System

Abstract

An object of a system according to an embodiment is to make it possible to easily understand the meaning of an unknown word on the spot when the unknown word appears during a conversation.SOLUTION: A system according to an embodiment includes a voice input unit, a generation unit, and a voice output unit. The voice input unit receives voice or character input. The generation unit analyzes the input received by the voice input unit and generates a brief description. The audio output unit outputs the explanation generated by the generation unit as audio through bone conduction earphones or bone conduction glasses.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] With conventional technology, when an unfamiliar word comes up during a conversation, there is no easy way to understand its meaning on the spot, which can hinder smooth communication.

[0005] The system according to the embodiment aims to enable users to easily understand the meaning of an unfamiliar word on the spot when it comes up during a conversation. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, a generation unit, and a voice output unit. The voice input unit accepts voice or text input. The generation unit analyzes the input accepted by the voice input unit and generates a concise explanation. The voice output unit outputs the explanation generated by the generation unit as voice through bone conduction earphones or bone conduction glasses. [Effects of the Invention]

[0007] The system according to the embodiment can make it possible to easily understand the meaning of an unfamiliar word on the spot when it comes up during a conversation. [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) The explanation system according to an embodiment of the present invention uses a generation AI to provide a concise explanation on the spot when an unfamiliar word comes up during a business negotiation or meeting. This explanation system works by asking the generation AI questions via voice or text input, and receiving the answer via audio via bone conduction earphones or bone conduction glasses. This allows the explanation system to quickly input knowledge without embarrassment, enabling smooth communication.

[0029] An explanation system according to an embodiment includes a voice input unit, a generation unit, and a voice output unit. The voice input unit accepts voice or text input. For example, a user can tap a voice input button through a dedicated app to communicate words they do not understand. The voice input unit can also accept text input using a keyboard. The generation unit analyzes the input accepted by the voice input unit and generates a concise explanation. For example, the generation AI analyzes the input question using a text generation AI (e.g., LLM) and generates a concise explanation. The generation AI can also analyze the input content and generate a concise explanation using a multimodal generation AI. For example, the generation AI generates an answer in the form of, for example, "Commoditization is a state in which it has become difficult to differentiate products." The voice output unit outputs the explanation generated by the generation unit via bone conduction earphones or bone conduction glasses. For example, the answer generated by the generation AI is communicated to the user via bone conduction earphones. This allows the user to input knowledge without being noticed by those around them. As a result, the explanation system according to the embodiment can quickly and accurately input knowledge when an unfamiliar word comes up during a business negotiation or meeting, thereby realizing smooth communication.

[0030] The voice input unit can analyze the tone or speed of the user's voice, estimate the urgency or importance, and assign priorities. For example, when the user inputs voice, the voice input unit analyzes the tone and speed of the voice and estimates the urgency or importance. For example, if the user is in a hurry or nervous, the voice input unit can prioritize processing. The voice input unit can also estimate the user's emotional state based on the tone and speed of the voice and determine the urgency or importance. This allows processing to be prioritized according to the user's urgency or importance.

[0031] The voice input unit can automatically search past conversation history and provide supplemental information according to the content of the question. For example, when a user inputs a question, the voice input unit automatically searches past conversation history and provides related information. For example, if the same question has been asked before, the answer to that question is presented. The voice input unit can also provide supplemental information based on past conversation history related to the content of the question. This allows the user to deepen their understanding by providing supplemental information based on past conversation history.

[0032] The voice input unit can provide a more intuitive way to ask questions by combining not only voice input but also gesture input or gaze input. The voice input unit can provide a more intuitive way to ask questions by, for example, combining gesture input with voice input. For example, a question can be input by recognizing hand movements or finger gestures. Furthermore, by combining gaze input with the voice input unit, the user can also input questions using their gaze. This allows the user to input questions intuitively.

[0033] The voice input unit can translate the content of a question in real time and support communication between users who speak different languages. For example, when a user inputs a question, the voice input unit translates it in real time and supports communication between users who speak different languages. For example, a question in Japanese can be translated into English and an answer can be provided. The voice input unit also supports multiple languages ​​and can translate and provide the answer in a language selected by the user. This facilitates communication between users who speak different languages.

