system

Abstract

The system according to this embodiment aims to convert information from a user into a format suitable for the recipient and transmit it. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a conversion unit, and an output unit. The reception unit receives voice, video, and text input from the user. The analysis unit analyzes the information received by the reception unit and converts it into a format suitable for the recipient. The conversion unit converts the voice, video, and text into a format suitable for the recipient based on the information generated by the analysis unit. The output unit transmits the information converted by the conversion unit to the recipient.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that communication with hearing-impaired people or foreign language speakers is difficult and there is a lack of means to transmit information in an appropriate form.

[0005] The system according to the embodiment aims to convert information from a user into an appropriate form and transmit it to the other party.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a conversion unit, and an output unit. The reception unit receives voice, video, and text input from the user. The analysis unit analyzes the information received by the reception unit and converts it into a format suitable for the recipient. The conversion unit converts the voice, video, and text into a format suitable for the recipient based on the information generated by the analysis unit. The output unit transmits the information converted by the conversion unit to the recipient. [Effects of the Invention]

[0007] The system according to this embodiment can convert information from a user into a format suitable for the recipient and transmit it. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 multi-translation app according to an embodiment of the present invention is intended for people in Japan who have difficulty making phone calls in Japanese, such as those with hearing impairments or inbound tourists who do not speak Japanese. Unlike general translation functions, this app enables smooth communication by converting and transmitting audio, video, and text in a format suitable for the recipient. Specifically, it consists of the following steps: First, the user inputs information using audio, video, or text. For example, when a person with a hearing impairment makes a restaurant reservation, they input the reservation details in text. This input is received by the app. Next, the app analyzes the input information and converts it into a format suitable for the recipient. For example, it converts the text-input reservation details into audio and transmits them to the restaurant staff. Also, if an inbound tourist does not speak Japanese, it translates the English input into Japanese and transmits it verbally. Furthermore, the app receives the response from the other party, converts it into a format suitable for the user, and returns it. For example, it converts the verbal response from the restaurant staff into text and displays it to the person with a hearing impairment. It also translates the Japanese response into English and transmits it verbally to the inbound tourist. This system allows users to smoothly carry out what they want to do, improving their quality of life. It also offers expanded business opportunities for participating businesses. For example, people with hearing impairments can make restaurant reservations more easily, leading to an increase in restaurant customers. Furthermore, inbound tourists can use services without feeling the language barrier, stimulating the tourism industry. This app is equipped with advanced technology for the mutual conversion of voice, video, and text, enabling flexible responses to user needs. For instance, it uses speech recognition and natural language processing technologies to accurately analyze input information and convert it into the appropriate format. It can also use video analysis technology to analyze visual information such as sign language and convert it into voice or text. Thus, this invention provides smooth communication for people in Japan who have difficulty making phone calls in Japanese, improving their quality of life and expanding business opportunities for participating businesses. As a result, the multi-translation app can flexibly respond to user needs and provide smooth communication.

[0029] The multi-translation application according to this embodiment comprises a reception unit, an analysis unit, a conversion unit, and an output unit. The reception unit receives voice, video, and text input from the user. User input includes, but is not limited to, voice, video, and text. For example, the reception unit receives voice input via a microphone. The reception unit can also receive video input via a camera. Furthermore, the reception unit can also receive text input via a keyboard or touchscreen. For example, the reception unit receives voice input via a high-precision microphone and acquires clear audio data using noise-canceling technology. Video input is received via a high-resolution camera, and visual information such as sign language is analyzed using video analysis technology. Text input can be directly entered by the user using a touchscreen or keyboard. The analysis unit analyzes the information received by the reception unit and generates information for conversion into a format suitable for the recipient. The analysis unit accurately analyzes the input information using, for example, speech recognition technology or natural language processing technology. For example, the analysis unit converts voice input into text data using speech recognition technology. Furthermore, the analysis unit can analyze text data and understand its meaning using natural language processing technology. In addition, the analysis unit can analyze visual information such as sign language using video analysis technology and convert it into text data. For example, the analysis unit performs highly accurate speech recognition using deep learning-based speech recognition technology. Natural language processing technology understands the meaning of text data using morphological and grammatical analysis. Video analysis technology analyzes visual information such as sign language using computer vision technology and converts it into text data. The conversion unit converts audio, video, and text into a format suitable for the recipient based on the information generated by the analysis unit. For example, the conversion unit converts text data into audio. The conversion unit can also convert text data into video. Furthermore, the conversion unit can translate text data into other languages. For example, the conversion unit converts text data into natural-sounding speech using text generation AI (e.g., LLM). It converts text data into video using video generation technology. It translates text data into other languages ​​using translation technology. The output unit transmits the information converted by the conversion unit to the recipient.The output unit can, for example, output audio through a speaker. It can also output video through a display. Furthermore, it can display text on a screen. For example, the output unit can output clear audio using a high-quality speaker. It can display video in high resolution using a display. It can display text using a highly legible font on a screen. This allows the multi-translation application according to the embodiment to respond flexibly to user needs and provide smooth communication.

[0030] The reception desk accepts voice, video, and text input from users. User input includes, but is not limited to, voice, video, and text. For example, the reception desk can accept voice input via a microphone. It can also accept video input via a camera. Furthermore, it can accept text input via a keyboard or touchscreen. For example, the reception desk can accept voice input with a high-precision microphone and acquire clear audio data using noise cancellation technology. Video input is received with a high-resolution camera, and visual information such as sign language is analyzed using video analysis technology. Text input can be entered directly by the user using a touchscreen or keyboard. The reception desk integrates these input methods to smoothly accept information regardless of the format in which the user provides it. For example, in the case of voice input, the microphone is equipped with advanced noise cancellation technology to remove ambient noise and clearly capture the user's voice. In the case of video input, the camera captures video at high resolution and can accurately capture visual information such as sign language and facial expressions. In the case of text input, the touchscreen or keyboard is designed to allow the user to enter text quickly and accurately. Furthermore, the reception unit is equipped with high-speed data transfer technology to process this input data in real time and transmit it to the analysis unit. This allows the system to respond quickly and accurately regardless of the format in which the user provides the information.

