System

Abstract

An object of a system according to an embodiment is to deal with the diversity of sign language and promote understanding and popularization of sign language.SOLUTION: A system includes an input unit, an analysis unit, an acquisition unit, and a display unit. The input unit inputs an action of sign language. The analysis part analyzes the action of the sign language input by the input part. The acquisition unit acquires a trigger or a meaning corresponding to the sign language action analyzed by the analysis unit from the database. The display unit displays the information acquired by the acquisition unit to the user.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 is unable to fully accommodate the diversity of sign language, posing challenges to the understanding and dissemination of sign language.

[0005] The system according to the embodiment aims to accommodate the diversity of sign languages ​​and promote the understanding and dissemination of sign languages. [Means for solving the problem]

[0006] A system according to an embodiment includes an input unit, an analysis unit, an acquisition unit, and a display unit. The input unit inputs sign language actions. The analysis unit analyzes the sign language actions input by the input unit. The acquisition unit acquires triggers or meanings corresponding to the sign language actions analyzed by the analysis unit from a database. The display unit displays the information acquired by the acquisition unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can accommodate the diversity of sign languages ​​and promote the understanding and dissemination of sign languages. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sign language information provision system according to an embodiment of the present invention is a system in which a user inputs a sign language action, and acquires and displays a "cue" or "meaning" corresponding to the action. The sign language information provision system inputs a sign language action, acquires the "cue" or "meaning" corresponding to the action from a database, and displays the acquired information to the user, thereby increasing a user's sense of familiarity with sign language. For example, the user inputs a sign language action into the sign language information provision system. For example, the sign language information provision system can capture the sign language action using a camera or input the sign language action as text. Next, the sign language information provision system analyzes the input sign language action. The sign language information provision system analyzes the sign language action and acquires the "cue" or "meaning" of the sign language corresponding to the action from a database. For example, for the sign language action for "thank you," the system acquires the background and meaning of the sign language from the database. Next, the sign language information provision system displays the acquired information to the user. For example, for the sign language action for "thank you," the background and meaning of the sign language are displayed. This allows the sign language information provision system to accommodate the diversity of sign languages ​​and increase a user's sense of familiarity with sign language. For example, by learning about the different meanings and backgrounds of sign languages ​​depending on the region and age group, people's understanding of sign language will deepen and it is expected that more people will become interested in sign language. This will enable the sign language information provision system to respond to the diversity of sign languages ​​and increase people's familiarity with sign language. For example, by learning about the different meanings and backgrounds of sign languages ​​depending on the region and age group, people's understanding of sign language will deepen and it is expected that more people will become interested in sign language.

[0029] A sign language information provision system according to an embodiment includes an input unit, an analysis unit, an acquisition unit, and a display unit. The input unit inputs sign language actions. The sign language actions include, but are not limited to, hand movements, facial expressions, and body movements, for example. The input unit, for example, uses a camera to capture the sign language actions. The input unit can also input the sign language actions as text. For example, a user captures the sign language action of saying "thank you" with a camera. This information is input into the system. The analysis unit analyzes the sign language actions input by the input unit. For example, the analysis unit analyzes the sign language actions and acquires a "cue" or "meaning" of the sign language corresponding to the action from a database. For example, for the sign language action of saying "thank you," the analysis unit acquires the background and meaning of the sign language from the database. The acquisition unit acquires the "cue" or "meaning" corresponding to the sign language action analyzed by the analysis unit from the database. For example, the acquisition unit acquires the "cue" or "meaning" of the sign language from the database. The display unit displays the information acquired by the acquisition unit to the user. The display unit displays the acquired information to the user in text format, audio, video, or the like. For example, for the sign language gesture of "thank you," the background and meaning of the sign language are displayed. In this way, the sign language information provision system according to the embodiment can increase a user's sense of familiarity with sign language by inputting a sign language gesture and acquiring and displaying the "trigger" and "meaning" corresponding to the gesture.

[0030] The input unit can capture a sign language action using a camera, or input the sign language action as text. Examples of cameras include, but are not limited to, a still image camera, a video camera, etc. The input unit, for example, captures a sign language action using a camera. For example, a user captures a sign language action for "thank you" using a camera. The input unit can also input the sign language action as text. For example, a user inputs the sign language action for "thank you" as text. This allows sign language actions to be input in a variety of ways, by capturing a sign language action with a camera or inputting it as text.

[0031] The display unit can display the acquired information to the user in text format, audio, or video. For example, the display unit displays the acquired information in text format. Examples of text format include, but are not limited to, font size and color. The display unit can also display the acquired information as audio. Examples of audio format include, but are not limited to, the type and speed of audio. The display unit can also display the acquired information as video. Examples of video format include, but are not limited to, resolution and frame rate. This allows the acquired information to be displayed in a variety of formats, making it possible to provide the user with information that is easy to understand.

[0032] The sign language information provision system includes a registration unit that registers and updates sign language triggers or meanings in a database. The registration unit registers and updates sign language triggers or meanings in the database. For example, the registration unit registers sign language triggers and meanings in the database. The registration unit can also update sign language triggers and meanings in the database. For example, if a new sign language trigger or meaning is discovered, the registration unit adds it to the database. Also, if an existing sign language trigger or meaning is changed, the registration unit reflects that in the database. In this way, by registering and updating sign language "triggers" and "meanings" in the database, the information can be kept up to date.

[0033] The sign language information provision system includes a management unit that manages the entire system. The management unit manages the entire system. For example, the management unit monitors the system. The management unit can also perform system maintenance. Furthermore, the management unit can also manage users. For example, the management unit monitors the system's operating status in real time and issues an alert if an abnormality occurs. The management unit also performs regular maintenance to ensure stable operation of the system. Furthermore, the management unit manages user registration information and sets access permissions. In this way, by managing the entire system, the operational efficiency of the system can be improved.

[0034] The input unit can analyze the user's past sign language input history and select an appropriate input method. For example, the input unit can prioritize and display sign language actions that the user has frequently used in the past, simplifying input. The input unit can also analyze input methods (camera, text, etc.) that the user has used in the past and suggest the optimal input method. Furthermore, the input unit can predict and suggest sign language actions to be used in a specific time period based on the user's past sign language input history. In this way, by analyzing the user's past sign language input history, the optimal input method can be suggested and input efficiency can be improved.

[0035] The input unit can perform filtering based on the user's current learning status or area of ​​interest when inputting sign language actions. For example, if the user is a beginner, the input unit can preferentially display basic sign language actions to support input. Also, if the user is interested in a specific area of ​​interest (e.g., medical terminology), the input unit can preferentially display sign language actions related to that area. Furthermore, the input unit can suggest sign language actions of an appropriate level of difficulty based on the user's learning progress. In this way, by filtering based on the user's learning status and area of ​​interest, appropriate sign language actions are preferentially displayed to support learning.

