System

Abstract

To provide a system that develops customer support using sign language nationwide.SOLUTION: A system according to the embodiment comprises a reception unit, a generation unit, a provision unit and a recognition unit. The reception unit receives a user's input. The generation unit analyzes the input received by the reception unit, and generates a sign language video. The provision unit provides the sign language video generated by the generation unit to the user. The recognition unit analyzes a sign language, and converts it into text or voice.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, customer support using sign language was limited to certain stores, making it difficult to expand nationwide.

[0005] The system according to the embodiment aims to provide customer support using sign language nationwide. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a providing unit, and a recognizing unit. The receiving unit receives a user's input. The generating unit analyzes the input received by the receiving unit and generates a sign language video. The providing unit provides the sign language video generated by the generating unit to a user. The recognizing unit analyzes the sign language and converts it into text or audio. Effect of the Invention

[0007] The system according to the embodiment can provide customer support using sign language nationwide. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sign language video generation system according to an embodiment of the present invention is a system that converts a user's input into a sign language video and provides customer support for the hearing impaired. In this sign language video generation system, a user inputs text or voice, and a generation AI analyzes the input and generates a sign language video. The generated sign language video is provided to the user. In addition, by utilizing a sign language recognition AI, even staff who do not know sign language can communicate efficiently. This makes it possible to expand customer support using sign language, which was previously limited to some stores, nationwide. For example, a user may input customer support inquiries in text or ask questions by voice. This input is sent to the generation AI. Next, the generation AI analyzes the input text or voice. The generation AI understands the input content using natural language processing technology and generates a sign language video. For example, when the text "Please tell me how to return a product" is input, a sign language video is generated based on the content. The generated sign language video is provided to the user. The user can understand the content of the customer support by watching the sign language video. This allows even hearing impaired people to receive customer support smoothly. Furthermore, by utilizing sign language recognition AI, even staff who do not know sign language can communicate efficiently. For example, when a staff member receives an inquiry in sign language, the sign language recognition AI analyzes the sign language and converts it into text or voice. This allows the staff member to understand the sign language and respond appropriately. This system makes it possible to expand customer support in sign language, which was previously limited to certain stores, nationwide. For example, providing customer support in sign language in stores nationwide would improve the quality of service for the hearing impaired and increase customer satisfaction. This allows the sign language video generation system to provide customer support to the hearing impaired.

[0029] The sign language video generation system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a recognition unit. The reception unit receives a user input. The user input includes, but is not limited to, for example, a text input, a voice input, and a gesture input. The reception unit receives, for example, a customer support inquiry in text form. The reception unit can also receive a voice inquiry. The reception unit can also receive a gesture input. For example, the reception unit uses a keyboard or a touch screen to receive the text input. The reception unit uses a microphone to receive the voice input. The reception unit uses a camera or a sensor to receive the gesture input. The generation unit uses a generation AI to analyze the input received by the reception unit and generate a sign language video. The generation AI uses, for example, a natural language processing technology to understand the input content and generate a sign language video. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI analyzes a text input and generates a sign language video. The generation AI can also analyze voice input and generate a sign language video. The generation AI can also analyze gesture input and generate a sign language video. For example, the generation AI uses natural language processing technology to analyze text input. The generation AI uses voice recognition technology to analyze voice input. The generation AI uses image recognition technology to analyze gesture input. The provision unit provides the sign language video generated by the generation unit to a user. The provision unit provides the sign language video by a method such as streaming, download, real-time distribution, etc. The provision unit provides the sign language video by streaming, for example. The provision unit can also provide the sign language video by download. The provision unit can also provide the sign language video by real-time distribution. For example, the provision unit uses an Internet connection to provide the sign language video by streaming. The provision unit uses a file server to provide the sign language video by download. The provision unit uses live streaming technology to provide the sign language video by real-time distribution. The recognition unit analyzes the sign language and converts it into text or voice. The recognition unit analyzes the sign language using, for example, a sign language recognition AI and converts it into text or voice.The recognition unit, for example, analyzes sign language and converts it into text. The recognition unit can also analyze sign language and convert it into speech. For example, the recognition unit uses sign language recognition AI to analyze sign language. It uses natural language processing technology to convert sign language into text. It uses speech synthesis technology to convert sign language into speech. In this way, the sign language video generation system according to the embodiment can convert user input into sign language video and provide customer support for the hearing impaired.

[0030] The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and gesture input. For example, the reception unit receives customer support inquiries in text. The reception unit can also receive voice inquiries. Furthermore, the reception unit can also receive gesture input. For example, the reception unit uses a keyboard or a touch screen to receive text input. The reception unit uses a microphone to receive voice input. The reception unit uses a camera or a sensor to receive gesture input. This allows the reception unit to provide a variety of input means and improve user convenience. Furthermore, the reception unit preprocesses the input data and checks the consistency of the data before passing it to the generation unit. For example, in the case of text input, spell checking and grammar checking are performed, and in the case of voice input, noise removal and voice clearing are performed. In the case of gesture input, the data acquired from the camera or sensor is filtered to extract only necessary information. As a result, the reception unit also plays a role in ensuring the quality of the input data so that the generation unit can efficiently generate sign language videos. Furthermore, the reception unit stores the user's input history, enabling quick responses to future inquiries. For example, by referring to the contents of past inquiries, the reception unit can quickly provide solutions to similar problems, thereby improving user satisfaction and increasing the efficiency of the entire system.

[0031] The generation unit uses the generation AI to analyze the input received by the reception unit and generate a sign language video. The generation AI understands the input content using, for example, natural language processing technology and generates a sign language video. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI analyzes the text input and generates a sign language video. The generation AI can also analyze the voice input and generate a sign language video. Furthermore, the generation AI can analyze the gesture input and generate a sign language video. For example, the generation AI uses natural language processing technology to analyze the text input. The generation AI uses voice recognition technology to analyze the voice input. The generation AI uses image recognition technology to analyze the gesture input. The generation AI combines these technologies to accurately understand the user's input content and generate an appropriate sign language video. Specifically, when analyzing the text input, the generation AI understands the context and meaning and converts it into sign language grammar and expressions. In the case of voice input, the generation AI converts the voice into text using voice recognition technology, and generates a sign language video based on the text. In the case of gesture input, image recognition technology is used to analyze the gestures and convert the movements into sign language. Based on the results of these analyses, the generation AI creates animation data for generating sign language videos, and generates the sign language videos. Before being provided to users, the generated sign language videos are quality checked to confirm accuracy and naturalness. This enables the generation unit to respond to a variety of user inputs and generate high-quality sign language videos.

[0032] The providing unit provides the sign language video generated by the generating unit to the user. The providing unit provides the sign language video by, for example, streaming, downloading, real-time distribution, or the like. The providing unit provides the sign language video by, for example, streaming. The providing unit can also provide the sign language video by downloading. Furthermore, the providing unit can also provide the sign language video by real-time distribution. For example, the providing unit uses an Internet connection to provide the sign language video by streaming. A file server is used to provide the sign language video by downloading. A live streaming technology is used to provide the sign language video by real-time distribution. This allows the providing unit to realize a flexible providing method according to the needs of the user. Furthermore, when providing the sign language video, the providing unit performs optimization according to the viewing environment and device of the user. For example, in the case of streaming, the image quality and bit rate of the video are adjusted according to the Internet connection speed of the user. In the case of downloading, the file size is optimized according to the storage capacity of the user's device. In the case of real-time distribution, a technology for minimizing delays is introduced to provide a smooth viewing experience. This allows the providing unit to provide high-quality sign language video to the user and improve the viewing experience. Furthermore, the provision unit monitors the status of the sign language video provision and responds promptly if a problem occurs. For example, if buffering occurs during streaming, measures are taken to distribute the server load. This enables the provision unit to provide stable sign language video provision and increase user satisfaction.

