System

Abstract

An object of a system according to an embodiment is to perform mutual translation between speech and sign language in real time.SOLUTION: A system according to an embodiment includes a voice input unit, a sign language translation unit, a sign language expression unit, a sign language analysis unit, a character conversion unit, and a voice conversion unit. The voice input unit receives a voice of a user. The sign language translation unit translates the speech received by the speech input unit into sign language. The sign language expression unit visually expresses the sign language translated by the sign language translation unit. The sign language analysis part analyzes sign language photographed by a camera. The character conversion unit converts the sign language analyzed by the sign language analysis unit into characters. The speech conversion unit converts the sign language analyzed by the sign language analysis unit into speech.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology was unable to translate between speech and sign language in real time, making it difficult to facilitate communication.

[0005] The system according to the embodiment aims to perform real-time translation between speech and sign language. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech input unit, a sign language translation unit, a sign language expression unit, a sign language analysis unit, a character conversion unit, and a speech conversion unit. The speech input unit receives a user's speech. The sign language translation unit translates the speech received by the speech input unit into sign language. The sign language expression unit visually expresses the sign language translated by the sign language translation unit. The sign language analysis unit analyzes sign language captured by a camera. The character conversion unit converts the sign language analyzed by the sign language analysis unit into character. The speech conversion unit converts the sign language analyzed by the sign language analysis unit into speech. [Effects of the Invention]

[0007] The system according to the embodiment can translate between speech and sign language in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A sign language interpretation system according to an embodiment of the present invention is a system that translates a user's speech into sign language and converts the sign language into text and speech. By translating a user's speech into sign language and converting the sign language into text and speech, the sign language interpretation system can facilitate communication with people with hearing impairments.

[0029] A sign language interpretation system according to an embodiment includes a voice input unit, a sign language translation unit, a sign language expression unit, a sign language analysis unit, a character conversion unit, and a voice conversion unit. The voice input unit receives a user's voice. For example, the voice can be input using a microphone. Alternatively, the voice input unit can use a built-in microphone of a smartphone or tablet. The sign language translation unit translates the voice received by the voice input unit into sign language. For example, a generation AI analyzes voice data and translates it into sign language. The sign language expression unit visually expresses the sign language translated by the sign language translation unit. For example, an AI character expresses it using sign language on a screen. The sign language analysis unit analyzes sign language captured by a camera. For example, the generation AI analyzes video data of the sign language and understands the content of the sign language. The character conversion unit converts the sign language analyzed by the sign language analysis unit into text. For example, the generation AI converts the content of the sign language into text data. The voice conversion unit converts the sign language analyzed by the sign language analysis unit into voice. For example, the generation AI converts the content of the sign language into voice data. As a result, the sign language interpretation system according to the embodiment can facilitate communication with the hearing impaired by translating the user's speech into sign language and converting the sign language into text and speech.

[0030] The sign language translation unit can analyze a speaker's mouth movements and facial expressions in real time and combine them with audio data to perform sign language translation. For example, the generation AI in the sign language translation unit analyzes a speaker's mouth movements and facial expressions in real time and combines them with audio data to perform sign language translation. For example, it analyzes the mouth movements and facial expressions when a speaker says "thank you" and reflects them in the sign language movements. The sign language translation unit also uses a camera to acquire video data to analyze the speaker's mouth movements and facial expressions, and the generation AI performs sign language translation based on that data. For example, if the speaker's mouth movements match "hello," it generates that sign language. The sign language translation unit also builds a system in which the generation AI analyzes the speaker's mouth movements and facial expressions and integrates them with audio data to perform sign language translation. For example, if the speaker's facial expression indicates "surprise," that emotion can be reflected in the sign language. This enables more accurate sign language translation by analyzing the speaker's mouth movements and facial expressions.

[0031] The sign language translation unit can translate spoken words not only into sign language but also into visual icons and emojis. For example, the sign language translation unit not only translates spoken words by the generation AI into sign language but also adds a function to convert them into visual icons and emojis. For example, "thank you" is displayed as a thankful emoji along with the sign language. The sign language translation unit also builds a system that simultaneously displays visual icons and emojis when translating spoken words into sign language. For example, "hello" is displayed as a greeting icon along with the sign language. The sign language translation unit not only translates spoken words by the generation AI into sign language but also adds a function to convert them into visual icons and emojis, allowing the user to select. For example, "congratulations" is displayed as a congratulatory emoji along with the sign language. This allows for a greater variety of expressions by translating spoken words not only into sign language but also into visual icons and emojis.

