System

Abstract

An object of a system according to an embodiment is to enable people who speak different languages to naturally enjoy a conversation.SOLUTION: A system according to an embodiment includes a glasses-type wearable device, a microphone, a speaker, a smartphone application, a generation AI, and a communicator. The glasses-type wearable device includes a microphone and a speaker. The microphone acquires audio data. The smartphone application receives the voice data acquired by the microphone. The generated AI is analyzed and translated in the smartphone app. The communication unit transmits the voice AI to the speaker.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 technologies have had the problem of making it difficult for people who speak different languages ​​to enjoy natural conversations.

[0005] The system according to the embodiment aims to enable people who speak different languages ​​to enjoy natural conversations with each other. [Means for solving the problem]

[0006] The system according to the embodiment includes a glasses-type wearable device, a microphone, a speaker, a smartphone app, a generation AI, and a communication unit. The glasses-type wearable device is equipped with a microphone and a speaker. The microphone acquires voice data. The smartphone app receives the voice data acquired by the microphone. The generation AI analyzes and translates the voice data in the smartphone app. The communication unit transmits the voice data translated by the generation AI to the speaker. [Effects of the Invention]

[0007] The system according to the embodiment allows people who speak different languages ​​to enjoy natural conversations with each other. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A translation system according to an embodiment of the present invention utilizes the microphone and speaker functions of a wearable eyeglasses device and works in conjunction with a smartphone app with translation functionality. This system uses the microphone of the wearable eyeglasses device to pick up the speaker's voice, transmits it to the smartphone app, and a generation AI analyzes and translates the language in real time, then transmits the translation results to the speaker of the wearable eyeglasses device. This allows people with different native languages ​​to enjoy natural conversations.

[0029] A translation system according to an embodiment includes a glasses-type wearable device, a microphone, a speaker, a smartphone app, a generation AI, and a communication unit. The glasses-type wearable device is equipped with a microphone for picking up the speaker's voice. For example, the microphone has high performance and can remove ambient noise to clearly pick up the speaker's voice. The glasses-type wearable device is also equipped with a speaker for playing back the translation result. For example, the speaker can transmit voice without blocking the ears using bone conduction technology. The smartphone app receives voice data acquired by the microphone of the glasses-type wearable device. For example, the voice data is received in real time using Bluetooth or Wi-Fi. The generation AI analyzes and translates the voice data in the smartphone app. For example, the generation AI converts the voice data into text using a text generation AI (e.g., LLM) and translates the text. The communication unit transmits the voice data translated by the generation AI to the speaker. For example, the communication unit transmits the translation result to the speaker in real time using Bluetooth or Wi-Fi. This allows the translation system according to an embodiment to enable people with different native languages ​​to enjoy natural conversations. For example, a user wearing a glasses-type wearable device can have a real-time translated conversation with a person speaking a different language.

[0030] The microphone automatically filters out surrounding environmental noise and clearly picks up only the speaker's voice. For example, the microphone analyzes surrounding environmental noise in real time and uses noise-canceling technology to emphasize only the speaker's voice. For example, it automatically removes cafe noise and wind noise to clearly pick up the speaker's voice. This allows the speaker's voice to be picked up clearly.

[0031] A microphone can detect the movement of a speaker's mouth and use it as auxiliary data to improve the accuracy of speech recognition. For example, a microphone equipped with a camera can analyze the movement of a speaker's mouth in real time. For example, the accuracy of speech recognition can be improved by combining the movement of the mouth with voice data. This can improve the accuracy of speech recognition.

[0032] The microphone can simultaneously detect the speaker's gestures and facial expressions and use them as communication data. The microphone, for example, is equipped with a camera and analyzes the speaker's gestures and facial expressions in real time. For example, hand movements and facial expressions can be detected and combined with voice data to be used as comprehensive communication data. This allows for use as comprehensive communication data.

