System

Abstract

To provide a system capable of facilitating communication with the deaf and mute.SOLUTION: A system in an embodiment includes a reception unit, an analysis unit, a conversion unit, and a display unit. The reception unit inputs voices. The analysis unit analyzes the voice input by the reception unit. The conversion unit converts the text analyzed by the analysis unit into sign languages. The display unit displays images of sign language converted by the conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not make it easy to communicate with deaf and mute people, and there is room for improvement.

[0005] The system according to the embodiment aims to facilitate communication with deaf and mute people. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a conversion unit, and a display unit. The reception unit inputs a voice. The analysis unit analyzes the voice input by the reception unit. The conversion unit converts the text analyzed by the analysis unit into sign language. The display unit displays an image of the sign language converted by the conversion unit. Effect of the Invention

[0007] The system according to the embodiment can facilitate communication with deaf and mute people. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A conversation support system according to an embodiment of the present invention is a smartphone application that allows a user to easily have a conversation with a deaf-mute person. This conversation support system is a system in which a user inputs voice into a smartphone, an AI analyzes the voice, converts it into sign language, and displays the converted sign language as an image on the smartphone screen. For example, when a user inputs "Hello, how are you?" by voice, an AI analyzes the voice, converts it into sign language, and an image of the sign language is displayed on the smartphone screen. This allows a user to easily have a conversation with a deaf-mute person. In addition, this conversation support system supports many languages, and users who speak different languages ​​can communicate with each other through sign language. For example, users who speak different languages, such as English, French, and Chinese, can communicate with each other through sign language. This allows the conversation support system to easily have a conversation with a deaf-mute person. In addition, users who speak different languages ​​can communicate with each other through sign language.

[0029] The conversation support system according to the embodiment includes a reception unit, an analysis unit, a conversion unit, and a display unit. The reception unit is a unit for a user to input voice to a smartphone. For example, the reception unit can input voice using a microphone of the smartphone. The analysis unit is a unit that analyzes the voice input by the reception unit. The analysis unit converts the input voice into text, for example, using a voice recognition technology. For example, the analysis unit converts the voice "Hello, how are you?" into text "Hello, how are you?". The conversion unit is a unit that converts the text analyzed by the analysis unit into sign language. The conversion unit, for example, generates a sign language action and displays it as an image. For example, the conversion unit converts the text "Hello, how are you?" into a sign language action and generates the image. The display unit is a unit that displays the image of the sign language converted by the conversion unit. The display unit, for example, displays the image of the sign language on the screen of the smartphone. This allows the user to easily have a conversation with a deaf-mute person. Each of the reception unit, analysis unit, conversion unit, and display unit can perform processing using AI. For example, the reception unit can convert voice input into a format that is easy for AI to analyze. The analysis unit can convert voice into text using AI. The conversion unit can convert text into sign language using AI. The display unit can smoothly display images of sign language using AI. This allows the conversation support system to easily converse with deaf and mute people.

[0030] The reception unit is a part for a user to input voice into a smartphone. For example, the reception unit can input voice using a microphone of the smartphone. Specifically, the reception unit captures the user's voice with high accuracy using the built-in microphone of the smartphone. When inputting voice, noise canceling technology can be used to remove ambient noise and obtain clear voice data. Furthermore, the reception unit has a function to automatically detect the start and end of voice input, allowing the user to input voice naturally without pressing a button. For example, a specific keyword or voice pattern can be set as a trigger to detect the start of voice input. As a result, the user can start voice input simply by saying a greeting such as "hello." In addition, in order to improve the quality of voice input, the reception unit can identify the direction of the voice using multiple microphones and obtain optimal voice data. As a result, the reception unit can capture the user's voice with high accuracy and prepare to send it to the analysis unit.

[0031] The analysis unit is a part that analyzes the voice input by the reception unit. The analysis unit converts the input voice into text, for example, using a voice recognition technology. Specifically, the analysis unit uses a voice recognition engine to analyze the voice data in real time and generate corresponding text data. The voice recognition engine utilizes deep learning technology and achieves high recognition accuracy by learning a large amount of voice data. For example, when converting a voice saying "Hello, how are you?" into a text saying "Hello, how are you?", an accurate text is generated by extracting features of the voice and matching them with a model learned in advance. Furthermore, the analysis unit can take into account context information and the user's speech pattern in order to improve the accuracy of the voice recognition. For example, more appropriate text conversion is performed based on past conversation history and the user's speech tendency. In addition, the analysis unit supports multiple languages ​​and can automatically identify voices in different languages ​​and convert them into corresponding text. As a result, the analysis unit can quickly and accurately convert the user's voice into text and send it to the next processing step.

[0032] The conversion unit is a part that converts the text analyzed by the analysis unit into sign language. The conversion unit, for example, generates sign language actions and displays them as images. Specifically, in order to generate sign language actions, the conversion unit refers to a sign language dictionary database and obtains a corresponding sign language action sequence. For example, when converting a text such as "Hello, how are you?" into sign language, the conversion unit sequentially generates sign language actions corresponding to each word and displays them continuously. When generating sign language actions, the conversion unit considers the grammar and expression method of the sign language to realize natural sign language expression. Furthermore, in order to generate sign language actions, the conversion unit utilizes 3D animation technology, and can generate sign language actions in real time and display them as smooth images. For example, in order to generate sign language actions, a 3D model is used to reproduce hand movements and facial expressions in detail. In addition, the conversion unit can adjust the style and speed of the sign language according to the user's preferences. As a result, the conversion unit can convert the analyzed text into accurate and natural sign language and provide it to the user.

[0033] The display unit is a part that displays the image of the sign language converted by the conversion unit. The display unit displays the image of the sign language on the screen of a smartphone, for example. Specifically, the display unit uses the high-resolution display of the smartphone to clearly display the image of the sign language. The display unit displays the image at a high frame rate in order to smoothly reproduce the actions of the sign language, so that the user can easily understand the actions of the sign language. Furthermore, the display unit has a function of enlarging or reducing the image of the sign language, so that the user can easily check the details of the sign language. For example, when the actions of the sign language are detailed, the image is enlarged and displayed, so that the user can check the hand movements and facial expressions in detail. Furthermore, the display unit has a function of displaying the corresponding text together with the image of the sign language, so that the user can check the sign language and the text at the same time. As a result, the display unit displays the converted image of the sign language in high quality, so that the user can smoothly converse with a deaf person. Furthermore, the display unit has a function of automatically adjusting the brightness and contrast of the image in order to reduce the visual burden on the user. This enables the display unit to display sign language images in an easily viewable manner even in a variety of environments, improving user convenience.

[0034] The conversation support system includes a generation unit that generates sign language actions. The generation unit is a part that generates sign language actions. For example, the generation unit can generate hand movements and facial expressions. The generation unit can generate sign language actions using AI. For example, the generation unit uses AI to convert text such as "Hello, how are you?" into sign language actions and generate the actions. In this way, the generation of sign language actions makes it possible to express sign language.

[0035] The display unit can smoothly display the sign language actions. The display unit is a part that smoothly displays the sign language actions. For example, the display unit can display the sign language actions at a high frame rate. The display unit can smoothly display the sign language actions using AI. For example, the display unit can display the sign language actions in real time using AI. Thereby, the sign language actions are displayed smoothly, thereby improving visibility.

