System

Abstract

An object of a system according to an embodiment is to detect a risky interaction from voice data in real time and appropriately handle the interaction.SOLUTION: A system includes a voice acquisition unit, an analysis unit, a detection unit, a warning unit, and a recording unit. The voice acquisition unit acquires voice data. The analysis unit analyzes the voice data acquired by the voice acquisition unit in real time. The detection unit detects risky interaction from the voice data analyzed by the analysis unit. The warning unit issues a warning for the risky interaction detected by the detection unit. The recording unit records voice of the risky interaction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to detect risky interactions from voice data in real time and respond appropriately.

[0005] The system according to the embodiment aims to detect risky interactions from voice data in real time and respond appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes an audio acquisition unit, an analysis unit, a detection unit, a warning unit, and a recording unit. The audio acquisition unit acquires audio data. The analysis unit analyzes the audio data acquired by the audio acquisition unit in real time. The detection unit detects risky interactions from the audio data analyzed by the analysis unit. The warning unit issues a warning about risky interactions detected by the detection unit. The recording unit records audio of the risky interactions. [Effects of the Invention]

[0007] The system according to the embodiment can detect risky interactions from voice data in real time and respond appropriately. [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 voice monitoring AI device according to an embodiment of the present invention is a system that automatically acquires voice data, analyzes it using a generation AI, detects risky interactions, and automatically issues warnings and records the audio. This allows the voice monitoring AI device to effectively detect risky interactions such as fraud and harassment, and automatically issues warnings and records the audio.

[0029] A voice monitoring AI device according to an embodiment includes an audio acquisition unit, an analysis unit, a detection unit, a warning unit, and a recording unit. The audio acquisition unit acquires audio data. For example, it collects audio data through a microphone. The audio acquisition unit can also acquire audio data in real time. The analysis unit analyzes the audio data acquired by the audio acquisition unit in real time. For example, a generation AI analyzes the audio data and identifies specific patterns. The generation AI performs the analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The detection unit detects risky interactions from the audio data analyzed by the analysis unit. For example, it detects typical fraudulent phrases or harassing words. The warning unit issues a warning for risky interactions detected by the detection unit. For example, the device issues a warning via a voice message. It can also send an alert notification to a smartphone or PC. The recording unit records the audio of risky interactions. For example, it stores the recorded data as evidence of suspected fraudulent interactions or harassment. As a result, the voice monitoring AI device according to the embodiment can detect risky interactions in real time and automatically issue a warning and record the conversation. For example, to protect the safety of children at home, it can detect fraudulent phone calls and harassing conversations and issue a warning. It can also record risky conversations and save them as evidence to prevent harassment of employees in the office.

[0030] The analysis unit can analyze background sounds or environmental sounds included in the voice data and identify risks in a specific environment. The analysis unit, for example, analyzes background sounds or environmental sounds included in the voice data and identifies risks in a specific environment. For example, it detects interactions in a noisy environment as a risk. The analysis unit also analyzes background sounds or environmental sounds included in the voice data in real time and identifies risks. For example, it detects interactions in an environment where the siren of an emergency vehicle can be heard as a risk. The analysis unit also analyzes background sounds or environmental sounds included in the voice data and identifies risks in a specific environment. For example, it detects interactions in an environment where the noise of a construction site can be heard as a risk. In this way, by analyzing background sounds and environmental sounds, it is possible to identify risks in a specific environment.

[0031] The analysis unit can automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, the analysis unit can add a function to automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, multilingual support such as English, Spanish, and Chinese can be achieved. The analysis unit can also automatically recognize different languages ​​and dialects and analyze voice data. For example, it can recognize dialects specific to a region and perform accurate analysis. The analysis unit can also add a function to automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, it can analyze interactions in international business environments. This allows global support by recognizing different languages ​​and dialects.

[0032] The analysis unit can visualize the analysis results of the voice data and provide them to the user. The analysis unit, for example, visualizes the analysis results of the voice data and provides them to the user. For example, the analysis results may be displayed in graphs or charts to enable intuitive understanding. The analysis unit also visualizes the analysis results of the voice data and provides them to the user. For example, the analysis results may be displayed in dashboard format to enable important information to be grasped at a glance. The analysis unit also visualizes the analysis results of the voice data and provides them to the user. For example, the analysis results may be displayed in a heat map to highlight high-risk areas. In this way, visualizing the analysis results allows the user to intuitively understand.

[0033] The detection unit can learn risk patterns by referring to the history of past interactions. In the detection unit, for example, the generation AI refers to the history of past interactions to learn risk patterns. For example, risks are identified based on past interactions involving fraud or harassment. The detection unit also refers to the history of past interactions to learn risk patterns. For example, high-risk phrases and words are identified based on past data. In addition, the detection unit can learn risk patterns by referring to the history of past interactions. For example, risky interactions are identified based on data from past interactions. In this way, by referring to the history of past interactions, risk patterns can be learned, enabling more accurate risk detection.

