System

Abstract

An object of a system according to an embodiment is to analyze the content of a telephone conversation in real time and to immediately handle a case where there is a suspicion of special fraud.SOLUTION: A system includes a transcription part, an analysis part, and a notification part. The transcriber transcribes the telephone conversation in real time. The analysis unit analyzes the text data transcribed by the transcription unit and determines whether or not there is a suspicion of special fraud. When the analysis unit determines that there is a suspicion of a special fraud, the notification unit transmits an alert notification to the person in question or a necessary contact address.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 the problem that it is difficult to analyze telephone conversations in real time and respond immediately if special fraud is suspected.

[0005] The system of the embodiment aims to analyze the content of telephone conversations in real time and respond immediately if there is suspicion of special fraud. [Means for solving the problem]

[0006] The system according to the embodiment includes a transcription unit, an analysis unit, and a notification unit. The transcription unit transcribes telephone conversations in real time. The analysis unit analyzes the text data transcribed by the transcription unit and determines whether there is a suspicion of special fraud. If the analysis unit determines that there is a suspicion of special fraud, the notification unit sends an alert notification to the person in question or a necessary contact. [Effects of the Invention]

[0007] The system according to the embodiment analyzes the contents of telephone conversations in real time and can respond immediately if there is suspicion of special fraud. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The new social problem-solving service according to the embodiment of the present invention is a system in which AI transcribes telephone conversations and analyzes their contents to detect conversations that are suspected of being fraudulent, and immediately sends an alert to the person in question and necessary contacts. This new social problem-solving service transcribes telephone conversations in real time and immediately sends an alert if there is a suspicion of fraudulent activity, thereby preventing damage before it occurs.

[0029] A new social problem-solving service according to an embodiment includes a transcription unit, an analysis unit, and a notification unit. The transcription unit transcribes telephone conversations in real time. For example, the transcription unit converts the contents of the call into text data using speech recognition technology. The transcription unit can also analyze the audio data of the call and convert it into text. For example, the speech recognition technology can use deep learning-based speech recognition technology. The analysis unit analyzes the text data transcribed by the transcription unit and determines whether there is a suspicion of special fraud. For example, the analysis unit detects fraud patterns using keyword matching technology. The analysis unit can also analyze the text data using natural language processing technology. For example, the analysis unit compares the text data with a database of past fraud cases to detect phrases that may be fraudulent. If the analysis unit determines that there is a suspicion of special fraud, the notification unit sends an alert notification to the person or necessary contacts. For example, the notification unit sends the alert notification via email, SMS, or app notification. The notification unit can also customize the content of the alert notification. For example, the notification unit generates notification content based on the recipient's emotions. As a result, the new social problem-solving service according to the embodiment can prevent damage by transcribing telephone conversations in real time and sending an immediate alert notification if there is suspicion of special fraud.

[0030] The transcription unit can convert the contents of a phone call into text data using speech recognition technology. For example, the transcription unit estimates the speaker's emotions from the text transcribed by the generation AI and records changes in emotions in real time. For example, the transcription unit analyzes the speaker's tone and speed of voice from the text transcribed by the generation AI and records changes in emotions in real time. For example, if a speaker suddenly raises their voice, that change in emotion is reflected in the text. In addition, during transcription, to estimate the speaker's emotions, emotional features are extracted from the voice data and emotion tags are assigned to the text data. For example, the emotion tag "Calm" is assigned to the text "Hello, this is the bank." The transcription unit also applies an emotion estimation algorithm to the transcribed text and records changes in emotion in real time. For example, the emotion tag "Tense" is assigned to the phrase "Please transfer money." This allows the transcription unit to accurately convert the contents of a phone call into text data using speech recognition technology.

[0031] The analysis unit can compare the text data with a database of past fraud cases and calculate the similarity. For example, the analysis unit develops an algorithm that compares the data with a database of past fraud cases and calculates the similarity in order to detect fraud patterns. For example, fraud phrases and patterns are registered in the database. A system is also built that compares the transcript data with the database of past fraud cases and calculates the similarity. For example, phrases that are suspected to be fraudulent are compared with the database. Also, to detect fraud patterns, an algorithm is applied that uses the database of past fraud cases to calculate the similarity. For example, the content of phone calls that are suspected to be fraudulent is compared with the database. This makes it possible to detect fraud patterns with a high degree of accuracy by comparing the data with the database of past fraud cases.

[0032] The transcription unit can automatically remove background sounds or noise during transcription to generate clear text data. For example, the transcription unit uses noise canceling technology to automatically remove background sounds and noise during transcription. For example, noise and echoes during phone calls are removed. The transcription unit also analyzes audio data and applies an algorithm to identify and remove background sounds and noise. For example, car sounds and wind noise during phone calls are removed. Furthermore, to improve transcription accuracy, a noise removal filter is applied in real time to generate clear text data. For example, background music during phone calls is removed. This removes background sounds and noise, improving transcription accuracy.