[0034] The generation unit can automatically generate visuals or graphs related to the generated explanation to promote visual understanding. For example, the generation unit automatically generates visuals or graphs related to the explanation provided by the generation AI to make it easier to understand visually. For example, it visualizes or illustrates data. The generation unit can also generate visuals or graphs related to the content of the explanation and provide them to the user. This can deepen the user's understanding by making it easier to understand visually.

[0035] The generation unit can evaluate the user's level of understanding of the explanation content in real time and provide additional explanation as needed. For example, the generation unit can evaluate the user's level of understanding of the explanation provided by the generation AI in real time and provide additional explanation as needed. For example, it can provide supplementary explanation if the level of understanding is low. The generation unit can also evaluate the user's level of understanding and provide appropriate additional explanation. This makes it possible to deepen understanding by providing appropriate explanation according to the user's level of understanding.

[0036] The generation unit can also explain the generated explanation from the perspective of different fields of expertise, promoting a multifaceted understanding. For example, the generation unit can promote a more multifaceted understanding by explaining the explanation provided by the generation AI from the perspective of different fields of expertise. For example, it can combine a technical perspective with a business perspective. The generation unit can also provide perspectives from different fields of expertise related to the content of the explanation. This allows the user to deepen their understanding by providing explanations from the perspectives of different fields of expertise.

[0037] The generation unit can customize the explanation content based on the user's learning history or interests, and provide personalized information. For example, the generation unit can customize the explanation provided by the generation AI based on the user's learning history or interests, and provide personalized information. For example, it can provide information related to past learning content preferentially. The generation unit can also provide appropriate information based on the user's interests. This allows for deeper understanding by providing information customized based on the user's learning history and interests.

[0038] The audio output unit can automatically adjust the sound quality of the bone conduction earphones to match the shape of the user's ears, thereby achieving optimal audio output. The audio output unit, for example, builds a system that automatically adjusts the sound quality of the bone conduction earphones to match the shape of the user's ears. For example, the audio output unit scans the shape of the ears to set the optimal sound quality. The audio output unit can also optimize the sound quality based on the shape of the user's ears. This allows for better audio output by optimizing the sound quality to match the shape of the user's ears.

[0039] The audio output unit can analyze surrounding environmental sounds in real time through bone conduction earphones and perform noise cancellation as needed. The audio output unit, for example, constructs a system that analyzes surrounding environmental sounds in real time through bone conduction earphones and performs noise cancellation as needed. For example, it detects surrounding noise and reduces it. The audio output unit can also perform appropriate noise cancellation depending on the type of environmental sound. This allows for real-time analysis of surrounding environmental sounds and noise cancellation, thereby achieving clear audio output.

[0040] The audio output unit can also use the bone conduction earphones as sensors to monitor the user's health condition and provide health information in real time. For example, the audio output unit can be equipped with a health monitoring sensor in the bone conduction earphones to build a system that provides health information in real time. For example, it can measure and notify the user's heart rate and body temperature. The audio output unit can also analyze the user's health condition and provide appropriate health information. This allows the system to monitor the user's health condition and provide health information in real time, supporting health management.

[0041] The audio output unit can seamlessly link the bone conduction earphones with different devices (smartphones, tablets, PCs) to realize audio output on multiple devices. The audio output unit, for example, seamlessly links the bone conduction earphones with different devices to build a system that realizes audio output on multiple devices. For example, it can switch audio between a smartphone and a PC. The audio output unit can also support multiple devices and output audio to a device selected by the user. This allows seamless linking with different devices and realizes audio output on multiple devices, improving convenience.

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

[0043] The explanation system can also record a user's learning history and customize answers to new questions based on what they have learned in the past. For example, if a user has previously studied a particular topic, it can provide a more detailed explanation for a new question related to that topic. It can also provide answers in a format that is easy for the user to understand based on the user's learning history. This allows for a deeper understanding by providing customized information based on the user's learning history.

[0044] The explanation system can also automatically generate relevant visuals and graphs based on the content of the user's question, providing them in a format that is visually easy to understand. For example, visualizing and illustrating data can help users understand the information more intuitively. It can also generate visuals and graphs related to the explanation content and provide them to users. This can deepen the user's understanding by making it easier to understand visually.

