System

Abstract

An object of the system according to the embodiment is to detect a conversation in which a cash card fraud is suspected in real time and to quickly respond to the conversation.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and an alert unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit and detects a conversation suspected of a cash card fraud. The alert unit issues an alert based on the conversation suspected of fraud detected by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to detect conversations suspected of being cash card fraud in real time and respond quickly.

[0005] The system according to the embodiment aims to detect conversations that are suspected of being cash card fraud in real time and to respond promptly. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and an alert unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit and detects conversations that are suspected of being cash card fraud. The alert unit issues an alert based on the conversations that are suspected of being fraudulent detected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect conversations that are suspected of being cash card fraud in real time and respond quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A fraud detection system according to an embodiment of the present invention collects and analyzes voice data, detects potentially fraudulent conversations, and issues an alert. The fraud detection system uses a dedicated device or smartphone with a dedicated app. A voice collection device equipped with a generation AI is deployed to constantly listen to the owner's conversations. When a conversation that may be a cash card fraud is detected, an alert is sent to family members and the police. For example, the fraud detection system installs a dedicated app on a dedicated device or smartphone. The owner then wears a voice collection device equipped with a generation AI. This device constantly listens to the owner's conversations and analyzes them in real time. For example, it collects all types of voice, including telephone conversations and face-to-face conversations. The generation AI analyzes the collected voice data and detects conversations that may be potentially fraudulent. For example, phrases such as "We're taking your cash card" or "This is a call from the bank" are deemed to be highly likely to be fraudulent. In this case, the generation AI envisions countless possible scenarios for victimization and quickly issues an alert. When an alert is detected, family members and the police are notified via a dedicated app. For example, a message such as "A possible fraudulent conversation has been detected" is sent to family members, and a notification such as "A possible fraudulent conversation has been detected. Please check for more information" is sent to the police. Privacy is thoroughly protected, and collected voice data is encrypted and never leaked. Furthermore, the generating AI analyzes only necessary data and immediately deletes unnecessary data, thereby protecting the owner's privacy. This allows the fraud detection system to prevent fraudulent billing scams before they occur. For example, even if an elderly person receives a fraudulent phone call, the generating AI can quickly issue an alert and contact family members and the police, preventing the victim from falling victim. Furthermore, even if the owner does not realize there is a possibility of fraud, the generating AI automatically detects it and issues an alert, allowing them to go about their daily life with peace of mind.

[0029] A fraud detection system according to an embodiment includes a collection unit, an analysis unit, and an alert unit. The collection unit collects audio data. The collection unit collects the audio data using, for example, a microphone installed on a dedicated terminal or a smartphone. The collection unit can also encrypt the collected audio data to prevent it from leaking to the outside. For example, the collection unit encrypts the collected audio data using an encryption algorithm such as AES (Advanced Encryption Standard). The analysis unit analyzes the audio data collected by the collection unit and detects conversations that are suspected of being cash card fraud. The analysis unit analyzes the audio data using, for example, a generation AI to identify fraud patterns. The generation AI analyzes, for example, specific keywords or the flow of conversations to determine the possibility of fraud. The analysis unit can also analyze only necessary data and immediately delete unnecessary data. For example, the analysis unit filters out noise and irrelevant conversations and analyzes only important data. The alert unit issues an alert based on the suspected fraud conversations detected by the analysis unit. The alert unit issues an alert to, for example, family members or the police. The alert unit can send an alert using, for example, SMS or email. The alert unit can also estimate a user's emotions and adjust the alert method based on the estimated user emotions. For example, if a user is nervous, the alert unit can provide a simple, highly visible alert. This allows the fraud detection system according to the embodiment to collect and analyze voice data, detect potentially fraudulent conversations, and issue an alert.

[0030] The collection unit can collect voice data using a microphone mounted on a dedicated terminal or a smartphone. The collection unit collects voice data using, for example, a microphone mounted on a dedicated terminal or a smartphone. For example, the collection unit can collect voice data using a high-sensitivity microphone mounted on a dedicated terminal. The collection unit can also collect voice data using a microphone mounted on a smartphone. For example, the collection unit collects voice data using a built-in microphone of a smartphone. Furthermore, the collection unit can also collect voice data by connecting an external microphone. For example, the collection unit collects voice data using an external microphone connected via Bluetooth (registered trademark). This makes it possible to collect voice data using a microphone mounted on a dedicated terminal or a smartphone.

[0031] The analysis unit can analyze the collected voice data and identify fraud patterns. For example, the analysis unit can analyze the collected voice data and identify fraud patterns. For example, the analysis unit can analyze the voice data using generative AI to detect specific phrases or conversation flows. The analysis unit can also analyze the characteristics of the voice data to determine the possibility of fraud. For example, the analysis unit can analyze the tone of voice and speaker attributes to identify fraud patterns. The analysis unit can also learn from past fraud cases and detect new fraud patterns. For example, the analysis unit can store past fraud cases in a database and compare them with new voice data to identify fraud patterns. In this way, the analysis unit can analyze the collected voice data and detect fraud patterns, thereby increasing the possibility of fraud.

[0032] The alert unit can issue an alert to family members or the police based on the detected conversation that is suspected to be fraudulent. For example, the alert unit can send an alert to family members or the police based on the detected conversation that is suspected to be fraudulent. For example, the alert unit can send an alert to family members using SMS or email. The alert unit can also send an alert to the police. For example, the alert unit notifies the police of details of the suspected fraudulent conversation. The alert unit can also estimate the user's emotions and adjust the alert method based on the estimated user's emotions. For example, the alert unit provides a simple, highly visible alert if the user is nervous. This allows for a quick response by detecting a conversation that may be fraudulent and alerting family members or the police.