[0033] The transcription unit makes the transcription data multilingual, and can transcribe conversations in different languages ​​in real time. For example, to make the transcription data multilingual, the transcription unit builds a speech recognition model that supports multiple languages. For example, it transcribes speech in English, Japanese, Chinese, etc. in real time. In addition, to transcribe conversations in different languages ​​in real time, a system is developed that dynamically switches speech recognition models. For example, it responds immediately when the language changes during a call. In addition, to make the transcription data multilingual, the speech recognition model is trained with speech data in multiple languages. For example, speech data in multiple languages ​​is collected and trained into the model. This makes it multilingual, allowing conversations in different languages ​​to be transcribed in real time.

[0034] The transcription unit generates subtitles in real time to visually display the transcription data, and can also support video calls. The transcription unit, for example, develops a system that generates subtitles in real time to visually display the transcription data. For example, subtitles are displayed during a video call. Also, to support video calls, a function is added to display the transcription data as subtitles in real time. For example, text data during a call is displayed on the screen. Also, to visually display the transcription data, a subtitle generation algorithm is applied to generate subtitles in real time. For example, text data during a call is automatically displayed as subtitles. This makes it possible to support video calls by visually displaying the transcription data.

[0035] The analysis unit can apply the results of conversation content analysis to different industries and applications, and detect risks other than fraud. For example, the analysis unit can apply the results of conversation content analysis to customer support and build a system that detects customer dissatisfaction and complaints in real time. For example, an alert can be sent when a customer expresses dissatisfaction. In addition, the analysis unit can use the results of conversation content analysis to develop a system that detects patient emergencies and risks in real time in medical consultations. For example, an alert can be sent when a patient complains of emergency symptoms. In addition, the analysis results of conversation content can be applied to different industries and build a system that detects risks other than fraud. For example, it can detect suspected fraudulent transactions in the financial industry. This makes it possible to apply the analysis results to different industries and applications and detect risks other than fraud.

[0036] The analysis unit can visualize the analysis results and provide a dashboard that can be intuitively understood by the user. For example, the analysis unit develops a dashboard that can be intuitively understood by the user in order to visualize the analysis results. For example, the content of calls suspected of being fraudulent is displayed in graphs and charts. The analysis unit also provides a dashboard that visualizes the analysis results in real time and allows the user to respond immediately. For example, the content of calls suspected of being fraudulent is displayed in color-coded form. The analysis unit also provides a dashboard that can be customized by the user in order to visualize the analysis results. For example, the user can select and display data that interests them. In this way, the analysis results can be visualized so that they can be intuitively understood by the user.

[0037] The notification unit can simultaneously send alert notifications to smartphones, tablets, and PCs before sending them to ensure they are received. The notification unit, for example, develops a system that simultaneously sends alert notifications to multiple devices before sending them. For example, notifications are sent simultaneously to smartphones, tablets, and PCs. In addition, to simultaneously send alert notifications to multiple devices, a system is built that synchronizes between devices in real time. For example, confirmation is performed between devices to ensure that notifications are received reliably. In addition, to simultaneously send alert notifications to multiple devices, a system is developed that automatically adjusts notification settings for each device. For example, the notification sound and display method are customized for each device. As a result, alert notifications are reliably received by sending them to multiple devices simultaneously.

[0038] The notification unit can save the history of alert notifications so that they can be checked later. The notification unit, for example, develops a system that saves the history of alert notifications so that they can be checked later. For example, a function is added to display the contents of past notifications in a list. Also, in order to save the history of alert notifications, a notification database is constructed so that users can search for past notifications. For example, a function is provided to search for notifications by specific date and time or content. Also, in order to save the history of alert notifications so that they can be checked later, a system is developed that automatically backs up the notification history. For example, the notification history is saved on a regular basis in the cloud. This allows the history of alert notifications to be saved and checked later.

[0039] The notification unit can also send alert notifications as audio messages or video messages. The notification unit develops, for example, a system that can send alert notifications as audio messages. For example, if fraud is suspected, a notification that issues an audio warning is sent. Also, a video recording function is added to enable the alert notifications to be sent as video messages. For example, if fraud is suspected, a video notification that shows specific steps to take is sent. Also, a system is built that allows the user to select the notification format in order to send alert notifications as audio messages or video messages. For example, the user is allowed to select between audio and video notifications. This allows the notifications to be sent as audio or video messages, making it possible to notify recipients more effectively.

[0040] The notification unit can automatically send alert notifications not only to family and friends, but also to local communities and police. For example, the notification unit develops a system that automatically sends alert notifications not only to family and friends, but also to local communities and police. For example, a notification is sent to a local crime prevention network when fraud is suspected. In addition, a function is added to pre-set a list of notification recipients in order to automatically send alert notifications. For example, it is made possible for users to customize notification recipients. In addition, a system is built that automatically obtains contact information for notification recipients in order to send alert notifications to local communities and police. For example, contact information for a local crime prevention network is automatically obtained. This makes it possible to send notifications to local communities and police, thereby preventing damage on a wider scale.

[0041] The damage prevention unit can learn from past alert notifications and their results and propose the optimal response method in order to prevent damage before it occurs. For example, the damage prevention unit develops a system that learns from past alert notifications and their results in order to prevent damage before it occurs. For example, it proposes the optimal response method based on past success stories and failure stories. It also applies a machine learning algorithm to learn from past alert notifications and their results and propose the optimal response method. For example, it automatically generates the optimal response method based on past data. It also builds a system that stores past alert notifications and their results in a database and allows it to learn from them in order to prevent damage before it occurs. For example, it proposes the optimal response method based on past data. In this way, it can propose the optimal response method by learning from past alert notifications and their results.

