infers the possibility of a scam. The context analysis unit also translates the contents of phone calls in different languages ​​in real time, and the generation AI develops an algorithm to analyze the context. For example, it translates a phone call in Chinese into Japanese and infers the possibility of a scam. The context analysis unit also allows the generation AI to translate the contents of phone calls in real time, and infers the possibility of a scam based on that data. For example, it translates a phone call in Spanish into English and infers the possibility of a scam. This makes it possible to respond to scam calls in different languages.

[0033] The context analysis unit can infer the possibility of fraud based on the user's calendar and schedule. For example, the context analysis unit has the generation AI refer to the user's calendar and schedule and analyze the context of the phone call. For example, if an unexpected call comes in, it determines that there is a high possibility of fraud. The context analysis unit also develops an algorithm that allows the generation AI to analyze the context based on calendar and schedule data. For example, if an unexpected call comes in, it infers that the call is likely to be fraud. The context analysis unit also has the generation AI refer to the user's calendar and schedule and infers the possibility of fraud based on that data. For example, if an unexpected call comes in, it increases the possibility that the call is fraud. This allows the generation AI to infer the possibility of fraud by referencing the user's calendar and schedule.

[0034] The blacklist filter unit can analyze the patterns of phone numbers registered on the blacklist and develop an algorithm that predicts new fraudulent phone numbers. For example, the blacklist filter unit analyzes the patterns of phone numbers registered on the blacklist and develops an algorithm that predicts new fraudulent phone numbers. For example, it analyzes phone numbers that are concentrated in specific areas or time periods. The blacklist filter unit also develops an algorithm that uses generation AI to predict new fraudulent phone numbers based on blacklist data. For example, it learns the patterns of past fraudulent phone numbers and predicts new fraudulent phone numbers. The blacklist filter unit also analyzes the patterns of phone numbers registered on the blacklist and builds a system that predicts new fraudulent phone numbers based on that data. For example, it analyzes the patterns of specific phone numbers and predicts new fraudulent phone numbers. This makes it possible to predict new fraudulent phone numbers.

[0035] The blacklist filter unit analyzes the region and time zone information of phone numbers registered on the blacklist, and can identify time zones and areas where fraud is more likely. For example, the blacklist filter unit analyzes the region and time zone information of phone numbers registered on the blacklist, and can identify time zones and areas where fraud is more likely. For example, it analyzes phone numbers that are concentrated in specific regions and time zones. The blacklist filter unit also develops an algorithm using generation AI to identify time zones and areas where fraud is more likely based on region and time zone data. For example, it identifies time zones and areas where fraud is more likely based on past data. The blacklist filter unit also analyzes the region and time zone information of phone numbers registered on the blacklist, and can build a system based on that data to identify time zones and areas where fraud is more likely. For example, it analyzes phone numbers that are concentrated in specific regions and time zones. This makes it possible to identify time zones and areas where fraud is more likely.

[0036] The blacklist filter unit can share phone numbers registered on the blacklist with other users, thereby strengthening fraud prevention throughout the community. For example, the blacklist filter unit can share phone numbers registered on the blacklist with other users, thereby building a system that strengthens fraud prevention throughout the community. For example, it can share a database of fraudulent phone numbers. The blacklist filter unit can also develop algorithms that strengthen fraud prevention by sharing the blacklist with other users. For example, it can share information about fraudulent phone numbers in real time. The blacklist filter unit can also share phone numbers registered on the blacklist with other users, thereby building a system that strengthens fraud prevention based on that data. For example, sharing information about fraudulent phone numbers reduces the possibility of fraud. This can strengthen fraud prevention throughout the community.

[0037] The blacklist filter unit synchronizes phone numbers registered on the blacklist with other devices, thereby providing multi-layered fraud prevention. The blacklist filter unit, for example, synchronizes phone numbers registered on the blacklist with other devices, thereby building a system that provides multi-layered fraud prevention. For example, it synchronizes information on fraudulent phone numbers with smartwatches and tablets. The blacklist filter unit also develops an algorithm that provides multi-layered fraud prevention by synchronizing the blacklist with other devices. For example, it synchronizes information on fraudulent phone numbers with smartwatches and tablets in real time. The blacklist filter unit also synchronizes phone numbers registered on the blacklist with other devices, thereby building a system that provides multi-layered fraud prevention based on that data. For example, it reduces the possibility of fraud by synchronizing information on fraudulent phone numbers with smartwatches and tablets. This provides multi-layered fraud prevention.