[0045] The explanation system can also combine not only voice input but also gesture input or gaze input to provide a more intuitive way to ask questions. For example, questions can be entered by recognizing hand movements or finger gestures. Furthermore, by combining gaze input, users can also enter questions using their gaze. This allows users to enter questions intuitively.

[0046] The explanation system can also generate explanations from different disciplinary perspectives, promoting multifaceted understanding. For example, it can combine technical and business perspectives. It can also provide perspectives from different disciplinary fields related to the content of the explanation. This allows users to deepen their understanding by providing explanations from different disciplinary perspectives.

[0047] The explanation system can further customize the explanations it generates based on the user's learning history or interests, providing personalized information. For example, it can prioritize information related to past learning content. It can also provide appropriate information based on the user's interests. This allows for deeper understanding by providing information customized based on the user's learning history and interests.

[0048] The explanation system can also evaluate the user's level of understanding of the generated explanation in real time and provide additional explanation as needed. For example, it can provide supplementary explanation if the user's level of understanding is low. It can also evaluate the user's level of understanding and provide appropriate additional explanation. This allows the user to deepen their understanding by providing an appropriate explanation according to their level of understanding.

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

[0050] Step 1: The voice input unit accepts voice or text input. For example, a user can tap the voice input button through a dedicated app to communicate words they don't understand. The voice input unit can also accept text input using a keyboard. Step 2: The generation unit analyzes the input received by the voice input unit and generates a concise explanation. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the input question and generate a concise explanation. The generation AI may also use a multimodal generation AI to analyze the input content and generate a concise explanation. For example, the generation unit may generate an answer in the form of "Commoditization is a state in which it has become difficult to differentiate products." Step 3: The audio output unit outputs the explanation generated by the generation unit through bone conduction earphones or bone conduction glasses. For example, the answer generated by the generation AI is conveyed to the user through bone conduction earphones. This allows the user to input knowledge without being noticed by those around them.

[0051] (Example 2) The explanation system according to an embodiment of the present invention uses a generation AI to provide a concise explanation on the spot when an unfamiliar word comes up during a business negotiation or meeting. This explanation system works by asking the generation AI questions via voice or text input, and receiving the answer via audio via bone conduction earphones or bone conduction glasses. This allows the explanation system to quickly input knowledge without embarrassment, enabling smooth communication.

[0052] An explanation system according to an embodiment includes a voice input unit, a generation unit, and a voice output unit. The voice input unit accepts voice or text input. For example, a user can tap a voice input button through a dedicated app to communicate words they do not understand. The voice input unit can also accept text input using a keyboard. The generation unit analyzes the input accepted by the voice input unit and generates a concise explanation. For example, the generation AI analyzes the input question using a text generation AI (e.g., LLM) and generates a concise explanation. The generation AI can also analyze the input content and generate a concise explanation using a multimodal generation AI. For example, the generation AI generates an answer in the form of, for example, "Commoditization is a state in which it has become difficult to differentiate products." The voice output unit outputs the explanation generated by the generation unit via bone conduction earphones or bone conduction glasses. For example, the answer generated by the generation AI is communicated to the user via bone conduction earphones. This allows the user to input knowledge without being noticed by those around them. As a result, the explanation system according to the embodiment can quickly and accurately input knowledge when an unfamiliar word comes up during a business negotiation or meeting, thereby realizing smooth communication.

[0053] The voice input unit can analyze the tone or speed of the user's voice, estimate the urgency or importance, and assign priorities. For example, when the user inputs voice, the voice input unit analyzes the tone and speed of the voice and estimates the urgency or importance. For example, if the user is in a hurry or nervous, the voice input unit can prioritize processing. The voice input unit can also estimate the user's emotional state based on the tone and speed of the voice and determine the urgency or importance. This allows processing to be prioritized according to the user's urgency or importance.

[0054] The voice input unit can automatically search past conversation history and provide supplemental information according to the content of the question. For example, when a user inputs a question, the voice input unit automatically searches past conversation history and provides related information. For example, if the same question has been asked before, the answer to that question is presented. The voice input unit can also provide supplemental information based on past conversation history related to the content of the question. This allows the user to deepen their understanding by providing supplemental information based on past conversation history.