[0042] The damage prevention department can send alert notifications for damage prevention in the form of email, SMS, and app notification. The damage prevention department, for example, develops a system for sending alert notifications for damage prevention in different formats. For example, notifications are sent in multiple formats, such as email, SMS, and app notification. In addition, a system is built that automatically selects the notification format in order to send alert notifications in different formats. For example, the system selects the optimal notification format based on the user's settings. In addition, in order to send alert notifications for damage prevention in different formats, templates are created for each notification format. For example, templates are prepared for email, SMS, and app notification. In this way, alert notifications can be sent in different formats, ensuring that the notifications reach the recipients reliably.

[0043] The damage prevention department can link alert notifications for damage prevention with a local crime prevention network and share information widely. The damage prevention department, for example, develops a system that links with a local crime prevention network to send alert notifications for damage prevention. For example, if there is suspicion of fraud, a notification is sent to the local crime prevention network. In addition, a function for linking with the local crime prevention network is added to share alert notifications widely. For example, contact information for the local crime prevention network is automatically obtained. In addition, a system is built that links alert notifications for damage prevention with a local crime prevention network and shares information widely. For example, if there is suspicion of fraud, a notification is sent to the entire region. In this way, by linking with the local crime prevention network, information can be shared widely and the effectiveness of damage prevention can be improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The transcription unit can automatically remove background sounds or noise during transcription to generate clear text data. For example, noise canceling technology is used to automatically remove background sounds and noise during transcription. For example, noise and echoes during phone calls are removed. The transcription unit also analyzes audio data and applies algorithms to identify and remove background sounds and noise. For example, car sounds and wind noise during phone calls are removed. To improve transcription accuracy, a noise removal filter is applied in real time to generate clear text data. For example, background music during phone calls is removed. This removes background sounds and noise, improving transcription accuracy.

[0046] The transcription unit makes the transcription data multilingual, enabling it to transcribe conversations in different languages ​​in real time. For example, to make the transcription data multilingual, a speech recognition model is built that supports multiple languages. For example, speech in English, Japanese, Chinese, etc. is transcribed in real time. In addition, to transcribe conversations in different languages ​​in real time, a system is developed that dynamically switches speech recognition models. For example, it responds immediately when the language changes during a call. In addition, to make the transcription data multilingual, the speech recognition model is trained with speech data in multiple languages. For example, speech data in multiple languages ​​is collected and trained into the model. This makes it multilingual, allowing it to transcribe conversations in different languages ​​in real time.

[0047] The transcription unit generates subtitles in real time to visually display the transcription data, making it possible to support video calls. For example, a system is developed to generate subtitles in real time to visually display the transcription data. For example, subtitles are displayed during a video call. Also, to support video calls, a function is added to display the transcription data as subtitles in real time. For example, text data during a call is displayed on the screen. Also, to visually display the transcription data, a subtitle generation algorithm is applied to generate subtitles in real time. For example, text data during a call is automatically displayed as subtitles. This makes it possible to support video calls by visually displaying the transcription data.

[0048] The analysis unit can apply the results of conversation content analysis to different industries and applications, enabling it to detect risks other than fraud. For example, the results of conversation content analysis can be applied to customer support to build a system that detects customer dissatisfaction and complaints in real time. For example, an alert can be sent if a customer expresses dissatisfaction. In addition, the results of conversation content analysis can be used to develop a system that detects patient emergencies and risks in real time in medical consultations. For example, an alert can be sent if a patient complains of emergency symptoms. In addition, the results of conversation content analysis can be applied to different industries to build a system that detects risks other than fraud. For example, it can detect suspected fraudulent transactions in the financial industry. This makes it possible to apply the results of conversation content analysis to different industries and applications, enabling it to detect risks other than fraud.

[0049] The analysis unit can visualize the analysis results and provide a dashboard that can be intuitively understood by the user. For example, a dashboard that can be intuitively understood by the user is developed to visualize the analysis results. For example, the content of calls suspected of being fraudulent may be displayed in graphs and charts. A dashboard that visualizes the analysis results in real time and allows the user to respond immediately may be provided. For example, the content of calls suspected of being fraudulent may be displayed in color-coded. A dashboard that can be customized by the user to visualize the analysis results may be provided. For example, the user may be able to select and display data that interests them. In this way, the visualization of the analysis results allows the user to intuitively understand.

[0050] The notification unit can simultaneously send alert notifications to smartphones, tablets, and PCs before sending them to ensure they are received. For example, a system is developed that simultaneously sends alert notifications to multiple devices before sending them. For example, notifications are sent simultaneously to smartphones, tablets, and PCs. Also, in order to simultaneously send alert notifications to multiple devices, a system is built that synchronizes between devices in real time. For example, confirmation is performed between devices to ensure that notifications are received reliably. Also, in order to simultaneously send alert notifications to multiple devices, a system is developed that automatically adjusts notification settings for each device. For example, the notification sound and display method are customized for each device. As a result, alert notifications are reliably received by sending them to multiple devices simultaneously.

