System

Abstract

An object of a system according to an embodiment is to support sign language learning by analyzing the diversity of sign language and providing background information thereof.SOLUTION: A system includes a sign language analysis unit, a background information providing unit, and a learning support unit. The sign language analysis unit analyzes the diversity of sign language. The background information providing unit provides background information of the sign language analyzed by the sign language analyzing unit. The learning support unit supports sign language learning on the basis of the information provided by the background information providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 has had the difficulty of responding to the diversity of sign languages, making it difficult for sign language learners to feel familiar with sign language.

[0005] The system according to the embodiment aims to support sign language learning by analyzing the diversity of sign languages ​​and providing background information. [Means for solving the problem]

[0006] A system according to an embodiment includes a sign language analysis unit, a background information provision unit, and a learning support unit. The sign language analysis unit analyzes the diversity of sign languages. The background information provision unit provides background information of the sign language analyzed by the sign language analysis unit. The learning support unit supports learning of sign language based on the information provided by the background information provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can support sign language learning by analyzing the diversity of sign languages ​​and providing background information. [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) The sign language understanding support system according to an embodiment of the present invention is a system that analyzes the diversity of sign languages ​​and provides additional information on the origins and meanings of the sign languages. This makes sign language understanding support system more approachable and attracts people who have not previously been interested in sign language.

[0029] A sign language understanding support system according to an embodiment includes a sign language analysis unit, a background information provision unit, and a learning support unit. The sign language analysis unit analyzes the diversity of sign languages. For example, the generation AI analyzes video data of sign languages ​​to understand differences in sign languages ​​across regions and eras. The generation AI can also perform analysis taking into account differences in the speed and rhythm of sign language movements. The background information provision unit provides background information on the sign language analyzed by the sign language analysis unit. For example, the generation AI provides additional information such as the "trigger" and "meaning" of the origin of a sign language. The generation AI can also explain the historical and cultural background of a sign language. The learning support unit supports learning sign language based on the information provided by the background information provision unit. For example, the generation AI provides videos and texts for sign language practice, making it easier for users to learn sign language. The generation AI can also explain the movements and meanings of sign language in an easy-to-understand manner. As a result, the sign language understanding support system according to an embodiment facilitates understanding the diversity of sign languages ​​and increases familiarity with sign language. For example, learning background information about sign language deepens understanding of sign language and increases motivation to learn it. Also, participating in a sign language community increases opportunities to use sign language and promotes the spread of sign language.

[0030] The sign language analysis unit can perform analysis taking into account differences in the speed and rhythm of sign language movements. The sign language analysis unit, for example, uses generation AI to analyze differences in the speed and rhythm of sign language movements. For example, by analyzing video data of sign language and extracting patterns of the speed and rhythm of movements, it can clarify the characteristics of sign language by region and era. In addition, the generation AI can input video data of sign language and analyze changes in the speed and rhythm of movements to perform analysis taking into account differences in the speed and rhythm of sign language movements. This allows for a more detailed understanding of the diversity of sign language.

[0031] The sign language analysis unit performs analysis based on the frequency of sign language use and context, and can track changes in the meaning of sign language. The sign language analysis unit performs analysis based on the frequency of sign language use and context, for example, using a generation AI. For example, by analyzing video data of sign language and tracking the context in which a specific sign language is used, changes in the meaning of sign language can be clarified. In addition, the generation AI can input video data of sign language and analyze the context in which a specific sign language is used in order to perform analysis based on the frequency of sign language use and context. This makes it possible to track changes in the meaning of sign language.

[0032] The sign language analysis unit can analyze sign languages ​​from different cultural and linguistic regions, providing a diversity of sign languages ​​from a global perspective. The sign language analysis unit can analyze sign languages ​​from different cultural and linguistic regions, for example, using a generation AI. For example, by collecting video data of sign languages ​​and analyzing the characteristics of sign languages ​​from different cultural and linguistic regions, the diversity of sign languages ​​from a global perspective can be provided. In addition, in order to analyze sign languages ​​from different cultural and linguistic regions, the generation AI can also input video data of sign languages ​​and analyze the characteristics of sign languages ​​from different cultural and linguistic regions. This makes it possible to provide a diversity of sign languages ​​from a global perspective.