[0033] The collection unit can encrypt the collected voice data to prevent it from being leaked to the outside. The collection unit, for example, encrypts the collected voice data to prevent it from being leaked to the outside. For example, the collection unit encrypts the voice data using an encryption algorithm such as AES (Advanced Encryption Standard). The collection unit can also securely store the encrypted voice data. For example, the collection unit stores the encrypted voice data in cloud storage. Furthermore, the collection unit can securely manage keys for decrypting the encrypted voice data. For example, the collection unit manages encryption keys using a key management system. In this way, privacy protection is strengthened by encrypting the voice data.

[0034] The analysis unit can analyze only necessary data and immediately delete unnecessary data. For example, the analysis unit can analyze only necessary data and immediately delete unnecessary data. For example, the analysis unit can filter out noise and irrelevant conversations and analyze only important data. The analysis unit can also use noise canceling technology to remove noise during collection. For example, the analysis unit can clean up audio data using a noise canceling algorithm. The analysis unit can also detect and delete duplicate data. For example, the analysis unit can detect and delete duplicate data if the same audio data is collected multiple times. This allows for efficient data analysis by immediately deleting unnecessary data.

[0035] When collecting voice data, the collection unit can analyze the user's past conversation history and select an appropriate collection method. For example, when collecting voice data, the collection unit analyzes the user's past conversation history and selects an appropriate collection method. For example, if the collection unit has had a conversation in the past that could be fraudulent, the generation AI learns that pattern and quickly collects similar conversations when they occur. Furthermore, if the collection unit determines from the user's past conversation history that conversations that are likely to be fraudulent tend to occur during certain time periods, it can strengthen collection during those time periods. Furthermore, the collection unit can prioritize the collection of conversations that contain specific keywords based on the user's past conversation history. This allows the optimal collection method to be selected by analyzing past conversation history, enabling efficient data collection.

[0036] The collection unit can perform filtering based on the user's current environment when collecting voice data. For example, the collection unit performs filtering based on the user's current environment (e.g., noise level and surrounding conditions) when collecting voice data. For example, if the user is in a noisy environment, the collection unit causes the generation AI to collect voice data using noise canceling technology. Also, if the user is in a quiet environment, the collection unit can collect detailed voice data using a high-sensitivity microphone. Also, if the user is moving, the collection unit can cause the generation AI to filter environmental sounds and collect only important conversations. This makes it possible to remove noise and collect important data by filtering according to the user's environment.

[0037] When collecting voice data, the collection unit can select an appropriate collection means depending on the user's input method. For example, when collecting voice data, the collection unit selects the optimal collection means depending on the user's input method (voice, text, gesture, etc.). For example, when the user is using voice input, the collection unit causes the generation AI to preferentially collect voice data. Also, when the user is using text input, the collection unit can cause the generation AI to analyze the text data and collect related voice data. Also, when the user is using gesture input, the collection unit can cause the generation AI to analyze the meaning of the gesture and collect related voice data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0038] When collecting voice data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting voice data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects voice data based on fraud patterns that are likely to occur in that area. Furthermore, when the user is moving, the collection unit can also collect highly relevant voice data based on the user's current location. Furthermore, when the user is in a specific building, the collection unit can collect voice data based on fraud patterns that are likely to occur in that building. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting voice data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting voice data. For example, if the user posts about fraud on social media, the collection unit collects related voice data based on the content of the post. The collection unit can also collect related voice data by referring to the activities of the user's friends on social media. The collection unit can also collect related voice data based on the user's check-in information on social media. In this way, related data can be efficiently collected by analyzing social media activities.

[0040] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting voice data. For example, when collecting voice data, the collection unit adjusts the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the collection method using the generation AI based on feedback provided by the user in the past. Furthermore, if the user prefers a particular collection method, the collection unit can also preferentially use that method. Furthermore, the collection unit can analyze the user's past feedback and suggest the optimal collection method. This allows the collection method to be customized by reflecting past feedback, enabling efficient data collection.

[0041] The analysis unit can adjust the accuracy of the analysis based on the importance of the audio data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the audio data during analysis. For example, in the case of audio data containing important conversations, the analysis unit allows the generation AI to perform a detailed analysis. In addition, in the case of audio data containing ordinary conversations, the analysis unit can also allow the generation AI to perform a simplified analysis. In addition, in the case of audio data containing conversations that are likely to be fraudulent, the analysis unit can also allow the generation AI to perform a focused analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the audio data, efficient analysis is possible.

[0042] The analysis unit can use different analysis algorithms depending on the category of voice data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of voice data during analysis. For example, in the case of a conversation that may be fraudulent, the analysis unit causes the generation AI to apply an algorithm that detects specific fraud patterns. In addition, in the case of a normal conversation, the analysis unit can also cause the generation AI to apply a general voice analysis algorithm. In addition, in the case of a conversation that includes a specific keyword, the analysis unit can also cause the generation AI to apply an analysis algorithm related to that keyword. In this way, by applying different analysis algorithms depending on the category of voice data, highly accurate analysis is possible.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can have the generation AI adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results, and the generation AI can suggest the optimal analysis method. The analysis unit can also have the generation AI improve the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0044] The analysis unit can set an analysis priority based on the time when the voice data was collected during analysis. For example, the analysis unit determines an analysis priority based on the time when the voice data was collected during analysis. For example, the analysis unit prioritizes analysis of recently collected voice data. The analysis unit can also prioritize analysis of voice data collected during a specific time period. The analysis unit can also prioritize analysis of voice data collected during a time period specified by the user. In this way, efficient analysis is possible by determining an analysis priority based on the time when the voice data was collected.

