system

Abstract

The system according to the embodiment aims to effectively prevent telephone fraud and nuisance calls. [Solution] The system according to the embodiment includes a call unit, an analysis unit, and an intermediary unit. When a call comes in, the call unit uses AI to automatically make the call. The analysis unit analyzes the other party's information and requirements collected by the call unit. The intermediary unit connects the call to the user based on the results of the analysis by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to effectively prevent telephone fraud and nuisance calls.

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

[0006] The system according to the embodiment includes a call unit, an analysis unit, and an intermediary unit. When a call comes in, the call unit uses AI to automatically make the call. The analysis unit analyzes the other party's information and requirements collected by the call unit. The intermediary unit connects the call to the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) In a call forwarding system according to an embodiment of the present invention, when a call is received, AI automatically makes the call, asks for the caller's identification and requirements, analyzes the content, and only transfers the call to the appropriate party if it is determined that there are no problems. In a call forwarding system, AI automatically makes the call, asks for the caller's identification and requirements, and analyzes the content. Only if the analysis determines that there are no problems, the call is transferred to the appropriate party. For example, when a call is received, the AI ​​in the call forwarding system plays a message such as "This is a call forwarding service. We would like to hear your business," and asks for the caller's identification and requirements. At this time, the AI ​​analyzes the caller's voice, speaking style, and content to determine whether there is a possibility of fraud. Next, in the call forwarding system, AI analyzes the caller's identification and requirements. For example, the AI ​​analyzes the caller's tone of voice, speaking style, and content to determine whether there is a possibility of fraud. The AI ​​has studied data from past fraudulent calls and understands the characteristics of fraud, allowing it to accurately determine the possibility of fraud. Only if the analysis determines that there are no problems, the call is transferred to the appropriate party. For example, the AI ​​will convey a message to the caller such as, "The matter you need is ____. May I transfer your call?" and will only transfer the call if the caller agrees. This can effectively prevent nuisance and fraudulent calls. For example, it can significantly reduce the risk of elderly people falling for fraudulent calls. It can also allow busy businessmen to concentrate on their work without being bothered by nuisance calls. Furthermore, because the call is made by AI, there is no need for the caller to answer the phone, saving time and effort. This allows the call forwarding system to effectively prevent nuisance and fraudulent calls. For example, it can significantly reduce the risk of elderly people falling for fraudulent calls. It can also allow busy businessmen to concentrate on their work without being bothered by nuisance calls. Furthermore, because the call is made by AI, there is no need for the caller to answer the phone, saving time and effort.

[0029] The call forwarding system according to the embodiment includes a call unit, an analysis unit, and a transfer unit. When a call is received, the call unit uses AI to automatically make the call. For example, when a call is received, the call unit uses AI to play a message such as, "This is a call forwarding service. We would like to hear your business," and listen for the caller's confirmation and requirements. The call unit can also collect the caller's voice, speaking style, and content. For example, the call unit can collect the caller's tone of voice, speaking style, and content. The call unit can also analyze the caller's voice characteristics in real time to immediately determine the possibility of fraud. For example, the call unit can analyze the caller's tone and pitch in real time to determine the possibility of fraud. The analysis unit analyzes the caller's information and requirements collected by the call unit. For example, the analysis unit analyzes the caller's tone of voice, speaking style, and content to determine the possibility of fraud. The analysis unit can also learn data from past fraudulent calls to identify the characteristics of fraud. For example, the analysis unit can learn voice data from past fraudulent calls to identify the characteristics of fraud. Furthermore, the analysis unit can analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. For example, the analysis unit analyzes changes in the other party's tone of voice in real time to determine the possibility of fraud. The relay unit transfers the call to the user based on the results of the analysis by the analysis unit. For example, the AI ​​conveys a message to the user such as, "I'm sorry, but your business is with you. May I transfer the call?" and only transfers the call if the user agrees. The relay unit can also re-analyze the other party's tone of voice and speaking style before transferring the call to the user to determine the final possibility of fraud. For example, the relay unit re-analyzes the other party's tone of voice to determine the final possibility of fraud. This allows the telephone relay system according to the embodiment to prevent telephone fraud. For example, the call unit collects the other party's tone of voice, speaking style, and content, and the analysis unit analyzes the other party's tone of voice, speaking style, and content to determine the possibility of fraud. The relay unit transfers the call to the user based on the results of the analysis by the analysis unit. This allows the call handling system to effectively prevent nuisance and fraudulent calls.

[0030] The analysis unit can learn data from past scam calls to identify characteristics of scams. The analysis unit can, for example, learn voice data from past scam calls to identify characteristics of scams. For example, the analysis unit collects voice data from scam calls and learns characteristics of voice patterns and speaking styles. The analysis unit can also learn text data from scam calls to identify characteristics of scams. For example, the analysis unit collects text data from scam calls and learns characteristics of text patterns and phrases. The analysis unit can also comprehensively learn data from scam calls to identify characteristics of scams. For example, the analysis unit learns a combination of voice data and text data to identify characteristics of scams. In this way, by identifying characteristics of scams, it is possible to determine the possibility of scams with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data from scam calls into a generation AI and have the generation AI identify characteristics of scams.

[0031] The call unit can collect the other party's voice, speaking style, and content. The call unit, for example, collects the other party's tone of voice, speaking style, and content. For example, the call unit collects the other party's tone of voice and pitch. The call unit can also collect characteristics of the other party's speaking style. For example, the call unit collects the rhythm and strength of the other party's speaking style. The call unit can also collect the content of the other party's speech. For example, the call unit collects the content of the other party's speech as text data. In this way, by collecting the other party's voice, speaking style, and content, information for determining the possibility of fraud can be obtained. Some or all of the above-mentioned processing in the call unit may be performed using, or without, AI, for example. For example, the call unit can input the other party's tone of voice, speaking style, and content into a generation AI and have the generation AI determine the possibility of fraud.

[0032] Before transferring the call to the person, the AI ​​can convey a message to the person, such as "The matter you need is ____. May I transfer your call?" For example, in the transfer unit, the AI ​​can convey a message to the person, such as "The matter you need is ____. May I transfer your call?" For example, in the transfer unit, the AI ​​summarizes the caller's requirements and conveys them to the person. The transfer unit can also transfer the call only if the person agrees. For example, the transfer unit only transfers the call if the person agrees. Furthermore, before transferring the call to the person, the transfer unit can also re-analyze the caller's tone of voice and speaking style to ultimately determine the possibility of fraud. For example, the transfer unit re-analyzes the caller's tone of voice to ultimately determine the possibility of fraud. By confirming before transferring the call to the person, nuisance calls and fraudulent calls can be prevented. Some or all of the above-described processing in the transfer unit may be performed, for example, using AI, or may be performed without using AI. For example, the mediation unit can input the other party's requirements into the generation AI and have the generation AI execute a summary.