[0033] The microphone automatically adjusts different audio frequency bands to pick up the voices of multiple speakers individually, even when multiple speakers are speaking at the same time.The microphone, for example, analyzes different audio frequency bands in real time and implements an algorithm to pick up the voices of multiple speakers individually.For example, it automatically adjusts different frequency bands to separate the voices of each speaker, so that the voices of multiple speakers can be picked up individually, even when multiple speakers are speaking at the same time.

[0034] A smartphone app can minimize delays when transmitting audio data by using data compression technology. For example, when transmitting audio data, a smartphone app can use a data compression algorithm to reduce the amount of data and minimize delays. For example, the smartphone app can compress audio data in real time to improve transmission speed. This can minimize delays in transmitting audio data.

[0035] The smartphone app can apply noise reduction technology before transmitting the audio data to transmit clear audio data. For example, the smartphone app can apply a noise reduction algorithm before transmitting the audio data to generate clear audio data. For example, the smartphone app can remove background noise and emphasize the speaker's voice. This allows the transmission of clear audio data.

[0036] Smartphone apps can simultaneously transmit location and time information when transmitting voice data, enabling translation based on context. For example, a smartphone app can simultaneously transmit location and time information when transmitting voice data, enabling translation based on context. For example, information related to a specific location or time can be reflected in the translation. This allows translation based on context.

[0037] A smartphone app can add speaker profile information when voice data is transmitted and provide individually optimized translations. For example, a smartphone app can add speaker profile information when voice data is transmitted and provide individually optimized translations. For example, a system can be developed in which translations are performed according to the speaker's language level and preferences. This makes it possible to provide individually optimized translations.

[0038] When analyzing voice data, generative AI can refer to past conversation history to understand the context. For example, when analyzing voice data, generative AI can refer to past conversation history to develop a system that understands the context. For example, it can provide an appropriate translation based on the content of past conversations. This allows it to refer to past conversation history to understand the context.

[0039] When analyzing voice data, generative AI can automatically recognize the accent or dialect of the speaker and provide an appropriate translation. For example, generative AI will develop a system that automatically recognizes the accent or dialect of the speaker and provides an appropriate translation when analyzing voice data. For example, it will accurately translate dialects from specific regions. This will enable generative AI to automatically recognize the accent or dialect of the speaker and provide an appropriate translation.

[0040] Generative AI can provide translations that take into account cultural nuances between different languages ​​when analyzing voice data. For example, generative AI develops systems that provide translations that take into account cultural nuances between different languages ​​when analyzing voice data. For example, it appropriately translates expressions related to specific cultures. This makes it possible to provide translations that take into account cultural nuances between different languages.

[0041] When analyzing voice data, the generative AI receives feedback from users in real time, allowing it to continuously improve translation accuracy. For example, when analyzing voice data, the generative AI receives feedback from users in real time, allowing it to develop a system that continuously improves translation accuracy. For example, the translation algorithm is adjusted based on user feedback. This allows it to continuously improve translation accuracy.

[0042] The speaker can automatically optimize the quality of the voice data when transmitting the translation result, thereby achieving clear voice output. The speaker develops a system that automatically optimizes the quality of the voice data when transmitting the translation result, for example, by removing noise from the voice data to achieve clear voice output. This makes it possible to achieve clear voice output.

[0043] When transmitting translation results, speakers can reproduce the tone and pitch of the speaker's voice, providing a more natural voice output. For example, speakers can develop a system that reproduces the tone and pitch of the speaker's voice when transmitting translation results. For example, the speaker can analyze the characteristics of the speaker's voice and reflect them in the translated audio. This allows for a more natural voice output.

[0044] When transmitting translation results, speakers can add visual information to the audio data to support comprehension both visually and aurally. For example, speakers can develop a system that adds visual information (e.g., subtitles) to audio data when transmitting translation results. For example, subtitles corresponding to the translated audio can be displayed. This allows for comprehension to be supported both visually and aurally.