[0036] The conversion unit can support multiple languages. The conversion unit is a part that supports multiple languages. For example, the conversion unit can convert multiple languages, such as English, Japanese, French, and Chinese, into sign language. The conversion unit can convert multiple languages ​​into sign language using AI. For example, the conversion unit can convert English text into sign language using AI and generate the sign language movements. In this way, by supporting multiple languages, users who speak different languages ​​can communicate with each other via sign language.

[0037] The reception unit is a part that allows users who speak multiple languages ​​to communicate with each other through sign language. For example, the reception unit allows an English-speaking user and a Japanese-speaking user to communicate with each other through sign language. The reception unit can convert multiple languages ​​into sign language using AI. For example, the reception unit can convert English speech into sign language using AI and display an image of the sign language. This allows users who speak different languages ​​to communicate with each other through sign language.

[0038] The reception unit can analyze the user's past voice input history and select a voice input method. The reception unit is a part that analyzes the user's past voice input history and selects a voice input method. For example, the reception unit can preferentially suggest a voice input method that the user has frequently used in the past. The reception unit can analyze the user's past voice input history using AI and select an optimal voice input method. For example, the reception unit can analyze the user's past voice input history using AI and automatically complete a specific phrase. This improves the accuracy of voice input by selecting an optimal voice input method based on the past voice input history.

[0039] The reception unit can filter the user's current environmental sound to remove noise when the voice is input. The reception unit is a part that filters the user's current environmental sound to remove noise when the voice is input. For example, when the user is in a noisy environment, the reception unit can filter the environmental sound to make the voice input clear. The reception unit can filter the environmental sound and remove noise using AI. For example, the reception unit can remove background noise using AI to optimize the voice input. As a result, the accuracy of the voice input is improved by filtering the environmental sound and removing noise.

[0040] The reception unit can prioritize processing of highly relevant voice inputs in consideration of the geographical location information of the user at the time of voice input. The reception unit is a part that prioritizes processing of highly relevant voice inputs in consideration of the geographical location information of the user at the time of voice input. For example, when the user is in a specific location, the reception unit can prioritize processing of voice inputs related to the location. The reception unit can analyze geographical location information using AI and prioritize processing of highly relevant voice inputs. For example, the reception unit can analyze the user's current location using AI and prioritize processing of voice inputs related to the location. As a result, highly relevant voice inputs can be processed by processing the voice inputs in consideration of the geographical location information.

[0041] The reception unit can analyze the user's social media activity at the time of voice input and prioritize processing of related voice input. The reception unit is a part that analyzes the user's social media activity at the time of voice input and prioritizes processing of related voice input. For example, the reception unit can prioritize processing of phrases that the user frequently uses on social media. The reception unit can analyze the user's social media activity using AI and prioritize processing of related voice input. For example, the reception unit can analyze the content of the user's social media posts using AI and prioritize processing of voice input related to a specific topic. As a result, by analyzing social media activity and processing the voice input, highly relevant voice input is possible.

[0042] The analysis unit can improve the analysis accuracy by taking into account the context of the voice during voice analysis. The analysis unit is a part that improves the analysis accuracy by taking into account the context of the voice during voice analysis. For example, the analysis unit can perform accurate analysis by taking into account the context before and after the voice. The analysis unit can analyze the context of the voice using AI and improve the analysis accuracy. For example, the analysis unit can analyze the context of a specific phrase or word using AI and improve the analysis accuracy. In this way, by improving the analysis accuracy by taking into account the context of the voice, more accurate voice analysis is possible.

[0043] The analysis unit can improve the analysis accuracy by learning the user's pronunciation characteristics during voice analysis. The analysis unit is a part that improves the analysis accuracy by learning the user's pronunciation characteristics during voice analysis. For example, the analysis unit can learn the user's pronunciation characteristics and perform individually optimized voice analysis. The analysis unit can learn the user's pronunciation characteristics using AI and improve the analysis accuracy. For example, the analysis unit can learn the user's pronunciation habits and features using AI and improve the analysis accuracy. Thereby, by learning the user's pronunciation characteristics and improving the analysis accuracy, more accurate voice analysis is possible.

[0044] The analysis unit can improve the analysis accuracy by taking into account the geographical location information of the user during voice analysis. The analysis unit is a part that improves the analysis accuracy by taking into account the geographical location information of the user during voice analysis. For example, when the user is in a specific location, the analysis unit can prioritize analyzing voice related to that location. The analysis unit can analyze the geographical location information using AI and improve the analysis accuracy. For example, the analysis unit can analyze the user's current location using AI and prioritize analyzing voice related to that location. This allows for more accurate voice analysis by improving the analysis accuracy by taking into account the geographical location information.

[0045] The analysis unit can analyze the user's social media activity during the voice analysis and prioritize processing of related analysis results. The analysis unit is a part that analyzes the user's social media activity during the voice analysis and prioritizes processing of related analysis results. For example, the analysis unit can prioritize analysis of phrases that the user frequently uses on social media. The analysis unit can analyze the user's social media activity using AI and prioritize processing of related analysis results. For example, the analysis unit can analyze the content of the user's social media posts using AI and prioritize analysis of voices related to a specific topic. In this way, by analyzing social media activity and processing the analysis results, highly relevant analysis results can be obtained.

[0046] The conversion unit can improve conversion accuracy by taking into account the context of the text during sign language conversion. The conversion unit is a part that improves conversion accuracy by taking into account the context of the text during sign language conversion. For example, the conversion unit can perform accurate sign language conversion by taking into account the context before and after the text. The conversion unit can analyze the context of the text using AI and improve conversion accuracy. For example, the conversion unit can analyze the context of a specific phrase or word using AI and improve conversion accuracy. As a result, more accurate sign language conversion is possible by improving conversion accuracy by taking into account the context of the text.

[0047] The conversion unit can improve conversion accuracy by learning the user's sign language comprehension during sign language conversion. The conversion unit is a part that improves conversion accuracy by learning the user's sign language comprehension during sign language conversion. For example, the conversion unit can learn the user's sign language comprehension and perform individually optimized sign language conversion. The conversion unit can learn the user's sign language comprehension using AI and improve conversion accuracy. For example, the conversion unit can learn the user's sign language habits and characteristics using AI and improve conversion accuracy. Thereby, by learning the user's sign language comprehension and improving conversion accuracy, more accurate sign language conversion is possible.

[0048] The conversion unit can improve the conversion accuracy by considering the geographical location information of the user during sign language conversion. The conversion unit is a part that improves the conversion accuracy by considering the geographical location information of the user during sign language conversion. For example, when the user is in a specific location, the conversion unit can preferentially convert sign language related to the location. The conversion unit can analyze the geographical location information using AI and improve the conversion accuracy. For example, the conversion unit can analyze the user's current location using AI and preferentially convert sign language related to the location. As a result, more accurate sign language conversion is possible by improving the conversion accuracy by considering the geographical location information.

[0049] The conversion unit can analyze the user's social media activities and prioritize processing of related sign language conversions during sign language conversion. The conversion unit is a part that analyzes the user's social media activities and prioritizes processing of related sign language conversions during sign language conversion. For example, the conversion unit can prioritize converting phrases that the user frequently uses on social media into sign language. The conversion unit can analyze the user's social media activities using AI and prioritize processing of related sign language conversions. For example, the conversion unit can analyze the content of the user's social media posts using AI and prioritize conversion of sign language related to a specific topic. This enables highly relevant sign language conversion by analyzing social media activities and processing sign language conversion.