[0032] The sign language translation unit can analyze different dialects and accents and perform sign language translation accordingly. For example, the sign language translation unit adds a function that allows the generation AI to analyze different dialects and accents and perform sign language translation accordingly. For example, it translates the Kansai dialect "Okini" into sign language. The sign language translation unit also strengthens the speech analysis function of the generation AI to analyze different dialects and accents, improving the accuracy of sign language translation. For example, it translates the Okinawa dialect "Haisai" into sign language. The sign language translation unit also builds a system that allows the generation AI to analyze different dialects and accents and perform sign language translation accordingly. For example, it translates the Tohoku dialect "Nda" into sign language. This improves the accuracy of sign language translation by making it possible to handle different dialects and accents.

[0033] The sign language expression unit can adjust the speed and movements of the sign language according to the user's level of understanding. For example, when an AI character uses sign language, the sign language expression unit adds a function to adjust the speed and movements of the sign language according to the user's level of understanding. For example, the sign language expression unit can slow down the speed of the sign language for beginners. The sign language expression unit also analyzes the user's level of understanding in real time and builds a system in which the generation AI adjusts the speed and movements of the sign language. For example, the sign language expression unit can speed up the speed of the sign language for advanced users. The sign language expression unit also enhances the function to adjust the speed and movements of the sign language according to the user's level of understanding when the AI ​​character uses sign language, thereby providing more appropriate sign language expression. For example, the sign language movements can be simplified for children. This makes it possible to adjust the speed and movements of the sign language according to the user's level of understanding, thereby enabling more appropriate sign language expression.

[0034] The sign language expression unit can prepare multiple AI characters and allow the user to select a character of their choice. For example, the sign language expression unit prepares multiple AI characters and adds a function that allows the user to select a character of their choice. For example, it allows the user to select a male character or a female character. The sign language expression unit also builds a system in which the generation AI generates multiple characters so that the user can select a character of their choice. For example, it provides characters with different clothing and hairstyles. The sign language expression unit also prepares multiple AI characters and enhances the function that allows the user to select a character of their choice, providing a wider variety of options. For example, it allows the user to select an animal character or an anime-style character. In this way, by providing multiple AI characters, the user can select a character of their choice.

[0035] The sign language expression unit can display visual information related to the background when the sign language is being used to aid understanding. For example, the sign language expression unit adds a function to display visual information related to the background when the AI ​​character is using sign language. For example, when expressing "thank you," a scene of gratitude is displayed in the background. In addition, the sign language expression unit builds a system in which the generation AI automatically generates a background according to the content of the sign language to display visual information related to the background. For example, when expressing "hello," a greeting scene is displayed in the background. In addition, the sign language expression unit enhances the function to display visual information related to the background when the AI ​​character is using sign language, providing sign language expressions that are easier to understand. For example, when expressing "congratulations," a congratulatory scene is displayed in the background. In this way, by displaying visual information related to the background when the sign language is being used, it is possible to aid understanding.

[0036] The sign language analysis unit analyzes not only the position and speed of the hand but also the fine finger movements, allowing it to convert the sign language movements into more accurate characters and speech. For example, when the generation AI analyzes sign language movements, the sign language analysis unit adds a function that analyzes not only the position and speed of the hand but also the fine finger movements. For example, if the finger movements indicate "thank you," the movement is converted into characters and speech. The sign language analysis unit also builds a system in which the generation AI uses high-precision video analysis technology to analyze not only the position and speed of the hand but also the fine finger movements. For example, if the finger movements indicate "hello," the movement is converted into characters and speech. The sign language analysis unit also analyzes the fine finger movements when the generation AI analyzes sign language movements, allowing it to convert the sign language movements into more accurate characters and speech. For example, if the finger movements indicate "sorry," the movement is converted into characters and speech. This allows for more accurate conversion into characters and speech by analyzing not only the position and speed of the hand but also the fine finger movements.

[0037] The sign language analysis unit can understand the context of sign language and convert it into appropriate grammar and expressions. For example, the sign language analysis unit adds a function that enables the generation AI to understand the context of sign language and convert it into appropriate grammar and expressions. For example, when expressing "thank you" in sign language, it can convert it to "I appreciate it" depending on the context. In addition, the sign language analysis unit builds a system in which the generation AI analyzes the context of sign language and converts it into appropriate grammar and expressions in order to understand the context of sign language. For example, when expressing "hello" in sign language, it can convert it to "good morning" depending on the context. In addition, the sign language analysis unit strengthens the generation AI's ability to understand the context of sign language and convert it into appropriate grammar and expressions, converting it into more natural-looking text and speech. For example, when expressing "sorry" in sign language, it can convert it to "I apologize" depending on the context. This enables the context of sign language to be understood and converted into appropriate grammar and expressions.