[0055] The voice input unit can use the emotion estimation function to analyze the emotion of the user when asking a question and simultaneously provide relaxing voice to reduce stress or anxiety. For example, when the user inputs a question, the voice input unit can use the emotion estimation function to analyze the emotion and provide relaxing voice to reduce stress or anxiety. For example, it can play music that has a relaxing effect. The voice input unit can also analyze the user's emotional state and select and provide appropriate relaxing voice. This reduces the user's stress and anxiety and allows the user to ask the question in a relaxed state.

[0056] The voice input unit can provide a more intuitive way to ask questions by combining not only voice input but also gesture input or gaze input. The voice input unit can provide a more intuitive way to ask questions by, for example, combining gesture input with voice input. For example, a question can be input by recognizing hand movements or finger gestures. Furthermore, by combining gaze input with the voice input unit, the user can also input questions using their gaze. This allows the user to input questions intuitively.

[0057] The voice input unit can translate the content of a question in real time and support communication between users who speak different languages. For example, when a user inputs a question, the voice input unit translates it in real time and supports communication between users who speak different languages. For example, a question in Japanese can be translated into English and an answer can be provided. The voice input unit also supports multiple languages ​​and can translate and provide the answer in a language selected by the user. This facilitates communication between users who speak different languages.

[0058] The voice input unit uses the emotion estimation function to analyze the emotion of the user when asking a question and provides positive feedback, thereby lowering the hurdle for asking a question. For example, when the user inputs a question, the voice input unit uses the emotion estimation function to analyze the emotion and provides positive feedback. For example, if the user is nervous, an encouraging message is displayed. The voice input unit can also analyze the user's emotional state and provide appropriate positive feedback, making it easier for the user to ask a question.

[0059] The generation unit can automatically generate visuals or graphs related to the generated explanation to promote visual understanding. For example, the generation unit automatically generates visuals or graphs related to the explanation provided by the generation AI to make it easier to understand visually. For example, it visualizes or illustrates data. The generation unit can also generate visuals or graphs related to the content of the explanation and provide them to the user. This can deepen the user's understanding by making it easier to understand visually.

[0060] The generation unit can evaluate the user's level of understanding of the explanation content in real time and provide additional explanation as needed. For example, the generation unit can evaluate the user's level of understanding of the explanation provided by the generation AI in real time and provide additional explanation as needed. For example, it can provide supplementary explanation if the level of understanding is low. The generation unit can also evaluate the user's level of understanding and provide appropriate additional explanation. This makes it possible to deepen understanding by providing appropriate explanation according to the user's level of understanding.

[0061] The generation unit can evaluate the emotional impact of the explanation generated using the emotion estimation function on the user and adjust it to elicit positive emotions. For example, the generation unit uses the emotion estimation function to evaluate the emotional impact of the generated explanation and adjusts it to elicit positive emotions. For example, the generation unit changes the explanation to a more positive expression. The generation unit can also analyze the user's emotional state and make adjustments to elicit appropriate positive emotions. This can elicit positive emotions in the user, thereby improving the acceptability of the explanation.

[0062] The generation unit can also explain the generated explanation from the perspective of different fields of expertise, promoting a multifaceted understanding. For example, the generation unit can promote a more multifaceted understanding by explaining the explanation provided by the generation AI from the perspective of different fields of expertise. For example, it can combine a technical perspective with a business perspective. The generation unit can also provide perspectives from different fields of expertise related to the content of the explanation. This allows the user to deepen their understanding by providing explanations from the perspectives of different fields of expertise.

[0063] The generation unit can customize the explanation content based on the user's learning history or interests, and provide personalized information. For example, the generation unit can customize the explanation provided by the generation AI based on the user's learning history or interests, and provide personalized information. For example, it can provide information related to past learning content preferentially. The generation unit can also provide appropriate information based on the user's interests. This allows for deeper understanding by providing information customized based on the user's learning history and interests.