[0038] The in-device AI processing unit can acquire clear audio data by using noise canceling technology when analyzing audio data. For example, when the in-device AI analyzes telephone audio data, the in-device AI processing unit uses noise canceling technology to remove background sounds and acquire clear audio data. For example, it filters out ambient noise. The in-device AI processing unit also uses noise canceling technology to develop an algorithm for the in-device AI to analyze telephone audio data. For example, it removes environmental sounds such as wind noise and traffic noise. The in-device AI processing unit also builds a system for the in-device AI to use noise canceling technology to make telephone audio data clear. For example, it removes background sounds during calls in real time, improving the quality of the audio data. This allows for acquisition of clearer audio data.

[0039] The in-device AI processing unit learns the user's past call patterns when analyzing voice data, and is able to infer the possibility of fraud with a high degree of accuracy. For example, the in-device AI processing unit learns the user's past call patterns and infers the possibility of fraud in the current call with a high degree of accuracy. For example, it compares the content of past calls with the content of the current call. The in-device AI processing unit also develops an algorithm that enables the in-device AI to infer the possibility of fraud based on past call patterns. For example, it learns call patterns that have been suspected of being fraudulent in the past, and infers the possibility of fraud in the current call. The in-device AI processing unit also builds a system in which the in-device AI learns the user's past call patterns, and uses that data to infer the possibility of fraud with a high degree of accuracy. For example, it analyzes past call patterns and determines the possibility of fraud in the current call. This allows the possibility of fraud to be inferred with a high degree of accuracy.

[0040] The in-device AI processing unit can refer to the user's health condition when analyzing voice data and infer the possibility of fraud. For example, the in-device AI processing unit refers to the user's health condition and analyzes the voice data from the phone. For example, if the heart rate or stress level is high, it determines that there is a high possibility of fraud. The in-device AI processing unit also develops an algorithm that allows the in-device AI to infer the possibility of fraud based on the health condition data. For example, it determines that there is a high possibility of fraud if the heart rate or stress level is high. The in-device AI processing unit also builds a system in which the in-device AI refers to the user's health condition and infers the possibility of fraud based on that data. For example, it determines that there is a high possibility of fraud if the heart rate or stress level is high. In this way, it is possible to infer the possibility of fraud by referring to the user's health condition.

[0041] The in-device AI processing unit can refer to the user's location information when analyzing voice data and infer the possibility of fraud. For example, the in-device AI processing unit refers to the user's location information and analyzes the voice data from the phone. For example, it determines that there is a high possibility of fraud if the user is in an unusual location. The in-device AI processing unit also develops an algorithm that allows the in-device AI to infer the possibility of fraud based on the location information data. For example, it determines that there is a high possibility of fraud if the user is in an unusual location. The in-device AI processing unit also builds a system that allows the in-device AI to refer to the user's location information and infer the possibility of fraud based on that data. For example, it determines that there is a high possibility of fraud if the user is in an unusual location. This makes it possible to infer the possibility of fraud by referring to the user's location information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Telephone Samurai system can further include a health monitoring unit that monitors the user's health status. For example, it can measure the user's heart rate and stress level in real time and issue an alert if an abnormality is detected. The health monitoring unit can also develop an algorithm that infers the possibility of fraud based on the user's health data. For example, a sudden increase in heart rate or high stress level can be determined to indicate a high possibility of fraud. The health monitoring unit can also build a system that monitors the user's health status and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's health status.

[0044] The Telephone Samurai system can further include a location information acquisition unit that acquires the user's location information. For example, if the user is in an unusual location, it can determine that there is a high possibility of fraud. The location information acquisition unit can also develop an algorithm that infers the possibility of fraud based on the user's location information. For example, it can determine that there is a high possibility of fraud when the user is traveling or on a business trip. The location information acquisition unit can also build a system that monitors the user's location information in real time and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's location information.

[0045] The Telephone Samurai system can further include a schedule reference unit that references the user's calendar and schedule. For example, if an unscheduled call is received, it can determine that there is a high possibility of fraud. The schedule reference unit can also develop an algorithm that infers the possibility of fraud based on the data from the user's calendar and schedule. For example, if an unscheduled call is received, it can determine that there is a high possibility of fraud. The schedule reference unit can also build a system that monitors the user's calendar and schedule in real time and infers the possibility of fraud based on that data. This allows for highly accurate inference of the possibility of fraud by referencing the user's calendar and schedule.