[0045] The analysis unit can set the order of analysis based on the relevance of the voice data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the voice data during analysis. For example, the analysis unit prioritizes analyzing voice data that is likely to be fraudulent. The analysis unit can also postpone analyzing voice data that includes normal conversations. The analysis unit can also prioritize analyzing voice data that includes specific keywords. In this way, by adjusting the order of analysis based on the relevance of the voice data, important data can be analyzed with priority.

[0046] The analysis unit can set the use of technical terms for analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms for analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit can cause the generation AI to provide analysis results using technical terms. Also, if the user does not have technical knowledge, the analysis unit can cause the generation AI to provide analysis results in simple language. Also, the analysis unit can adjust the way the generation AI expresses the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms for analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0047] The alert unit can set the level of detail of the alert based on the importance of the conversation suspected of being fraudulent when issuing an alert. For example, the alert unit adjusts the level of detail of the alert based on the importance of the conversation suspected of being fraudulent when issuing an alert. For example, the alert unit allows the generation AI to provide a detailed alert for a conversation that is highly likely to be fraudulent. The alert unit can also allow the generation AI to provide a simplified alert for a normal conversation. The alert unit can also allow the generation AI to provide a focused alert for a conversation that includes specific keywords. This enables efficient alerts by adjusting the level of detail of the alert based on the importance of a conversation that may be fraudulent.

[0048] The alert unit can use different alert means depending on the fraud category when issuing an alert. For example, the alert unit applies different alert means depending on the fraud category when issuing an alert. For example, in the case of cash card fraud, the alert unit can have the generation AI provide an audio alert. In addition, in the case of fictitious billing fraud, the alert unit can have the generation AI provide an alert by text message. In addition, in the case of telephone fraud, the alert unit can have the generation AI provide a video alert. In this way, by applying different alert means depending on the fraud category, appropriate alerts can be provided.

[0049] The alert unit can set a priority of the alert at the time of issuing an alert based on when the conversation suspected of being fraudulent was collected. For example, the alert unit determines the priority of the alert at the time of issuing an alert based on when the conversation suspected of being fraudulent was collected. For example, the alert unit prioritizes alerting on conversations that have recently been collected that may be fraudulent. The alert unit can also prioritize alerting on conversations that have been collected during a specific time period that may be fraudulent. The alert unit can also prioritize alerting on conversations that have been collected during a time period specified by the user. This enables a quick response by determining the priority of the alert based on when the conversation suspected of being fraudulent was collected.

[0050] The alert unit can set the order of alerts based on the relevance of conversations that may be suspected of fraud when issuing an alert. For example, the alert unit adjusts the order of alerts based on the relevance of conversations that may be suspected of fraud when issuing an alert. For example, the alert unit prioritizes alerts for conversations that are highly likely to be fraudulent. The alert unit can also postpone normal conversations. The alert unit can also prioritize alerts for conversations that include specific keywords. In this way, by adjusting the order of alerts based on the relevance of conversations that may be suspected of fraud, important alerts can be provided preferentially.

[0051] The alert unit can set the content of the alert according to the user's level of expertise when issuing an alert. For example, the alert unit adjusts the content of the alert according to the user's level of expertise when issuing an alert. For example, if the user has specialized knowledge, the alert unit causes the generation AI to provide an alert using technical terms. Also, if the user does not have specialized knowledge, the alert unit can cause the generation AI to provide an alert in simple language. Also, the alert unit can cause the generation AI to adjust the way the alert is expressed according to the user's level of expertise. In this way, by adjusting the content of the alert according to the user's level of expertise, it is possible to provide an alert that is easy to understand.

[0052] The alert unit can improve the accuracy of the alert by referring to the user's past alert results when issuing an alert. For example, the alert unit improves the accuracy of the alert by referring to the user's past alert results when issuing an alert. For example, the alert unit allows the generation AI to adjust the content of the alert based on feedback provided by the user in the past. The alert unit can also analyze the user's past alert results and the generation AI can suggest the optimal alert method. The alert unit can also allow the generation AI to improve the accuracy of the alert based on the user's past alert results. In this way, the accuracy of the alert can be improved by referring to the past alert results.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can collect voice data taking into account the user's geographical location information. For example, if the user is in a specific area, voice data can be collected based on fraud patterns that are likely to occur in that area. Also, if the user is on the move, highly relevant voice data can be collected based on the user's current location. Furthermore, if the user is in a specific building, voice data can be collected based on fraud patterns that are likely to occur in that building. In this way, by taking into account the geographical location information, highly relevant data can be collected preferentially.

[0055] The analysis unit can set analysis priorities based on the time when the voice data was collected. For example, it can prioritize analysis of recently collected voice data. It can also prioritize analysis of voice data collected during a specific time period. It can also prioritize analysis of voice data collected during a time period specified by the user. This allows for efficient analysis by determining analysis priorities based on the time when the voice data was collected.

[0056] The analysis unit can use different analysis algorithms depending on the category of voice data. For example, in the case of a conversation that may be fraudulent, the generation AI applies an algorithm that detects specific fraud patterns. In addition, in the case of a normal conversation, the generation AI can also apply a general voice analysis algorithm. Furthermore, in the case of a conversation that includes specific keywords, the generation AI can also apply an analysis algorithm related to those keywords. In this way, applying different analysis algorithms depending on the category of voice data enables highly accurate analysis.