[0033] The recognition unit analyzes the sign language and converts it into text or voice. The recognition unit, for example, uses a sign language recognition AI to analyze the sign language and converts it into text or voice. The recognition unit, for example, analyzes the sign language and converts it into text. The recognition unit can also analyze the sign language and convert it into voice. For example, the recognition unit uses a sign language recognition AI to analyze the sign language. A natural language processing technique is used to convert the sign language into text. A voice synthesis technique is used to convert the sign language into voice. This allows the recognition unit to understand the sign language and convert it into text or voice. Specifically, the recognition unit uses a sign language recognition AI to analyze the actions and facial expressions of the sign language and understand their meaning. The sign language recognition AI uses a model to analyze the actions of the sign language in detail and understand the grammar and expressions of the sign language. When converting the sign language into text, a natural language processing technique is used to convert the meaning of the sign language into text. When converting the sign language into voice, a voice synthesis technique is used to convert the text into voice. This allows the recognition unit to accurately analyze the sign language and convert it into text or voice. Furthermore, the recognition unit can provide feedback to the user based on the sign language analysis results. For example, if the sign language analysis results are unclear, the recognition unit can improve the accuracy of the analysis by requesting additional input from the user. The recognition unit can also save the sign language analysis results and use them for future analyses. This allows the recognition unit to continuously improve the sign language analysis accuracy and provide high-quality services to the user.

[0034] The generation unit can analyze the input text or voice by the generation AI and generate a sign language video. The generation unit, for example, uses the generation AI to analyze the input text and generate a sign language video. The generation AI, for example, uses natural language processing technology to analyze the text and generate a sign language video. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI uses natural language processing technology to analyze the text. The generation AI understands the meaning of the text and generates a sign language video. The generation AI, for example, analyzes the text "Please tell me how to return the product" and generates a sign language video based on the content. The generation unit can also analyze the input voice and generate a sign language video by using the generation AI. The generation AI, for example, uses speech recognition technology to analyze the voice and generates a sign language video. The generation AI converts the voice into text using speech recognition technology and generates a sign language video based on the text. For example, the generation AI analyzes the voice "Please tell me how to return the product" and generates a sign language video based on the content. This improves the accuracy of converting input text or audio into sign language video by using the generation AI. Some or all of the above-mentioned processes in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit may input text or audio to the generation AI and cause the generation AI to generate a sign language video.

[0035] The recognition unit can analyze sign language and convert it into text or voice. The recognition unit can, for example, use a sign language recognition AI to analyze sign language and convert it into text. The sign language recognition AI can, for example, use image recognition technology to analyze sign language and convert the content into text. The sign language recognition AI can analyze the movement of the sign language and generate text corresponding to the movement. For example, the sign language recognition AI can analyze the sign language "Please tell me how to return the product" and convert the content into text. The recognition unit can also analyze sign language and convert it into voice using the sign language recognition AI. The sign language recognition AI can, for example, use image recognition technology to analyze sign language and convert the content into voice. The sign language recognition AI can, for example, analyze the movement of the sign language and generate voice corresponding to the movement. For example, the sign language recognition AI can analyze the sign language "Please tell me how to return the product" and convert the content into voice. By converting sign language into text or voice, even staff who do not remember sign language can communicate efficiently. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input sign language into a sign language recognition AI and have the sign language recognition AI convert it into text or speech.

[0036] The providing unit can provide the generated sign language video to a user. The providing unit, for example, provides the generated sign language video by streaming. The providing unit, for example, uses an Internet connection to provide the sign language video by streaming. The providing unit can also provide the generated sign language video by download. The providing unit, for example, uses a file server to provide the sign language video by download. Furthermore, the providing unit can also provide the generated sign language video by real-time distribution. The providing unit, for example, uses live streaming technology to provide the sign language video by real-time distribution. Thereby, by providing the generated sign language video to a user, a hearing-impaired person can receive customer support. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated sign language video to AI and cause AI to execute provision to the user.

[0037] The reception unit can receive the customer support inquiry content in text or voice. The reception unit, for example, receives the customer support inquiry content in text. The reception unit, for example, uses a keyboard or a touch screen to receive text input. The reception unit can also receive the customer support inquiry content in voice. The reception unit, for example, uses a microphone to receive voice input. This improves the user's convenience by receiving the customer support inquiry content in text or voice. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the customer support inquiry content into AI and have the AI ​​execute the reception in text or voice.

[0038] The recognition unit can analyze sign language and provide it to the staff in text or voice. The recognition unit, for example, uses a sign language recognition AI to analyze sign language and convert it into text. The sign language recognition AI, for example, uses image recognition technology to analyze sign language and convert the content into text. The sign language recognition AI analyzes the movement of the sign language and generates text corresponding to the movement. For example, the sign language recognition AI analyzes the sign language "Please tell me how to return the product" and converts the content into text. The recognition unit can also analyze sign language using a sign language recognition AI and convert it into voice. The sign language recognition AI, for example, uses image recognition technology to analyze sign language and converts the content into voice. The sign language recognition AI analyzes the movement of the sign language and generates voice corresponding to the movement. For example, the sign language recognition AI analyzes the sign language "Please tell me how to return the product" and converts the content into voice. As a result, by providing the sign language to the staff in text or voice, the staff can easily understand the sign language. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input sign language into a sign language recognition AI and have the sign language recognition AI convert it into text or speech.

[0039] The reception unit can analyze the user's past inquiry history and select an appropriate reception method. For example, if the user has made an inquiry by text in the past, the reception unit preferentially suggests text input. For example, if the user has made an inquiry by voice in the past, the reception unit preferentially suggests voice input. For example, the reception unit automatically selects an optimal reception method from the content of the user's past inquiries. In this way, the optimal reception method can be provided by analyzing the user's past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past inquiry history into AI and cause the AI ​​to select the optimal reception method.

[0040] The reception unit can perform filtering based on the user's current situation and field of interest at the time of reception. For example, when the user inputs the current situation, the reception unit filters the related inquiry content based on the situation. For example, the reception unit registers the user's field of interest in advance and preferentially receives inquiry content related to that field. For example, the reception unit excludes unnecessary inquiry content based on the user's current situation and field of interest. As a result, by performing filtering based on the user's current situation and field of interest, it is possible to preferentially receive highly relevant inquiry content. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation and field of interest to AI and have the AI ​​execute filtering.

[0041] The reception unit can preferentially receive inquiries with high relevance in consideration of the geographical location information of the user at the time of reception. For example, when the user is in a specific area, the reception unit preferentially receives inquiries related to the area. For example, the reception unit proposes optimal inquiry content based on the geographical location information of the user. For example, when the user is moving, the reception unit preferentially receives related inquiries based on the current location. In this way, by considering the geographical location information of the user, it is possible to preferentially receive inquiries with high relevance. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to perform preferential reception of inquiries with high relevance.

