system

Abstract

The system according to this embodiment aims to enable smooth communication between multiple languages ​​and cultures. [Solution] The system according to the embodiment comprises an analysis unit, an understanding unit, a conversion unit, and a provision unit. The analysis unit analyzes speech and text and gestures. The understanding unit understands the context and emotions based on the information analyzed by the analysis unit. The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. The provision unit provides the information converted by the conversion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, smooth communication between multiple languages and cultures is difficult, and there is room for improvement, especially in sign language translation.

[0005] The system according to the embodiment aims to achieve smooth communication between multiple languages and cultures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an understanding unit, a conversion unit, and a provision unit. The analysis unit analyzes speech, text, and gestures. The understanding unit understands the context and emotions based on the information analyzed by the analysis unit. The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. The provision unit provides the information converted by the conversion unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable smooth communication between multiple languages ​​and cultures. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The translation system according to an embodiment of the present invention is a system that uses advanced AI-based translation technology to realize smooth communication between multiple languages ​​and cultures. This translation system uses AI to analyze speech, text, and gestures in order to translate the user's spoken language or sign language in real time. Next, the AI ​​understands the context and emotions and provides an appropriate translation. Furthermore, the AI ​​converts sign language into text and speech, facilitating communication between the deaf and the hearing. This technology aims to support international business and exchange, and to eliminate language barriers and cultural misunderstandings. For example, if a user speaks in English, the AI ​​analyzes the speech and translates it into another language. Also, if a user uses sign language, the AI ​​analyzes the sign language and converts it into text and speech. This enables smooth communication between people who speak different languages ​​and between the deaf and the hearing. Next, the AI ​​understands the context and emotions and provides an appropriate translation. For example, it can accurately translate specialized terminology used in business meetings and expressions based on cultural background. This helps prevent misunderstandings and problems in international business and exchange. Furthermore, the AI ​​converts sign language into text and speech, facilitating communication between the deaf and the hearing. For example, AI can analyze sign language and convert its content into text or speech, enabling deaf individuals to communicate smoothly with others. This promotes social participation for the deaf. This technology aims to support international business and exchange, overcoming language barriers and cultural misunderstandings. For instance, multinational corporations can communicate more smoothly with employees and partners who speak different languages. International exchange organizations can also use it as a tool to connect people from different cultures and backgrounds. This strengthens global networks and contributes to solving international problems. As a result, translation systems can enable smooth interlingual and intercultural communication.

[0029] The translation system according to this embodiment comprises an analysis unit, an understanding unit, a conversion unit, and a provision unit. The analysis unit analyzes speech and text, and gestures. The analysis unit analyzes, for example, the language spoken by the user or sign language in real time. The analysis unit can convert speech into text data using speech recognition technology. For example, the analysis unit analyzes the language spoken by the user using speech recognition technology and converts it into text data. The analysis unit can also capture sign language with a camera and convert it into text data using sign language recognition technology. For example, the analysis unit captures sign language with a camera, analyzes it using sign language recognition technology, and converts it into text data. Furthermore, the analysis unit can capture gestures with a camera and analyze them using gesture recognition technology. For example, the analysis unit captures gestures with a camera and analyzes them using gesture recognition technology. The understanding unit understands context and emotions based on the information analyzed by the analysis unit. The understanding unit understands, for example, specialized terminology used in business meetings and expressions based on cultural background. The understanding unit can understand context using natural language processing technology. For example, the understanding unit understands the context of the text data analyzed using natural language processing technology. The understanding unit can also understand emotions using sentiment analysis technology. For example, the understanding unit understands the emotions of the text data analyzed using sentiment analysis technology. The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. For example, the conversion unit converts sign language into text and speech. The conversion unit can convert sign language into text using sign language recognition technology. For example, the conversion unit converts sign language into text using sign language recognition technology. The conversion unit can also convert sign language into speech using speech synthesis technology. For example, the conversion unit converts sign language into speech using speech synthesis technology. The providing unit provides the information converted by the conversion unit. For example, the providing unit provides the converted information to the user. The providing unit can display the converted information using a display. For example, the providing unit displays the converted information using a display. The providing unit can also output the converted information as speech using a speaker. For example, the providing unit outputs the converted information as speech using a speaker.As a result, the translation system according to this embodiment can enable smooth interlingual and intercultural communication.

[0030] The analysis unit analyzes speech, text, and gestures. For example, it analyzes the language spoken by the user and sign language in real time. The analysis unit can convert speech into text data using speech recognition technology. Specifically, speech recognition technology converts the audio signal into digital data and converts the audio into text using an acoustic model and a language model. The acoustic model extracts features from the audio signal, and the language model analyzes grammatical and vocabulary patterns. This allows the analysis unit to convert the language spoken by the user into text data with high accuracy. The analysis unit can also capture sign language with a camera and convert it into text data using sign language recognition technology. Sign language recognition technology analyzes the movement of sign language captured by the camera and recognizes the shape and movement patterns of the hands. This allows the meaning of the sign language to be converted into text data. Furthermore, the analysis unit can capture gestures with a camera and analyze them using gesture recognition technology. Gesture recognition technology analyzes the movement of gestures captured by the camera and recognizes the movement patterns of the hands and arms. This allows the meaning of the gestures to be analyzed and appropriate actions to be triggered. By combining these technologies, the analysis unit can analyze users' diverse communication methods and convert them into text data in real time.