[0045] The speaker can perform voice filtering according to the user's preferences and provide individually optimized voice output. The speaker develops a system that performs voice filtering according to the user's preferences, for example, when transmitting translation results. For example, the speaker adjusts the tone and pitch of the voice according to the user's preferences. This allows the speaker to provide individually optimized voice output.

[0046] The system can analyze the conversation history of people with different native languages ​​and provide an appropriate translation based on the content of the past conversation. For example, the system can analyze the conversation history of people with different native languages ​​and provide an appropriate translation based on the content of the past conversation. For example, the system can provide an appropriate translation based on the content of the past conversation.

[0047] The system can provide translations that take cultural backgrounds and nuances into account during conversations between people with different native languages. For example, the system develops a system that provides translations that take cultural backgrounds and nuances into account during conversations between people with different native languages. For example, the system appropriately translates expressions related to a specific culture. This makes it possible to provide translations that take cultural backgrounds and nuances into account.

[0048] The system can convert conversations between people with different native languages ​​into text in real time and provide visual support. For example, a system can be developed that converts conversations between people with different native languages ​​into text in real time and provides visual support. For example, the content of the conversation can be displayed as subtitles in real time, thereby providing visual support.

[0049] The system provides a function to record conversations between people with different native languages ​​and play them back later, thereby supporting review and confirmation of the conversation. For example, a system is developed that provides a function to record conversations between people with different native languages ​​and play them back later. For example, the content of a conversation can be recorded and played back later for confirmation. This can support review and confirmation of the conversation.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The translation system can also be equipped with a health management unit that monitors the user's health condition. For example, a wearable eyeglass device could be equipped with a heart rate sensor and a body temperature sensor to monitor the user's health condition in real time. This allows the user to receive alerts according to their health condition. Furthermore, health data can be sent to a smartphone app and linked with the health management app to support the user's health management.

[0052] The translation system can also be equipped with a location information acquisition unit that acquires the user's location information. For example, it can be equipped with a GPS function to grasp the user's current location in real time. This allows the system to provide translations specific to that region when the user is in an area where a different language is spoken. Furthermore, the system can provide services such as tourist information and restaurant recommendations based on the location information.

[0053] The translation system can also be equipped with a gesture recognition unit that recognizes the user's gestures. For example, it can be equipped with a camera and analyze the user's hand movements and facial expressions in real time. This allows the system to translate sign language when the user uses it. Furthermore, it can provide an operation interface using gestures, allowing the user to operate the system using their hands.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The wearable glasses device is equipped with a microphone to pick up the speaker's voice. For example, the microphone is highly efficient and can filter out surrounding noise to clearly pick up the speaker's voice. The wearable glasses device also has a speaker to play back the translation results. For example, the speaker uses bone conduction technology to transmit sound without blocking the ears. Step 2: The smartphone app receives the audio data captured by the microphone of the glasses-type wearable device, for example, via Bluetooth or Wi-Fi in real time. Step 3: The generation AI analyzes and translates the voice data in the smartphone app. For example, the generation AI converts the voice data into text using a text generation AI (e.g., LLM) and then translates the text. Step 4: The communication unit transmits the voice data translated by the AI ​​to the speaker. For example, the communication unit transmits the translation results in real time to the speaker using Bluetooth or Wi-Fi.

[0056] (Example 2) A translation system according to an embodiment of the present invention utilizes the microphone and speaker functions of a wearable eyeglasses device and works in conjunction with a smartphone app with translation functionality. This system uses the microphone of the wearable eyeglasses device to pick up the speaker's voice, transmits it to the smartphone app, and a generation AI analyzes and translates the language in real time, then transmits the translation results to the speaker of the wearable eyeglasses device. This allows people with different native languages ​​to enjoy natural conversations.