[0033] The sign language analysis unit analyzes combinations of sign language gestures and speech, and can clarify the interrelationship between sign language and speech. The sign language analysis unit, for example, uses a generation AI to analyze combinations of sign language gestures and speech. For example, it analyzes video data and audio data of sign language to clarify the interrelationship between sign language and speech. In addition, the generation AI can input video data and audio data of sign language to analyze combinations of sign language gestures and speech, and analyze the interrelationship between sign language and speech. This makes it possible to clarify the interrelationship between sign language and speech.

[0034] The sign language analysis unit can perform analysis taking into account the social background and occupation of the sign language user. For example, when using the generation AI to analyze differences in sign language by region or age, the sign language analysis unit takes into account the social background and occupation of the sign language user. For example, it can analyze video data of sign language and clarify differences in sign language based on the user's social background and occupation. In addition, the generation AI can input video data of sign language and analyze it taking into account the user's social background and occupation in order to analyze differences in sign language by region or age. This allows for a more detailed understanding of differences in sign language.

[0035] The sign language analysis unit performs analysis based on the scene and situation in which the sign language is used, and can track changes in the meaning of the sign language. The sign language analysis unit performs analysis based on the scene and situation in which the sign language is used, for example, using a generation AI. For example, it analyzes video data of sign language and tracks changes in the meaning of sign language used in specific scenes and situations. In addition, in order to perform analysis based on the scene and situation in which the sign language is used, the generation AI can also input video data of sign language and analyze changes in the meaning of sign language used in specific scenes and situations. This makes it possible to track changes in the meaning of sign language.

[0036] The sign language analysis unit can analyze sign languages ​​from different cultural and linguistic regions and provide differences in sign languages ​​from a global perspective. The sign language analysis unit analyzes sign languages ​​from different cultural and linguistic regions, for example, using a generation AI. For example, by collecting video data of sign languages ​​and analyzing the characteristics of sign languages ​​from different cultural and linguistic regions, differences in sign languages ​​from a global perspective can be provided. In addition, in order to analyze sign languages ​​from different cultural and linguistic regions, the generation AI can also input video data of sign languages ​​and analyze the characteristics of sign languages ​​from different cultural and linguistic regions. This makes it possible to provide differences in sign languages ​​from a global perspective.

[0037] The sign language analysis unit analyzes combinations of sign language gestures and speech, and can clarify the interrelationship between sign language and speech. The sign language analysis unit, for example, uses a generation AI to analyze combinations of sign language gestures and speech. For example, it analyzes video data and audio data of sign language to clarify the interrelationship between sign language and speech. In addition, the generation AI can input video data and audio data of sign language to analyze combinations of sign language gestures and speech, and analyze the interrelationship between sign language and speech. This makes it possible to clarify the interrelationship between sign language and speech.

[0038] The background information providing unit can explain not only the historical and cultural background of sign language, but also the evolutionary process of sign language. For example, when providing background information of sign language using the generation AI, the background information providing unit can explain not only the historical and cultural background of sign language, but also the evolutionary process of sign language. For example, it can analyze video data of sign language to clarify the evolutionary process of sign language. In addition, in order to provide background information of sign language, the generation AI can input video data of sign language and analyze the historical and cultural background and evolutionary process of sign language. This makes it possible to provide more detailed background information of sign language.

[0039] The background information providing unit collects personal anecdotes and stories of sign language users, allowing for a deeper understanding of the meaning of the sign language. The background information providing unit, for example, uses a generation AI to collect personal anecdotes and stories of sign language users and provide them as background information for the sign language. For example, it analyzes video data of sign language and extracts the user's anecdotes and stories. In addition, the generation AI can input video data of sign language and collect the user's personal anecdotes and stories to provide background information for the sign language. This allows for a deeper understanding of the meaning of the sign language.

[0040] The background information providing unit can collect background information on sign languages ​​from different cultural and linguistic regions and provide background information on sign languages ​​from a global perspective. The background information providing unit collects background information on sign languages ​​from different cultural and linguistic regions, for example, using a generation AI. For example, the generation AI can collect video data of sign languages ​​and analyze the background information on sign languages ​​from different cultural and linguistic regions to provide background information on sign languages ​​from a global perspective. In addition, the generation AI can input video data of sign languages ​​and analyze the background information on sign languages ​​from different cultural and linguistic regions in order to collect background information on sign languages ​​from different cultural and linguistic regions. This makes it possible to provide background information on sign languages ​​from a global perspective.