[0031] The understanding unit understands context and sentiment based on information analyzed by the analysis unit. For example, the understanding unit can understand specialized terminology used in business meetings and expressions based on cultural background. The understanding unit can understand context using natural language processing techniques. Specifically, natural language processing techniques analyze the grammatical structure of text data and understand the meaning and relationships of words. This allows the understanding unit to accurately grasp the context of the text data. The understanding unit can also understand sentiment using sentiment analysis techniques. Sentiment analysis techniques analyze the emotional tone and nuances of text data and identify emotions such as positive, negative, and neutral. This allows the understanding unit to accurately grasp the user's emotions and generate appropriate responses. Furthermore, the understanding unit can perform a deeper contextual understanding by utilizing past conversation history and user profile information. For example, it can generate more personalized responses by considering expressions used by a particular user in the past and their preferred communication style. By combining these techniques, the understanding unit can accurately understand the user's intentions and emotions and generate appropriate responses.

[0032] The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. For example, the conversion unit converts sign language into text and speech. The conversion unit can convert sign language into text using sign language recognition technology. Specifically, sign language recognition technology analyzes sign language movements and generates corresponding strings of characters. This allows the content of sign language to be accurately converted into text data. The conversion unit can also convert sign language into speech using speech synthesis technology. Speech synthesis technology converts text data into speech signals and generates speech with natural pronunciation. This allows the content of sign language to be conveyed through speech. Furthermore, the conversion unit can add appropriate tone and intonation depending on the context and emotions. For example, it can understand the user's emotions using emotion analysis technology and generate speech in a bright tone for positive emotions and a calm tone for negative emotions. By combining these technologies, the conversion unit can accurately and naturally convert the content of sign language into text and speech.

[0033] The providing unit provides the information converted by the conversion unit. For example, the providing unit provides the converted information to the user. The providing unit can display the converted information using a display. Specifically, the display has a high-resolution screen and can clearly display text data. This allows the user to visually confirm the converted information. The providing unit can also output the converted information as audio using a speaker. The speaker has high-quality audio output and can convey information with clear sound. This allows the user to auditorily confirm the converted information. Furthermore, the providing unit can also transmit the information to the user's device. For example, it can transmit the converted information to a smartphone or tablet so that the user can check the information anytime, anywhere. By combining these technologies, the providing unit can effectively provide the converted information to the user. As a result, the translation system according to the embodiment can realize smooth interlingual and intercultural communication.

[0034] The analysis unit can analyze the language spoken by the user and sign language in real time. For example, the analysis unit can analyze the language spoken by the user in real time. The analysis unit can convert the language spoken by the user into text data in real time using speech recognition technology. For example, the analysis unit analyzes the language spoken by the user in real time using speech recognition technology and converts it into text data. The analysis unit can also analyze sign language in real time. The analysis unit can convert sign language into text data in real time using sign language recognition technology. For example, the analysis unit analyzes sign language in real time using sign language recognition technology and converts it into text data. This enables real-time analysis of language and sign language. Specific methods and criteria for real-time analysis include the acceptable range of latency and analysis speed. For example, the analysis unit uses a processor with high processing power to minimize latency and achieve real-time analysis. The analysis unit can also use parallel processing technology to improve analysis speed. For example, the analysis unit achieves real-time analysis by performing analysis in parallel using multiple processors. This enables real-time analysis of the language spoken by the user and sign language.

[0035] The understanding unit can understand specialized terminology and culturally contextualized expressions used in business meetings. For example, the understanding unit can understand specialized terminology used in business meetings. The understanding unit can understand specialized terminology used in business meetings using a specialized terminology dictionary. For example, the understanding unit can understand specialized terminology used in business meetings using a specialized terminology dictionary. Furthermore, the understanding unit can also understand culturally contextualized expressions. The understanding unit can understand culturally contextualized expressions using a cultural context database. For example, the understanding unit can understand culturally contextualized expressions using a cultural context database. This improves the understanding of specialized terminology and culturally contextualized expressions. Specific methods and criteria for understanding specialized terminology and culturally contextualized expressions include the use of specialized terminology dictionaries and cultural context databases. For example, the understanding unit can understand the meaning of specialized terminology using a specialized terminology dictionary and the meaning of culturally contextualized expressions using a cultural context database. This enables accurate understanding of specialized terminology and culturally contextualized expressions used in business meetings.

[0036] The conversion unit can convert sign language into text and speech. For example, the conversion unit can convert sign language into text. The conversion unit can convert sign language into text using sign language recognition technology. For example, the conversion unit can convert sign language into text using sign language recognition technology. The conversion unit can also convert sign language into speech. The conversion unit can convert sign language into speech using speech synthesis technology. For example, the conversion unit can convert sign language into speech using speech synthesis technology. This facilitates communication between the deaf and the hearing by converting sign language into text and speech. Specific methods and criteria for converting sign language into text and speech include the use of sign language recognition technology and speech synthesis technology. For example, the conversion unit analyzes sign language movements using sign language recognition technology and converts them into text. The conversion unit also analyzes sign language movements using speech synthesis technology and converts them into speech. This allows sign language to be converted into text and speech.

[0037] The service provider can provide the converted information to the user. For example, the service provider can provide the converted information to the user. The service provider can display the converted information using a display. For example, the service provider can display the converted information using a display. The service provider can also output the converted information as sound using a speaker. For example, the service provider can output the converted information as sound using a speaker. This enables smooth communication by providing the converted information to the user. Specific methods and formats for providing the converted information include the use of displays and speakers. For example, the service provider can visually display the converted information using a display and output the converted information as sound using a speaker. This allows the user to receive the converted information visually and aurally.

