system

Abstract

The system according to this embodiment aims to support multidimensional communication for people who are unable to speak. [Solution] The system according to the embodiment comprises a reception unit, a synthesis unit, an analysis unit, and a recognition unit. The reception unit receives text input. The synthesis unit converts the text received by the reception unit into speech. The analysis unit analyzes visual information. The recognition unit recognizes emotions.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] [[ID=:35]]In the conventional technology, a system for assisting the multi-dimensional communication of people who cannot speak has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to assist the multi-dimensional communication of people who cannot speak.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a synthesis unit, an analysis unit, and a recognition unit. The reception unit receives a text input. The synthesis unit converts the text received by the reception unit into voice. The analysis unit analyzes visual information. The recognition unit recognizes emotions.

Effects of the Invention

[0007] The system according to this embodiment can support multidimensional communication for people who are unable to speak. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The SocialVoice system according to an embodiment of the present invention is a comprehensive platform that supports people who are unable to speak. This SocialVoice system enables multidimensional communication using text input, speech synthesis, visual information, and emotion recognition. The SocialVoice system also provides remote work support, real-time translation, customization features, and wellness monitoring, promoting the social and economic integration of users. For example, the SocialVoice system allows users to input text and then use speech synthesis technology to produce speech. This makes it easier for people with speech impairments to communicate with others. Furthermore, by combining visual information and emotion recognition technology, the SocialVoice system can more accurately convey the user's emotions and intentions. For example, if a user smiles, the SocialVoice system recognizes that emotion and generates an appropriate response. The SocialVoice system also enables communication with people who speak different languages ​​by utilizing its real-time translation function. For example, when an English-speaking user communicates with a Japanese-speaking person, the SocialVoice system performs real-time translation, enabling smooth conversation. Furthermore, the SocialVoice system provides a customizable interface, offering a user-friendly environment tailored to the user's needs. Equipped with a wellness monitoring function, the SocialVoice system monitors the user's health status in real time. For example, the SocialVoice system monitors the user's heart rate and stress level, and issues appropriate alerts if abnormalities are detected. In this way, the SocialVoice system supports the user's health management. This platform has the effect of improving the social participation rate and employment rate of people with speech disorders. Furthermore, it is expected to improve communication satisfaction through emotion recognition and enhance the user's quality of life. It is anticipated to be used in a wide range of industries, including medical facilities, educational institutions, and corporations.This enables the SocialVoice system to provide multi-dimensional communication to support people who are unable to speak.

[0029] The SocialVoice system according to this embodiment comprises a reception unit, a synthesis unit, an analysis unit, and a recognition unit. The reception unit receives text input. Text input includes, but is not limited to, keyboard input, voice input, and handwriting input. For example, the reception unit receives text entered by a user using a keyboard. The reception unit can also convert spoken content by a user using voice input into text and receive it. Furthermore, the reception unit can convert written content by a user using handwriting input into text and receive it. The synthesis unit converts the text received by the reception unit into speech. The synthesis unit converts text into speech using, for example, speech synthesis technology. Speech synthesis technology includes, but is not limited to, deep learning-based speech synthesis algorithms. For example, the synthesis unit generates natural-sounding speech using a deep learning-based speech synthesis algorithm. The synthesis unit can also generate speech based on specific rules using a rule-based speech synthesis algorithm. Furthermore, the synthesis unit can generate speech by combining multiple algorithms using a hybrid speech synthesis algorithm. The analysis unit analyzes visual information. Visual information includes, but is not limited to, images, videos, and real-time camera footage. The analysis unit analyzes visual information using, for example, image recognition technology. Image recognition technology includes, but is not limited to, deep learning-based image recognition algorithms. The analysis unit recognizes objects in an image using, for example, deep learning-based image recognition algorithms. The analysis unit can also analyze an image based on specific rules using rule-based image recognition algorithms. Furthermore, the analysis unit can analyze an image by combining multiple algorithms using hybrid image recognition algorithms. The recognition unit recognizes emotions. Emotion recognition includes, but is not limited to, facial expression recognition, speech analysis, and text analysis. The recognition unit recognizes emotions using, for example, facial expression recognition technology. Facial expression recognition technology includes, but is not limited to, deep learning-based facial expression recognition algorithms.The recognition unit can recognize emotions from the user's facial expressions, for example, using a deep learning-based facial recognition algorithm. The recognition unit can also recognize emotions using speech analysis technology. Speech analysis technology includes, but is not limited to, deep learning-based speech analysis algorithms. The recognition unit can recognize emotions from the user's voice, for example, using a deep learning-based speech analysis algorithm. Furthermore, the recognition unit can also recognize emotions using text analysis technology. Text analysis technology includes, but is not limited to, deep learning-based text analysis algorithms. The recognition unit can recognize emotions from the user's text, for example, using a deep learning-based text analysis algorithm. As a result, the SocialVoice system according to the embodiment can realize multidimensional communication to support people who are unable to speak.

[0030] The reception desk accepts text input. Text input includes, but is not limited to, keyboard input, voice input, and handwriting input. For example, the reception desk accepts text entered by the user using a keyboard. Specifically, it acquires the string of characters entered by the user using a keyboard in real time and sends it to the system. The reception desk can also accept spoken content from the user converted into text using voice input. In the case of voice input, the user's speech is collected through a microphone and converted into text using speech recognition technology. Speech recognition technology may include, for example, a deep learning-based speech recognition algorithm, which enables highly accurate speech-to-text conversion. Furthermore, the reception desk can also accept content written by the user converted into text using handwriting input. In the case of handwriting input, the user enters handwritten characters using a digital pen or finger on a tablet or smartphone touchscreen, and handwriting recognition technology converts those handwritten characters into text. Handwriting recognition technology may include, for example, a deep learning-based handwriting recognition algorithm, which enables highly accurate recognition of handwritten characters. As a result, the reception desk can accurately acquire text data and send it to the system regardless of the input method chosen by the user. Furthermore, the reception unit can accept multiple input methods simultaneously, improving user convenience. For example, if a user uses both keyboard input and voice input, the reception unit can process both inputs simultaneously and generate integrated text data. This allows the reception unit to respond to diverse user needs and achieve flexible and efficient text input.

[0031] The synthesis unit converts text received by the reception unit into speech. The synthesis unit converts text to speech using, for example, speech synthesis technology. Speech synthesis technology includes, but is not limited to, deep learning-based speech synthesis algorithms. Specifically, it uses deep learning-based speech synthesis algorithms to generate natural-sounding speech. Deep learning-based speech synthesis algorithms can generate speech with natural intonation and intonation from text by learning from large amounts of speech data. The synthesis unit can also generate speech based on specific rules using rule-based speech synthesis algorithms. Because rule-based speech synthesis algorithms generate speech based on combinations of phonemes and phonologies, they can generate speech that follows specific pronunciation rules. Furthermore, the synthesis unit can also generate speech by combining multiple algorithms using hybrid speech synthesis algorithms. Hybrid speech synthesis algorithms combine the advantages of deep learning-based and rule-based algorithms to produce more natural and high-quality speech. This allows the synthesis unit to convert user-inputted text into high-quality speech and provide it to the user. Additionally, the synthesis unit can customize speech parameters such as speed, pitch, and volume by adjusting them. This enables voice output tailored to the user's preferences and circumstances. For example, if a user desires fast voice output, the synthesis unit can adjust the voice speed to generate the desired voice. This allows the synthesis unit to respond to diverse user needs and achieve flexible and high-quality speech synthesis.

[0032] The analysis unit analyzes visual information. Visual information includes, but is not limited to, images, videos, and real-time camera footage. The analysis unit analyzes visual information using, for example, image recognition technology. Image recognition technology includes, but is not limited to, deep learning-based image recognition algorithms. Specifically, it uses deep learning-based image recognition algorithms to recognize objects in images. Deep learning-based image recognition algorithms can recognize objects and features in images with high accuracy by learning from large amounts of image data. The analysis unit can also analyze images based on specific rules using rule-based image recognition algorithms. Rule-based image recognition algorithms analyze images based on predefined rules and patterns, enabling analysis suitable for specific conditions. Furthermore, the analysis unit can combine multiple algorithms to analyze images using hybrid image recognition algorithms. Hybrid image recognition algorithms combine the advantages of deep learning-based algorithms and rule-based algorithms to achieve more accurate and flexible image analysis. As a result, the analysis unit can analyze visual information with high accuracy and provide it to the user. Furthermore, the analysis unit can analyze visual information in real time and respond to dynamic situations. For example, by analyzing real-time camera footage, it's possible to track moving objects or detect abnormal movements. This allows the analysis unit to quickly and accurately analyze visual information and provide it to the user.