[0034] The detection unit can analyze the characteristics of a speaker's voice and identify risks. For example, the generation AI in the detection unit analyzes the characteristics of a speaker's voice and identifies risks. For example, if the speaker's voice sounds tense, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds angry, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds excited, the risk is evaluated as high. In this way, risks can be identified by analyzing the characteristics of a speaker's voice.

[0035] The detection unit can be customized by introducing risk patterns specialized for different industries or applications. The detection unit, for example, introduces risk patterns specialized for different industries or applications to detect risky interactions. For example, it uses risk patterns specialized for the financial industry or the medical industry. The detection unit also introduces risk patterns specialized for different industries or applications to detect risky interactions. For example, it uses risk patterns specialized for the education industry or the entertainment industry. The detection unit also introduces risk patterns specialized for different industries or applications to detect risky interactions. For example, it uses risk patterns specialized for the manufacturing industry or the service industry. In this way, customizable risk detection can be achieved by introducing risk patterns specialized for different industries or applications.

[0036] The detection unit can link the detection results of risky interactions with other security systems to achieve comprehensive risk management. The detection unit, for example, links the detection results of risky interactions with other security systems to achieve comprehensive risk management. For example, it links with a surveillance camera system to identify risks. The detection unit also links with other security systems to integrate the detection results of risky interactions. For example, it links with an access control system to identify risks. The detection unit also links the detection results of risky interactions with other security systems to achieve comprehensive risk management. For example, it links with an intrusion detection system to identify risks. In this way, by linking with other security systems, comprehensive risk management can be achieved.

[0037] The warning unit can automatically generate the content of the warning and provide the optimal warning message depending on the situation. For example, the generation AI of the warning unit automatically generates the content of the warning and provides the optimal warning message depending on the situation. For example, if there is a high risk of fraud, the warning unit issues a warning saying, "There is a possibility of fraud. Please be careful." The warning unit also automatically generates the content of the warning and provides the optimal warning message depending on the situation. For example, if there is a high risk of harassment, the warning unit issues a warning saying, "There is a possibility of harassment. Please be careful." The warning unit also automatically generates the content of the warning and provides the optimal warning message depending on the situation. For example, if there is a high risk of an emergency, the warning unit issues a warning saying, "There is a possibility of an emergency. Please be careful." This allows the user to take appropriate action by providing the optimal warning message depending on the situation.

[0038] The warning unit can optimize the timing of the warning and issue a warning before the risk increases. For example, the generation AI optimizes the timing of the warning and issues a warning before the risk increases. For example, the generation AI issues a warning, "There is a possibility of fraud. Please be careful," before the risk of fraud increases. The warning unit also optimizes the timing of the warning and issues a warning before the risk increases. For example, the generation AI issues a warning, "There is a possibility of harassment. Please be careful," before the risk of harassment increases. The warning unit also optimizes the timing of the warning and issues a warning before the risk increases. For example, the generation AI issues a warning, "There is a possibility of an emergency. Please be careful," before the risk of an emergency increases. This allows the user to take early action by issuing a warning before the risk increases.

[0039] The warning unit can link different devices together to provide a warning through multiple channels. For example, the warning unit links different devices together to issue a warning and provides a warning through multiple channels. For example, a smart watch and a smart speaker are linked together to issue a warning. The warning unit also links different devices together to provide a warning through multiple channels. For example, a smartphone and a PC are linked together to issue a warning. The warning unit also links different devices together to issue a warning and provides a warning through multiple channels. For example, a tablet and a smart speaker are linked together to issue a warning. By providing a warning through multiple channels, the user can take prompt action.

[0040] The warning unit can link the result of issuing a warning with the user's behavior history and evaluate the effectiveness of the warning. The warning unit, for example, links the result of issuing a warning with the user's behavior history and evaluates the effectiveness of the warning. For example, it analyzes the user's behavior after the warning and measures the effectiveness of the warning. The warning unit also links with the user's behavior history and evaluates the result of issuing a warning. For example, it analyzes the user's behavior pattern after the warning and measures the effectiveness of the warning. The warning unit also links the result of issuing a warning with the user's behavior history and evaluates the effectiveness of the warning. For example, it analyzes changes in the user's behavior after the warning and measures the effectiveness of the warning. In this way, the accuracy of the warning can be improved by evaluating the effectiveness of the warning.

[0041] The recording unit can automatically summarize the recorded data and extract only the important parts. For example, the generation AI in the recording unit automatically summarizes the recorded data and extracts only the important parts. For example, it summarizes and extracts parts that are suspected of fraud. The recording unit can also automatically summarize the recorded data and extract only the important parts. For example, it can summarize and extract parts that are suspected of harassment. The recording unit can also automatically summarize the recorded data and extract only the important parts. For example, it can summarize and extract parts that are suspected of being an emergency. This allows for efficient data management by summarizing the recorded data and extracting only the important parts.