[0031] The analysis unit analyzes the information received by the reception unit and generates information to convert it into a format suitable for the recipient. The analysis unit accurately analyzes the input information using, for example, speech recognition technology and natural language processing technology. For example, the analysis unit uses speech recognition technology to convert voice input into text data. The analysis unit can also use natural language processing technology to analyze text data and understand its meaning. Furthermore, the analysis unit can use video analysis technology to analyze visual information such as sign language and convert it into text data. For example, the analysis unit performs highly accurate speech recognition using deep learning-based speech recognition technology. Natural language processing technology uses morphological and grammatical analysis to understand the meaning of text data. Video analysis technology uses computer vision technology to analyze visual information such as sign language and convert it into text data. The analysis unit can combine these technologies to analyze complex information quickly and accurately. For example, speech recognition technology converts user speech into text in real time, and that text is analyzed by natural language processing technology to understand the context and intent. Video analysis technology analyzes visual information such as sign language and facial expressions and converts it into text data. This allows the analysis unit to handle input in any format—audio, video, or text—and generate information to convert it into a format suitable for the recipient. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and user history. For example, by learning from past audio and text data and understanding the user's speech patterns and context, it can perform more accurate analysis. This enables the analysis unit to respond flexibly to user needs and provide smoother communication.

[0032] The conversion unit converts audio, video, and text into formats suitable for the recipient based on the information generated by the analysis unit. For example, the conversion unit converts text data into audio. It can also convert text data into video. Furthermore, the conversion unit can translate text data into other languages. For instance, the conversion unit uses text generation AI (e.g., LLM) to convert text data into natural-sounding speech. It uses video generation technology to convert text data into video. It uses translation technology to translate text data into other languages. The conversion unit can combine these technologies to provide flexible solutions tailored to user needs. For example, text generation AI considers the user's speaking style and context to generate natural and fluent speech. Video generation technology generates and provides users with videos that include visual information such as sign language and facial expressions. Translation technology supports multiple languages ​​and can translate text data quickly and accurately. Furthermore, the conversion unit possesses high-speed processing capabilities to enable real-time conversion. This allows for the instant conversion of user-input information into a format suitable for the recipient, facilitating smooth communication. For example, information entered by a user via voice can be converted into text in real time, that text can be translated into another language, and then transmitted to the other party via voice. This allows the conversion unit to respond flexibly to the user's needs, enabling smooth communication.

[0033] The output unit transmits the information converted by the conversion unit to the recipient. For example, the output unit can output audio via a speaker. It can also output video via a display. Furthermore, it can display text on a screen. For instance, the output unit can use high-quality speakers to produce clear audio, a display to show high-resolution video, or a screen to display text in a highly legible font. The output unit integrates these output methods to ensure that users can smoothly understand information regardless of the format in which it is received. For example, in the case of audio output, high-quality speakers reproduce clear and natural sound, conveying accurate information to the user. In the case of video output, a high-resolution display clearly displays visual information such as sign language and facial expressions, conveying accurate information to the user. In the case of text output, the screen uses a highly legible font, making it easy for users to read the information. Furthermore, the output unit can collect user feedback and continuously improve the accuracy and effectiveness of its output. For example, if the user provides feedback on the volume and sound quality of the audio output, the output unit can adjust the settings accordingly to achieve better audio output. Furthermore, the output unit can reliably transmit information to the user by using multiple output methods in combination. For example, by simultaneously outputting audio and text, the user can more reliably understand the information. This allows the output unit to provide information to the user quickly and reliably, enabling smooth communication.

[0034] The analysis unit can accurately analyze input information using speech recognition technology and natural language processing technology. For example, the analysis unit can convert speech input into text data using speech recognition technology. For example, the analysis unit can perform highly accurate speech recognition using deep learning-based speech recognition technology. The analysis unit can also analyze text data and understand its meaning using natural language processing technology. For example, the analysis unit can understand the meaning of text data using morphological analysis and grammatical analysis. Furthermore, the analysis unit can analyze visual information such as sign language using video analysis technology and convert it into text data. For example, the analysis unit can analyze visual information such as sign language using computer vision technology and convert it into text data. As a result, the accuracy of input information analysis is improved by using speech recognition technology and natural language processing technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input speech data into a generating AI and have the generating AI perform the conversion from speech data to text data.

[0035] The conversion unit can analyze visual information, such as sign language, using video analysis technology and convert it into speech or text. For example, the conversion unit can analyze visual information, such as sign language, using video analysis technology and convert it into text data. For example, the conversion unit can analyze visual information, such as sign language, using computer vision technology and convert it into text data. Furthermore, the conversion unit can convert text data into speech. For example, the conversion unit can convert text data into natural-sounding speech using text generation AI (e.g., LLM). In addition, the conversion unit can translate text data into other languages. For example, the conversion unit can translate text data into other languages ​​using translation technology. This allows for accurate conversion of visual information using video analysis technology. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input video data into a generation AI and have the generation AI perform the conversion from video data to text data.

[0036] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can customize input methods based on the content the user has entered in the past. For example, the reception desk can prioritize suggesting voice input, which the user has frequently used in the past. It can predict and suggest input methods to be used during specific time periods based on the user's past input history. It can customize input methods based on the content the user has entered in the past. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into a generating AI and have the generating AI select the optimal input method.

[0037] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can prioritize receiving relevant information based on the user's current location. It can also prioritize receiving relevant input based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate input methods based on the user's current situation (e.g., in a meeting, traveling). For example, the reception unit prioritizes receiving relevant information based on the user's current location. It prioritizes receiving relevant input based on the user's areas of interest. It suggests appropriate input methods based on the user's current situation. This allows for the priority reception of highly relevant information by filtering based on the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0038] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving information related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving information about their home area. This allows for the priority of receiving highly relevant information by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input geographical location data into a generating AI and have the generating AI prioritize highly relevant inputs.