[0041] The background information providing unit can analyze combinations of sign language gestures and speech and clarify the interrelationships between sign language and speech. The background information providing unit, for example, uses a generation AI to analyze combinations of sign language gestures and speech. For example, it analyzes video data and audio data of sign language and clarifies the interrelationships between sign language and speech. In addition, the generation AI can input video data and audio data of sign language to analyze combinations of sign language gestures and speech and analyze the interrelationships between sign language and speech. This makes it possible to clarify the interrelationships between sign language and speech.

[0042] The learning support unit provides videos and texts for sign language practice, making it easier for users to learn sign language. The learning support unit provides videos and texts for sign language practice, for example, by using a generation AI. For example, the learning support unit analyzes sign language video data and generates practice videos and texts. In addition, in order to provide videos and texts for sign language practice, the generation AI can also input sign language video data and generate practice videos and texts. This makes it easier for users to learn sign language.

[0043] The learning support unit can explain the actions and meanings of sign language in an easy-to-understand manner. The learning support unit, for example, uses a generation AI to explain the actions and meanings of sign language in an easy-to-understand manner. For example, the learning support unit analyzes video data of sign language and explains the actions and meanings in an easy-to-understand manner. In addition, the generation AI can input video data of sign language and explain the actions and meanings in an easy-to-understand manner. This makes it easier for users to understand sign language.

[0044] The learning support unit can provide information to support learning of sign language. The learning support unit provides information to support learning of sign language, for example, using a generation AI. For example, the learning support unit analyzes video data of sign language and generates information to support learning. In addition, in order to provide information to support learning of sign language, the generation AI can also input video data of sign language and generate information to support learning. This makes it easier for users to learn sign language.

[0045] The learning support unit provides information about events and workshops related to sign language, making it easier for users to participate in sign language communities. The learning support unit provides information about events and workshops related to sign language, for example, by using a generation AI. For example, the generation AI may analyze video data of sign language and generate information about events and workshops. In addition, in order to provide information about events and workshops related to sign language, the generation AI may input video data of sign language and generate information about events and workshops. This makes it easier for users to participate in sign language communities.

[0046] The learning support unit can provide information to promote the spread of sign language. The learning support unit provides information to promote the spread of sign language, for example, using a generation AI. For example, the learning support unit analyzes video data of sign language and generates information to promote its spread. In addition, in order to provide information to promote the spread of sign language, the generation AI can also input video data of sign language and generate information to promote its spread. This makes it possible to promote the spread of sign language.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] A sign language understanding support system can also track a user's progress in learning sign language and provide an individualized learning plan. For example, if a user is struggling with a particular sign language action, it can provide practice videos and texts specific to that action. It can also generate a list of sign languages ​​to learn next based on the user's learning history, supporting efficient learning. It can also have a function to test the sign languages ​​the user has learned and evaluate their level of understanding. This allows users to learn sign language at their own pace and effectively improve their skills.

[0049] The sign language understanding support system can also analyze combinations of sign language gestures and speech to clarify the interrelationship between sign language and speech. For example, it can analyze video data and audio data of sign language to clarify the interrelationship between sign language and speech. In addition, to analyze combinations of sign language gestures and speech, it can also input video data and audio data of sign language and analyze the interrelationship between sign language and speech. This makes it possible to clarify the interrelationship between sign language and speech.

[0050] The sign language understanding support system can also perform analysis based on the scene and situation in which the sign language is used and track changes in the meaning of the sign language. For example, it can analyze video data of sign language and track changes in the meaning of sign language used in a specific scene or situation. In order to perform analysis based on the scene and situation in which the sign language is used, it can also input video data of sign language and analyze changes in the meaning of sign language used in a specific scene or situation. This makes it possible to track changes in the meaning of sign language.

[0051] The sign language understanding support system can also perform analysis taking into account the social background and occupation of the sign language user. For example, it can analyze video data of sign language and clarify differences in sign language based on the user's social background and occupation. It can also input video data of sign language and analyze it taking into account the user's social background and occupation in order to analyze differences in sign language by region or age. This allows for a more detailed understanding of the differences in sign language.