[0051] The notification unit can save the history of alert notifications so that they can be checked later. For example, a system can be developed that saves the history of alert notifications so that they can be checked later. For example, a function can be added to display the contents of past notifications in a list. Also, in order to save the history of alert notifications, a notification database can be constructed so that users can search for past notifications. For example, a function can be provided to search for notifications by specific date and time or content. Also, in order to save the history of alert notifications so that they can be checked later, a system can be developed that automatically backs up the notification history. For example, the notification history can be saved on a regular basis to the cloud. This allows the history of alert notifications to be saved and checked later.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The transcription unit transcribes the telephone conversation in real time. For example, the transcription unit may use speech recognition technology to convert the contents of the call into text data. The transcription unit may also analyze the audio data of the call and convert it into text. For example, the speech recognition technology may use deep learning-based speech recognition technology. Step 2: The analysis unit analyzes the text data transcribed by the transcription unit and determines whether there is any suspicion of special fraud. For example, the analysis unit may use keyword matching technology to detect fraud patterns. The analysis unit may also analyze the text data using natural language processing technology. For example, the analysis unit may compare the text data with a database of past fraud cases to detect phrases that may be fraudulent. Step 3: If the analysis unit determines that a special fraud is suspected, the notification unit sends an alert notification to the individual or necessary contacts. For example, the notification unit may send the alert notification via email, SMS, or app notification. The notification unit can also customize the content of the alert notification. For example, the notification unit may generate notification content based on the recipient's emotions.

[0054] (Example 2) The new social problem-solving service according to the embodiment of the present invention is a system in which AI transcribes telephone conversations and analyzes their contents to detect conversations that are suspected of being fraudulent, and immediately sends an alert to the person in question and necessary contacts. This new social problem-solving service transcribes telephone conversations in real time and immediately sends an alert if there is a suspicion of fraudulent activity, thereby preventing damage before it occurs.

[0055] A new social problem-solving service according to an embodiment includes a transcription unit, an analysis unit, and a notification unit. The transcription unit transcribes telephone conversations in real time. For example, the transcription unit converts the contents of the call into text data using speech recognition technology. The transcription unit can also analyze the audio data of the call and convert it into text. For example, the speech recognition technology can use deep learning-based speech recognition technology. The analysis unit analyzes the text data transcribed by the transcription unit and determines whether there is a suspicion of special fraud. For example, the analysis unit detects fraud patterns using keyword matching technology. The analysis unit can also analyze the text data using natural language processing technology. For example, the analysis unit compares the text data with a database of past fraud cases to detect phrases that may be fraudulent. If the analysis unit determines that there is a suspicion of special fraud, the notification unit sends an alert notification to the person or necessary contacts. For example, the notification unit sends the alert notification via email, SMS, or app notification. The notification unit can also customize the content of the alert notification. For example, the notification unit generates notification content based on the recipient's emotions. As a result, the new social problem-solving service according to the embodiment can prevent damage by transcribing telephone conversations in real time and sending an immediate alert notification if there is suspicion of special fraud.

[0056] The transcription unit can convert the contents of a phone call into text data using speech recognition technology. For example, the transcription unit estimates the speaker's emotions from the text transcribed by the generation AI and records changes in emotions in real time. For example, the transcription unit analyzes the speaker's tone and speed of voice from the text transcribed by the generation AI and records changes in emotions in real time. For example, if a speaker suddenly raises their voice, that change in emotion is reflected in the text. In addition, during transcription, to estimate the speaker's emotions, emotional features are extracted from the voice data and emotion tags are assigned to the text data. For example, the emotion tag "Calm" is assigned to the text "Hello, this is the bank." The transcription unit also applies an emotion estimation algorithm to the transcribed text and records changes in emotion in real time. For example, the emotion tag "Tense" is assigned to the phrase "Please transfer money." This allows the transcription unit to accurately convert the contents of a phone call into text data using speech recognition technology.

[0057] The analysis unit can compare the text data with a database of past fraud cases and calculate the similarity. For example, the analysis unit develops an algorithm that compares the data with a database of past fraud cases and calculates the similarity in order to detect fraud patterns. For example, fraud phrases and patterns are registered in the database. A system is also built that compares the transcript data with the database of past fraud cases and calculates the similarity. For example, phrases that are suspected to be fraudulent are compared with the database. Also, to detect fraud patterns, an algorithm is applied that uses the database of past fraud cases to calculate the similarity. For example, the content of phone calls that are suspected to be fraudulent is compared with the database. This makes it possible to detect fraud patterns with a high degree of accuracy by comparing the data with the database of past fraud cases.

[0058] The notification unit can customize the notification content according to the emotions of the recipient when sending an alert notification. For example, the notification unit develops a system that estimates the emotions of the recipient when sending an alert notification and customizes the notification content according to that emotion. For example, if the recipient is nervous, it sends a notification urging them to stay calm. In addition, it uses the emotion estimation function to customize the notification content according to the recipient's emotions in real time. For example, if the recipient is surprised, it sends a notification with reassuring content. In addition, it builds a system that analyzes the emotions of the recipient when sending an alert notification and adjusts the notification content based on that emotion. For example, if the recipient is feeling anxious, it sends a notification indicating specific ways to respond. In this way, customizing the notification content according to the recipient's emotions enables more effective alert notifications.