[0033] The analysis unit can analyze the tone of the other party's voice, speaking style, and content to determine the possibility of fraud. The analysis unit can, for example, analyze the tone of the other party's voice, speaking style, and content to determine the possibility of fraud. For example, the analysis unit can analyze the tone and pitch of the other party's voice to determine the possibility of fraud. The analysis unit can also analyze the characteristics of the other party's speaking style to determine the possibility of fraud. For example, the analysis unit can analyze the rhythm and strength of the other party's speaking style to determine the possibility of fraud. The analysis unit can also analyze the content of the other party's speech to determine the possibility of fraud. For example, the analysis unit can analyze the content of the other party's speech as text data to determine the possibility of fraud. In this way, by analyzing the tone of the other party's voice, speaking style, and content, the possibility of fraud can be determined with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the tone of the other party's voice, speaking style, and content into the generation AI and have the generation AI determine the possibility of fraud.

[0034] The call unit can analyze the characteristics of the other party's voice in real time during a call and instantly determine the possibility of fraud. The call unit, for example, can analyze the characteristics of the other party's voice in real time during a call and instantly determine the possibility of fraud. For example, the call unit can use AI to analyze the tone and pitch of the other party's voice in real time and determine the possibility of fraud. The call unit can also use AI to analyze the strength and rhythm of the other party's voice in real time and determine the possibility of fraud. For example, the call unit can use AI to analyze the emotional expressions of the other party's voice in real time and determine the possibility of fraud. In this way, by analyzing the characteristics of the other party's voice in real time, the possibility of fraud can be instantly determined. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the characteristics of the other party's voice into a generation AI and have the generation AI determine the possibility of fraud.

[0035] The call unit can analyze the speaking speed and pauses of the other party during a call to determine the possibility of fraud. The call unit, for example, can analyze the speaking speed and pauses of the other party during a call to determine the possibility of fraud. For example, the call unit can use AI to analyze the speaking speed of the other party in real time to determine the possibility of fraud. The call unit can also use AI to analyze the pausing of the other party in real time to determine the possibility of fraud. For example, the call unit can use AI to analyze the rhythm of the other party's speech in real time to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the speaking speed and pauses of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the speaking speed and pauses of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0036] The call unit can analyze the background sound of the other party during a call and determine the possibility of fraud. The call unit, for example, can analyze the background sound of the other party during a call and determine the possibility of fraud. For example, the call unit can use AI to analyze the background sound of the other party in real time and determine the possibility of fraud. The call unit can also use AI to analyze the type and pattern of the background sound of the other party and determine the possibility of fraud. For example, the call unit can use AI to analyze changes in the background sound of the other party and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the background sound of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the background sound of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0037] The call unit can analyze the geographical location information of the other party during a call and determine the possibility of fraud. The call unit, for example, analyzes the geographical location information of the other party during a call and determines the possibility of fraud. For example, the call unit uses AI to analyze the geographical location information of the other party in real time and determine the possibility of fraud. The call unit can also use AI to confirm a match between the geographical location information of the other party and the content of the call and determine the possibility of fraud. For example, the call unit uses AI to analyze fluctuations in the geographical location information of the other party and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the geographical location information of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the geographical location information of the other party to a generation AI and have the generation AI determine the possibility of fraud.

[0038] The call unit can refer to the other party's past call history during a call to determine the possibility of fraud. The call unit, for example, can refer to the other party's past call history during a call to determine the possibility of fraud. For example, the call unit can use AI to refer to the other party's past call history to determine the possibility of fraud. The call unit can also use AI to compare the content of the other party's past calls with the content of the current call to determine the possibility of fraud. For example, the call unit can use AI to analyze the other party's past call frequency and patterns to determine the possibility of fraud. In this way, the possibility of fraud can be determined by referring to the other party's past call history. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the other party's past call history into a generation AI and have the generation AI determine the possibility of fraud.

[0039] The call unit can analyze the social media activity of the other party during a call and determine the possibility of fraud. The call unit, for example, can analyze the social media activity of the other party during a call and determine the possibility of fraud. For example, the call unit can use AI to analyze the social media activity of the other party and determine the possibility of fraud. The call unit can also use AI to compare the content of the call with the content of the other party's social media posts and determine the possibility of fraud. For example, the call unit can use AI to analyze the other party's social media friendships and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's social media activity. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the other party's social media activity into a generation AI and have the generation AI determine the possibility of fraud.

[0040] The analysis unit can analyze changes in the other party's tone of voice and speaking style in real time during analysis to determine the possibility of fraud. For example, the analysis unit can analyze changes in the other party's tone of voice and speaking style in real time during analysis to determine the possibility of fraud. For example, the analysis unit can use AI to analyze changes in the other party's tone of voice in real time to determine the possibility of fraud. The analysis unit can also analyze changes in the other party's speaking style in real time to determine the possibility of fraud. For example, the analysis unit can use AI to analyze a combination of the other party's tone of voice and speaking style to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing changes in the other party's tone of voice and speaking style in real time. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input changes in the other party's tone of voice and speaking style into a generation AI and have the generation AI determine the possibility of fraud.

[0041] During analysis, the analysis unit can analyze the other party's language and phrase patterns to determine the possibility of fraud. During analysis, the analysis unit, for example, can analyze the other party's language and phrase patterns to determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's language and phrase patterns to determine the possibility of fraud. The analysis unit can also use AI to analyze the other party's phrase patterns to determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's language and phrase patterns in combination to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's language and phrase patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the other party's language and phrase patterns into a generation AI and have the generation AI determine the possibility of fraud.

[0042] The analysis unit can analyze the frequency components of the other party's voice during analysis and determine the possibility of fraud. For example, the analysis unit can analyze the frequency components of the other party's voice during analysis and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the frequency components of the other party's voice and determine the possibility of fraud. The analysis unit can also use AI to analyze fluctuations in the frequency components of the other party's voice and determine the possibility of fraud. For example, the analysis unit can analyze the frequency components of the other party's voice in combination with other voice features and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the frequency components of the other party's voice. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the frequency components of the other party's voice into a generation AI and have the generation AI determine the possibility of fraud.

[0043] The analysis unit can analyze the other party's geographical location information during analysis and determine the possibility of fraud. The analysis unit, for example, can analyze the other party's geographical location information during analysis and determine the possibility of fraud. For example, the analysis unit can have an AI analyze the other party's geographical location information and determine the possibility of fraud. The analysis unit can also have an AI confirm a match between the other party's geographical location information and the content of the call and determine the possibility of fraud. For example, the analysis unit can have an AI analyze fluctuations in the other party's geographical location information and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's geographical location information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the other party's geographical location information into the generation AI and have the generation AI determine the possibility of fraud.