[0064] The generation unit can evaluate the emotional impact of the explanation generated using the emotion estimation function on the user and provide supplemental information to reduce negative emotions. For example, the generation unit can evaluate the emotional impact of the generated explanation using the emotion estimation function and provide supplemental information to reduce negative emotions. For example, the generation unit can add positive elements to the explanation. The generation unit can also analyze the user's emotional state and provide appropriate supplemental information. This can reduce negative emotions and deepen the user's understanding.

[0065] The audio output unit can automatically adjust the sound quality of the bone conduction earphones to match the shape of the user's ears, thereby achieving optimal audio output. The audio output unit, for example, builds a system that automatically adjusts the sound quality of the bone conduction earphones to match the shape of the user's ears. For example, the audio output unit scans the shape of the ears to set the optimal sound quality. The audio output unit can also optimize the sound quality based on the shape of the user's ears. This allows for better audio output by optimizing the sound quality to match the shape of the user's ears.

[0066] The audio output unit can analyze surrounding environmental sounds in real time through bone conduction earphones and perform noise cancellation as needed. The audio output unit, for example, constructs a system that analyzes surrounding environmental sounds in real time through bone conduction earphones and performs noise cancellation as needed. For example, it detects surrounding noise and reduces it. The audio output unit can also perform appropriate noise cancellation depending on the type of environmental sound. This allows for real-time analysis of surrounding environmental sounds and noise cancellation, thereby achieving clear audio output.

[0067] The audio output unit can use the emotion estimation function to analyze the emotion of the user when receiving the audio output and simultaneously provide relaxing music or audio. The audio output unit can, for example, use the emotion estimation function to analyze the emotion of the user when receiving the audio output and simultaneously provide relaxing music or audio. For example, relaxing music can be played when the user is nervous. The audio output unit can also analyze the user's emotional state and select and provide appropriate relaxing music or audio. In this way, by providing relaxing music or audio according to the user's emotion, the user can receive the audio in a relaxed state.

[0068] The audio output unit can also use the bone conduction earphones as sensors to monitor the user's health condition and provide health information in real time. For example, the audio output unit can be equipped with a health monitoring sensor in the bone conduction earphones to build a system that provides health information in real time. For example, it can measure and notify the user's heart rate and body temperature. The audio output unit can also analyze the user's health condition and provide appropriate health information. This allows the system to monitor the user's health condition and provide health information in real time, supporting health management.

[0069] The audio output unit can seamlessly link the bone conduction earphones with different devices (smartphones, tablets, PCs) to realize audio output on multiple devices. The audio output unit, for example, seamlessly links the bone conduction earphones with different devices to build a system that realizes audio output on multiple devices. For example, it can switch audio between a smartphone and a PC. The audio output unit can also support multiple devices and output audio to a device selected by the user. This allows seamless linking with different devices and realizes audio output on multiple devices, improving convenience.

[0070] The audio output unit can use the emotion estimation function to analyze the emotion of the user when receiving the audio output and provide audio feedback to elicit positive emotions. The audio output unit can, for example, use the emotion estimation function to analyze the emotion of the user when receiving the audio output and provide audio feedback to elicit positive emotions. For example, the audio output unit can play an encouraging message. The audio output unit can also analyze the user's emotional state and provide appropriate positive feedback. In this way, positive emotions can be elicited by providing positive audio feedback according to the user's emotions.

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

[0072] The explanation system can also record a user's learning history and customize answers to new questions based on what they have learned in the past. For example, if a user has previously studied a particular topic, it can provide a more detailed explanation for a new question related to that topic. It can also provide answers in a format that is easy for the user to understand based on the user's learning history. This allows for a deeper understanding by providing customized information based on the user's learning history.

[0073] The explanation system can also estimate the user's emotions and, based on the estimated emotions, provide relaxing voices to reduce stress and anxiety when the user asks a question. For example, if the user is nervous, it can play relaxing music. It can also analyze the user's emotional state and select and provide appropriate relaxing voices. This reduces the user's stress and anxiety and allows them to ask questions in a relaxed state.

[0074] The explanation system can also automatically generate relevant visuals and graphs based on the content of the user's question, providing them in a format that is visually easy to understand. For example, visualizing and illustrating data can help users understand the information more intuitively. It can also generate visuals and graphs related to the explanation content and provide them to users. This can deepen the user's understanding by making it easier to understand visually.