[0050] The display unit can learn the visual characteristics of the user and select the optimal display method when displaying a sign language video. The display unit is a part that learns the visual characteristics of the user and selects the optimal display method when displaying a sign language video. For example, the display unit can learn the visual characteristics of the user and display an individually optimized sign language video. The display unit can learn the visual characteristics of the user using AI and select the optimal display method. For example, the display unit can learn the visual habits and characteristics of the user using AI and improve display accuracy. As a result, by learning the visual characteristics and selecting the optimal display method, a sign language video that is easier to view is provided.

[0051] The display unit can select the optimal display method by referring to the user's past display history when displaying a sign language video. The display unit is a part that selects the optimal display method by referring to the user's past display history when displaying a sign language video. For example, the display unit can suggest the optimal display method based on the display method of the sign language video that the user has viewed in the past. The display unit can analyze the user's past display history using AI and select the optimal display method. For example, the display unit can analyze the user's past display history using AI and preferentially suggest a specific display method. In this way, by selecting the optimal display method by referring to the past display history, a sign language video that is easier to view is provided.

[0052] The display unit can select the optimal display method in consideration of the device information of the user when displaying the sign language video. The display unit is a part that selects the optimal display method in consideration of the device information of the user when displaying the sign language video. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. The display unit can analyze the device information of the user using AI and select the optimal display method. For example, the display unit can analyze the device information of the user using AI and provide a display method optimized for devices such as smartphones, tablets, and smartwatches. As a result, by selecting the optimal display method in consideration of the device information, a sign language video that is easier to view is provided.

[0053] The display unit, when displaying the sign language video, can analyze the user's social media activity and preferentially display related sign language video. The display unit is a part that, when displaying the sign language video, analyzes the user's social media activity and preferentially displays related sign language video. For example, the display unit can preferentially display sign language video that the user frequently watches on social media. The display unit can analyze the user's social media activity using AI and preferentially display related sign language video. For example, the display unit can analyze the content of the user's social media posts using AI and preferentially display sign language video related to a specific topic. In this way, highly relevant sign language video is provided by analyzing social media activity and displaying the sign language video.

[0054] The generation unit can improve the generation accuracy by taking into account the context of the text when generating a sign language action. The generation unit is a part that improves the generation accuracy by taking into account the context of the text when generating a sign language action. For example, the generation unit can generate an accurate sign language action by taking into account the context before and after the text. The generation unit can analyze the context of the text using AI and improve the generation accuracy. For example, the generation unit can analyze the context of a specific phrase or word using AI and improve the generation accuracy. In this way, by improving the generation accuracy by taking into account the context of the text, a more accurate sign language action is generated.

[0055] The generation unit can improve the generation accuracy by learning the user's sign language comprehension when generating a sign language action. The generation unit is a part that improves the generation accuracy by learning the user's sign language comprehension when generating a sign language action. For example, the generation unit can learn the user's sign language comprehension and generate individually optimized sign language action. The generation unit can learn the user's sign language comprehension using AI and improve the generation accuracy. For example, the generation unit can learn the user's sign language habits and characteristics using AI and improve the generation accuracy. Thereby, by learning the user's sign language comprehension and improving the generation accuracy, more accurate sign language action is generated.

[0056] The generation unit can improve the generation accuracy by considering the geographical location information of the user when generating a sign language action. The generation unit is a part that improves the generation accuracy by considering the geographical location information of the user when generating a sign language action. For example, when the user is in a specific location, the generation unit can preferentially generate sign language related to the location. The generation unit can analyze the geographical location information using AI and improve the generation accuracy. For example, the generation unit can analyze the user's current location using AI and preferentially generate sign language related to the location. In this way, by improving the generation accuracy by considering the geographical location information, more accurate sign language action is generated.

[0057] The generation unit can analyze the user's social media activity and generate related sign language actions preferentially when generating sign language actions. The generation unit is a part that analyzes the user's social media activity and generates related sign language actions preferentially when generating sign language actions. For example, the generation unit can generate a sign language action preferentially for a phrase that the user frequently uses on social media. The generation unit can analyze the user's social media activity using AI and generate related sign language actions preferentially. For example, the generation unit can analyze the content of the user's social media posts using AI and generate sign language actions related to a specific topic preferentially. In this way, by generating sign language actions by analyzing social media activities, highly relevant sign language actions are generated.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can suggest appropriate sign language expressions by referring to the user's past conversation history when the user inputs voice. For example, the reception unit can learn phrases and expressions used by the user in the past and automatically suggest them when they are used again in a similar situation. This allows the user to maintain consistent communication. The reception unit can also preferentially display specific sign language expressions based on the user's past conversation history. This allows the user to use sign language quickly and efficiently. Furthermore, the reception unit can analyze the past conversation history and automatically complete phrases frequently used by the user. This improves the efficiency of voice input and reduces the burden on the user.

[0060] The conversion unit can learn the user's sign language comprehension level and provide individually optimized sign language expressions. For example, the conversion unit can learn sign language expressions that the user found easy to understand in the past and provide them preferentially when used again in a similar situation. This allows the user to understand sign language more effectively. The conversion unit can also adjust the difficulty of the sign language expressions according to the user's sign language comprehension level. For example, it can provide simple sign language expressions to beginner users and complex sign language expressions to advanced users. Furthermore, the conversion unit can adjust the speed of the sign language expressions based on the user's sign language comprehension level. This allows the user to learn and understand sign language at his or her own pace.

[0061] The display unit can learn the visual characteristics of the user and provide an optimal sign language image display method. For example, the display unit can learn a display method of a sign language image that was easy for the user to view in the past, and provide it preferentially when used again in a similar situation. This allows the user to view the sign language image more effectively. The display unit can also adjust the display size and color of the sign language image according to the visual characteristics of the user. For example, a large display size can be provided for a user with poor eyesight, and an appropriate color can be provided for a user with color vision deficiency. Furthermore, the display unit can adjust the display speed of the sign language image based on the visual characteristics of the user. This allows the user to view and understand the sign language image at his or her own pace.

[0062] The reception unit can provide a relevant sign language expression preferentially in consideration of the geographical location information of the user. For example, when the user is in a specific region, the reception unit can provide a sign language expression commonly used in the region preferentially. This allows the user to use a sign language expression suitable for the region. The reception unit can also learn a sign language expression specific to the region based on the geographical location information of the user and provide it in an appropriate situation. Furthermore, the reception unit can provide a sign language expression according to the culture and customs of the region based on the geographical location information of the user. This allows the user to use a sign language expression suitable for the culture and customs of the region.

[0063] The reception unit may filter the user's current environmental sound to remove noise when the user inputs voice. For example, when the user is in a noisy environment, the reception unit may filter the environmental sound to make the voice input clear. The reception unit may use AI to filter the environmental sound and remove noise. For example, the reception unit may use AI to remove background noise and optimize the voice input. Thereby, the accuracy of the voice input is improved by filtering the environmental sound and removing noise.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The reception unit is a part for a user to input voice into the smartphone. For example, the reception unit can input voice using a microphone of the smartphone. Step 2: The analysis unit is a part that analyzes the voice input by the reception unit. The analysis unit converts the input voice into text, for example, using voice recognition technology. For example, the analysis unit converts the voice "Hello, how are you?" into the text "Hello, how are you?" Step 3: The conversion unit is a part that converts the text analyzed by the analysis unit into sign language. The conversion unit, for example, generates sign language actions and displays them as images. For example, the conversion unit converts the text "Hello, how are you?" into sign language actions and generates the images. Step 4: The display unit is a part that displays the image of the sign language converted by the conversion unit. For example, the display unit displays the image of the sign language on the screen of a smartphone. This allows the user to easily converse with the deaf and mute.