[0036] When inputting sign language actions, the input unit can select an appropriate input means according to the user's input method. For example, if the user desires voice input, the input unit inputs sign language actions using voice recognition technology. Furthermore, if the user desires text input, the input unit can also provide a text input interface and input sign language actions. Furthermore, if the user desires image input, the input unit can also use a camera to capture and input sign language actions. This allows for smooth input of sign language actions by selecting the optimal input means according to the user's input method.

[0037] When inputting sign language actions, the input unit can preferentially input sign language actions that are highly relevant based on the user's geographical location information. For example, when the user is in a specific area, the input unit can preferentially display sign language actions that are commonly used in that area. Furthermore, when the user is traveling, the input unit can preferentially display sign language actions that are used in the area where the user is traveling. Furthermore, when the user is participating in a specific event, the input unit can preferentially display sign language actions related to the event. In this way, by taking the user's geographical location information into consideration, sign language actions related to the area are preferentially displayed to support input.

[0038] When inputting sign language actions, the input unit can analyze the user's social media activities and input related sign language actions. For example, the input unit can prioritize and display sign language actions related to words and phrases frequently used by the user on social media. The input unit can also analyze the content of the user's social media posts and suggest related sign language actions. Furthermore, the input unit can also suggest related sign language actions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related sign language actions can be prioritized and input can be supported.

[0039] The input unit can customize the input method by reflecting the user's past feedback when inputting sign language actions. For example, the input unit can suggest the optimal input method based on feedback provided by the user in the past. The input unit can also preferentially display a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the input unit can analyze the user's past feedback and customize the input interface. In this way, the optimal input method is suggested by reflecting the user's past feedback, improving input efficiency.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the sign language. For example, the analysis unit provides detailed analysis results for sign language actions with high importance. The analysis unit can also provide concise analysis results for sign language actions with low importance. Furthermore, the analysis unit can also adjust the display order of the analysis results according to the importance of the sign language actions. In this way, by adjusting the level of detail of the analysis based on the importance of the sign language, appropriate analysis results can be provided.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the sign language category. For example, the analysis unit applies a simple analysis algorithm to sign language actions used in everyday conversation. The analysis unit can also apply a detailed analysis algorithm to sign language actions used in technical terms. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of sign language actions. In this way, the accuracy of analysis is improved by applying the optimal analysis algorithm depending on the sign language category.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring to, for example, sign language actions that the user has analyzed in the past. The analysis unit can also optimize the analysis algorithm for a specific sign language action from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the sign language was submitted. For example, the analysis unit prioritizes analysis of the most recently submitted sign language actions. The analysis unit can also prioritize analysis of the sign language actions submitted within a specific period. Furthermore, the analysis unit can adjust the priority of analysis based on the time when the sign language actions were submitted. In this way, by determining the priority of analysis based on the time when the sign language was submitted, it is possible to prioritize analysis of the most recent sign language actions.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sign language. For example, the analysis unit prioritizes analysis of highly relevant sign language actions. The analysis unit can also postpone analysis of less relevant sign language actions. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the sign language actions. In this way, by adjusting the order of analysis based on the relevance of the sign language, highly relevant sign language actions can be analyzed with priority.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terms. If the user is an intermediate user, the analysis unit can also provide analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can also provide analysis results using detailed technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0046] The acquisition unit can improve the accuracy of acquisition by taking into account the interrelationships between sign languages ​​during acquisition. For example, the acquisition unit acquires and displays related sign language actions together. The acquisition unit can also analyze the interrelationships between sign language actions and acquire optimal information. Furthermore, the acquisition unit can improve the accuracy of the acquired information based on the interrelationships between sign language actions. In this way, by taking into account the interrelationships between sign languages, related sign language actions are acquired together and the accuracy of the information is improved.

[0047] The acquisition unit can acquire the information while taking into consideration the attribute information of the sign language submitter. The acquisition unit acquires the related information based on, for example, the age and gender of the sign language submitter. The acquisition unit can also acquire the related information based on the occupation and expertise of the sign language submitter. Furthermore, the acquisition unit can also acquire the related information based on the region and cultural background of the sign language submitter. This allows the related information to be appropriately acquired by taking into consideration the attribute information of the sign language submitter.

[0048] The acquisition unit can weight the acquisition based on the frequency of sign language submission during acquisition. For example, the acquisition unit preferentially acquires and displays sign language actions that are submitted more frequently. The acquisition unit can also postpone acquisition of sign language actions that are submitted less frequently. Furthermore, the acquisition unit can weight the information to be acquired based on the frequency of sign language submission. Thus, by weighting acquisition based on the frequency of sign language submission, frequently used sign language actions can be preferentially acquired.

[0049] The acquisition unit can acquire the information while taking into consideration the geographical distribution of sign languages. For example, the acquisition unit can preferentially acquire sign language actions commonly used in a specific region. The acquisition unit can also analyze the geographical distribution of sign language actions and acquire optimal information. Furthermore, the acquisition unit can adjust the information to be acquired based on the geographical distribution of sign language actions. In this way, by taking into consideration the geographical distribution of sign languages, it is possible to preferentially acquire sign language actions related to a region.

[0050] The acquisition unit can improve the accuracy of acquisition by referring to literature related to sign language during acquisition. For example, the acquisition unit refers to literature related to sign language movements to acquire optimal information. The acquisition unit can also analyze literature related to sign language movements to improve the accuracy of the acquired information. Furthermore, the acquisition unit can adjust the information to be acquired based on literature related to sign language movements. In this way, the accuracy of the acquired information is improved by referring to literature related to sign language.

[0051] The acquisition unit can acquire the sign language taking into consideration the market value of the sign language. For example, the acquisition unit prioritizes acquiring sign language actions with high market value. The acquisition unit can also postpone acquiring sign language actions with low market value. Furthermore, the acquisition unit can adjust the information to be acquired based on the market value of the sign language actions. In this way, by taking into consideration the market value of the sign language, it is possible to prioritize acquiring sign language actions with high value.

[0052] The display unit can select the optimal display method by referring to the user's past display history when displaying. For example, the display unit preferentially provides a display method that the user has used favorably in the past. The display unit can also suggest the optimal display method based on the user's past display history. Furthermore, the display unit can analyze the user's past display history and customize the display method. In this way, the optimal display method is provided by referring to the user's past display history, improving user convenience.

[0053] The display unit can customize the display content according to the user's current task when displaying the information. For example, if the user is studying, the display unit can prioritize displaying information related to the study. Also, if the user is working, the display unit can prioritize displaying information related to the work. Furthermore, the display unit can provide optimal display content according to the user's current task. In this way, by customizing the display content according to the user's current task, information necessary for the user can be provided with priority.

[0054] The display unit can improve the display method by reflecting user feedback during display. For example, the display unit improves the display method based on feedback provided by the user. The display unit can also provide a specific display method with priority based on the user feedback. Furthermore, the display unit can analyze the user feedback and customize the display interface. In this way, the display method can be improved by reflecting the user feedback, thereby improving user convenience.