[0057] The alert unit can set the level of detail of the alert based on the importance of the conversation suspected of being fraudulent. For example, if the conversation is highly likely to be fraudulent, the generation AI will provide a detailed alert. In addition, if the conversation is normal, the generation AI can provide a simplified alert. Furthermore, if the conversation contains specific keywords, the generation AI can provide a focused alert. This allows for efficient alerting by adjusting the level of detail of the alert based on the importance of the conversation suspected of being fraudulent.

[0058] The collection unit can analyze a user's past conversation history and select an appropriate collection method. For example, if a user has had a conversation in the past that could be fraudulent, the generation AI can learn that pattern and quickly collect similar conversations when they occur. Also, if the user's past conversation history shows that conversations with a high probability of fraud tend to occur during certain time periods, collection can be strengthened during those times. Furthermore, based on the user's past conversation history, it can also prioritize the collection of conversations that contain specific keywords. This makes it possible to select the optimal collection method by analyzing past conversation history and collect data efficiently.

[0059] The alert unit can use different alert methods depending on the fraud category. For example, in the case of cash card fraud, the generation AI can provide a voice alert. In the case of fictitious billing fraud, the generation AI can also provide an alert via text message. Furthermore, in the case of telephone fraud, the generation AI can also provide a video alert. This allows appropriate alerts to be provided by applying different alert methods depending on the fraud category.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects voice data. The collection unit collects voice data using, for example, a dedicated terminal or a microphone installed on a smartphone. The collection unit can also encrypt the collected voice data to prevent it from leaking to the outside. For example, the collection unit encrypts the collected voice data using an encryption algorithm such as AES (Advanced Encryption Standard). Step 2: The analysis unit analyzes the voice data collected by the collection unit and detects conversations that are suspected of being cash card fraud. The analysis unit, for example, uses a generation AI to analyze the voice data and identify fraud patterns. The generation AI, for example, analyzes specific keywords and the flow of conversation to determine the possibility of fraud. The analysis unit can also analyze only the necessary data and immediately delete unnecessary data. For example, the analysis unit can filter out noise and irrelevant conversations and analyze only the important data. Step 3: The alert unit issues an alert based on the suspected fraudulent conversation detected by the analysis unit. The alert unit issues an alert, for example, to family members or the police. The alert unit can send the alert, for example, by SMS or email. The alert unit can also estimate the user's emotions and adjust the alert method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible alert is provided.

[0062] (Example 2) A fraud detection system according to an embodiment of the present invention collects and analyzes voice data, detects potentially fraudulent conversations, and issues an alert. The fraud detection system uses a dedicated device or smartphone with a dedicated app. A voice collection device equipped with a generation AI is deployed to constantly listen to the owner's conversations. When a conversation that may be a cash card fraud is detected, an alert is sent to family members and the police. For example, the fraud detection system installs a dedicated app on a dedicated device or smartphone. The owner then wears a voice collection device equipped with a generation AI. This device constantly listens to the owner's conversations and analyzes them in real time. For example, it collects all types of voice, including telephone conversations and face-to-face conversations. The generation AI analyzes the collected voice data and detects conversations that may be potentially fraudulent. For example, phrases such as "We're taking your cash card" or "This is a call from the bank" are deemed to be highly likely to be fraudulent. In this case, the generation AI envisions countless possible scenarios for victimization and quickly issues an alert. When an alert is detected, family members and the police are notified via a dedicated app. For example, a message such as "A possible fraudulent conversation has been detected" is sent to family members, and a notification such as "A possible fraudulent conversation has been detected. Please check for more information" is sent to the police. Privacy is thoroughly protected, and collected voice data is encrypted and never leaked. Furthermore, the generating AI analyzes only necessary data and immediately deletes unnecessary data, thereby protecting the owner's privacy. This allows the fraud detection system to prevent fraudulent billing scams before they occur. For example, even if an elderly person receives a fraudulent phone call, the generating AI can quickly issue an alert and contact family members and the police, preventing the victim from falling victim. Furthermore, even if the owner does not realize there is a possibility of fraud, the generating AI automatically detects it and issues an alert, allowing them to go about their daily life with peace of mind.

[0063] A fraud detection system according to an embodiment includes a collection unit, an analysis unit, and an alert unit. The collection unit collects audio data. The collection unit collects the audio data using, for example, a microphone installed on a dedicated terminal or a smartphone. The collection unit can also encrypt the collected audio data to prevent it from leaking to the outside. For example, the collection unit encrypts the collected audio data using an encryption algorithm such as AES (Advanced Encryption Standard). The analysis unit analyzes the audio data collected by the collection unit and detects conversations that are suspected of being cash card fraud. The analysis unit analyzes the audio data using, for example, a generation AI to identify fraud patterns. The generation AI analyzes, for example, specific keywords or the flow of conversations to determine the possibility of fraud. The analysis unit can also analyze only necessary data and immediately delete unnecessary data. For example, the analysis unit filters out noise and irrelevant conversations and analyzes only important data. The alert unit issues an alert based on the suspected fraud conversations detected by the analysis unit. The alert unit issues an alert to, for example, family members or the police. The alert unit can send an alert using, for example, SMS or email. The alert unit can also estimate a user's emotions and adjust the alert method based on the estimated user emotions. For example, if a user is nervous, the alert unit can provide a simple, highly visible alert. This allows the fraud detection system according to the embodiment to collect and analyze voice data, detect potentially fraudulent conversations, and issue an alert.