[0038] The analysis unit can analyze gestures and support communication through body language. For example, the analysis unit can analyze gestures. The analysis unit can analyze gestures using gesture recognition technology. For example, the analysis unit can analyze gestures using gesture recognition technology and understand the meaning of body language. This supports communication through body language by analyzing gestures. The use of gesture recognition technology is included as a specific method or criterion for analyzing gestures. For example, the analysis unit analyzes video of gestures captured by a camera and recognizes the movement of the gestures. This allows it to analyze gestures and understand the meaning of body language.

[0039] The analysis unit can analyze a user's past utterance history and select an appropriate analysis method. For example, the analysis unit can analyze a user's past utterance history. The analysis unit can analyze a user's past utterance history using utterance history analysis technology. For example, the analysis unit can analyze a user's past utterance history using utterance history analysis technology and select an appropriate analysis method. This allows the optimal analysis method to be selected by analyzing the user's past utterance history. Specific methods and criteria for analyzing a user's past utterance history include methods for collecting utterance history and the use of analysis algorithms. For example, the analysis unit collects a user's past utterance history and analyzes it using a utterance history analysis algorithm. This allows the optimal analysis method to be selected based on the user's past utterance history.

[0040] The analysis unit can perform filtering based on the user's current situation and areas of interest during analysis. For example, the analysis unit considers the user's current situation. The analysis unit can identify the user's current situation using situation recognition technology. For example, the analysis unit uses situation recognition technology to identify the user's current situation and reflects it in the analysis. The analysis unit can also consider the user's areas of interest. The analysis unit can identify the user's areas of interest using area of ​​interest analysis technology. For example, the analysis unit uses area of ​​interest analysis technology to identify the user's areas of interest and reflects it in the analysis. This allows for the analysis of highly relevant information by filtering based on the user's current situation and areas of interest. Specific methods and criteria for filtering based on the user's current situation and areas of interest include the use of situation recognition technology and area of ​​interest analysis technology. For example, the analysis unit uses situation recognition technology to identify the user's current situation and area of ​​interest analysis technology to identify the user's areas of interest. This allows for the analysis of highly relevant information based on the user's current situation and areas of interest.

[0041] The analysis unit can prioritize the analysis of highly relevant information by considering the user's geographical location information during analysis. For example, the analysis unit considers the user's geographical location information. The analysis unit can acquire the user's geographical location information using location information acquisition technology. For example, the analysis unit acquires the user's geographical location information using location information acquisition technology and incorporates it into the analysis. This allows the analysis to prioritize the analysis of highly relevant information by considering the user's geographical location information. The use of location information acquisition technology is a specific method or criterion for prioritizing the analysis of highly relevant information based on the user's geographical location information. For example, the analysis unit acquires the user's geographical location information using location information acquisition technology and prioritizes the analysis of highly relevant information based on that information. This allows the analysis to prioritize the analysis of highly relevant information by considering the user's geographical location information.

[0042] The analysis unit can analyze users' social media activity and analyze related information during the analysis process. For example, the analysis unit can analyze users' social media activity. The analysis unit can analyze users' social media activity using social media analysis techniques. For example, the analysis unit analyzes users' social media activity using social media analysis techniques and analyzes related information. This allows for the priority analysis of relevant information based on users' social media activity. Specific methods and criteria for analyzing users' social media activity include methods for collecting social media data and the use of analysis algorithms. For example, the analysis unit collects social media data and analyzes it using social media analysis algorithms. This allows for the priority analysis of relevant information based on users' social media activity.

[0043] The understanding unit can adjust the level of detail of its understanding based on the frequency of use of technical terms. For example, the understanding unit considers the frequency of use of technical terms. The understanding unit can identify the frequency of use of technical terms using techniques for measuring their frequency. For example, the understanding unit identifies the frequency of use of technical terms using techniques for measuring their frequency and adjusts the level of detail of its understanding. This allows for a more accurate understanding by adjusting the level of detail of understanding based on the frequency of use of technical terms. Specific methods and criteria for adjusting the level of detail of understanding based on the frequency of use of technical terms include the use of methods for measuring frequency and criteria for adjusting the level of detail. For example, the understanding unit measures the frequency of use of technical terms and adjusts the level of detail of its understanding based on that frequency. This allows for adjusting the level of detail of understanding based on the frequency of use of technical terms.

[0044] The understanding unit can apply different understanding algorithms depending on the cultural background during understanding. For example, the understanding unit considers the cultural background. The understanding unit can identify the cultural background using techniques for identifying cultural backgrounds. For example, the understanding unit identifies the cultural background using techniques for identifying cultural backgrounds and applies an understanding algorithm. This allows for a more appropriate understanding by applying an understanding algorithm appropriate to the cultural background. Specific methods and criteria for applying different understanding algorithms depending on the cultural background include methods for identifying the cultural background and the type of algorithm to apply. For example, the understanding unit identifies the cultural background and applies an understanding algorithm appropriate to that background. This allows for the application of different understanding algorithms depending on the cultural background.