[0042] The recording unit can automatically improve the sound quality of the recorded data to provide clear audio. For example, the generation AI in the recording unit can automatically improve the sound quality of the recorded data to provide clear audio. For example, the sound quality can be improved using noise reduction technology. The recording unit can also automatically improve the sound quality of the recorded data to provide clear audio. For example, the sound quality can be improved using echo cancellation technology. The recording unit can also automatically improve the sound quality of the recorded data to provide clear audio. For example, the sound quality can be improved using voice enhancement technology. This improves the sound quality of the recorded data to provide clear audio.

[0043] The recording unit can integrate the audio recording data with other data to provide comprehensive evidence. For example, the recording unit can integrate the audio recording data of the relevant exchange with other data to provide comprehensive evidence. For example, text messages and audio recordings can be integrated to provide evidence. The recording unit can also integrate the audio recording data with other data to provide comprehensive evidence. For example, video and audio recordings can be integrated to provide evidence. The recording unit can also integrate the audio recording data of the relevant exchange with other data to provide comprehensive evidence. For example, text messages, videos, and audio recordings can be integrated to provide evidence. In this way, the audio recording data can be integrated with other data to provide comprehensive evidence.

[0044] The recording unit can automatically store the recorded data in cloud storage for easy access. For example, the recording unit automatically stores the voice recording data of the corresponding interaction in cloud storage for easy access. For example, the recording data is uploaded and stored in the cloud. The recording unit also automatically stores the voice recording data in cloud storage for easy access. For example, the recorded data stored in cloud storage can be accessed at any time. The recording unit also automatically stores the voice recording data of the corresponding interaction in cloud storage for easy access. For example, a function is provided for sharing the recorded data stored in cloud storage. This makes the recorded data easier to access by storing it in cloud storage.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The voice monitoring AI device can further include a voice data translation unit. The translation unit translates the acquired voice data into other languages ​​in real time. For example, content spoken in English can be translated into Japanese and provided to the user. The translation unit can also support multiple languages, facilitating communication between different languages. For example, it can support multiple languages ​​such as French, German, and Spanish. Furthermore, the translation unit can accurately translate technical terms and industry-specific language. For example, it can accurately translate technical terms such as medical and legal terms and provide them to the user. This allows the voice monitoring AI device to support communication between different languages ​​and enable global support.

[0047] The analysis unit can analyze background sounds in the voice data and identify risks in specific environments. For example, it detects interactions in noisy environments as risks. The analysis unit also analyzes background sounds and environmental sounds included in the voice data in real time and identifies risks. For example, it detects interactions in an environment where the siren of an emergency vehicle can be heard as risks. The analysis unit also analyzes background sounds and environmental sounds included in the voice data and identifies risks in specific environments. For example, it detects interactions in an environment where the noise of a construction site can be heard as risks. In this way, by analyzing background sounds and environmental sounds, it is possible to identify risks in specific environments.

[0048] The analysis unit can automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, it can support multiple languages ​​such as English, Spanish, and Chinese. The analysis unit also automatically recognizes different languages ​​and dialects and analyzes voice data. For example, it can recognize dialects specific to a region and perform accurate analysis. The analysis unit can also add a function to automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, it can analyze interactions in international business environments. This allows it to recognize different languages ​​and dialects and enable global support.

[0049] The analysis unit can visualize the analysis results of the voice data and provide them to the user. For example, the analysis results can be displayed in graphs or charts to enable intuitive understanding. The analysis unit can also visualize the analysis results of the voice data and provide them to the user. For example, the analysis results can be displayed in dashboard format to enable important information to be grasped at a glance. The analysis unit can also visualize the analysis results of the voice data and provide them to the user. For example, the analysis results can be displayed in a heat map to highlight high-risk areas. In this way, visualizing the analysis results allows the user to intuitively understand.

[0050] The detection unit can learn risk patterns by referring to the history of past interactions. For example, the generation AI can learn risk patterns by referring to the history of past interactions. For example, it can identify risks based on past interactions involving fraud or harassment. The detection unit can also learn risk patterns by referring to the history of past interactions. For example, it can identify high-risk phrases and words based on past data. The detection unit can also learn risk patterns by referring to the history of past interactions. For example, it can identify risky interactions based on data from past interactions. In this way, by referring to the history of past interactions, it can learn risk patterns and enable more accurate risk detection.

[0051] The detection unit can analyze the characteristics of a speaker's voice and identify risks. For example, the generation AI analyzes the characteristics of a speaker's voice and identifies risks. For example, if the speaker's voice sounds tense, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds angry, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds excited, the risk is evaluated as high. In this way, risks can be identified by analyzing the characteristics of a speaker's voice.