[0033] The recognition unit recognizes emotions. Emotion recognition includes, but is not limited to, facial expression recognition, speech analysis, and text analysis. For example, the recognition unit recognizes emotions using facial expression recognition technology. Facial expression recognition technology includes, but is not limited to, deep learning-based facial expression recognition algorithms. Specifically, it recognizes emotions from the user's facial expressions using a deep learning-based facial expression recognition algorithm. By learning from a large amount of facial expression data, the deep learning-based facial expression recognition algorithm can recognize subtle changes in facial expressions with high accuracy. The recognition unit can also recognize emotions using speech analysis technology. Speech analysis technology includes, but is not limited to, deep learning-based speech analysis algorithms. By using speech analysis technology, it can recognize emotions from the tone, pitch, and rhythm of the user's voice. Furthermore, the recognition unit can also recognize emotions using text analysis technology. Text analysis technology includes, but is not limited to, deep learning-based text analysis algorithms. By using text analysis technology, it can recognize emotions from the content and context of the text entered by the user. As a result, the recognition unit can recognize a wide range of user emotions with high accuracy and reflect them in the system. Furthermore, the recognition unit can achieve more accurate emotion recognition by combining multiple emotion recognition technologies. For example, by combining facial expression recognition and voice analysis, it can recognize emotions from both the user's facial expressions and voice, enabling more accurate emotion recognition. As a result, the recognition unit can recognize the user's emotions with high accuracy and reflect them in the system.

[0034] The SocialVoice system includes a translation unit that performs real-time translation. The translation unit translates text into different languages ​​in real time, for example. The translation unit uses neural machine translation technology to translate text in real time. Neural machine translation technology includes, but is not limited to, deep learning-based translation algorithms. The translation unit uses deep learning-based translation algorithms to translate text with high accuracy. The translation unit can also use rule-based translation algorithms to translate text based on specific rules. Furthermore, the translation unit can use hybrid translation algorithms to translate text by combining multiple algorithms. This enables the SocialVoice system to communicate with people who speak different languages. For example, when an English-speaking user communicates with a Japanese-speaking person, the translation unit translates English into Japanese in real time, enabling a smooth conversation. In addition, the translation unit supports multiple languages, allowing users to communicate in their native language. For example, the translation unit supports multiple languages ​​such as English, Japanese, French, German, and Chinese. This allows the SocialVoice system to facilitate communication between people who speak different languages.

[0035] The SocialVoice system includes a component that provides a customizable interface. This component can, for example, customize the interface to meet user needs. For instance, it can provide functionality that allows users to change the interface's design and layout. It can also provide functionality that allows users to change the interface's colors and fonts. Furthermore, it can provide functionality that allows users to change the layout of the interface and the position of buttons. In addition, it can provide functionality that allows users to add or remove interface features. This enables the SocialVoice system to provide a user-friendly environment tailored to user needs. For example, the component can change the interface's colors to improve visibility for visually impaired users. It can also increase the font size of the interface for easier reading for the elderly. Furthermore, the component can provide interfaces specialized for specific tasks. For example, it can provide an interface for medical facilities and customize it for ease of use by healthcare professionals. This allows the SocialVoice system to provide a user-friendly environment tailored to user needs.

[0036] The SocialVoice system includes a monitoring unit that performs wellness monitoring. The monitoring unit, for example, monitors the user's health status in real time. The monitoring unit monitors health data such as heart rate, blood pressure, and activity level. The monitoring unit, for example, monitors the user's heart rate in real time using a heart rate sensor. The heart rate sensor includes, but is not limited to, a photoelectric heart rate sensor. The monitoring unit measures the user's heart rate with high accuracy using, for example, a photoelectric heart rate sensor. The monitoring unit can also monitor the user's blood pressure in real time using a blood pressure sensor. The blood pressure sensor includes, but is not limited to, a cuff-type blood pressure sensor. The monitoring unit measures the user's blood pressure with high accuracy using, for example, a cuff-type blood pressure sensor. Furthermore, the monitoring unit can also monitor the user's activity level in real time using an activity level sensor. The activity level sensor includes, but is not limited to, an accelerometer. The monitoring unit measures the user's activity level with high accuracy using, for example, an accelerometer. This allows the SocialVoice system to monitor the user's health status in real time and issue appropriate alerts if an abnormality is detected. For example, the monitoring unit can issue an alert and notify the user if their heart rate is abnormally high. The monitoring unit can also issue an alert and notify the user if their blood pressure is abnormally low. Furthermore, the monitoring unit can issue an alert and notify the user if their activity level is abnormally low. In this way, the SocialVoice system can support the user's health management.

[0037] The synthesis unit can convert text into speech. The synthesis unit converts text into speech using, for example, speech synthesis technology. Speech synthesis technology includes, but is not limited to, deep learning-based speech synthesis algorithms. The synthesis unit can generate natural-sounding speech using, for example, deep learning-based speech synthesis algorithms. The synthesis unit can also generate speech based on specific rules using rule-based speech synthesis algorithms. Furthermore, the synthesis unit can generate speech by combining multiple algorithms using hybrid speech synthesis algorithms. This makes it easier for people with speech impairments to communicate with others by converting text into speech. Some or all of the above processing in the synthesis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the synthesis unit can input text into a generative AI, and the generative AI can generate speech.

[0038] The analysis unit can analyze visual information. For example, the analysis unit analyzes visual information using image recognition technology. Image recognition technology includes, but is not limited to, deep learning-based image recognition algorithms. For example, the analysis unit can recognize objects in an image using a deep learning-based image recognition algorithm. The analysis unit can also analyze an image based on specific rules using a rule-based image recognition algorithm. Furthermore, the analysis unit can analyze an image by combining multiple algorithms using a hybrid image recognition algorithm. This allows for a more accurate conveyance of the user's emotions and intentions by analyzing visual information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input visual information into a generative AI, which can then analyze the visual information.

[0039] The recognition unit can recognize emotions. The recognition unit recognizes emotions using, for example, facial expression recognition technology. Facial expression recognition technology includes, but is not limited to, deep learning-based facial expression recognition algorithms. The recognition unit recognizes emotions from the user's facial expressions using, for example, a deep learning-based facial expression recognition algorithm. The recognition unit can also recognize emotions using speech analysis technology. Speech analysis technology includes, but is not limited to, deep learning-based speech analysis algorithms. The recognition unit recognizes emotions from the user's voice using, for example, a deep learning-based speech analysis algorithm. Furthermore, the recognition unit can also recognize emotions using text analysis technology. Text analysis technology includes, but is not limited to, deep learning-based text analysis algorithms. The recognition unit recognizes emotions from the user's text using, for example, a deep learning-based text analysis algorithm. This allows for a more accurate transmission of the user's emotions and intentions by recognizing emotions. Some or all of the above processing in the recognition unit may be performed, for example, using generative AI, or without using generative AI. For example, the recognition unit inputs the user's facial expression data into a generating AI, which can then recognize emotions.

[0040] The reception desk can analyze the user's past text input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may perform predictive input based on the user's past input to reduce the effort required for input. For example, the reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the optimal input method to be selected by analyzing the user's past text input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past input data into a generative AI, which can then select the optimal input method.

[0041] The reception system can filter text input based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception system will prioritize business-related text input. If the user is relaxed, the reception system will prioritize entertainment-related text input. If the user is working on a specific project, the reception system will prioritize text input related to that project. This allows for more appropriate text input by filtering the input based on the user's current situation and areas of interest. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception system can input the user's current situation data into a generative AI, which can then filter the input.

[0042] The reception unit can prioritize inputting highly relevant text by considering the user's geographical location when text is entered. For example, if the user is in a specific location, the reception unit will prioritize inputting text related to that location. For example, if the user is traveling, the reception unit will prioritize inputting text related to the travel destination. For example, if the user is at home, the reception unit will prioritize inputting text related to home. In this way, highly relevant text can be prioritized by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, and the generative AI can select highly relevant text.

[0043] The reception unit can analyze the user's social media activity and input relevant text when text is entered. For example, the reception unit can input relevant text based on what the user has recently been talking about on social media. For example, if the user is using a specific hashtag, the reception unit can input text related to that hashtag. For example, the reception unit can input relevant text based on the content of posts from accounts the user follows on social media. In this way, relevant text can be input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, and the generative AI can select relevant text.

[0044] The synthesis unit can apply different speech synthesis algorithms depending on the content of the text. For example, in the case of a business document, the synthesis unit applies a formal speech synthesis algorithm. For example, in the case of a casual conversation, the synthesis unit applies a relaxed speech synthesis algorithm. For example, in the case of an emotional message, the synthesis unit applies an emotionally emphasizing speech synthesis algorithm. By applying different speech synthesis algorithms depending on the content of the text, it is possible to generate more appropriate speech. Some or all of the above processing in the synthesis unit may be performed using a generation AI, for example, or without a generation AI. For example, the synthesis unit can input text data into a generation AI, which can then select the optimal speech synthesis algorithm.

[0045] The synthesis unit can generate the optimal voice by referring to the user's past voice history during speech synthesis. For example, the synthesis unit can generate the optimal voice based on the tone and pitch of voices the user has used in the past. For example, the synthesis unit can generate the optimal voice based on the voice style the user has preferred to use in the past. For example, the synthesis unit can generate the optimal voice for a specific situation from the user's past voice history. In this way, the optimal voice can be generated by referring to the user's past voice history. Some or all of the above processing in the synthesis unit may be performed using a generation AI, for example, or without a generation AI. For example, the synthesis unit can input the user's past voice data into a generation AI, and the generation AI can generate the optimal voice.

