System

Abstract

A system in accordance with an embodiment aims to efficiently transcribe telephone conversations, determine fraudulent calls, and adjust speech speed for the elderly.SOLUTION: A system includes a voice acquisition unit, a transcription unit, a transmission unit, a fraud determination unit, and a voice adjustment unit. The voice acquisition unit acquires a voice of a conversation. The transcription unit converts the voice acquired by the voice acquisition unit into text data. The transmission unit transmits the text data converted by the transcription unit to the user. The fraud determination unit determines a fraudulent call by analyzing the content of the call. The voice adjustment unit adjusts the voice speed for elderly people.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] Existing technology does not adequately transcribe telephone conversations, identify fraudulent calls, or adjust speech speed for elderly users, so there is room for improvement.

[0005] The system of the embodiment aims to efficiently transcribe telephone conversations, identify fraudulent calls, and adjust speech speed for elderly people. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice acquisition unit, a transcription unit, a transmission unit, a fraud determination unit, and a voice adjustment unit. The voice acquisition unit acquires the voice of a conversation. The transcription unit converts the voice acquired by the voice acquisition unit into text data. The transmission unit transmits the text data converted by the transcription unit to the user. The fraud determination unit analyzes the contents of the call to determine whether it is a fraudulent call. The voice adjustment unit adjusts the voice speed to suit the elderly. [Effects of the Invention]

[0007] Embodiments of the system can efficiently transcribe phone conversations, identify fraudulent calls, and adjust speech speed for seniors. [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 audio logging system according to an embodiment of the present invention is a system in which a generation AI automatically transcribes conversations and transmits the data to reduce the effort required for taking notes during business negotiations, meetings, and telephone conversations. This allows the audio logging system to automatically record the content of conversations and provide them to users. It also has functions for adjusting the voice speed for elderly people and detecting fraudulent calls.

[0029] The audio log system according to the embodiment includes a voice capture unit, a transcription unit, a transmission unit, a fraud determination unit, and a voice adjustment unit. The voice capture unit captures the voice of a conversation. For example, it captures telephone conversations in real time. It can also capture the voice of face-to-face conversations and video conferences. The voice capture unit uses noise canceling technology to remove background noise and obtain clear voice. For example, the voice capture unit captures telephone conversations with a high-precision microphone and removes background noise using noise canceling technology. Face-to-face conversations can be captured using a microphone system in a conference room. Video conference voice can be captured directly from the conference system. The transcription unit converts the voice captured by the voice capture unit into text data. For example, the generation AI converts the voice into text data using speech recognition technology. The generation AI can also understand context and perform accurate transcription using natural language processing technology. The generation AI can also analyze the intonation and emotion of the voice and reflect them in the text data. For example, the generation AI converts the content of a conversation into text data in real time using speech recognition technology. The system uses natural language processing technology to understand context and provide accurate transcription. It analyzes the intonation and emotion of the voice and reflects them in the text data. The transmission unit transmits the text data converted by the transcription unit to the user. For example, the transmission unit may send the text data via email. It can also send the text data via a messenger app. Furthermore, the transmission unit may store the text data in cloud storage and make it accessible to the user. For example, the transmission unit may send the text data via email. It can also send the text data via a messenger app. It can also store the text data in cloud storage and make it accessible to the user. The fraud detection unit analyzes the content of the call to determine whether it is a fraudulent call. For example, the generation AI analyzes the content of the call to detect phrases and patterns that may be fraudulent. The generation AI can also use machine learning algorithms to improve the accuracy of fraudulent call detection. Furthermore, the fraud detection unit can notify the user of the fraudulent call detection results in real time.For example, the generation AI analyzes the content of a phone call and detects phrases or patterns that may be fraudulent. It uses a machine learning algorithm to improve the accuracy of fraudulent call detection. It notifies the user of the fraudulent call detection results in real time. The voice adjustment unit adjusts the voice speed for elderly users. For example, when an elderly user answers a phone call, the generation AI analyzes the voice speed and adjusts it to an appropriate speed. The generation AI can also adjust the voice frequency band according to the elderly user's hearing characteristics. Furthermore, if the elderly user misses a specific keyword or phrase, the voice adjustment unit can automatically repeat that part. For example, when an elderly user answers a phone call, the generation AI analyzes the voice speed and adjusts it to an appropriate speed. The voice frequency band according to the elderly user's hearing characteristics. If the elderly user misses a specific keyword or phrase, the voice adjustment unit can automatically repeat that part. In this way, the audio logging system according to the embodiment can automatically record the content of the conversation and provide it to the user. It also has functions for adjusting the voice speed for elderly users and detecting fraudulent calls. For example, the output unit displays the recorded conversation content to the user via a web application or mobile application. If you want to receive feedback on paper, you can print the results using a printer. Sending the results by email provides quick feedback by sending the results directly to the user.