[0064] The collection unit can collect voice data using a microphone mounted on a dedicated terminal or a smartphone. The collection unit collects voice data using, for example, a microphone mounted on a dedicated terminal or a smartphone. For example, the collection unit can collect voice data using a high-sensitivity microphone mounted on a dedicated terminal. The collection unit can also collect voice data using a microphone mounted on a smartphone. For example, the collection unit collects voice data using a built-in microphone of a smartphone. Furthermore, the collection unit can also collect voice data by connecting an external microphone. For example, the collection unit collects voice data using an external microphone connected via Bluetooth. This makes it possible to collect voice data using a microphone mounted on a dedicated terminal or a smartphone.

[0065] The analysis unit can analyze the collected voice data and identify fraud patterns. For example, the analysis unit can analyze the collected voice data and identify fraud patterns. For example, the analysis unit can analyze the voice data using generative AI to detect specific phrases or conversation flows. The analysis unit can also analyze the characteristics of the voice data to determine the possibility of fraud. For example, the analysis unit can analyze the tone of voice and speaker attributes to identify fraud patterns. The analysis unit can also learn from past fraud cases and detect new fraud patterns. For example, the analysis unit can store past fraud cases in a database and compare them with new voice data to identify fraud patterns. In this way, the analysis unit can analyze the collected voice data and detect fraud patterns, thereby increasing the possibility of fraud.

[0066] The alert unit can issue an alert to family members or the police based on the detected conversation that is suspected to be fraudulent. For example, the alert unit can send an alert to family members or the police based on the detected conversation that is suspected to be fraudulent. For example, the alert unit can send an alert to family members using SMS or email. The alert unit can also send an alert to the police. For example, the alert unit notifies the police of details of the suspected fraudulent conversation. The alert unit can also estimate the user's emotions and adjust the alert method based on the estimated user's emotions. For example, the alert unit provides a simple, highly visible alert if the user is nervous. This allows for a quick response by detecting a conversation that may be fraudulent and alerting family members or the police.

[0067] The collection unit can encrypt the collected voice data to prevent it from being leaked to the outside. The collection unit, for example, encrypts the collected voice data to prevent it from being leaked to the outside. For example, the collection unit encrypts the voice data using an encryption algorithm such as AES (Advanced Encryption Standard). The collection unit can also securely store the encrypted voice data. For example, the collection unit stores the encrypted voice data in cloud storage. Furthermore, the collection unit can securely manage keys for decrypting the encrypted voice data. For example, the collection unit manages encryption keys using a key management system. In this way, privacy protection is strengthened by encrypting the voice data.

[0068] The analysis unit can analyze only necessary data and immediately delete unnecessary data. For example, the analysis unit can analyze only necessary data and immediately delete unnecessary data. For example, the analysis unit can filter out noise and irrelevant conversations and analyze only important data. The analysis unit can also use noise canceling technology to remove noise during collection. For example, the analysis unit can clean up audio data using a noise canceling algorithm. The analysis unit can also detect and delete duplicate data. For example, the analysis unit can detect and delete duplicate data if the same audio data is collected multiple times. This allows for efficient data analysis by immediately deleting unnecessary data.

[0069] The collection unit can infer the user's emotions and adjust the timing of collecting voice data based on the inferred user emotions. The collection unit, for example, infers the user's emotions and adjusts the timing of collecting voice data based on the inferred user emotions. For example, if the user is nervous, the collection unit causes the generation AI to increase the frequency of voice data collection and collect detailed data. Also, if the user is relaxed, the collection unit can cause the generation AI to reduce the frequency of voice data collection and collect the minimum amount of data necessary. Also, if the user is in a hurry, the collection unit can cause the generation AI to collect voice data quickly so as not to miss important information. This enables more appropriate data collection by adjusting the timing of voice data collection according to the user's emotions.

[0070] When collecting voice data, the collection unit can analyze the user's past conversation history and select an appropriate collection method. For example, when collecting voice data, the collection unit analyzes the user's past conversation history and selects an appropriate collection method. For example, if the collection unit has had a conversation in the past that could be fraudulent, the generation AI learns that pattern and quickly collects similar conversations when they occur. Furthermore, if the collection unit determines from the user's past conversation history that conversations that are likely to be fraudulent tend to occur during certain time periods, it can strengthen collection during those time periods. Furthermore, the collection unit can prioritize the collection of conversations that contain specific keywords based on the user's past conversation history. This allows the optimal collection method to be selected by analyzing past conversation history, enabling efficient data collection.

[0071] The collection unit can perform filtering based on the user's current environment when collecting voice data. For example, the collection unit performs filtering based on the user's current environment (e.g., noise level and surrounding conditions) when collecting voice data. For example, if the user is in a noisy environment, the collection unit causes the generation AI to collect voice data using noise canceling technology. Also, if the user is in a quiet environment, the collection unit can collect detailed voice data using a high-sensitivity microphone. Also, if the user is moving, the collection unit can cause the generation AI to filter environmental sounds and collect only important conversations. This makes it possible to remove noise and collect important data by filtering according to the user's environment.

[0072] When collecting voice data, the collection unit can select an appropriate collection means depending on the user's input method. For example, when collecting voice data, the collection unit selects the optimal collection means depending on the user's input method (voice, text, gesture, etc.). For example, when the user is using voice input, the collection unit causes the generation AI to preferentially collect voice data. Also, when the user is using text input, the collection unit can cause the generation AI to analyze the text data and collect related voice data. Also, when the user is using gesture input, the collection unit can cause the generation AI to analyze the meaning of the gesture and collect related voice data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method.