[0045] The understanding unit can determine the priority of understanding based on the timing of the statements made. For example, the understanding unit considers the timing of statements. The understanding unit can identify the timing of statements using techniques for identifying the timing of statements. For example, the understanding unit identifies the timing of statements using techniques for identifying the timing of statements and determines the priority of understanding. This allows important information to be understood preferentially by determining the priority of understanding based on the timing of statements. Specific methods and criteria for determining the priority of understanding based on the timing of statements include the use of timing identification methods and priority determination criteria. For example, the understanding unit identifies the timing of statements and determines the priority of understanding based on that timing. This allows the understanding unit to determine the priority of understanding based on the timing of statements.

[0046] The understanding unit can improve the accuracy of its understanding by referring to relevant literature and materials during the understanding process. For example, the understanding unit can refer to relevant literature. The understanding unit can refer to relevant literature using literature referencing techniques. For example, the understanding unit can refer to relevant literature using literature referencing techniques to improve the accuracy of its understanding. The understanding unit can also refer to relevant materials. The understanding unit can refer to relevant materials using material referencing techniques. For example, the understanding unit can refer to relevant materials using material referencing techniques to improve the accuracy of its understanding. This improves the accuracy of understanding by referring to relevant literature and materials. Specific methods and criteria for referring to relevant literature and materials include the use of methods for selecting and referencing literature and materials. For example, the understanding unit can improve the accuracy of its understanding by selecting relevant literature and materials and referring to them. This improves the accuracy of understanding by referring to relevant literature and materials.

[0047] The conversion unit can adjust the level of detail in the conversion based on the frequency of sign language use during the conversion process. For example, the conversion unit considers the frequency of sign language use. The conversion unit can identify the frequency of sign language use using a technique for measuring the frequency of sign language use. For example, the conversion unit identifies the frequency of sign language use using a technique for measuring the frequency of sign language use and adjusts the level of detail in the conversion. This allows for a more accurate conversion by adjusting the level of detail based on the frequency of sign language use. Specific methods and criteria for adjusting the level of detail based on the frequency of sign language use include the use of methods for measuring frequency and criteria for adjusting the level of detail. For example, the conversion unit measures the frequency of sign language use and adjusts the level of detail in the conversion based on that frequency. This allows for adjusting the level of detail in the conversion based on the frequency of sign language use.

[0048] The conversion unit can apply different conversion algorithms depending on the type of sign language during conversion. For example, the conversion unit considers the type of sign language. The conversion unit can identify the type of sign language using a technique for identifying the type of sign language. For example, the conversion unit identifies the type of sign language using a technique for identifying the type of sign language and applies a conversion algorithm. This allows for more accurate conversion by applying the optimal conversion algorithm according to the type of sign language. Specific methods and criteria for applying different conversion algorithms depending on the type of sign language include methods for identifying the type of sign language and the type of algorithm to apply. For example, the conversion unit identifies the type of sign language and applies a conversion algorithm corresponding to that type. This allows for the application of different conversion algorithms depending on the type of sign language.

[0049] The conversion unit can determine the conversion priority based on the sign language usage during conversion. For example, the conversion unit considers the sign language usage. The conversion unit can identify the sign language usage using a technique for identifying sign language usage. For example, the conversion unit identifies the sign language usage using a technique for identifying sign language usage and determines the conversion priority. This allows important information to be converted preferentially by determining the conversion priority based on the sign language usage. Specific methods and criteria for determining the conversion priority based on the sign language usage include methods for identifying usage and criteria for determining priority. For example, the conversion unit identifies the sign language usage and determines the conversion priority based on that usage. This allows the conversion priority to be determined based on the sign language usage.

[0050] The conversion unit can improve the accuracy of the conversion by referring to relevant sign language dictionaries during the conversion process. For example, the conversion unit can refer to relevant sign language dictionaries. The conversion unit can refer to relevant sign language dictionaries using sign language dictionary referencing technology. For example, the conversion unit can improve the accuracy of the conversion by referring to relevant sign language dictionaries using sign language dictionary referencing technology. This improves the accuracy of the conversion by referring to relevant sign language dictionaries. Specific methods and criteria for referring to relevant sign language dictionaries include methods for selecting sign language dictionaries and using referencing methods. For example, the conversion unit can improve the accuracy of the conversion by selecting relevant sign language dictionaries and referring to them. This improves the accuracy of the conversion by referring to relevant sign language dictionaries.

[0051] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can refer to the user's past usage history. The service provider can refer to the user's past usage history using usage history referencing technology. For example, the service provider can refer to the user's past usage history using usage history referencing technology and select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history. Specific methods and criteria for referring to the user's past usage history include the use of methods for collecting and referencing usage history. For example, the service provider can collect the user's past usage history and refer to it to select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history.

[0052] The service provider can select the optimal service delivery method by considering the user's device information at the time of delivery. For example, the service provider can consider the user's device information. The service provider can acquire the user's device information using device information acquisition technology. For example, the service provider can acquire the user's device information using device information acquisition technology and select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's device information. Specific methods and criteria for considering the user's device information include the use of device information acquisition and consideration methods. For example, the service provider can acquire the user's device information and select the optimal service delivery method based on that information. This allows the service provider to select the optimal service delivery method by considering the user's device information.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The analysis unit can analyze the user's speech rate and provide that information to the comprehension unit. For example, if the user speaks quickly, the analysis unit analyzes the speech rate and provides it to the comprehension unit. Based on the provided speech rate, the comprehension unit can adjust how it understands the context. For example, if the user speaks quickly, the comprehension unit improves the accuracy of its understanding to avoid missing important information. Also, if the user speaks slowly, the comprehension unit can understand the context in more detail. This enables appropriate understanding according to the user's speech rate.