[0066] (Example 2) A conversation support system according to an embodiment of the present invention is a smartphone application that allows a user to easily have a conversation with a deaf-mute person. This conversation support system is a system in which a user inputs voice into a smartphone, an AI analyzes the voice, converts it into sign language, and displays the converted sign language as an image on the smartphone screen. For example, when a user inputs "Hello, how are you?" by voice, an AI analyzes the voice, converts it into sign language, and an image of the sign language is displayed on the smartphone screen. This allows a user to easily have a conversation with a deaf-mute person. In addition, this conversation support system supports many languages, and users who speak different languages ​​can communicate with each other through sign language. For example, users who speak different languages, such as English, French, and Chinese, can communicate with each other through sign language. This allows the conversation support system to easily have a conversation with a deaf-mute person. In addition, users who speak different languages ​​can communicate with each other through sign language.

[0067] The conversation support system according to the embodiment includes a reception unit, an analysis unit, a conversion unit, and a display unit. The reception unit is a unit for a user to input voice to a smartphone. For example, the reception unit can input voice using a microphone of the smartphone. The analysis unit is a unit that analyzes the voice input by the reception unit. The analysis unit converts the input voice into text, for example, using a voice recognition technology. For example, the analysis unit converts the voice "Hello, how are you?" into text "Hello, how are you?". The conversion unit is a unit that converts the text analyzed by the analysis unit into sign language. The conversion unit, for example, generates a sign language action and displays it as an image. For example, the conversion unit converts the text "Hello, how are you?" into a sign language action and generates the image. The display unit is a unit that displays the image of the sign language converted by the conversion unit. The display unit, for example, displays the image of the sign language on the screen of the smartphone. This allows the user to easily have a conversation with a deaf-mute person. Each of the reception unit, analysis unit, conversion unit, and display unit can perform processing using AI. For example, the reception unit can convert voice input into a format that is easy for AI to analyze. The analysis unit can convert voice into text using AI. The conversion unit can convert text into sign language using AI. The display unit can smoothly display images of sign language using AI. This allows the conversation support system to easily converse with deaf and mute people.

[0068] The reception unit is a part for a user to input voice into a smartphone. For example, the reception unit can input voice using a microphone of the smartphone. Specifically, the reception unit captures the user's voice with high accuracy using the built-in microphone of the smartphone. When inputting voice, noise canceling technology can be used to remove ambient noise and obtain clear voice data. Furthermore, the reception unit has a function to automatically detect the start and end of voice input, allowing the user to input voice naturally without pressing a button. For example, a specific keyword or voice pattern can be set as a trigger to detect the start of voice input. As a result, the user can start voice input simply by saying a greeting such as "hello." In addition, in order to improve the quality of voice input, the reception unit can identify the direction of the voice using multiple microphones and obtain optimal voice data. As a result, the reception unit can capture the user's voice with high accuracy and prepare to send it to the analysis unit.

[0069] The analysis unit is a part that analyzes the voice input by the reception unit. The analysis unit converts the input voice into text, for example, using a voice recognition technology. Specifically, the analysis unit uses a voice recognition engine to analyze the voice data in real time and generate corresponding text data. The voice recognition engine utilizes deep learning technology and achieves high recognition accuracy by learning a large amount of voice data. For example, when converting a voice saying "Hello, how are you?" into a text saying "Hello, how are you?", an accurate text is generated by extracting features of the voice and matching them with a model learned in advance. Furthermore, the analysis unit can take into account context information and the user's speech pattern in order to improve the accuracy of the voice recognition. For example, more appropriate text conversion is performed based on past conversation history and the user's speech tendency. In addition, the analysis unit supports multiple languages ​​and can automatically identify voices in different languages ​​and convert them into corresponding text. As a result, the analysis unit can quickly and accurately convert the user's voice into text and send it to the next processing step.

[0070] The conversion unit is a part that converts the text analyzed by the analysis unit into sign language. The conversion unit, for example, generates sign language actions and displays them as images. Specifically, in order to generate sign language actions, the conversion unit refers to a sign language dictionary database and obtains a corresponding sign language action sequence. For example, when converting a text such as "Hello, how are you?" into sign language, the conversion unit sequentially generates sign language actions corresponding to each word and displays them continuously. When generating sign language actions, the conversion unit considers the grammar and expression method of the sign language to realize natural sign language expression. Furthermore, in order to generate sign language actions, the conversion unit utilizes 3D animation technology, and can generate sign language actions in real time and display them as smooth images. For example, in order to generate sign language actions, a 3D model is used to reproduce hand movements and facial expressions in detail. In addition, the conversion unit can adjust the style and speed of the sign language according to the user's preferences. As a result, the conversion unit can convert the analyzed text into accurate and natural sign language and provide it to the user.

[0071] The display unit is a part that displays the image of the sign language converted by the conversion unit. The display unit displays the image of the sign language on the screen of a smartphone, for example. Specifically, the display unit uses the high-resolution display of the smartphone to clearly display the image of the sign language. The display unit displays the image at a high frame rate in order to smoothly reproduce the actions of the sign language, so that the user can easily understand the actions of the sign language. Furthermore, the display unit has a function of enlarging or reducing the image of the sign language, so that the user can easily check the details of the sign language. For example, when the actions of the sign language are detailed, the image is enlarged and displayed, so that the user can check the hand movements and facial expressions in detail. Furthermore, the display unit has a function of displaying the corresponding text together with the image of the sign language, so that the user can check the sign language and the text at the same time. As a result, the display unit displays the converted image of the sign language in high quality, so that the user can smoothly converse with a deaf person. Furthermore, the display unit has a function of automatically adjusting the brightness and contrast of the image in order to reduce the visual burden on the user. This enables the display unit to display sign language images in an easily viewable manner even in a variety of environments, improving user convenience.

[0072] The conversation support system includes a generation unit that generates sign language actions. The generation unit is a part that generates sign language actions. For example, the generation unit can generate hand movements and facial expressions. The generation unit can generate sign language actions using AI. For example, the generation unit uses AI to convert text such as "Hello, how are you?" into sign language actions and generate the actions. In this way, the generation of sign language actions makes it possible to express sign language.

[0073] The display unit can smoothly display the sign language actions. The display unit is a part that smoothly displays the sign language actions. For example, the display unit can display the sign language actions at a high frame rate. The display unit can smoothly display the sign language actions using AI. For example, the display unit can display the sign language actions in real time using AI. Thereby, the sign language actions are displayed smoothly, thereby improving visibility.

[0074] The conversion unit can support multiple languages. The conversion unit is a part that supports multiple languages. For example, the conversion unit can convert multiple languages, such as English, Japanese, French, and Chinese, into sign language. The conversion unit can convert multiple languages ​​into sign language using AI. For example, the conversion unit can convert English text into sign language using AI and generate the sign language movements. In this way, by supporting multiple languages, users who speak different languages ​​can communicate with each other via sign language.