[0039] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on information the user has shared on social media. The reception unit can also analyze the activity of the user's social media followers and friends and accept relevant inputs. Furthermore, the reception unit can accept relevant inputs based on topics the user has shown interest in on social media. For example, the reception unit can prioritize accepting relevant inputs based on information the user has shared on social media. It can analyze the activity of the user's social media followers and friends and accept relevant inputs. It can accept relevant inputs based on topics the user has shown interest in on social media. This allows for the priority acceptance of relevant inputs by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input social media activity data into a generating AI and have the generating AI prioritize relevant inputs.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. For example, the analysis unit performs a detailed analysis for important information. The analysis unit can also perform a simplified analysis for general information. Furthermore, the analysis unit can perform a rapid analysis for urgent information. For example, the analysis unit performs a detailed analysis for important information. It performs a simplified analysis for general information. It performs a rapid analysis for urgent information. This allows for a detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the input information during analysis. For example, the analysis unit can apply a speech recognition algorithm to audio information. It can also apply a video analysis algorithm to video information. Furthermore, it can apply a natural language processing algorithm to text information. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of the input information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of the input information into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the submission timing of the input information during analysis. For example, the analysis unit may prioritize the analysis of the most recent information. It can also prioritize the analysis of urgent information. Furthermore, the analysis unit can analyze current information by referring to past information. For example, the analysis unit may prioritize the analysis of the most recent information, prioritize the analysis of urgent information, and analyze current information by referring to past information. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of analysis based on the submission timing of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can group relevant information together for analysis. For example, the analysis unit can prioritize the analysis of highly relevant information, postpone the analysis of less relevant information, and group relevant information together for analysis. This allows the analysis unit to prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0044] The conversion unit can improve the accuracy of the conversion by considering the interrelationships of the input information during the conversion process. For example, the conversion unit can perform accurate conversions by considering the interrelationships between audio and text information. Furthermore, the conversion unit can also perform accurate conversions by considering the interrelationships between video and audio information. In addition, the conversion unit can perform accurate conversions by considering the interrelationships between text and video information. For example, the conversion unit can perform accurate conversions by considering the interrelationships between audio and text information. It can perform accurate conversions by considering the interrelationships between video and audio information. It can perform accurate conversions by considering the interrelationships between text and video information. This improves the accuracy of the conversion by considering the interrelationships of the input information. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input data on the interrelationships of the input information into a generating AI and have the generating AI perform the conversion accuracy improvement.

[0045] The conversion unit can perform conversions while considering the attribute information of the submitter of the input information. For example, the conversion unit can perform appropriate conversions based on the submitter's age. The conversion unit can also perform appropriate conversions based on the submitter's language ability. Furthermore, the conversion unit can also perform appropriate conversions based on the submitter's expertise. For example, the conversion unit can perform appropriate conversions based on the submitter's age. For example, it can perform appropriate conversions based on the submitter's language ability. For example, it can perform appropriate conversions based on the submitter's expertise. This allows for the provision of more appropriate conversion results by considering the submitter's attribute information. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the conversion.

[0046] The conversion unit can perform conversions while considering the geographical distribution of the input information. For example, the conversion unit can prioritize the conversion of geographically close information. It can also postpone the conversion of geographically distant information. Furthermore, the conversion unit can perform appropriate conversions based on the geographical distribution. For example, the conversion unit can prioritize the conversion of geographically close information, postpone the conversion of geographically distant information, and perform appropriate conversions based on the geographical distribution. This allows for the provision of more appropriate conversion results by considering the geographical distribution. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input geographical distribution data into a generating AI and have the generating AI perform the conversion.

[0047] The conversion unit can improve the accuracy of the conversion by referring to related literature of the input information during the conversion process. For example, the conversion unit can perform an accurate conversion by referring to related literature. The conversion unit can also perform an appropriate conversion based on related literature. Furthermore, the conversion unit can improve the accuracy of the conversion by referring to related literature. For example, the conversion unit can perform an accurate conversion by referring to related literature. It can perform an appropriate conversion based on related literature. It can improve the accuracy of the conversion by referring to related literature. As a result, the accuracy of the conversion is improved by referring to related literature. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input related literature data into a generating AI and have the generating AI perform the conversion.

[0048] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods previously used by the user. The output unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the output unit can analyze the user's past operation history and provide an appropriate display method. For example, the output unit can prioritize providing display methods previously used by the user. It can suggest the optimal display method based on the user's past operation history. It can analyze the user's past operation history and provide an appropriate display method. In this way, by referring to past operation history, the output unit can provide the user with the optimal display method. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input past operation history data into a generating AI and have the generating AI select the optimal display method.

[0049] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. This allows the output unit to provide the optimal display method by considering device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input device information data into a generating AI and have the generating AI select the optimal display method.

[0050] The output unit can provide a multilingual display according to the user's language settings when outputting. For example, the output unit can automatically set the display language based on the language settings of the user's device. The output unit can also provide a language switching function if the user uses multiple languages. Furthermore, the output unit can provide a display in a specific language if the user selects one. For example, the output unit can automatically set the display language based on the language settings of the user's device. It can provide a language switching function if the user uses multiple languages. It can provide a display in a specific language if the user selects one. This makes it possible to provide information tailored to the user by providing a multilingual display according to language settings. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input language setting data into a generating AI and have the generating AI execute a multilingual display.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze background noise and perform noise cancellation when receiving user voice input. For example, if the user is using voice input in a noisy environment, the reception unit can analyze the background noise and use noise cancellation technology to obtain clear audio data. Furthermore, if the user is using voice input in a quiet environment, the reception unit can analyze the background noise and minimize noise cancellation. Additionally, if the user is using voice input while moving, the reception unit can analyze the background noise and cancel out noise associated with movement. This allows the reception unit to adjust noise cancellation according to the user's environment and obtain clear audio data.