[0038] The sign language analysis unit can convert sign language into a visual format, in addition to text and audio. For example, the sign language analysis unit adds a function to the generation AI that converts sign language into animation, in addition to text and audio. For example, when expressing "thank you" in sign language, an animation of a gesture of gratitude is displayed. The sign language analysis unit also builds a system in which the generation AI visually displays sign language gestures in order to convert sign language into visual notes. For example, when expressing "hello" in sign language, a greeting gesture is displayed in the visual note. The sign language analysis unit also enhances the generation AI's function to convert sign language into other visual formats, in addition to text and audio, to provide more diverse methods of expression. For example, when expressing "congratulations" in sign language, a congratulatory gesture is displayed in the visual note. This enables more diverse expressions by converting sign language into other visual formats, in addition to text and audio.

[0039] The sign language analysis unit can display the sign language analysis results in real time and provide an interface that allows the user to check and correct the results. For example, the sign language analysis unit adds a function that allows the generation AI to display the sign language analysis results in real time and provide an interface that allows the user to check and correct the results. For example, when expressing "thank you" in sign language, the results are converted into text or speech in real time, allowing the user to make corrections. In addition, to display the sign language analysis results in real time, the sign language analysis unit builds a system in which the generation AI analyzes sign language movements and instantly displays the results. For example, when expressing "hello" in sign language, the results are converted into text or speech in real time, allowing the user to make corrections. In addition, the sign language analysis unit enhances the function that allows the generation AI to display the sign language analysis results in real time and provide an interface that allows the user to check and correct the results, providing an easier-to-use system. For example, when expressing "I'm sorry" in sign language, the results are converted into text or speech in real time, allowing the user to make corrections. This enables more accurate sign language translation by displaying the sign language analysis results in real time and providing an interface that allows the user to check and correct the results.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The sign language interpretation system can further include a location information acquisition unit that acquires the user's location information and provides sign language expressions according to the location. For example, if the user is in a specific area, sign language expressions according to the dialect and culture of that area are provided. The location information acquisition unit can also update the location information in real time as the user moves and provide appropriate sign language expressions. For example, if the user is in a tourist spot, sign language expressions related to that tourist spot are provided. The location information acquisition unit can also provide information on nearby sign language interpreters and sign language classes based on the user's location information. This enables more appropriate communication by providing sign language expressions and related information according to the user's location information.

[0042] The sign language interpretation system may further include a health condition monitoring unit that monitors the user's health condition and provides sign language expressions according to the user's health condition. For example, if the user is tired, the speed of the sign language may be slowed down. The health condition monitoring unit may also measure the user's heart rate and stress level in real time and provide appropriate sign language expressions. For example, if the user is relaxed, the sign language expressions may be softer. The health condition monitoring unit may also display not only sign language expressions but also messages encouraging the user to take a break according to the user's health condition. This allows for more comfortable communication by providing sign language expressions and support according to the user's health condition.

[0043] The sign language interpretation system may further include a learning history recording unit that records the user's learning history and provides sign language expressions according to the user's learning progress. For example, if the user is learning a specific sign language expression, the system allows the user to repeatedly practice that expression. The learning history recording unit may also track the user's learning progress in real time and provide appropriate sign language expressions. For example, if the user is at a beginner's level, the system may provide basic sign language expressions. The learning history recording unit may also suggest the next sign language expression to be learned based on the user's learning history. This allows for effective sign language learning by providing sign language expressions and learning support according to the user's learning progress.

[0044] The sign language interpretation system can further include a personalized sign language representation unit that provides sign language representations according to the user's preferences and interests. For example, if the user is interested in sports, sign language representations related to sports are provided. The personalized sign language representation unit can also suggest appropriate sign language representations based on the user's past usage history. For example, it can prioritize the display of sign language representations that the user frequently uses. The personalized sign language representation unit can also provide not only sign language representations but also related information and content according to the user's preferences and interests. This enables more engaging communication by providing sign language representations and information according to the user's preferences and interests.

[0045] The sign language interpretation system may further include a schedule management unit that acquires the user's schedule information and provides sign language expressions according to the schedule. For example, if the user plans to attend a meeting, sign language expressions related to the meeting are provided. The schedule management unit can also update the user's schedule information in real time and provide appropriate sign language expressions. For example, if the user plans to go on a trip, sign language expressions related to the trip are provided. The schedule management unit can also display not only sign language expressions but also related reminders and notifications based on the user's schedule. This enables more efficient communication by providing sign language expressions and support according to the user's schedule.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The voice input unit receives the user's voice. For example, the voice can be input using a microphone. The voice input unit can also use the built-in microphone of a smartphone or tablet. Step 2: The sign language translation unit translates the speech received by the speech input unit into sign language. For example, a generation AI analyzes the speech data and translates it into sign language. Step 3: The sign language expression unit visually expresses the sign language translated by the sign language translation unit. For example, an AI character expresses the sign language on the screen. Step 4: The sign language analysis unit analyzes the sign language captured on the camera. For example, the generation AI analyzes the sign language video data and understands the content of the sign language. Step 5: The character conversion unit converts the sign language analyzed by the sign language analysis unit into text. For example, the generation AI converts the sign language content into text data. Step 6: The speech conversion unit converts the sign language analyzed by the sign language analysis unit into speech. For example, the generation AI converts the sign language content into speech data.