[0059] The transcription unit can automatically remove background sounds or noise during transcription to generate clear text data. For example, the transcription unit uses noise canceling technology to automatically remove background sounds and noise during transcription. For example, noise and echoes during phone calls are removed. The transcription unit also analyzes audio data and applies an algorithm to identify and remove background sounds and noise. For example, car sounds and wind noise during phone calls are removed. Furthermore, to improve transcription accuracy, a noise removal filter is applied in real time to generate clear text data. For example, background music during phone calls is removed. This removes background sounds and noise, improving transcription accuracy.

[0060] The transcription unit makes the transcription data multilingual, and can transcribe conversations in different languages ​​in real time. For example, to make the transcription data multilingual, the transcription unit builds a speech recognition model that supports multiple languages. For example, it transcribes speech in English, Japanese, Chinese, etc. in real time. In addition, to transcribe conversations in different languages ​​in real time, a system is developed that dynamically switches speech recognition models. For example, it responds immediately when the language changes during a call. In addition, to make the transcription data multilingual, the speech recognition model is trained with speech data in multiple languages. For example, speech data in multiple languages ​​is collected and trained into the model. This makes it multilingual, allowing conversations in different languages ​​to be transcribed in real time.

[0061] The transcription unit generates subtitles in real time to visually display the transcription data, and can also support video calls. The transcription unit, for example, develops a system that generates subtitles in real time to visually display the transcription data. For example, subtitles are displayed during a video call. Also, to support video calls, a function is added to display the transcription data as subtitles in real time. For example, text data during a call is displayed on the screen. Also, to visually display the transcription data, a subtitle generation algorithm is applied to generate subtitles in real time. For example, text data during a call is automatically displayed as subtitles. This makes it possible to support video calls by visually displaying the transcription data.

[0062] The transcription unit can use an emotion estimation function to display the speaker's emotion in real time while transcribing, and provide visual feedback on changes in emotion. The transcription unit, for example, develops a system that uses the emotion estimation function to display the speaker's emotion in real time while transcribing. For example, the speaker's emotion is displayed using a color or icon. In addition, an emotion estimation algorithm is applied to display the speaker's emotion in real time while transcribing. For example, the tone and speed of the speaker's voice are analyzed to estimate the emotion. In addition, in order to provide visual feedback on changes in emotion, emotion tags are assigned to the transcription data and displayed in real time. For example, if the speaker's emotion changes, they are displayed immediately. This makes it easier to understand the speaker's emotional state by providing visual feedback on changes in emotion in real time.

[0063] The analysis unit uses an emotion estimation function to analyze the speaker's emotions and can detect changes in emotion if fraud is suspected. For example, the analysis unit applies an emotion estimation algorithm to text transcribed by the generative AI to analyze the speaker's emotions. For example, it analyzes the tone and speed of the speaker's voice to detect changes in emotion. The emotion estimation function is also used to analyze text data to detect changes in emotion if fraud is suspected. For example, the phrase "Please transfer money" is tagged with the emotion "nervous." The emotion estimation function is also used to analyze the speaker's emotions in real time and detect changes in emotion if fraud is suspected. For example, a change in emotion is detected if the speaker suddenly raises their voice. This allows for highly accurate judgment of suspected fraud by detecting changes in emotion.

[0064] The analysis unit can apply the results of conversation content analysis to different industries and applications, and detect risks other than fraud. For example, the analysis unit can apply the results of conversation content analysis to customer support and build a system that detects customer dissatisfaction and complaints in real time. For example, an alert can be sent when a customer expresses dissatisfaction. In addition, the analysis unit can use the results of conversation content analysis to develop a system that detects patient emergencies and risks in real time in medical consultations. For example, an alert can be sent when a patient complains of emergency symptoms. In addition, the analysis results of conversation content can be applied to different industries and build a system that detects risks other than fraud. For example, it can detect suspected fraudulent transactions in the financial industry. This makes it possible to apply the analysis results to different industries and applications and detect risks other than fraud.

[0065] The analysis unit can visualize the analysis results and provide a dashboard that can be intuitively understood by the user. For example, the analysis unit develops a dashboard that can be intuitively understood by the user in order to visualize the analysis results. For example, the content of calls suspected of being fraudulent is displayed in graphs and charts. The analysis unit also provides a dashboard that visualizes the analysis results in real time and allows the user to respond immediately. For example, the content of calls suspected of being fraudulent is displayed in color-coded form. The analysis unit also provides a dashboard that can be customized by the user in order to visualize the analysis results. For example, the user can select and display data that interests them. In this way, the analysis results can be visualized so that they can be intuitively understood by the user.