[0052] The sign language understanding support system can also analyze sign languages ​​from different cultural and linguistic regions, providing a global perspective on the diversity of sign languages. For example, by collecting video data of sign languages ​​and analyzing the characteristics of sign languages ​​from different cultural and linguistic regions, a global perspective on the diversity of sign languages ​​can be provided. In addition, to analyze sign languages ​​from different cultural and linguistic regions, video data of sign languages ​​can be input and the characteristics of sign languages ​​from different cultural and linguistic regions can be analyzed. This makes it possible to provide a global perspective on the diversity of sign languages.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The sign language analysis unit analyzes the diversity of sign languages. For example, the generation AI analyzes video data of sign language to understand the differences in sign language by region and generation. The generation AI can also take into account differences in the speed and rhythm of sign language movements during analysis. Step 2: The background information provider provides background information about the sign language analyzed by the sign language analyzer. For example, the generation AI can provide additional information about the origins and meaning of the sign language. The generation AI can also explain the historical and cultural background of the sign language. Step 3: The learning support unit supports sign language learning based on the information provided by the background information provider. For example, the generation AI can provide videos and texts for sign language practice, making it easier for users to learn sign language. The generation AI can also provide easy-to-understand explanations of the actions and meanings of sign language.

[0055] (Example 2) The sign language understanding support system according to an embodiment of the present invention is a system that analyzes the diversity of sign languages ​​and provides additional information on the origins and meanings of the sign languages. This makes sign language understanding support system more approachable and attracts people who have not previously been interested in sign language.

[0056] A sign language understanding support system according to an embodiment includes a sign language analysis unit, a background information provision unit, and a learning support unit. The sign language analysis unit analyzes the diversity of sign languages. For example, the generation AI analyzes video data of sign languages ​​to understand differences in sign languages ​​across regions and eras. The generation AI can also perform analysis taking into account differences in the speed and rhythm of sign language movements. The background information provision unit provides background information on the sign language analyzed by the sign language analysis unit. For example, the generation AI provides additional information such as the "trigger" and "meaning" of the origin of a sign language. The generation AI can also explain the historical and cultural background of a sign language. The learning support unit supports learning sign language based on the information provided by the background information provision unit. For example, the generation AI provides videos and texts for sign language practice, making it easier for users to learn sign language. The generation AI can also explain the movements and meanings of sign language in an easy-to-understand manner. As a result, the sign language understanding support system according to an embodiment facilitates understanding the diversity of sign languages ​​and increases familiarity with sign language. For example, learning background information about sign language deepens understanding of sign language and increases motivation to learn it. Also, participating in a sign language community increases opportunities to use sign language and promotes the spread of sign language.

[0057] The sign language analysis unit can perform analysis taking into account differences in the speed and rhythm of sign language movements. The sign language analysis unit, for example, uses generation AI to analyze differences in the speed and rhythm of sign language movements. For example, by analyzing video data of sign language and extracting patterns of the speed and rhythm of movements, it can clarify the characteristics of sign language by region and era. In addition, the generation AI can input video data of sign language and analyze changes in the speed and rhythm of movements to perform analysis taking into account differences in the speed and rhythm of sign language movements. This allows for a more detailed understanding of the diversity of sign language.

[0058] The sign language analysis unit performs analysis based on the frequency of sign language use and context, and can track changes in the meaning of sign language. The sign language analysis unit performs analysis based on the frequency of sign language use and context, for example, using a generation AI. For example, by analyzing video data of sign language and tracking the context in which a specific sign language is used, changes in the meaning of sign language can be clarified. In addition, the generation AI can input video data of sign language and analyze the context in which a specific sign language is used in order to perform analysis based on the frequency of sign language use and context. This makes it possible to track changes in the meaning of sign language.

[0059] The sign language analysis unit can use the emotion estimation function to analyze the user's emotional response to the diversity of sign language and identify features of sign language that elicit positive emotions. The sign language analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to the diversity of sign language. For example, it analyzes video data of sign language, estimates emotions from the user's facial expressions and voice, and identifies features of sign language that elicit positive emotions. In addition, the generation AI can also analyze video data of sign language using the emotion estimation function to track the user's emotional response in order to analyze the user's emotional response to the diversity of sign language. This makes it possible to identify features of sign language that elicit positive emotions.