[0053] The analysis unit can automatically search for relevant information based on user input and add it to the analysis results. For example, if a user enters a restaurant reservation, the analysis unit can automatically search for relevant information such as the restaurant's menu and opening hours and add it to the analysis results. Similarly, if a user enters information about a tourist destination, the analysis unit can automatically search for relevant information such as how to access that destination and nearby tourist attractions and add it to the analysis results. Furthermore, if a user enters information about an event, the analysis unit can automatically search for relevant information such as event details and ticket information and add it to the analysis results. This allows the analysis unit to provide more comprehensive information by automatically searching for relevant information based on user input and adding it to the analysis results.

[0054] The conversion unit can analyze user input and convert it to an appropriate format, providing a customized format based on user preferences. For example, the conversion unit can display text data using the user's preferred font and color. It can also generate audio data using the user's preferred tone and speed. Furthermore, it can generate video data using the user's preferred video style and effects. This allows the conversion unit to provide a more satisfying information experience by offering a customized format based on user preferences.

[0055] The reception desk can analyze a user's past input history and select the most suitable 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 predict and suggest input methods that a user will use at specific times of the day based on their past input history. Furthermore, the reception desk can customize input methods based on the user's past input. This allows the system to provide users with the most optimal input method by analyzing their past input history.

[0056] The reception system can filter input based on the user's current situation and areas of interest. For example, it can prioritize receiving relevant information based on the user's current location. It can also prioritize input based on the user's areas of interest. Furthermore, it can suggest appropriate input methods based on the user's current situation (e.g., in a meeting, traveling). This allows for the priority of receiving highly relevant information by filtering based on the user's situation and areas of interest.

[0057] The reception desk can prioritize receiving highly relevant input by considering the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving information related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving information about their home area. This allows the reception desk to prioritize receiving highly relevant information by considering geographical location.

[0058] The reception desk can analyze the user's social media activity when receiving input and accept relevant input. For example, the reception desk can prioritize accepting relevant input based on information the user has shared on social media. It can also analyze the activity of the user's social media followers and friends and accept relevant input. Furthermore, the reception desk can accept relevant input based on topics the user has shown interest in on social media. This allows for the prioritization of relevant input by analyzing social media activity.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception area receives voice, video, and text input from the user. For example, voice input is received via a microphone, video input via a camera, and text input via a keyboard or touchscreen. Furthermore, noise cancellation technology is used for voice input, and a high-resolution camera and video analysis technology are used for video input. Step 2: The analysis unit analyzes the information received by the reception unit and generates information to convert it into a format suitable for the recipient. For example, it converts voice input into text data using speech recognition technology and natural language processing technology, and converts visual information such as sign language into text data using video analysis technology. Step 3: The conversion unit converts audio, video, and text into a format suitable for the recipient based on the information generated by the analysis unit. For example, it converts text data to audio, video, or translates it into another language. Step 4: The output unit transmits the information converted by the conversion unit to the recipient. For example, audio output is performed by a speaker, video output by a display, and text output is displayed on a screen.

[0061] (Example of form 2) The multi-translation app according to an embodiment of the present invention is intended for people in Japan who have difficulty making phone calls in Japanese, such as those with hearing impairments or inbound tourists who do not speak Japanese. Unlike general translation functions, this app enables smooth communication by converting and transmitting audio, video, and text in a format suitable for the recipient. Specifically, it consists of the following steps: First, the user inputs information using audio, video, or text. For example, when a person with a hearing impairment makes a restaurant reservation, they input the reservation details in text. This input is received by the app. Next, the app analyzes the input information and converts it into a format suitable for the recipient. For example, it converts the text-input reservation details into audio and transmits them to the restaurant staff. Also, if an inbound tourist does not speak Japanese, it translates the English input into Japanese and transmits it verbally. Furthermore, the app receives the response from the other party, converts it into a format suitable for the user, and returns it. For example, it converts the verbal response from the restaurant staff into text and displays it to the person with a hearing impairment. It also translates the Japanese response into English and transmits it verbally to the inbound tourist. This system allows users to smoothly carry out what they want to do, improving their quality of life. It also offers expanded business opportunities for participating businesses. For example, people with hearing impairments can make restaurant reservations more easily, leading to an increase in restaurant customers. Furthermore, inbound tourists can use services without feeling the language barrier, stimulating the tourism industry. This app is equipped with advanced technology for the mutual conversion of voice, video, and text, enabling flexible responses to user needs. For instance, it uses speech recognition and natural language processing technologies to accurately analyze input information and convert it into the appropriate format. It can also use video analysis technology to analyze visual information such as sign language and convert it into voice or text. Thus, this invention provides smooth communication for people in Japan who have difficulty making phone calls in Japanese, improving their quality of life and expanding business opportunities for participating businesses. As a result, the multi-translation app can flexibly respond to user needs and provide smooth communication.

[0062] The multi-translation application according to this embodiment comprises a reception unit, an analysis unit, a conversion unit, and an output unit. The reception unit receives voice, video, and text input from the user. User input includes, but is not limited to, voice, video, and text. For example, the reception unit receives voice input via a microphone. The reception unit can also receive video input via a camera. Furthermore, the reception unit can also receive text input via a keyboard or touchscreen. For example, the reception unit receives voice input via a high-precision microphone and acquires clear audio data using noise-canceling technology. Video input is received via a high-resolution camera, and visual information such as sign language is analyzed using video analysis technology. Text input can be directly entered by the user using a touchscreen or keyboard. The analysis unit analyzes the information received by the reception unit and generates information for conversion into a format suitable for the recipient. The analysis unit accurately analyzes the input information using, for example, speech recognition technology or natural language processing technology. For example, the analysis unit converts voice input into text data using speech recognition technology. Furthermore, the analysis unit can analyze text data and understand its meaning using natural language processing technology. In addition, the analysis unit can analyze visual information such as sign language using video analysis technology and convert it into text data. For example, the analysis unit performs highly accurate speech recognition using deep learning-based speech recognition technology. Natural language processing technology understands the meaning of text data using morphological and grammatical analysis. Video analysis technology analyzes visual information such as sign language using computer vision technology and converts it into text data. The conversion unit converts audio, video, and text into a format suitable for the recipient based on the information generated by the analysis unit. For example, the conversion unit converts text data into audio. The conversion unit can also convert text data into video. Furthermore, the conversion unit can translate text data into other languages. For example, the conversion unit converts text data into natural-sounding speech using text generation AI (e.g., LLM). It converts text data into video using video generation technology. It translates text data into other languages ​​using translation technology. The output unit transmits the information converted by the conversion unit to the recipient.The output unit can, for example, output audio through a speaker. It can also output video through a display. Furthermore, it can display text on a screen. For example, the output unit can output clear audio using a high-quality speaker. It can display video in high resolution using a display. It can display text using a highly legible font on a screen. This allows the multi-translation application according to the embodiment to respond flexibly to user needs and provide smooth communication.