[0055] The display unit can select the optimal display method when displaying information by taking into consideration the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. In this way, by taking into consideration the user's device information, a display method optimized for the device is provided, improving user convenience.

[0056] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. In this way, by making the display content multilingual according to the user's language setting, a display that is easy for the user to understand is provided.

[0057] The display unit can adjust the display method according to the visual and auditory characteristics of the user when displaying. For example, if the user is visually impaired, the display unit can provide audio guidance preferentially. Also, if the user is hearing impaired, the display unit can provide a visual display method preferentially. Furthermore, the display unit can provide the optimal display method according to the visual and auditory characteristics of the user. In this way, the display method can be adjusted according to the visual and auditory characteristics of the user, thereby providing the optimal display for the user.

[0058] The registration unit can improve the accuracy of registration by taking into account the interrelationships between sign languages ​​during registration. For example, the registration unit registers related sign language actions together to improve the accuracy of the database. The registration unit can also analyze the interrelationships between sign language actions and register optimal information. Furthermore, the registration unit can improve the accuracy of the information to be registered based on the interrelationships between sign language actions. In this way, by taking into account the interrelationships between sign languages, related sign language actions are registered together to improve the accuracy of the database.

[0059] The registration unit can perform registration while taking into consideration attribute information of the sign language submitter. The registration unit registers related information based on, for example, the age and gender of the sign language submitter. The registration unit can also register related information based on the occupation and expertise of the sign language submitter. Furthermore, the registration unit can register related information based on the region and cultural background of the sign language submitter. In this way, related information can be appropriately registered by taking into consideration the attribute information of the sign language submitter.

[0060] The registration unit can weight the registration based on the frequency of submission of the sign language at the time of registration. For example, the registration unit preferentially registers sign language actions that are submitted more frequently. The registration unit can also postpone registration of sign language actions that are submitted less frequently. Furthermore, the registration unit can weight the information to be registered based on the frequency of submission of the sign language actions. Thus, by weighting the registration based on the frequency of submission of the sign language, frequently used sign language actions can be preferentially registered.

[0061] The registration unit can perform registration while taking into consideration the geographical distribution of sign languages. For example, the registration unit preferentially registers sign language actions that are commonly used in a specific region. The registration unit can also analyze the geographical distribution of sign language actions and register optimal information. Furthermore, the registration unit can adjust the information to be registered based on the geographical distribution of sign language actions. In this way, by taking into consideration the geographical distribution of sign languages, sign language actions related to a region can be preferentially registered.

[0062] The registration unit can improve the accuracy of registration by referring to literature related to sign language during registration. For example, the registration unit refers to literature related to sign language actions and registers optimal information. The registration unit can also analyze literature related to sign language actions and improve the accuracy of the information to be registered. Furthermore, the registration unit can adjust the information to be registered based on literature related to sign language actions. In this way, the accuracy of the information to be registered is improved by referring to literature related to sign language.

[0063] The registration unit can take into consideration the market value of the sign language when registering. For example, the registration unit preferentially registers sign language actions with high market value. The registration unit can also postpone registration of sign language actions with low market value. Furthermore, the registration unit can adjust the information to be registered based on the market value of the sign language actions. In this way, by taking into consideration the market value of the sign language, it is possible to preferentially register sign language actions with high value.

[0064] During management, the management unit can refer to the system's past management history to select the optimal management method. For example, the management unit analyzes the system's past management history and proposes the optimal management method. The management unit can also provide a specific management method with priority based on the past management history. Furthermore, the management unit can customize the management interface based on the system's past management history. In this way, by referring to the system's past management history, the optimal management method can be provided and the operational efficiency of the system can be improved.

[0065] The management unit can customize the management content according to the current state of the system during management. For example, the management unit monitors the current state of the system in real time and provides the optimal management method. The management unit can also customize the management interface based on the current state of the system. Furthermore, the management unit can analyze the current state of the system and adjust the management content. In this way, customizing the management content according to the current state of the system improves the operational efficiency of the system.

[0066] The management unit can improve the management method by reflecting user feedback during management. For example, the management unit improves the management method based on feedback provided by the user. The management unit can also provide a specific management method with priority based on user feedback. Furthermore, the management unit can analyze user feedback and customize the management interface. In this way, the management method can be improved by reflecting user feedback, thereby improving the operational efficiency of the system.

[0067] The management unit can select the optimal management method during management by taking into consideration device information of the system. For example, if the system is running on a smartphone, the management unit provides a management interface that matches the screen size. Also, if the system is running on a tablet, the management unit can provide a management interface that is optimized for a large screen. Furthermore, if the system is running on a smartwatch, the management unit can provide a simple and highly visible management interface. In this way, by taking into consideration device information of the system, a management method optimized for the device is provided, improving the operational efficiency of the system.

[0068] During management, the management unit can make the management content multilingual in accordance with the system's language setting. For example, the management unit automatically sets the language of the management interface based on the system's language setting. The management unit can also provide a language switching function if the system supports multiple languages. Furthermore, the management unit can also make the management content multilingual in accordance with the system's language setting. This provides a management interface that is easy for users to understand by making the management content multilingual in accordance with the system's language setting.

[0069] The management unit can adjust the management method according to the visual and auditory characteristics of the system during management. For example, the management unit can prioritize providing audio guidance to users with visual impairments. The management unit can also prioritize providing a visual management interface to users with hearing impairments. Furthermore, the management unit can provide the optimal management method according to the visual and auditory characteristics of the system. In this way, by adjusting the management method according to the visual and auditory characteristics of the system, the optimal management interface for the user is provided.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The sign language information provision system may further include a learning tracking unit that tracks the user's learning progress. The learning tracking unit records which sign languages ​​the user has learned and their level of understanding, and suggests appropriate sign languages ​​for the next learning session. For example, if the user has learned the sign language for "thank you," the learning tracking unit records the user's progress and suggests related sign languages ​​such as "you're welcome" for the next learning session. The learning tracking unit may also prioritize the display of sign languages ​​that require review based on the user's level of understanding. Furthermore, the learning tracking unit may analyze the user's learning history and suggest an optimal learning plan. This improves the user's learning efficiency and supports the user's acquisition of sign language.

[0072] The sign language information provision system can further include an interest suggestion unit that suggests sign language actions based on the user's interests and concerns. For example, if the user is interested in sports, the interest suggestion unit can preferentially display sign language related to sports. Also, if the user is interested in music, the interest suggestion unit can suggest sign language related to music. Furthermore, the interest suggestion unit can analyze the user's past search history and learning history and suggest optimal sign language actions. In this way, suggesting sign language actions based on the user's interests and concerns increases motivation to learn.