[0073] The collection unit can infer the user's emotions and determine the priority of the voice data to be collected based on the inferred user emotions. The collection unit, for example, can infer the user's emotions and determine the priority of the voice data to be collected based on the inferred user emotions. For example, if the user is nervous, the collection unit can cause the generation AI to prioritize collecting important conversations. Also, if the user is relaxed, the collection unit can cause the generation AI to prioritize collecting normal conversations. Also, if the user is in a hurry, the collection unit can cause the generation AI to collect important information in a short amount of time. In this way, by determining the priority of voice data according to the user's emotions, important data can be collected preferentially.

[0074] When collecting voice data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when collecting voice data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects voice data based on fraud patterns that are likely to occur in that area. Furthermore, when the user is moving, the collection unit can also collect highly relevant voice data based on the user's current location. Furthermore, when the user is in a specific building, the collection unit can collect voice data based on fraud patterns that are likely to occur in that building. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information.

[0075] The collection unit can analyze the user's social media activities and collect related data when collecting voice data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting voice data. For example, if the user posts about fraud on social media, the collection unit collects related voice data based on the content of the post. The collection unit can also collect related voice data by referring to the activities of the user's friends on social media. The collection unit can also collect related voice data based on the user's check-in information on social media. In this way, related data can be efficiently collected by analyzing social media activities.

[0076] The collection unit can adjust the collection method by reflecting the user's past feedback when collecting voice data. For example, when collecting voice data, the collection unit adjusts the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the collection method using the generation AI based on feedback provided by the user in the past. Furthermore, if the user prefers a particular collection method, the collection unit can also preferentially use that method. Furthermore, the collection unit can analyze the user's past feedback and suggest the optimal collection method. This allows the collection method to be customized by reflecting past feedback, enabling efficient data collection.

[0077] The analysis unit can infer the user's emotions and adjust the way the analysis is presented based on the inferred user emotions. The analysis unit, for example, can infer the user's emotions and adjust the way the analysis is presented based on the inferred user emotions. For example, if the user is nervous, the analysis unit can cause the generation AI to provide a simple, highly visible analysis result. Also, if the user is relaxed, the analysis unit can cause the generation AI to provide a detailed analysis result. Also, if the user is in a hurry, the analysis unit can cause the generation AI to provide an analysis result that focuses on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, more appropriate analysis results can be provided.

[0078] The analysis unit can adjust the accuracy of the analysis based on the importance of the audio data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the audio data during analysis. For example, in the case of audio data containing important conversations, the analysis unit allows the generation AI to perform a detailed analysis. In addition, in the case of audio data containing ordinary conversations, the analysis unit can also allow the generation AI to perform a simplified analysis. In addition, in the case of audio data containing conversations that are likely to be fraudulent, the analysis unit can also allow the generation AI to perform a focused analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the audio data, efficient analysis is possible.

[0079] The analysis unit can use different analysis algorithms depending on the category of voice data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of voice data during analysis. For example, in the case of a conversation that may be fraudulent, the analysis unit causes the generation AI to apply an algorithm that detects specific fraud patterns. In addition, in the case of a normal conversation, the analysis unit can also cause the generation AI to apply a general voice analysis algorithm. In addition, in the case of a conversation that includes a specific keyword, the analysis unit can also cause the generation AI to apply an analysis algorithm related to that keyword. In this way, by applying different analysis algorithms depending on the category of voice data, highly accurate analysis is possible.

[0080] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can have the generation AI adjust the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results, and the generation AI can suggest the optimal analysis method. The analysis unit can also have the generation AI improve the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the past analysis results.

[0081] The analysis unit can infer the user's emotions and adjust the length of the analysis based on the inferred user emotions. The analysis unit, for example, can infer the user's emotions and adjust the length of the analysis based on the inferred user emotions. For example, if the user is in a hurry, the analysis unit can cause the generation AI to provide a short, to-the-point analysis result. Also, if the user is relaxed, the analysis unit can cause the generation AI to provide a detailed analysis result. Also, if the user is excited, the analysis unit can cause the generation AI to provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0082] The analysis unit can set an analysis priority based on the time when the voice data was collected during analysis. For example, the analysis unit determines an analysis priority based on the time when the voice data was collected during analysis. For example, the analysis unit prioritizes analysis of recently collected voice data. The analysis unit can also prioritize analysis of voice data collected during a specific time period. The analysis unit can also prioritize analysis of voice data collected during a time period specified by the user. In this way, efficient analysis is possible by determining an analysis priority based on the time when the voice data was collected.

[0083] The analysis unit can set the order of analysis based on the relevance of the voice data during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the voice data during analysis. For example, the analysis unit prioritizes analyzing voice data that is likely to be fraudulent. The analysis unit can also postpone analyzing voice data that includes normal conversations. The analysis unit can also prioritize analyzing voice data that includes specific keywords. In this way, by adjusting the order of analysis based on the relevance of the voice data, important data can be analyzed with priority.

[0084] The analysis unit can set the use of technical terms for analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms for analysis according to the user's level of expertise during analysis. For example, if the user has technical knowledge, the analysis unit can cause the generation AI to provide analysis results using technical terms. Also, if the user does not have technical knowledge, the analysis unit can cause the generation AI to provide analysis results in simple language. Also, the analysis unit can adjust the way the generation AI expresses the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms for analysis according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0085] The alert unit can infer the user's emotions and adjust the alert method based on the inferred user emotions. The alert unit can, for example, infer the user's emotions and adjust the alert method based on the inferred user emotions. For example, if the user is nervous, the alert unit's generation AI can provide a simple, highly visible alert. Also, if the user is relaxed, the alert unit's generation AI can provide a detailed alert. Also, if the user is in a hurry, the alert unit's generation AI can provide an alert that focuses on the main points. In this way, by adjusting the alert method according to the user's emotions, more appropriate alerts can be provided.