[0046] The Telephone Samurai system can further include a history reference unit that references a user's call history and message history. For example, if a call with the same content has been made in the past, it can determine that there is a high possibility of fraud. The history reference unit can also develop an algorithm that infers the possibility of fraud based on the user's call history and message history data. For example, it can compare the content of past calls with the content of current calls to infer the possibility of fraud. The history reference unit can also monitor a user's call history and message history in real time and build a system that infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referencing the user's call history and message history.

[0047] The Telephone Samurai system can also use noise-canceling technology when analyzing the user's voice data. For example, it can filter out ambient noise to obtain clear voice data. Furthermore, noise-canceling technology can be used to develop algorithms for analyzing telephone voice data. For example, it can remove environmental sounds such as wind noise and traffic noise. Furthermore, noise-canceling technology can be used to build a system that makes telephone voice data clearer. This allows for even clearer voice data to be obtained.

[0048] The Telephone Samurai system can further include a learning unit that learns a user's past call patterns. For example, it compares the content of past calls with the content of current calls to accurately infer the possibility of fraud. The learning unit can also develop an algorithm that infers the possibility of fraud based on past call patterns. For example, it can learn call patterns that have been suspected of being fraudulent in the past and infer the possibility of fraud in a current call. The learning unit can also build a system that learns a user's past call patterns and, based on that data, infers the possibility of fraud with high accuracy. This allows for highly accurate inference of the possibility of fraud.

[0049] The Telephone Samurai system can further include a schedule reference unit that infers the possibility of fraud based on the user's calendar and schedule. For example, if an unscheduled call is received, it may determine that there is a high possibility of fraud. The schedule reference unit can also develop an algorithm that infers the possibility of fraud based on the user's calendar and schedule data. For example, if an unscheduled call is received, it may determine that there is a high possibility of fraud. The schedule reference unit can also build a system that monitors the user's calendar and schedule in real time and infers the possibility of fraud based on that data. This allows for highly accurate inference of the possibility of fraud by referencing the user's calendar and schedule.

[0050] The Telephone Samurai system can further include a location information acquisition unit that acquires the user's location information. For example, if the user is in an unusual location, it can determine that there is a high possibility of fraud. The location information acquisition unit can also develop an algorithm that infers the possibility of fraud based on the user's location information. For example, it can determine that there is a high possibility of fraud when the user is traveling or on a business trip. The location information acquisition unit can also build a system that monitors the user's location information in real time and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's location information.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The context analysis unit analyzes the context of the call. For example, the generative AI receives the voice and text data of the call as input, analyzes its content, and infers the possibility of fraud. In addition, when multiple calls are made in succession, the context analysis unit can analyze their context and determine that there is a high possibility of fraud. Step 2: The blacklist filter unit performs filtering using an existing phone number blacklist. For example, if a phone number that has been used for fraud in the past is registered on the blacklist, calls from that number will be automatically rejected. The blacklist filter unit also periodically updates the blacklist and can add new fraudulent phone numbers. Step 3: The in-device AI processing unit performs AI processing within the device. For example, a generation AI runs within the device and analyzes the voice and text data of the call. This protects privacy as the data is not transmitted externally. The generation AI can use text generation AI (e.g., LLM) or multimodal generation AI to analyze the contents of the call and infer the possibility of fraud.

[0053] (Example 2) The Telephone Samurai System, an embodiment of the present invention, is a basic telephone application service for protecting the elderly. It is a system that prevents telephone fraud before it occurs by using LLM technology, a generative AI with the characteristic of understanding context. As a result, the Telephone Samurai System can reduce fraud victims among the elderly and realize a safer society.