[0057] A translation system according to an embodiment includes a glasses-type wearable device, a microphone, a speaker, a smartphone app, a generation AI, and a communication unit. The glasses-type wearable device is equipped with a microphone for picking up the speaker's voice. For example, the microphone has high performance and can remove ambient noise to clearly pick up the speaker's voice. The glasses-type wearable device is also equipped with a speaker for playing back the translation result. For example, the speaker can transmit voice without blocking the ears using bone conduction technology. The smartphone app receives voice data acquired by the microphone of the glasses-type wearable device. For example, the voice data is received in real time using Bluetooth or Wi-Fi. The generation AI analyzes and translates the voice data in the smartphone app. For example, the generation AI converts the voice data into text using a text generation AI (e.g., LLM) and translates the text. The communication unit transmits the voice data translated by the generation AI to the speaker. For example, the communication unit transmits the translation result to the speaker in real time using Bluetooth or Wi-Fi. This allows the translation system according to an embodiment to enable people with different native languages ​​to enjoy natural conversations. For example, a user wearing a glasses-type wearable device can have a real-time translated conversation with a person speaking a different language.

[0058] The microphone automatically filters out surrounding environmental noise and clearly picks up only the speaker's voice. For example, the microphone analyzes surrounding environmental noise in real time and uses noise-canceling technology to emphasize only the speaker's voice. For example, it automatically removes cafe noise and wind noise to clearly pick up the speaker's voice. This allows the speaker's voice to be picked up clearly.

[0059] A microphone can detect the movement of a speaker's mouth and use it as auxiliary data to improve the accuracy of speech recognition. For example, a microphone equipped with a camera can analyze the movement of a speaker's mouth in real time. For example, the accuracy of speech recognition can be improved by combining the movement of the mouth with voice data. This can improve the accuracy of speech recognition.

[0060] The microphone can estimate the speaker's emotions in real time and perform voice filtering according to those emotions. For example, the microphone analyzes the tone and pitch of the speaker's voice and uses an emotion estimation algorithm to estimate the speaker's emotions in real time. For example, it can detect emotions such as anger or joy and perform voice filtering according to those emotions. This makes it possible to filter voice according to the speaker's emotions.

[0061] The microphone can simultaneously detect the speaker's gestures and facial expressions and use them as communication data. The microphone, for example, is equipped with a camera and analyzes the speaker's gestures and facial expressions in real time. For example, hand movements and facial expressions can be detected and combined with voice data to be used as comprehensive communication data. This allows for use as comprehensive communication data.

[0062] The microphone automatically adjusts different audio frequency bands to pick up the voices of multiple speakers individually, even when multiple speakers are speaking at the same time.The microphone, for example, analyzes different audio frequency bands in real time and implements an algorithm to pick up the voices of multiple speakers individually.For example, it automatically adjusts different frequency bands to separate the voices of each speaker, so that the voices of multiple speakers can be picked up individually, even when multiple speakers are speaking at the same time.

[0063] Mike will develop a system that uses emotion estimation functionality to provide real-time voice feedback according to the speaker's emotions. For example, if the speaker is happy, positive feedback will be provided. This will enable more natural conversations.

[0064] A smartphone app can minimize delays when transmitting audio data by using data compression technology. For example, when transmitting audio data, a smartphone app can use a data compression algorithm to reduce the amount of data and minimize delays. For example, the smartphone app can compress audio data in real time to improve transmission speed. This can minimize delays in transmitting audio data.

[0065] The smartphone app can apply noise reduction technology before transmitting the audio data to transmit clear audio data. For example, the smartphone app can apply a noise reduction algorithm before transmitting the audio data to generate clear audio data. For example, the smartphone app can remove background noise and emphasize the speaker's voice. This allows the transmission of clear audio data.

[0066] The smartphone app can use its emotion estimation function to add emotional information contained in the transmitted voice data, thereby achieving more accurate translation. For example, the smartphone app can use its emotion estimation function to analyze the emotional information contained in the transmitted voice data in real time and add it to the translation data. For example, the speaker's emotions can be reflected in the text data. This allows for more accurate translation.