[0060] The sign language analysis unit can analyze sign languages ​​from different cultural and linguistic regions, providing a diversity of sign languages ​​from a global perspective. The sign language analysis unit can analyze sign languages ​​from different cultural and linguistic regions, for example, using a generation AI. For example, by collecting video data of sign languages ​​and analyzing the characteristics of sign languages ​​from different cultural and linguistic regions, the diversity of sign languages ​​from a global perspective can be provided. In addition, in order to analyze sign languages ​​from different cultural and linguistic regions, the generation AI can also input video data of sign languages ​​and analyze the characteristics of sign languages ​​from different cultural and linguistic regions. This makes it possible to provide a diversity of sign languages ​​from a global perspective.

[0061] The sign language analysis unit analyzes combinations of sign language gestures and speech, and can clarify the interrelationship between sign language and speech. The sign language analysis unit, for example, uses a generation AI to analyze combinations of sign language gestures and speech. For example, it analyzes video data and audio data of sign language to clarify the interrelationship between sign language and speech. In addition, the generation AI can input video data and audio data of sign language to analyze combinations of sign language gestures and speech, and analyze the interrelationship between sign language and speech. This makes it possible to clarify the interrelationship between sign language and speech.

[0062] The sign language analysis unit uses the emotion estimation function to monitor the user's emotional response to the diversity of sign languages ​​in real time, and can identify features of sign language that pique the user's interest. The sign language analysis unit, for example, uses the emotion estimation function to monitor the user's emotional response to the diversity of sign languages ​​in real time. For example, it analyzes video data of sign language, infers emotions from the user's facial expressions and voice, and identifies features of sign language that pique the user's interest. In addition, the generation AI can also analyze video data of sign language using the emotion estimation function to track the user's emotional response in order to monitor the user's emotional response to the diversity of sign languages ​​in real time. This makes it possible to identify features of sign language that pique the user's interest.

[0063] The sign language analysis unit can perform analysis taking into account the social background and occupation of the sign language user. For example, when using the generation AI to analyze differences in sign language by region or age, the sign language analysis unit takes into account the social background and occupation of the sign language user. For example, it can analyze video data of sign language and clarify differences in sign language based on the user's social background and occupation. In addition, the generation AI can input video data of sign language and analyze it taking into account the user's social background and occupation in order to analyze differences in sign language by region or age. This allows for a more detailed understanding of differences in sign language.

[0064] The sign language analysis unit performs analysis based on the scene and situation in which the sign language is used, and can track changes in the meaning of the sign language. The sign language analysis unit performs analysis based on the scene and situation in which the sign language is used, for example, using a generation AI. For example, it analyzes video data of sign language and tracks changes in the meaning of sign language used in specific scenes and situations. In addition, in order to perform analysis based on the scene and situation in which the sign language is used, the generation AI can also input video data of sign language and analyze changes in the meaning of sign language used in specific scenes and situations. This makes it possible to track changes in the meaning of sign language.

[0065] The sign language analysis unit uses the emotion estimation function to analyze the user's emotional response to differences in sign language by region and age group, and can identify features of sign language that elicit positive emotions. The sign language analysis unit, for example, uses the emotion estimation function to analyze the user's emotional response to differences in sign language by region and age group. For example, it analyzes video data of sign language, estimates emotions from the user's facial expressions and voice, and identifies features of sign language that elicit positive emotions. In addition, the generation AI can also analyze video data of sign language using the emotion estimation function and track the user's emotional response in order to analyze the user's emotional response to differences in sign language by region and age group. This makes it possible to identify features of sign language that elicit positive emotions.

[0066] The sign language analysis unit can analyze sign languages ​​from different cultural and linguistic regions and provide differences in sign languages ​​from a global perspective. The sign language analysis unit analyzes sign languages ​​from different cultural and linguistic regions, for example, using a generation AI. For example, by collecting video data of sign languages ​​and analyzing the characteristics of sign languages ​​from different cultural and linguistic regions, differences in sign languages ​​from a global perspective can be provided. In addition, in order to analyze sign languages ​​from different cultural and linguistic regions, the generation AI can also input video data of sign languages ​​and analyze the characteristics of sign languages ​​from different cultural and linguistic regions. This makes it possible to provide differences in sign languages ​​from a global perspective.