[0055] The analysis unit can analyze the volume of the user's speech and provide that information to the understanding unit. For example, if the user speaks loudly, the analysis unit analyzes the volume and provides it to the understanding unit. Based on the provided volume, the understanding unit can adjust how it understands the context. For example, if the user speaks loudly, the understanding unit will emphasize and understand important information. Also, if the user speaks softly, the understanding unit can understand the context more carefully. This enables appropriate understanding according to the volume of the user's speech.

[0056] The analysis unit can analyze the accent of the user's speech and provide that information to the understanding unit. For example, if the user speaks with a specific accent, the analysis unit can analyze that accent and provide it to the understanding unit. The understanding unit can then adjust how it understands the context based on the provided accent. For example, if the user speaks with a specific accent, the understanding unit will understand the context in a way that is appropriate for that accent. Furthermore, in order to accurately understand the speech of users with different accents, the understanding unit can take accent differences into account when understanding the context. This enables appropriate understanding according to the accent of the user's speech.

[0057] The analysis unit can analyze the rhythm of the user's speech and provide that information to the understanding unit. For example, if the user speaks with a specific rhythm, the analysis unit can analyze that rhythm and provide it to the understanding unit. The understanding unit can then adjust its contextual understanding based on the provided rhythm. For example, if the user speaks with a specific rhythm, the understanding unit will perform contextual understanding according to that rhythm. Furthermore, in order to accurately understand the speech of users with different rhythms, the understanding unit can take rhythmic differences into account when understanding context. This enables appropriate understanding according to the rhythm of the user's speech.

[0058] The analysis unit can analyze the intonation of the user's speech and provide that information to the understanding unit. For example, if the user speaks with a specific intonation, the analysis unit can analyze that intonation and provide it to the understanding unit. The understanding unit can then adjust its contextual understanding based on the provided intonation. For example, if the user speaks with a specific intonation, the understanding unit will perform contextual understanding according to that intonation. Furthermore, in order to accurately understand the speech of users with different intonations, the understanding unit can consider the differences in intonation when understanding the context. This enables appropriate understanding according to the intonation of the user's speech.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The analysis unit analyzes speech, text, and gestures. The analysis unit analyzes the user's spoken language and sign language in real time and converts speech into text data using speech recognition technology. It also captures sign language with a camera and converts it into text data using sign language recognition technology. Furthermore, it captures gestures with a camera and analyzes them using gesture recognition technology. Step 2: The understanding unit understands the context and emotions based on the information analyzed by the analysis unit. The understanding unit understands the jargon and cultural background-based expressions used in business meetings and understands the context using natural language processing techniques. It also understands emotions using sentiment analysis techniques. Step 3: The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. The conversion unit converts sign language into text using sign language recognition technology and converts sign language into speech using speech synthesis technology. Step 4: The providing unit provides the information converted by the conversion unit. The providing unit displays the converted information using a display and outputs the converted information as sound using a speaker.

[0061] (Example of form 2) The translation system according to an embodiment of the present invention is a system that uses advanced AI-based translation technology to realize smooth communication between multiple languages ​​and cultures. This translation system uses AI to analyze speech, text, and gestures in order to translate the user's spoken language or sign language in real time. Next, the AI ​​understands the context and emotions and provides an appropriate translation. Furthermore, the AI ​​converts sign language into text and speech, facilitating communication between the deaf and the hearing. This technology aims to support international business and exchange, and to eliminate language barriers and cultural misunderstandings. For example, if a user speaks in English, the AI ​​analyzes the speech and translates it into another language. Also, if a user uses sign language, the AI ​​analyzes the sign language and converts it into text and speech. This enables smooth communication between people who speak different languages ​​and between the deaf and the hearing. Next, the AI ​​understands the context and emotions and provides an appropriate translation. For example, it can accurately translate specialized terminology used in business meetings and expressions based on cultural background. This helps prevent misunderstandings and problems in international business and exchange. Furthermore, the AI ​​converts sign language into text and speech, facilitating communication between the deaf and the hearing. For example, AI can analyze sign language and convert its content into text or speech, enabling deaf individuals to communicate smoothly with others. This promotes social participation for the deaf. This technology aims to support international business and exchange, overcoming language barriers and cultural misunderstandings. For instance, multinational corporations can communicate more smoothly with employees and partners who speak different languages. International exchange organizations can also use it as a tool to connect people from different cultures and backgrounds. This strengthens global networks and contributes to solving international problems. As a result, translation systems can enable smooth interlingual and intercultural communication.

[0062] The translation system according to this embodiment comprises an analysis unit, an understanding unit, a conversion unit, and a provision unit. The analysis unit analyzes speech and text, and gestures. The analysis unit analyzes, for example, the language spoken by the user or sign language in real time. The analysis unit can convert speech into text data using speech recognition technology. For example, the analysis unit analyzes the language spoken by the user using speech recognition technology and converts it into text data. The analysis unit can also capture sign language with a camera and convert it into text data using sign language recognition technology. For example, the analysis unit captures sign language with a camera, analyzes it using sign language recognition technology, and converts it into text data. Furthermore, the analysis unit can capture gestures with a camera and analyze them using gesture recognition technology. For example, the analysis unit captures gestures with a camera and analyzes them using gesture recognition technology. The understanding unit understands context and emotions based on the information analyzed by the analysis unit. The understanding unit understands, for example, specialized terminology used in business meetings and expressions based on cultural background. The understanding unit can understand context using natural language processing technology. For example, the understanding unit understands the context of the text data analyzed using natural language processing technology. The understanding unit can also understand emotions using sentiment analysis technology. For example, the understanding unit understands the emotions of the text data analyzed using sentiment analysis technology. The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. For example, the conversion unit converts sign language into text and speech. The conversion unit can convert sign language into text using sign language recognition technology. For example, the conversion unit converts sign language into text using sign language recognition technology. The conversion unit can also convert sign language into speech using speech synthesis technology. For example, the conversion unit converts sign language into speech using speech synthesis technology. The providing unit provides the information converted by the conversion unit. For example, the providing unit provides the converted information to the user. The providing unit can display the converted information using a display. For example, the providing unit displays the converted information using a display. The providing unit can also output the converted information as speech using a speaker. For example, the providing unit outputs the converted information as speech using a speaker.As a result, the translation system according to this embodiment can enable smooth interlingual and intercultural communication.