[0042] The reception unit can analyze the user's social media activity at the time of reception and receive related inquiries. The reception unit, for example, preferentially receives inquiries related to topics of interest from the user's social media activity. The reception unit, for example, analyzes the user's comments on social media and suggests related inquiry content. The reception unit, for example, filters optimal inquiry content based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related inquiry content. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​execute the reception of related inquiries.

[0043] The generation unit can adjust the level of detail of the sign language video based on the importance of the inquiry content at the time of generation. For example, in the case of important inquiry content, the generation unit generates detailed sign language video. For example, in the case of general inquiry content, the generation unit generates standard sign language video. For example, in the case of simple inquiry content, the generation unit generates simple sign language video. In this way, by adjusting the level of detail of the sign language video based on the importance of the inquiry content, appropriate information can be provided. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the inquiry content to AI and cause the AI ​​to adjust the level of detail of the sign language video.

[0044] The generation unit can apply different generation algorithms depending on the category of the inquiry at the time of generation. For example, in the case of a technical inquiry, the generation unit generates a sign language video including technical terms. For example, in the case of a general inquiry, the generation unit generates a standard sign language video. For example, in the case of an inquiry regarding customer support, the generation unit generates a familiar sign language video. In this way, by applying different generation algorithms depending on the category of the inquiry, more appropriate sign language video can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of the inquiry to AI and cause the AI ​​to apply the generation algorithm.

[0045] The generation unit can determine the priority of the sign language videos based on the submission time of the inquiry at the time of generation. For example, the generation unit preferentially converts the most recently submitted inquiry into the sign language video. For example, the generation unit generates the sign language video after an inquiry that was submitted earlier. For example, the generation unit adjusts the order of generation of the sign language videos based on the submission time. This enables a quick response by determining the priority of the sign language videos based on the submission time of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the inquiry to AI and cause the AI ​​to determine the priority of the sign language videos.

[0046] The generation unit can adjust the order of the sign language videos based on the relevance of the queries at the time of generation. The generation unit, for example, preferentially converts highly relevant queries into sign language videos. The generation unit, for example, generates sign language videos after leaving queries with low relevance. The generation unit, for example, adjusts the generation order of the sign language videos based on the relevance of the queries. In this way, by adjusting the order of the sign language videos based on the relevance of the queries, highly relevant information can be provided preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the queries to AI and cause the AI ​​to adjust the order of the sign language videos.

[0047] The providing unit can select an appropriate providing method by referring to the user's past viewing history at the time of providing. The providing unit selects the optimal providing method based on, for example, a history of sign language videos that the user has viewed in the past. The providing unit preferentially provides a preferred sign language video from, for example, the user's viewing history. The providing unit, for example, analyzes the user's viewing history and suggests an optimal providing method. In this way, it is possible to select an optimal providing method for the sign language video by referring to the user's past viewing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's viewing history into AI and cause the AI ​​to select the optimal providing method.

[0048] The providing unit can customize the means for providing the sign language video based on the user's current situation at the time of providing. For example, when the user is on the move, the providing unit provides a sign language video optimized for a smartphone. For example, when the user is at home, the providing unit provides a sign language video that can be viewed on a large screen. For example, the providing unit selects an optimal means for providing the sign language video based on the user's current situation. This allows more appropriate sign language video to be provided by customizing the means for providing the sign language video based on the user's current situation. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation to AI and cause AI to customize the means for providing the sign language video.

[0049] The providing unit can select an appropriate providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit preferentially provides a sign language video related to the area. For example, the providing unit proposes an optimal sign language video providing method based on the geographical location information of the user. For example, when the user is moving, the providing unit provides an optimal sign language video based on the current location. In this way, it is possible to select an optimal sign language video providing method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to select an optimal providing method.

[0050] The providing unit can analyze the user's social media activity at the time of providing and suggest a means of providing the sign language video. For example, the providing unit preferentially provides sign language videos related to topics of interest from the user's social media activity. For example, the providing unit analyzes the user's comments on social media and suggests related sign language videos. For example, the providing unit selects an optimal means of providing the sign language video based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially provide related sign language videos. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI and cause the AI ​​to execute a suggestion of a means of providing the sign language video.

[0051] The recognition unit can improve the accuracy of analysis by taking into account the context of the sign language during recognition. The recognition unit improves the accuracy of analysis by taking into account, for example, the context before and after the sign language. The recognition unit interprets the meaning of ambiguous sign language, for example, based on the context of the sign language. The recognition unit understands, for example, the context of the sign language and converts it into appropriate text or voice. In this way, the accuracy of analysis can be improved by taking into account the context of the sign language. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the context data of the sign language to the AI ​​and cause the AI ​​to improve the accuracy of the analysis.

[0052] The recognition unit can perform analysis while taking into consideration attribute information of the person submitting the sign language at the time of recognition. The recognition unit improves the accuracy of the analysis by taking into consideration, for example, the age and gender of the person submitting the sign language. The recognition unit performs an appropriate interpretation by taking into consideration, for example, the region and cultural background of the person submitting the sign language. The recognition unit selects an optimal analysis method based on, for example, the attribute information of the person submitting the sign language. In this way, the accuracy of the analysis can be improved by taking into consideration the attribute information of the person submitting the sign language. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the attribute information of the person submitting the sign language to AI and cause the AI ​​to perform the analysis.

[0053] The recognition unit can perform analysis taking into account the geographical distribution of the sign language during recognition. The recognition unit performs analysis taking into account, for example, expressions specific to a region of the sign language. The recognition unit performs appropriate interpretation based on, for example, the geographical distribution of the sign language. The recognition unit understands, for example, the geographical background of the sign language and selects an optimal analysis method. In this way, appropriate analysis can be performed by taking into account the geographical distribution of the sign language. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input geographical distribution data of the sign language to AI and have the AI ​​perform the analysis.

[0054] The recognition unit can improve the accuracy of analysis by referring to literature related to sign language during recognition. The recognition unit, for example, refers to literature related to sign language to improve the accuracy of analysis. The recognition unit performs an appropriate interpretation based on, for example, a specialized book or dictionary on sign language. The recognition unit, for example, utilizes literature related to sign language to select an optimal analysis method. In this way, the accuracy of analysis can be improved by referring to literature related to sign language. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input literature data related to sign language to AI and cause the AI ​​to improve the accuracy of analysis.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When accepting an input from a user, the accepting unit can suggest an optimal input method by referring to the user's past input history. For example, if the user has frequently used text input in the past, text input can be suggested preferentially. Also, if the user has preferred voice input, voice input can be suggested preferentially. Furthermore, if the user has frequently used gesture input, gesture input can be suggested preferentially. This makes it possible to improve user convenience by suggesting an optimal input method based on the user's past input history.

[0057] The generation unit can generate more appropriate sign language videos by analyzing the text or voice input by the generation AI and referring to the user's past inquiries when generating sign language videos. For example, if the user has made a similar inquiry in the past, the sign language video is generated by referring to the inquiry. Also, if the user has preferred a particular expression in the past, the expression can be reflected in the sign language video. Furthermore, if the user has shown interest in a particular topic in the past, a sign language video related to that topic can be generated. This makes it possible to provide more appropriate sign language videos based on the user's past inquiries.

[0058] The recognition unit can improve the accuracy of the analysis by taking into account the context of the sign language when analyzing the sign language and converting it into text or speech. For example, the meaning of ambiguous sign language is interpreted by taking into account the context before and after the sign language. Also, the recognition unit can convert into appropriate text or speech based on the context of the sign language. Furthermore, the accuracy of the analysis can be improved by understanding the context of the sign language and making an appropriate interpretation. As a result, more accurate analysis results can be provided by taking into account the context of the sign language.