[0048] (Example 2) A sign language interpretation system according to an embodiment of the present invention is a system that translates a user's speech into sign language and converts the sign language into text and speech. By translating a user's speech into sign language and converting the sign language into text and speech, the sign language interpretation system can facilitate communication with people with hearing impairments.

[0049] A sign language interpretation system according to an embodiment includes a voice input unit, a sign language translation unit, a sign language expression unit, a sign language analysis unit, a character conversion unit, and a voice conversion unit. The voice input unit receives a user's voice. For example, the voice can be input using a microphone. Alternatively, the voice input unit can use a built-in microphone of a smartphone or tablet. The sign language translation unit translates the voice received by the voice input unit into sign language. For example, a generation AI analyzes voice data and translates it into sign language. The sign language expression unit visually expresses the sign language translated by the sign language translation unit. For example, an AI character expresses it using sign language on a screen. The sign language analysis unit analyzes sign language captured by a camera. For example, the generation AI analyzes video data of the sign language and understands the content of the sign language. The character conversion unit converts the sign language analyzed by the sign language analysis unit into text. For example, the generation AI converts the content of the sign language into text data. The voice conversion unit converts the sign language analyzed by the sign language analysis unit into voice. For example, the generation AI converts the content of the sign language into voice data. As a result, the sign language interpretation system according to the embodiment can facilitate communication with the hearing impaired by translating the user's speech into sign language and converting the sign language into text and speech.

[0050] The sign language translation unit can analyze a speaker's mouth movements and facial expressions in real time and combine them with audio data to perform sign language translation. For example, the generation AI in the sign language translation unit analyzes a speaker's mouth movements and facial expressions in real time and combines them with audio data to perform sign language translation. For example, it analyzes the mouth movements and facial expressions when a speaker says "thank you" and reflects them in the sign language movements. The sign language translation unit also uses a camera to acquire video data to analyze the speaker's mouth movements and facial expressions, and the generation AI performs sign language translation based on that data. For example, if the speaker's mouth movements match "hello," it generates that sign language. The sign language translation unit also builds a system in which the generation AI analyzes the speaker's mouth movements and facial expressions and integrates them with audio data to perform sign language translation. For example, if the speaker's facial expression indicates "surprise," that emotion can be reflected in the sign language. This enables more accurate sign language translation by analyzing the speaker's mouth movements and facial expressions.

[0051] The sign language translation unit analyzes the speaker's tone of voice and emotion, and can add emotional expressions to the sign language. For example, the generation AI in the sign language translation unit analyzes the speaker's tone of voice and generates a sign language expression corresponding to that tone. For example, if a speaker says "happy" in a high tone, that emotion is reflected in the sign language. The sign language translation unit also analyzes audio data in real time to analyze the speaker's tone of voice and emotion, and the generation AI performs sign language translation based on that data. For example, if a speaker says "sad" in a low tone, that emotion is reflected in the sign language. The sign language translation unit also builds a system in which the generation AI analyzes the speaker's tone of voice and emotion and adds emotional expressions to the sign language. For example, if the speaker sounds excited, that emotion is reflected in the sign language. In this way, emotional expressions can be added to the sign language by analyzing the speaker's tone of voice and emotion.

[0052] The sign language translation unit can use the emotion estimation function to estimate the speaker's emotion and generate a sign language expression corresponding to that emotion. In the sign language translation unit, for example, the generation AI estimates the speaker's emotion and generates a sign language expression corresponding to that emotion. For example, when a speaker says "thank you," it estimates the emotion of gratitude and reflects that emotion in the sign language. The sign language translation unit also uses the emotion estimation function to analyze the speaker's emotion in real time, and the generation AI performs sign language translation based on that data. For example, when a speaker says "I'm sorry," it estimates the emotion of apology and reflects that emotion in the sign language. The sign language translation unit also builds a system in which the generation AI estimates the speaker's emotion using the emotion estimation function and generates a sign language expression corresponding to that emotion. For example, when a speaker says "I'm happy," it estimates the emotion of joy and reflects that emotion in the sign language. In this way, the emotion estimation function can be used to generate a sign language expression corresponding to the speaker's emotion.