[0046] The synthesis unit can determine the priority of speech based on the submission timing of the text during speech synthesis. For example, the synthesis unit prioritizes speech synthesis for urgent texts. For example, the synthesis unit determines the priority of speech synthesis based on the submission timing for periodic reports. For example, the synthesis unit determines the priority of speech synthesis based on a deadline specified by the user. This allows for the generation of more appropriate speech by prioritizing speech based on the submission timing of the text. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input text data into a generation AI, which can then determine the priority of speech.

[0047] The synthesis unit can adjust the order of speech based on the relevance of the text during speech synthesis. For example, the synthesis unit prioritizes speech synthesis of text containing important information. For example, the synthesis unit prioritizes speech synthesis of highly relevant text based on user interests. For example, the synthesis unit performs speech synthesis in the optimal order according to the content of the text. By adjusting the order of speech based on the relevance of the text, more appropriate speech can be generated. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input text data into a generation AI, and the generation AI can determine the order of speech.

[0048] The analysis unit can select the optimal analysis method by referring to the user's past visual information history when analyzing visual information. For example, the analysis unit may select the optimal analysis method based on the visual information the user has previously preferred to view. For example, the analysis unit may select the optimal analysis method according to a specific situation from the user's past visual information history. For example, the analysis unit may select the optimal analysis method based on feedback from when the user previously analyzed visual information. In this way, the optimal analysis method can be selected by referring to the user's past visual information history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's past visual information data into a generating AI, and the generating AI can select the optimal analysis method.

[0049] The analysis unit can customize the means of analysis based on the user's current situation when analyzing visual information. For example, if the user is in a meeting, the analysis unit will prioritize analyzing business-related visual information. For example, if the user is relaxed, the analysis unit will prioritize analyzing entertainment-related visual information. For example, if the user is working on a specific project, the analysis unit will prioritize analyzing visual information related to that project. This allows for more appropriate analysis by customizing the means of analysis based on the user's current situation. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's current situation data into the generative AI, which can then customize the means of analysis.

[0050] The analysis unit can select the optimal analysis method when analyzing visual information, taking into account the user's geographical location. For example, if the user is in a specific location, the analysis unit will prioritize analyzing visual information related to that location. For example, if the user is traveling, the analysis unit will prioritize analyzing visual information related to the travel destination. For example, if the user is at home, the analysis unit will prioritize analyzing visual information related to home. This allows the analysis unit to select the optimal analysis method by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then select the optimal analysis method.

[0051] The analysis unit can analyze the user's social media activity and propose analysis methods when analyzing visual information. For example, the analysis unit analyzes relevant visual information based on what the user has recently been talking about on social media. For example, if the user is using a specific hashtag, the analysis unit analyzes visual information related to that hashtag. For example, the analysis unit analyzes relevant visual information based on the content of posts from accounts the user follows on social media. By doing so, the analysis unit can propose the most suitable analysis method by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, which can then propose the most suitable analysis method.

[0052] The translation unit can apply different translation algorithms depending on the content of the text during translation. For example, the translation unit may apply a formal translation algorithm to business documents, a relaxed translation algorithm to casual conversations, and an emotionally emphasizing translation algorithm to emotional messages. By applying different translation algorithms depending on the content of the text, more appropriate translations can be achieved. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input text data into a generative AI, which can then select the optimal translation algorithm.

[0053] The translation unit can provide the optimal translation by referring to the user's past translation history during translation. For example, the translation unit can provide the optimal translation based on the translation style the user has used in the past. For example, the translation unit can provide the optimal translation for a specific situation based on the user's past translation history. For example, the translation unit can provide the optimal translation based on feedback from the user's past translations. In this way, the translation unit can provide the optimal translation by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input the user's past translation data into a generative AI, and the generative AI can provide the optimal translation.

[0054] The translation department can prioritize translations based on the submission date of the text. For example, it may prioritize urgent texts. For example, it may prioritize regular reports based on their submission date. For example, it may prioritize translations based on deadlines specified by the user. This allows for more appropriate translations by prioritizing translations based on the submission date of the text. Some or all of the above processes in the translation department may be performed using, for example, a generative AI, or not. For example, the translation department can input text data into a generative AI, which can then determine the translation priority.

[0055] The translation unit can adjust the order of translations based on the relevance of the texts during the translation process. For example, the translation unit may prioritize translating texts containing important information. For example, the translation unit may prioritize translating texts that are highly relevant based on the user's interests. For example, the translation unit may perform translations in the optimal order according to the content of the texts. By adjusting the order of translations based on the relevance of the texts, more appropriate translations become possible. Some or all of the above processes in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input text data into a generative AI, and the generative AI can determine the order of translations.

[0056] The service provider can select the optimal display method when displaying the interface by referring to the user's past operation history. For example, the service provider can select the optimal display method based on the interface design that the user has previously preferred to use. For example, the service provider can select the optimal display method according to a specific situation from the user's past operation history. For example, the service provider can select the optimal display method based on the user's feedback when they previously used the interface. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past operation data into a generation AI, and the generation AI can select the optimal display method.

[0057] The service provider can customize the display methods based on the user's current situation when displaying interfaces. For example, if the user is in a meeting, the service provider will prioritize displaying business-related interfaces. For example, if the user is relaxing, the service provider will prioritize displaying entertainment-related interfaces. For example, if the user is working on a specific project, the service provider will prioritize displaying interfaces related to that project. This allows for a more appropriate display by customizing the display methods based on the user's current situation. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's current situation data into a generative AI, which can then customize the display methods.

[0058] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. This allows the service provider to select the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0059] The service provider can analyze the user's social media activity and suggest display methods when displaying an interface. For example, the service provider can display relevant interfaces based on what the user has recently been talking about on social media. For example, if the user is using a specific hashtag, the service provider can display interfaces related to that hashtag. For example, the service provider can display relevant interfaces based on the content of posts from accounts the user follows on social media. In this way, by analyzing the user's social media activity, the service provider can suggest the most suitable display method. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then suggest the most suitable display method.

[0060] The monitoring unit can select the optimal monitoring method by referring to the user's past health data during monitoring. For example, the monitoring unit selects the optimal monitoring method based on the health data the user has previously provided. For example, the monitoring unit selects the optimal monitoring method according to a specific situation from the user's past health data. For example, the monitoring unit selects the optimal monitoring method based on feedback from the user's past monitoring. In this way, the optimal monitoring method can be selected by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the monitoring unit can input the user's past health data into a generating AI, and the generating AI can select the optimal monitoring method.

[0061] The monitoring unit can customize the monitoring methods based on the user's current health status during monitoring. For example, if the user is tired, the monitoring unit will focus on monitoring heart rate and stress levels. For example, if the user is healthy, the monitoring unit will monitor general health data. For example, if the user is unwell, the monitoring unit will focus on monitoring data related to specific symptoms. This allows for more appropriate monitoring by customizing the monitoring methods based on the user's current health status. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's current health status data into a generative AI, which can then customize the monitoring methods.

[0062] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is in a specific location, the monitoring unit will prioritize monitoring health data related to that location. For example, if the user is traveling, the monitoring unit will prioritize monitoring health data related to the travel destination. For example, if the user is at home, the monitoring unit will prioritize monitoring health data related to home. This allows the optimal monitoring method to be selected by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI, which can then select the optimal monitoring method.

[0063] The monitoring unit can analyze a user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit monitors relevant health data based on health-related content that the user has recently been discussing on social media. For example, if a user is using a specific hashtag, the monitoring unit monitors health data related to that hashtag. For example, the monitoring unit monitors relevant health data based on the content of posts from accounts that the user follows on social media. By analyzing the user's social media activity, the monitoring unit can propose the most suitable monitoring method. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's social media activity data into a generative AI, which can then propose the most suitable monitoring method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The SocialVoice system can also include a reception unit that analyzes the user's past text input history and selects the optimal input method. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also reduce input effort by performing predictive input based on the user's past input content. Furthermore, it can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the system can select the optimal input method by analyzing the user's past text input history.

[0066] The SocialVoice system can also include a reception section that prioritizes inputting highly relevant text by considering the user's geographical location. For example, if the user is in a specific location, text related to that location can be prioritized. Similarly, if the user is traveling, text related to their travel destination can be prioritized. Furthermore, if the user is at home, text related to their home can be prioritized. This allows for the prioritization of highly relevant text by considering the user's geographical location.

[0067] The SocialVoice system can also include an analysis unit that selects the optimal analysis method by referring to the user's past visual information history. For example, it can select the optimal analysis method based on the visual information the user has previously viewed with preference. It can also select the optimal analysis method for a specific situation based on the user's past visual information history. Furthermore, it can select the optimal analysis method based on feedback from when the user previously analyzed visual information. In this way, the optimal analysis method can be selected by referring to the user's past visual information history.

[0068] The SocialVoice system can also include a translation unit that provides the optimal translation by referring to the user's past translation history. For example, it can provide the optimal translation based on the translation style the user has used in the past. It can also provide the optimal translation for a specific situation based on the user's past translation history. Furthermore, it can provide the optimal translation based on feedback from the user's past translations. In this way, the system can provide the optimal translation by referring to the user's past translation history.