[0075] The reception unit is a part that allows users who speak multiple languages ​​to communicate with each other through sign language. For example, the reception unit allows an English-speaking user and a Japanese-speaking user to communicate with each other through sign language. The reception unit can convert multiple languages ​​into sign language using AI. For example, the reception unit can convert English speech into sign language using AI and display an image of the sign language. This allows users who speak different languages ​​to communicate with each other through sign language.

[0076] The reception unit can estimate the user's emotion and adjust the timing of the voice input based on the estimated user's emotion. The reception unit is a part that estimates the user's emotion and adjusts the timing of the voice input based on the estimated user's emotion. For example, if the user is nervous, the reception unit can delay the timing of the voice input to relax the user. The reception unit can estimate the user's emotion using AI and adjust the timing of the voice input. For example, the reception unit can analyze the user's facial expression using AI and delay the timing of the voice input if the user is nervous. This allows for more appropriate voice input by adjusting the timing of the voice input according to the user's emotion.

[0077] The reception unit can analyze the user's past voice input history and select a voice input method. The reception unit is a part that analyzes the user's past voice input history and selects a voice input method. For example, the reception unit can preferentially suggest a voice input method that the user has frequently used in the past. The reception unit can analyze the user's past voice input history using AI and select an optimal voice input method. For example, the reception unit can analyze the user's past voice input history using AI and automatically complete a specific phrase. This improves the accuracy of voice input by selecting an optimal voice input method based on the past voice input history.

[0078] The reception unit can filter the user's current environmental sound to remove noise when the voice is input. The reception unit is a part that filters the user's current environmental sound to remove noise when the voice is input. For example, when the user is in a noisy environment, the reception unit can filter the environmental sound to make the voice input clear. The reception unit can filter the environmental sound and remove noise using AI. For example, the reception unit can remove background noise using AI to optimize the voice input. As a result, the accuracy of the voice input is improved by filtering the environmental sound and removing noise.

[0079] The reception unit can estimate the user's emotion and determine the priority of the voice input based on the estimated user's emotion. The reception unit is a part that estimates the user's emotion and determines the priority of the voice input based on the estimated user's emotion. For example, when the user is nervous, the reception unit can prioritize processing of important voice input. The reception unit can estimate the user's emotion using AI and determine the priority of the voice input. For example, the reception unit can analyze the user's facial expression using AI and prioritize processing of important voice input when the user is nervous. In this way, by determining the priority of the voice input according to the user's emotion, important voice input can be processed preferentially.

[0080] The reception unit can prioritize processing of highly relevant voice inputs in consideration of the geographical location information of the user at the time of voice input. The reception unit is a part that prioritizes processing of highly relevant voice inputs in consideration of the geographical location information of the user at the time of voice input. For example, when the user is in a specific location, the reception unit can prioritize processing of voice inputs related to the location. The reception unit can analyze geographical location information using AI and prioritize processing of highly relevant voice inputs. For example, the reception unit can analyze the user's current location using AI and prioritize processing of voice inputs related to the location. As a result, highly relevant voice inputs can be processed by processing the voice inputs in consideration of the geographical location information.

[0081] The reception unit can analyze the user's social media activity at the time of voice input and prioritize processing of related voice input. The reception unit is a part that analyzes the user's social media activity at the time of voice input and prioritizes processing of related voice input. For example, the reception unit can prioritize processing of phrases that the user frequently uses on social media. The reception unit can analyze the user's social media activity using AI and prioritize processing of related voice input. For example, the reception unit can analyze the content of the user's social media posts using AI and prioritize processing of voice input related to a specific topic. As a result, by analyzing social media activity and processing the voice input, highly relevant voice input is possible.

[0082] The analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user's emotions. The analysis unit is a part that estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated user's emotions. For example, the analysis unit can increase the accuracy of the voice analysis to prevent erroneous recognition when the user is nervous. The analysis unit can estimate the user's emotions using AI and adjust the accuracy of the voice analysis. For example, the analysis unit can analyze the user's facial expressions using AI and increase the accuracy of the voice analysis when the user is nervous. This allows for more accurate voice analysis by adjusting the accuracy of the voice analysis according to the user's emotions.

[0083] The analysis unit can improve the analysis accuracy by taking into account the context of the voice during voice analysis. The analysis unit is a part that improves the analysis accuracy by taking into account the context of the voice during voice analysis. For example, the analysis unit can perform accurate analysis by taking into account the context before and after the voice. The analysis unit can analyze the context of the voice using AI and improve the analysis accuracy. For example, the analysis unit can analyze the context of a specific phrase or word using AI and improve the analysis accuracy. In this way, by improving the analysis accuracy by taking into account the context of the voice, more accurate voice analysis is possible.

[0084] The analysis unit can improve the analysis accuracy by learning the user's pronunciation characteristics during voice analysis. The analysis unit is a part that improves the analysis accuracy by learning the user's pronunciation characteristics during voice analysis. For example, the analysis unit can learn the user's pronunciation characteristics and perform individually optimized voice analysis. The analysis unit can learn the user's pronunciation characteristics using AI and improve the analysis accuracy. For example, the analysis unit can learn the user's pronunciation habits and features using AI and improve the analysis accuracy. Thereby, by learning the user's pronunciation characteristics and improving the analysis accuracy, more accurate voice analysis is possible.

[0085] The analysis unit can estimate the user's emotions and determine the priority order of the analysis results based on the estimated user's emotions. The analysis unit is a part that estimates the user's emotions and determines the priority order of the analysis results based on the estimated user's emotions. For example, the analysis unit can preferentially display important analysis results when the user is nervous. The analysis unit can estimate the user's emotions using AI and determine the priority order of the analysis results. For example, the analysis unit can analyze the user's facial expressions using AI and preferentially display important analysis results when the user is nervous. In this way, by determining the priority order of the analysis results according to the user's emotions, important analysis results can be preferentially displayed.

[0086] The analysis unit can improve the analysis accuracy by taking into account the geographical location information of the user during voice analysis. The analysis unit is a part that improves the analysis accuracy by taking into account the geographical location information of the user during voice analysis. For example, when the user is in a specific location, the analysis unit can prioritize analyzing voice related to that location. The analysis unit can analyze the geographical location information using AI and improve the analysis accuracy. For example, the analysis unit can analyze the user's current location using AI and prioritize analyzing voice related to that location. This allows for more accurate voice analysis by improving the analysis accuracy by taking into account the geographical location information.

[0087] The analysis unit can analyze the user's social media activity during the voice analysis and prioritize processing of related analysis results. The analysis unit is a part that analyzes the user's social media activity during the voice analysis and prioritizes processing of related analysis results. For example, the analysis unit can prioritize analysis of phrases that the user frequently uses on social media. The analysis unit can analyze the user's social media activity using AI and prioritize processing of related analysis results. For example, the analysis unit can analyze the content of the user's social media posts using AI and prioritize analysis of voices related to a specific topic. In this way, by analyzing social media activity and processing the analysis results, highly relevant analysis results can be obtained.

[0088] The conversion unit can estimate the user's emotion and adjust the expression method of the sign language conversion based on the estimated user's emotion. The conversion unit is a part that estimates the user's emotion and adjusts the expression method of the sign language conversion based on the estimated user's emotion. For example, the conversion unit can provide a simple and easy-to-understand sign language expression when the user is nervous. The conversion unit can estimate the user's emotion using AI and adjust the expression method of the sign language conversion. For example, the conversion unit can analyze the user's facial expression using AI and provide a simple sign language expression when the user is nervous. This allows for more appropriate sign language expression by adjusting the expression method of the sign language conversion according to the user's emotion.