[0067] Smartphone apps can simultaneously transmit location and time information when transmitting voice data, enabling translation based on context. For example, a smartphone app can simultaneously transmit location and time information when transmitting voice data, enabling translation based on context. For example, information related to a specific location or time can be reflected in the translation. This allows translation based on context.

[0068] A smartphone app can add speaker profile information when voice data is transmitted and provide individually optimized translations. For example, a smartphone app can add speaker profile information when voice data is transmitted and provide individually optimized translations. For example, a system can be developed in which translations are performed according to the speaker's language level and preferences. This makes it possible to provide individually optimized translations.

[0069] A smartphone app can improve the quality of conversation by using an emotion estimation function to provide real-time emotion feedback based on transmitted voice data. For example, a smartphone app can develop a system that uses an emotion estimation function to provide real-time emotion feedback based on transmitted voice data. For example, the smartphone app can analyze the speaker's emotion and provide appropriate feedback. This can improve the quality of conversation.

[0070] When analyzing voice data, generative AI can take the speaker's emotions into consideration and provide translations that correspond to those emotions. For example, when analyzing voice data, generative AI will develop a system that estimates the speaker's emotions in real time and provides translations that correspond to those emotions. For example, if the speaker is angry, a calm translation will be provided. This makes it possible to provide translations that correspond to emotions.

[0071] When analyzing voice data, generative AI can refer to past conversation history to understand the context. For example, when analyzing voice data, generative AI can refer to past conversation history to develop a system that understands the context. For example, it can provide an appropriate translation based on the content of past conversations. This allows it to refer to past conversation history to understand the context.

[0072] When analyzing voice data, generative AI can automatically recognize the accent or dialect of the speaker and provide an appropriate translation. For example, generative AI will develop a system that automatically recognizes the accent or dialect of the speaker and provides an appropriate translation when analyzing voice data. For example, it will accurately translate dialects from specific regions. This will enable generative AI to automatically recognize the accent or dialect of the speaker and provide an appropriate translation.

[0073] Generative AI can provide translations that take into account cultural nuances between different languages ​​when analyzing voice data. For example, generative AI develops systems that provide translations that take into account cultural nuances between different languages ​​when analyzing voice data. For example, it appropriately translates expressions related to specific cultures. This makes it possible to provide translations that take into account cultural nuances between different languages.

[0074] When analyzing voice data, the generative AI receives feedback from users in real time, allowing it to continuously improve translation accuracy. For example, when analyzing voice data, the generative AI receives feedback from users in real time, allowing it to develop a system that continuously improves translation accuracy. For example, the translation algorithm is adjusted based on user feedback. This allows it to continuously improve translation accuracy.

[0075] Generative AI uses emotion estimation functionality to provide translations based on the speaker's emotions when analyzing voice data, enabling more natural conversations. For example, generative AI will develop a system that uses emotion estimation functionality to provide translations based on the speaker's emotions when analyzing voice data. For example, if the speaker is happy, it will provide a positive translation. This will enable more natural conversations.

[0076] The speaker can automatically optimize the quality of the voice data when transmitting the translation result, thereby achieving clear voice output. The speaker develops a system that automatically optimizes the quality of the voice data when transmitting the translation result, for example, by removing noise from the voice data to achieve clear voice output. This makes it possible to achieve clear voice output.

[0077] When transmitting translation results, speakers can reproduce the tone and pitch of the speaker's voice, providing a more natural voice output. For example, speakers can develop a system that reproduces the tone and pitch of the speaker's voice when transmitting translation results. For example, the speaker can analyze the characteristics of the speaker's voice and reflect them in the translated audio. This allows for a more natural voice output.

[0078] The speaker can use the emotion estimation function to add emotion to the speech output of the translation result, thereby realizing a more emotionally rich conversation. For example, the speaker can develop a system that uses the emotion estimation function to add emotion to the speech output of the translation result. For example, the speaker's emotion can be analyzed and reflected in the translated speech. This makes it possible to realize a more emotionally rich conversation.