[0063] The analysis unit analyzes speech, text, and gestures. For example, it analyzes the language spoken by the user and sign language in real time. The analysis unit can convert speech into text data using speech recognition technology. Specifically, speech recognition technology converts the audio signal into digital data and converts the audio into text using an acoustic model and a language model. The acoustic model extracts features from the audio signal, and the language model analyzes grammatical and vocabulary patterns. This allows the analysis unit to convert the language spoken by the user into text data with high accuracy. The analysis unit can also capture sign language with a camera and convert it into text data using sign language recognition technology. Sign language recognition technology analyzes the movement of sign language captured by the camera and recognizes the shape and movement patterns of the hands. This allows the meaning of the sign language to be converted into text data. Furthermore, the analysis unit can capture gestures with a camera and analyze them using gesture recognition technology. Gesture recognition technology analyzes the movement of gestures captured by the camera and recognizes the movement patterns of the hands and arms. This allows the meaning of the gestures to be analyzed and appropriate actions to be triggered. By combining these technologies, the analysis unit can analyze users' diverse communication methods and convert them into text data in real time.

[0064] The understanding unit understands context and sentiment based on information analyzed by the analysis unit. For example, the understanding unit can understand specialized terminology used in business meetings and expressions based on cultural background. The understanding unit can understand context using natural language processing techniques. Specifically, natural language processing techniques analyze the grammatical structure of text data and understand the meaning and relationships of words. This allows the understanding unit to accurately grasp the context of the text data. The understanding unit can also understand sentiment using sentiment analysis techniques. Sentiment analysis techniques analyze the emotional tone and nuances of text data and identify emotions such as positive, negative, and neutral. This allows the understanding unit to accurately grasp the user's emotions and generate appropriate responses. Furthermore, the understanding unit can perform a deeper contextual understanding by utilizing past conversation history and user profile information. For example, it can generate more personalized responses by considering expressions used by a particular user in the past and their preferred communication style. By combining these techniques, the understanding unit can accurately understand the user's intentions and emotions and generate appropriate responses.

[0065] The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. For example, the conversion unit converts sign language into text and speech. The conversion unit can convert sign language into text using sign language recognition technology. Specifically, sign language recognition technology analyzes sign language movements and generates corresponding strings of characters. This allows the content of sign language to be accurately converted into text data. The conversion unit can also convert sign language into speech using speech synthesis technology. Speech synthesis technology converts text data into speech signals and generates speech with natural pronunciation. This allows the content of sign language to be conveyed through speech. Furthermore, the conversion unit can add appropriate tone and intonation depending on the context and emotions. For example, it can understand the user's emotions using emotion analysis technology and generate speech in a bright tone for positive emotions and a calm tone for negative emotions. By combining these technologies, the conversion unit can accurately and naturally convert the content of sign language into text and speech.

[0066] The providing unit provides the information converted by the conversion unit. For example, the providing unit provides the converted information to the user. The providing unit can display the converted information using a display. Specifically, the display has a high-resolution screen and can clearly display text data. This allows the user to visually confirm the converted information. The providing unit can also output the converted information as audio using a speaker. The speaker has high-quality audio output and can convey information with clear sound. This allows the user to auditorily confirm the converted information. Furthermore, the providing unit can also transmit the information to the user's device. For example, it can transmit the converted information to a smartphone or tablet so that the user can check the information anytime, anywhere. By combining these technologies, the providing unit can effectively provide the converted information to the user. As a result, the translation system according to the embodiment can realize smooth interlingual and intercultural communication.

[0067] The analysis unit can analyze the language spoken by the user and sign language in real time. For example, the analysis unit can analyze the language spoken by the user in real time. The analysis unit can convert the language spoken by the user into text data in real time using speech recognition technology. For example, the analysis unit analyzes the language spoken by the user in real time using speech recognition technology and converts it into text data. The analysis unit can also analyze sign language in real time. The analysis unit can convert sign language into text data in real time using sign language recognition technology. For example, the analysis unit analyzes sign language in real time using sign language recognition technology and converts it into text data. This enables real-time analysis of language and sign language. Specific methods and criteria for real-time analysis include the acceptable range of latency and analysis speed. For example, the analysis unit uses a processor with high processing power to minimize latency and achieve real-time analysis. The analysis unit can also use parallel processing technology to improve analysis speed. For example, the analysis unit achieves real-time analysis by performing analysis in parallel using multiple processors. This enables real-time analysis of the language spoken by the user and sign language.