[0069] The SocialVoice system can further include a provisioning unit that analyzes the user's social media activity and suggests display methods. For example, it can display relevant interfaces based on what the user has recently been talking about on social media. It can also display interfaces related to specific hashtags if the user is using them. Furthermore, it can display relevant interfaces based on the content of posts from accounts the user follows on social media. This allows the system to suggest the most suitable display method by analyzing the user's social media activity.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk accepts text input. Text input includes keyboard input, voice input, and handwriting input. For example, it accepts text entered by the user using a keyboard. It can also convert spoken content from the user into text using voice input and accept it. Furthermore, it can convert written content from the user into text using handwriting input and accept it. Step 2: The synthesis unit converts the text received by the reception unit into speech. The synthesis unit converts text into speech using speech synthesis technology. For example, it can generate natural-sounding speech using a deep learning-based speech synthesis algorithm. It can also generate speech using a rule-based speech synthesis algorithm or a hybrid speech synthesis algorithm. Step 3: The analysis unit analyzes the visual information. Visual information includes images, videos, and real-time camera footage. The analysis unit analyzes the visual information using image recognition technology. For example, it recognizes objects in an image using a deep learning-based image recognition algorithm. It can also analyze images using rule-based image recognition algorithms or hybrid image recognition algorithms. Step 4: The recognition unit recognizes emotions. Emotion recognition includes facial expression recognition, speech analysis, and text analysis. The recognition unit recognizes emotions using facial expression recognition technology. For example, it recognizes emotions from the user's facial expressions using a deep learning-based facial expression recognition algorithm. It can also recognize emotions using speech analysis technology and text analysis technology.

[0072] (Example of form 2) The SocialVoice system according to an embodiment of the present invention is a comprehensive platform that supports people who are unable to speak. This SocialVoice system enables multidimensional communication using text input, speech synthesis, visual information, and emotion recognition. The SocialVoice system also provides remote work support, real-time translation, customization features, and wellness monitoring, promoting the social and economic integration of users. For example, the SocialVoice system allows users to input text and then use speech synthesis technology to produce speech. This makes it easier for people with speech impairments to communicate with others. Furthermore, by combining visual information and emotion recognition technology, the SocialVoice system can more accurately convey the user's emotions and intentions. For example, if a user smiles, the SocialVoice system recognizes that emotion and generates an appropriate response. The SocialVoice system also enables communication with people who speak different languages ​​by utilizing its real-time translation function. For example, when an English-speaking user communicates with a Japanese-speaking person, the SocialVoice system performs real-time translation, enabling smooth conversation. Furthermore, the SocialVoice system provides a customizable interface, offering a user-friendly environment tailored to the user's needs. Equipped with a wellness monitoring function, the SocialVoice system monitors the user's health status in real time. For example, the SocialVoice system monitors the user's heart rate and stress level, and issues appropriate alerts if abnormalities are detected. In this way, the SocialVoice system supports the user's health management. This platform has the effect of improving the social participation rate and employment rate of people with speech disorders. Furthermore, it is expected to improve communication satisfaction through emotion recognition and enhance the user's quality of life. It is anticipated to be used in a wide range of industries, including medical facilities, educational institutions, and corporations.This enables the SocialVoice system to provide multi-dimensional communication to support people who are unable to speak.

[0073] The SocialVoice system according to this embodiment comprises a reception unit, a synthesis unit, an analysis unit, and a recognition unit. The reception unit receives text input. Text input includes, but is not limited to, keyboard input, voice input, and handwriting input. For example, the reception unit receives text entered by a user using a keyboard. The reception unit can also convert spoken content by a user using voice input into text and receive it. Furthermore, the reception unit can convert written content by a user using handwriting input into text and receive it. The synthesis unit converts the text received by the reception unit into speech. The synthesis unit converts text into speech using, for example, speech synthesis technology. Speech synthesis technology includes, but is not limited to, deep learning-based speech synthesis algorithms. For example, the synthesis unit generates natural-sounding speech using a deep learning-based speech synthesis algorithm. The synthesis unit can also generate speech based on specific rules using a rule-based speech synthesis algorithm. Furthermore, the synthesis unit can generate speech by combining multiple algorithms using a hybrid speech synthesis algorithm. The analysis unit analyzes visual information. Visual information includes, but is not limited to, images, videos, and real-time camera footage. The analysis unit analyzes visual information using, for example, image recognition technology. Image recognition technology includes, but is not limited to, deep learning-based image recognition algorithms. The analysis unit recognizes objects in an image using, for example, deep learning-based image recognition algorithms. The analysis unit can also analyze an image based on specific rules using rule-based image recognition algorithms. Furthermore, the analysis unit can analyze an image by combining multiple algorithms using hybrid image recognition algorithms. The recognition unit recognizes emotions. Emotion recognition includes, but is not limited to, facial expression recognition, speech analysis, and text analysis. The recognition unit recognizes emotions using, for example, facial expression recognition technology. Facial expression recognition technology includes, but is not limited to, deep learning-based facial expression recognition algorithms.The recognition unit can recognize emotions from the user's facial expressions, for example, using a deep learning-based facial recognition algorithm. The recognition unit can also recognize emotions using speech analysis technology. Speech analysis technology includes, but is not limited to, deep learning-based speech analysis algorithms. The recognition unit can recognize emotions from the user's voice, for example, using a deep learning-based speech analysis algorithm. Furthermore, the recognition unit can also recognize emotions using text analysis technology. Text analysis technology includes, but is not limited to, deep learning-based text analysis algorithms. The recognition unit can recognize emotions from the user's text, for example, using a deep learning-based text analysis algorithm. As a result, the SocialVoice system according to the embodiment can realize multidimensional communication to support people who are unable to speak.

[0074] The reception desk accepts text input. Text input includes, but is not limited to, keyboard input, voice input, and handwriting input. For example, the reception desk accepts text entered by the user using a keyboard. Specifically, it acquires the string of characters entered by the user using a keyboard in real time and sends it to the system. The reception desk can also accept spoken content from the user converted into text using voice input. In the case of voice input, the user's speech is collected through a microphone and converted into text using speech recognition technology. Speech recognition technology may include, for example, a deep learning-based speech recognition algorithm, which enables highly accurate speech-to-text conversion. Furthermore, the reception desk can also accept content written by the user converted into text using handwriting input. In the case of handwriting input, the user enters handwritten characters using a digital pen or finger on a tablet or smartphone touchscreen, and handwriting recognition technology converts those handwritten characters into text. Handwriting recognition technology may include, for example, a deep learning-based handwriting recognition algorithm, which enables highly accurate recognition of handwritten characters. As a result, the reception desk can accurately acquire text data and send it to the system regardless of the input method chosen by the user. Furthermore, the reception unit can accept multiple input methods simultaneously, improving user convenience. For example, if a user uses both keyboard input and voice input, the reception unit can process both inputs simultaneously and generate integrated text data. This allows the reception unit to respond to diverse user needs and achieve flexible and efficient text input.

[0075] The synthesis unit converts text received by the reception unit into speech. The synthesis unit converts text to speech using, for example, speech synthesis technology. Speech synthesis technology includes, but is not limited to, deep learning-based speech synthesis algorithms. Specifically, it uses deep learning-based speech synthesis algorithms to generate natural-sounding speech. Deep learning-based speech synthesis algorithms can generate speech with natural intonation and intonation from text by learning from large amounts of speech data. The synthesis unit can also generate speech based on specific rules using rule-based speech synthesis algorithms. Because rule-based speech synthesis algorithms generate speech based on combinations of phonemes and phonologies, they can generate speech that follows specific pronunciation rules. Furthermore, the synthesis unit can also generate speech by combining multiple algorithms using hybrid speech synthesis algorithms. Hybrid speech synthesis algorithms combine the advantages of deep learning-based and rule-based algorithms to produce more natural and high-quality speech. This allows the synthesis unit to convert user-inputted text into high-quality speech and provide it to the user. Additionally, the synthesis unit can customize speech parameters such as speed, pitch, and volume by adjusting them. This enables voice output tailored to the user's preferences and circumstances. For example, if a user desires fast voice output, the synthesis unit can adjust the voice speed to generate the desired voice. This allows the synthesis unit to respond to diverse user needs and achieve flexible and high-quality speech synthesis.

[0076] The analysis unit analyzes visual information. Visual information includes, but is not limited to, images, videos, and real-time camera footage. The analysis unit analyzes visual information using, for example, image recognition technology. Image recognition technology includes, but is not limited to, deep learning-based image recognition algorithms. Specifically, it uses deep learning-based image recognition algorithms to recognize objects in images. Deep learning-based image recognition algorithms can recognize objects and features in images with high accuracy by learning from large amounts of image data. The analysis unit can also analyze images based on specific rules using rule-based image recognition algorithms. Rule-based image recognition algorithms analyze images based on predefined rules and patterns, enabling analysis suitable for specific conditions. Furthermore, the analysis unit can combine multiple algorithms to analyze images using hybrid image recognition algorithms. Hybrid image recognition algorithms combine the advantages of deep learning-based algorithms and rule-based algorithms to achieve more accurate and flexible image analysis. As a result, the analysis unit can analyze visual information with high accuracy and provide it to the user. Furthermore, the analysis unit can analyze visual information in real time and respond to dynamic situations. For example, by analyzing real-time camera footage, it's possible to track moving objects or detect abnormal movements. This allows the analysis unit to quickly and accurately analyze visual information and provide it to the user.