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

[0053] Step 1: The voice acquisition unit acquires voice data. For example, the voice data is collected through a microphone. The voice acquisition unit can also acquire voice data in real time. Step 2: The analysis unit analyzes the voice data acquired by the voice acquisition unit in real time. For example, a generation AI analyzes the voice data and identifies specific patterns. The generation AI performs the analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The detection unit detects risky interactions from the voice data analyzed by the analysis unit, such as detecting typical fraudulent phrases or harassing language. Step 4: The warning unit issues a warning about the risky interactions detected by the detection unit. For example, the device can issue a warning with a voice message, or it can send an alert notification to a smartphone or PC. Step 5: The Recording Department records audio of potentially dangerous interactions, for example, suspected fraud or harassment, and stores the recordings as evidence.

[0054] (Example 2) The voice monitoring AI device according to an embodiment of the present invention is a system that automatically acquires voice data, analyzes it using a generation AI, detects risky interactions, and automatically issues warnings and records the audio. This allows the voice monitoring AI device to effectively detect risky interactions such as fraud and harassment, and automatically issues warnings and records the audio.

[0055] A voice monitoring AI device according to an embodiment includes an audio acquisition unit, an analysis unit, a detection unit, a warning unit, and a recording unit. The audio acquisition unit acquires audio data. For example, it collects audio data through a microphone. The audio acquisition unit can also acquire audio data in real time. The analysis unit analyzes the audio data acquired by the audio acquisition unit in real time. For example, a generation AI analyzes the audio data and identifies specific patterns. The generation AI performs the analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The detection unit detects risky interactions from the audio data analyzed by the analysis unit. For example, it detects typical fraudulent phrases or harassing words. The warning unit issues a warning for risky interactions detected by the detection unit. For example, the device issues a warning via a voice message. It can also send an alert notification to a smartphone or PC. The recording unit records the audio of risky interactions. For example, it stores the recorded data as evidence of suspected fraudulent interactions or harassment. As a result, the voice monitoring AI device according to the embodiment can detect risky interactions in real time and automatically issue a warning and record the conversation. For example, to protect the safety of children at home, it can detect fraudulent phone calls and harassing conversations and issue a warning. It can also record risky conversations and save them as evidence to prevent harassment of employees in the office.

[0056] The analysis unit can detect changes in emotion by analyzing changes in tone or speed of the voice data. For example, the analysis unit analyzes changes in tone or speed of the voice data to detect changes in the speaker's emotion. For example, if a speaker suddenly raises their voice, it detects anger. The analysis unit also analyzes changes in tone or speed of the voice data in real time to detect changes in emotion. For example, if a speaker suddenly lowers their voice, it detects sadness. The analysis unit also analyzes changes in tone or speed of the voice data to detect changes in emotion. For example, if a speaker suddenly increases their speaking speed, it detects excitement. In this way, by detecting changes in the speaker's emotion, risky interactions can be more accurately identified.

[0057] The analysis unit can analyze background sounds or environmental sounds included in the voice data and identify risks in a specific environment. The analysis unit, for example, analyzes background sounds or environmental sounds included in the voice data and identifies risks in a specific environment. For example, it detects interactions in a noisy environment as a risk. The analysis unit also analyzes background sounds or environmental sounds included in the voice data in real time and identifies risks. For example, it detects interactions in an environment where the siren of an emergency vehicle can be heard as a risk. The analysis unit also analyzes background sounds or environmental sounds included in the voice data and identifies risks in a specific environment. For example, it detects interactions in an environment where the noise of a construction site can be heard as a risk. In this way, by analyzing background sounds and environmental sounds, it is possible to identify risks in a specific environment.

[0058] The analysis unit can use the emotion estimation function to estimate the emotional state of the speaker in real time and evaluate risk based on changes in emotion. The analysis unit, for example, uses the emotion estimation function to estimate the emotional state of the speaker in real time and evaluate risk based on changes in emotion. For example, if the speaker suddenly shows anger, the risk is evaluated as high. The analysis unit also uses the emotion estimation function to estimate the emotional state of the speaker in real time and evaluate risk based on changes in emotion. For example, if the speaker suddenly shows sadness, the risk is evaluated as high. The analysis unit also uses the emotion estimation function to estimate the emotional state of the speaker in real time and evaluate risk based on changes in emotion. For example, if the speaker suddenly shows excitement, the risk is evaluated as high. This enables more accurate risk detection by evaluating risk based on changes in emotion.

[0059] The analysis unit can automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, the analysis unit can add a function to automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, multilingual support such as English, Spanish, and Chinese can be achieved. The analysis unit can also automatically recognize different languages ​​and dialects and analyze voice data. For example, it can recognize dialects specific to a region and perform accurate analysis. The analysis unit can also add a function to automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, it can analyze interactions in international business environments. This allows global support by recognizing different languages ​​and dialects.

[0060] The analysis unit can visualize the analysis results of the voice data and provide them to the user. The analysis unit, for example, visualizes the analysis results of the voice data and provides them to the user. For example, the analysis results may be displayed in graphs or charts to enable intuitive understanding. The analysis unit also visualizes the analysis results of the voice data and provides them to the user. For example, the analysis results may be displayed in dashboard format to enable important information to be grasped at a glance. The analysis unit also visualizes the analysis results of the voice data and provides them to the user. For example, the analysis results may be displayed in a heat map to highlight high-risk areas. In this way, visualizing the analysis results allows the user to intuitively understand.