[0067] The sign language analysis unit analyzes combinations of sign language gestures and speech, and can clarify the interrelationship between sign language and speech. The sign language analysis unit, for example, uses a generation AI to analyze combinations of sign language gestures and speech. For example, it analyzes video data and audio data of sign language to clarify the interrelationship between sign language and speech. In addition, the generation AI can input video data and audio data of sign language to analyze combinations of sign language gestures and speech, and analyze the interrelationship between sign language and speech. This makes it possible to clarify the interrelationship between sign language and speech.

[0068] The sign language analysis unit uses the emotion estimation function to monitor the user's emotional response to regional and age-specific differences in sign language in real time, and can identify features of sign language that pique the user's interest. The sign language analysis unit, for example, uses the emotion estimation function to monitor the user's emotional response to regional and age-specific differences in sign language in real time. For example, it analyzes sign language video data, infers emotions from the user's facial expressions and voice, and identifies sign language features that pique the user's interest. In addition, the generation AI can also analyze sign language video data using the emotion estimation function to track the user's emotional response in order to monitor the user's emotional response to regional and age-specific differences in sign language in real time. This makes it possible to identify features of sign language that pique the user's interest.

[0069] The background information providing unit can explain not only the historical and cultural background of sign language, but also the evolutionary process of sign language. For example, when providing background information of sign language using the generation AI, the background information providing unit can explain not only the historical and cultural background of sign language, but also the evolutionary process of sign language. For example, it can analyze video data of sign language to clarify the evolutionary process of sign language. In addition, in order to provide background information of sign language, the generation AI can input video data of sign language and analyze the historical and cultural background and evolutionary process of sign language. This makes it possible to provide more detailed background information of sign language.

[0070] The background information providing unit collects personal anecdotes and stories of sign language users, allowing for a deeper understanding of the meaning of the sign language. The background information providing unit, for example, uses a generation AI to collect personal anecdotes and stories of sign language users and provide them as background information for the sign language. For example, it analyzes video data of sign language and extracts the user's anecdotes and stories. In addition, the generation AI can input video data of sign language and collect the user's personal anecdotes and stories to provide background information for the sign language. This allows for a deeper understanding of the meaning of the sign language.

[0071] The background information providing unit can use the emotion estimation function to analyze the user's emotional response to the sign language background information and identify features of the sign language that elicit positive emotions. The background information providing unit, for example, uses the emotion estimation function to analyze the user's emotional response to the sign language background information. For example, it analyzes video data of sign language, estimates emotions from the user's facial expressions and voice, and identifies features of sign language that elicit positive emotions. In addition, the generation AI can also analyze video data of sign language using the emotion estimation function to track the user's emotional response in order to analyze the user's emotional response to the sign language background information. This makes it possible to identify features of sign language that elicit positive emotions.

[0072] The background information providing unit can collect background information on sign languages ​​from different cultural and linguistic regions and provide background information on sign languages ​​from a global perspective. The background information providing unit collects background information on sign languages ​​from different cultural and linguistic regions, for example, using a generation AI. For example, the generation AI can collect video data of sign languages ​​and analyze the background information on sign languages ​​from different cultural and linguistic regions to provide background information on sign languages ​​from a global perspective. In addition, the generation AI can input video data of sign languages ​​and analyze the background information on sign languages ​​from different cultural and linguistic regions in order to collect background information on sign languages ​​from different cultural and linguistic regions. This makes it possible to provide background information on sign languages ​​from a global perspective.

[0073] The background information providing unit can analyze combinations of sign language gestures and speech and clarify the interrelationships between sign language and speech. The background information providing unit, for example, uses a generation AI to analyze combinations of sign language gestures and speech. For example, it analyzes video data and audio data of sign language and clarifies the interrelationships between sign language and speech. In addition, the generation AI can input video data and audio data of sign language to analyze combinations of sign language gestures and speech and analyze the interrelationships between sign language and speech. This makes it possible to clarify the interrelationships between sign language and speech.