[0077] The recognition unit recognizes emotions. Emotion recognition includes, but is not limited to, facial expression recognition, speech analysis, and text analysis. For example, the recognition unit recognizes emotions using facial expression recognition technology. Facial expression recognition technology includes, but is not limited to, deep learning-based facial expression recognition algorithms. Specifically, it recognizes emotions from the user's facial expressions using a deep learning-based facial expression recognition algorithm. By learning from a large amount of facial expression data, the deep learning-based facial expression recognition algorithm can recognize subtle changes in facial expressions with high accuracy. The recognition unit can also recognize emotions using speech analysis technology. Speech analysis technology includes, but is not limited to, deep learning-based speech analysis algorithms. By using speech analysis technology, it can recognize emotions from the tone, pitch, and rhythm of the user's voice. Furthermore, the recognition unit can also recognize emotions using text analysis technology. Text analysis technology includes, but is not limited to, deep learning-based text analysis algorithms. By using text analysis technology, it can recognize emotions from the content and context of the text entered by the user. As a result, the recognition unit can recognize a wide range of user emotions with high accuracy and reflect them in the system. Furthermore, the recognition unit can achieve more accurate emotion recognition by combining multiple emotion recognition technologies. For example, by combining facial expression recognition and voice analysis, it can recognize emotions from both the user's facial expressions and voice, enabling more accurate emotion recognition. As a result, the recognition unit can recognize the user's emotions with high accuracy and reflect them in the system.

[0078] The SocialVoice system includes a translation unit that performs real-time translation. The translation unit translates text into different languages ​​in real time, for example. The translation unit uses neural machine translation technology to translate text in real time. Neural machine translation technology includes, but is not limited to, deep learning-based translation algorithms. The translation unit uses deep learning-based translation algorithms to translate text with high accuracy. The translation unit can also use rule-based translation algorithms to translate text based on specific rules. Furthermore, the translation unit can use hybrid translation algorithms to translate text by combining multiple algorithms. This enables the SocialVoice system to communicate with people who speak different languages. For example, when an English-speaking user communicates with a Japanese-speaking person, the translation unit translates English into Japanese in real time, enabling a smooth conversation. In addition, the translation unit supports multiple languages, allowing users to communicate in their native language. For example, the translation unit supports multiple languages ​​such as English, Japanese, French, German, and Chinese. This allows the SocialVoice system to facilitate communication between people who speak different languages.

[0079] The SocialVoice system includes a component that provides a customizable interface. This component can, for example, customize the interface to meet user needs. For instance, it can provide functionality that allows users to change the interface's design and layout. It can also provide functionality that allows users to change the interface's colors and fonts. Furthermore, it can provide functionality that allows users to change the layout of the interface and the position of buttons. In addition, it can provide functionality that allows users to add or remove interface features. This enables the SocialVoice system to provide a user-friendly environment tailored to user needs. For example, the component can change the interface's colors to improve visibility for visually impaired users. It can also increase the font size of the interface for easier reading for the elderly. Furthermore, the component can provide interfaces specialized for specific tasks. For example, it can provide an interface for medical facilities and customize it for ease of use by healthcare professionals. This allows the SocialVoice system to provide a user-friendly environment tailored to user needs.

[0080] The SocialVoice system includes a monitoring unit that performs wellness monitoring. The monitoring unit, for example, monitors the user's health status in real time. The monitoring unit monitors health data such as heart rate, blood pressure, and activity level. The monitoring unit, for example, monitors the user's heart rate in real time using a heart rate sensor. The heart rate sensor includes, but is not limited to, a photoelectric heart rate sensor. The monitoring unit measures the user's heart rate with high accuracy using, for example, a photoelectric heart rate sensor. The monitoring unit can also monitor the user's blood pressure in real time using a blood pressure sensor. The blood pressure sensor includes, but is not limited to, a cuff-type blood pressure sensor. The monitoring unit measures the user's blood pressure with high accuracy using, for example, a cuff-type blood pressure sensor. Furthermore, the monitoring unit can also monitor the user's activity level in real time using an activity level sensor. The activity level sensor includes, but is not limited to, an accelerometer. The monitoring unit measures the user's activity level with high accuracy using, for example, an accelerometer. This allows the SocialVoice system to monitor the user's health status in real time and issue appropriate alerts if an abnormality is detected. For example, the monitoring unit can issue an alert and notify the user if their heart rate is abnormally high. The monitoring unit can also issue an alert and notify the user if their blood pressure is abnormally low. Furthermore, the monitoring unit can issue an alert and notify the user if their activity level is abnormally low. In this way, the SocialVoice system can support the user's health management.

[0081] The synthesis unit can convert text into speech. The synthesis unit converts text into speech using, for example, speech synthesis technology. Speech synthesis technology includes, but is not limited to, deep learning-based speech synthesis algorithms. The synthesis unit can generate natural-sounding speech using, for example, deep learning-based speech synthesis algorithms. The synthesis unit can also generate speech based on specific rules using rule-based speech synthesis algorithms. Furthermore, the synthesis unit can generate speech by combining multiple algorithms using hybrid speech synthesis algorithms. This makes it easier for people with speech impairments to communicate with others by converting text into speech. Some or all of the above processing in the synthesis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the synthesis unit can input text into a generative AI, and the generative AI can generate speech.

[0082] The analysis unit can analyze visual information. For example, the analysis unit analyzes visual information using image recognition technology. Image recognition technology includes, but is not limited to, deep learning-based image recognition algorithms. For example, the analysis unit can recognize objects in an image using a deep learning-based image recognition algorithm. The analysis unit can also analyze an image based on specific rules using a rule-based image recognition algorithm. Furthermore, the analysis unit can analyze an image by combining multiple algorithms using a hybrid image recognition algorithm. This allows for a more accurate conveyance of the user's emotions and intentions by analyzing visual information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input visual information into a generative AI, which can then analyze the visual information.

[0083] The recognition unit can recognize emotions. The recognition unit recognizes emotions using, for example, facial expression recognition technology. Facial expression recognition technology includes, but is not limited to, deep learning-based facial expression recognition algorithms. The recognition unit recognizes emotions from the user's facial expressions using, for example, a deep learning-based facial expression recognition algorithm. The recognition unit can also recognize emotions using speech analysis technology. Speech analysis technology includes, but is not limited to, deep learning-based speech analysis algorithms. The recognition unit recognizes emotions from the user's voice using, for example, a deep learning-based speech analysis algorithm. Furthermore, the recognition unit can also recognize emotions using text analysis technology. Text analysis technology includes, but is not limited to, deep learning-based text analysis algorithms. The recognition unit recognizes emotions from the user's text using, for example, a deep learning-based text analysis algorithm. This allows for a more accurate transmission of the user's emotions and intentions by recognizing emotions. Some or all of the above processing in the recognition unit may be performed, for example, using generative AI, or without using generative AI. For example, the recognition unit inputs the user's facial expression data into a generating AI, which can then recognize emotions.

[0084] The reception unit can estimate the user's emotions and adjust the timing of text input based on the estimated emotions. For example, if the user is stressed, the reception unit simplifies the input interface and minimizes the input steps. For example, if the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. For example, if the user is in a hurry, the reception unit prioritizes voice input to allow for quick text input. This allows for more appropriate input by adjusting the timing of text input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using or without a generative AI. For example, the reception unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0085] The reception desk can analyze the user's past text input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may perform predictive input based on the user's past input to reduce the effort required for input. For example, the reception desk may predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the optimal input method to be selected by analyzing the user's past text input history. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's past input data into a generative AI, which can then select the optimal input method.

[0086] The reception system can filter text input based on the user's current situation and areas of interest. For example, if the user is in a meeting, the reception system will prioritize business-related text input. If the user is relaxed, the reception system will prioritize entertainment-related text input. If the user is working on a specific project, the reception system will prioritize text input related to that project. This allows for more appropriate text input by filtering the input based on the user's current situation and areas of interest. Some or all of the above processing in the reception system may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception system can input the user's current situation data into a generative AI, which can then filter the input.

[0087] The reception desk can estimate the user's emotions and determine the priority of the text to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize important text. For example, if the user is relaxed, the reception desk will prioritize casual text. For example, if the user is in a hurry, the reception desk will prioritize urgent text. This allows for more appropriate text input by determining the priority of the text to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not using a generative AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions.

[0088] The reception unit can prioritize inputting highly relevant text by considering the user's geographical location when text is entered. For example, if the user is in a specific location, the reception unit will prioritize inputting text related to that location. For example, if the user is traveling, the reception unit will prioritize inputting text related to the travel destination. For example, if the user is at home, the reception unit will prioritize inputting text related to home. In this way, highly relevant text can be prioritized by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, and the generative AI can select highly relevant text.

[0089] The reception unit can analyze the user's social media activity and input relevant text when text is entered. For example, the reception unit can input relevant text based on what the user has recently been talking about on social media. For example, if the user is using a specific hashtag, the reception unit can input text related to that hashtag. For example, the reception unit can input relevant text based on the content of posts from accounts the user follows on social media. In this way, relevant text can be input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, and the generative AI can select relevant text.