[0061] The analysis unit can use the emotion estimation function to provide real-time feedback according to the speaker's emotion, thereby promoting positive communication. The analysis unit, for example, uses the emotion estimation function to provide real-time feedback according to the speaker's emotion. For example, if the speaker shows anger, advice to stay calm is provided. The analysis unit also uses the emotion estimation function to provide real-time feedback according to the speaker's emotion. For example, if the speaker shows sadness, an encouraging message is provided. The analysis unit also uses the emotion estimation function to provide real-time feedback according to the speaker's emotion. For example, if the speaker shows excitement, advice to stay calm is provided. In this way, by providing feedback according to emotions, positive communication can be promoted.

[0062] The detection unit can learn risk patterns by referring to the history of past interactions. In the detection unit, for example, the generation AI refers to the history of past interactions to learn risk patterns. For example, risks are identified based on past interactions involving fraud or harassment. The detection unit also refers to the history of past interactions to learn risk patterns. For example, high-risk phrases and words are identified based on past data. In addition, the detection unit can learn risk patterns by referring to the history of past interactions. For example, risky interactions are identified based on data from past interactions. In this way, by referring to the history of past interactions, risk patterns can be learned, enabling more accurate risk detection.

[0063] The detection unit can analyze the characteristics of a speaker's voice and identify risks. For example, the generation AI in the detection unit analyzes the characteristics of a speaker's voice and identifies risks. For example, if the speaker's voice sounds tense, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds angry, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds excited, the risk is evaluated as high. In this way, risks can be identified by analyzing the characteristics of a speaker's voice.

[0064] The detection unit can use the emotion estimation function to detect risky interactions based on the emotional state of the speaker and perform risk assessment according to changes in emotion. The detection unit, for example, uses the emotion estimation function to detect risky interactions based on the emotional state of the speaker. For example, if the speaker suddenly shows anger, the risk is assessed as high. The detection unit also uses the emotion estimation function to detect risky interactions based on the emotional state of the speaker. For example, if the speaker suddenly shows sadness, the risk is assessed as high. The detection unit also uses the emotion estimation function to detect risky interactions based on the emotional state of the speaker. For example, if the speaker suddenly shows excitement, the risk is assessed as high. This enables more accurate risk detection by assessing risk based on changes in emotion.

[0065] The detection unit can be customized by introducing risk patterns specialized for different industries or applications. The detection unit, for example, introduces risk patterns specialized for different industries or applications to detect risky interactions. For example, it uses risk patterns specialized for the financial industry or the medical industry. The detection unit also introduces risk patterns specialized for different industries or applications to detect risky interactions. For example, it uses risk patterns specialized for the education industry or the entertainment industry. The detection unit also introduces risk patterns specialized for different industries or applications to detect risky interactions. For example, it uses risk patterns specialized for the manufacturing industry or the service industry. In this way, customizable risk detection can be achieved by introducing risk patterns specialized for different industries or applications.

[0066] The detection unit can link the detection results of risky interactions with other security systems to achieve comprehensive risk management. The detection unit, for example, links the detection results of risky interactions with other security systems to achieve comprehensive risk management. For example, it links with a surveillance camera system to identify risks. The detection unit also links with other security systems to integrate the detection results of risky interactions. For example, it links with an access control system to identify risks. The detection unit also links the detection results of risky interactions with other security systems to achieve comprehensive risk management. For example, it links with an intrusion detection system to identify risks. In this way, by linking with other security systems, comprehensive risk management can be achieved.

[0067] The detection unit can use the emotion estimation function to feed back the detection result of a risky interaction to the user and suggest a countermeasure based on the emotion. The detection unit, for example, uses the emotion estimation function to feed back the detection result of a risky interaction to the user. For example, if the speaker expresses anger, the detection unit suggests a countermeasure to help the user stay calm. The detection unit also uses the emotion estimation function to feed back the detection result of a risky interaction to the user. For example, if the speaker expresses sadness, the detection unit suggests a message of encouragement. The detection unit also uses the emotion estimation function to feed back the detection result of a risky interaction to the user. For example, if the speaker expresses excitement, the detection unit suggests a countermeasure to help the user stay calm. In this way, by suggesting a countermeasure based on the emotion, the user can take an appropriate action.

[0068] The warning unit can automatically generate the content of the warning and provide the optimal warning message depending on the situation. For example, the generation AI of the warning unit automatically generates the content of the warning and provides the optimal warning message depending on the situation. For example, if there is a high risk of fraud, the warning unit issues a warning saying, "There is a possibility of fraud. Please be careful." The warning unit also automatically generates the content of the warning and provides the optimal warning message depending on the situation. For example, if there is a high risk of harassment, the warning unit issues a warning saying, "There is a possibility of harassment. Please be careful." The warning unit also automatically generates the content of the warning and provides the optimal warning message depending on the situation. For example, if there is a high risk of an emergency, the warning unit issues a warning saying, "There is a possibility of an emergency. Please be careful." This allows the user to take appropriate action by providing the optimal warning message depending on the situation.