[0030] The transcription unit can understand the context of the conversation and automatically highlight important keywords or phrases. For example, the generation AI in the transcription unit analyzes the context of the conversation and automatically highlights important keywords and phrases. For example, it highlights important contract terms and price negotiation points during a business meeting. The transcription unit also analyzes the content of the conversation in real time and the generation AI automatically highlights important parts. For example, it highlights decisions and action items during a meeting. The transcription unit also adds a function where the generation AI understands the context of the conversation and automatically highlights important keywords and phrases. For example, it highlights important instructions or requests over the phone. This highlights important information to prevent users from missing important points.

[0031] The transcription unit translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, the generation AI in the transcription unit translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, a conversation in Japanese may be translated into English and provided to a foreign participant. The transcription unit also translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, a conversation in English may be translated into Chinese and provided to a Chinese participant. The transcription unit also translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, a conversation in French may be translated into Spanish and provided to a Spanish participant. This facilitates communication between participants who speak different languages.

[0032] The transcription unit can automatically summarize the transcript data of a conversation and generate a summary. In the transcription unit, for example, a generation AI automatically summarizes the transcript data of a conversation and generates a summary. For example, it provides a summary that concisely summarizes the main points and conclusions of a business negotiation. The transcription unit also adds a function to automatically summarize the transcript data of a conversation and generate a summary. For example, it provides a summary that concisely summarizes the minutes of a meeting. In addition, the transcription unit can automatically summarize the transcript data of a conversation and generate a summary. For example, it provides a summary that concisely summarizes important instructions and requests made over the phone. This provides a summary that concisely summarizes the main points of a conversation.

[0033] The transcription unit analyzes not only the audio of the conversation content but also the video data of the video conference, and can transcribe from both the video and audio. For example, the transcription unit uses a generation AI to analyze the video data of a video conference and transcribe from both the video and audio. For example, it transcribes the content of a presentation during a meeting from both the video and audio. The transcription unit also analyzes not only the audio of the conversation content but also the video data of the video conference and transcribes from both the video and audio. For example, it analyzes gestures and facial expressions during business negotiations from the video and reflects them in the transcription. The transcription unit also uses a generation AI to analyze the video data of a video conference and transcribe from both the video and audio. For example, it analyzes the content of a screen shared during a conference call from the video and reflects it in the transcription. This allows for more accurate recording by transcribing from both the video and audio.

[0034] The audio adjustment unit can adjust the frequency band of the audio according to the hearing characteristics of the elderly, converting it into audio that is easier to hear. For example, the generation AI analyzes the hearing characteristics of the elderly and adjusts the frequency band of the audio. For example, it converts audio into a frequency band that is easier for the elderly to hear. The audio adjustment unit also adds a function to adjust the frequency band of the audio according to the hearing characteristics of the elderly, converting it into audio that is easier to hear. For example, it converts high-pitched sounds that are difficult for the elderly to hear into low-pitched sounds. The generation AI also analyzes the hearing characteristics of the elderly and adjusts the frequency band of the audio. For example, it removes noise to convert it into audio that is easier for the elderly to hear. This converts the audio into audio that is easier for the elderly to hear, helping them understand the conversation.

[0035] The voice adjustment unit can automatically repeat a part of a conversation if the elderly person misses a specific keyword or phrase. For example, the voice adjustment unit adds a function to automatically repeat a part of a conversation if the generation AI misses a specific keyword or phrase. For example, by repeatedly playing important instructions or requests. The voice adjustment unit also adds a function to automatically repeat a part of a conversation if the elderly person misses a specific keyword or phrase. For example, by repeatedly playing important information on the phone. The voice adjustment unit also adds a function to automatically repeat a part of a conversation if the generation AI misses a specific keyword or phrase. For example, by repeatedly playing important points during a conversation. This ensures that the elderly person does not miss important information.

[0036] The audio adjustment unit can also apply the audio speed adjustment function for the elderly to other audio media such as television or radio. For example, the generation AI in the audio adjustment unit applies the audio speed adjustment function for the elderly to other audio media such as television and radio. For example, the audio of a television program is adjusted to a speed that is easy for the elderly to hear. The audio adjustment unit also applies the audio speed adjustment function for the elderly to other audio media such as television and radio. For example, the audio of a radio program is adjusted to a speed that is easy for the elderly to hear. The generation AI in the audio adjustment unit also applies the audio speed adjustment function for the elderly to other audio media such as television and radio. For example, the audio of a news program is adjusted to a speed that is easy for the elderly to hear. This makes it easier for the elderly to hear audio media such as television and radio.

[0037] The voice adjustment unit can not only adjust the voice speed but also improve the clarity of the voice when an elderly person makes a phone call. For example, the voice adjustment unit adds a function to not only adjust the voice speed but also improve the clarity of the voice when the generation AI makes a phone call. For example, it removes noise and makes the voice clearer. The voice adjustment unit also adds a function to not only adjust the voice speed but also improve the clarity of the voice when the elderly person makes a phone call. For example, it removes echo and makes the voice clearer. The voice adjustment unit also adds a function to not only adjust the voice speed but also improve the clarity of the voice when the generation AI makes a phone call. For example, it removes background noise and makes the voice clearer. This improves the clarity of the voice when the elderly person makes a phone call.