[0073] The sign language information provision system can further include a learning style adaptation unit that customizes sign language actions according to the user's learning style. For example, the learning style adaptation unit can provide sign language actions that make extensive use of videos and images to a user who prefers visual learning. It can also provide sign language actions that include audio guides to a user who prefers auditory learning. It can also provide interactive sign language practice to a user who prefers practical learning. In this way, the effectiveness of learning is maximized by customizing sign language actions according to the user's learning style.

[0074] The sign language information provision system can further include a goal suggestion unit that suggests sign language actions based on the user's learning goals. For example, if the user's goal is everyday conversation, the goal suggestion unit can preferentially display sign language actions related to everyday conversation. Also, if the user's goal is business communication, the goal suggestion unit can suggest sign language actions related to business. Furthermore, if the user is learning sign language for a specific event, the goal suggestion unit can suggest sign language actions related to the event. In this way, by suggesting sign language actions based on the user's learning goals, the effectiveness of learning is improved.

[0075] The sign language information provision system can further include a review suggestion unit that reviews sign language actions based on the user's learning history. The review suggestion unit, for example, periodically displays sign language actions that the user has learned in the past to encourage review. It can also support review by preferentially displaying sign language actions that the user has a low level of understanding. Furthermore, it can analyze the user's learning history and suggest an optimal review plan. In this way, reviewing sign language actions based on the user's learning history promotes the consolidation of learning.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The input unit inputs sign language actions. Sign language actions include hand movements, facial expressions, body movements, etc. The input unit can capture the sign language actions using a camera, and can also input the sign language actions as text. For example, a user can capture the sign language action of saying "thank you" with a camera. This information is input into the system. Step 2: The analysis unit analyzes the sign language movements input by the input unit. The analysis unit analyzes the sign language movements and retrieves the sign language "cues" and "meanings" corresponding to the movements from the database. Step 3: The acquisition unit acquires from the database the "cue" and "meaning" corresponding to the sign language action analyzed by the analysis unit. For example, the acquisition unit acquires the "cue" and "meaning" of the sign language from the database. Step 4: The display unit displays the information acquired by the acquisition unit to the user. The display unit displays the acquired information to the user in text format, audio, video, etc. For example, for the sign language gesture for "thank you," the background and meaning of the sign language are displayed.

[0078] (Example 2) A sign language information provision system according to an embodiment of the present invention is a system in which a user inputs a sign language action, and acquires and displays a "cue" or "meaning" corresponding to the action. The sign language information provision system inputs a sign language action, acquires the "cue" or "meaning" corresponding to the action from a database, and displays the acquired information to the user, thereby increasing a user's sense of familiarity with sign language. For example, the user inputs a sign language action into the sign language information provision system. For example, the sign language information provision system can capture the sign language action using a camera or input the sign language action as text. Next, the sign language information provision system analyzes the input sign language action. The sign language information provision system analyzes the sign language action and acquires the "cue" or "meaning" of the sign language corresponding to the action from a database. For example, for the sign language action for "thank you," the system acquires the background and meaning of the sign language from the database. Next, the sign language information provision system displays the acquired information to the user. For example, for the sign language action for "thank you," the background and meaning of the sign language are displayed. This allows the sign language information provision system to accommodate the diversity of sign languages ​​and increase a user's sense of familiarity with sign language. For example, by learning about the different meanings and backgrounds of sign languages ​​depending on the region and age group, people's understanding of sign language will deepen and it is expected that more people will become interested in sign language. This will enable the sign language information provision system to respond to the diversity of sign languages ​​and increase people's familiarity with sign language. For example, by learning about the different meanings and backgrounds of sign languages ​​depending on the region and age group, people's understanding of sign language will deepen and it is expected that more people will become interested in sign language.

[0079] A sign language information provision system according to an embodiment includes an input unit, an analysis unit, an acquisition unit, and a display unit. The input unit inputs sign language actions. The sign language actions include, but are not limited to, hand movements, facial expressions, and body movements, for example. The input unit, for example, uses a camera to capture the sign language actions. The input unit can also input the sign language actions as text. For example, a user captures the sign language action of saying "thank you" with a camera. This information is input into the system. The analysis unit analyzes the sign language actions input by the input unit. For example, the analysis unit analyzes the sign language actions and acquires a "cue" or "meaning" of the sign language corresponding to the action from a database. For example, for the sign language action of saying "thank you," the analysis unit acquires the background and meaning of the sign language from the database. The acquisition unit acquires the "cue" or "meaning" corresponding to the sign language action analyzed by the analysis unit from the database. For example, the acquisition unit acquires the "cue" or "meaning" of the sign language from the database. The display unit displays the information acquired by the acquisition unit to the user. The display unit displays the acquired information to the user in text format, audio, video, or the like. For example, for the sign language gesture of "thank you," the background and meaning of the sign language are displayed. In this way, the sign language information provision system according to the embodiment can increase a user's sense of familiarity with sign language by inputting a sign language gesture and acquiring and displaying the "trigger" and "meaning" corresponding to the gesture.

[0080] The input unit can capture a sign language action using a camera, or input the sign language action as text. Examples of cameras include, but are not limited to, a still image camera, a video camera, etc. The input unit, for example, captures a sign language action using a camera. For example, a user captures a sign language action for "thank you" using a camera. The input unit can also input the sign language action as text. For example, a user inputs the sign language action for "thank you" as text. This allows sign language actions to be input in a variety of ways, by capturing a sign language action with a camera or inputting it as text.

[0081] The display unit can display the acquired information to the user in text format, audio, or video. For example, the display unit displays the acquired information in text format. Examples of text format include, but are not limited to, font size and color. The display unit can also display the acquired information as audio. Examples of audio format include, but are not limited to, the type and speed of audio. The display unit can also display the acquired information as video. Examples of video format include, but are not limited to, resolution and frame rate. This allows the acquired information to be displayed in a variety of formats, making it possible to provide the user with information that is easy to understand.

[0082] The sign language information provision system includes a registration unit that registers and updates sign language triggers or meanings in a database. The registration unit registers and updates sign language triggers or meanings in the database. For example, the registration unit registers sign language triggers and meanings in the database. The registration unit can also update sign language triggers and meanings in the database. For example, if a new sign language trigger or meaning is discovered, the registration unit adds it to the database. Also, if an existing sign language trigger or meaning is changed, the registration unit reflects that in the database. In this way, by registering and updating sign language "triggers" and "meanings" in the database, the information can be kept up to date.

[0083] The sign language information provision system includes a management unit that manages the entire system. The management unit manages the entire system. For example, the management unit monitors the system. The management unit can also perform system maintenance. Furthermore, the management unit can also manage users. For example, the management unit monitors the system's operating status in real time and issues an alert if an abnormality occurs. The management unit also performs regular maintenance to ensure stable operation of the system. Furthermore, the management unit manages user registration information and sets access permissions. In this way, by managing the entire system, the operational efficiency of the system can be improved.