[0066] The analysis unit can use the emotion estimation function to suggest an appropriate way to respond to the user based on the analysis results. The analysis unit, for example, uses the emotion estimation function to develop a system that suggests an appropriate way to respond to the user based on the analysis results. For example, if fraud is suspected, it suggests reporting to the police. Furthermore, an emotion estimation algorithm is applied to suggest an appropriate way to respond to the user in real time based on the analysis results. For example, if the speaker is nervous, it suggests a calm response. Furthermore, the emotion estimation function is used to learn past data in order to suggest an appropriate way to respond to the user. For example, it suggests the optimal way to respond based on past success cases. In this way, the emotion estimation function can be used to suggest an appropriate way to respond to the user.

[0067] The notification unit can simultaneously send alert notifications to smartphones, tablets, and PCs before sending them to ensure they are received. The notification unit, for example, develops a system that simultaneously sends alert notifications to multiple devices before sending them. For example, notifications are sent simultaneously to smartphones, tablets, and PCs. In addition, to simultaneously send alert notifications to multiple devices, a system is built that synchronizes between devices in real time. For example, confirmation is performed between devices to ensure that notifications are received reliably. In addition, to simultaneously send alert notifications to multiple devices, a system is developed that automatically adjusts notification settings for each device. For example, the notification sound and display method are customized for each device. As a result, alert notifications are reliably received by sending them to multiple devices simultaneously.

[0068] The notification unit can save the history of alert notifications so that they can be checked later. The notification unit, for example, develops a system that saves the history of alert notifications so that they can be checked later. For example, a function is added to display the contents of past notifications in a list. Also, in order to save the history of alert notifications, a notification database is constructed so that users can search for past notifications. For example, a function is provided to search for notifications by specific date and time or content. Also, in order to save the history of alert notifications so that they can be checked later, a system is developed that automatically backs up the notification history. For example, the notification history is saved on a regular basis in the cloud. This allows the history of alert notifications to be saved and checked later.

[0069] The notification unit can also send alert notifications as audio messages or video messages. The notification unit develops, for example, a system that can send alert notifications as audio messages. For example, if fraud is suspected, a notification that issues an audio warning is sent. Also, a video recording function is added to enable the alert notifications to be sent as video messages. For example, if fraud is suspected, a video notification that shows specific steps to take is sent. Also, a system is built that allows the user to select the notification format in order to send alert notifications as audio messages or video messages. For example, the user is allowed to select between audio and video notifications. This allows the notifications to be sent as audio or video messages, making it possible to notify recipients more effectively.

[0070] The notification unit can automatically send alert notifications not only to family and friends, but also to local communities and police. For example, the notification unit develops a system that automatically sends alert notifications not only to family and friends, but also to local communities and police. For example, a notification is sent to a local crime prevention network when fraud is suspected. In addition, a function is added to pre-set a list of notification recipients in order to automatically send alert notifications. For example, it is made possible for users to customize notification recipients. In addition, a system is built that automatically obtains contact information for notification recipients in order to send alert notifications to local communities and police. For example, contact information for a local crime prevention network is automatically obtained. This makes it possible to send notifications to local communities and police, thereby preventing damage on a wider scale.

[0071] The notification unit uses the emotion estimation function to monitor the emotional reactions of alert notification recipients when they receive the notification and can provide appropriate follow-up. For example, the notification unit develops a system that uses the emotion estimation function to monitor the emotional reactions of alert notification recipients when they receive the notification. For example, the system analyzes the recipient's facial expressions and voice and calculates an emotion score. The notification unit also builds a system that monitors the emotional reactions of alert notification recipients when they receive the notification and provides appropriate follow-up based on the results. For example, the system provides additional support if the recipient is feeling anxious. The emotion estimation function is also used to monitor the emotional reactions of alert notification recipients in real time and customize the content of the follow-up. For example, the system sends a message that reassures the recipient. This enables more effective responses by monitoring the recipient's emotional reactions and providing appropriate follow-up.

[0072] Furthermore, the new social problem-solving service will be equipped with a damage prevention unit that uses an emotion estimation function to analyze the emotions of the person who receives the alert notification and related parties, and encourages them to take an appropriate action. The damage prevention unit will develop a system that uses the emotion estimation function to analyze the emotions of the person who receives the alert notification and related parties. For example, it will analyze the recipient's facial expressions and voice and calculate an emotion score. It will also build a system that analyzes the emotions of the person who receives the alert notification and related parties, and encourages them to take an appropriate action based on the results. For example, it will suggest specific ways to respond if the recipient is feeling anxious. It will also use the emotion estimation function to analyze the emotions of the person who receives the alert notification and related parties in real time, and encourage them to take an appropriate action. For example, if the recipient is nervous, it will send a message encouraging them to take a calm action. In this way, by analyzing emotions and encouraging them to take an appropriate action, it is possible to prevent damage from occurring.

[0073] The damage prevention unit can learn from past alert notifications and their results and propose the optimal response method in order to prevent damage before it occurs. For example, the damage prevention unit develops a system that learns from past alert notifications and their results in order to prevent damage before it occurs. For example, it proposes the optimal response method based on past success stories and failure stories. It also applies a machine learning algorithm to learn from past alert notifications and their results and propose the optimal response method. For example, it automatically generates the optimal response method based on past data. It also builds a system that stores past alert notifications and their results in a database and allows it to learn from them in order to prevent damage before it occurs. For example, it proposes the optimal response method based on past data. In this way, it can propose the optimal response method by learning from past alert notifications and their results.