[0089] The conversion unit can improve conversion accuracy by taking into account the context of the text during sign language conversion. The conversion unit is a part that improves conversion accuracy by taking into account the context of the text during sign language conversion. For example, the conversion unit can perform accurate sign language conversion by taking into account the context before and after the text. The conversion unit can analyze the context of the text using AI and improve conversion accuracy. For example, the conversion unit can analyze the context of a specific phrase or word using AI and improve conversion accuracy. As a result, more accurate sign language conversion is possible by improving conversion accuracy by taking into account the context of the text.

[0090] The conversion unit can improve conversion accuracy by learning the user's sign language comprehension during sign language conversion. The conversion unit is a part that improves conversion accuracy by learning the user's sign language comprehension during sign language conversion. For example, the conversion unit can learn the user's sign language comprehension and perform individually optimized sign language conversion. The conversion unit can learn the user's sign language comprehension using AI and improve conversion accuracy. For example, the conversion unit can learn the user's sign language habits and characteristics using AI and improve conversion accuracy. Thereby, by learning the user's sign language comprehension and improving conversion accuracy, more accurate sign language conversion is possible.

[0091] The conversion unit can estimate the user's emotion and determine the priority of the sign language conversion based on the estimated user's emotion. The conversion unit is a part that estimates the user's emotion and determines the priority of the sign language conversion based on the estimated user's emotion. For example, when the user is nervous, the conversion unit can prioritize important sign language conversion. The conversion unit can estimate the user's emotion using AI and determine the priority of the sign language conversion. For example, the conversion unit can analyze the user's facial expression using AI and prioritize important sign language conversion when the user is nervous. In this way, by determining the priority of the sign language conversion according to the user's emotion, important sign language conversion can be prioritized.

[0092] The conversion unit can improve the conversion accuracy by considering the geographical location information of the user during sign language conversion. The conversion unit is a part that improves the conversion accuracy by considering the geographical location information of the user during sign language conversion. For example, when the user is in a specific location, the conversion unit can preferentially convert sign language related to the location. The conversion unit can analyze the geographical location information using AI and improve the conversion accuracy. For example, the conversion unit can analyze the user's current location using AI and preferentially convert sign language related to the location. As a result, more accurate sign language conversion is possible by improving the conversion accuracy by considering the geographical location information.

[0093] The conversion unit can analyze the user's social media activities and prioritize processing of related sign language conversions during sign language conversion. The conversion unit is a part that analyzes the user's social media activities and prioritizes processing of related sign language conversions during sign language conversion. For example, the conversion unit can prioritize converting phrases that the user frequently uses on social media into sign language. The conversion unit can analyze the user's social media activities using AI and prioritize processing of related sign language conversions. For example, the conversion unit can analyze the content of the user's social media posts using AI and prioritize conversion of sign language related to a specific topic. This enables highly relevant sign language conversion by analyzing social media activities and processing sign language conversion.

[0094] The display unit can estimate the user's emotion and adjust the display method of the sign language video based on the estimated user's emotion. The display unit is a part that estimates the user's emotion and adjusts the display method of the sign language video based on the estimated user's emotion. For example, the display unit can provide a simple and highly visible display method when the user is nervous. The display unit can estimate the user's emotion using AI and adjust the display method of the sign language video. For example, the display unit can analyze the user's facial expression using AI and provide a simple display method when the user is nervous. This allows for more appropriate display by adjusting the display method of the sign language video according to the user's emotion.

[0095] The display unit can learn the visual characteristics of the user and select the optimal display method when displaying a sign language video. The display unit is a part that learns the visual characteristics of the user and selects the optimal display method when displaying a sign language video. For example, the display unit can learn the visual characteristics of the user and display an individually optimized sign language video. The display unit can learn the visual characteristics of the user using AI and select the optimal display method. For example, the display unit can learn the visual habits and characteristics of the user using AI and improve display accuracy. As a result, by learning the visual characteristics and selecting the optimal display method, a sign language video that is easier to view is provided.

[0096] The display unit can select the optimal display method by referring to the user's past display history when displaying a sign language video. The display unit is a part that selects the optimal display method by referring to the user's past display history when displaying a sign language video. For example, the display unit can suggest the optimal display method based on the display method of the sign language video that the user has viewed in the past. The display unit can analyze the user's past display history using AI and select the optimal display method. For example, the display unit can analyze the user's past display history using AI and preferentially suggest a specific display method. In this way, by selecting the optimal display method by referring to the past display history, a sign language video that is easier to view is provided.

[0097] The display unit can estimate the user's emotion and adjust the display order of the sign language video based on the estimated user's emotion. The display unit is a part that estimates the user's emotion and adjusts the display order of the sign language video based on the estimated user's emotion. For example, when the user is nervous, the display unit can preferentially display important sign language video. The display unit can estimate the user's emotion using AI and adjust the display order of the sign language video. For example, the display unit can analyze the user's facial expression using AI and preferentially display important sign language video when the user is nervous. In this way, by adjusting the display order of the sign language video according to the user's emotion, important sign language video can be preferentially displayed.

[0098] The display unit can select the optimal display method in consideration of the device information of the user when displaying the sign language video. The display unit is a part that selects the optimal display method in consideration of the device information of the user when displaying the sign language video. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. The display unit can analyze the device information of the user using AI and select the optimal display method. For example, the display unit can analyze the device information of the user using AI and provide a display method optimized for devices such as smartphones, tablets, and smartwatches. As a result, by selecting the optimal display method in consideration of the device information, a sign language video that is easier to view is provided.

[0099] The display unit, when displaying the sign language video, can analyze the user's social media activity and preferentially display related sign language video. The display unit is a part that, when displaying the sign language video, analyzes the user's social media activity and preferentially displays related sign language video. For example, the display unit can preferentially display sign language video that the user frequently watches on social media. The display unit can analyze the user's social media activity using AI and preferentially display related sign language video. For example, the display unit can analyze the content of the user's social media posts using AI and preferentially display sign language video related to a specific topic. In this way, highly relevant sign language video is provided by analyzing social media activity and displaying the sign language video.

[0100] The generation unit can estimate the user's emotion and adjust the generation method of the sign language action based on the estimated user's emotion. The generation unit is a part that estimates the user's emotion and adjusts the generation method of the sign language action based on the estimated user's emotion. For example, the generation unit can generate a simple and easy-to-understand sign language action when the user is nervous. The generation unit can estimate the user's emotion using AI and adjust the generation method of the sign language action. For example, the generation unit can analyze the user's facial expression using AI and generate a simple sign language action when the user is nervous. In this way, a more appropriate sign language action is generated by adjusting the generation method of the sign language action according to the user's emotion.

[0101] The generation unit can improve the generation accuracy by taking into account the context of the text when generating a sign language action. The generation unit is a part that improves the generation accuracy by taking into account the context of the text when generating a sign language action. For example, the generation unit can generate an accurate sign language action by taking into account the context before and after the text. The generation unit can analyze the context of the text using AI and improve the generation accuracy. For example, the generation unit can analyze the context of a specific phrase or word using AI and improve the generation accuracy. In this way, by improving the generation accuracy by taking into account the context of the text, a more accurate sign language action is generated.