[0084] The input unit can estimate the user's emotions and adjust the timing of sign language action input based on the estimated user emotions. For example, if the user is nervous, the input unit can adjust the timing of the sign language action input slowly to allow the user to relax. Furthermore, if the user is relaxed, the input unit can set the timing of the sign language action input to a normal speed to encourage smooth input. Furthermore, if the user is in a hurry, the input unit can quickly adjust the timing of the sign language action input to support efficient input. This reduces the user's stress by adjusting the timing of the sign language action input according to the user's emotions and supports smooth input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The input unit can analyze the user's past sign language input history and select an appropriate input method. For example, the input unit can prioritize and display sign language actions that the user has frequently used in the past, simplifying input. The input unit can also analyze input methods (camera, text, etc.) that the user has used in the past and suggest the optimal input method. Furthermore, the input unit can predict and suggest sign language actions to be used in a specific time period based on the user's past sign language input history. In this way, by analyzing the user's past sign language input history, the optimal input method can be suggested and input efficiency can be improved.

[0086] The input unit can perform filtering based on the user's current learning status or area of ​​interest when inputting sign language actions. For example, if the user is a beginner, the input unit can preferentially display basic sign language actions to support input. Also, if the user is interested in a specific area of ​​interest (e.g., medical terminology), the input unit can preferentially display sign language actions related to that area. Furthermore, the input unit can suggest sign language actions of an appropriate level of difficulty based on the user's learning progress. In this way, by filtering based on the user's learning status and area of ​​interest, appropriate sign language actions are preferentially displayed to support learning.

[0087] When inputting sign language actions, the input unit can select an appropriate input means according to the user's input method. For example, if the user desires voice input, the input unit inputs sign language actions using voice recognition technology. Furthermore, if the user desires text input, the input unit can also provide a text input interface and input sign language actions. Furthermore, if the user desires image input, the input unit can also use a camera to capture and input sign language actions. This allows for smooth input of sign language actions by selecting the optimal input means according to the user's input method.

[0088] The input unit can estimate the user's emotions and prioritize sign language actions to be input based on the estimated user emotions. For example, if the user is nervous, the input unit can prioritize displaying simple sign language actions to support input. Furthermore, if the user is relaxed, the input unit can also display complex sign language actions to encourage input. Furthermore, if the user is in a hurry, the input unit can prioritize displaying frequently used sign language actions to support quick input. Thus, by prioritizing sign language actions according to the user's emotions, sign language actions appropriate for the user's situation are displayed and input is supported. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When inputting sign language actions, the input unit can preferentially input sign language actions that are highly relevant based on the user's geographical location information. For example, when the user is in a specific area, the input unit can preferentially display sign language actions that are commonly used in that area. Furthermore, when the user is traveling, the input unit can preferentially display sign language actions that are used in the area where the user is traveling. Furthermore, when the user is participating in a specific event, the input unit can preferentially display sign language actions related to the event. In this way, by taking the user's geographical location information into consideration, sign language actions related to the area are preferentially displayed to support input.

[0090] When inputting sign language actions, the input unit can analyze the user's social media activities and input related sign language actions. For example, the input unit can prioritize and display sign language actions related to words and phrases frequently used by the user on social media. The input unit can also analyze the content of the user's social media posts and suggest related sign language actions. Furthermore, the input unit can also suggest related sign language actions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, related sign language actions can be prioritized and input can be supported.

[0091] The input unit can customize the input method by reflecting the user's past feedback when inputting sign language actions. For example, the input unit can suggest the optimal input method based on feedback provided by the user in the past. The input unit can also preferentially display a specific input method (voice, text, etc.) based on the user's past feedback. Furthermore, the input unit can analyze the user's past feedback and customize the input interface. In this way, the optimal input method is suggested by reflecting the user's past feedback, improving input efficiency.

[0092] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result, explaining in detail the background and meaning of the sign language movements. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. In this way, by adjusting the way the analysis is presented according to the user's emotions, analysis results that are easy for the user to understand are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the sign language. For example, the analysis unit provides detailed analysis results for sign language actions with high importance. The analysis unit can also provide concise analysis results for sign language actions with low importance. Furthermore, the analysis unit can also adjust the display order of the analysis results according to the importance of the sign language actions. In this way, by adjusting the level of detail of the analysis based on the importance of the sign language, appropriate analysis results can be provided.

[0094] During analysis, the analysis unit can apply different analysis algorithms depending on the sign language category. For example, the analysis unit applies a simple analysis algorithm to sign language actions used in everyday conversation. The analysis unit can also apply a detailed analysis algorithm to sign language actions used in technical terms. Furthermore, the analysis unit can select the optimal analysis algorithm depending on the category of sign language actions. In this way, the accuracy of analysis is improved by applying the optimal analysis algorithm depending on the sign language category.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring to, for example, sign language actions that the user has analyzed in the past. The analysis unit can also optimize the analysis algorithm for a specific sign language action from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a longer analysis result with detailed explanations. Furthermore, if the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. In this way, by adjusting the length of the analysis according to the user's emotions, appropriate analysis results can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] During analysis, the analysis unit can determine the priority of analysis based on the time when the sign language was submitted. For example, the analysis unit prioritizes analysis of the most recently submitted sign language actions. The analysis unit can also prioritize analysis of the sign language actions submitted within a specific period. Furthermore, the analysis unit can adjust the priority of analysis based on the time when the sign language actions were submitted. In this way, by determining the priority of analysis based on the time when the sign language was submitted, it is possible to prioritize analysis of the most recent sign language actions.

[0098] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the sign language. For example, the analysis unit prioritizes analysis of highly relevant sign language actions. The analysis unit can also postpone analysis of less relevant sign language actions. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the sign language actions. In this way, by adjusting the order of analysis based on the relevance of the sign language, highly relevant sign language actions can be analyzed with priority.

[0099] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is a beginner, the analysis unit can provide analysis results that avoid technical terms. If the user is an intermediate user, the analysis unit can also provide analysis results using appropriate technical terms. Furthermore, if the user is an advanced user, the analysis unit can also provide analysis results using detailed technical terms. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided.

[0100] The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. For example, if the user is nervous, the acquisition unit can prioritize acquiring and display simple information. Furthermore, if the user is relaxed, the acquisition unit can prioritize acquiring and display detailed information. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring and display information that focuses on the main points. In this way, by determining the priority of information to be acquired according to the user's emotions, it is possible to prioritize acquiring information that is appropriate for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The acquisition unit can improve the accuracy of acquisition by taking into account the interrelationships between sign languages ​​during acquisition. For example, the acquisition unit acquires and displays related sign language actions together. The acquisition unit can also analyze the interrelationships between sign language actions and acquire optimal information. Furthermore, the acquisition unit can improve the accuracy of the acquired information based on the interrelationships between sign language actions. In this way, by taking into account the interrelationships between sign languages, related sign language actions are acquired together and the accuracy of the information is improved.