[0063] The reception desk accepts voice, video, and text input from users. User input includes, but is not limited to, voice, video, and text. For example, the reception desk can accept voice input via a microphone. It can also accept video input via a camera. Furthermore, it can accept text input via a keyboard or touchscreen. For example, the reception desk can accept voice input with a high-precision microphone and acquire clear audio data using noise cancellation technology. Video input is received with a high-resolution camera, and visual information such as sign language is analyzed using video analysis technology. Text input can be entered directly by the user using a touchscreen or keyboard. The reception desk integrates these input methods to smoothly accept information regardless of the format in which the user provides it. For example, in the case of voice input, the microphone is equipped with advanced noise cancellation technology to remove ambient noise and clearly capture the user's voice. In the case of video input, the camera captures video at high resolution and can accurately capture visual information such as sign language and facial expressions. In the case of text input, the touchscreen or keyboard is designed to allow the user to enter text quickly and accurately. Furthermore, the reception unit is equipped with high-speed data transfer technology to process this input data in real time and transmit it to the analysis unit. This allows the system to respond quickly and accurately regardless of the format in which the user provides the information.

[0064] The analysis unit analyzes the information received by the reception unit and generates information to convert it into a format suitable for the recipient. The analysis unit accurately analyzes the input information using, for example, speech recognition technology and natural language processing technology. For example, the analysis unit uses speech recognition technology to convert voice input into text data. The analysis unit can also use natural language processing technology to analyze text data and understand its meaning. Furthermore, the analysis unit can use video analysis technology to analyze visual information such as sign language and convert it into text data. For example, the analysis unit performs highly accurate speech recognition using deep learning-based speech recognition technology. Natural language processing technology uses morphological and grammatical analysis to understand the meaning of text data. Video analysis technology uses computer vision technology to analyze visual information such as sign language and convert it into text data. The analysis unit can combine these technologies to analyze complex information quickly and accurately. For example, speech recognition technology converts user speech into text in real time, and that text is analyzed by natural language processing technology to understand the context and intent. Video analysis technology analyzes visual information such as sign language and facial expressions and converts it into text data. This allows the analysis unit to handle input in any format—audio, video, or text—and generate information to convert it into a format suitable for the recipient. Furthermore, the analysis unit can perform more accurate analysis by utilizing past data and user history. For example, by learning from past audio and text data and understanding the user's speech patterns and context, it can perform more accurate analysis. This enables the analysis unit to respond flexibly to user needs and provide smoother communication.

[0065] The conversion unit converts audio, video, and text into formats suitable for the recipient based on the information generated by the analysis unit. For example, the conversion unit converts text data into audio. It can also convert text data into video. Furthermore, the conversion unit can translate text data into other languages. For instance, the conversion unit uses text generation AI (e.g., LLM) to convert text data into natural-sounding speech. It uses video generation technology to convert text data into video. It uses translation technology to translate text data into other languages. The conversion unit can combine these technologies to provide flexible solutions tailored to user needs. For example, text generation AI considers the user's speaking style and context to generate natural and fluent speech. Video generation technology generates and provides users with videos that include visual information such as sign language and facial expressions. Translation technology supports multiple languages ​​and can translate text data quickly and accurately. Furthermore, the conversion unit possesses high-speed processing capabilities to enable real-time conversion. This allows for the instant conversion of user-input information into a format suitable for the recipient, facilitating smooth communication. For example, information entered by a user via voice can be converted into text in real time, that text can be translated into another language, and then transmitted to the other party via voice. This allows the conversion unit to respond flexibly to the user's needs, enabling smooth communication.

[0066] The output unit transmits the information converted by the conversion unit to the recipient. For example, the output unit can output audio via a speaker. It can also output video via a display. Furthermore, it can display text on a screen. For instance, the output unit can use high-quality speakers to produce clear audio, a display to show high-resolution video, or a screen to display text in a highly legible font. The output unit integrates these output methods to ensure that users can smoothly understand information regardless of the format in which it is received. For example, in the case of audio output, high-quality speakers reproduce clear and natural sound, conveying accurate information to the user. In the case of video output, a high-resolution display clearly displays visual information such as sign language and facial expressions, conveying accurate information to the user. In the case of text output, the screen uses a highly legible font, making it easy for users to read the information. Furthermore, the output unit can collect user feedback and continuously improve the accuracy and effectiveness of its output. For example, if the user provides feedback on the volume and sound quality of the audio output, the output unit can adjust the settings accordingly to achieve better audio output. Furthermore, the output unit can reliably transmit information to the user by using multiple output methods in combination. For example, by simultaneously outputting audio and text, the user can more reliably understand the information. This allows the output unit to provide information to the user quickly and reliably, enabling smooth communication.

[0067] The analysis unit can accurately analyze input information using speech recognition technology and natural language processing technology. For example, the analysis unit can convert speech input into text data using speech recognition technology. For example, the analysis unit can perform highly accurate speech recognition using deep learning-based speech recognition technology. The analysis unit can also analyze text data and understand its meaning using natural language processing technology. For example, the analysis unit can understand the meaning of text data using morphological analysis and grammatical analysis. Furthermore, the analysis unit can analyze visual information such as sign language using video analysis technology and convert it into text data. For example, the analysis unit can analyze visual information such as sign language using computer vision technology and convert it into text data. As a result, the accuracy of input information analysis is improved by using speech recognition technology and natural language processing technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input speech data into a generating AI and have the generating AI perform the conversion from speech data to text data.