[0053] The sign language translation unit can translate spoken words not only into sign language but also into visual icons and emojis. For example, the sign language translation unit not only translates spoken words by the generation AI into sign language but also adds a function to convert them into visual icons and emojis. For example, "thank you" is displayed as a thankful emoji along with the sign language. The sign language translation unit also builds a system that simultaneously displays visual icons and emojis when translating spoken words into sign language. For example, "hello" is displayed as a greeting icon along with the sign language. The sign language translation unit not only translates spoken words by the generation AI into sign language but also adds a function to convert them into visual icons and emojis, allowing the user to select. For example, "congratulations" is displayed as a congratulatory emoji along with the sign language. This allows for a greater variety of expressions by translating spoken words not only into sign language but also into visual icons and emojis.

[0054] The sign language translation unit can analyze different dialects and accents and perform sign language translation accordingly. For example, the sign language translation unit adds a function that allows the generation AI to analyze different dialects and accents and perform sign language translation accordingly. For example, it translates the Kansai dialect "Okini" into sign language. The sign language translation unit also strengthens the speech analysis function of the generation AI to analyze different dialects and accents, improving the accuracy of sign language translation. For example, it translates the Okinawa dialect "Haisai" into sign language. The sign language translation unit also builds a system that allows the generation AI to analyze different dialects and accents and perform sign language translation accordingly. For example, it translates the Tohoku dialect "Nda" into sign language. This improves the accuracy of sign language translation by making it possible to handle different dialects and accents.

[0055] The sign language translation unit can use the emotion estimation function to display background colors and effects on the screen according to the speaker's emotions. For example, the sign language translation unit adds a function in which the generation AI estimates the speaker's emotions and displays background colors and effects on the screen according to that emotion. For example, when the speaker says "happy," the background color is changed to a brighter color. The sign language translation unit also uses the emotion estimation function to analyze the speaker's emotions in real time, and builds a system in which the generation AI changes the background color and effects based on that data. For example, when the speaker says "sad," the background color is changed to a darker color. The sign language translation unit also develops a system in which the generation AI estimates the speaker's emotions using the emotion estimation function and displays background colors and effects on the screen according to that emotion. For example, an effect is added when the speaker says "surprised." As a result, the emotion estimation function can be used to display background colors and effects on the screen according to the speaker's emotions.

[0056] The sign language expression unit can link the AI ​​character's facial expressions and body movements to achieve more natural sign language expressions. For example, the sign language expression unit adds a function to link the character's facial expressions and body movements when the AI ​​character uses sign language. For example, when expressing "thank you" in sign language, the character smiles. In addition, the sign language expression unit builds a system in which the generation AI simultaneously generates sign language actions and facial expressions to link the character's facial expressions and body movements. For example, when expressing "I'm sorry" in sign language, the character makes a sad expression. In addition, the sign language expression unit strengthens the function to link facial expressions and body movements when the AI ​​character uses sign language, achieving more natural sign language expressions. For example, when expressing "I'm happy" in sign language, the character makes a happy expression. This links the AI ​​character's facial expressions and body movements, enabling more natural sign language expressions.

[0057] The sign language expression unit can adjust the speed and movements of the sign language according to the user's level of understanding. For example, when an AI character uses sign language, the sign language expression unit adds a function to adjust the speed and movements of the sign language according to the user's level of understanding. For example, the sign language expression unit can slow down the speed of the sign language for beginners. The sign language expression unit also analyzes the user's level of understanding in real time and builds a system in which the generation AI adjusts the speed and movements of the sign language. For example, the sign language expression unit can speed up the speed of the sign language for advanced users. The sign language expression unit also enhances the function to adjust the speed and movements of the sign language according to the user's level of understanding when the AI ​​character uses sign language, thereby providing more appropriate sign language expression. For example, the sign language movements can be simplified for children. This makes it possible to adjust the speed and movements of the sign language according to the user's level of understanding, thereby enabling more appropriate sign language expression.

[0058] The sign language expression unit can use the emotion estimation function to change the AI ​​character's facial expression and movements according to the user's emotions. For example, the sign language expression unit adds a function that allows the generation AI to estimate the user's emotions and change the AI ​​character's facial expression and movements according to those emotions. For example, when the user says "happy," the character smiles. The sign language expression unit also uses the emotion estimation function to analyze the user's emotions in real time, and builds a system in which the generation AI changes the character's facial expression and movements based on that data. For example, when the user says "sad," the character makes a sad expression. The sign language expression unit also develops a system in which the generation AI uses the emotion estimation function to estimate the user's emotions and change the AI ​​character's facial expression and movements according to those emotions. For example, when the user says "surprised," the character makes a surprised expression. This makes it possible to change the AI ​​character's facial expression and movements according to the user's emotions by using the emotion estimation function.