[0044] During analysis, the analysis unit can refer to the other party's past call history and determine the possibility of fraud. During analysis, the analysis unit, for example, can refer to the other party's past call history and determine the possibility of fraud. For example, the analysis unit can use AI to refer to the other party's past call history and determine the possibility of fraud. The analysis unit can also use AI to compare the content of the other party's past calls with the content of the current call and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's past call frequency and patterns and determine the possibility of fraud. In this way, the possibility of fraud can be determined by referring to the other party's past call history. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the other party's past call history into the generation AI and have the generation AI determine the possibility of fraud.

[0045] The analysis unit can analyze the other party's social media activity during the analysis and determine the possibility of fraud. The analysis unit, for example, can analyze the other party's social media activity during the analysis and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's social media activity and determine the possibility of fraud. The analysis unit can also use AI to compare the other party's social media posts with the content of phone calls and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's social media friendships and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the other party's social media activity into the generation AI and have the generation AI determine the possibility of fraud.

[0046] The intermediary unit can re-analyze the other party's tone of voice and speaking style when transferring a call, and make a final judgment on the possibility of fraud. For example, the intermediary unit can re-analyze the other party's tone of voice and speaking style when transferring a call, and make a final judgment on the possibility of fraud. For example, the intermediary unit can use AI to re-analyze the other party's tone of voice and speaking style to make a final judgment on the possibility of fraud. The intermediary unit can also use AI to re-analyze the other party's speaking style to make a final judgment on the possibility of fraud. For example, the intermediary unit can use AI to re-analyze the other party's tone of voice and speaking style in combination to make a final judgment on the possibility of fraud. In this way, by re-analyzing the other party's tone of voice and speaking style, the final judgment on the possibility of fraud can be made. Some or all of the above-mentioned processing in the intermediary unit may be performed using AI, for example, or may be performed without using AI. For example, the intermediary unit can input the other party's tone of voice and speaking style into a generation AI, and have the generation AI make a final judgment on the possibility of fraud.

[0047] The intermediary unit can summarize what the other party says when transferring a call and provide it to the user. For example, the intermediary unit summarizes what the other party says and provides it to the user when transferring a call. For example, in the intermediary unit, AI summarizes what the other party says and conveys it concisely to the user. The intermediary unit can also extract important points from what the other party says and provide them to the user. For example, in the intermediary unit, AI summarizes what the other party says and visually displays it to the user. This allows the user to concisely grasp important information by summarizing what the other party says. Some or all of the above-mentioned processing in the intermediary unit may be performed using AI, or may be performed without using AI, for example. For example, the intermediary unit can input what the other party says into a generation AI and have the generation AI execute the summary.

[0048] The relay unit can save the characteristics of the other party's voice when relaying a call and refer to them the next time the call is made. For example, the relay unit can save the characteristics of the other party's voice when relaying a call and refer to them the next time the call is made. For example, the relay unit can have an AI save the characteristics of the other party's voice and refer to them the next time the call is made. The relay unit can also have an AI save the characteristics of the other party's voice in a database and check for a match the next time the call is made. For example, the relay unit can have an AI save the characteristics of the other party's voice and re-evaluate the possibility of fraud the next time the call is made. In this way, by saving the characteristics of the other party's voice, the possibility of fraud can be re-evaluated the next time the call is made. Some or all of the above-mentioned processing in the relay unit may be performed using AI, for example, or may be performed without using AI. For example, the relay unit can input the characteristics of the other party's voice into a generation AI and have the generation AI save the characteristics and refer to them the next time the call is made.

[0049] The relay unit can analyze the geographical location information of the other party when relaying a call and determine the possibility of fraud. For example, the relay unit can analyze the geographical location information of the other party when relaying a call and determine the possibility of fraud. For example, the relay unit can use AI to analyze the geographical location information of the other party and determine the possibility of fraud. The relay unit can also use AI to confirm a match between the geographical location information of the other party and the content of the call and determine the possibility of fraud. For example, the relay unit can use AI to analyze changes in the geographical location information of the other party and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the geographical location information of the other party. Some or all of the above-mentioned processing in the relay unit can be performed using AI, for example, or without AI. For example, the relay unit can input the geographical location information of the other party to a generation AI and have the generation AI determine the possibility of fraud.

[0050] When transferring a call, the relay unit can refer to the other party's past call history to determine the possibility of fraud. When transferring a call, the relay unit can, for example, refer to the other party's past call history to determine the possibility of fraud. For example, the relay unit can use AI to refer to the other party's past call history to determine the possibility of fraud. The relay unit can also use AI to compare the content of the other party's past calls with the content of the current call to determine the possibility of fraud. For example, the relay unit can use AI to analyze the other party's past call frequency and patterns to determine the possibility of fraud. In this way, the possibility of fraud can be determined by referring to the other party's past call history. Some or all of the above-mentioned processing in the relay unit can be performed using AI, for example, or without AI. For example, the relay unit can input the other party's past call history into a generation AI and have the generation AI determine the possibility of fraud.

[0051] The relay unit can analyze the other party's social media activity when relaying a call and determine the possibility of fraud. For example, the relay unit can analyze the other party's social media activity when relaying a call and determine the possibility of fraud. For example, the relay unit can use AI to analyze the other party's social media activity and determine the possibility of fraud. The relay unit can also use AI to compare the other party's social media posts with the content of the call and determine the possibility of fraud. For example, the relay unit can use AI to analyze the other party's social media friendships and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's social media activity. Some or all of the above-mentioned processing in the relay unit may be performed using AI, for example, or may be performed without using AI. For example, the relay unit can input the other party's social media activity into the generation AI and have the generation AI determine the possibility of fraud.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The analysis unit can analyze the tone, speaking style, and content of the other party's voice to determine the possibility of fraud. For example, it can analyze the tone and pitch of the other party's voice to determine the possibility of fraud. It can also analyze the characteristics of the other party's speaking style to determine the possibility of fraud. It can also analyze the content of the other party's speech as text data to determine the possibility of fraud. In this way, by analyzing the tone, speaking style, and content of the other party's voice, it is possible to determine the possibility of fraud with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the tone, speaking style, and content of the other party's voice into the generation AI and have the generation AI perform a determination of the possibility of fraud.