[0054] The Telephone Samurai system according to the present embodiment includes a context analysis unit, a blacklist filter unit, and an in-device AI processing unit. The context analysis unit analyzes the context of a call. For example, the generation AI receives voice and text data from a call, analyzes the content, and infers the possibility of fraud. Furthermore, when multiple consecutive calls are received, the context analysis unit can analyze the context and determine whether the calls are likely to be fraudulent. The blacklist filter unit performs filtering using an existing phone number blacklist. For example, if a phone number previously used in a fraudulent transaction is registered on the blacklist, calls from that number are automatically rejected. The blacklist filter unit also periodically updates the blacklist and can add new fraudulent phone numbers. The in-device AI processing unit performs AI processing within the device. For example, the generation AI operates within the device and analyzes the voice and text data from the call. This prevents data from being transmitted externally, protecting privacy. The generation AI can analyze the content of the call and infer the possibility of fraud using text generation AI (e.g., LLM) or multimodal generation AI. As a result, the Telephone Samurai system according to the embodiment enables effective telephone fraud prevention to protect the elderly. For example, if the content of a call is judged to be potentially fraudulent, the generation AI automatically issues a warning to warn the elderly. It also has a function to contact family members or the police if necessary.

[0055] The context analysis unit analyzes the tone and speed of a speaker's voice from audio data and can infer the possibility of fraud. For example, the context analysis unit uses a generation AI to analyze telephone audio data in real time and detect the speaker's tone and speed. For example, if the tone of voice suddenly changes or the speaking speed increases, it determines that there is a high possibility of fraud. The context analysis unit also analyzes changes in the speaker's tone and speed and develops an algorithm to infer the possibility of fraud. For example, if the speaker appears nervous or in a hurry, it determines that there is a high possibility of fraud. The context analysis unit also uses a generation AI to analyze the tone and speed of a speaker's voice and infer the possibility of fraud based on that data. For example, if the speaker appears emotional or their voice is trembling, it determines that there is a high possibility of fraud. This allows for highly accurate inference of the possibility of fraud.

[0056] The context analysis unit can analyze context based on call history and message history. For example, the generation AI analyzes past call history and message history to understand the context of the current call. For example, if there has been a call with the same content in the past, the possibility of that call being fraudulent increases. The context analysis unit also refers to the call history and message history and develops an algorithm for the generation AI to analyze the context. For example, it compares the content of past calls with the content of the current call to infer the possibility of fraud. The context analysis unit also allows the generation AI to analyze context based on past call history and message history to infer the possibility of fraud. For example, if there has been a call suspected of being fraudulent in the past, the call history can be referenced to determine the possibility of fraud in the current call. This enables more detailed context analysis.

[0057] The context analysis unit uses the emotion estimation function to estimate the emotions of the caller on the other end and can automatically issue a warning if there is a high possibility of fraud. For example, the context analysis unit uses a generation AI to analyze the emotions of the caller in real time and automatically issue a warning if there is a high possibility of fraud. For example, it issues a warning if the caller is angry or nervous. The context analysis unit also uses the emotion estimation function to analyze the emotions of the caller and develops an algorithm to infer the possibility of fraud. For example, it determines that there is a high possibility of fraud if the caller is emotional. The context analysis unit also uses a generation AI to analyze the emotions of the caller and infer the possibility of fraud based on that data. For example, it determines that there is a high possibility of fraud if the caller sounds anxious or has a trembling voice. This makes it possible to automatically issue a warning if there is a high possibility of fraud.

[0058] The context analysis unit can translate the contents of phone calls in real time, enabling it to respond to scam calls in different languages. The context analysis unit, for example, allows the generation AI to translate the contents of phone calls in real time, enabling it to respond to scam calls in different languages. For example, it translates a phone call in Japanese into English and infers the possibility of a scam. The context analysis unit also translates the contents of phone calls in different languages ​​in real time, and the generation AI develops an algorithm to analyze the context. For example, it translates a phone call in Chinese into Japanese and infers the possibility of a scam. The context analysis unit also allows the generation AI to translate the contents of phone calls in real time, and infers the possibility of a scam based on that data. For example, it translates a phone call in Spanish into English and infers the possibility of a scam. This makes it possible to respond to scam calls in different languages.

[0059] The context analysis unit can infer the possibility of fraud based on the user's calendar and schedule. For example, the context analysis unit has the generation AI refer to the user's calendar and schedule and analyze the context of the phone call. For example, if an unexpected call comes in, it determines that there is a high possibility of fraud. The context analysis unit also develops an algorithm that allows the generation AI to analyze the context based on calendar and schedule data. For example, if an unexpected call comes in, it infers that the call is likely to be fraud. The context analysis unit also has the generation AI refer to the user's calendar and schedule and infers the possibility of fraud based on that data. For example, if an unexpected call comes in, it increases the possibility that the call is fraud. This allows the generation AI to infer the possibility of fraud by referencing the user's calendar and schedule.