[0059] The sign language expression unit can prepare multiple AI characters and allow the user to select a character of their choice. For example, the sign language expression unit prepares multiple AI characters and adds a function that allows the user to select a character of their choice. For example, it allows the user to select a male character or a female character. The sign language expression unit also builds a system in which the generation AI generates multiple characters so that the user can select a character of their choice. For example, it provides characters with different clothing and hairstyles. The sign language expression unit also prepares multiple AI characters and enhances the function that allows the user to select a character of their choice, providing a wider variety of options. For example, it allows the user to select an animal character or an anime-style character. In this way, by providing multiple AI characters, the user can select a character of their choice.

[0060] The sign language expression unit can display visual information related to the background when the sign language is being used to aid understanding. For example, the sign language expression unit adds a function to display visual information related to the background when the AI ​​character is using sign language. For example, when expressing "thank you," a scene of gratitude is displayed in the background. In addition, the sign language expression unit builds a system in which the generation AI automatically generates a background according to the content of the sign language to display visual information related to the background. For example, when expressing "hello," a greeting scene is displayed in the background. In addition, the sign language expression unit enhances the function to display visual information related to the background when the AI ​​character is using sign language, providing sign language expressions that are easier to understand. For example, when expressing "congratulations," a congratulatory scene is displayed in the background. In this way, by displaying visual information related to the background when the sign language is being used, it is possible to aid understanding.

[0061] The sign language expression unit can use the emotion estimation function to change the character's costume or background according to the user's emotion. For example, the sign language expression unit adds a function in which the generation AI estimates the user's emotion and changes the character's costume or background according to that emotion. For example, when the user says "happy," the character's costume can be changed to a brighter color. The sign language expression unit also uses the emotion estimation function to analyze the user's emotion in real time, and builds a system in which the generation AI changes the character's costume or background based on that data. For example, when the user says "sad," the background can be changed to a darker color. The sign language expression unit also develops a system in which the generation AI estimates the user's emotion using the emotion estimation function and changes the character's costume or background according to that emotion. For example, when the user says "surprised," an effect can be added to the background. In this way, the emotion estimation function can be used to change the character's costume or background according to the user's emotion.

[0062] The sign language analysis unit analyzes not only the position and speed of the hand but also the fine finger movements, allowing it to convert the sign language movements into more accurate characters and speech. For example, when the generation AI analyzes sign language movements, the sign language analysis unit adds a function that analyzes not only the position and speed of the hand but also the fine finger movements. For example, if the finger movements indicate "thank you," the movement is converted into characters and speech. The sign language analysis unit also builds a system in which the generation AI uses high-precision video analysis technology to analyze not only the position and speed of the hand but also the fine finger movements. For example, if the finger movements indicate "hello," the movement is converted into characters and speech. The sign language analysis unit also analyzes the fine finger movements when the generation AI analyzes sign language movements, allowing it to convert the sign language movements into more accurate characters and speech. For example, if the finger movements indicate "sorry," the movement is converted into characters and speech. This allows for more accurate conversion into characters and speech by analyzing not only the position and speed of the hand but also the fine finger movements.

[0063] The sign language analysis unit can understand the context of sign language and convert it into appropriate grammar and expressions. For example, the sign language analysis unit adds a function that enables the generation AI to understand the context of sign language and convert it into appropriate grammar and expressions. For example, when expressing "thank you" in sign language, it can convert it to "I appreciate it" depending on the context. In addition, the sign language analysis unit builds a system in which the generation AI analyzes the context of sign language and converts it into appropriate grammar and expressions in order to understand the context of sign language. For example, when expressing "hello" in sign language, it can convert it to "good morning" depending on the context. In addition, the sign language analysis unit strengthens the generation AI's ability to understand the context of sign language and convert it into appropriate grammar and expressions, converting it into more natural-looking text and speech. For example, when expressing "sorry" in sign language, it can convert it to "I apologize" depending on the context. This enables the context of sign language to be understood and converted into appropriate grammar and expressions.

[0064] The sign language analysis unit can use the emotion estimation function to estimate the emotion of a user using sign language and generate a voice tone or text expression corresponding to that emotion. For example, the sign language analysis unit adds a function in which the generation AI estimates the emotion of a user using sign language and generates a voice tone or text expression corresponding to that emotion. For example, when a user expresses "thank you" in sign language, the sign language analysis unit estimates the emotion of gratitude and pronounces "thank you" with a voice tone corresponding to that emotion. The sign language analysis unit also uses the emotion estimation function to analyze the emotion of a user using sign language in real time, and a generation AI generates a voice tone or text expression based on that data. For example, when a user expresses "I'm sorry" in sign language, the sign language analysis unit estimates the emotion of apology and pronounces "I'm sorry" with a voice tone corresponding to that emotion. The sign language analysis unit also develops a system in which the generation AI estimates the emotion of a user using sign language using the emotion estimation function and generates a voice tone or text expression corresponding to that emotion. For example, when a user expresses "I'm happy" in sign language, the emotion of joy is inferred and "I'm happy" is pronounced with a voice tone that corresponds to that emotion. By using the emotion inference function, it is possible to generate a voice tone and text expression that corresponds to the user's emotion.