[0086] The alert unit can set the level of detail of the alert based on the importance of the conversation suspected of being fraudulent when issuing an alert. For example, the alert unit adjusts the level of detail of the alert based on the importance of the conversation suspected of being fraudulent when issuing an alert. For example, the alert unit allows the generation AI to provide a detailed alert for a conversation that is highly likely to be fraudulent. The alert unit can also allow the generation AI to provide a simplified alert for a normal conversation. The alert unit can also allow the generation AI to provide a focused alert for a conversation that includes specific keywords. This enables efficient alerts by adjusting the level of detail of the alert based on the importance of a conversation that may be fraudulent.

[0087] The alert unit can use different alert means depending on the fraud category when issuing an alert. For example, the alert unit applies different alert means depending on the fraud category when issuing an alert. For example, in the case of cash card fraud, the alert unit can have the generation AI provide an audio alert. In addition, in the case of fictitious billing fraud, the alert unit can have the generation AI provide an alert by text message. In addition, in the case of telephone fraud, the alert unit can have the generation AI provide a video alert. In this way, by applying different alert means depending on the fraud category, appropriate alerts can be provided.

[0088] The alert unit can set a priority of the alert at the time of issuing an alert based on when the conversation suspected of being fraudulent was collected. For example, the alert unit determines the priority of the alert at the time of issuing an alert based on when the conversation suspected of being fraudulent was collected. For example, the alert unit prioritizes alerting on conversations that have recently been collected that may be fraudulent. The alert unit can also prioritize alerting on conversations that have been collected during a specific time period that may be fraudulent. The alert unit can also prioritize alerting on conversations that have been collected during a time period specified by the user. This enables a quick response by determining the priority of the alert based on when the conversation suspected of being fraudulent was collected.

[0089] The alert unit can infer the user's emotions and set the priority of alerts based on the inferred user emotions. The alert unit, for example, infers the user's emotions and determines the priority of alerts based on the inferred user emotions. For example, if the user is nervous, the alert unit can cause the generation AI to provide important alerts with priority. Also, if the user is relaxed, the alert unit can cause the generation AI to provide normal alerts with priority. Also, if the user is in a hurry, the alert unit can cause the generation AI to provide alerts quickly. In this way, by determining the priority of alerts according to the user's emotions, important alerts can be provided with priority.

[0090] The alert unit can set the order of alerts based on the relevance of conversations that may be suspected of fraud when issuing an alert. For example, the alert unit adjusts the order of alerts based on the relevance of conversations that may be suspected of fraud when issuing an alert. For example, the alert unit prioritizes alerts for conversations that are highly likely to be fraudulent. The alert unit can also postpone normal conversations. The alert unit can also prioritize alerts for conversations that include specific keywords. In this way, by adjusting the order of alerts based on the relevance of conversations that may be suspected of fraud, important alerts can be provided preferentially.

[0091] The alert unit can set the content of the alert according to the user's level of expertise when issuing an alert. For example, the alert unit adjusts the content of the alert according to the user's level of expertise when issuing an alert. For example, if the user has specialized knowledge, the alert unit causes the generation AI to provide an alert using technical terms. Also, if the user does not have specialized knowledge, the alert unit can cause the generation AI to provide an alert in simple language. Also, the alert unit can cause the generation AI to adjust the way the alert is expressed according to the user's level of expertise. In this way, by adjusting the content of the alert according to the user's level of expertise, it is possible to provide an alert that is easy to understand.

[0092] The alert unit can improve the accuracy of the alert by referring to the user's past alert results when issuing an alert. For example, the alert unit improves the accuracy of the alert by referring to the user's past alert results when issuing an alert. For example, the alert unit allows the generation AI to adjust the content of the alert based on feedback provided by the user in the past. The alert unit can also analyze the user's past alert results and the generation AI can suggest the optimal alert method. The alert unit can also allow the generation AI to improve the accuracy of the alert based on the user's past alert results. In this way, the accuracy of the alert can be improved by referring to the past alert results. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and alert unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects voice data using the microphone 38B of the smart device 14, and the collected voice data is encrypted by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generation AI to identify fraud patterns. The alert unit is realized, for example, by the control unit 46A of the smart device 14, and sends an alert to family members or the police via SMS or email. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and alert unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects voice data using the microphone 238 of the smart glasses 214, and the collected voice data is encrypted by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generative AI to identify fraud patterns. The alert unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends an alert to family members or the police via SMS or email. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and alert unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects voice data using the microphone 238 of the headset type terminal 314, and the collected voice data is encrypted by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generation AI to identify fraud patterns. The alert unit is realized, for example, by the control unit 46A of the headset type terminal 314, and sends an alert to family members or the police via SMS or email. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and alert unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects voice data using the microphone 238 of the robot 414, and the collected voice data is encrypted by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generation AI to identify fraud patterns. The alert unit is realized, for example, by the control unit 46A of the robot 414, and sends an alert to family members or the police via SMS or email.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] 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 nervous, the generation AI will prioritize analyzing important conversations. Also, if the user is relaxed, the generation AI can prioritize analyzing normal conversations. Furthermore, if the user is in a hurry, the generation AI can analyze important information in a short amount of time. In this way, by determining the analysis priority according to the user's emotions, important data can be analyzed with priority.