[0054] The analysis unit can analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. For example, it can analyze changes in the other party's tone of voice in real time to determine the possibility of fraud. It can also analyze changes in the other party's speaking style in real time to determine the possibility of fraud. It can also analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. It can also analyze changes in the other party's tone of voice and speaking style in combination to determine the possibility of fraud. In this way, it is possible to determine the possibility of fraud by analyzing changes in the other party's tone of voice and speaking style in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input changes in the other party's tone of voice and speaking style into the generation AI and have the generation AI determine the possibility of fraud.

[0055] The call unit can analyze the speaking speed and pauses of the other party during a call to determine the possibility of fraud. For example, the call unit can analyze the speaking speed of the other party in real time to determine the possibility of fraud. The call unit can also analyze the pauses of the other party in real time to determine the possibility of fraud. The call unit can also analyze the rhythm of the other party's speech in real time to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the speaking speed and pauses of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, or may be performed without using AI. For example, the call unit can input the speaking speed and pauses of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0056] The call unit can analyze the background sounds of the other party during a call and determine the possibility of fraud. For example, the call unit can analyze the background sounds of the other party in real time and determine the possibility of fraud. The call unit can also analyze the type and pattern of the background sounds of the other party to determine the possibility of fraud. Furthermore, the call unit can analyze changes in the background sounds of the other party to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the background sounds of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, or may be performed without using AI. For example, the call unit can input the background sounds of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0057] When transferring a call, the relay unit can save the characteristics of the other party's voice and refer to them the next time the call is made. For example, the relay unit can save the characteristics of the other party's voice and refer to them the next time the call is made. The characteristics of the other party's voice can also be saved in a database and a match can be confirmed the next time the call is made. Furthermore, the characteristics of the other party's voice can be saved and the possibility of fraud can be reevaluated the next time the call is made. In this way, by saving the characteristics of the other party's voice, the possibility of fraud can be reevaluated the next time the call is made. Some or all of the above-mentioned processing in the relay unit may be performed using AI, or may be performed without using AI. For example, the relay unit can input the characteristics of the other party's voice into a generation AI and have the generation AI save the characteristics and refer to them the next time the call is made.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: When a call comes in, the AI ​​automatically makes the call. For example, when a call comes in, the AI ​​plays a message such as, "This is a call forwarding service. We would like to hear your business," and asks for the caller's confirmation and requirements. The call processing unit can also collect information about the caller's voice, speaking style, and content. Furthermore, the call processing unit can analyze the characteristics of the caller's voice in real time and immediately determine the possibility of fraud. Step 2: The analysis unit analyzes the other party's information and requirements collected by the call unit. For example, it analyzes the other party's tone of voice, speaking style, and content to determine the possibility of fraud. The analysis unit can also learn from data on past fraudulent calls to identify the characteristics of fraud. It can also analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. Step 3: The relay unit connects the call to the user based on the results of the analysis by the analysis unit. For example, the AI ​​will convey a message such as "The matter you need is ____. May I connect you?" and only connect the call if the user agrees. The relay unit can also re-analyze the tone of the caller's voice and speaking style before connecting the call to the user, to ultimately determine the possibility of fraud.

[0060] (Example 2) In a call forwarding system according to an embodiment of the present invention, when a call is received, AI automatically makes the call, asks for the caller's identification and requirements, analyzes the content, and only transfers the call to the appropriate party if it is determined that there are no problems. In a call forwarding system, AI automatically makes the call, asks for the caller's identification and requirements, and analyzes the content. Only if the analysis determines that there are no problems, the call is transferred to the appropriate party. For example, when a call is received, the AI ​​in the call forwarding system plays a message such as "This is a call forwarding service. We would like to hear your business," and asks for the caller's identification and requirements. At this time, the AI ​​analyzes the caller's voice, speaking style, and content to determine whether there is a possibility of fraud. Next, in the call forwarding system, AI analyzes the caller's identification and requirements. For example, the AI ​​analyzes the caller's tone of voice, speaking style, and content to determine whether there is a possibility of fraud. The AI ​​has studied data from past fraudulent calls and understands the characteristics of fraud, allowing it to accurately determine the possibility of fraud. Only if the analysis determines that there are no problems, the call is transferred to the appropriate party. For example, the AI ​​will convey a message to the caller such as, "The matter you need is ____. May I transfer your call?" and will only transfer the call if the caller agrees. This can effectively prevent nuisance and fraudulent calls. For example, it can significantly reduce the risk of elderly people falling for fraudulent calls. It can also allow busy businessmen to concentrate on their work without being bothered by nuisance calls. Furthermore, because the call is made by AI, there is no need for the caller to answer the phone, saving time and effort. This allows the call forwarding system to effectively prevent nuisance and fraudulent calls. For example, it can significantly reduce the risk of elderly people falling for fraudulent calls. It can also allow busy businessmen to concentrate on their work without being bothered by nuisance calls. Furthermore, because the call is made by AI, there is no need for the caller to answer the phone, saving time and effort.

[0061] The call forwarding system according to the embodiment includes a call unit, an analysis unit, and a transfer unit. When a call is received, the call unit uses AI to automatically make the call. For example, when a call is received, the call unit uses AI to play a message such as, "This is a call forwarding service. We would like to hear your business," and listen for the caller's confirmation and requirements. The call unit can also collect the caller's voice, speaking style, and content. For example, the call unit can collect the caller's tone of voice, speaking style, and content. The call unit can also analyze the caller's voice characteristics in real time to immediately determine the possibility of fraud. For example, the call unit can analyze the caller's tone and pitch in real time to determine the possibility of fraud. The analysis unit analyzes the caller's information and requirements collected by the call unit. For example, the analysis unit analyzes the caller's tone of voice, speaking style, and content to determine the possibility of fraud. The analysis unit can also learn data from past fraudulent calls to identify the characteristics of fraud. For example, the analysis unit can learn voice data from past fraudulent calls to identify the characteristics of fraud. Furthermore, the analysis unit can analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. For example, the analysis unit analyzes changes in the other party's tone of voice in real time to determine the possibility of fraud. The relay unit transfers the call to the user based on the results of the analysis by the analysis unit. For example, the AI ​​conveys a message to the user such as, "I'm sorry, but your business is with you. May I transfer the call?" and only transfers the call if the user agrees. The relay unit can also re-analyze the other party's tone of voice and speaking style before transferring the call to the user to determine the final possibility of fraud. For example, the relay unit re-analyzes the other party's tone of voice to determine the final possibility of fraud. This allows the telephone relay system according to the embodiment to prevent telephone fraud. For example, the call unit collects the other party's tone of voice, speaking style, and content, and the analysis unit analyzes the other party's tone of voice, speaking style, and content to determine the possibility of fraud. The relay unit transfers the call to the user based on the results of the analysis by the analysis unit. This allows the call handling system to effectively prevent nuisance and fraudulent calls.