[0065] The sign language analysis unit can convert sign language into a visual format, in addition to text and audio. For example, the sign language analysis unit adds a function to the generation AI that converts sign language into animation, in addition to text and audio. For example, when expressing "thank you" in sign language, an animation of a gesture of gratitude is displayed. The sign language analysis unit also builds a system in which the generation AI visually displays sign language gestures in order to convert sign language into visual notes. For example, when expressing "hello" in sign language, a greeting gesture is displayed in the visual note. The sign language analysis unit also enhances the generation AI's function to convert sign language into other visual formats, in addition to text and audio, to provide more diverse methods of expression. For example, when expressing "congratulations" in sign language, a congratulatory gesture is displayed in the visual note. This enables more diverse expressions by converting sign language into other visual formats, in addition to text and audio.

[0066] The sign language analysis unit can display the sign language analysis results in real time and provide an interface that allows the user to check and correct the results. For example, the sign language analysis unit adds a function that allows the generation AI to display the sign language analysis results in real time and provide an interface that allows the user to check and correct the results. For example, when expressing "thank you" in sign language, the results are converted into text or speech in real time, allowing the user to make corrections. In addition, to display the sign language analysis results in real time, the sign language analysis unit builds a system in which the generation AI analyzes sign language movements and instantly displays the results. For example, when expressing "hello" in sign language, the results are converted into text or speech in real time, allowing the user to make corrections. In addition, the sign language analysis unit enhances the function that allows the generation AI to display the sign language analysis results in real time and provide an interface that allows the user to check and correct the results, providing an easier-to-use system. For example, when expressing "I'm sorry" in sign language, the results are converted into text or speech in real time, allowing the user to make corrections. This enables more accurate sign language translation by displaying the sign language analysis results in real time and providing an interface that allows the user to check and correct the results.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The sign language interpretation system can further include a location information acquisition unit that acquires the user's location information and provides sign language expressions according to the location. For example, if the user is in a specific area, sign language expressions according to the dialect and culture of that area are provided. The location information acquisition unit can also update the location information in real time as the user moves and provide appropriate sign language expressions. For example, if the user is in a tourist spot, sign language expressions related to that tourist spot are provided. The location information acquisition unit can also provide information on nearby sign language interpreters and sign language classes based on the user's location information. This enables more appropriate communication by providing sign language expressions and related information according to the user's location information.

[0069] The sign language interpretation system may further include a health condition monitoring unit that monitors the user's health condition and provides sign language expressions according to the user's health condition. For example, if the user is tired, the speed of the sign language may be slowed down. The health condition monitoring unit may also measure the user's heart rate and stress level in real time and provide appropriate sign language expressions. For example, if the user is relaxed, the sign language expressions may be softer. The health condition monitoring unit may also display not only sign language expressions but also messages encouraging the user to take a break according to the user's health condition. This allows for more comfortable communication by providing sign language expressions and support according to the user's health condition.

[0070] The sign language interpretation system may further include a learning history recording unit that records the user's learning history and provides sign language expressions according to the user's learning progress. For example, if the user is learning a specific sign language expression, the system allows the user to repeatedly practice that expression. The learning history recording unit may also track the user's learning progress in real time and provide appropriate sign language expressions. For example, if the user is at a beginner's level, the system may provide basic sign language expressions. The learning history recording unit may also suggest the next sign language expression to be learned based on the user's learning history. This allows for effective sign language learning by providing sign language expressions and learning support according to the user's learning progress.

[0071] The sign language interpretation system can further include a personalized sign language representation unit that provides sign language representations according to the user's preferences and interests. For example, if the user is interested in sports, sign language representations related to sports are provided. The personalized sign language representation unit can also suggest appropriate sign language representations based on the user's past usage history. For example, it can prioritize the display of sign language representations that the user frequently uses. The personalized sign language representation unit can also provide not only sign language representations but also related information and content according to the user's preferences and interests. This enables more engaging communication by providing sign language representations and information according to the user's preferences and interests.

[0072] The sign language interpretation system may further include a schedule management unit that acquires the user's schedule information and provides sign language expressions according to the schedule. For example, if the user plans to attend a meeting, sign language expressions related to the meeting are provided. The schedule management unit can also update the user's schedule information in real time and provide appropriate sign language expressions. For example, if the user plans to go on a trip, sign language expressions related to the trip are provided. The schedule management unit can also display not only sign language expressions but also related reminders and notifications based on the user's schedule. This enables more efficient communication by providing sign language expressions and support according to the user's schedule.