[0074] The damage prevention department can send alert notifications for damage prevention in the form of email, SMS, and app notification. The damage prevention department, for example, develops a system for sending alert notifications for damage prevention in different formats. For example, notifications are sent in multiple formats, such as email, SMS, and app notification. In addition, a system is built that automatically selects the notification format in order to send alert notifications in different formats. For example, the system selects the optimal notification format based on the user's settings. In addition, in order to send alert notifications for damage prevention in different formats, templates are created for each notification format. For example, templates are prepared for email, SMS, and app notification. In this way, alert notifications can be sent in different formats, ensuring that the notifications reach the recipients reliably.

[0075] The damage prevention department can link alert notifications for damage prevention with a local crime prevention network and share information widely. The damage prevention department, for example, develops a system that links with a local crime prevention network to send alert notifications for damage prevention. For example, if there is suspicion of fraud, a notification is sent to the local crime prevention network. In addition, a function for linking with the local crime prevention network is added to share alert notifications widely. For example, contact information for the local crime prevention network is automatically obtained. In addition, a system is built that links alert notifications for damage prevention with a local crime prevention network and shares information widely. For example, if there is suspicion of fraud, a notification is sent to the entire region. In this way, by linking with the local crime prevention network, information can be shared widely and the effectiveness of damage prevention can be improved.

[0076] The damage prevention unit can use the emotion estimation function to track the user's behavior after receiving an alert notification and evaluate the effectiveness of damage prevention. The damage prevention unit, for example, uses the emotion estimation function to develop a system that tracks the user's behavior after receiving an alert notification. For example, it records what actions the user took after receiving the notification. It also builds a system that tracks the user's behavior after receiving the alert notification and evaluates the effectiveness of damage prevention based on that data. For example, it checks whether the user who received the notification called the police. It also uses the emotion estimation function to track the user's emotional changes after receiving the alert notification and evaluates the effectiveness of damage prevention based on the results. For example, it checks whether the user who received the notification felt relieved. In this way, tracking user behavior and evaluating the effectiveness of damage prevention can be useful for improving the system.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The transcription unit can automatically remove background sounds or noise during transcription to generate clear text data. For example, noise canceling technology is used to automatically remove background sounds and noise during transcription. For example, noise and echoes during phone calls are removed. The transcription unit also analyzes audio data and applies algorithms to identify and remove background sounds and noise. For example, car sounds and wind noise during phone calls are removed. To improve transcription accuracy, a noise removal filter is applied in real time to generate clear text data. For example, background music during phone calls is removed. This removes background sounds and noise, improving transcription accuracy.

[0079] The transcription unit makes the transcription data multilingual, enabling it to transcribe conversations in different languages ​​in real time. For example, to make the transcription data multilingual, a speech recognition model is built that supports multiple languages. For example, speech in English, Japanese, Chinese, etc. is transcribed in real time. In addition, to transcribe conversations in different languages ​​in real time, a system is developed that dynamically switches speech recognition models. For example, it responds immediately when the language changes during a call. In addition, to make the transcription data multilingual, the speech recognition model is trained with speech data in multiple languages. For example, speech data in multiple languages ​​is collected and trained into the model. This makes it multilingual, allowing it to transcribe conversations in different languages ​​in real time.

[0080] The transcription unit generates subtitles in real time to visually display the transcription data, making it possible to support video calls. For example, a system is developed to generate subtitles in real time to visually display the transcription data. For example, subtitles are displayed during a video call. Also, to support video calls, a function is added to display the transcription data as subtitles in real time. For example, text data during a call is displayed on the screen. Also, to visually display the transcription data, a subtitle generation algorithm is applied to generate subtitles in real time. For example, text data during a call is automatically displayed as subtitles. This makes it possible to support video calls by visually displaying the transcription data.

[0081] The transcription unit can use an emotion estimation function to display the speaker's emotion in real time while transcribing, providing visual feedback on changes in emotion. For example, we will develop a system that uses the emotion estimation function to display the speaker's emotion in real time while transcribing. For example, the speaker's emotion can be displayed using a color or icon. In addition, an emotion estimation algorithm is applied to display the speaker's emotion in real time while transcribing. For example, the tone and speed of the speaker's voice can be analyzed to estimate the emotion. In addition, emotion tags can be added to the transcription data and displayed in real time to provide visual feedback on changes in emotion. For example, if the speaker's emotion changes, they can be displayed immediately. This makes it easier to understand the speaker's emotional state by providing visual feedback on changes in emotion in real time.

[0082] The analysis unit uses the emotion estimation function to analyze the speaker's emotions and can detect changes in emotion if fraud is suspected. For example, an emotion estimation algorithm is applied to text transcribed by the generation AI to analyze the speaker's emotions. For example, the tone and speed of the speaker's voice are analyzed to detect changes in emotion. The emotion estimation function is also used to analyze text data to detect changes in emotion if fraud is suspected. For example, the phrase "Please transfer money" is tagged with the emotion "nervous." The emotion estimation function is also used to analyze the speaker's emotions in real time and detect changes in emotion if fraud is suspected. For example, a change in emotion is detected if the speaker suddenly raises their voice. This allows for highly accurate judgment of suspected fraud by detecting changes in emotion.