[0075] The explanation system can also estimate the user's emotions and provide positive feedback based on the estimated emotions, lowering the barrier to asking questions. For example, if the user is nervous, it can display an encouraging message. It can also analyze the user's emotional state and provide appropriate positive feedback, making it easier for users to ask questions.

[0076] The explanation system can also combine not only voice input but also gesture input or gaze input to provide a more intuitive way to ask questions. For example, questions can be entered by recognizing hand movements or finger gestures. Furthermore, by combining gaze input, users can also enter questions using their gaze. This allows users to enter questions intuitively.

[0077] The explanation system can further estimate the user's emotions, evaluate the emotional impact of the generated explanation on the user based on the estimated emotions, and adjust the explanation to elicit positive emotions. For example, the explanation can be changed to more positive expressions. The system can also analyze the user's emotional state and make adjustments to elicit appropriate positive emotions. This can elicit positive emotions in the user, thereby improving the acceptability of the explanation.

[0078] The explanation system can also generate explanations from different disciplinary perspectives, promoting multifaceted understanding. For example, it can combine technical and business perspectives. It can also provide perspectives from different disciplinary fields related to the content of the explanation. This allows users to deepen their understanding by providing explanations from different disciplinary perspectives.

[0079] The explanation system can further estimate the user's emotions, evaluate the emotional impact of the generated explanation on the user based on the estimated emotions, and provide supplementary information to alleviate negative emotions. For example, it can add positive elements to the explanation. It can also analyze the user's emotional state and provide appropriate supplementary information. This can alleviate negative emotions and deepen the user's understanding.

[0080] The explanation system can further customize the explanations it generates based on the user's learning history or interests, providing personalized information. For example, it can prioritize information related to past learning content. It can also provide appropriate information based on the user's interests. This allows for deeper understanding by providing information customized based on the user's learning history and interests.

[0081] The explanation system can also evaluate the user's level of understanding of the generated explanation in real time and provide additional explanation as needed. For example, it can provide supplementary explanation if the user's level of understanding is low. It can also evaluate the user's level of understanding and provide appropriate additional explanation. This allows the user to deepen their understanding by providing an appropriate explanation according to their level of understanding.

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

[0083] Step 1: The voice input unit accepts voice or text input. For example, a user can tap the voice input button through a dedicated app to communicate words they don't understand. The voice input unit can also accept text input using a keyboard. Step 2: The generation unit analyzes the input received by the voice input unit and generates a concise explanation. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the input question and generate a concise explanation. The generation AI may also use a multimodal generation AI to analyze the input content and generate a concise explanation. For example, the generation unit may generate an answer in the form of "Commoditization is a state in which it has become difficult to differentiate products." Step 3: The audio output unit outputs the explanation generated by the generation unit through bone conduction earphones or bone conduction glasses. For example, the answer generated by the generation AI is conveyed to the user through bone conduction earphones. This allows the user to input knowledge without being noticed by those around them.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0118] 7, 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0128] 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.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice input unit that accepts voice or character input; a generation unit that analyzes the input received by the speech input unit and generates a concise explanation; an audio output unit that outputs the explanation generated by the generation unit as audio through bone conduction earphones or bone conduction glasses; A system characterized by:

2. The voice input unit Analyze the tone or speed of the user's voice to estimate and prioritize urgency or importance 2. The system of claim 1.

3. The voice input unit Provide a more intuitive way to ask questions by combining voice input with gesture or gaze input.

2. The system of claim 1.

4. The generation unit Automatically generating visuals or graphs related to the generated explanation to facilitate visual understanding.

2. The system of claim 1.

5. The audio output unit The sound quality of the bone conduction earphone is automatically adjusted to match the shape of the user's ear, thereby achieving optimal sound output.

2. The system of claim 1.

6. The voice input unit Analyzes the emotions of users when asking questions and simultaneously provides relaxing voices to reduce stress or anxiety 2. The system of claim 1.

7. The generation unit Evaluating the emotional impact of the generated explanation on the user and adjusting it to elicit positive emotions.

2. The system of claim 1.

8. The audio output unit Analyzing the emotions of the user when receiving audio output and simultaneously providing relaxing music or audio with the relaxing effect.

2. The system of claim 1.

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