[0059] When providing the generated sign language video to the user, the providing unit can select an appropriate providing method by referring to the user's past viewing history. For example, the optimal providing method is selected based on the history of sign language videos the user has viewed in the past. Also, it is possible to provide the user's preferred sign language video preferentially based on the user's viewing history. Furthermore, it is possible to analyze the user's viewing history and propose the optimal providing method. In this way, it is possible to select the optimal providing method of the sign language video by referring to the user's past viewing history.

[0060] When accepting customer support inquiries by text or voice, the reception unit can filter the inquiries based on the user's current situation and field of interest. For example, when the user inputs the current situation, related inquiry contents are filtered based on the situation. In addition, the user's field of interest can be registered in advance, and inquiries related to that field can be preferentially accepted. Furthermore, unnecessary inquiry contents can be excluded based on the user's current situation and field of interest. In this way, by filtering based on the user's current situation and field of interest, it is possible to preferentially accept highly relevant inquiry contents.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives a user input. The user input includes text input, voice input, gesture input, etc. For example, the reception unit uses a keyboard or a touch screen to receive text input, a microphone to receive voice input, and a camera or a sensor to receive gesture input. Step 2: The generation unit uses the generation AI to analyze the input received by the reception unit and generate a sign language video. The generation AI understands the input content using natural language processing technology, voice recognition technology, and image recognition technology, and generates a sign language video. For example, the generation AI analyzes text input, voice input, and gesture input, and generates a corresponding sign language video. Step 3: The providing unit provides the sign language video generated by the generating unit to the user. The providing unit provides the sign language video by a method such as streaming, downloading, real-time delivery, etc. For example, the providing unit uses an Internet connection to provide the sign language video by streaming, uses a file server to provide the sign language video by downloading, and uses live streaming technology to provide the sign language video by real-time delivery. Step 4: The recognition unit analyzes the sign language and converts it to text or speech. The recognition unit analyzes the sign language using sign language recognition AI, converts it to text using natural language processing technology, and converts it to speech using speech synthesis technology. For example, the recognition unit analyzes the sign language and converts it to text. It can also analyze the sign language and convert it to speech.

[0063] (Example 2) A sign language video generation system according to an embodiment of the present invention is a system that converts a user's input into a sign language video and provides customer support for the hearing impaired. In this sign language video generation system, a user inputs text or voice, and a generation AI analyzes the input and generates a sign language video. The generated sign language video is provided to the user. In addition, by utilizing a sign language recognition AI, even staff who do not know sign language can communicate efficiently. This makes it possible to expand customer support using sign language, which was previously limited to some stores, nationwide. For example, a user may input customer support inquiries in text or ask questions by voice. This input is sent to the generation AI. Next, the generation AI analyzes the input text or voice. The generation AI understands the input content using natural language processing technology and generates a sign language video. For example, when the text "Please tell me how to return a product" is input, a sign language video is generated based on the content. The generated sign language video is provided to the user. The user can understand the content of the customer support by watching the sign language video. This allows even hearing impaired people to receive customer support smoothly. Furthermore, by utilizing sign language recognition AI, even staff who do not know sign language can communicate efficiently. For example, when a staff member receives an inquiry in sign language, the sign language recognition AI analyzes the sign language and converts it into text or voice. This allows the staff member to understand the sign language and respond appropriately. This system makes it possible to expand customer support in sign language, which was previously limited to certain stores, nationwide. For example, providing customer support in sign language in stores nationwide would improve the quality of service for the hearing impaired and increase customer satisfaction. This allows the sign language video generation system to provide customer support to the hearing impaired.

[0064] The sign language video generation system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a recognition unit. The reception unit receives a user input. The user input includes, but is not limited to, for example, a text input, a voice input, and a gesture input. The reception unit receives, for example, a customer support inquiry in text form. The reception unit can also receive a voice inquiry. The reception unit can also receive a gesture input. For example, the reception unit uses a keyboard or a touch screen to receive the text input. The reception unit uses a microphone to receive the voice input. The reception unit uses a camera or a sensor to receive the gesture input. The generation unit uses a generation AI to analyze the input received by the reception unit and generate a sign language video. The generation AI uses, for example, a natural language processing technology to understand the input content and generate a sign language video. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI analyzes a text input and generates a sign language video. The generation AI can also analyze voice input and generate a sign language video. The generation AI can also analyze gesture input and generate a sign language video. For example, the generation AI uses natural language processing technology to analyze text input. The generation AI uses voice recognition technology to analyze voice input. The generation AI uses image recognition technology to analyze gesture input. The provision unit provides the sign language video generated by the generation unit to a user. The provision unit provides the sign language video by a method such as streaming, download, real-time distribution, etc. The provision unit provides the sign language video by streaming, for example. The provision unit can also provide the sign language video by download. The provision unit can also provide the sign language video by real-time distribution. For example, the provision unit uses an Internet connection to provide the sign language video by streaming. The provision unit uses a file server to provide the sign language video by download. The provision unit uses live streaming technology to provide the sign language video by real-time distribution. The recognition unit analyzes the sign language and converts it into text or voice. The recognition unit analyzes the sign language using, for example, a sign language recognition AI and converts it into text or voice.The recognition unit, for example, analyzes sign language and converts it into text. The recognition unit can also analyze sign language and convert it into speech. For example, the recognition unit uses sign language recognition AI to analyze sign language. It uses natural language processing technology to convert sign language into text. It uses speech synthesis technology to convert sign language into speech. In this way, the sign language video generation system according to the embodiment can convert user input into sign language video and provide customer support for the hearing impaired.

[0065] The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and gesture input. For example, the reception unit receives customer support inquiries in text. The reception unit can also receive voice inquiries. Furthermore, the reception unit can also receive gesture input. For example, the reception unit uses a keyboard or a touch screen to receive text input. The reception unit uses a microphone to receive voice input. The reception unit uses a camera or a sensor to receive gesture input. This allows the reception unit to provide a variety of input means and improve user convenience. Furthermore, the reception unit preprocesses the input data and checks the consistency of the data before passing it to the generation unit. For example, in the case of text input, spell checking and grammar checking are performed, and in the case of voice input, noise removal and voice clearing are performed. In the case of gesture input, the data acquired from the camera or sensor is filtered to extract only necessary information. As a result, the reception unit also plays a role in ensuring the quality of the input data so that the generation unit can efficiently generate sign language videos. Furthermore, the reception unit stores the user's input history, enabling quick responses to future inquiries. For example, by referring to the contents of past inquiries, the reception unit can quickly provide solutions to similar problems, thereby improving user satisfaction and increasing the efficiency of the entire system.