[0083] The analysis unit can apply the results of conversation content analysis to different industries and applications, enabling it to detect risks other than fraud. For example, the results of conversation content analysis can be applied to customer support to build a system that detects customer dissatisfaction and complaints in real time. For example, an alert can be sent if a customer expresses dissatisfaction. In addition, the results of conversation content analysis can be used to develop a system that detects patient emergencies and risks in real time in medical consultations. For example, an alert can be sent if a patient complains of emergency symptoms. In addition, the results of conversation content analysis can be applied to different industries to build a system that detects risks other than fraud. For example, it can detect suspected fraudulent transactions in the financial industry. This makes it possible to apply the results of conversation content analysis to different industries and applications, enabling it to detect risks other than fraud.

[0084] The analysis unit can visualize the analysis results and provide a dashboard that can be intuitively understood by the user. For example, a dashboard that can be intuitively understood by the user is developed to visualize the analysis results. For example, the content of calls suspected of being fraudulent may be displayed in graphs and charts. A dashboard that visualizes the analysis results in real time and allows the user to respond immediately may be provided. For example, the content of calls suspected of being fraudulent may be displayed in color-coded. A dashboard that can be customized by the user to visualize the analysis results may be provided. For example, the user may be able to select and display data that interests them. In this way, the visualization of the analysis results allows the user to intuitively understand.

[0085] The analysis unit can use the emotion estimation function to suggest an appropriate way to respond to the user based on the analysis results. For example, a system can be developed that uses the emotion estimation function to suggest an appropriate way to respond to the user based on the analysis results. For example, if there is suspicion of fraud, it can suggest calling the police. Furthermore, an emotion estimation algorithm can be applied to suggest an appropriate way to respond to the user in real time based on the analysis results. For example, if the speaker is nervous, it can suggest a calm response. Furthermore, the emotion estimation function can be used to learn from past data to suggest an appropriate way to respond to the user. For example, it can suggest the optimal way to respond based on past success stories. In this way, the emotion estimation function can be used to suggest an appropriate way to respond to the user.

[0086] The notification unit can simultaneously send alert notifications to smartphones, tablets, and PCs before sending them to ensure they are received. For example, a system is developed that simultaneously sends alert notifications to multiple devices before sending them. For example, notifications are sent simultaneously to smartphones, tablets, and PCs. Also, in order to simultaneously send alert notifications to multiple devices, a system is built that synchronizes between devices in real time. For example, confirmation is performed between devices to ensure that notifications are received reliably. Also, in order to simultaneously send alert notifications to multiple devices, a system is developed that automatically adjusts notification settings for each device. For example, the notification sound and display method are customized for each device. As a result, alert notifications are reliably received by sending them to multiple devices simultaneously.

[0087] The notification unit can save the history of alert notifications so that they can be checked later. For example, a system can be developed that saves the history of alert notifications so that they can be checked later. For example, a function can be added to display the contents of past notifications in a list. Also, in order to save the history of alert notifications, a notification database can be constructed so that users can search for past notifications. For example, a function can be provided to search for notifications by specific date and time or content. Also, in order to save the history of alert notifications so that they can be checked later, a system can be developed that automatically backs up the notification history. For example, the notification history can be saved on a regular basis to the cloud. This allows the history of alert notifications to be saved and checked later.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The transcription unit transcribes the telephone conversation in real time. For example, the transcription unit may use speech recognition technology to convert the contents of the call into text data. The transcription unit may also analyze the audio data of the call and convert it into text. For example, the speech recognition technology may use deep learning-based speech recognition technology. Step 2: The analysis unit analyzes the text data transcribed by the transcription unit and determines whether there is any suspicion of special fraud. For example, the analysis unit may use keyword matching technology to detect fraud patterns. The analysis unit may also analyze the text data using natural language processing technology. For example, the analysis unit may compare the text data with a database of past fraud cases to detect phrases that may be fraudulent. Step 3: If the analysis unit determines that a special fraud is suspected, the notification unit sends an alert notification to the individual or necessary contacts. For example, the notification unit may send the alert notification via email, SMS, or app notification. The notification unit can also customize the content of the alert notification. For example, the notification unit may generate notification content based on the recipient's emotions.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 AI 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 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 transcription department that transcribes phone conversations in real time, an analysis unit that analyzes the text data transcribed by the transcription unit and determines whether there is a suspicion of special fraud; and a notification unit that sends an alert notification to the person in question or a necessary contact when the analysis unit determines that there is a suspicion of special fraud. A system characterized by:

2. The transcription unit Convert the contents of the call into the text data using voice recognition technology 2. The system of claim 1.

3. The analysis unit The text data is compared with a database of past fraud cases to calculate the degree of similarity.

2. The system of claim 1.

4. The notification unit When sending the alert notification, the notification content is customized according to the emotion of the recipient.

2. The system of claim 1.

5. The transcription unit Automatically remove background sounds or noise during transcription to produce clear text data 2. The system of claim 1.

Images