[0062] The analysis unit can learn data from past scam calls to identify characteristics of scams. The analysis unit can, for example, learn voice data from past scam calls to identify characteristics of scams. For example, the analysis unit collects voice data from scam calls and learns characteristics of voice patterns and speaking styles. The analysis unit can also learn text data from scam calls to identify characteristics of scams. For example, the analysis unit collects text data from scam calls and learns characteristics of text patterns and phrases. The analysis unit can also comprehensively learn data from scam calls to identify characteristics of scams. For example, the analysis unit learns a combination of voice data and text data to identify characteristics of scams. In this way, by identifying characteristics of scams, it is possible to determine the possibility of scams with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice data from scam calls into a generation AI and have the generation AI identify characteristics of scams.

[0063] The call unit can collect the other party's voice, speaking style, and content. The call unit, for example, collects the other party's tone of voice, speaking style, and content. For example, the call unit collects the other party's tone of voice and pitch. The call unit can also collect characteristics of the other party's speaking style. For example, the call unit collects the rhythm and strength of the other party's speaking style. The call unit can also collect the content of the other party's speech. For example, the call unit collects the content of the other party's speech as text data. In this way, by collecting the other party's voice, speaking style, and content, information for determining the possibility of fraud can be obtained. Some or all of the above-mentioned processing in the call unit may be performed using, or without, AI, for example. For example, the call unit can input the other party's tone of voice, speaking style, and content into a generation AI and have the generation AI determine the possibility of fraud.

[0064] Before transferring the call to the person, the AI ​​can convey a message to the person, such as "The matter you need is ____. May I transfer your call?" For example, in the transfer unit, the AI ​​can convey a message to the person, such as "The matter you need is ____. May I transfer your call?" For example, in the transfer unit, the AI ​​summarizes the caller's requirements and conveys them to the person. The transfer unit can also transfer the call only if the person agrees. For example, the transfer unit only transfers the call if the person agrees. Furthermore, before transferring the call to the person, the transfer unit can also re-analyze the caller's tone of voice and speaking style to ultimately determine the possibility of fraud. For example, the transfer unit re-analyzes the caller's tone of voice to ultimately determine the possibility of fraud. By confirming before transferring the call to the person, nuisance calls and fraudulent calls can be prevented. Some or all of the above-described processing in the transfer unit may be performed, for example, using AI, or may be performed without using AI. For example, the mediation unit can input the other party's requirements into the generation AI and have the generation AI execute a summary.

[0065] The analysis unit can analyze the tone of the other party's voice, speaking style, and content to determine the possibility of fraud. The analysis unit can, for example, analyze the tone of the other party's voice, speaking style, and content to determine the possibility of fraud. For example, the analysis unit can analyze the tone and pitch of the other party's voice to determine the possibility of fraud. The analysis unit can also analyze the characteristics of the other party's speaking style to determine the possibility of fraud. For example, the analysis unit can analyze the rhythm and strength of the other party's speaking style to determine the possibility of fraud. The analysis unit can also analyze the content of the other party's speech to determine the possibility of fraud. For example, the analysis unit can analyze the content of the other party's speech as text data to determine the possibility of fraud. In this way, by analyzing the tone of the other party's voice, speaking style, and content, the possibility of fraud can be determined with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the tone of the other party's voice, speaking style, and content into the generation AI and have the generation AI determine the possibility of fraud.

[0066] The call unit can estimate the user's emotions and adjust the timing of the start of the call based on the estimated user emotions. For example, the call unit can estimate the user's emotions and adjust the timing of the start of the call based on the estimated user emotions. For example, if the user is feeling stressed, the call unit can play a relaxing message before the AI ​​starts the call. Furthermore, if the user is busy, the call unit can delay the timing of the AI ​​starting the call and start the call after the user has calmed down. For example, if the user is relaxed, the call unit can immediately start the call and smoothly ask the other party for confirmation and requirements. This allows the user's stress to be reduced by adjusting the timing of the start of the call according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the call unit can be performed using an AI, for example, or without an AI. For example, the call unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of the start of the call.

[0067] The call unit can analyze the characteristics of the other party's voice in real time during a call and instantly determine the possibility of fraud. The call unit, for example, can analyze the characteristics of the other party's voice in real time during a call and instantly determine the possibility of fraud. For example, the call unit can use AI to analyze the tone and pitch of the other party's voice in real time and determine the possibility of fraud. The call unit can also use AI to analyze the strength and rhythm of the other party's voice in real time and determine the possibility of fraud. For example, the call unit can use AI to analyze the emotional expressions of the other party's voice in real time and determine the possibility of fraud. In this way, by analyzing the characteristics of the other party's voice in real time, the possibility of fraud can be instantly determined. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the characteristics of the other party's voice into a generation AI and have the generation AI determine the possibility of fraud.

[0068] The call unit can analyze the speaking speed and pauses of the other party during a call to determine the possibility of fraud. The call unit, for example, can analyze the speaking speed and pauses of the other party during a call to determine the possibility of fraud. For example, the call unit can use AI to analyze the speaking speed of the other party in real time to determine the possibility of fraud. The call unit can also use AI to analyze the pausing of the other party in real time to determine the possibility of fraud. For example, the call unit can use AI to analyze the rhythm of the other party's speech in real time to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the speaking speed and pauses of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the speaking speed and pauses of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0069] The call unit can analyze the background sound of the other party during a call and determine the possibility of fraud. The call unit, for example, can analyze the background sound of the other party during a call and determine the possibility of fraud. For example, the call unit can use AI to analyze the background sound of the other party in real time and determine the possibility of fraud. The call unit can also use AI to analyze the type and pattern of the background sound of the other party and determine the possibility of fraud. For example, the call unit can use AI to analyze changes in the background sound of the other party and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the background sound of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the background sound of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0070] The call unit can estimate the user's emotions and adjust the content of the call based on the estimated user emotions. For example, the call unit can estimate the user's emotions and adjust the content of the call based on the estimated user emotions. For example, if the user is feeling stressed, the call unit can have the AI ​​briefly summarize the content of the call. Furthermore, if the user is relaxed, the call unit can have the AI ​​provide detailed content of the call. For example, if the user is in a hurry, the AI ​​can provide only the important points. This allows the user's stress to be reduced by adjusting the content of the call according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the call unit can be performed using, for example, an AI, or without an AI. For example, the call unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the call.

[0071] The call unit can analyze the geographical location information of the other party during a call and determine the possibility of fraud. The call unit, for example, analyzes the geographical location information of the other party during a call and determines the possibility of fraud. For example, the call unit uses AI to analyze the geographical location information of the other party in real time and determine the possibility of fraud. The call unit can also use AI to confirm a match between the geographical location information of the other party and the content of the call and determine the possibility of fraud. For example, the call unit uses AI to analyze fluctuations in the geographical location information of the other party and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the geographical location information of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the geographical location information of the other party to a generation AI and have the generation AI determine the possibility of fraud.