[0066] The generation unit uses the generation AI to analyze the input received by the reception unit and generate a sign language video. The generation AI understands the input content using, for example, natural language processing technology and generates a sign language video. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI analyzes the text input and generates a sign language video. The generation AI can also analyze the voice input and generate a sign language video. Furthermore, the generation AI can analyze the gesture input and generate a sign language video. For example, the generation AI uses natural language processing technology to analyze the text input. The generation AI uses voice recognition technology to analyze the voice input. The generation AI uses image recognition technology to analyze the gesture input. The generation AI combines these technologies to accurately understand the user's input content and generate an appropriate sign language video. Specifically, when analyzing the text input, the generation AI understands the context and meaning and converts it into sign language grammar and expressions. In the case of voice input, the generation AI converts the voice into text using voice recognition technology, and generates a sign language video based on the text. In the case of gesture input, image recognition technology is used to analyze the gestures and convert the movements into sign language. Based on the results of these analyses, the generation AI creates animation data for generating sign language videos, and generates the sign language videos. Before being provided to users, the generated sign language videos are quality checked to confirm accuracy and naturalness. This enables the generation unit to respond to a variety of user inputs and generate high-quality sign language videos.

[0067] The providing unit provides the sign language video generated by the generating unit to the user. The providing unit provides the sign language video by, for example, streaming, downloading, real-time distribution, or the like. The providing unit provides the sign language video by, for example, streaming. The providing unit can also provide the sign language video by downloading. Furthermore, the providing unit can also provide the sign language video by real-time distribution. For example, the providing unit uses an Internet connection to provide the sign language video by streaming. A file server is used to provide the sign language video by downloading. A live streaming technology is used to provide the sign language video by real-time distribution. This allows the providing unit to realize a flexible providing method according to the needs of the user. Furthermore, when providing the sign language video, the providing unit performs optimization according to the viewing environment and device of the user. For example, in the case of streaming, the image quality and bit rate of the video are adjusted according to the Internet connection speed of the user. In the case of downloading, the file size is optimized according to the storage capacity of the user's device. In the case of real-time distribution, a technology for minimizing delays is introduced to provide a smooth viewing experience. This allows the providing unit to provide high-quality sign language video to the user and improve the viewing experience. Furthermore, the provision unit monitors the status of the sign language video provision and responds promptly if a problem occurs. For example, if buffering occurs during streaming, measures are taken to distribute the server load. This enables the provision unit to provide stable sign language video provision and increase user satisfaction.

[0068] The recognition unit analyzes the sign language and converts it into text or voice. The recognition unit, for example, uses a sign language recognition AI to analyze the sign language and converts it into text or voice. The recognition unit, for example, analyzes the sign language and converts it into text. The recognition unit can also analyze the sign language and convert it into voice. For example, the recognition unit uses a sign language recognition AI to analyze the sign language. A natural language processing technique is used to convert the sign language into text. A voice synthesis technique is used to convert the sign language into voice. This allows the recognition unit to understand the sign language and convert it into text or voice. Specifically, the recognition unit uses a sign language recognition AI to analyze the actions and facial expressions of the sign language and understand their meaning. The sign language recognition AI uses a model to analyze the actions of the sign language in detail and understand the grammar and expressions of the sign language. When converting the sign language into text, a natural language processing technique is used to convert the meaning of the sign language into text. When converting the sign language into voice, a voice synthesis technique is used to convert the text into voice. This allows the recognition unit to accurately analyze the sign language and convert it into text or voice. Furthermore, the recognition unit can provide feedback to the user based on the sign language analysis results. For example, if the sign language analysis results are unclear, the recognition unit can improve the accuracy of the analysis by requesting additional input from the user. The recognition unit can also save the sign language analysis results and use them for future analyses. This allows the recognition unit to continuously improve the sign language analysis accuracy and provide high-quality services to the user.

[0069] The generation unit can analyze the input text or voice by the generation AI and generate a sign language video. The generation unit, for example, uses the generation AI to analyze the input text and generate a sign language video. The generation AI, for example, uses natural language processing technology to analyze the text and generate a sign language video. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the generation AI uses natural language processing technology to analyze the text. The generation AI understands the meaning of the text and generates a sign language video. The generation AI, for example, analyzes the text "Please tell me how to return the product" and generates a sign language video based on the content. The generation unit can also analyze the input voice and generate a sign language video by using the generation AI. The generation AI, for example, uses speech recognition technology to analyze the voice and generates a sign language video. The generation AI converts the voice into text using speech recognition technology and generates a sign language video based on the text. For example, the generation AI analyzes the voice "Please tell me how to return the product" and generates a sign language video based on the content. This improves the accuracy of converting input text or audio into sign language video by using the generation AI. Some or all of the above-mentioned processes in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit may input text or audio to the generation AI and cause the generation AI to generate a sign language video.

[0070] The recognition unit can analyze sign language and convert it into text or voice. The recognition unit can, for example, use a sign language recognition AI to analyze sign language and convert it into text. The sign language recognition AI can, for example, use image recognition technology to analyze sign language and convert the content into text. The sign language recognition AI can analyze the movement of the sign language and generate text corresponding to the movement. For example, the sign language recognition AI can analyze the sign language "Please tell me how to return the product" and convert the content into text. The recognition unit can also analyze sign language and convert it into voice using the sign language recognition AI. The sign language recognition AI can, for example, use image recognition technology to analyze sign language and convert the content into voice. The sign language recognition AI can, for example, analyze the movement of the sign language and generate voice corresponding to the movement. For example, the sign language recognition AI can analyze the sign language "Please tell me how to return the product" and convert the content into voice. By converting sign language into text or voice, even staff who do not remember sign language can communicate efficiently. Some or all of the above-mentioned processing in the recognition unit may be performed using AI, for example, or may be performed without using AI. For example, the recognition unit can input sign language into a sign language recognition AI and have the sign language recognition AI convert it into text or speech.

[0071] The providing unit can provide the generated sign language video to a user. The providing unit, for example, provides the generated sign language video by streaming. The providing unit, for example, uses an Internet connection to provide the sign language video by streaming. The providing unit can also provide the generated sign language video by download. The providing unit, for example, uses a file server to provide the sign language video by download. Furthermore, the providing unit can also provide the generated sign language video by real-time distribution. The providing unit, for example, uses live streaming technology to provide the sign language video by real-time distribution. Thereby, by providing the generated sign language video to a user, a hearing-impaired person can receive customer support. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the generated sign language video to AI and cause AI to execute provision to the user.

[0072] The reception unit can receive the customer support inquiry content in text or voice. The reception unit, for example, receives the customer support inquiry content in text. The reception unit, for example, uses a keyboard or a touch screen to receive text input. The reception unit can also receive the customer support inquiry content in voice. The reception unit, for example, uses a microphone to receive voice input. This improves the user's convenience by receiving the customer support inquiry content in text or voice. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the customer support inquiry content into AI and have the AI ​​execute the reception in text or voice.

[0073] The recognition unit can analyze sign language and provide it to the staff in text or voice. The recognition unit, for example, uses a sign language recognition AI to analyze sign language and convert it into text. The sign language recognition AI, for example, uses image recognition technology to analyze sign language and convert the content into text. The sign language recognition AI analyzes the movement of the sign language and generates text corresponding to the movement. For example, the sign language recognition AI analyzes the sign language "Please tell me how to return the product" and converts the content into text. The recognition unit can also analyze sign language using a sign language recognition AI and convert it into voice. The sign language recognition AI, for example, uses image recognition technology to analyze sign language and converts the content into voice. The sign language recognition AI analyzes the movement of the sign language and generates voice corresponding to the movement. For example, the sign language recognition AI analyzes the sign language "Please tell me how to return the product" and converts the content into voice. As a result, by providing the sign language to the staff in text or voice, the staff can easily understand the sign language. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input sign language into a sign language recognition AI and have the sign language recognition AI convert it into text or speech.