[0060] The context analysis unit uses the emotion estimation function to monitor the emotions of the caller in real time and can automatically notify family members or the police if there is a high possibility of fraud. For example, the context analysis unit uses a generation AI to monitor the emotions of the caller in real time and automatically notify family members or the police if there is a high possibility of fraud. For example, a notification is sent if the caller is angry or nervous. The context analysis unit also uses the emotion estimation function to analyze the emotions of the caller and develop an algorithm to infer the possibility of fraud. For example, if the caller appears emotional, it will determine that there is a high possibility of fraud and send a notification. The context analysis unit also uses a generation AI to analyze the emotions of the caller and infer the possibility of fraud based on that data and notify family members or the police. For example, a notification is sent if the caller sounds anxious or has a trembling voice. This allows for automatic notification of family members or the police if there is a high possibility of fraud.

[0061] The blacklist filter unit can analyze the patterns of phone numbers registered on the blacklist and develop an algorithm that predicts new fraudulent phone numbers. For example, the blacklist filter unit analyzes the patterns of phone numbers registered on the blacklist and develops an algorithm that predicts new fraudulent phone numbers. For example, it analyzes phone numbers that are concentrated in specific areas or time periods. The blacklist filter unit also develops an algorithm that uses generation AI to predict new fraudulent phone numbers based on blacklist data. For example, it learns the patterns of past fraudulent phone numbers and predicts new fraudulent phone numbers. The blacklist filter unit also analyzes the patterns of phone numbers registered on the blacklist and builds a system that predicts new fraudulent phone numbers based on that data. For example, it analyzes the patterns of specific phone numbers and predicts new fraudulent phone numbers. This makes it possible to predict new fraudulent phone numbers.

[0062] The blacklist filter unit analyzes the region and time zone information of phone numbers registered on the blacklist, and can identify time zones and areas where fraud is more likely. For example, the blacklist filter unit analyzes the region and time zone information of phone numbers registered on the blacklist, and can identify time zones and areas where fraud is more likely. For example, it analyzes phone numbers that are concentrated in specific regions and time zones. The blacklist filter unit also develops an algorithm using generation AI to identify time zones and areas where fraud is more likely based on region and time zone data. For example, it identifies time zones and areas where fraud is more likely based on past data. The blacklist filter unit also analyzes the region and time zone information of phone numbers registered on the blacklist, and can build a system based on that data to identify time zones and areas where fraud is more likely. For example, it analyzes phone numbers that are concentrated in specific regions and time zones. This makes it possible to identify time zones and areas where fraud is more likely.

[0063] The blacklist filter unit uses an emotion estimation function to analyze the emotion of the caller when a call comes from a blacklisted phone number, further increasing the likelihood of fraud. The blacklist filter unit, for example, uses the emotion estimation function to analyze the emotion of the caller when a call comes from a blacklisted phone number, further increasing the likelihood of fraud. For example, if the caller is angry or nervous, it determines that the call is likely to be fraud. The blacklist filter unit also develops an algorithm in which the generative AI analyzes the emotion of the caller when a call comes from a blacklisted phone number and infers the likelihood of fraud. For example, if the caller appears emotional, it determines that the call is likely to be fraud. The blacklist filter unit also uses the emotion estimation function to analyze the emotion of the caller when a call comes from a blacklisted phone number, and infers the likelihood of fraud based on that data. For example, if the caller sounds anxious or has a trembling voice, it determines that the call is likely to be fraud. This further increases the likelihood of fraud.

[0064] The blacklist filter unit can share phone numbers registered on the blacklist with other users, thereby strengthening fraud prevention throughout the community. For example, the blacklist filter unit can share phone numbers registered on the blacklist with other users, thereby building a system that strengthens fraud prevention throughout the community. For example, it can share a database of fraudulent phone numbers. The blacklist filter unit can also develop algorithms that strengthen fraud prevention by sharing the blacklist with other users. For example, it can share information about fraudulent phone numbers in real time. The blacklist filter unit can also share phone numbers registered on the blacklist with other users, thereby building a system that strengthens fraud prevention based on that data. For example, sharing information about fraudulent phone numbers reduces the possibility of fraud. This can strengthen fraud prevention throughout the community.