[0079] When transmitting translation results, speakers can add visual information to the audio data to support comprehension both visually and aurally. For example, speakers can develop a system that adds visual information (e.g., subtitles) to audio data when transmitting translation results. For example, subtitles corresponding to the translated audio can be displayed. This allows for comprehension to be supported both visually and aurally.

[0080] The speaker can perform voice filtering according to the user's preferences and provide individually optimized voice output. The speaker develops a system that performs voice filtering according to the user's preferences, for example, when transmitting translation results. For example, the speaker adjusts the tone and pitch of the voice according to the user's preferences. This allows the speaker to provide individually optimized voice output.

[0081] A speaker can use the emotion estimation function to provide emotional feedback in real time based on the speech output of the translation result, thereby improving the quality of the conversation. For example, a speaker can develop a system that uses the emotion estimation function to provide emotional feedback in real time based on the speech output of the translation result. For example, the speaker's emotions are analyzed and appropriate feedback is provided. This can improve the quality of the conversation.

[0082] The system uses an emotion estimation function to analyze the emotions of people with different native languages ​​in real time and provide translations that correspond to those emotions. For example, a system is developed that uses an emotion estimation function to analyze the emotions of people with different native languages ​​in real time and provide translations that correspond to those emotions. For example, if the speaker is happy, a positive translation is provided. This makes it possible to provide translations that correspond to the emotions of people with different native languages.

[0083] The system can analyze the conversation history of people with different native languages ​​and provide an appropriate translation based on the content of the past conversation. For example, the system can analyze the conversation history of people with different native languages ​​and provide an appropriate translation based on the content of the past conversation. For example, the system can provide an appropriate translation based on the content of the past conversation.

[0084] The system can provide translations that take cultural backgrounds and nuances into account during conversations between people with different native languages. For example, the system develops a system that provides translations that take cultural backgrounds and nuances into account during conversations between people with different native languages. For example, the system appropriately translates expressions related to a specific culture. This makes it possible to provide translations that take cultural backgrounds and nuances into account.

[0085] The system can convert conversations between people with different native languages ​​into text in real time and provide visual support. For example, a system can be developed that converts conversations between people with different native languages ​​into text in real time and provides visual support. For example, the content of the conversation can be displayed as subtitles in real time, thereby providing visual support.

[0086] The system provides a function to record conversations between people with different native languages ​​and play them back later, thereby supporting review and confirmation of the conversation. For example, a system is developed that provides a function to record conversations between people with different native languages ​​and play them back later. For example, the content of a conversation can be recorded and played back later for confirmation. This can support review and confirmation of the conversation.

[0087] The system uses the emotion estimation function to provide emotion-based feedback during a conversation between people with different native languages, thereby enabling a more natural conversation. For example, the system uses the emotion estimation function to develop a system that provides emotion-based feedback during a conversation between people with different native languages. For example, the system analyzes the speaker's emotion and provides appropriate feedback. This enables a more natural conversation.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The translation system can also be equipped with a health management unit that monitors the user's health condition. For example, a wearable eyeglass device could be equipped with a heart rate sensor and a body temperature sensor to monitor the user's health condition in real time. This allows the user to receive alerts according to their health condition. Furthermore, health data can be sent to a smartphone app and linked with the health management app to support the user's health management.

[0090] The translation system can also be equipped with a location information acquisition unit that acquires the user's location information. For example, it can be equipped with a GPS function to grasp the user's current location in real time. This allows the system to provide translations specific to that region when the user is in an area where a different language is spoken. Furthermore, the system can provide services such as tourist information and restaurant recommendations based on the location information.