[0102] The acquisition unit can acquire the information while taking into consideration the attribute information of the sign language submitter. The acquisition unit acquires the related information based on, for example, the age and gender of the sign language submitter. The acquisition unit can also acquire the related information based on the occupation and expertise of the sign language submitter. Furthermore, the acquisition unit can also acquire the related information based on the region and cultural background of the sign language submitter. This allows the related information to be appropriately acquired by taking into consideration the attribute information of the sign language submitter.

[0103] The acquisition unit can weight the acquisition based on the frequency of sign language submission during acquisition. For example, the acquisition unit preferentially acquires and displays sign language actions that are submitted more frequently. The acquisition unit can also postpone acquisition of sign language actions that are submitted less frequently. Furthermore, the acquisition unit can weight the information to be acquired based on the frequency of sign language submission. Thus, by weighting acquisition based on the frequency of sign language submission, frequently used sign language actions can be preferentially acquired.

[0104] The acquisition unit can estimate the user's emotions and adjust the display method of the acquired information based on the estimated user emotions. For example, if the user is nervous, the acquisition unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the acquisition unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the acquisition unit can also provide a display method that focuses on the main points. In this way, by adjusting the information display method according to the user's emotions, a display that is easy for the user to understand is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The acquisition unit can acquire the information while taking into consideration the geographical distribution of sign languages. For example, the acquisition unit can preferentially acquire sign language actions commonly used in a specific region. The acquisition unit can also analyze the geographical distribution of sign language actions and acquire optimal information. Furthermore, the acquisition unit can adjust the information to be acquired based on the geographical distribution of sign language actions. In this way, by taking into consideration the geographical distribution of sign languages, it is possible to preferentially acquire sign language actions related to a region.

[0106] The acquisition unit can improve the accuracy of acquisition by referring to literature related to sign language during acquisition. For example, the acquisition unit refers to literature related to sign language movements to acquire optimal information. The acquisition unit can also analyze literature related to sign language movements to improve the accuracy of the acquired information. Furthermore, the acquisition unit can adjust the information to be acquired based on literature related to sign language movements. In this way, the accuracy of the acquired information is improved by referring to literature related to sign language.

[0107] The acquisition unit can acquire the sign language taking into consideration the market value of the sign language. For example, the acquisition unit prioritizes acquiring sign language actions with high market value. The acquisition unit can also postpone acquiring sign language actions with low market value. Furthermore, the acquisition unit can adjust the information to be acquired based on the market value of the sign language actions. In this way, by taking into consideration the market value of the sign language, it is possible to prioritize acquiring sign language actions with high value.

[0108] The display unit can estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, when the user is nervous, the display unit provides a simple, highly visible display method. When the user is relaxed, the display unit can also provide a display method that includes detailed information. When the user is in a hurry, the display unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotions, a display that is easy for the user to understand is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0109] The display unit can select the optimal display method by referring to the user's past display history when displaying. For example, the display unit preferentially provides a display method that the user has used favorably in the past. The display unit can also suggest the optimal display method based on the user's past display history. Furthermore, the display unit can analyze the user's past display history and customize the display method. In this way, the optimal display method is provided by referring to the user's past display history, improving user convenience.

[0110] The display unit can customize the display content according to the user's current task when displaying the information. For example, if the user is studying, the display unit can prioritize displaying information related to the study. Also, if the user is working, the display unit can prioritize displaying information related to the work. Furthermore, the display unit can provide optimal display content according to the user's current task. In this way, by customizing the display content according to the user's current task, information necessary for the user can be provided with priority.

[0111] The display unit can improve the display method by reflecting user feedback during display. For example, the display unit improves the display method based on feedback provided by the user. The display unit can also provide a specific display method with priority based on the user feedback. Furthermore, the display unit can analyze the user feedback and customize the display interface. In this way, the display method can be improved by reflecting the user feedback, thereby improving user convenience.

[0112] The display unit can estimate the user's emotions and determine display priorities based on the estimated user emotions. For example, when the user is nervous, the display unit can prioritize displaying simple and important information. Furthermore, when the user is relaxed, the display unit can also prioritize displaying detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying information that focuses on the main points. In this way, by determining the display priorities according to the user's emotions, information that is important to the user is prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The display unit can select the optimal display method when displaying information by taking into consideration the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. In this way, by taking into consideration the user's device information, a display method optimized for the device is provided, improving user convenience.

[0114] The display unit can make the display content multilingual according to the user's language setting when displaying. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the display unit can provide the display content in that language. In this way, by making the display content multilingual according to the user's language setting, a display that is easy for the user to understand is provided.

[0115] The display unit can adjust the display method according to the visual and auditory characteristics of the user when displaying. For example, if the user is visually impaired, the display unit can provide audio guidance preferentially. Also, if the user is hearing impaired, the display unit can provide a visual display method preferentially. Furthermore, the display unit can provide the optimal display method according to the visual and auditory characteristics of the user. In this way, the display method can be adjusted according to the visual and auditory characteristics of the user, thereby providing the optimal display for the user.

[0116] The registration unit can estimate the user's emotions and determine the priority of information to be registered based on the estimated user emotions. For example, if the user is nervous, the registration unit can prioritize registering simple and important information. Furthermore, if the user is relaxed, the registration unit can also prioritize registering detailed information. Furthermore, if the user is in a hurry, the registration unit can also prioritize registering information that covers the main points. In this way, by determining the priority of information to be registered according to the user's emotions, information that is important to the user can be prioritized and registered. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] The registration unit can improve the accuracy of registration by taking into account the interrelationships between sign languages ​​during registration. For example, the registration unit registers related sign language actions together to improve the accuracy of the database. The registration unit can also analyze the interrelationships between sign language actions and register optimal information. Furthermore, the registration unit can improve the accuracy of the information to be registered based on the interrelationships between sign language actions. In this way, by taking into account the interrelationships between sign languages, related sign language actions are registered together to improve the accuracy of the database.

[0118] The registration unit can perform registration while taking into consideration attribute information of the sign language submitter. The registration unit registers related information based on, for example, the age and gender of the sign language submitter. The registration unit can also register related information based on the occupation and expertise of the sign language submitter. Furthermore, the registration unit can register related information based on the region and cultural background of the sign language submitter. In this way, related information can be appropriately registered by taking into consideration the attribute information of the sign language submitter.

[0119] The registration unit can weight the registration based on the frequency of submission of the sign language at the time of registration. For example, the registration unit preferentially registers sign language actions that are submitted more frequently. The registration unit can also postpone registration of sign language actions that are submitted less frequently. Furthermore, the registration unit can weight the information to be registered based on the frequency of submission of the sign language actions. Thus, by weighting the registration based on the frequency of submission of the sign language, frequently used sign language actions can be preferentially registered.