[0069] The warning unit can optimize the timing of the warning and issue a warning before the risk increases. For example, the generation AI optimizes the timing of the warning and issues a warning before the risk increases. For example, the generation AI issues a warning, "There is a possibility of fraud. Please be careful," before the risk of fraud increases. The warning unit also optimizes the timing of the warning and issues a warning before the risk increases. For example, the generation AI issues a warning, "There is a possibility of harassment. Please be careful," before the risk of harassment increases. The warning unit also optimizes the timing of the warning and issues a warning before the risk increases. For example, the generation AI issues a warning, "There is a possibility of an emergency. Please be careful," before the risk of an emergency increases. This allows the user to take early action by issuing a warning before the risk increases.

[0070] The warning unit uses the emotion estimation function to generate a warning message according to the emotional state of the user, and can provide a warning that takes emotions into consideration. The warning unit, for example, uses the emotion estimation function to generate a warning message according to the emotional state of the user. For example, if the user is nervous, a message urging the user to stay calm is provided. The warning unit also uses the emotion estimation function to generate a warning message according to the emotional state of the user. For example, if the user is angry, a message urging the user to stay calm is provided. The warning unit also uses the emotion estimation function to generate a warning message according to the emotional state of the user. For example, if the user is sad, an encouraging message is provided. In this way, a warning that takes emotions into consideration allows the user to take an appropriate action.

[0071] The warning unit can link different devices together to provide a warning through multiple channels. For example, the warning unit links different devices together to issue a warning and provides a warning through multiple channels. For example, a smart watch and a smart speaker are linked together to issue a warning. The warning unit also links different devices together to provide a warning through multiple channels. For example, a smartphone and a PC are linked together to issue a warning. The warning unit also links different devices together to issue a warning and provides a warning through multiple channels. For example, a tablet and a smart speaker are linked together to issue a warning. By providing a warning through multiple channels, the user can take prompt action.

[0072] The warning unit can link the result of issuing a warning with the user's behavior history and evaluate the effectiveness of the warning. The warning unit, for example, links the result of issuing a warning with the user's behavior history and evaluates the effectiveness of the warning. For example, it analyzes the user's behavior after the warning and measures the effectiveness of the warning. The warning unit also links with the user's behavior history and evaluates the result of issuing a warning. For example, it analyzes the user's behavior pattern after the warning and measures the effectiveness of the warning. The warning unit also links the result of issuing a warning with the user's behavior history and evaluates the effectiveness of the warning. For example, it analyzes changes in the user's behavior after the warning and measures the effectiveness of the warning. In this way, the accuracy of the warning can be improved by evaluating the effectiveness of the warning.

[0073] The warning unit uses the emotion estimation function to monitor the user's emotional response after the issuance of the warning, and can continuously improve the effectiveness of the warning. The warning unit, for example, uses the emotion estimation function to monitor the user's emotional response after the issuance of the warning. For example, it analyzes the user's emotional changes after the warning and evaluates the effectiveness of the warning. The warning unit also uses the emotion estimation function to monitor the user's emotional response after the issuance of the warning. For example, it analyzes the user's emotional score after the warning and evaluates the effectiveness of the warning. The warning unit also uses the emotion estimation function to monitor the user's emotional response after the issuance of the warning. For example, it analyzes the user's emotional changes after the warning in real time and evaluates the effectiveness of the warning. This continuously improves the effectiveness of the warning, allowing the user to take appropriate action.

[0074] The recording unit can automatically summarize the recorded data and extract only the important parts. For example, the generation AI in the recording unit automatically summarizes the recorded data and extracts only the important parts. For example, it summarizes and extracts parts that are suspected of fraud. The recording unit can also automatically summarize the recorded data and extract only the important parts. For example, it can summarize and extract parts that are suspected of harassment. The recording unit can also automatically summarize the recorded data and extract only the important parts. For example, it can summarize and extract parts that are suspected of being an emergency. This allows for efficient data management by summarizing the recorded data and extracting only the important parts.

[0075] The recording unit can automatically improve the sound quality of the recorded data to provide clear audio. For example, the generation AI in the recording unit can automatically improve the sound quality of the recorded data to provide clear audio. For example, the sound quality can be improved using noise reduction technology. The recording unit can also automatically improve the sound quality of the recorded data to provide clear audio. For example, the sound quality can be improved using echo cancellation technology. The recording unit can also automatically improve the sound quality of the recorded data to provide clear audio. For example, the sound quality can be improved using voice enhancement technology. This improves the sound quality of the recorded data to provide clear audio.