[0090] The synthesis unit can estimate the user's emotions and adjust the tone and pitch of the voice based on the estimated emotions. For example, if the user is relaxed, the synthesis unit will generate a calm tone of voice. For example, if the user is excited, the synthesis unit will generate a bright and energetic tone of voice. For example, if the user is sad, the synthesis unit will generate a gentle tone of voice. This allows for the generation of more appropriate voices by adjusting the tone and pitch of the voice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the synthesis unit may be performed using a generative AI, or not using a generative AI. For example, the synthesis unit can input user facial expression data into a generative AI, which can then estimate the emotions.

[0091] The synthesis unit can apply different speech synthesis algorithms depending on the content of the text. For example, in the case of a business document, the synthesis unit applies a formal speech synthesis algorithm. For example, in the case of a casual conversation, the synthesis unit applies a relaxed speech synthesis algorithm. For example, in the case of an emotional message, the synthesis unit applies an emotionally emphasizing speech synthesis algorithm. By applying different speech synthesis algorithms depending on the content of the text, it is possible to generate more appropriate speech. Some or all of the above processing in the synthesis unit may be performed using a generation AI, for example, or without a generation AI. For example, the synthesis unit can input text data into a generation AI, which can then select the optimal speech synthesis algorithm.

[0092] The synthesis unit can generate the optimal voice by referring to the user's past voice history during speech synthesis. For example, the synthesis unit can generate the optimal voice based on the tone and pitch of voices the user has used in the past. For example, the synthesis unit can generate the optimal voice based on the voice style the user has preferred to use in the past. For example, the synthesis unit can generate the optimal voice for a specific situation from the user's past voice history. In this way, the optimal voice can be generated by referring to the user's past voice history. Some or all of the above processing in the synthesis unit may be performed using a generation AI, for example, or without a generation AI. For example, the synthesis unit can input the user's past voice data into a generation AI, and the generation AI can generate the optimal voice.

[0093] The synthesis unit can estimate the user's emotions and adjust the speed of the audio based on the estimated emotions. For example, if the user is relaxed, the synthesis unit will generate audio at a slow speed. For example, if the user is in a hurry, the synthesis unit will generate audio at a fast speed. For example, if the user is excited, the synthesis unit will generate audio at a moderate speed. By adjusting the speed of the audio according to the user's emotions, more appropriate audio can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the synthesis unit may be performed using a generative AI, or not using a generative AI. For example, the synthesis unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0094] The synthesis unit can determine the priority of speech based on the submission timing of the text during speech synthesis. For example, the synthesis unit prioritizes speech synthesis for urgent texts. For example, the synthesis unit determines the priority of speech synthesis based on the submission timing for periodic reports. For example, the synthesis unit determines the priority of speech synthesis based on a deadline specified by the user. This allows for the generation of more appropriate speech by prioritizing speech based on the submission timing of the text. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input text data into a generation AI, which can then determine the priority of speech.

[0095] The synthesis unit can adjust the order of speech based on the relevance of the text during speech synthesis. For example, the synthesis unit prioritizes speech synthesis of text containing important information. For example, the synthesis unit prioritizes speech synthesis of highly relevant text based on user interests. For example, the synthesis unit performs speech synthesis in the optimal order according to the content of the text. By adjusting the order of speech based on the relevance of the text, more appropriate speech can be generated. Some or all of the above processing in the synthesis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the synthesis unit can input text data into a generation AI, and the generation AI can determine the order of speech.

[0096] The analysis unit can estimate the user's emotions and adjust the method of analyzing visual information based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will analyze detailed visual information. For example, if the user is in a hurry, the analysis unit will prioritize the analysis of important visual information. For example, if the user is excited, the analysis unit will analyze visually stimulating information. By adjusting the method of analyzing visual information according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI, which can then estimate the emotions.

[0097] The analysis unit can select the optimal analysis method by referring to the user's past visual information history when analyzing visual information. For example, the analysis unit may select the optimal analysis method based on the visual information the user has previously preferred to view. For example, the analysis unit may select the optimal analysis method according to a specific situation from the user's past visual information history. For example, the analysis unit may select the optimal analysis method based on feedback from when the user previously analyzed visual information. In this way, the optimal analysis method can be selected by referring to the user's past visual information history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's past visual information data into a generating AI, and the generating AI can select the optimal analysis method.

[0098] The analysis unit can customize the means of analysis based on the user's current situation when analyzing visual information. For example, if the user is in a meeting, the analysis unit will prioritize analyzing business-related visual information. For example, if the user is relaxed, the analysis unit will prioritize analyzing entertainment-related visual information. For example, if the user is working on a specific project, the analysis unit will prioritize analyzing visual information related to that project. This allows for more appropriate analysis by customizing the means of analysis based on the user's current situation. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's current situation data into the generative AI, which can then customize the means of analysis.

[0099] The analysis unit can estimate the user's emotions and prioritize visual information based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing important visual information. For example, if the user is relaxed, the analysis unit will prioritize analyzing casual visual information. For example, if the user is in a hurry, the analysis unit will prioritize analyzing urgent visual information. This allows for more appropriate analysis by prioritizing visual information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0100] The analysis unit can select the optimal analysis method when analyzing visual information, taking into account the user's geographical location. For example, if the user is in a specific location, the analysis unit will prioritize analyzing visual information related to that location. For example, if the user is traveling, the analysis unit will prioritize analyzing visual information related to the travel destination. For example, if the user is at home, the analysis unit will prioritize analyzing visual information related to home. This allows the analysis unit to select the optimal analysis method by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then select the optimal analysis method.

[0101] The analysis unit can analyze the user's social media activity and propose analysis methods when analyzing visual information. For example, the analysis unit analyzes relevant visual information based on what the user has recently been talking about on social media. For example, if the user is using a specific hashtag, the analysis unit analyzes visual information related to that hashtag. For example, the analysis unit analyzes relevant visual information based on the content of posts from accounts the user follows on social media. By doing so, the analysis unit can propose the most suitable analysis method by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, which can then propose the most suitable analysis method.

[0102] The recognition unit can estimate the user's emotions and adjust the accuracy of emotion recognition based on the estimated user emotions. For example, the recognition unit improves the accuracy of emotion recognition when the user is relaxed. For example, the recognition unit prioritizes recognizing important emotions when the user is in a hurry. For example, the recognition unit quickly recognizes changes in emotions when the user is excited. By adjusting the accuracy of emotion recognition according to the user's emotions, more accurate emotion recognition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using a generative AI, or not using a generative AI. For example, the recognition unit can input user facial expression data into a generative AI, and the generative AI can estimate emotions.

[0103] The recognition unit can select the optimal recognition method by referring to the user's past emotional history when recognizing emotions. For example, the recognition unit may select the optimal recognition method based on the emotional patterns the user has shown in the past. For example, the recognition unit may select the optimal recognition method according to a specific situation from the user's past emotional history. For example, the recognition unit may select the optimal recognition method based on the feedback the user received when they recognized emotions in the past. In this way, the optimal recognition method can be selected by referring to the user's past emotional history. Some or all of the above processing in the recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recognition unit can input the user's past emotional data into a generative AI, and the generative AI can select the optimal recognition method.

[0104] The recognition unit can customize its recognition methods based on the user's current situation when recognizing emotions. For example, if the user is in a meeting, the recognition unit will prioritize recognizing business-related emotions. If the user is relaxed, the recognition unit will prioritize recognizing casual emotions. If the user is working on a specific project, the recognition unit will prioritize recognizing emotions related to that project. By customizing the recognition methods based on the user's current situation, more appropriate emotion recognition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using a generative AI, or not using a generative AI. For example, the recognition unit can input the user's facial expression data into a generative AI, which can then estimate emotions.

[0105] The recognition unit can estimate the user's emotions and determine the priority of emotion recognition based on the estimated user emotions. For example, if the user is stressed, the recognition unit will prioritize recognizing important emotions. For example, if the user is relaxed, the recognition unit will prioritize recognizing casual emotions. For example, if the user is in a hurry, the recognition unit will prioritize recognizing urgent emotions. This allows for more appropriate emotion recognition by determining the priority of emotion recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using a generative AI, or not using a generative AI. For example, the recognition unit can input the user's facial expression data into a generative AI, which can then estimate emotions.

[0106] The recognition unit can select the optimal recognition method when recognizing emotions, taking into account the user's geographical location information. For example, if the user is in a specific location, the recognition unit will prioritize recognizing emotions associated with that location. For example, if the user is traveling, the recognition unit will prioritize recognizing emotions associated with the travel destination. For example, if the user is at home, the recognition unit will prioritize recognizing emotions associated with home. This allows the recognition unit to select the optimal recognition method by taking into account the user's geographical location information. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using a generative AI, or not using a generative AI. For example, the recognition unit can input the user's geographical location information into the generative AI, which can then select the optimal recognition method.

[0107] The recognition unit can analyze the user's social media activity and propose a means of recognition when recognizing emotions. For example, the recognition unit can recognize relevant emotions based on what the user has recently been talking about on social media. For example, if the user is using a specific hashtag, the recognition unit can recognize emotions associated with that hashtag. For example, the recognition unit can recognize relevant emotions based on the content of posts from accounts the user follows on social media. In this way, by analyzing the user's social media activity, the recognition unit can propose the optimal means of recognition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using a generative AI, or not using a generative AI. For example, the recognition unit can input the user's social media activity data into a generative AI, which can then propose the optimal means of recognition.