[0068] The understanding unit can understand specialized terminology and culturally contextualized expressions used in business meetings. For example, the understanding unit can understand specialized terminology used in business meetings. The understanding unit can understand specialized terminology used in business meetings using a specialized terminology dictionary. For example, the understanding unit can understand specialized terminology used in business meetings using a specialized terminology dictionary. Furthermore, the understanding unit can also understand culturally contextualized expressions. The understanding unit can understand culturally contextualized expressions using a cultural context database. For example, the understanding unit can understand culturally contextualized expressions using a cultural context database. This improves the understanding of specialized terminology and culturally contextualized expressions. Specific methods and criteria for understanding specialized terminology and culturally contextualized expressions include the use of specialized terminology dictionaries and cultural context databases. For example, the understanding unit can understand the meaning of specialized terminology using a specialized terminology dictionary and the meaning of culturally contextualized expressions using a cultural context database. This enables accurate understanding of specialized terminology and culturally contextualized expressions used in business meetings.

[0069] The conversion unit can convert sign language into text and speech. For example, the conversion unit can convert sign language into text. The conversion unit can convert sign language into text using sign language recognition technology. For example, the conversion unit can convert sign language into text using sign language recognition technology. The conversion unit can also convert sign language into speech. The conversion unit can convert sign language into speech using speech synthesis technology. For example, the conversion unit can convert sign language into speech using speech synthesis technology. This facilitates communication between the deaf and the hearing by converting sign language into text and speech. Specific methods and criteria for converting sign language into text and speech include the use of sign language recognition technology and speech synthesis technology. For example, the conversion unit analyzes sign language movements using sign language recognition technology and converts them into text. The conversion unit also analyzes sign language movements using speech synthesis technology and converts them into speech. This allows sign language to be converted into text and speech.

[0070] The service provider can provide the converted information to the user. For example, the service provider can provide the converted information to the user. The service provider can display the converted information using a display. For example, the service provider can display the converted information using a display. The service provider can also output the converted information as sound using a speaker. For example, the service provider can output the converted information as sound using a speaker. This enables smooth communication by providing the converted information to the user. Specific methods and formats for providing the converted information include the use of displays and speakers. For example, the service provider can visually display the converted information using a display and output the converted information as sound using a speaker. This allows the user to receive the converted information visually and aurally.

[0071] The analysis unit can analyze gestures and support communication through body language. For example, the analysis unit can analyze gestures. The analysis unit can analyze gestures using gesture recognition technology. For example, the analysis unit can analyze gestures using gesture recognition technology and understand the meaning of body language. This supports communication through body language by analyzing gestures. The use of gesture recognition technology is included as a specific method or criterion for analyzing gestures. For example, the analysis unit analyzes video of gestures captured by a camera and recognizes the movement of the gestures. This allows it to analyze gestures and understand the meaning of body language.

[0072] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using an emotion engine. For example, the analysis unit can estimate emotions from the user's facial expressions and voice using an emotion engine. Furthermore, the analysis unit can adjust the accuracy of the analysis based on the estimated emotions. The analysis unit can adjust the accuracy of the analysis based on the emotions estimated using the emotion engine. For example, if the user is tense, the analysis unit can use the emotion engine to improve the accuracy of the analysis to alleviate the tension. Also, if the user is relaxed, the analysis unit can use the emotion engine to adjust the accuracy of the analysis to maintain a relaxed state. Furthermore, if the user is excited, the analysis unit can use the emotion engine to adjust the accuracy of the analysis to suppress the excitement. This allows for more appropriate analysis by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0073] The analysis unit can analyze a user's past utterance history and select an appropriate analysis method. For example, the analysis unit can analyze a user's past utterance history. The analysis unit can analyze a user's past utterance history using utterance history analysis technology. For example, the analysis unit can analyze a user's past utterance history using utterance history analysis technology and select an appropriate analysis method. This allows the optimal analysis method to be selected by analyzing the user's past utterance history. Specific methods and criteria for analyzing a user's past utterance history include methods for collecting utterance history and the use of analysis algorithms. For example, the analysis unit collects a user's past utterance history and analyzes it using a utterance history analysis algorithm. This allows the optimal analysis method to be selected based on the user's past utterance history.

[0074] The analysis unit can perform filtering based on the user's current situation and areas of interest during analysis. For example, the analysis unit considers the user's current situation. The analysis unit can identify the user's current situation using situation recognition technology. For example, the analysis unit uses situation recognition technology to identify the user's current situation and reflects it in the analysis. The analysis unit can also consider the user's areas of interest. The analysis unit can identify the user's areas of interest using area of ​​interest analysis technology. For example, the analysis unit uses area of ​​interest analysis technology to identify the user's areas of interest and reflects it in the analysis. This allows for the analysis of highly relevant information by filtering based on the user's current situation and areas of interest. Specific methods and criteria for filtering based on the user's current situation and areas of interest include the use of situation recognition technology and area of ​​interest analysis technology. For example, the analysis unit uses situation recognition technology to identify the user's current situation and area of ​​interest analysis technology to identify the user's areas of interest. This allows for the analysis of highly relevant information based on the user's current situation and areas of interest.