[0076] The recording unit can use the emotion estimation function to analyze changes in emotion contained in the recorded data and identify important portions based on the changes in emotion. The recording unit, for example, uses the emotion estimation function to analyze changes in emotion contained in the recorded data and identify important portions. For example, it identifies a portion where the speaker suddenly shows anger. The recording unit also uses the emotion estimation function to analyze changes in emotion contained in the recorded data and identify important portions. For example, it identifies a portion where the speaker suddenly shows sadness. The recording unit also uses the emotion estimation function to analyze changes in emotion contained in the recorded data and identify important portions. For example, it identifies a portion where the speaker suddenly shows excitement. This enables efficient data management by identifying important portions based on changes in emotion.

[0077] The recording unit can integrate the audio recording data with other data to provide comprehensive evidence. For example, the recording unit can integrate the audio recording data of the relevant exchange with other data to provide comprehensive evidence. For example, text messages and audio recordings can be integrated to provide evidence. The recording unit can also integrate the audio recording data with other data to provide comprehensive evidence. For example, video and audio recordings can be integrated to provide evidence. The recording unit can also integrate the audio recording data of the relevant exchange with other data to provide comprehensive evidence. For example, text messages, videos, and audio recordings can be integrated to provide evidence. In this way, the audio recording data can be integrated with other data to provide comprehensive evidence.

[0078] The recording unit can automatically store the recorded data in cloud storage for easy access. For example, the recording unit automatically stores the voice recording data of the corresponding interaction in cloud storage for easy access. For example, the recording data is uploaded and stored in the cloud. The recording unit also automatically stores the voice recording data in cloud storage for easy access. For example, the recorded data stored in cloud storage can be accessed at any time. The recording unit also automatically stores the voice recording data of the corresponding interaction in cloud storage for easy access. For example, a function is provided for sharing the recorded data stored in cloud storage. This makes the recorded data easier to access by storing it in cloud storage.

[0079] The recording unit can use the emotion estimation function to collect the user's emotional reactions to the recorded data and evaluate the importance of the recorded data. The recording unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the recorded data and evaluate the importance of the recorded data. For example, it evaluates recorded data in which the user has a strong emotional reaction as important. The recording unit also uses the emotion estimation function to collect the user's emotional reactions to the recorded data and evaluate the importance of the recorded data. For example, it evaluates recorded data in which the user has a positive emotional reaction as important. The recording unit also uses the emotion estimation function to collect the user's emotional reactions to the recorded data and evaluate the importance of the recorded data. For example, it evaluates recorded data in which the user has a negative emotional reaction as important. In this way, the importance of the recorded data can be evaluated by collecting the user's emotional reactions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The voice monitoring AI device can further include a voice data translation unit. The translation unit translates the acquired voice data into other languages ​​in real time. For example, content spoken in English can be translated into Japanese and provided to the user. The translation unit can also support multiple languages, facilitating communication between different languages. For example, it can support multiple languages ​​such as French, German, and Spanish. Furthermore, the translation unit can accurately translate technical terms and industry-specific language. For example, it can accurately translate technical terms such as medical and legal terms and provide them to the user. This allows the voice monitoring AI device to support communication between different languages ​​and enable global support.

[0082] The analysis unit can analyze background sounds in the voice data and identify risks in specific environments. For example, it detects interactions in noisy environments as risks. The analysis unit also analyzes background sounds and environmental sounds included in the voice data in real time and identifies risks. For example, it detects interactions in an environment where the siren of an emergency vehicle can be heard as risks. The analysis unit also analyzes background sounds and environmental sounds included in the voice data and identifies risks in specific environments. For example, it detects interactions in an environment where the noise of a construction site can be heard as risks. In this way, by analyzing background sounds and environmental sounds, it is possible to identify risks in specific environments.

[0083] The analysis unit can detect changes in emotion by analyzing changes in tone or speed of the voice data. For example, if a speaker suddenly raises their voice, it detects anger. The analysis unit can also analyze changes in tone or speed of the voice data in real time to detect changes in emotion. For example, if a speaker suddenly lowers their voice, it detects sadness. The analysis unit can also analyze changes in tone or speed of the voice data to detect changes in emotion. For example, if a speaker suddenly increases their speaking speed, it detects excitement. In this way, risky interactions can be more accurately identified by detecting changes in the speaker's emotions.

[0084] The analysis unit can use the emotion estimation function to estimate the emotional state of the speaker in real time and evaluate risk based on changes in emotion. For example, if the speaker suddenly shows anger, the risk is evaluated as high. The analysis unit also uses the emotion estimation function to estimate the emotional state of the speaker in real time and evaluate risk based on changes in emotion. For example, if the speaker suddenly shows sadness, the risk is evaluated as high. The analysis unit also uses the emotion estimation function to estimate the emotional state of the speaker in real time and evaluate risk based on changes in emotion. For example, if the speaker suddenly shows excitement, the risk is evaluated as high. This enables more accurate risk detection by evaluating risk based on changes in emotion.