[0091] The translation system can also be equipped with a gesture recognition unit that recognizes the user's gestures. For example, it can be equipped with a camera and analyze the user's hand movements and facial expressions in real time. This allows the system to translate sign language when the user uses it. Furthermore, it can provide an operation interface using gestures, allowing the user to operate the system using their hands.

[0092] The translation system can also be equipped with an emotion estimation function that analyzes the user's voice data and estimates the speaker's emotions. For example, it can analyze the speaker's tone and pitch and use an emotion estimation algorithm to estimate the speaker's emotions in real time. This allows it to provide a calm translation if the speaker is angry, and a positive translation if the speaker is happy.

[0093] The translation system can also analyze the user's speech data and provide speech feedback based on the speaker's emotions, for example, providing comforting words if the speaker is sad, or calming words if the speaker is excited, resulting in a more natural conversation.

[0094] The translation system can also analyze the user's voice data and perform voice filtering based on the speaker's emotions. For example, if the speaker is nervous, the system will perform voice filtering to relax the speaker, and if the speaker is relaxed, the system will provide the speaker's voice as is. This makes it possible to filter voices according to the speaker's emotions.

[0095] The translation system can also analyze the user's speech data and provide translations based on the speaker's emotions. For example, if the speaker is angry, it will provide a calm translation, and if the speaker is happy, it will provide a positive translation. This allows it to provide translations that correspond to the speaker's emotions.

[0096] The translation system can also analyze the user's speech data and provide speech feedback based on the speaker's emotions, for example, providing comforting words if the speaker is sad, or calming words if the speaker is excited, resulting in a more natural conversation.

[0097] The translation system can also analyze the user's voice data and perform voice filtering based on the speaker's emotions. For example, if the speaker is nervous, the system will perform voice filtering to relax the speaker, and if the speaker is relaxed, the system will provide the speaker's voice as is. This makes it possible to filter voices according to the speaker's emotions.

[0098] The translation system can also analyze the user's speech data and provide translations based on the speaker's emotions. For example, if the speaker is angry, it will provide a calm translation, and if the speaker is happy, it will provide a positive translation. This allows it to provide translations that correspond to the speaker's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The wearable glasses device is equipped with a microphone to pick up the speaker's voice. For example, the microphone is highly efficient and can filter out surrounding noise to clearly pick up the speaker's voice. The wearable glasses device also has a speaker to play back the translation results. For example, the speaker uses bone conduction technology to transmit sound without blocking the ears. Step 2: The smartphone app receives the audio data captured by the microphone of the glasses-type wearable device, for example, via Bluetooth or Wi-Fi in real time. Step 3: The generation AI analyzes and translates the voice data in the smartphone app. For example, the generation AI converts the voice data into text using a text generation AI (e.g., LLM) and then translates the text. Step 4: The communication unit transmits the voice data translated by the AI ​​to the speaker. For example, the communication unit transmits the translation results in real time to the speaker using Bluetooth or Wi-Fi.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. Glasses-type wearable device, a microphone mounted on the glasses-type wearable device; a speaker mounted on the glasses-type wearable device; a smartphone application that receives audio data acquired by the microphone; A generation AI that analyzes and translates the voice data in the smartphone app; a communication unit that transmits the voice data translated by the generation AI to the speaker; A system characterized by:

2. The microphone is Automatically filters out ambient noise and clearly picks up only the speaker's voice 2. The system of claim 1.

3. The microphone is The speaker's gestures and facial expressions are simultaneously detected and used as communication data.

2. The system of claim 1.

4. The smartphone app When transmitting the audio data, data compression technology is used to minimize delays.

2. The system of claim 1.

5. The generated AI is When analyzing the speech data, the speaker's emotions are taken into consideration, and the translation is provided according to the emotions.

2. The system of claim 1.

6. The speaker is When sending translation results, the quality of the voice data is automatically optimized to ensure clear voice output.

2. The system of claim 1.

7. The system comprises: Analyzing emotions of people with different native languages ​​in real time and providing the translation according to the emotions 2. The system of claim 1.

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