[0108] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is relaxed, the translation unit will use a casual expression. If the user is in a business setting, the translation unit will use a formal expression. If the user wants to convey an emotional message, the translation unit will use an expression that emphasizes emotion. By adjusting the translation's expression according to the user's emotions, a more appropriate translation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input user facial expression data into a generative AI, which can then estimate the emotions.

[0109] The translation unit can apply different translation algorithms depending on the content of the text during translation. For example, the translation unit may apply a formal translation algorithm to business documents, a relaxed translation algorithm to casual conversations, and an emotionally emphasizing translation algorithm to emotional messages. By applying different translation algorithms depending on the content of the text, more appropriate translations can be achieved. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input text data into a generative AI, which can then select the optimal translation algorithm.

[0110] The translation unit can provide the optimal translation by referring to the user's past translation history during translation. For example, the translation unit can provide the optimal translation based on the translation style the user has used in the past. For example, the translation unit can provide the optimal translation for a specific situation based on the user's past translation history. For example, the translation unit can provide the optimal translation based on feedback from the user's past translations. In this way, the translation unit can provide the optimal translation by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input the user's past translation data into a generative AI, and the generative AI can provide the optimal translation.

[0111] The translation unit can estimate the user's emotions and adjust the translation speed based on the estimated emotions. For example, if the user is in a hurry, the translation unit will translate quickly. If the user is relaxed, the translation unit will translate at a slow pace. If the user wants to convey an emotional message, the translation unit will translate at a moderate speed. By adjusting the translation speed according to the user's emotions, more appropriate translations become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using a generative AI, or not using a generative AI. For example, the translation unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0112] The translation department can prioritize translations based on the submission date of the text. For example, it may prioritize urgent texts. For example, it may prioritize regular reports based on their submission date. For example, it may prioritize translations based on deadlines specified by the user. This allows for more appropriate translations by prioritizing translations based on the submission date of the text. Some or all of the above processes in the translation department may be performed using, for example, a generative AI, or not. For example, the translation department can input text data into a generative AI, which can then determine the translation priority.

[0113] The translation unit can adjust the order of translations based on the relevance of the texts during the translation process. For example, the translation unit may prioritize translating texts containing important information. For example, the translation unit may prioritize translating texts that are highly relevant based on the user's interests. For example, the translation unit may perform translations in the optimal order according to the content of the texts. By adjusting the order of translations based on the relevance of the texts, more appropriate translations become possible. Some or all of the above processes in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input text data into a generative AI, and the generative AI can determine the order of translations.

[0114] The service provider can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the service provider can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the service provider can provide an interface with bright colors to make the input process enjoyable. For example, if the user is tired, the service provider can provide a simple and highly visible interface to facilitate the input process. This allows for a more appropriate display by adjusting the interface display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user facial expression data into a generative AI, which can then estimate emotions.

[0115] The service provider can select the optimal display method when displaying the interface by referring to the user's past operation history. For example, the service provider can select the optimal display method based on the interface design that the user has previously preferred to use. For example, the service provider can select the optimal display method according to a specific situation from the user's past operation history. For example, the service provider can select the optimal display method based on the user's feedback when they previously used the interface. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past operation data into a generation AI, and the generation AI can select the optimal display method.

[0116] The service provider can customize the display methods based on the user's current situation when displaying interfaces. For example, if the user is in a meeting, the service provider will prioritize displaying business-related interfaces. For example, if the user is relaxing, the service provider will prioritize displaying entertainment-related interfaces. For example, if the user is working on a specific project, the service provider will prioritize displaying interfaces related to that project. This allows for a more appropriate display by customizing the display methods based on the user's current situation. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's current situation data into a generative AI, which can then customize the display methods.

[0117] The service provider can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, if the user is tense, the service provider may simplify the operation procedures to reduce visual stress. For example, if the user is enjoying themselves, the service provider may make the operation procedures more detailed to make the operation more enjoyable. For example, if the user is tired, the service provider may provide simple and highly visible operation procedures to make the operation easier. This allows for more appropriate operation by adjusting the interface's operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user facial expression data into a generative AI, which can then estimate emotions.

[0118] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. This allows the service provider to select the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0119] The service provider can analyze the user's social media activity and suggest display methods when displaying an interface. For example, the service provider can display relevant interfaces based on what the user has recently been talking about on social media. For example, if the user is using a specific hashtag, the service provider can display interfaces related to that hashtag. For example, the service provider can display relevant interfaces based on the content of posts from accounts the user follows on social media. In this way, by analyzing the user's social media activity, the service provider can suggest the most suitable display method. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then suggest the most suitable display method.

[0120] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, if the user is relaxed, the monitoring unit will perform detailed monitoring. If the user is in a hurry, the monitoring unit will prioritize monitoring important health data. If the user is excited, the monitoring unit will quickly monitor changes in emotions. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0121] The monitoring unit can select the optimal monitoring method by referring to the user's past health data during monitoring. For example, the monitoring unit selects the optimal monitoring method based on the health data the user has previously provided. For example, the monitoring unit selects the optimal monitoring method according to a specific situation from the user's past health data. For example, the monitoring unit selects the optimal monitoring method based on feedback from the user's past monitoring. In this way, the optimal monitoring method can be selected by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the monitoring unit can input the user's past health data into a generating AI, and the generating AI can select the optimal monitoring method.

[0122] The monitoring unit can customize the monitoring methods based on the user's current health status during monitoring. For example, if the user is tired, the monitoring unit will focus on monitoring heart rate and stress levels. For example, if the user is healthy, the monitoring unit will monitor general health data. For example, if the user is unwell, the monitoring unit will focus on monitoring data related to specific symptoms. This allows for more appropriate monitoring by customizing the monitoring methods based on the user's current health status. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's current health status data into a generative AI, which can then customize the monitoring methods.

[0123] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring the stress level. For example, if the user is relaxed, the monitoring unit will monitor general health data. For example, if the user is in a hurry, the monitoring unit will prioritize monitoring important health data. This allows for more appropriate monitoring by determining monitoring priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0124] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is in a specific location, the monitoring unit will prioritize monitoring health data related to that location. For example, if the user is traveling, the monitoring unit will prioritize monitoring health data related to the travel destination. For example, if the user is at home, the monitoring unit will prioritize monitoring health data related to home. This allows the optimal monitoring method to be selected by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI, which can then select the optimal monitoring method.

[0125] The monitoring unit can analyze a user's social media activity during monitoring and propose monitoring methods. For example, the monitoring unit monitors relevant health data based on health-related content that the user has recently been discussing on social media. For example, if a user is using a specific hashtag, the monitoring unit monitors health data related to that hashtag. For example, the monitoring unit monitors relevant health data based on the content of posts from accounts that the user follows on social media. By analyzing the user's social media activity, the monitoring unit can propose the most suitable monitoring method. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's social media activity data into a generative AI, which can then propose the most suitable monitoring method.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The SocialVoice system can further include a synthesis unit that estimates the user's emotions and adjusts the tone and pitch of the synthesized speech based on those emotions. For example, if the user is relaxed, the synthesis unit can generate a calm tone of voice. If the user is excited, the synthesis unit can generate a bright and energetic tone of voice. Furthermore, if the user is sad, the synthesis unit can generate a gentle tone of voice. This allows for the generation of more appropriate speech by adjusting the tone and pitch of the voice according to the user's emotions.

[0128] The SocialVoice system can also include a reception unit that analyzes the user's past text input history and selects the optimal input method. For example, it can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. It can also reduce input effort by performing predictive input based on the user's past input content. Furthermore, it can predict and suggest input methods to be used during specific time periods based on the user's past input history. In this way, the system can select the optimal input method by analyzing the user's past text input history.

[0129] The SocialVoice system may also include an analysis unit that estimates the user's emotions and adjusts the method of analyzing visual information based on those emotions. For example, if the user is relaxed, the analysis unit can analyze detailed visual information. If the user is in a hurry, the analysis unit can prioritize the analysis of important visual information. Furthermore, if the user is excited, the analysis unit can analyze visually stimulating information. By adjusting the method of analyzing visual information according to the user's emotions, more appropriate analysis becomes possible.

[0130] The SocialVoice system can also include a reception section that prioritizes inputting highly relevant text by considering the user's geographical location. For example, if the user is in a specific location, text related to that location can be prioritized. Similarly, if the user is traveling, text related to their travel destination can be prioritized. Furthermore, if the user is at home, text related to their home can be prioritized. This allows for the prioritization of highly relevant text by considering the user's geographical location.

[0131] The SocialVoice system can further include a recognition unit that estimates the user's emotions and adjusts the accuracy of emotion recognition based on the estimated emotions. For example, when the user is relaxed, the recognition unit can improve the accuracy of emotion recognition. Also, when the user is in a hurry, the recognition unit can prioritize the recognition of important emotions. Furthermore, when the user is excited, the recognition unit can quickly recognize changes in emotions. This allows for more accurate emotion recognition by adjusting the accuracy of emotion recognition according to the user's emotions.

[0132] The SocialVoice system can also include an analysis unit that selects the optimal analysis method by referring to the user's past visual information history. For example, it can select the optimal analysis method based on the visual information the user has previously viewed with preference. It can also select the optimal analysis method for a specific situation based on the user's past visual information history. Furthermore, it can select the optimal analysis method based on feedback from when the user previously analyzed visual information. In this way, the optimal analysis method can be selected by referring to the user's past visual information history.