[0068] The conversion unit can analyze visual information, such as sign language, using video analysis technology and convert it into speech or text. For example, the conversion unit can analyze visual information, such as sign language, using video analysis technology and convert it into text data. For example, the conversion unit can analyze visual information, such as sign language, using computer vision technology and convert it into text data. Furthermore, the conversion unit can convert text data into speech. For example, the conversion unit can convert text data into natural-sounding speech using text generation AI (e.g., LLM). In addition, the conversion unit can translate text data into other languages. For example, the conversion unit can translate text data into other languages ​​using translation technology. This allows for accurate conversion of visual information using video analysis technology. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input video data into a generation AI and have the generation AI perform the conversion from video data to text data.

[0069] The reception desk can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is nervous, the reception desk can delay the input acceptance to allow them to relax. Conversely, if the user is in a hurry, the reception desk can speed up the input acceptance to provide a quick response. Furthermore, if the user is tired, the reception desk can adjust the input acceptance timing to allow for breaks. For example, the reception desk can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate input acceptance by adjusting the timing of input acceptance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception desk may be performed using AI, or not. For example, the reception desk can input image data of the user captured by the camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can customize input methods based on the content the user has entered in the past. For example, the reception desk can prioritize suggesting voice input, which the user has frequently used in the past. It can predict and suggest input methods to be used during specific time periods based on the user's past input history. It can customize input methods based on the content the user has entered in the past. In this way, by analyzing past input history, the reception desk can provide the user with the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into a generating AI and have the generating AI select the optimal input method.

[0071] The reception unit can filter input based on the user's current situation and areas of interest. For example, the reception unit can prioritize receiving relevant information based on the user's current location. It can also prioritize receiving relevant input based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate input methods based on the user's current situation (e.g., in a meeting, traveling). For example, the reception unit prioritizes receiving relevant information based on the user's current location. It prioritizes receiving relevant input based on the user's areas of interest. It suggests appropriate input methods based on the user's current situation. This allows for the priority reception of highly relevant information by filtering based on the user's situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0072] The reception unit can estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize important inputs. Conversely, if the user is relaxed, the reception unit can prioritize normal inputs. Furthermore, if the user is in a hurry, the reception unit can prioritize urgent inputs. For example, the reception unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows the system to prioritize important inputs by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception unit may be performed using AI, or not. For example, the reception unit can input image data of the user captured by the camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The reception unit can prioritize receiving inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. Furthermore, if the user is traveling, the reception unit can prioritize receiving information related to their travel destination. Additionally, if the user is at home, the reception unit can prioritize receiving information about their home area. This allows for the priority of receiving highly relevant information by considering geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input geographical location data into a generating AI and have the generating AI prioritize highly relevant inputs.

[0074] The reception unit can analyze the user's social media activity and accept relevant inputs when receiving input. For example, the reception unit can prioritize accepting relevant inputs based on information the user has shared on social media. The reception unit can also analyze the activity of the user's social media followers and friends and accept relevant inputs. Furthermore, the reception unit can accept relevant inputs based on topics the user has shown interest in on social media. For example, the reception unit can prioritize accepting relevant inputs based on information the user has shared on social media. It can analyze the activity of the user's social media followers and friends and accept relevant inputs. It can accept relevant inputs based on topics the user has shown interest in on social media. This allows for the priority acceptance of relevant inputs by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input social media activity data into a generating AI and have the generating AI prioritize relevant inputs.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit may be performed using AI, or not. For example, the analysis unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the input information during the analysis. For example, the analysis unit performs a detailed analysis for important information. The analysis unit can also perform a simplified analysis for general information. Furthermore, the analysis unit can perform a rapid analysis for urgent information. For example, the analysis unit performs a detailed analysis for important information. It performs a simplified analysis for general information. It performs a rapid analysis for urgent information. This allows for a detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input importance data of the input information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the input information during analysis. For example, the analysis unit can apply a speech recognition algorithm to audio information. It can also apply a video analysis algorithm to video information. Furthermore, it can apply a natural language processing algorithm to text information. This improves analysis accuracy by applying the appropriate analysis algorithm according to the category of the input information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input category data of the input information into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. By adjusting the length of the analysis according to the user's emotions, it can provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0079] The analysis unit can determine the priority of analysis based on the submission timing of the input information during analysis. For example, the analysis unit may prioritize the analysis of the most recent information. It can also prioritize the analysis of urgent information. Furthermore, the analysis unit can analyze current information by referring to past information. For example, the analysis unit may prioritize the analysis of the most recent information, prioritize the analysis of urgent information, and analyze current information by referring to past information. This allows the analysis unit to prioritize the analysis of the most recent information by determining the priority of analysis based on the submission timing of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input submission timing data into a generating AI and have the generating AI determine the priority of analysis.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the input information during analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can group relevant information together for analysis. For example, the analysis unit can prioritize the analysis of highly relevant information, postpone the analysis of less relevant information, and group relevant information together for analysis. This allows the analysis unit to prioritize the analysis of highly relevant information by adjusting the order of analysis based on the relevance of the input information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0081] The transformation unit can estimate the user's emotions and adjust the transformation criteria based on the estimated emotions. For example, if the user is nervous, the transformation unit will perform a simple and highly visible transformation. If the user is relaxed, the transformation unit can also perform a transformation that includes detailed information. Furthermore, if the user is in a hurry, the transformation unit can perform a transformation that gets straight to the point. For example, the transformation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate transformation results by adjusting the transformation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 transformation unit may be performed using AI, or not using AI. For example, the transformation unit can input the user's image data captured by the camera into the generative AI and have the generative AI perform the user's emotion estimation.