[0038] The fraud detection unit analyzes the content of phone calls and automatically learns phrases or patterns that are likely to be fraudulent, thereby improving detection accuracy. For example, the fraud detection unit uses a generation AI to analyze the content of phone calls and automatically learns phrases and patterns that are likely to be fraudulent. For example, typical fraud methods and phrases are registered in a database to improve detection accuracy. The fraud detection unit also adds a function to analyze the content of phone calls and automatically learn phrases and patterns that are likely to be fraudulent. For example, signs of fraud are detected based on data from past fraudulent calls. The fraud detection unit also uses a generation AI to analyze the content of phone calls and automatically learn phrases and patterns that are likely to be fraudulent. For example, as fraud methods evolve, the AI ​​learns new patterns to improve detection accuracy. This automatically learns phrases and patterns that are likely to be fraudulent, thereby improving detection accuracy.

[0039] The fraud determination unit can notify the user of the fraud call determination result in real time and suggest actions to take immediate countermeasures. For example, the generation AI in the fraud determination unit notifies the user of the fraud call determination result in real time and suggests actions to take immediate countermeasures. For example, if there is a high possibility of fraud, it urges the user to hang up the phone. The fraud determination unit also adds a function to notify the user of the fraud call determination result in real time and suggest actions to take immediate countermeasures. For example, if there is a high possibility of fraud, it urges the user to report the call to the police. The fraud determination unit also adds a function to notify the user of the fraud call determination result in real time and suggest actions to take immediate countermeasures. For example, if there is a high possibility of fraud, it urges the user to consult with family or friends. In this way, the generation AI notifies the user of the fraud call determination result in real time and suggests actions to take immediate countermeasures.

[0040] The fraud determination unit can apply the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud. For example, the generation AI applies the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud. For example, it detects typical phrases and patterns of fraudulent emails. The fraud determination unit also applies the fraudulent call detection function to emails or text messages in messaging apps to add a function to detect the possibility of fraud. For example, it detects links and attachments in fraudulent messages. The fraud determination unit also applies the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud. For example, it analyzes the sender of the fraudulent message and evaluates its credibility. This allows the generation AI to apply the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud.

[0041] The fraud determination unit can automatically notify family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, the generation AI in the fraud determination unit automatically notifies family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, if there is a high possibility of fraud, it sends a warning message to family members. The fraud determination unit also adds a function to automatically notify family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, if there is a high possibility of fraud, it sends a warning message to a trusted friend. The fraud determination unit also adds a function to automatically notify family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, if there is a high possibility of fraud, it encourages them to report the call to the police. This allows family members or trusted third parties to automatically notify the fraud call determination result, encouraging them to take prompt action.

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

[0043] The audio logging system can also learn a user's conversation patterns and analyze the frequency with which the user uses specific phrases and keywords. For example, it can identify contract terms and price negotiation points frequently used during business negotiations and provide them to the user. It can also analyze the frequency of decisions and action items made during meetings and highlight important points. It can also analyze the frequency of important instructions and requests made over the phone and notify the user. This allows it to identify frequently used phrases and keywords and provide efficient conversation support.

[0044] The audio logging system can also analyze the content of a user's conversation to determine whether the user is discussing a specific topic. For example, if a specific product or service is being discussed during a business meeting, that information can be automatically highlighted. It can also automatically highlight information about a specific project or task during a meeting. It can also automatically highlight information about specific instructions or requests over the phone. This allows users to highlight information when they are discussing a specific topic, ensuring that important information is not missed.

[0045] The audio logging system can further analyze the content of a user's conversation and determine whether the user should take a specific action. For example, it can determine whether a contract should be concluded during a business negotiation and notify the user. It can also determine whether a specific task should be performed during a meeting and notify the user. It can also determine whether specific instructions or requests made over the phone should be carried out and notify the user. This makes it possible to determine whether the user should take a specific action and provide efficient conversation support.

[0046] The audio log system can also analyze the content of a user's conversation and determine whether the user should share specific information. For example, during a business meeting, it can determine whether specific contract terms or price negotiation points should be shared and notify the user. It can also determine whether the progress of a specific project or task should be shared during a meeting and notify the user. It can also determine whether specific instructions or requests over the phone should be shared and notify the user. This makes it possible to determine whether the user should share specific information and provide efficient conversation support.

[0047] The audio log system can further analyze the content of a user's conversation and provide support for the user to search for specific information. For example, support can be provided for searching for specific contract terms or price negotiation points during a business meeting. Support can also be provided for searching for the progress of a specific project or task during a meeting. Furthermore, support can be provided for searching for specific instructions or requests over the phone. This allows the system to support the user in searching for specific information and provide support for efficient conversations.