[0065] The blacklist filter unit synchronizes phone numbers registered on the blacklist with other devices, thereby providing multi-layered fraud prevention. The blacklist filter unit, for example, synchronizes phone numbers registered on the blacklist with other devices, thereby building a system that provides multi-layered fraud prevention. For example, it synchronizes information on fraudulent phone numbers with smartwatches and tablets. The blacklist filter unit also develops an algorithm that provides multi-layered fraud prevention by synchronizing the blacklist with other devices. For example, it synchronizes information on fraudulent phone numbers with smartwatches and tablets in real time. The blacklist filter unit also synchronizes phone numbers registered on the blacklist with other devices, thereby building a system that provides multi-layered fraud prevention based on that data. For example, it reduces the possibility of fraud by synchronizing information on fraudulent phone numbers with smartwatches and tablets. This provides multi-layered fraud prevention.

[0066] The blacklist filter unit uses an emotion estimation function to monitor the user's emotional reactions when a call comes from a blacklisted phone number, and can evaluate the possibility of fraud in real time. For example, the blacklist filter unit uses the emotion estimation function to monitor the user's emotional reactions when a call comes from a blacklisted phone number, and can evaluate the possibility of fraud in real time. For example, if the user feels anxious or scared, it determines that the call is likely to be fraud. In addition, the blacklist filter unit develops an algorithm that uses a generative AI to analyze the user's emotional reactions when a call comes from a blacklisted phone number, and infers the possibility of fraud. For example, it determines that the call is likely to be fraud if the user feels nervous. In addition, the blacklist filter unit uses the emotion estimation function to monitor the user's emotional reactions when a call comes from a blacklisted phone number, and can evaluate the possibility of fraud in real time based on that data. For example, it determines that the call is likely to be fraud if the user feels anger or anxiety. This allows for real-time evaluation of the possibility of fraud.

[0067] The in-device AI processing unit can acquire clear audio data by using noise canceling technology when analyzing audio data. For example, when the in-device AI analyzes telephone audio data, the in-device AI processing unit uses noise canceling technology to remove background sounds and acquire clear audio data. For example, it filters out ambient noise. The in-device AI processing unit also uses noise canceling technology to develop an algorithm for the in-device AI to analyze telephone audio data. For example, it removes environmental sounds such as wind noise and traffic noise. The in-device AI processing unit also builds a system for the in-device AI to use noise canceling technology to make telephone audio data clear. For example, it removes background sounds during calls in real time, improving the quality of the audio data. This allows for acquisition of clearer audio data.

[0068] The in-device AI processing unit learns the user's past call patterns when analyzing voice data, and is able to infer the possibility of fraud with a high degree of accuracy. For example, the in-device AI processing unit learns the user's past call patterns and infers the possibility of fraud in the current call with a high degree of accuracy. For example, it compares the content of past calls with the content of the current call. The in-device AI processing unit also develops an algorithm that enables the in-device AI to infer the possibility of fraud based on past call patterns. For example, it learns call patterns that have been suspected of being fraudulent in the past, and infers the possibility of fraud in the current call. The in-device AI processing unit also builds a system in which the in-device AI learns the user's past call patterns, and uses that data to infer the possibility of fraud with a high degree of accuracy. For example, it analyzes past call patterns and determines the possibility of fraud in the current call. This allows the possibility of fraud to be inferred with a high degree of accuracy.

[0069] The in-device AI processing unit can use the emotion estimation function to analyze the emotions of the caller and automatically issue a warning if there is a high possibility of fraud. For example, the in-device AI processing unit can analyze the emotions of the caller in real time and automatically issue a warning if there is a high possibility of fraud. For example, it can issue a warning if the caller is angry or nervous. The in-device AI processing unit can also use the emotion estimation function to develop an algorithm that analyzes the emotions of the caller and infers the possibility of fraud. For example, it can determine that there is a high possibility of fraud if the caller appears emotional. The in-device AI processing unit can also build a system in which the in-device AI analyzes the emotions of the caller and, based on that data, infers the possibility of fraud and issues a warning. For example, it can issue a warning if the caller sounds anxious or has a trembling voice. This makes it possible to automatically issue a warning if there is a high possibility of fraud.