[0073] The sign language interpretation system may further include an emotion estimation unit that estimates the user's emotion and adjusts the sign language expression based on the estimated emotion. For example, if the user is nervous, the sign language expression will be made slower. The emotion estimation unit can also analyze the user's facial expression and tone of voice to estimate the emotion in real time. For example, if the user is happy, the sign language expression will be made brighter. The emotion estimation unit can also display not only sign language expressions but also encouraging messages according to the user's emotion. This enables more appropriate communication by providing sign language expressions and support according to the user's emotion.

[0074] The sign language interpretation system can further estimate the user's emotions and adjust the speed and emphasis of the sign language based on the estimated emotions. For example, if the user is excited, the speed of the sign language can be increased and the movements can be emphasized. The emotion estimation unit can also analyze the user's emotions in real time and provide appropriate sign language expressions. For example, if the user is calm, the speed of the sign language can be decreased and the movements can be made gentler. The emotion estimation unit can also display not only sign language expressions but also messages encouraging relaxation according to the user's emotions. This allows for more natural communication by providing sign language expressions and support that correspond to the user's emotions.

[0075] The sign language interpretation system can further estimate the user's emotions and adjust the sign language content based on the estimated emotions. For example, if the user is sad, the sign language content is changed to a gentler expression. The emotion estimation unit can also analyze the user's emotions in real time and provide appropriate sign language expressions. For example, if the user is angry, the sign language content is changed to a calmer expression. The emotion estimation unit can also display not only sign language expressions but also messages to soothe the emotions depending on the user's emotions. This enables more appropriate communication by providing sign language expressions and support that correspond to the user's emotions.

[0076] The sign language interpretation system can further estimate the user's emotions and adjust the sign language expression method based on the estimated emotions. For example, if the user is surprised, the sign language movements are increased. The emotion estimation unit can also analyze the user's emotions in real time and provide appropriate sign language expressions. For example, if the user is tired, the sign language movements are decreased. The emotion estimation unit can also display not only sign language expressions but also messages encouraging the user to take a break depending on the user's emotions. This allows for more natural communication by providing sign language expressions and support according to the user's emotions.

[0077] The sign language interpretation system can further estimate the user's emotions and emphasize the sign language expressions based on the estimated emotions. For example, if the user is feeling grateful, the sign language movements are increased and the expression is emphasized. The emotion estimation unit can also analyze the user's emotions in real time and provide appropriate sign language expressions. For example, if the user is feeling apologetic, the sign language movements are reduced and the expression is subdued. The emotion estimation unit can also display not only sign language expressions but also messages that convey the emotions, depending on the user's emotions. This enables more appropriate communication by providing sign language expressions and support that correspond to the user's emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The voice input unit receives the user's voice. For example, the voice can be input using a microphone. The voice input unit can also use the built-in microphone of a smartphone or tablet. Step 2: The sign language translation unit translates the speech received by the speech input unit into sign language. For example, a generation AI analyzes the speech data and translates it into sign language. Step 3: The sign language expression unit visually expresses the sign language translated by the sign language translation unit. For example, an AI character expresses the sign language on the screen. Step 4: The sign language analysis unit analyzes the sign language captured on the camera. For example, the generation AI analyzes the sign language video data and understands the content of the sign language. Step 5: The character conversion unit converts the sign language analyzed by the sign language analysis unit into text. For example, the generation AI converts the sign language content into text data. Step 6: The speech conversion unit converts the sign language analyzed by the sign language analysis unit into speech. For example, the generation AI converts the sign language content into speech data.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] 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.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] 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.

[0092] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] 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.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] 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.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0139] 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.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice input unit for receiving a user's voice; a sign language translation unit that translates the speech received by the speech input unit into sign language; a sign language representation unit that visually represents the sign language translated by the sign language translation unit; a sign language analysis unit that analyzes sign language captured by a camera; a character conversion unit that converts the sign language analyzed by the sign language analysis unit into characters; a speech conversion unit that converts the sign language analyzed by the sign language analysis unit into speech. A system characterized by:

2. The sign language translation unit The speaker's mouth movements and facial expressions are analyzed in real time, and combined with the voice data to translate into sign language.

2. The system of claim 1.

3. The sign language translation unit Analyzing the speaker's tone of voice and emotions to add emotional expressions to sign language 2. The system of claim 1.

4. The sign language translation unit Estimate the speaker's emotions and generate sign language expressions that correspond to those emotions 2. The system of claim 1.

5. The sign language translation unit Translate spoken words into visual icons and emojis, as well as sign language 2. The system of claim 1.

6. The sign language translation unit Analyze different dialects and accents and translate into sign language accordingly 2. The system of claim 1.

7. The sign language translation unit Display background colors and effects on the screen according to the speaker's emotions 2. The system of claim 1.

8. The sign language expression unit The AI ​​character's facial expressions and body movements will be linked to achieve more natural sign language expression.

2. The system of claim 1.

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