[0048] The audio logging system can also analyze the content of a user's conversation and determine whether the user should save specific information. For example, during a business meeting, it can determine whether specific contract terms or price negotiation points should be saved and notify the user. It can also determine whether the progress of a specific project or task should be saved during a meeting and notify the user. It can also determine whether specific instructions or requests made over the phone should be saved and notify the user. This makes it possible to determine whether the user should save specific information and provide efficient conversation support.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The audio capture unit captures the audio of a conversation. For example, it can capture telephone conversations in real time, as well as audio from face-to-face conversations and video conferences. Furthermore, it uses noise-canceling technology to remove background noise and capture clear audio. Step 2: The transcription unit converts the audio captured by the audio capture unit into text data. For example, the generation AI uses speech recognition technology to convert the audio into text data, and natural language processing technology to understand the context and produce an accurate transcription. It can also analyze the intonation and emotion of the audio and reflect them in the text data. Step 3: The sending unit sends the text data converted by the transcription unit to the user, for example, by email or via a messenger app, or by storing the text data in cloud storage so that the user can access it. Step 4: The fraud detection unit analyzes the content of the call to determine whether it is a fraudulent call. For example, the generation AI analyzes the content of the call, detects phrases and patterns that may be fraudulent, and uses machine learning algorithms to improve the accuracy of the detection. The user is notified of the fraudulent call detection results in real time. Step 5: The audio adjustment section adjusts the audio speed for seniors. For example, the AI ​​generator analyzes the audio speed and adjusts it to an appropriate speed. It also adjusts the audio frequency band according to the hearing characteristics of seniors, and automatically repeats certain keywords or phrases if they are missed.

[0051] (Example 2) The audio logging system according to an embodiment of the present invention is a system in which a generation AI automatically transcribes conversations and transmits the data to reduce the effort required for taking notes during business negotiations, meetings, and telephone conversations. This allows the audio logging system to automatically record the content of conversations and provide them to users. It also has functions for adjusting the voice speed for elderly people and detecting fraudulent calls.

[0052] The audio log system according to the embodiment includes a voice capture unit, a transcription unit, a transmission unit, a fraud determination unit, and a voice adjustment unit. The voice capture unit captures the voice of a conversation. For example, it captures telephone conversations in real time. It can also capture the voice of face-to-face conversations and video conferences. The voice capture unit uses noise canceling technology to remove background noise and obtain clear voice. For example, the voice capture unit captures telephone conversations with a high-precision microphone and removes background noise using noise canceling technology. Face-to-face conversations can be captured using a microphone system in a conference room. Video conference voice can be captured directly from the conference system. The transcription unit converts the voice captured by the voice capture unit into text data. For example, the generation AI converts the voice into text data using speech recognition technology. The generation AI can also understand context and perform accurate transcription using natural language processing technology. The generation AI can also analyze the intonation and emotion of the voice and reflect them in the text data. For example, the generation AI converts the content of a conversation into text data in real time using speech recognition technology. The system uses natural language processing technology to understand context and provide accurate transcription. It analyzes the intonation and emotion of the voice and reflects them in the text data. The transmission unit transmits the text data converted by the transcription unit to the user. For example, the transmission unit may send the text data via email. It can also send the text data via a messenger app. Furthermore, the transmission unit may store the text data in cloud storage and make it accessible to the user. For example, the transmission unit may send the text data via email. It can also send the text data via a messenger app. It can also store the text data in cloud storage and make it accessible to the user. The fraud detection unit analyzes the content of the call to determine whether it is a fraudulent call. For example, the generation AI analyzes the content of the call to detect phrases and patterns that may be fraudulent. The generation AI can also use machine learning algorithms to improve the accuracy of fraudulent call detection. Furthermore, the fraud detection unit can notify the user of the fraudulent call detection results in real time.For example, the generation AI analyzes the content of a phone call and detects phrases or patterns that may be fraudulent. It uses a machine learning algorithm to improve the accuracy of fraudulent call detection. It notifies the user of the fraudulent call detection results in real time. The voice adjustment unit adjusts the voice speed for elderly users. For example, when an elderly user answers a phone call, the generation AI analyzes the voice speed and adjusts it to an appropriate speed. The generation AI can also adjust the voice frequency band according to the elderly user's hearing characteristics. Furthermore, if the elderly user misses a specific keyword or phrase, the voice adjustment unit can automatically repeat that part. For example, when an elderly user answers a phone call, the generation AI analyzes the voice speed and adjusts it to an appropriate speed. The voice frequency band according to the elderly user's hearing characteristics. If the elderly user misses a specific keyword or phrase, the voice adjustment unit can automatically repeat that part. In this way, the audio logging system according to the embodiment can automatically record the content of the conversation and provide it to the user. It also has functions for adjusting the voice speed for elderly users and detecting fraudulent calls. For example, the output unit displays the recorded conversation content to the user via a web application or mobile application. If you want to receive feedback on paper, you can print the results using a printer. Sending the results by email provides quick feedback by sending the results directly to the user.