[0102] The generation unit can improve the generation accuracy by learning the user's sign language comprehension when generating a sign language action. The generation unit is a part that improves the generation accuracy by learning the user's sign language comprehension when generating a sign language action. For example, the generation unit can learn the user's sign language comprehension and generate individually optimized sign language action. The generation unit can learn the user's sign language comprehension using AI and improve the generation accuracy. For example, the generation unit can learn the user's sign language habits and characteristics using AI and improve the generation accuracy. Thereby, by learning the user's sign language comprehension and improving the generation accuracy, more accurate sign language action is generated.

[0103] The generation unit can estimate the user's emotion and determine the priority of the sign language actions based on the estimated user's emotion. The generation unit is a part that estimates the user's emotion and determines the priority of the sign language actions based on the estimated user's emotion. For example, when the user is nervous, the generation unit can generate important sign language actions with priority. The generation unit can estimate the user's emotion using AI and determine the priority of the sign language actions. For example, the generation unit can analyze the user's facial expression using AI and generate important sign language actions with priority when the user is nervous. In this way, by determining the priority of the sign language actions according to the user's emotion, important sign language actions can be generated with priority.

[0104] The generation unit can improve the generation accuracy by considering the geographical location information of the user when generating a sign language action. The generation unit is a part that improves the generation accuracy by considering the geographical location information of the user when generating a sign language action. For example, when the user is in a specific location, the generation unit can preferentially generate sign language related to the location. The generation unit can analyze the geographical location information using AI and improve the generation accuracy. For example, the generation unit can analyze the user's current location using AI and preferentially generate sign language related to the location. In this way, by improving the generation accuracy by considering the geographical location information, more accurate sign language action is generated.

[0105] The generation unit can analyze the user's social media activity and generate related sign language actions preferentially when generating sign language actions. The generation unit is a part that analyzes the user's social media activity and generates related sign language actions preferentially when generating sign language actions. For example, the generation unit can generate a sign language action preferentially for a phrase that the user frequently uses on social media. The generation unit can analyze the user's social media activity using AI and generate related sign language actions preferentially. For example, the generation unit can analyze the content of the user's social media posts using AI and generate sign language actions related to a specific topic preferentially. In this way, by generating sign language actions by analyzing social media activities, highly relevant sign language actions are generated.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can suggest appropriate sign language expressions by referring to the user's past conversation history when the user inputs voice. For example, the reception unit can learn phrases and expressions used by the user in the past and automatically suggest them when they are used again in a similar situation. This allows the user to maintain consistent communication. The reception unit can also preferentially display specific sign language expressions based on the user's past conversation history. This allows the user to use sign language quickly and efficiently. Furthermore, the reception unit can analyze the past conversation history and automatically complete phrases frequently used by the user. This improves the efficiency of voice input and reduces the burden on the user.

[0108] The analysis unit can analyze the tone and speed of the voice when the user inputs the voice, and estimate the user's emotions. For example, the analysis unit can detect that the tone of the voice becomes higher and the speed becomes faster when the user is excited, and estimate the emotions. This allows the analysis unit to select an appropriate sign language expression according to the user's emotions. The analysis unit can also analyze changes in the tone and speed of the voice in real time and track changes in the user's emotions. This allows an appropriate sign language expression according to the user's emotions to be provided. Furthermore, the analysis unit can estimate the intensity of the user's emotions based on changes in the tone and speed of the voice. This allows a sign language expression according to the intensity of the user's emotions to be provided.

[0109] The conversion unit can learn the user's sign language comprehension level and provide individually optimized sign language expressions. For example, the conversion unit can learn sign language expressions that the user found easy to understand in the past and provide them preferentially when used again in a similar situation. This allows the user to understand sign language more effectively. The conversion unit can also adjust the difficulty of the sign language expressions according to the user's sign language comprehension level. For example, it can provide simple sign language expressions to beginner users and complex sign language expressions to advanced users. Furthermore, the conversion unit can adjust the speed of the sign language expressions based on the user's sign language comprehension level. This allows the user to learn and understand sign language at his or her own pace.

[0110] The display unit can learn the visual characteristics of the user and provide an optimal sign language image display method. For example, the display unit can learn a display method of a sign language image that was easy for the user to view in the past, and provide it preferentially when used again in a similar situation. This allows the user to view the sign language image more effectively. The display unit can also adjust the display size and color of the sign language image according to the visual characteristics of the user. For example, a large display size can be provided for a user with poor eyesight, and an appropriate color can be provided for a user with color vision deficiency. Furthermore, the display unit can adjust the display speed of the sign language image based on the visual characteristics of the user. This allows the user to view and understand the sign language image at his or her own pace.

[0111] The reception unit can provide a relevant sign language expression preferentially in consideration of the geographical location information of the user. For example, when the user is in a specific region, the reception unit can provide a sign language expression commonly used in the region preferentially. This allows the user to use a sign language expression suitable for the region. The reception unit can also learn a sign language expression specific to the region based on the geographical location information of the user and provide it in an appropriate situation. Furthermore, the reception unit can provide a sign language expression according to the culture and customs of the region based on the geographical location information of the user. This allows the user to use a sign language expression suitable for the culture and customs of the region.

[0112] The analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can increase the accuracy of the voice analysis to prevent erroneous recognition. The analysis unit can estimate the user's emotions using AI and adjust the accuracy of the voice analysis. For example, the analysis unit can analyze the user's facial expressions using AI and increase the accuracy of the voice analysis if the user is nervous. This allows for more accurate voice analysis by adjusting the accuracy of the voice analysis according to the user's emotions.

[0113] The conversion unit can estimate the user's emotions and adjust the method of expression of the sign language conversion based on the estimated user's emotions. For example, the conversion unit can provide a simple and easy-to-understand sign language expression when the user is nervous. The conversion unit can estimate the user's emotions using AI and adjust the method of expression of the sign language conversion. For example, the conversion unit can analyze the user's facial expressions using AI and provide a simple sign language expression when the user is nervous. This enables more appropriate sign language expression by adjusting the method of expression of the sign language conversion according to the user's emotions.

[0114] The display unit can estimate the user's emotion and adjust the display method of the sign language video based on the estimated user's emotion. For example, the display unit can provide a simple and highly visible display method when the user is nervous. The display unit can estimate the user's emotion using AI and adjust the display method of the sign language video. For example, the display unit can analyze the user's facial expression using AI and provide a simple display method when the user is nervous. This allows for more appropriate display by adjusting the display method of the sign language video according to the user's emotion.

[0115] The generation unit can estimate the user's emotion and adjust the generation method of the sign language motion based on the estimated user's emotion. For example, the generation unit can generate a simple and easy-to-understand sign language motion when the user is nervous. The generation unit can estimate the user's emotion using AI and adjust the generation method of the sign language motion. For example, the generation unit can analyze the user's facial expression using AI and generate a simple sign language motion when the user is nervous. In this way, by adjusting the generation method of the sign language motion according to the user's emotion, more appropriate sign language motion is generated.