[0072] The call unit can refer to the other party's past call history during a call to determine the possibility of fraud. The call unit, for example, can refer to the other party's past call history during a call to determine the possibility of fraud. For example, the call unit can use AI to refer to the other party's past call history to determine the possibility of fraud. The call unit can also use AI to compare the content of the other party's past calls with the content of the current call to determine the possibility of fraud. For example, the call unit can use AI to analyze the other party's past call frequency and patterns to determine the possibility of fraud. In this way, the possibility of fraud can be determined by referring to the other party's past call history. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the other party's past call history into a generation AI and have the generation AI determine the possibility of fraud.

[0073] The call unit can analyze the social media activity of the other party during a call and determine the possibility of fraud. The call unit, for example, can analyze the social media activity of the other party during a call and determine the possibility of fraud. For example, the call unit can use AI to analyze the social media activity of the other party and determine the possibility of fraud. The call unit can also use AI to compare the content of the call with the content of the other party's social media posts and determine the possibility of fraud. For example, the call unit can use AI to analyze the other party's social media friendships and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's social media activity. Some or all of the above-mentioned processing in the call unit may be performed using AI, for example, or may be performed without using AI. For example, the call unit can input the other party's social media activity into a generation AI and have the generation AI determine the possibility of fraud.

[0074] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit causes the AI ​​to prioritize important analyses. Furthermore, if the user is relaxed, the analysis unit can also cause the AI ​​to perform detailed analyses. For example, if the user is in a hurry, the analysis unit causes the AI ​​to perform analyses quickly. This allows the user's stress to be reduced by determining the analysis priorities based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0075] The analysis unit can analyze changes in the other party's tone of voice and speaking style in real time during analysis to determine the possibility of fraud. For example, the analysis unit can analyze changes in the other party's tone of voice and speaking style in real time during analysis to determine the possibility of fraud. For example, the analysis unit can use AI to analyze changes in the other party's tone of voice in real time to determine the possibility of fraud. The analysis unit can also analyze changes in the other party's speaking style in real time to determine the possibility of fraud. For example, the analysis unit can use AI to analyze a combination of the other party's tone of voice and speaking style to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing changes in the other party's tone of voice and speaking style in real time. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input changes in the other party's tone of voice and speaking style into a generation AI and have the generation AI determine the possibility of fraud.

[0076] During analysis, the analysis unit can analyze the other party's language and phrase patterns to determine the possibility of fraud. During analysis, the analysis unit, for example, can analyze the other party's language and phrase patterns to determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's language and phrase patterns to determine the possibility of fraud. The analysis unit can also use AI to analyze the other party's phrase patterns to determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's language and phrase patterns in combination to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's language and phrase patterns. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the other party's language and phrase patterns into a generation AI and have the generation AI determine the possibility of fraud.

[0077] The analysis unit can analyze the frequency components of the other party's voice during analysis and determine the possibility of fraud. For example, the analysis unit can analyze the frequency components of the other party's voice during analysis and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the frequency components of the other party's voice and determine the possibility of fraud. The analysis unit can also use AI to analyze fluctuations in the frequency components of the other party's voice and determine the possibility of fraud. For example, the analysis unit can analyze the frequency components of the other party's voice in combination with other voice features and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the frequency components of the other party's voice. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the frequency components of the other party's voice into a generation AI and have the generation AI determine the possibility of fraud.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit causes the AI ​​to display concise analysis results. Alternatively, if the user is relaxed, the analysis unit can cause the AI ​​to display detailed analysis results. For example, if the user is in a hurry, the analysis unit causes the AI ​​to display only the important points. This allows the user's stress to be reduced by adjusting the display method of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0079] The analysis unit can analyze the other party's geographical location information during analysis and determine the possibility of fraud. The analysis unit, for example, can analyze the other party's geographical location information during analysis and determine the possibility of fraud. For example, the analysis unit can have an AI analyze the other party's geographical location information and determine the possibility of fraud. The analysis unit can also have an AI confirm a match between the other party's geographical location information and the content of the call and determine the possibility of fraud. For example, the analysis unit can have an AI analyze fluctuations in the other party's geographical location information and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's geographical location information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the other party's geographical location information into the generation AI and have the generation AI determine the possibility of fraud.

[0080] During analysis, the analysis unit can refer to the other party's past call history and determine the possibility of fraud. During analysis, the analysis unit, for example, can refer to the other party's past call history and determine the possibility of fraud. For example, the analysis unit can use AI to refer to the other party's past call history and determine the possibility of fraud. The analysis unit can also use AI to compare the content of the other party's past calls with the content of the current call and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's past call frequency and patterns and determine the possibility of fraud. In this way, the possibility of fraud can be determined by referring to the other party's past call history. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the other party's past call history into the generation AI and have the generation AI determine the possibility of fraud.

[0081] The analysis unit can analyze the other party's social media activity during the analysis and determine the possibility of fraud. The analysis unit, for example, can analyze the other party's social media activity during the analysis and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's social media activity and determine the possibility of fraud. The analysis unit can also use AI to compare the other party's social media posts with the content of phone calls and determine the possibility of fraud. For example, the analysis unit can use AI to analyze the other party's social media friendships and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the other party's social media activity into the generation AI and have the generation AI determine the possibility of fraud.

[0082] The mediation unit can estimate the user's emotions and adjust the timing of the call transfer based on the estimated user emotions. For example, the mediation unit can estimate the user's emotions and adjust the timing of the call transfer based on the estimated user emotions. For example, if the user is feeling stressed, the mediation unit uses the AI ​​to delay the timing of the call transfer and wait until the user has calmed down before transferring the call. Furthermore, if the user is relaxed, the AI ​​can immediately transfer the call. For example, if the user is in a hurry, the mediation unit uses the AI ​​to quickly transfer the call. This allows the user's stress to be reduced by adjusting the timing of the call transfer according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the mediation unit can be performed, for example, using an AI, or without an AI. For example, the relay unit can input the user's emotional data into the generation AI and have the generation AI adjust the timing of the relay.