[0095] The collection unit can collect voice data taking into account the user's geographical location information. For example, if the user is in a specific area, voice data can be collected based on fraud patterns that are likely to occur in that area. Also, if the user is on the move, highly relevant voice data can be collected based on the user's current location. Furthermore, if the user is in a specific building, voice data can be collected based on fraud patterns that are likely to occur in that building. In this way, by taking into account the geographical location information, highly relevant data can be collected preferentially.

[0096] The alert unit can estimate the user's emotions and adjust the alert method based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible alert. If the user is relaxed, the generation AI can also provide a detailed alert. Furthermore, if the user is in a hurry, the generation AI can also provide an alert that focuses on the main points. This allows the system to provide more appropriate alerts by adjusting the alert method according to the user's emotions.

[0097] The analysis unit can set analysis priorities based on the time when the voice data was collected. For example, it can prioritize analysis of recently collected voice data. It can also prioritize analysis of voice data collected during a specific time period. It can also prioritize analysis of voice data collected during a time period specified by the user. This allows for efficient analysis by determining analysis priorities based on the time when the voice data was collected.

[0098] The collection unit can estimate the user's emotions and adjust the timing of voice data collection based on the estimated user emotions. For example, if the user is nervous, the generation AI can increase the frequency of voice data collection and collect detailed data. Alternatively, if the user is relaxed, the generation AI can reduce the frequency of voice data collection and collect the minimum amount of data necessary. Furthermore, if the user is in a hurry, the generation AI can collect voice data quickly so as not to miss important information. This allows for more appropriate data collection by adjusting the timing of voice data collection according to the user's emotions.

[0099] The analysis unit can use different analysis algorithms depending on the category of voice data. For example, in the case of a conversation that may be fraudulent, the generation AI applies an algorithm that detects specific fraud patterns. In addition, in the case of a normal conversation, the generation AI can also apply a general voice analysis algorithm. Furthermore, in the case of a conversation that includes specific keywords, the generation AI can also apply an analysis algorithm related to those keywords. In this way, applying different analysis algorithms depending on the category of voice data enables highly accurate analysis.

[0100] The alert unit can set the level of detail of the alert based on the importance of the conversation suspected of being fraudulent. For example, if the conversation is highly likely to be fraudulent, the generation AI will provide a detailed alert. In addition, if the conversation is normal, the generation AI can provide a simplified alert. Furthermore, if the conversation contains specific keywords, the generation AI can provide a focused alert. This allows for efficient alerting by adjusting the level of detail of the alert based on the importance of the conversation suspected of being fraudulent.

[0101] The collection unit can analyze a user's past conversation history and select an appropriate collection method. For example, if a user has had a conversation in the past that could be fraudulent, the generation AI can learn that pattern and quickly collect similar conversations when they occur. Also, if the user's past conversation history shows that conversations with a high probability of fraud tend to occur during certain time periods, collection can be strengthened during those times. Furthermore, based on the user's past conversation history, it can also prioritize the collection of conversations that contain specific keywords. This makes it possible to select the optimal collection method by analyzing past conversation history and collect data efficiently.

[0102] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide simple, highly visible analysis results. If the user is relaxed, the generation AI can also provide detailed analysis results. Furthermore, if the user is in a hurry, the generation AI can also provide analysis results that focus on the main points. This allows the system to provide more appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions.

[0103] The alert unit can use different alert methods depending on the fraud category. For example, in the case of cash card fraud, the generation AI can provide a voice alert. In the case of fictitious billing fraud, the generation AI can also provide an alert via text message. Furthermore, in the case of telephone fraud, the generation AI can also provide a video alert. This allows appropriate alerts to be provided by applying different alert methods depending on the fraud category.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects voice data. The collection unit collects voice data using, for example, a dedicated terminal or a microphone installed on a smartphone. The collection unit can also encrypt the collected voice data to prevent it from leaking to the outside. For example, the collection unit encrypts the collected voice data using an encryption algorithm such as AES (Advanced Encryption Standard). Step 2: The analysis unit analyzes the voice data collected by the collection unit and detects conversations that are suspected of being cash card fraud. The analysis unit, for example, uses a generation AI to analyze the voice data and identify fraud patterns. The generation AI, for example, analyzes specific keywords and the flow of conversation to determine the possibility of fraud. The analysis unit can also analyze only the necessary data and immediately delete unnecessary data. For example, the analysis unit can filter out noise and irrelevant conversations and analyze only the important data. Step 3: The alert unit issues an alert based on the suspected fraudulent conversation detected by the analysis unit. The alert unit issues an alert, for example, to family members or the police. The alert unit can send the alert, for example, by SMS or email. The alert unit can also estimate the user's emotions and adjust the alert method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible alert is provided.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

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

[0129] The 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.

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

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects voice data; an analysis unit that analyzes the voice data collected by the collection unit and detects conversations that are suspected of being cash card fraud; an alert unit that issues an alert based on the conversation suspected of fraud detected by the analysis unit. A system characterized by:

2. The collecting unit Collect voice data using a dedicated device or a microphone on a smartphone 2. The system of claim 1.

3. The analysis unit Analyzing collected voice data to identify fraud patterns 2. The system of claim 1.

4. The alert unit Alert family or police based on detected fraudulent conversations 2. The system of claim 1.

5. The collecting unit Collected voice data is encrypted to prevent it from leaking to the outside.

2. The system of claim 1.

6. The analysis unit Analyze only the data you need and immediately delete unnecessary data 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of voice data collection based on the inferred user emotions 2. The system of claim 1.

8. The collecting unit When collecting voice data, analyze the user's past conversation history and select the appropriate collection method.

2. The system of claim 1.

Images