[0085] The analysis unit can automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, it can support multiple languages ​​such as English, Spanish, and Chinese. The analysis unit also automatically recognizes different languages ​​and dialects and analyzes voice data. For example, it can recognize dialects specific to a region and perform accurate analysis. The analysis unit can also add a function to automatically recognize different languages ​​and dialects when analyzing voice data, enabling global support. For example, it can analyze interactions in international business environments. This allows it to recognize different languages ​​and dialects and enable global support.

[0086] The analysis unit can visualize the analysis results of the voice data and provide them to the user. For example, the analysis results can be displayed in graphs or charts to enable intuitive understanding. The analysis unit can also visualize the analysis results of the voice data and provide them to the user. For example, the analysis results can be displayed in dashboard format to enable important information to be grasped at a glance. The analysis unit can also visualize the analysis results of the voice data and provide them to the user. For example, the analysis results can be displayed in a heat map to highlight high-risk areas. In this way, visualizing the analysis results allows the user to intuitively understand.

[0087] The analysis unit uses the emotion estimation function to provide real-time feedback according to the speaker's emotions, thereby promoting positive communication. For example, if the speaker expresses anger, advice to stay calm is provided. The analysis unit also uses the emotion estimation function to provide real-time feedback according to the speaker's emotions. For example, if the speaker expresses sadness, an encouraging message is provided. The analysis unit also uses the emotion estimation function to provide real-time feedback according to the speaker's emotions. For example, if the speaker expresses excitement, advice to stay calm is provided. In this way, by providing feedback according to emotions, positive communication can be promoted.

[0088] The detection unit can learn risk patterns by referring to the history of past interactions. For example, the generation AI can learn risk patterns by referring to the history of past interactions. For example, it can identify risks based on past interactions involving fraud or harassment. The detection unit can also learn risk patterns by referring to the history of past interactions. For example, it can identify high-risk phrases and words based on past data. The detection unit can also learn risk patterns by referring to the history of past interactions. For example, it can identify risky interactions based on data from past interactions. In this way, by referring to the history of past interactions, it can learn risk patterns and enable more accurate risk detection.

[0089] The detection unit can analyze the characteristics of a speaker's voice and identify risks. For example, the generation AI analyzes the characteristics of a speaker's voice and identifies risks. For example, if the speaker's voice sounds tense, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds angry, the risk is evaluated as high. The detection unit can also analyze the characteristics of a speaker's voice and identify risks. For example, if the speaker's voice sounds excited, the risk is evaluated as high. In this way, risks can be identified by analyzing the characteristics of a speaker's voice.

[0090] The detection unit uses the emotion estimation function to detect risky interactions based on the emotional state of the speaker and can perform risk assessment according to changes in emotion. For example, if the speaker suddenly shows anger, the risk is assessed as high. The detection unit also uses the emotion estimation function to detect risky interactions based on the emotional state of the speaker. For example, if the speaker suddenly shows sadness, the risk is assessed as high. The detection unit also uses the emotion estimation function to detect risky interactions based on the emotional state of the speaker. For example, if the speaker suddenly shows excitement, the risk is assessed as high. This enables more accurate risk detection by assessing risk based on changes in emotion.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The voice acquisition unit acquires voice data. For example, the voice data is collected through a microphone. The voice acquisition unit can also acquire voice data in real time. Step 2: The analysis unit analyzes the voice data acquired by the voice acquisition unit in real time. For example, a generation AI analyzes the voice data and identifies specific patterns. The generation AI performs the analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The detection unit detects risky interactions from the voice data analyzed by the analysis unit, such as detecting typical fraudulent phrases or harassing language. Step 4: The warning unit issues a warning about the risky interactions detected by the detection unit. For example, the device can issue a warning with a voice message, or it can send an alert notification to a smartphone or PC. Step 5: The Recording Department records audio of potentially dangerous interactions, for example, suspected fraud or harassment, and stores the recordings as evidence.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a 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.

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

[0129] The 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.

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

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

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

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 that acquires voice data; an analysis unit that analyzes the voice data acquired by the voice acquisition unit in real time; a detection unit that detects risky interactions from the voice data analyzed by the analysis unit; a warning unit that issues a warning about the risky interaction detected by the detection unit; a recording unit that records the audio of the risky exchange. A system characterized by:

2. The analysis unit Analyzing changes in tone or speed of the audio data to detect changes in emotion 2. The system of claim 1.

3. The analysis unit Analyzing background or environmental sounds contained in the audio data to identify the risk in a specific environment 2. The system of claim 1.

4. The analysis unit Estimating the emotional state of a speaker in real time and assessing the risk based on changes in emotion 2. The system of claim 1.

5. The analysis unit The analysis of the voice data automatically recognizes different languages ​​or dialects, enabling global compatibility.

2. The system of claim 1.

6. The analysis unit The analysis results of the voice data are visualized and provided to the user.

2. The system of claim 1.

7. The analysis unit Providing real-time feedback based on the speaker's emotions to promote positive communication 2. The system of claim 1.

8. The detection unit Refer to the history of past interactions and learn the patterns of the risk.

2. The system of claim 1.

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