[0133] The SocialVoice system can also include a translation unit that estimates the user's emotions and adjusts the translation's expression based on those emotions. For example, if the user is relaxed, the translation unit can translate using casual language. If the user is in a business setting, the translation unit can translate using formal language. Furthermore, if the user wants to convey an emotional message, the translation unit can translate using language that emphasizes those emotions. This allows for more appropriate translations by adjusting the translation's expression according to the user's emotions.

[0134] The SocialVoice system can also include a translation unit that provides the optimal translation by referring to the user's past translation history. For example, it can provide the optimal translation based on the translation style the user has used in the past. It can also provide the optimal translation for a specific situation based on the user's past translation history. Furthermore, it can provide the optimal translation based on feedback from the user's past translations. In this way, the system can provide the optimal translation by referring to the user's past translation history.

[0135] The SocialVoice system can further include a component that estimates the user's emotions and adjusts the interface display based on those emotions. For example, if the user is stressed, the component can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the component can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the component can provide a simple and highly visible interface to facilitate the input process. This allows for a more appropriate display by adjusting the interface display according to the user's emotions.

[0136] The SocialVoice system can further include a provisioning unit that analyzes the user's social media activity and suggests display methods. For example, it can display relevant interfaces based on what the user has recently been talking about on social media. It can also display interfaces related to specific hashtags if the user is using them. Furthermore, it can display relevant interfaces based on the content of posts from accounts the user follows on social media. This allows the system to suggest the most suitable display method by analyzing the user's social media activity.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The reception desk accepts text input. Text input includes keyboard input, voice input, and handwriting input. For example, it accepts text entered by the user using a keyboard. It can also convert spoken content from the user into text using voice input and accept it. Furthermore, it can convert written content from the user into text using handwriting input and accept it. Step 2: The synthesis unit converts the text received by the reception unit into speech. The synthesis unit converts text into speech using speech synthesis technology. For example, it can generate natural-sounding speech using a deep learning-based speech synthesis algorithm. It can also generate speech using a rule-based speech synthesis algorithm or a hybrid speech synthesis algorithm. Step 3: The analysis unit analyzes the visual information. Visual information includes images, videos, and real-time camera footage. The analysis unit analyzes the visual information using image recognition technology. For example, it recognizes objects in an image using a deep learning-based image recognition algorithm. It can also analyze images using rule-based image recognition algorithms or hybrid image recognition algorithms. Step 4: The recognition unit recognizes emotions. Emotion recognition includes facial expression recognition, speech analysis, and text analysis. The recognition unit recognizes emotions using facial expression recognition technology. For example, it recognizes emotions from the user's facial expressions using a deep learning-based facial expression recognition algorithm. It can also recognize emotions using speech analysis technology and text analysis technology.

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0140] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0141] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0142] Each of the multiple elements described above, including the reception unit, synthesis unit, analysis unit, recognition unit, translation unit, provision unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives text input from the user. The synthesis unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts text into speech. The analysis unit analyzes visual information using the camera 42 of the smart device 14. The recognition unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes emotions. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates text in real time. The provision unit is implemented by the control unit 46A of the smart device 14 and provides a customizable interface. The monitoring unit monitors the user's health status using the sensors of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, synthesis unit, analysis unit, recognition unit, translation unit, provision unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives text input from the user. The synthesis unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts text into speech. The analysis unit analyzes visual information using the camera 42 of the smart glasses 214. The recognition unit is implemented by the identification processing unit 290 of the data processing unit 12 and recognizes emotions. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and translates text in real time. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides a customizable interface. The monitoring unit monitors the user's health status using the sensors of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, synthesis unit, analysis unit, recognition unit, translation unit, provision unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives text input from the user. The synthesis unit is implemented by the identification processing unit 290 of the data processing unit 12 and converts text into speech. The analysis unit analyzes visual information using the camera 42 of the headset terminal 314. The recognition unit is implemented by the identification processing unit 290 of the data processing unit 12 and recognizes emotions. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and translates text in real time. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides a customizable interface. The monitoring unit monitors the user's health status using the sensors of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0177] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0179] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0181] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0182] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0183] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0184] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0185] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0186] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0187] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0188] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0189] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0190] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0191] Each of the multiple elements described above, including the reception unit, synthesis unit, analysis unit, recognition unit, translation unit, provision unit, and monitoring unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives text input from the user. The synthesis unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts text into speech. The analysis unit analyzes visual information using the camera 42 of the robot 414. The recognition unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes emotions. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates text in real time. The provision unit is implemented by the control unit 46A of the robot 414 and provides a customizable interface. The monitoring unit monitors the user's health status using the sensors of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0192] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0194] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0195] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0196] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0200] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0201] 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.

[0202] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0203] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0204] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0205] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0207] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0208] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0209] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0210] (Note 1) A reception area that accepts text input, A synthesis unit that converts text received by the reception unit into speech, It comprises an analysis unit that analyzes visual information and a recognition unit that recognizes emotions. A system characterized by the following features. (Note 2) Equipped with a translation department that performs real-time translations. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a component that provides a customizable interface. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a monitoring unit for wellness monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned synthesis section is Convert text to speech The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze visual information The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recognition unit, Recognizing emotions The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It includes a reception unit that estimates the user's emotions and adjusts the timing of text input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past text input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering text, the input content is filtered based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It includes a reception unit that estimates the user's emotions and determines the priority of the input text based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering text, the system prioritizes inputting highly relevant text by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering text, the system analyzes the user's social media activity and inputs relevant text. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned synthesis section is It includes a synthesis unit that estimates the user's emotions and adjusts the tone and pitch of the voice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned synthesis section is Apply different speech synthesis algorithms depending on the content of the text. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned synthesis section is During speech synthesis, the system generates the optimal voice by referring to the user's past speech history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned synthesis section is It includes a synthesis unit that estimates the user's emotions and adjusts the speed of the speech based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned synthesis section is During speech synthesis, the priority of speech is determined based on when the text was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned synthesis section is During speech synthesis, the order of speech is adjusted based on the relevance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, It includes an analysis unit that estimates the user's emotions and adjusts the method of analyzing visual information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing visual information, the system selects the optimal analysis method by referring to the user's past visual information history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, When analyzing visual information, the analysis method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It includes an analysis unit that estimates the user's emotions and determines the priority of visual information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, When analyzing visual information, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, When analyzing visual information, we analyze users' social media activity and propose methods for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recognition unit, It includes a recognition unit that estimates the user's emotions and adjusts the accuracy of emotion recognition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recognition unit, During emotion recognition, the system selects the optimal recognition method by referring to the user's past emotional history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recognition unit, When recognizing emotions, the means of recognition are customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recognition unit, It includes a recognition unit that estimates the user's emotions and determines the priority of emotion recognition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recognition unit, When recognizing emotions, the system selects the optimal recognition method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recognition unit, When recognizing emotions, we analyze users' social media activity and propose methods for recognition. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned translation department, It includes a translation unit that estimates the user's emotions and adjusts the translation's expression based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned translation department, During translation, different translation algorithms are applied depending on the content of the text. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned translation department, During translation, the system provides the best possible translation by referencing the user's past translation history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned translation department, It includes a translation unit that estimates the user's emotions and adjusts the translation speed based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned translation department, During the translation process, translation priorities are determined based on the submission date of the text. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the text. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned supply unit is, It includes a component that estimates the user's emotions and adjusts the interface display method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned supply unit is, When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned supply unit is, When displaying the interface, customize the display method based on the user's current status. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned supply unit is, It includes a component that estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned supply unit is, When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned supply unit is, When displaying the interface, the system analyzes the user's social media activity and suggests display methods. The system described in Appendix 3, characterized by the features described herein. (Note 44) The monitoring unit, It includes a monitoring unit that estimates the user's emotions and adjusts the monitoring method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The monitoring unit, During monitoring, the system selects the optimal monitoring method by referring to the user's past health data. The system described in Appendix 4, characterized by the features described herein. (Note 46) The monitoring unit, During monitoring, the monitoring methods are customized based on the user's current health status. The system described in Appendix 4, characterized by the features described herein. (Note 47) The monitoring unit, It includes a monitoring unit that estimates the user's emotions and determines the monitoring priority based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. (Note 49) The monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts text input, A synthesis unit that converts text received by the reception unit into speech, It comprises an analysis unit that analyzes visual information and a recognition unit that recognizes emotions. A system characterized by the following features.

2. Equipped with a translation department that performs real-time translations. The system according to feature 1.

3. It includes a component that provides a customizable interface. The system according to feature 1.

4. Equipped with a monitoring unit for wellness monitoring. The system according to feature 1.

5. The aforementioned synthesis section is Convert text to speech The system according to feature 1.

6. The aforementioned analysis unit, Analyze visual information The system according to feature 1.

7. The aforementioned recognition unit, Recognizing emotions The system according to feature 1.

8. The aforementioned reception unit is It includes a reception unit that estimates the user's emotions and adjusts the timing of text input based on the estimated user emotions. The system according to feature 1.

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