[0120] The registration unit can estimate the user's emotions and adjust the display method of the registered information based on the estimated user emotions. For example, if the user is nervous, the registration unit can provide a simple, highly visible display method. If the user is relaxed, the registration unit can also provide a display method that includes detailed information. If the user is in a hurry, the registration unit can also provide a display method that focuses on the main points. In this way, by adjusting the information display method according to the user's emotions, a display that is easy for the user to understand is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0121] The registration unit can perform registration while taking into consideration the geographical distribution of sign languages. For example, the registration unit preferentially registers sign language actions that are commonly used in a specific region. The registration unit can also analyze the geographical distribution of sign language actions and register optimal information. Furthermore, the registration unit can adjust the information to be registered based on the geographical distribution of sign language actions. In this way, by taking into consideration the geographical distribution of sign languages, sign language actions related to a region can be preferentially registered.

[0122] The registration unit can improve the accuracy of registration by referring to literature related to sign language during registration. For example, the registration unit refers to literature related to sign language actions and registers optimal information. The registration unit can also analyze literature related to sign language actions and improve the accuracy of the information to be registered. Furthermore, the registration unit can adjust the information to be registered based on literature related to sign language actions. In this way, the accuracy of the information to be registered is improved by referring to literature related to sign language.

[0123] The registration unit can take into consideration the market value of the sign language when registering. For example, the registration unit preferentially registers sign language actions with high market value. The registration unit can also postpone registration of sign language actions with low market value. Furthermore, the registration unit can adjust the information to be registered based on the market value of the sign language actions. In this way, by taking into consideration the market value of the sign language, it is possible to preferentially register sign language actions with high value.

[0124] The management unit can estimate the user's emotions and adjust the management method based on the estimated user emotions. For example, if the user is nervous, the management unit can provide a simple and highly visible management interface. If the user is relaxed, the management unit can also provide detailed management options and suggest a customizable management method. Furthermore, if the user is in a hurry, the management unit can provide a management interface that can be operated quickly. In this way, by adjusting the management method according to the user's emotions, a management interface that is easy for the user to use is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0125] During management, the management unit can refer to the system's past management history to select the optimal management method. For example, the management unit analyzes the system's past management history and proposes the optimal management method. The management unit can also provide a specific management method with priority based on the past management history. Furthermore, the management unit can customize the management interface based on the system's past management history. In this way, by referring to the system's past management history, the optimal management method can be provided and the operational efficiency of the system can be improved.

[0126] The management unit can customize the management content according to the current state of the system during management. For example, the management unit monitors the current state of the system in real time and provides the optimal management method. The management unit can also customize the management interface based on the current state of the system. Furthermore, the management unit can analyze the current state of the system and adjust the management content. In this way, customizing the management content according to the current state of the system improves the operational efficiency of the system.

[0127] The management unit can improve the management method by reflecting user feedback during management. For example, the management unit improves the management method based on feedback provided by the user. The management unit can also provide a specific management method with priority based on user feedback. Furthermore, the management unit can analyze user feedback and customize the management interface. In this way, the management method can be improved by reflecting user feedback, thereby improving the operational efficiency of the system.

[0128] The management unit can estimate the user's emotions and determine management priorities based on the estimated user emotions. For example, if the user is nervous, the management unit can prioritize providing simple but important management tasks. Furthermore, if the user is relaxed, the management unit can prioritize providing detailed management tasks. Furthermore, if the user is in a hurry, the management unit can prioritize providing management tasks that focus on the main points. In this way, by determining management priorities according to the user's emotions, management tasks that are important to the user are prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0129] The management unit can select the optimal management method during management by taking into consideration device information of the system. For example, if the system is running on a smartphone, the management unit provides a management interface that matches the screen size. Also, if the system is running on a tablet, the management unit can provide a management interface that is optimized for a large screen. Furthermore, if the system is running on a smartwatch, the management unit can provide a simple and highly visible management interface. In this way, by taking into consideration device information of the system, a management method optimized for the device is provided, improving the operational efficiency of the system.

[0130] During management, the management unit can make the management content multilingual in accordance with the system's language setting. For example, the management unit automatically sets the language of the management interface based on the system's language setting. The management unit can also provide a language switching function if the system supports multiple languages. Furthermore, the management unit can also make the management content multilingual in accordance with the system's language setting. This provides a management interface that is easy for users to understand by making the management content multilingual in accordance with the system's language setting.

[0131] The management unit can adjust the management method according to the visual and auditory characteristics of the system during management. For example, the management unit can prioritize providing audio guidance to users with visual impairments. The management unit can also prioritize providing a visual management interface to users with hearing impairments. Furthermore, the management unit can provide the optimal management method according to the visual and auditory characteristics of the system. In this way, by adjusting the management method according to the visual and auditory characteristics of the system, the optimal management interface for the user is provided. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, acquisition unit, display unit, registration unit, and management unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit inputs sign language actions using the camera 42 or touch panel 38A of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sign language actions. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires the "trigger" and "meaning" of the sign language from the database 24. The display unit displays the acquired information to the user using the display 40A or speaker 40B of the smart device 14. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and registers and updates the sign language trigger and meaning in the database 24. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the entire system. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, acquisition unit, display unit, registration unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit inputs sign language actions using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sign language actions. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires the "trigger" and "meaning" of the sign language from the database 24. The display unit displays the acquired information to the user using the display and speaker 240 of the smart glasses 214. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and registers and updates the sign language trigger and meaning in the database 24. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the entire system. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, analysis unit, acquisition unit, display unit, registration unit, and management unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit inputs sign language actions using the camera 42 or microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sign language actions. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires the "trigger" and "meaning" of the sign language from the database 24. The display unit displays the acquired information to the user using the display 343 or speaker 240 of the headset-type terminal 314. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and registers and updates the sign language trigger and meaning in the database 24. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the entire system. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, analysis unit, acquisition unit, display unit, registration unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit inputs sign language movements using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the sign language movements. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires the "trigger" and "meaning" of the sign language from the database 24. The display unit displays the acquired information to the user using the display and speaker 240 of the robot 414. The registration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and registers and updates the trigger and meaning of the sign language in the database 24. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the entire system.

[0132] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0133] The sign language information provision system may further include a learning tracking unit that tracks the user's learning progress. The learning tracking unit records which sign languages ​​the user has learned and their level of understanding, and suggests appropriate sign languages ​​for the next learning session. For example, if the user has learned the sign language for "thank you," the learning tracking unit records the user's progress and suggests related sign languages ​​such as "you're welcome" for the next learning session. The learning tracking unit may also prioritize the display of sign languages ​​that require review based on the user's level of understanding. Furthermore, the learning tracking unit may analyze the user's learning history and suggest an optimal learning plan. This improves the user's learning efficiency and supports the user's acquisition of sign language.