[0083] The intermediary unit can re-analyze the other party's tone of voice and speaking style when transferring a call, and make a final judgment on the possibility of fraud. For example, the intermediary unit can re-analyze the other party's tone of voice and speaking style when transferring a call, and make a final judgment on the possibility of fraud. For example, the intermediary unit can use AI to re-analyze the other party's tone of voice and speaking style to make a final judgment on the possibility of fraud. The intermediary unit can also use AI to re-analyze the other party's speaking style to make a final judgment on the possibility of fraud. For example, the intermediary unit can use AI to re-analyze the other party's tone of voice and speaking style in combination to make a final judgment on the possibility of fraud. In this way, by re-analyzing the other party's tone of voice and speaking style, the final judgment on the possibility of fraud can be made. Some or all of the above-mentioned processing in the intermediary unit may be performed using AI, for example, or may be performed without using AI. For example, the intermediary unit can input the other party's tone of voice and speaking style into a generation AI, and have the generation AI make a final judgment on the possibility of fraud.

[0084] The intermediary unit can summarize what the other party says when transferring a call and provide it to the user. For example, the intermediary unit summarizes what the other party says and provides it to the user when transferring a call. For example, in the intermediary unit, AI summarizes what the other party says and conveys it concisely to the user. The intermediary unit can also extract important points from what the other party says and provide them to the user. For example, in the intermediary unit, AI summarizes what the other party says and visually displays it to the user. This allows the user to concisely grasp important information by summarizing what the other party says. Some or all of the above-mentioned processing in the intermediary unit may be performed using AI, or may be performed without using AI, for example. For example, the intermediary unit can input what the other party says into a generation AI and have the generation AI execute the summary.

[0085] The relay unit can save the characteristics of the other party's voice when relaying a call and refer to them the next time the call is made. For example, the relay unit can save the characteristics of the other party's voice when relaying a call and refer to them the next time the call is made. For example, the relay unit can have an AI save the characteristics of the other party's voice and refer to them the next time the call is made. The relay unit can also have an AI save the characteristics of the other party's voice in a database and check for a match the next time the call is made. For example, the relay unit can have an AI save the characteristics of the other party's voice and re-evaluate the possibility of fraud the next time the call is made. In this way, by saving the characteristics of the other party's voice, the possibility of fraud can be re-evaluated the next time the call is made. Some or all of the above-mentioned processing in the relay unit may be performed using AI, for example, or may be performed without using AI. For example, the relay unit can input the characteristics of the other party's voice into a generation AI and have the generation AI save the characteristics and refer to them the next time the call is made.

[0086] The intermediary unit can estimate the user's emotions and adjust the intermediary content based on the estimated user emotions. For example, the intermediary unit estimates the user's emotions and adjusts the intermediary content based on the estimated user emotions. For example, if the user is feeling stressed, the intermediary unit may provide concise intermediary content through the AI. Furthermore, if the user is relaxed, the intermediary unit may provide detailed intermediary content through the AI. For example, if the user is in a hurry, the intermediary unit may provide only important points as intermediary content. This allows the user's stress to be reduced by adjusting the intermediary content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the intermediary unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the intermediation unit can input the user's emotional data into the generation AI and have the generation AI adjust the content of the intermediation.

[0087] The relay unit can analyze the geographical location information of the other party when relaying a call and determine the possibility of fraud. For example, the relay unit can analyze the geographical location information of the other party when relaying a call and determine the possibility of fraud. For example, the relay unit can use AI to analyze the geographical location information of the other party and determine the possibility of fraud. The relay unit can also use AI to confirm a match between the geographical location information of the other party and the content of the call and determine the possibility of fraud. For example, the relay unit can use AI to analyze changes in the geographical location information of the other party and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the geographical location information of the other party. Some or all of the above-mentioned processing in the relay unit can be performed using AI, for example, or without AI. For example, the relay unit can input the geographical location information of the other party to a generation AI and have the generation AI determine the possibility of fraud.

[0088] When transferring a call, the relay unit can refer to the other party's past call history to determine the possibility of fraud. When transferring a call, the relay unit can, for example, refer to the other party's past call history to determine the possibility of fraud. For example, the relay unit can use AI to refer to the other party's past call history to determine the possibility of fraud. The relay unit can also use AI to compare the content of the other party's past calls with the content of the current call to determine the possibility of fraud. For example, the relay unit can use AI to analyze the other party's past call frequency and patterns to determine the possibility of fraud. In this way, the possibility of fraud can be determined by referring to the other party's past call history. Some or all of the above-mentioned processing in the relay unit can be performed using AI, for example, or without AI. For example, the relay unit can input the other party's past call history into a generation AI and have the generation AI determine the possibility of fraud.

[0089] The relay unit can analyze the other party's social media activity when relaying a call and determine the possibility of fraud. For example, the relay unit can analyze the other party's social media activity when relaying a call and determine the possibility of fraud. For example, the relay unit can use AI to analyze the other party's social media activity and determine the possibility of fraud. The relay unit can also use AI to compare the other party's social media posts with the content of the call and determine the possibility of fraud. For example, the relay unit can use AI to analyze the other party's social media friendships and determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the other party's social media activity. Some or all of the above-mentioned processing in the relay unit may be performed using AI, for example, or may be performed without using AI. For example, the relay unit can input the other party's social media activity into the generation AI and have the generation AI determine the possibility of fraud. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned calling unit, analysis unit, and relay unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the calling unit is realized by the processor 46 of the smart device 14, and when a call comes in, an AI automatically makes the call and asks for the caller's identification and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the caller's information and requirements collected by the calling unit to determine the possibility of fraud. The relay unit is realized, for example, by the control unit 46A of the smart device 14, and relays the call to the user based on the results of the analysis by the analysis unit. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned calling unit, analysis unit, and relay unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the calling unit is realized by the processor 46 of the smart glasses 214, and when a call comes in, AI automatically makes the call and asks for the caller's identification and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the caller's information and requirements collected by the calling unit to determine the possibility of fraud. The relay unit is realized, for example, by the control unit 46A of the smart glasses 214, and relays the call to the user based on the results of the analysis by the analysis unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned calling unit, analysis unit, and relay unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the calling unit is realized by the processor 46 of the headset terminal 314, and when a call comes in, AI automatically makes the call and asks for the caller's identification and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the caller's information and requirements collected by the calling unit to determine the possibility of fraud. The relay unit is realized, for example, by the control unit 46A of the headset terminal 314, and relays the call to the user based on the results of the analysis by the analysis unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned calling unit, analysis unit, and relay unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the calling unit is realized by the processor 46 of the robot 414, and when a call comes in, an AI automatically makes the call and asks for the caller's identification and requirements. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the caller's information and requirements collected by the calling unit to determine the possibility of fraud. The relay unit is realized, for example, by the control unit 46A of the robot 414, and relays the call to the user based on the results of the analysis by the analysis unit.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The call unit can estimate the user's emotions and adjust the content of the call based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can provide a concise summary of the call content. Furthermore, if the user is relaxed, the AI ​​can provide detailed information about the call. Furthermore, if the user is in a hurry, the AI ​​can provide only the important points. This allows the user's stress to be reduced by adjusting the content of the call according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the call unit may be performed using AI, or may be performed without AI. For example, the call unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the call.