[0053] The transcription unit can understand the context of the conversation and automatically highlight important keywords or phrases. For example, the generation AI in the transcription unit analyzes the context of the conversation and automatically highlights important keywords and phrases. For example, it highlights important contract terms and price negotiation points during a business meeting. The transcription unit also analyzes the content of the conversation in real time and the generation AI automatically highlights important parts. For example, it highlights decisions and action items during a meeting. The transcription unit also adds a function where the generation AI understands the context of the conversation and automatically highlights important keywords and phrases. For example, it highlights important instructions or requests over the phone. This highlights important information to prevent users from missing important points.

[0054] The transcription unit translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, the generation AI in the transcription unit translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, a conversation in Japanese may be translated into English and provided to a foreign participant. The transcription unit also translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, a conversation in English may be translated into Chinese and provided to a Chinese participant. The transcription unit also translates the content of a conversation in real time, facilitating communication between participants who speak different languages. For example, a conversation in French may be translated into Spanish and provided to a Spanish participant. This facilitates communication between participants who speak different languages.

[0055] The transcription unit can use the emotion estimation function to analyze changes in emotions during a conversation and add the intensity or type of emotion to the text data. In the transcription unit, for example, the generation AI analyzes changes in emotions during a conversation and adds the intensity and type of emotion to the text data. For example, tension and excitement during a business negotiation can be reflected in the text data. The transcription unit also uses the emotion estimation function to analyze changes in emotions during a conversation and add the intensity and type of emotion to the text data. For example, conflicts of opinion and agreements during a meeting can be reflected in the text data. The transcription unit also uses the generation AI to analyze changes in emotions during a conversation and add the intensity and type of emotion to the text data. For example, gratitude and dissatisfaction over the phone can be reflected in the text data. In this way, emotional nuances can be conveyed by reflecting changes in emotions during a conversation in the text data.

[0056] The transcription unit can automatically summarize the transcript data of a conversation and generate a summary. In the transcription unit, for example, a generation AI automatically summarizes the transcript data of a conversation and generates a summary. For example, it provides a summary that concisely summarizes the main points and conclusions of a business negotiation. The transcription unit also adds a function to automatically summarize the transcript data of a conversation and generate a summary. For example, it provides a summary that concisely summarizes the minutes of a meeting. In addition, the transcription unit can automatically summarize the transcript data of a conversation and generate a summary. For example, it provides a summary that concisely summarizes important instructions and requests made over the phone. This provides a summary that concisely summarizes the main points of a conversation.

[0057] The transcription unit analyzes not only the audio of the conversation content but also the video data of the video conference, and can transcribe from both the video and audio. For example, the transcription unit uses a generation AI to analyze the video data of a video conference and transcribe from both the video and audio. For example, it transcribes the content of a presentation during a meeting from both the video and audio. The transcription unit also analyzes not only the audio of the conversation content but also the video data of the video conference and transcribes from both the video and audio. For example, it analyzes gestures and facial expressions during business negotiations from the video and reflects them in the transcription. The transcription unit also uses a generation AI to analyze the video data of a video conference and transcribe from both the video and audio. For example, it analyzes the content of a screen shared during a conference call from the video and reflects it in the transcription. This allows for more accurate recording by transcribing from both the video and audio.

[0058] The transcription unit can use an emotion estimation function to display changes in emotions during a conversation in real time and provide feedback according to the changes in emotions. For example, the transcription unit uses an emotion estimation function to display changes in emotions during a conversation in real time and provide feedback according to the changes in emotions. For example, tension and excitement during a business meeting can be displayed in real time and appropriate feedback can be provided. The transcription unit can also use an emotion estimation function to display changes in emotions during a conversation in real time and provide feedback according to the changes in emotions. For example, conflicts of opinion and agreements during a meeting can be displayed in real time and appropriate feedback can be provided. The transcription unit can also use an emotion estimation function to display changes in emotions during a conversation in real time and provide feedback according to the changes in emotions. For example, gratitude and dissatisfaction over the phone can be displayed in real time and appropriate feedback can be provided. This allows changes in emotions during a conversation to be displayed in real time and appropriate feedback can be provided.

[0059] The audio adjustment unit can adjust the frequency band of the audio according to the hearing characteristics of the elderly, converting it into audio that is easier to hear. For example, the generation AI analyzes the hearing characteristics of the elderly and adjusts the frequency band of the audio. For example, it converts audio into a frequency band that is easier for the elderly to hear. The audio adjustment unit also adds a function to adjust the frequency band of the audio according to the hearing characteristics of the elderly, converting it into audio that is easier to hear. For example, it converts high-pitched sounds that are difficult for the elderly to hear into low-pitched sounds. The generation AI also analyzes the hearing characteristics of the elderly and adjusts the frequency band of the audio. For example, it removes noise to convert it into audio that is easier for the elderly to hear. This converts the audio into audio that is easier for the elderly to hear, helping them understand the conversation.