[0074] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated emotion. For example, when the user is stressed, the reception unit delays the timing of input reception to allow the user to relax. For example, when the user is in a hurry, the reception unit advances the timing of input reception to quickly respond. For example, when the user is relaxed, the reception unit accepts input at a normal timing. In this way, the user's stress can be reduced by adjusting the timing of input reception according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the user's past inquiry history and select an appropriate reception method. For example, if the user has made an inquiry by text in the past, the reception unit preferentially suggests text input. For example, if the user has made an inquiry by voice in the past, the reception unit preferentially suggests voice input. For example, the reception unit automatically selects an optimal reception method from the content of the user's past inquiries. In this way, the optimal reception method can be provided by analyzing the user's past inquiry history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past inquiry history into AI and cause the AI ​​to select the optimal reception method.

[0076] The reception unit can perform filtering based on the user's current situation and field of interest at the time of reception. For example, when the user inputs the current situation, the reception unit filters the related inquiry content based on the situation. For example, the reception unit registers the user's field of interest in advance and preferentially receives inquiry content related to that field. For example, the reception unit excludes unnecessary inquiry content based on the user's current situation and field of interest. As a result, by performing filtering based on the user's current situation and field of interest, it is possible to preferentially receive highly relevant inquiry content. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's current situation and field of interest to AI and have the AI ​​execute filtering.

[0077] The reception unit can estimate the user's emotion and determine the priority of the received inquiry based on the estimated emotion. For example, when the user is feeling stressed, the reception unit receives the inquiry with priority. For example, when the user is relaxed, the reception unit receives the inquiry with normal priority. For example, when the user is in a hurry, the reception unit receives the inquiry with top priority. This enables a prompt and appropriate response by determining the priority of the inquiry according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The reception unit can preferentially receive inquiries with high relevance in consideration of the geographical location information of the user at the time of reception. For example, when the user is in a specific area, the reception unit preferentially receives inquiries related to the area. For example, the reception unit proposes optimal inquiry content based on the geographical location information of the user. For example, when the user is moving, the reception unit preferentially receives related inquiries based on the current location. In this way, by considering the geographical location information of the user, it is possible to preferentially receive inquiries with high relevance. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to perform preferential reception of inquiries with high relevance.

[0079] The reception unit can analyze the user's social media activity at the time of reception and receive related inquiries. The reception unit, for example, preferentially receives inquiries related to topics of interest from the user's social media activity. The reception unit, for example, analyzes the user's comments on social media and suggests related inquiry content. The reception unit, for example, filters optimal inquiry content based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related inquiry content. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​execute the reception of related inquiries.

[0080] The generation unit can estimate the user's emotion and adjust the expression method of the sign language video based on the estimated emotion. For example, when the user is relaxed, the generation unit generates a relaxed sign language video. For example, when the user is in a hurry, the generation unit generates a concise and quick sign language video. For example, when the user is excited, the generation unit generates a visually stimulating sign language video. This allows a more appropriate sign language video to be provided by adjusting the expression method of the sign language video according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the sign language video.

[0081] The generation unit can adjust the level of detail of the sign language video based on the importance of the inquiry content at the time of generation. For example, in the case of important inquiry content, the generation unit generates detailed sign language video. For example, in the case of general inquiry content, the generation unit generates standard sign language video. For example, in the case of simple inquiry content, the generation unit generates simple sign language video. In this way, by adjusting the level of detail of the sign language video based on the importance of the inquiry content, appropriate information can be provided. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the inquiry content to AI and cause the AI ​​to adjust the level of detail of the sign language video.

[0082] The generation unit can apply different generation algorithms depending on the category of the inquiry at the time of generation. For example, in the case of a technical inquiry, the generation unit generates a sign language video including technical terms. For example, in the case of a general inquiry, the generation unit generates a standard sign language video. For example, in the case of an inquiry regarding customer support, the generation unit generates a familiar sign language video. In this way, by applying different generation algorithms depending on the category of the inquiry, more appropriate sign language video can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the category of the inquiry to AI and cause the AI ​​to apply the generation algorithm.

[0083] The generating unit can estimate the user's emotion and adjust the length of the sign language video based on the estimated emotion. For example, when the user is in a hurry, the generating unit generates a short and to the point sign language video. For example, when the user is relaxed, the generating unit generates a longer sign language video including detailed explanations. For example, when the user is excited, the generating unit generates a visually stimulating sign language video. This allows the length of the sign language video to be adjusted according to the user's emotion, thereby providing a more appropriate sign language video. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generating AI. The generating AI is, for example, a text generating AI (for example, LLM) or a multimodal generating AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating unit can input the user's emotion data to the generating AI and cause the generating AI to adjust the length of the sign language video.

[0084] The generation unit can determine the priority of the sign language videos based on the submission time of the inquiry at the time of generation. For example, the generation unit preferentially converts the most recently submitted inquiry into the sign language video. For example, the generation unit generates the sign language video after an inquiry that was submitted earlier. For example, the generation unit adjusts the order of generation of the sign language videos based on the submission time. This enables a quick response by determining the priority of the sign language videos based on the submission time of the inquiry. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time of the inquiry to AI and cause the AI ​​to determine the priority of the sign language videos.

[0085] The generation unit can adjust the order of the sign language videos based on the relevance of the queries at the time of generation. The generation unit, for example, preferentially converts highly relevant queries into sign language videos. The generation unit, for example, generates sign language videos after leaving queries with low relevance. The generation unit, for example, adjusts the generation order of the sign language videos based on the relevance of the queries. In this way, by adjusting the order of the sign language videos based on the relevance of the queries, highly relevant information can be provided preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the relevance of the queries to AI and cause the AI ​​to adjust the order of the sign language videos.

[0086] The providing unit can estimate the user's emotion and adjust the way in which the sign language video is provided based on the estimated emotion. For example, when the user is relaxed, the providing unit provides the sign language video at a leisurely pace. For example, when the user is in a hurry, the providing unit provides the sign language video quickly. For example, when the user is excited, the providing unit provides a visually stimulating sign language video. This allows a more appropriate sign language video to be provided by adjusting the way in which the sign language video is provided according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way in which the sign language video is provided.

[0087] The providing unit can select an appropriate providing method by referring to the user's past viewing history at the time of providing. The providing unit selects the optimal providing method based on, for example, a history of sign language videos that the user has viewed in the past. The providing unit preferentially provides a preferred sign language video from, for example, the user's viewing history. The providing unit, for example, analyzes the user's viewing history and suggests an optimal providing method. In this way, it is possible to select an optimal providing method for the sign language video by referring to the user's past viewing history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's viewing history into AI and cause the AI ​​to select the optimal providing method.

[0088] The providing unit can customize the means for providing the sign language video based on the user's current situation at the time of providing. For example, when the user is on the move, the providing unit provides a sign language video optimized for a smartphone. For example, when the user is at home, the providing unit provides a sign language video that can be viewed on a large screen. For example, the providing unit selects an optimal means for providing the sign language video based on the user's current situation. This allows more appropriate sign language video to be provided by customizing the means for providing the sign language video based on the user's current situation. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation to AI and cause AI to customize the means for providing the sign language video.

[0089] The providing unit can estimate the user's emotion and determine the priority order of providing the sign language video based on the estimated emotion. For example, when the user is feeling stressed, the providing unit provides the sign language video with priority. For example, when the user is relaxed, the providing unit provides the sign language video with normal priority. For example, when the user is in a hurry, the providing unit provides the sign language video with top priority. This enables a quick and appropriate response by determining the priority order of providing the sign language video according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of providing the sign language video.