[0092] The analysis unit can analyze the tone, speaking style, and content of the other party's voice to determine the possibility of fraud. For example, it can analyze the tone and pitch of the other party's voice to determine the possibility of fraud. It can also analyze the characteristics of the other party's speaking style to determine the possibility of fraud. It can also analyze the content of the other party's speech as text data to determine the possibility of fraud. In this way, by analyzing the tone, speaking style, and content of the other party's voice, it is possible to determine the possibility of fraud with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input the tone, speaking style, and content of the other party's voice into the generation AI and have the generation AI perform a determination of the possibility of fraud.

[0093] The call unit can estimate the user's emotions and adjust the timing of the start of the call based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can play a relaxing message before starting the call. Alternatively, if the user is busy, the AI ​​can delay the start of the call and wait until the user has calmed down before starting the call. Furthermore, if the user is relaxed, the AI ​​can immediately start the call and smoothly ask the other party for confirmation and requirements. This allows the user's stress to be reduced by adjusting the start of the call according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the call unit may be performed using AI, or may be performed without AI. For example, the call unit can input the user's emotion data into the generation AI and have the generation AI adjust the start of the call.

[0094] The analysis unit can analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. For example, it can analyze changes in the other party's tone of voice in real time to determine the possibility of fraud. It can also analyze changes in the other party's speaking style in real time to determine the possibility of fraud. It can also analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. It can also analyze changes in the other party's tone of voice and speaking style in combination to determine the possibility of fraud. In this way, it is possible to determine the possibility of fraud by analyzing changes in the other party's tone of voice and speaking style in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can input changes in the other party's tone of voice and speaking style into the generation AI and have the generation AI determine the possibility of fraud.

[0095] The call unit can analyze the speaking speed and pauses of the other party during a call to determine the possibility of fraud. For example, the call unit can analyze the speaking speed of the other party in real time to determine the possibility of fraud. The call unit can also analyze the pauses of the other party in real time to determine the possibility of fraud. The call unit can also analyze the rhythm of the other party's speech in real time to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the speaking speed and pauses of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, or may be performed without using AI. For example, the call unit can input the speaking speed and pauses of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0096] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can prioritize important analyses. Also, if the user is relaxed, the AI ​​can perform detailed analyses. Furthermore, if the user is in a hurry, the AI ​​can quickly perform analyses. This allows the user's stress to be reduced by determining the analysis priority according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0097] The call unit can analyze the background sounds of the other party during a call and determine the possibility of fraud. For example, the call unit can analyze the background sounds of the other party in real time and determine the possibility of fraud. The call unit can also analyze the type and pattern of the background sounds of the other party to determine the possibility of fraud. Furthermore, the call unit can analyze changes in the background sounds of the other party to determine the possibility of fraud. In this way, the possibility of fraud can be determined by analyzing the background sounds of the other party. Some or all of the above-mentioned processing in the call unit may be performed using AI, or may be performed without using AI. For example, the call unit can input the background sounds of the other party into a generation AI and have the generation AI determine the possibility of fraud.

[0098] The relay unit can estimate the user's emotions and adjust the timing of the call transfer based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can delay the timing of the call transfer and wait until the user has calmed down before transferring the call. Also, if the user is relaxed, the AI ​​can immediately transfer the call. Furthermore, if the user is in a hurry, the AI ​​can quickly transfer the call. This allows the user's stress to be reduced by adjusting the timing of the call transfer according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the relay unit can be performed using AI, or without AI. For example, the relay unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of the call transfer.

[0099] When transferring a call, the relay unit can save the characteristics of the other party's voice and refer to them the next time the call is made. For example, the relay unit can save the characteristics of the other party's voice and refer to them the next time the call is made. The characteristics of the other party's voice can also be saved in a database and a match can be confirmed the next time the call is made. Furthermore, the characteristics of the other party's voice can be saved and the possibility of fraud can be reevaluated the next time the call is made. In this way, by saving the characteristics of the other party's voice, the possibility of fraud can be reevaluated the next time the call is made. Some or all of the above-mentioned processing in the relay unit may be performed using AI, or may be performed without using AI. For example, the relay unit can input the characteristics of the other party's voice into a generation AI and have the generation AI save the characteristics and refer to them the next time the call is made.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can display concise analysis results. Furthermore, if the user is relaxed, the AI ​​can display detailed analysis results. Furthermore, if the user is in a hurry, the AI ​​can display only the important points. This allows the user's stress to be reduced by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: When a call comes in, the AI ​​automatically makes the call. For example, when a call comes in, the AI ​​plays a message such as, "This is a call forwarding service. We would like to hear your business," and asks for the caller's confirmation and requirements. The call processing unit can also collect information about the caller's voice, speaking style, and content. Furthermore, the call processing unit can analyze the characteristics of the caller's voice in real time and immediately determine the possibility of fraud. Step 2: The analysis unit analyzes the other party's information and requirements collected by the call unit. For example, it analyzes the other party's tone of voice, speaking style, and content to determine the possibility of fraud. The analysis unit can also learn from data on past fraudulent calls to identify the characteristics of fraud. It can also analyze changes in the other party's tone of voice and speaking style in real time to determine the possibility of fraud. Step 3: The relay unit connects the call to the user based on the results of the analysis by the analysis unit. For example, the AI ​​will convey a message such as "The matter you need is ____. May I connect you?" and only connect the call if the user agrees. The relay unit can also re-analyze the tone of the caller's voice and speaking style before connecting the call to the user, to ultimately determine the possibility of fraud.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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. When a call comes in, the AI ​​automatically makes the call, an analysis unit that analyzes the information and requirements of the other party collected by the call unit; and an intermediary unit that intermediates the user based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Learn from past fraudulent call data to understand the characteristics of fraud 2. The system of claim 1.

3. The call unit is Collect the other person's voice, speaking style, and content 2. The system of claim 1.

4. The analysis unit Analyze the tone of voice, manner of speaking, and content of the call to determine the possibility of fraud 2. The system of claim 1.

5. The call unit is Estimates user emotions and adjusts the timing of a call start based on the estimated user emotions.

2. The system of claim 1.

6. The call unit is During a call, the characteristics of the other party's voice are analyzed in real time to immediately determine the possibility of fraud.

2. The system of claim 1.

7. The call unit is During a call, the speed at which the other party speaks and the pauses they take are analyzed to determine the possibility of fraud.

2. The system of claim 1.

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