[0060] The voice adjustment unit can automatically repeat a part of a conversation if the elderly person misses a specific keyword or phrase. For example, the voice adjustment unit adds a function to automatically repeat a part of a conversation if the generation AI misses a specific keyword or phrase. For example, by repeatedly playing important instructions or requests. The voice adjustment unit also adds a function to automatically repeat a part of a conversation if the elderly person misses a specific keyword or phrase. For example, by repeatedly playing important information on the phone. The voice adjustment unit also adds a function to automatically repeat a part of a conversation if the generation AI misses a specific keyword or phrase. For example, by repeatedly playing important points during a conversation. This ensures that the elderly person does not miss important information.

[0061] The voice adjustment unit can use the emotion estimation function to analyze the emotional state of the elderly person and adjust the voice to reduce stress. For example, the generation AI in the voice adjustment unit analyzes the emotional state of the elderly person and adjusts the voice to reduce stress. For example, the tone of the voice is adjusted so that the elderly person feels relaxed. The voice adjustment unit also uses the emotion estimation function to analyze the emotional state of the elderly person and adjusts the voice to reduce stress. For example, the speed of the voice is adjusted so that the elderly person feels at ease. The voice adjustment unit also uses the generation AI to analyze the emotional state of the elderly person and adjusts the voice to reduce stress. For example, the volume of the voice is adjusted so that the elderly person feels calm. In this way, the voice adjustment is performed to reduce stress for the elderly person.

[0062] The audio adjustment unit can also apply the audio speed adjustment function for the elderly to other audio media such as television or radio. For example, the generation AI in the audio adjustment unit applies the audio speed adjustment function for the elderly to other audio media such as television and radio. For example, the audio of a television program is adjusted to a speed that is easy for the elderly to hear. The audio adjustment unit also applies the audio speed adjustment function for the elderly to other audio media such as television and radio. For example, the audio of a radio program is adjusted to a speed that is easy for the elderly to hear. The generation AI in the audio adjustment unit also applies the audio speed adjustment function for the elderly to other audio media such as television and radio. For example, the audio of a news program is adjusted to a speed that is easy for the elderly to hear. This makes it easier for the elderly to hear audio media such as television and radio.

[0063] The voice adjustment unit can not only adjust the voice speed but also improve the clarity of the voice when an elderly person makes a phone call. For example, the voice adjustment unit adds a function to not only adjust the voice speed but also improve the clarity of the voice when the generation AI makes a phone call. For example, it removes noise and makes the voice clearer. The voice adjustment unit also adds a function to not only adjust the voice speed but also improve the clarity of the voice when the elderly person makes a phone call. For example, it removes echo and makes the voice clearer. The voice adjustment unit also adds a function to not only adjust the voice speed but also improve the clarity of the voice when the generation AI makes a phone call. For example, it removes background noise and makes the voice clearer. This improves the clarity of the voice when the elderly person makes a phone call.

[0064] The voice adjustment unit can use the emotion estimation function to detect in real time any anxiety or confusion the elderly person feels during a conversation and provide appropriate support. For example, the voice adjustment unit uses the generation AI to detect in real time any anxiety or confusion the elderly person feels during a conversation and provide appropriate support. For example, if the elderly person seems confused, the voice adjustment unit repeats the content of the conversation. The voice adjustment unit also uses the emotion estimation function to detect in real time any anxiety or confusion the elderly person feels during a conversation and provide appropriate support. For example, if the elderly person feels anxious, the voice adjustment unit provides a message to reassure the elderly. The voice adjustment unit also uses the generation AI to detect in real time any anxiety or confusion the elderly person feels during a conversation and provide appropriate support. For example, if the elderly person seems confused, the voice adjustment unit further adjusts the speed of the conversation. This allows the voice adjustment unit to detect in real time any anxiety or confusion the elderly person feels during a conversation and provide appropriate support.

[0065] The fraud detection unit analyzes the content of phone calls and automatically learns phrases or patterns that are likely to be fraudulent, thereby improving detection accuracy. For example, the fraud detection unit uses a generation AI to analyze the content of phone calls and automatically learns phrases and patterns that are likely to be fraudulent. For example, typical fraud methods and phrases are registered in a database to improve detection accuracy. The fraud detection unit also adds a function to analyze the content of phone calls and automatically learn phrases and patterns that are likely to be fraudulent. For example, signs of fraud are detected based on data from past fraudulent calls. The fraud detection unit also uses a generation AI to analyze the content of phone calls and automatically learn phrases and patterns that are likely to be fraudulent. For example, as fraud methods evolve, the AI ​​learns new patterns to improve detection accuracy. This automatically learns phrases and patterns that are likely to be fraudulent, thereby improving detection accuracy.

[0066] The fraud determination unit can notify the user of the fraud call determination result in real time and suggest actions to take immediate countermeasures. For example, the generation AI in the fraud determination unit notifies the user of the fraud call determination result in real time and suggests actions to take immediate countermeasures. For example, if there is a high possibility of fraud, it urges the user to hang up the phone. The fraud determination unit also adds a function to notify the user of the fraud call determination result in real time and suggest actions to take immediate countermeasures. For example, if there is a high possibility of fraud, it urges the user to report the call to the police. The fraud determination unit also adds a function to notify the user of the fraud call determination result in real time and suggest actions to take immediate countermeasures. For example, if there is a high possibility of fraud, it urges the user to consult with family or friends. In this way, the generation AI notifies the user of the fraud call determination result in real time and suggests actions to take immediate countermeasures.