[0082] The conversion unit can improve the accuracy of the conversion by considering the interrelationships of the input information during the conversion process. For example, the conversion unit can perform accurate conversions by considering the interrelationships between audio and text information. Furthermore, the conversion unit can also perform accurate conversions by considering the interrelationships between video and audio information. In addition, the conversion unit can perform accurate conversions by considering the interrelationships between text and video information. For example, the conversion unit can perform accurate conversions by considering the interrelationships between audio and text information. It can perform accurate conversions by considering the interrelationships between video and audio information. It can perform accurate conversions by considering the interrelationships between text and video information. This improves the accuracy of the conversion by considering the interrelationships of the input information. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input data on the interrelationships of the input information into a generating AI and have the generating AI perform the conversion accuracy improvement.

[0083] The conversion unit can perform conversions while considering the attribute information of the submitter of the input information. For example, the conversion unit can perform appropriate conversions based on the submitter's age. The conversion unit can also perform appropriate conversions based on the submitter's language ability. Furthermore, the conversion unit can also perform appropriate conversions based on the submitter's expertise. For example, the conversion unit can perform appropriate conversions based on the submitter's age. For example, it can perform appropriate conversions based on the submitter's language ability. For example, it can perform appropriate conversions based on the submitter's expertise. This allows for the provision of more appropriate conversion results by considering the submitter's attribute information. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the submitter's attribute information data into a generating AI and have the generating AI perform the conversion.

[0084] The transformation unit can estimate the user's emotions and adjust the order in which the transformation results are displayed based on the estimated emotions. For example, if the user is nervous, the transformation unit can display important information first. It can also display detailed information first if the user is relaxed. Furthermore, if the user is in a hurry, it can display concise information first. For example, the transformation unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate information to be provided by adjusting the display order of the transformation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 transformation unit may be performed using AI, or not. For example, the transformation unit can input image data of the user captured by the camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The conversion unit can perform conversions while considering the geographical distribution of the input information. For example, the conversion unit can prioritize the conversion of geographically close information. It can also postpone the conversion of geographically distant information. Furthermore, the conversion unit can perform appropriate conversions based on the geographical distribution. For example, the conversion unit can prioritize the conversion of geographically close information, postpone the conversion of geographically distant information, and perform appropriate conversions based on the geographical distribution. This allows for the provision of more appropriate conversion results by considering the geographical distribution. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input geographical distribution data into a generating AI and have the generating AI perform the conversion.

[0086] The conversion unit can improve the accuracy of the conversion by referring to related literature of the input information during the conversion process. For example, the conversion unit can perform an accurate conversion by referring to related literature. The conversion unit can also perform an appropriate conversion based on related literature. Furthermore, the conversion unit can improve the accuracy of the conversion by referring to related literature. For example, the conversion unit can perform an accurate conversion by referring to related literature. It can perform an appropriate conversion based on related literature. It can improve the accuracy of the conversion by referring to related literature. As a result, the accuracy of the conversion is improved by referring to related literature. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input related literature data into a generating AI and have the generating AI perform the conversion.

[0087] The output unit can estimate the user's emotions and adjust the display method of the output based on the estimated user emotions. For example, if the user is nervous, the output unit can provide a simple and highly visible display method. If the user is relaxed, the output unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the output unit can provide a display method that gets straight to the point. For example, the output unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate information to be provided by adjusting the 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 output unit may be performed using AI, for example, or without AI. For example, the output unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0088] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit can prioritize providing display methods previously used by the user. The output unit can also suggest the optimal display method based on the user's past operation history. Furthermore, the output unit can analyze the user's past operation history and provide an appropriate display method. For example, the output unit can prioritize providing display methods previously used by the user. It can suggest the optimal display method based on the user's past operation history. It can analyze the user's past operation history and provide an appropriate display method. In this way, by referring to past operation history, the output unit can provide the user with the optimal display method. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input past operation history data into a generating AI and have the generating AI select the optimal display method.

[0089] The output unit can estimate the user's emotions and adjust the output operation procedure based on the estimated user emotions. For example, if the user is nervous, the output unit can provide a simple and highly visible operation procedure. If the user is relaxed, the output unit can also provide a detailed operation procedure. Furthermore, if the user is in a hurry, the output unit can provide a concise operation procedure. For example, the output unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate operation by adjusting the operation procedure 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 output unit may be performed using AI, or not using AI. For example, the output unit can input image data of the user captured by the camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0090] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the output unit can provide a concise and highly visible display method. This allows the output unit to provide the optimal display method by considering device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input device information data into a generating AI and have the generating AI select the optimal display method.

[0091] The output unit can provide a multilingual display according to the user's language settings when outputting. For example, the output unit can automatically set the display language based on the language settings of the user's device. The output unit can also provide a language switching function if the user uses multiple languages. Furthermore, the output unit can provide a display in a specific language if the user selects one. For example, the output unit can automatically set the display language based on the language settings of the user's device. It can provide a language switching function if the user uses multiple languages. It can provide a display in a specific language if the user selects one. This makes it possible to provide information tailored to the user by providing a multilingual display according to language settings. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input language setting data into a generating AI and have the generating AI execute a multilingual display.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The reception unit can analyze background noise and perform noise cancellation when receiving user voice input. For example, if the user is using voice input in a noisy environment, the reception unit can analyze the background noise and use noise cancellation technology to obtain clear audio data. Furthermore, if the user is using voice input in a quiet environment, the reception unit can analyze the background noise and minimize noise cancellation. Additionally, if the user is using voice input while moving, the reception unit can analyze the background noise and cancel out noise associated with movement. This allows the reception unit to adjust noise cancellation according to the user's environment and obtain clear audio data.

[0094] The analysis unit can automatically search for relevant information based on user input and add it to the analysis results. For example, if a user enters a restaurant reservation, the analysis unit can automatically search for relevant information such as the restaurant's menu and opening hours and add it to the analysis results. Similarly, if a user enters information about a tourist destination, the analysis unit can automatically search for relevant information such as how to access that destination and nearby tourist attractions and add it to the analysis results. Furthermore, if a user enters information about an event, the analysis unit can automatically search for relevant information such as event details and ticket information and add it to the analysis results. This allows the analysis unit to provide more comprehensive information by automatically searching for relevant information based on user input and adding it to the analysis results.