[0134] The sign language information provision system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the learning content based on the estimated emotions. For example, if the user is tired, the emotion adjustment unit can prioritize displaying simple sign language to reduce the learning burden. Also, if the user is excited, the emotion adjustment unit can suggest more difficult sign language to increase the user's motivation to try. Furthermore, if the user is relaxed, the emotion adjustment unit can provide a regular learning plan to support smooth learning. In this way, the learning content is adjusted according to the user's emotions, maximizing the effectiveness of learning.

[0135] The sign language information provision system can further include an interest suggestion unit that suggests sign language actions based on the user's interests and concerns. For example, if the user is interested in sports, the interest suggestion unit can preferentially display sign language related to sports. Also, if the user is interested in music, the interest suggestion unit can suggest sign language related to music. Furthermore, the interest suggestion unit can analyze the user's past search history and learning history and suggest optimal sign language actions. In this way, suggesting sign language actions based on the user's interests and concerns increases motivation to learn.

[0136] The sign language information provision system may further include a feedback unit that estimates the user's emotions and provides feedback on sign language actions based on the estimated emotions. For example, if the user is nervous, the feedback unit may preferentially provide positive feedback to increase the user's confidence. If the user is relaxed, the feedback unit may provide detailed feedback and point out areas for improvement in the sign language actions. Furthermore, if the user is in a hurry, the feedback unit may provide concise feedback to support efficient learning. In this way, the effectiveness of learning is improved by providing feedback according to the user's emotions.

[0137] The sign language information provision system can further include a learning style adaptation unit that customizes sign language actions according to the user's learning style. For example, the learning style adaptation unit can provide sign language actions that make extensive use of videos and images to a user who prefers visual learning. It can also provide sign language actions that include audio guides to a user who prefers auditory learning. It can also provide interactive sign language practice to a user who prefers practical learning. In this way, the effectiveness of learning is maximized by customizing sign language actions according to the user's learning style.

[0138] The sign language information provision system may further include a difficulty adjustment unit that estimates the user's emotions and adjusts the difficulty of the sign language actions based on the estimated emotions. For example, if the user is tired, the difficulty adjustment unit may preferentially display easy sign language actions to reduce the burden of learning. Also, if the user is excited, the difficulty adjustment unit may suggest more difficult sign language actions to increase the user's motivation to try. Furthermore, if the user is relaxed, the difficulty adjustment unit may provide sign language actions of normal difficulty to support smooth learning. In this way, the effectiveness of learning is maximized by adjusting the difficulty of the sign language actions according to the user's emotions.

[0139] The sign language information provision system can further include a goal suggestion unit that suggests sign language actions based on the user's learning goals. For example, if the user's goal is everyday conversation, the goal suggestion unit can preferentially display sign language actions related to everyday conversation. Also, if the user's goal is business communication, the goal suggestion unit can suggest sign language actions related to business. Furthermore, if the user is learning sign language for a specific event, the goal suggestion unit can suggest sign language actions related to the event. In this way, by suggesting sign language actions based on the user's learning goals, the effectiveness of learning is improved.

[0140] The sign language information provision system can further include a practice suggestion unit that estimates the user's emotions and suggests a practice method for sign language movements based on the estimated emotions. For example, if the user is nervous, the practice suggestion unit can suggest a practice method that will help the user relax, thereby relieving the tension. Also, if the user is relaxed, the practice suggestion unit can suggest a practice method that will increase concentration, thereby supporting effective learning. Furthermore, if the user is in a hurry, the practice suggestion unit can suggest an effective practice method that can be done in a short amount of time, thereby supporting efficient learning. In this way, the effectiveness of learning is maximized by suggesting a practice method according to the user's emotions.

[0141] The sign language information provision system can further include a review suggestion unit that reviews sign language actions based on the user's learning history. The review suggestion unit, for example, periodically displays sign language actions that the user has learned in the past to encourage review. It can also support review by preferentially displaying sign language actions that the user has a low level of understanding. Furthermore, it can analyze the user's learning history and suggest an optimal review plan. In this way, reviewing sign language actions based on the user's learning history promotes the consolidation of learning.

[0142] The sign language information provision system may further include a feedback unit that estimates the user's emotions and provides feedback on sign language actions based on the estimated emotions. For example, if the user is nervous, the feedback unit may preferentially provide positive feedback to increase the user's confidence. If the user is relaxed, the feedback unit may provide detailed feedback and point out areas for improvement in the sign language actions. Furthermore, if the user is in a hurry, the feedback unit may provide concise feedback to support efficient learning. In this way, the effectiveness of learning is improved by providing feedback according to the user's emotions.

[0143] The processing flow of the second embodiment will be briefly explained below.

[0144] Step 1: The input unit inputs sign language actions. Sign language actions include hand movements, facial expressions, body movements, etc. The input unit can capture the sign language actions using a camera, and can also input the sign language actions as text. For example, a user can capture the sign language action of saying "thank you" with a camera. This information is input into the system. Step 2: The analysis unit analyzes the sign language movements input by the input unit. The analysis unit analyzes the sign language movements and retrieves the sign language "cues" and "meanings" corresponding to the movements from the database. Step 3: The acquisition unit acquires from the database the "cue" and "meaning" corresponding to the sign language action analyzed by the analysis unit. For example, the acquisition unit acquires the "cue" and "meaning" of the sign language from the database. Step 4: The display unit displays the information acquired by the acquisition unit to the user. The display unit displays the acquired information to the user in text format, audio, video, etc. For example, for the sign language gesture for "thank you," the background and meaning of the sign language are displayed.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0150] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0166] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0176] 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.

[0177] 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.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0193] 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.

[0194] 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.

[0195] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0196] 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.

[0197] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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."

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Explanation of symbols]

[0217] 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. an input unit for inputting sign language actions; an analysis unit that analyzes the sign language gestures input by the input unit; an acquisition unit that acquires, from a database, a trigger or meaning corresponding to the sign language action analyzed by the analysis unit; a display unit that displays the information acquired by the acquisition unit to a user; Equipped with A system characterized by:

2. The input unit Use a camera to capture sign language movements or input sign language movements as text 2. The system of claim 1.

3. The display unit Presents the retrieved information to the user in text, audio, or video format 2. The system of claim 1.

4. Equipped with a registration unit that registers and updates sign language triggers or meanings in a database 2. The system of claim 1.

5. Equipped with a management department that manages the entire system 2. The system of claim 1.

6. The input unit Estimate the user's emotions and adjust the timing of sign language input based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyze the user's past sign language input history and select the appropriate input method 2. The system of claim 1.

8. The input unit Filter sign language input based on the user's current learning status or area of ​​interest 2. The system of claim 1.

9. The input unit When inputting sign language actions, select an appropriate input method according to the user's input method.

2. The system of claim 1.

10. The input unit Estimate the user's emotions and determine the priority of sign language actions to input based on the estimated user emotions.

2. The system of claim 1.

Images