[0090] The providing unit can select an appropriate providing method in consideration of the geographical location information of the user at the time of providing. For example, when the user is in a specific area, the providing unit preferentially provides a sign language video related to the area. For example, the providing unit proposes an optimal sign language video providing method based on the geographical location information of the user. For example, when the user is moving, the providing unit provides an optimal sign language video based on the current location. In this way, it is possible to select an optimal sign language video providing method by considering the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to select an optimal providing method.

[0091] The providing unit can analyze the user's social media activity at the time of providing and suggest a means of providing the sign language video. For example, the providing unit preferentially provides sign language videos related to topics of interest from the user's social media activity. For example, the providing unit analyzes the user's comments on social media and suggests related sign language videos. For example, the providing unit selects an optimal means of providing the sign language video based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to preferentially provide related sign language videos. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity into AI and cause the AI ​​to execute a suggestion of a means of providing the sign language video.

[0092] The recognition unit can estimate the user's emotion and adjust the sign language analysis method based on the estimated emotion. For example, when the user is relaxed, the recognition unit applies a normal sign language analysis method. For example, when the user is in a hurry, the recognition unit applies a quick sign language analysis method. For example, when the user is excited, the recognition unit applies a visually stimulating sign language analysis method. This allows a more appropriate analysis result to be provided by adjusting the sign language analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the sign language analysis method.

[0093] The recognition unit can improve the accuracy of analysis by taking into account the context of the sign language during recognition. The recognition unit improves the accuracy of analysis by taking into account, for example, the context before and after the sign language. The recognition unit interprets the meaning of ambiguous sign language, for example, based on the context of the sign language. The recognition unit understands, for example, the context of the sign language and converts it into appropriate text or voice. In this way, the accuracy of analysis can be improved by taking into account the context of the sign language. A part or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the context data of the sign language to the AI ​​and cause the AI ​​to improve the accuracy of the analysis.

[0094] The recognition unit can perform analysis while taking into consideration attribute information of the person submitting the sign language at the time of recognition. The recognition unit improves the accuracy of the analysis by taking into consideration, for example, the age and gender of the person submitting the sign language. The recognition unit performs an appropriate interpretation by taking into consideration, for example, the region and cultural background of the person submitting the sign language. The recognition unit selects an optimal analysis method based on, for example, the attribute information of the person submitting the sign language. In this way, the accuracy of the analysis can be improved by taking into consideration the attribute information of the person submitting the sign language. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the attribute information of the person submitting the sign language to AI and cause the AI ​​to perform the analysis.

[0095] The recognition unit can estimate the user's emotion and adjust the display order of the analysis results based on the estimated emotion. For example, when the user is in a hurry, the recognition unit preferentially displays important analysis results. For example, when the user is relaxed, the recognition unit displays analysis results in a normal order. For example, when the user is excited, the recognition unit preferentially displays visually stimulating analysis results. In this way, by adjusting the display order of the analysis results according to the user's emotion, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the analysis results.

[0096] The recognition unit can perform analysis taking into account the geographical distribution of the sign language during recognition. The recognition unit performs analysis taking into account, for example, expressions specific to a region of the sign language. The recognition unit performs appropriate interpretation based on, for example, the geographical distribution of the sign language. The recognition unit understands, for example, the geographical background of the sign language and selects an optimal analysis method. In this way, appropriate analysis can be performed by taking into account the geographical distribution of the sign language. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input geographical distribution data of the sign language to AI and have the AI ​​perform the analysis.

[0097] The recognition unit can improve the accuracy of analysis by referring to literature related to sign language during recognition. The recognition unit, for example, refers to literature related to sign language to improve the accuracy of analysis. The recognition unit performs an appropriate interpretation based on, for example, a specialized book or dictionary on sign language. The recognition unit, for example, utilizes literature related to sign language to select an optimal analysis method. In this way, the accuracy of analysis can be improved by referring to literature related to sign language. A part or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input literature data related to sign language to AI and cause the AI ​​to improve the accuracy of analysis.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When accepting an input from a user, the accepting unit can suggest an optimal input method by referring to the user's past input history. For example, if the user has frequently used text input in the past, text input can be suggested preferentially. Also, if the user has preferred voice input, voice input can be suggested preferentially. Furthermore, if the user has frequently used gesture input, gesture input can be suggested preferentially. This makes it possible to improve user convenience by suggesting an optimal input method based on the user's past input history.

[0100] The generation unit can generate more appropriate sign language videos by analyzing the text or voice input by the generation AI and referring to the user's past inquiries when generating sign language videos. For example, if the user has made a similar inquiry in the past, the sign language video is generated by referring to the inquiry. Also, if the user has preferred a particular expression in the past, the expression can be reflected in the sign language video. Furthermore, if the user has shown interest in a particular topic in the past, a sign language video related to that topic can be generated. This makes it possible to provide more appropriate sign language videos based on the user's past inquiries.

[0101] The recognition unit can improve the accuracy of the analysis by taking into account the context of the sign language when analyzing the sign language and converting it into text or speech. For example, the meaning of ambiguous sign language is interpreted by taking into account the context before and after the sign language. Also, the recognition unit can convert into appropriate text or speech based on the context of the sign language. Furthermore, the accuracy of the analysis can be improved by understanding the context of the sign language and making an appropriate interpretation. As a result, more accurate analysis results can be provided by taking into account the context of the sign language.

[0102] When providing the generated sign language video to the user, the providing unit can select an appropriate providing method by referring to the user's past viewing history. For example, the optimal providing method is selected based on the history of sign language videos the user has viewed in the past. Also, it is possible to provide the user's preferred sign language video preferentially based on the user's viewing history. Furthermore, it is possible to analyze the user's viewing history and propose the optimal providing method. In this way, it is possible to select the optimal providing method of the sign language video by referring to the user's past viewing history.

[0103] When accepting customer support inquiries by text or voice, the reception unit can filter the inquiries based on the user's current situation and field of interest. For example, when the user inputs the current situation, related inquiry contents are filtered based on the situation. In addition, the user's field of interest can be registered in advance, and inquiries related to that field can be preferentially accepted. Furthermore, unnecessary inquiry contents can be excluded based on the user's current situation and field of interest. In this way, by filtering based on the user's current situation and field of interest, it is possible to preferentially accept highly relevant inquiry contents.

[0104] The recognition unit can estimate the user's emotions when analyzing sign language and providing the information to staff in text or voice, and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is in a hurry, important analysis results can be displayed with priority. Also, if the user is relaxed, analysis results can be displayed in the normal order. Furthermore, if the user is excited, visually stimulating analysis results can be displayed with priority. In this way, more appropriate information can be provided by adjusting the display order of the analysis results according to the user's emotions.

[0105] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated emotion. For example, if the user is feeling stressed, the timing of input reception can be delayed to allow the user to relax. Also, if the user is in a hurry, the timing of input reception can be advanced to allow for a quick response. Furthermore, if the user is relaxed, input can be accepted at a normal timing. In this way, the timing of input reception can be adjusted according to the user's emotion, thereby reducing the user's stress.