[0095] The conversion unit can analyze user input and convert it to an appropriate format, providing a customized format based on user preferences. For example, the conversion unit can display text data using the user's preferred font and color. It can also generate audio data using the user's preferred tone and speed. Furthermore, it can generate video data using the user's preferred video style and effects. This allows the conversion unit to provide a more satisfying information experience by offering a customized format based on user preferences.

[0096] The reception desk can estimate the user's emotions and adjust the input reception interface based on those emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface. If the user is relaxed, it can also provide an interface with more detailed options. Furthermore, if the user is in a hurry, it can provide an interface that allows for quick input. In this way, the reception desk can provide more appropriate input reception by adjusting the interface according to the user's emotions.

[0097] The reception desk can analyze a user's past input history and select the most suitable 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 predict and suggest input methods that a user will use at specific times of the day based on their past input history. Furthermore, the reception desk can customize input methods based on the user's past input. This allows the system to provide users with the most optimal input method by analyzing their past input history.

[0098] The reception system can filter input based on the user's current situation and areas of interest. For example, it can prioritize receiving relevant information based on the user's current location. It can also prioritize input based on the user's areas of interest. Furthermore, it can suggest appropriate input methods based on the user's current situation (e.g., in a meeting, traveling). This allows for the priority of receiving highly relevant information by filtering based on the user's situation and areas of interest.

[0099] The reception desk can estimate the user's emotions and determine the priority of input to be received based on those emotions. For example, if the user is stressed, the reception desk will prioritize important input. Conversely, if the user is relaxed, the reception desk can prioritize normal input. Furthermore, if the user is in a hurry, the reception desk can prioritize urgent input. This allows for prioritizing important input based on the user's emotions.

[0100] The reception desk can prioritize receiving highly relevant input by considering the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving information related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving information about their home area. This allows the reception desk to prioritize receiving highly relevant information by considering geographical location.

[0101] The reception desk can analyze the user's social media activity when receiving input and accept relevant input. For example, the reception desk can prioritize accepting relevant input based on information the user has shared on social media. It can also analyze the activity of the user's social media followers and friends and accept relevant input. Furthermore, the reception desk can accept relevant input based on topics the user has shown interest in on social media. This allows for the prioritization of relevant input by analyzing social media activity.

[0102] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is tense, the analysis unit provides a simple and highly visual presentation. If the user is relaxed, the analysis unit can also provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise presentation. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception area receives voice, video, and text input from the user. For example, voice input is received via a microphone, video input via a camera, and text input via a keyboard or touchscreen. Furthermore, noise cancellation technology is used for voice input, and a high-resolution camera and video analysis technology are used for video input. Step 2: The analysis unit analyzes the information received by the reception unit and generates information to convert it into a format suitable for the recipient. For example, it converts voice input into text data using speech recognition technology and natural language processing technology, and converts visual information such as sign language into text data using video analysis technology. Step 3: The conversion unit converts audio, video, and text into a format suitable for the recipient based on the information generated by the analysis unit. For example, it converts text data to audio, video, or translates it into another language. Step 4: The output unit transmits the information converted by the conversion unit to the recipient. For example, audio output is performed by a speaker, video output by a display, and text output is displayed on a screen.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the reception unit, analysis unit, conversion unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice, video, and text input from the user using the microphone 38B, camera 42, and touch panel 38A of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using speech recognition technology and natural language processing technology. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the analyzed information into voice, video, and text. The output unit transmits the converted information to the recipient using the speaker 40B and display 40A of the smart device 14. 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.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, analysis unit, conversion unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice, video, and text input from the user using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using speech recognition technology and natural language processing technology. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the analyzed information into voice, video, and text. The output unit transmits the converted information to the recipient using the speaker 240 and display 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.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, conversion unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice, video, and text input from the user using the microphone 238 and camera 42 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using speech recognition technology and natural language processing technology. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the analyzed information into voice, video, and text. The output unit transmits the converted information to the recipient using the speaker 240 and display 343 of the headset terminal 314. 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.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the reception unit, analysis unit, conversion unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice, video, and text input from the user using the microphone 238 and camera 42 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using speech recognition technology and natural language processing technology. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the analyzed information into voice, video, and text. The output unit transmits the converted information to the recipient using the speaker 240 and display of the robot 414. 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A reception area that accepts voice, video, and text input from users, An analysis unit analyzes the information received by the reception unit and converts it into a format suitable for the recipient, A conversion unit that converts audio, video, and text into a format suitable for the recipient based on the information generated by the analysis unit, The system includes an output unit that transmits the information converted by the conversion unit to the other party. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Using speech recognition and natural language processing technologies, the input information is accurately analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is Using video analysis technology, visual information such as sign language is analyzed and converted into speech and text. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the input information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the input information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the input information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is It estimates the user's emotions and adjusts the conversion criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is During conversion, the system improves conversion accuracy by considering the interrelationships between input information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is During the conversion process, the attribute information of the person submitting the input information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The conversion unit is It estimates the user's sentiment and adjusts the order in which the conversion results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The conversion unit is During conversion, the geographical distribution of the input information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The conversion unit is During conversion, the system references relevant literature from the input information to improve conversion accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, It estimates the user's emotions and adjusts how the output is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When outputting data, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, It estimates the user's emotions and adjusts the output operation procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The output unit is, When outputting, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, When outputting, the display will support multiple languages ​​according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 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 voice, video, and text input from users, An analysis unit analyzes the information received by the reception unit and converts it into a format suitable for the recipient, A conversion unit that converts audio, video, and text into a format suitable for the recipient based on the information generated by the analysis unit, The system includes an output unit that transmits the information converted by the conversion unit to the other party. A system characterized by the following features.

2. The aforementioned analysis unit, Using speech recognition and natural language processing technologies, the input information is accurately analyzed. The system according to feature 1.

3. The conversion unit is Using video analysis technology, visual information such as sign language is analyzed and converted into speech and text. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

5. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

6. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system according to feature 1.

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