[0067] The fraud determination unit can use the emotion estimation function to analyze the anxiety or fear a user feels when receiving a fraudulent call and provide psychological support. For example, the generation AI in the fraud determination unit analyzes the anxiety or fear a user feels when receiving a fraudulent call and provides psychological support. For example, if the user feels anxious, it provides a message to reassure the user. The fraud determination unit also uses the emotion estimation function to analyze the anxiety or fear a user feels when receiving a fraudulent call and provides psychological support. For example, if the user feels fear, it provides advice to stay calm. The fraud determination unit also uses the generation AI to analyze the anxiety or fear a user feels when receiving a fraudulent call and provides psychological support. For example, if the user feels anxious, it urges the user to consult a trusted third party. In this way, the anxiety or fear a user feels when receiving a fraudulent call is analyzed and psychological support is provided.

[0068] The fraud determination unit can apply the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud. For example, the generation AI applies the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud. For example, it detects typical phrases and patterns of fraudulent emails. The fraud determination unit also applies the fraudulent call detection function to emails or text messages in messaging apps to add a function to detect the possibility of fraud. For example, it detects links and attachments in fraudulent messages. The fraud determination unit also applies the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud. For example, it analyzes the sender of the fraudulent message and evaluates its credibility. This allows the generation AI to apply the fraudulent call detection function to emails or text messages in messaging apps to detect the possibility of fraud.

[0069] The fraud determination unit can automatically notify family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, the generation AI in the fraud determination unit automatically notifies family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, if there is a high possibility of fraud, it sends a warning message to family members. The fraud determination unit also adds a function to automatically notify family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, if there is a high possibility of fraud, it sends a warning message to a trusted friend. The fraud determination unit also adds a function to automatically notify family members or trusted third parties of the fraud call determination result, encouraging them to take prompt action. For example, if there is a high possibility of fraud, it encourages them to report the call to the police. This allows family members or trusted third parties to automatically notify the fraud call determination result, encouraging them to take prompt action.

[0070] The fraud determination unit can use the emotion estimation function to detect in real time the anxiety or fear a user feels when receiving a fraudulent call and issue an appropriate alert. For example, the generation AI in the fraud determination unit detects in real time the anxiety or fear a user feels when receiving a fraudulent call and issues an appropriate alert. For example, if the user feels anxious, a warning message is displayed. The fraud determination unit can also use the emotion estimation function to detect in real time the anxiety or fear a user feels when receiving a fraudulent call and issue an appropriate alert. For example, if the user feels fear, the unit urges the user to hang up the phone. The fraud determination unit can also use the generation AI to detect in real time the anxiety or fear a user feels when receiving a fraudulent call and issue an appropriate alert. For example, if the user feels anxious, the unit urges the user to consult with family or friends. In this way, the generation AI can detect in real time the anxiety or fear a user feels when receiving a fraudulent call and issue an appropriate alert.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The audio logging system can also learn a user's conversation patterns and analyze the frequency with which the user uses specific phrases and keywords. For example, it can identify contract terms and price negotiation points frequently used during business negotiations and provide them to the user. It can also analyze the frequency of decisions and action items made during meetings and highlight important points. It can also analyze the frequency of important instructions and requests made over the phone and notify the user. This allows it to identify frequently used phrases and keywords and provide efficient conversation support.

[0073] The sound logging system can also analyze the tone of a user's conversation to determine whether the user is relaxed or tense. For example, it can analyze the tension during a business meeting and provide the user with advice on how to relax. It can also analyze conflicts of opinion during a meeting and provide the user with advice on how to stay calm. It can also analyze gratitude or dissatisfaction over the phone and provide the user with appropriate feedback. This allows the quality of conversations to be improved by analyzing the tone of a user's conversation and providing appropriate advice.

[0074] The audio logging system can also analyze the content of a user's conversation to determine whether the user is discussing a specific topic. For example, if a specific product or service is being discussed during a business meeting, that information can be automatically highlighted. It can also automatically highlight information about a specific project or task during a meeting. It can also automatically highlight information about specific instructions or requests over the phone. This allows users to highlight information when they are discussing a specific topic, ensuring that important information is not missed.

[0075] The sound logging system can also analyze the content of a user's conversation and determine whether the user is expressing a particular emotion. For example, if a user is excited during a business meeting, that information can be reflected in the text data. Also, if a user expresses dissatisfaction during a meeting, that information can be reflected in the text data. Furthermore, if a user expresses gratitude over the phone, that information can be reflected in the text data. In this way, by reflecting the user's emotions in the text data, it is possible to convey emotional nuances.

[0076] The audio logging system can further analyze the content of a user's conversation and determine whether the user should take a specific action. For example, it can determine whether a contract should be concluded during a business negotiation and notify the user. It can also determine whether a specific task should be performed during a meeting and notify the user. It can also determine whether specific instructions or requests made over the phone should be carried out and notify the user. This makes it possible to determine whether the user should take a specific action and provide efficient conversation support.