[0074] The background information providing unit uses the emotion estimation function to monitor the user's emotional response to the sign language background information in real time, and can identify features of the sign language that will interest the user. The background information providing unit, for example, uses the emotion estimation function to monitor the user's emotional response to the sign language background information in real time. For example, it analyzes sign language video data, estimates emotions from the user's facial expressions and voice, and identifies features of sign language that will interest the user. In addition, the generation AI can also analyze sign language video data using the emotion estimation function to track the user's emotional response in order to monitor the user's emotional response to the sign language background information in real time. This makes it possible to identify features of sign language that will interest the user.

[0075] The learning support unit provides videos and texts for sign language practice, making it easier for users to learn sign language. The learning support unit provides videos and texts for sign language practice, for example, by using a generation AI. For example, the learning support unit analyzes sign language video data and generates practice videos and texts. In addition, in order to provide videos and texts for sign language practice, the generation AI can also input sign language video data and generate practice videos and texts. This makes it easier for users to learn sign language.

[0076] The learning support unit can explain the actions and meanings of sign language in an easy-to-understand manner. The learning support unit, for example, uses a generation AI to explain the actions and meanings of sign language in an easy-to-understand manner. For example, the learning support unit analyzes video data of sign language and explains the actions and meanings in an easy-to-understand manner. In addition, the generation AI can input video data of sign language and explain the actions and meanings in an easy-to-understand manner. This makes it easier for users to understand sign language.

[0077] The learning support unit can provide information to support learning of sign language. The learning support unit provides information to support learning of sign language, for example, using a generation AI. For example, the learning support unit analyzes video data of sign language and generates information to support learning. In addition, in order to provide information to support learning of sign language, the generation AI can also input video data of sign language and generate information to support learning. This makes it easier for users to learn sign language.

[0078] The learning support unit provides information about events and workshops related to sign language, making it easier for users to participate in sign language communities. The learning support unit provides information about events and workshops related to sign language, for example, by using a generation AI. For example, the generation AI may analyze video data of sign language and generate information about events and workshops. In addition, in order to provide information about events and workshops related to sign language, the generation AI may input video data of sign language and generate information about events and workshops. This makes it easier for users to participate in sign language communities.

[0079] The learning support unit can provide information to promote the spread of sign language. The learning support unit provides information to promote the spread of sign language, for example, using a generation AI. For example, the learning support unit analyzes video data of sign language and generates information to promote its spread. In addition, in order to provide information to promote the spread of sign language, the generation AI can also input video data of sign language and generate information to promote its spread. This makes it possible to promote the spread of sign language.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] A sign language understanding support system can also track a user's progress in learning sign language and provide an individualized learning plan. For example, if a user is struggling with a particular sign language action, it can provide practice videos and texts specific to that action. It can also generate a list of sign languages ​​to learn next based on the user's learning history, supporting efficient learning. It can also have a function to test the sign languages ​​the user has learned and evaluate their level of understanding. This allows users to learn sign language at their own pace and effectively improve their skills.

[0082] The sign language understanding support system can also analyze combinations of sign language gestures and speech to clarify the interrelationship between sign language and speech. For example, it can analyze video data and audio data of sign language to clarify the interrelationship between sign language and speech. In addition, to analyze combinations of sign language gestures and speech, it can also input video data and audio data of sign language and analyze the interrelationship between sign language and speech. This makes it possible to clarify the interrelationship between sign language and speech.

[0083] The sign language understanding support system can also perform analysis based on the scene and situation in which the sign language is used and track changes in the meaning of the sign language. For example, it can analyze video data of sign language and track changes in the meaning of sign language used in a specific scene or situation. In order to perform analysis based on the scene and situation in which the sign language is used, it can also input video data of sign language and analyze changes in the meaning of sign language used in a specific scene or situation. This makes it possible to track changes in the meaning of sign language.

[0084] The sign language understanding support system can also perform analysis taking into account the social background and occupation of the sign language user. For example, it can analyze video data of sign language and clarify differences in sign language based on the user's social background and occupation. It can also input video data of sign language and analyze it taking into account the user's social background and occupation in order to analyze differences in sign language by region or age. This allows for a more detailed understanding of the differences in sign language.

[0085] The sign language understanding support system can also analyze sign languages ​​from different cultural and linguistic regions, providing a global perspective on the diversity of sign languages. For example, by collecting video data of sign languages ​​and analyzing the characteristics of sign languages ​​from different cultural and linguistic regions, a global perspective on the diversity of sign languages ​​can be provided. In addition, to analyze sign languages ​​from different cultural and linguistic regions, video data of sign languages ​​can be input and the characteristics of sign languages ​​from different cultural and linguistic regions can be analyzed. This makes it possible to provide a global perspective on the diversity of sign languages.