[0116] The reception unit may filter the user's current environmental sound to remove noise when the user inputs voice. For example, when the user is in a noisy environment, the reception unit may filter the environmental sound to make the voice input clear. The reception unit may use AI to filter the environmental sound and remove noise. For example, the reception unit may use AI to remove background noise and optimize the voice input. Thereby, the accuracy of the voice input is improved by filtering the environmental sound and removing noise.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The reception unit is a part for a user to input voice into the smartphone. For example, the reception unit can input voice using a microphone of the smartphone. Step 2: The analysis unit is a part that analyzes the voice input by the reception unit. The analysis unit converts the input voice into text, for example, using voice recognition technology. For example, the analysis unit converts the voice "Hello, how are you?" into the text "Hello, how are you?" Step 3: The conversion unit is a part that converts the text analyzed by the analysis unit into sign language. The conversion unit, for example, generates sign language actions and displays them as images. For example, the conversion unit converts the text "Hello, how are you?" into sign language actions and generates the images. Step 4: The display unit is a part that displays the image of the sign language converted by the conversion unit. For example, the display unit displays the image of the sign language on the screen of a smartphone. This allows the user to easily converse with the deaf and mute.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0121] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements including the above-mentioned reception unit, analysis unit, conversion unit, display unit, and generation unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input voice using the microphone 38B of the smart device 14. The analysis unit can convert voice into text by the specific processing unit 290 of the data processing device 12. The conversion unit can convert text into sign language by the specific processing unit 290 of the data processing device 12. The display unit can display an image of sign language by the display 40A of the smart device 14. The generation unit can generate a sign language action by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0130] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements including the above-mentioned reception unit, analysis unit, conversion unit, display unit, and generation unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input voice using the microphone 238 of the smart glasses 214. The analysis unit can convert voice into text by the specific processing unit 290 of the data processing device 12. The conversion unit can convert text into sign language by the specific processing unit 290 of the data processing device 12. The display unit can display an image of the sign language by the display of the smart glasses 214. The generation unit can generate the action of the sign language by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements including the above-mentioned reception unit, analysis unit, conversion unit, display unit, and generation unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can input voice using the microphone 238 of the headset type terminal 314. The analysis unit can convert voice into text by the specific processing unit 290 of the data processing device 12. The conversion unit can convert text into sign language by the specific processing unit 290 of the data processing device 12. The display unit can display an image of the sign language by the display 343 of the headset type terminal 314. The generation unit can generate the action of the sign language by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] Each of the multiple elements including the above-mentioned reception unit, analysis unit, conversion unit, display unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input voice using the microphone 238 of the robot 414. The analysis unit can convert voice into text by the specific processing unit 290 of the data processing device 12. The conversion unit can convert text into sign language by the specific processing unit 290 of the data processing device 12. The display unit can display an image of the sign language by the display of the robot 414. The generation unit can generate the action of the sign language by the specific processing unit 290 of the data processing device 12. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0173] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0176] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0177] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] In addition, 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 upon request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0184] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0188] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0190] (Appendix 1) A reception unit for inputting voice; an analysis unit that analyzes the voice input by the reception unit; a conversion unit that converts the text analyzed by the analysis unit into sign language; a display unit that displays the sign language image converted by the conversion unit; Equipped A system characterized by: (Appendix 2) Equipped with a generator that generates sign language actions 2. The system of claim 1, (Appendix 3) The display unit is Displaying sign language actions 2. The system of claim 1, (Appendix 4) The conversion unit is Supports multiple languages 2. The system of claim 1, (Appendix 5) The reception unit is Users who speak multiple languages ​​can communicate with each other through sign language. 2. The system of claim 1, (Appendix 6) The reception unit is Estimate the user's emotion and adjust the timing of voice input based on the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past voice input history and select a voice input method 2. The system of claim 1, (Appendix 8) The reception unit is Filters the user's current ambient sound to remove noise when speaking 2. The system of claim 1, (Appendix 9) The reception unit is Estimate user emotions and prioritize voice inputs based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The reception unit is When inputting voice, the system takes into account the user's geographic location information and prioritizes relevant voice input. 2. The system of claim 1, (Appendix 11) The reception unit is When you input voice, the system analyzes your social media activity and prioritizes relevant voice input. 2. The system of claim 1, (Appendix 12) The analysis unit is Estimate user emotions and adjust the accuracy of voice analysis based on the estimated user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is Improve accuracy of speech analysis by taking into account the context of the speech 2. The system of claim 1, (Appendix 14) The analysis unit is During voice analysis, the system learns the user's pronunciation characteristics to improve analysis accuracy. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and prioritize analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is When analyzing voice, the accuracy of analysis is improved by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing voice, analyze the user's social media activity and prioritize relevant analysis results. 2. The system of claim 1, (Appendix 18) The conversion unit is Estimating a user's emotion and adjusting the sign language conversion expression method based on the estimated user's emotion 2. The system of claim 1, (Appendix 19) The conversion unit is When converting sign language, improve conversion accuracy by taking into account the context of the text 2. The system of claim 1, (Appendix 20) The conversion unit is When converting sign language, the system learns the user's level of sign language comprehension to improve conversion accuracy. 2. The system of claim 1, (Appendix 21) The conversion unit is Estimate the user's emotions and determine the priority of sign language conversion based on the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The conversion unit is When converting sign language, the accuracy of conversion is improved by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 23) The conversion unit is When converting sign language, analyze the user's social media activity and prioritize relevant conversions. 2. The system of claim 1, (Appendix 24) The display unit is Estimating a user's emotions and adjusting the display method of the sign language video based on the estimated user's emotions 2. The system of claim 1, (Appendix 25) The display unit is When displaying sign language images, the system learns the user's visual characteristics and selects the optimal display method 2. The system of claim 1, (Appendix 26) The display unit is When displaying sign language video, the optimal display method is selected by referring to the user's past display history. 2. The system of claim 1, (Appendix 27) The display unit is Estimating a user's emotion and adjusting the display order of sign language videos based on the estimated user's emotion 2. The system of claim 1, (Appendix 28) The display unit is When displaying sign language video, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 29) The display unit is When displaying sign language videos, the system analyzes the user's social media activity and prioritizes displaying relevant sign language videos. 2. The system of claim 1, (Appendix 30) The generation unit is Estimating a user's emotion and adjusting a method for generating sign language actions based on the estimated user's emotion 3. The system of claim 2, (Appendix 31) The generation unit is Improve accuracy of sign language generation by considering text context 3. The system of claim 2, (Appendix 32) The generation unit is When generating sign language actions, the accuracy of generation is improved by learning the user's level of sign language comprehension 3. The system of claim 2, (Appendix 33) The generation unit is Estimating a user's emotion and prioritizing sign language actions based on the estimated user's emotion 3. The system of claim 2, (Appendix 34) The generation unit is Improve the accuracy of sign language generation by considering the user's geographic location information 3. The system of claim 2, (Appendix 35) The generation unit is When generating sign language actions, the system analyzes the user's social media activities and generates relevant sign language actions with priority. 3. The system of claim 2, [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting voice; an analysis unit that analyzes the voice input by the reception unit; a conversion unit that converts the text analyzed by the analysis unit into sign language; a display unit that displays the sign language image converted by the conversion unit; Equipped A system characterized by:

2. Equipped with a generator that generates sign language actions 2. The system of claim 1 .

3. The display unit is Displaying sign language actions 2. The system of claim 1 .

4. The conversion unit is Supports multiple languages 2. The system of claim 1 .

5. The reception unit is Users who speak multiple languages ​​can communicate with each other through sign language.

2. The system of claim 1 .

6. The reception unit is Estimate the user's emotion and adjust the timing of voice input based on the estimated user's emotion.

2. The system of claim 1 .

7. The reception unit is Analyze the user's past voice input history and select a voice input method 2. The system of claim 1 .

8. The reception unit is Filters the user's current ambient sound to remove noise when speaking 2. The system of claim 1 .

Images