[0077] The audio log system can also analyze the content of a user's conversation and determine whether the user should share specific information. For example, during a business meeting, it can determine whether specific contract terms or price negotiation points should be shared and notify the user. It can also determine whether the progress of a specific project or task should be shared during a meeting and notify the user. It can also determine whether specific instructions or requests over the phone should be shared and notify the user. This makes it possible to determine whether the user should share specific information and provide efficient conversation support.

[0078] The sound logging system can also analyze the content of a user's conversation and display in real time whether the user is expressing a particular emotion. For example, if a user is nervous during a business meeting, that information can be displayed in real time and appropriate feedback can be provided. Also, if a user is excited during a meeting, that information can be displayed in real time and appropriate feedback can be provided. Furthermore, if a user is expressing dissatisfaction over the phone, that information can be displayed in real time and appropriate feedback can be provided. In this way, by displaying a user's emotions in real time and providing appropriate feedback, the quality of conversations can be improved.

[0079] The audio log system can further analyze the content of a user's conversation and provide support for the user to search for specific information. For example, support can be provided for searching for specific contract terms or price negotiation points during a business meeting. Support can also be provided for searching for the progress of a specific project or task during a meeting. Furthermore, support can be provided for searching for specific instructions or requests over the phone. This allows the system to support the user in searching for specific information and provide support for efficient conversations.

[0080] The sound logging system can further analyze the content of a user's conversation, analyze whether the user is expressing a particular emotion, and suggest appropriate actions based on that emotion. For example, if a user is excited during a business meeting, the system can analyze that information and suggest actions to help the user relax. Also, if a user is expressing frustration during a meeting, the system can analyze that information and suggest actions to help the user calm down. Furthermore, if a user is expressing gratitude over the phone, the system can analyze that information and suggest actions to convey that gratitude. In this way, the quality of conversations can be improved by suggesting appropriate actions based on the user's emotions.

[0081] The audio logging system can also analyze the content of a user's conversation and determine whether the user should save specific information. For example, during a business meeting, it can determine whether specific contract terms or price negotiation points should be saved and notify the user. It can also determine whether the progress of a specific project or task should be saved during a meeting and notify the user. It can also determine whether specific instructions or requests made over the phone should be saved and notify the user. This makes it possible to determine whether the user should save specific information and provide efficient conversation support.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The audio capture unit captures the audio of a conversation. For example, it can capture telephone conversations in real time, as well as audio from face-to-face conversations and video conferences. Furthermore, it uses noise-canceling technology to remove background noise and capture clear audio. Step 2: The transcription unit converts the audio captured by the audio capture unit into text data. For example, the generation AI uses speech recognition technology to convert the audio into text data, and natural language processing technology to understand the context and produce an accurate transcription. It can also analyze the intonation and emotion of the audio and reflect them in the text data. Step 3: The sending unit sends the text data converted by the transcription unit to the user, for example, by email or via a messenger app, or by storing the text data in cloud storage so that the user can access it. Step 4: The fraud detection unit analyzes the content of the call to determine whether it is a fraudulent call. For example, the generation AI analyzes the content of the call, detects phrases and patterns that may be fraudulent, and uses machine learning algorithms to improve the accuracy of the detection. The user is notified of the fraudulent call detection results in real time. Step 5: The audio adjustment section adjusts the audio speed for seniors. For example, the AI ​​generator analyzes the audio speed and adjusts it to an appropriate speed. It also adjusts the audio frequency band according to the hearing characteristics of seniors, and automatically repeats certain keywords or phrases if they are missed.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0132] The data processing system 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 voice acquisition unit for acquiring voice of a conversation; a transcription unit that converts the speech acquired by the speech acquisition unit into text data; a transmitting unit that transmits the text data converted by the transcription unit to a user; a fraud determination unit that analyzes the contents of a call and determines whether the call is fraudulent; A voice adjustment unit that adjusts the voice speed for elderly people. A system characterized by:

2. The transcription unit Understand the context of the conversation and automatically highlight important keywords or phrases 2. The system of claim 1.

3. The transcription unit Translate the content of said conversation in real time to facilitate communication between participants who speak different languages 2. The system of claim 1.

4. The transcription unit Analyzing changes in emotions during the conversation and adding the intensity or type of emotions to the text data.

2. The system of claim 1.

5. The transcription unit The transcript data of the conversation is automatically summarized to generate a summary.

2. The system of claim 1.

6. The transcription unit The content of the conversation is analyzed not only through audio but also through video conferencing video data, and both video and audio are transcribed.

2. The system of claim 1.

7. The transcription unit Displaying changes in emotions during the conversation in real time and providing feedback according to the changes in emotions 2. The system of claim 1.

8. The audio adjustment unit Adjusts the frequency band of the voice to suit the hearing characteristics of the elderly, converting it into a voice that is easier to hear.

2. The system of claim 1.

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