[0106] The generation unit can estimate the user's emotion and adjust the way in which the sign language video is expressed based on the estimated emotion. For example, when the user is relaxed, a relaxed sign language video can be generated. When the user is in a hurry, a concise and quick sign language video can be generated. Furthermore, when the user is excited, a visually stimulating sign language video can be generated. In this way, by adjusting the way in which the sign language video is expressed according to the user's emotion, a more appropriate sign language video can be provided.

[0107] The providing unit can estimate the user's emotion and adjust the method of providing the sign language video based on the estimated emotion. For example, when the user is relaxed, the sign language video can be provided at a leisurely pace. When the user is in a hurry, the sign language video can be provided quickly. Furthermore, when the user is excited, a visually stimulating sign language video can be provided. In this way, by adjusting the method of providing the sign language video according to the user's emotion, it is possible to provide a more appropriate sign language video.

[0108] The providing unit can estimate the user's emotions and determine the priority order for providing the sign language video based on the estimated emotions. For example, if the user is feeling stressed, the sign language video can be provided with priority. Also, if the user is relaxed, the sign language video can be provided with normal priority. Furthermore, if the user is in a hurry, the sign language video can be provided with top priority. In this way, by determining the priority order for providing the sign language video according to the user's emotions, a quick and appropriate response can be made.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit receives a user input. The user input includes text input, voice input, gesture input, etc. For example, the reception unit uses a keyboard or a touch screen to receive text input, a microphone to receive voice input, and a camera or a sensor to receive gesture input. Step 2: The generation unit uses the generation AI to analyze the input received by the reception unit and generate a sign language video. The generation AI understands the input content using natural language processing technology, voice recognition technology, and image recognition technology, and generates a sign language video. For example, the generation AI analyzes text input, voice input, and gesture input, and generates a corresponding sign language video. Step 3: The providing unit provides the sign language video generated by the generating unit to the user. The providing unit provides the sign language video by a method such as streaming, downloading, real-time delivery, etc. For example, the providing unit uses an Internet connection to provide the sign language video by streaming, uses a file server to provide the sign language video by downloading, and uses live streaming technology to provide the sign language video by real-time delivery. Step 4: The recognition unit analyzes the sign language and converts it to text or speech. The recognition unit analyzes the sign language using sign language recognition AI, converts it to text using natural language processing technology, and converts it to speech using speech synthesis technology. For example, the recognition unit analyzes the sign language and converts it to text. It can also analyze the sign language and convert it to speech.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recognition unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's input using a touch screen or a microphone 38B of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input using a generation AI to generate a sign language video. The provision unit provides the sign language video to the user using the display 40A of the smart device 14. The recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the sign language and converts it into text or voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recognition unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's input using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input using a generation AI to generate a sign language video. The provision unit provides the sign language video to the user using the display of the smart glasses 214. The recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the sign language and converts it into text or voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recognition unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's input using the microphone 238 of the headset type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input using a generation AI to generate a sign language video. The provision unit provides the sign language video to the user using the display 343 of the headset type terminal 314. The recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the sign language and converts it into text or voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] 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.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and recognition unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's input using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input using a generation AI to generate a sign language video. The provision unit provides the sign language video to the user using the display of the robot 414. The recognition unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the sign language and converts it into text or voice. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A reception unit that receives input from a user; a generation unit that analyzes the input received by the reception unit and generates a sign language video; a providing unit that provides a user with the sign language video generated by the generating unit; Equipped with a recognition unit that analyzes sign language and converts it into text or speech A system characterized by: (Appendix 2) The generation unit is Generative AI analyzes input text or voice and generates sign language video 2. The system of claim 1, (Appendix 3) The recognition unit is Analyzes sign language and converts it into text or speech 2. The system of claim 1, (Appendix 4) The providing unit is Providing the generated sign language video to the user 2. The system of claim 1, (Appendix 5) The reception unit is Accept customer support inquiries via text or voice 2. The system of claim 1, (Appendix 6) The recognition unit is Analyzes sign language and provides it to staff as text or audio 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past inquiry history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is At the time of check-in, filtering is performed based on the user's current situation and interests. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize inquiries based on the estimated emotions. 2. The system of claim 1, (Appendix 11) The reception unit is At the time of reception, the system takes into account the user's geographic location information to prioritize inquiries that are more relevant to the user. 2. The system of claim 1, (Appendix 12) The reception unit is Upon reception, analyze the user's social media activity and receive related inquiries 2. The system of claim 1, (Appendix 13) The generation unit is Estimating user emotions and adjusting the way sign language videos are expressed based on the estimated emotions 2. The system of claim 1, (Appendix 14) The generation unit is During generation, the level of detail of the sign language video is adjusted based on the importance of the inquiry. 2. The system of claim 1, (Appendix 15) The generation unit is During generation, different generation algorithms are applied depending on the category of the query. 2. The system of claim 1, (Appendix 16) The generation unit is Estimating user emotions and adjusting the length of sign language videos based on the estimated emotions 2. The system of claim 1, (Appendix 17) The generation unit is At generation time, prioritize sign language videos based on when the query was submitted 2. The system of claim 1, (Appendix 18) The generation unit is At generation time, the order of sign language videos is adjusted based on the relevance of the query 2. The system of claim 1, (Appendix 19) The providing unit is Estimate the user's emotions and adjust the way sign language videos are presented based on the estimated emotions 2. The system of claim 1, (Appendix 20) The providing unit is When providing content, the appropriate delivery method is selected by referring to the user's past viewing history. 2. The system of claim 1, (Appendix 21) The providing unit is At the time of delivery, customize the delivery of sign language video based on the user's current context 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and determine the priority of providing sign language videos based on the estimated emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing information, the appropriate method of providing the information is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 24) The providing unit is At the time of provision, the system analyzes the user's social media activity to suggest ways to provide sign language videos. 2. The system of claim 1, (Appendix 25) The recognition unit is Estimate the user's emotions and adjust the sign language analysis method based on the estimated emotions. 2. The system of claim 1, (Appendix 26) The recognition unit is During recognition, the context of the sign language is taken into account to improve analysis accuracy. 2. The system of claim 1, (Appendix 27) The recognition unit is When recognizing, analysis is performed taking into account the attributes of the sign language submitter. 2. The system of claim 1, (Appendix 28) The recognition unit is Infer user emotions and adjust the display order of analysis results based on the inferred emotions 2. The system of claim 1, (Appendix 29) The recognition unit is During recognition, the analysis takes into account the geographical distribution of sign languages. 2. The system of claim 1, (Appendix 30) The recognition unit is During recognition, the accuracy of analysis is improved by referring to related literature on sign language. 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input from a user; a generation unit that analyzes the input received by the reception unit and generates a sign language video; a providing unit that provides a user with the sign language video generated by the generating unit; Equipped with a recognition unit that analyzes sign language and converts it into text or speech A system characterized by:

2. The generation unit is Analyze the input text or voice using generation AI and generate a sign language video 2. The system of claim 1 .

3. The reception unit is Accept customer support inquiries via text or voice 2. The system of claim 1 .

4. The recognition unit is Analyzes sign language and provides it to staff as text or audio 2. The system of claim 1 .

5. The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past inquiry history and select the appropriate reception method 2. The system of claim 1 .

7. The reception unit is At the time of check-in, filtering is performed based on the user's current situation and interests.

2. The system of claim 1 .

8. The reception unit is Estimate the user's emotions and prioritize inquiries based on the estimated emotions.

2. The system of claim 1 .

Images