[0070] The in-device AI processing unit can refer to the user's health condition when analyzing voice data and infer the possibility of fraud. For example, the in-device AI processing unit refers to the user's health condition and analyzes the voice data from the phone. For example, if the heart rate or stress level is high, it determines that there is a high possibility of fraud. The in-device AI processing unit also develops an algorithm that allows the in-device AI to infer the possibility of fraud based on the health condition data. For example, it determines that there is a high possibility of fraud if the heart rate or stress level is high. The in-device AI processing unit also builds a system in which the in-device AI refers to the user's health condition and infers the possibility of fraud based on that data. For example, it determines that there is a high possibility of fraud if the heart rate or stress level is high. In this way, it is possible to infer the possibility of fraud by referring to the user's health condition.

[0071] The in-device AI processing unit can refer to the user's location information when analyzing voice data and infer the possibility of fraud. For example, the in-device AI processing unit refers to the user's location information and analyzes the voice data from the phone. For example, it determines that there is a high possibility of fraud if the user is in an unusual location. The in-device AI processing unit also develops an algorithm that allows the in-device AI to infer the possibility of fraud based on the location information data. For example, it determines that there is a high possibility of fraud if the user is in an unusual location. The in-device AI processing unit also builds a system that allows the in-device AI to refer to the user's location information and infer the possibility of fraud based on that data. For example, it determines that there is a high possibility of fraud if the user is in an unusual location. This makes it possible to infer the possibility of fraud by referring to the user's location information.

[0072] The in-device AI processing unit can use the emotion estimation function to monitor the emotions of the caller in real time and automatically notify family members or the police if there is a high possibility of fraud. For example, the in-device AI processing unit can monitor the emotions of the caller in real time and automatically notify family members or the police if there is a high possibility of fraud. For example, it can notify if the caller is angry or nervous. The in-device AI processing unit can also use the emotion estimation function to develop an algorithm that analyzes the caller's emotions and infers the possibility of fraud. For example, if the caller appears emotional, it can determine that there is a high possibility of fraud and send a notification. The in-device AI processing unit can also analyze the caller's emotions and use that data to infer the possibility of fraud and build a system that notifies family members or the police. For example, it can notify if the caller sounds anxious or has a trembling voice. This allows for automatic notification of family members or the police if there is a high possibility of fraud.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The Telephone Samurai system can further include a health monitoring unit that monitors the user's health status. For example, it can measure the user's heart rate and stress level in real time and issue an alert if an abnormality is detected. The health monitoring unit can also develop an algorithm that infers the possibility of fraud based on the user's health data. For example, a sudden increase in heart rate or high stress level can be determined to indicate a high possibility of fraud. The health monitoring unit can also build a system that monitors the user's health status and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's health status.

[0075] The Telephone Samurai system can further include a location information acquisition unit that acquires the user's location information. For example, if the user is in an unusual location, it can determine that there is a high possibility of fraud. The location information acquisition unit can also develop an algorithm that infers the possibility of fraud based on the user's location information. For example, it can determine that there is a high possibility of fraud when the user is traveling or on a business trip. The location information acquisition unit can also build a system that monitors the user's location information in real time and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's location information.

[0076] The Telephone Samurai system can further include a schedule reference unit that references the user's calendar and schedule. For example, if an unscheduled call is received, it can determine that there is a high possibility of fraud. The schedule reference unit can also develop an algorithm that infers the possibility of fraud based on the data from the user's calendar and schedule. For example, if an unscheduled call is received, it can determine that there is a high possibility of fraud. The schedule reference unit can also build a system that monitors the user's calendar and schedule in real time and infers the possibility of fraud based on that data. This allows for highly accurate inference of the possibility of fraud by referencing the user's calendar and schedule.

[0077] The Telephone Samurai system can further include a history reference unit that references a user's call history and message history. For example, if a call with the same content has been made in the past, it can determine that there is a high possibility of fraud. The history reference unit can also develop an algorithm that infers the possibility of fraud based on the user's call history and message history data. For example, it can compare the content of past calls with the content of current calls to infer the possibility of fraud. The history reference unit can also monitor a user's call history and message history in real time and build a system that infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referencing the user's call history and message history.

[0078] The Telephone Samurai system can further include an emotion estimation unit that estimates the user's emotions. For example, if the user feels anxious or scared, it can determine that there is a high possibility of fraud. The emotion estimation unit can also develop an algorithm that infers the possibility of fraud based on the user's emotion data. For example, it can determine that there is a high possibility of fraud if the user feels nervous. The emotion estimation unit can also build a system that monitors the user's emotions in real time and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's emotions.