[0086] The sign language understanding support system can also use the emotion estimation function to analyze a user's emotional response to the diversity of sign language and identify the sign language features that elicit positive emotions. For example, it can analyze video data of sign language, estimate emotions from the user's facial expressions and voice, and identify the sign language features that elicit positive emotions. In addition, to analyze a user's emotional response to the diversity of sign language, it can also use the emotion estimation function to analyze video data of sign language and track the user's emotional response. This makes it possible to identify the sign language features that elicit positive emotions.

[0087] The sign language understanding support system can also use the emotion estimation function to monitor the user's emotional response to the diversity of sign languages ​​in real time and identify the features of sign language that interest the user. For example, it can analyze video data of sign language, estimate emotions from the user's facial expressions and voice, and identify the features of sign language that interest the user. In addition, in order to monitor the user's emotional response to the diversity of sign language in real time, it can also analyze video data of sign language using the emotion estimation function and track the user's emotional response. This makes it possible to identify the features of sign language that interest the user.

[0088] The sign language understanding support system can also use the emotion estimation function to analyze a user's emotional response to differences in sign language by region and age group, and identify the sign language features that elicit positive emotions. For example, it can analyze video data of sign language, estimate emotions from the user's facial expressions and voice, and identify the sign language features that elicit positive emotions. In addition, in order to analyze a user's emotional response to differences in sign language by region and age group, it can also use the emotion estimation function to analyze video data of sign language and track the user's emotional response. This makes it possible to identify the sign language features that elicit positive emotions.

[0089] The sign language understanding support system can also use the emotion estimation function to analyze a user's emotional response to background information about the sign language and identify features of the sign language that elicit positive emotions. For example, it can analyze video data of sign language, estimate emotions from the user's facial expressions and voice, and identify features of sign language that elicit positive emotions. In addition, to analyze a user's emotional response to background information about the sign language, it can also use the emotion estimation function to analyze video data of sign language and track the user's emotional response. This makes it possible to identify features of sign language that elicit positive emotions.

[0090] The sign language understanding support system can also use the emotion estimation function to monitor the user's emotional response to the sign language background information in real time and identify the sign language features that attract the user's interest. For example, it can analyze video data of sign language, estimate the user's emotion from the user's facial expressions and voice, and identify the sign language features that attract the user's interest. In addition, in order to monitor the user's emotional response to the sign language background information in real time, it can also analyze video data of sign language using the emotion estimation function and track the user's emotional response. This makes it possible to identify the sign language features that attract the user's interest.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The sign language analysis unit analyzes the diversity of sign languages. For example, the generation AI analyzes video data of sign language to understand the differences in sign language by region and generation. The generation AI can also take into account differences in the speed and rhythm of sign language movements during analysis. Step 2: The background information provider provides background information about the sign language analyzed by the sign language analyzer. For example, the generation AI can provide additional information about the origins and meaning of the sign language. The generation AI can also explain the historical and cultural background of the sign language. Step 3: The learning support unit supports sign language learning based on the information provided by the background information provider. For example, the generation AI can provide videos and texts for sign language practice, making it easier for users to learn sign language. The generation AI can also provide easy-to-understand explanations of the actions and meanings of sign language.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 AI 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 sign language analysis unit that analyzes the diversity of sign languages, a background information providing unit that provides background information of the sign language analyzed by the sign language analyzing unit; a learning support unit that supports learning of sign language based on the information provided by the background information providing unit. A system characterized by:

2. The sign language analysis unit The analysis takes into account the differences in the speed and rhythm of the sign language.

2. The system of claim 1.

3. The sign language analysis unit Analyze the sign language based on its frequency of use and context to track changes in its meaning.

2. The system of claim 1.

4. The sign language analysis unit Analyzing a user's emotional response to the diversity of sign languages ​​and identifying features of the sign languages ​​that elicit positive emotions 2. The system of claim 1.

5. The sign language analysis unit Analyzing the sign languages ​​of different cultural and linguistic regions and providing a global perspective on the diversity of sign languages 2. The system of claim 1.

Images