[0079] The Telephone Samurai system can also use noise-canceling technology when analyzing the user's voice data. For example, it can filter out ambient noise to obtain clear voice data. Furthermore, noise-canceling technology can be used to develop algorithms for analyzing telephone voice data. For example, it can remove environmental sounds such as wind noise and traffic noise. Furthermore, noise-canceling technology can be used to build a system that makes telephone voice data clearer. This allows for even clearer voice data to be obtained.

[0080] The Telephone Samurai system can further include a learning unit that learns a user's past call patterns. For example, it compares the content of past calls with the content of current calls to accurately infer the possibility of fraud. The learning unit can also develop an algorithm that infers the possibility of fraud based on past call patterns. For example, it can learn call patterns that have been suspected of being fraudulent in the past and infer the possibility of fraud in a current call. The learning unit can also build a system that learns a user's past call patterns and, based on that data, infers the possibility of fraud with high accuracy. This allows for highly accurate inference of the possibility of fraud.

[0081] The Telephone Samurai system can further include a schedule reference unit that infers the possibility of fraud based on the user's calendar and schedule. For example, if an unscheduled call is received, it may determine that there is a high possibility of fraud. The schedule reference unit can also develop an algorithm that infers the possibility of fraud based on the user's calendar and schedule data. For example, if an unscheduled call is received, it may determine that there is a high possibility of fraud. The schedule reference unit can also build a system that monitors the user's calendar and schedule in real time and infers the possibility of fraud based on that data. This allows for highly accurate inference of the possibility of fraud by referencing the user's calendar and schedule.

[0082] The Telephone Samurai system can further include an emotion estimation unit that estimates the user's emotions. For example, if the user feels anxious or scared, it can determine that there is a high possibility of fraud. The emotion estimation unit can also develop an algorithm that infers the possibility of fraud based on the user's emotion data. For example, it can determine that there is a high possibility of fraud if the user feels nervous. The emotion estimation unit can also build a system that monitors the user's emotions in real time and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's emotions.

[0083] The Telephone Samurai system can further include a location information acquisition unit that acquires the user's location information. For example, if the user is in an unusual location, it can determine that there is a high possibility of fraud. The location information acquisition unit can also develop an algorithm that infers the possibility of fraud based on the user's location information. For example, it can determine that there is a high possibility of fraud when the user is traveling or on a business trip. The location information acquisition unit can also build a system that monitors the user's location information in real time and infers the possibility of fraud based on that data. This allows the possibility of fraud to be inferred with high accuracy by referring to the user's location information.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The context analysis unit analyzes the context of the call. For example, the generative AI receives the voice and text data of the call as input, analyzes its content, and infers the possibility of fraud. In addition, when multiple calls are made in succession, the context analysis unit can analyze their context and determine that there is a high possibility of fraud. Step 2: The blacklist filter unit performs filtering using an existing phone number blacklist. For example, if a phone number that has been used for fraud in the past is registered on the blacklist, calls from that number will be automatically rejected. The blacklist filter unit also periodically updates the blacklist and can add new fraudulent phone numbers. Step 3: The in-device AI processing unit performs AI processing within the device. For example, a generation AI runs within the device and analyzes the voice and text data of the call. This protects privacy as the data is not transmitted externally. The generation AI can use text generation AI (e.g., LLM) or multimodal generation AI to analyze the contents of the call and infer the possibility of fraud.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] 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]

[0153] 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 context analysis unit that analyzes a context; a blacklist filter unit that performs filtering using an existing phone number blacklist; An AI processing unit within the terminal that performs AI processing within the terminal. A system characterized by:

2. The context analysis unit Analyzing the tone and speed of a speaker from audio data to infer the likelihood of fraud 2. The system of claim 1.

3. The context analysis unit Contextual analysis based on call and message history 2. The system of claim 1.

4. The context analysis unit It estimates the emotions of the caller and automatically issues a warning if there is a high possibility of fraud.

2. The system of claim 1.

5. The context analysis unit Translates phone calls in real time to help deal with scam calls in different languages 2. The system of claim 1.

6. The context analysis unit Inferring potential fraud based on a user's calendar and to-do list 2. The system of claim 1.

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