[0075] The analysis unit can estimate the user's emotions and determine the priority of information to analyze based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using an emotion engine. For example, the analysis unit can estimate emotions from the user's facial expressions and voice using an emotion engine. The analysis unit can also determine the priority of information to analyze based on the estimated user emotions. The analysis unit can determine the priority of information to analyze based on the emotions estimated using the emotion engine. For example, if the user is tense, the analysis unit can use the emotion engine to prioritize the analysis of information important to alleviate the tension. If the user is relaxed, the analysis unit can also use the emotion engine to adjust the priority of information to maintain a relaxed state. Furthermore, if the user is excited, the analysis unit can use the emotion engine to determine the priority of information to suppress the excitement. In this way, by determining the priority of information to analyze based on the user's emotions, important information can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0076] The analysis unit can prioritize the analysis of highly relevant information by considering the user's geographical location information during analysis. For example, the analysis unit considers the user's geographical location information. The analysis unit can acquire the user's geographical location information using location information acquisition technology. For example, the analysis unit acquires the user's geographical location information using location information acquisition technology and incorporates it into the analysis. This allows the analysis to prioritize the analysis of highly relevant information by considering the user's geographical location information. The use of location information acquisition technology is a specific method or criterion for prioritizing the analysis of highly relevant information based on the user's geographical location information. For example, the analysis unit acquires the user's geographical location information using location information acquisition technology and prioritizes the analysis of highly relevant information based on that information. This allows the analysis to prioritize the analysis of highly relevant information by considering the user's geographical location information.

[0077] The analysis unit can analyze users' social media activity and analyze related information during the analysis process. For example, the analysis unit can analyze users' social media activity. The analysis unit can analyze users' social media activity using social media analysis techniques. For example, the analysis unit analyzes users' social media activity using social media analysis techniques and analyzes related information. This allows for the priority analysis of relevant information based on users' social media activity. Specific methods and criteria for analyzing users' social media activity include methods for collecting social media data and the use of analysis algorithms. For example, the analysis unit collects social media data and analyzes it using social media analysis algorithms. This allows for the priority analysis of relevant information based on users' social media activity.

[0078] The understanding unit can estimate the user's emotions and adjust its contextual understanding method based on the estimated user emotions. For example, the understanding unit can estimate the user's emotions using an emotion engine. For example, the understanding unit can estimate emotions from the user's facial expressions and voice using an emotion engine. The understanding unit can also adjust its contextual understanding method based on the estimated user emotions. The understanding unit can adjust its contextual understanding method based on the emotions estimated using the emotion engine. For example, if the user is tense, the understanding unit can adjust its contextual understanding method using the emotion engine to alleviate the tension. Also, if the user is relaxed, the understanding unit can adjust its contextual understanding method using the emotion engine to maintain that relaxed state. Furthermore, if the user is excited, the understanding unit can adjust its contextual understanding method using the emotion engine to suppress the excitement. This allows for a more appropriate understanding by adjusting the contextual understanding method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI may be, but is not limited to, a text-generating AI (e.g., LLM) or a multimodal generative AI. Some or all of the processing described above in the understanding unit may be performed using AI, or not using AI. For example, the understanding unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0079] The understanding unit can adjust the level of detail of its understanding based on the frequency of use of technical terms. For example, the understanding unit considers the frequency of use of technical terms. The understanding unit can identify the frequency of use of technical terms using techniques for measuring their frequency. For example, the understanding unit identifies the frequency of use of technical terms using techniques for measuring their frequency and adjusts the level of detail of its understanding. This allows for a more accurate understanding by adjusting the level of detail of understanding based on the frequency of use of technical terms. Specific methods and criteria for adjusting the level of detail of understanding based on the frequency of use of technical terms include the use of methods for measuring frequency and criteria for adjusting the level of detail. For example, the understanding unit measures the frequency of use of technical terms and adjusts the level of detail of its understanding based on that frequency. This allows for adjusting the level of detail of understanding based on the frequency of use of technical terms.

[0080] The understanding unit can apply different understanding algorithms depending on the cultural background during understanding. For example, the understanding unit considers the cultural background. The understanding unit can identify the cultural background using techniques for identifying cultural backgrounds. For example, the understanding unit identifies the cultural background using techniques for identifying cultural backgrounds and applies an understanding algorithm. This allows for a more appropriate understanding by applying an understanding algorithm appropriate to the cultural background. Specific methods and criteria for applying different understanding algorithms depending on the cultural background include methods for identifying the cultural background and the type of algorithm to apply. For example, the understanding unit identifies the cultural background and applies an understanding algorithm appropriate to that background. This allows for the application of different understanding algorithms depending on the cultural background.

[0081] The understanding unit can estimate the user's emotions and determine the priority of understanding based on the estimated user emotions. For example, the understanding unit can estimate the user's emotions using an emotion engine. For example, the understanding unit can estimate emotions from the user's facial expressions and voice using an emotion engine. The understanding unit can also determine the priority of understanding based on the estimated user emotions. The understanding unit can determine the priority of understanding based on the emotions estimated using an emotion engine. For example, if the user is tense, the understanding unit can use the emotion engine to prioritize understanding information that is important to alleviate the tension. Also, if the user is relaxed, the understanding unit can use the emotion engine to adjust the priority of information to maintain a relaxed state. Furthermore, if the user is excited, the understanding unit can use the emotion engine to determine the priority of information to suppress the excitement. In this way, by determining the priority of understanding based on the user's emotions, important information can be understood preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the understanding unit may be performed using AI, for example, or without AI. For example, the understanding unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0082] The understanding unit can determine the priority of understanding based on the timing of the statements made. For example, the understanding unit considers the timing of statements. The understanding unit can identify the timing of statements using techniques for identifying the timing of statements. For example, the understanding unit identifies the timing of statements using techniques for identifying the timing of statements and determines the priority of understanding. This allows important information to be understood preferentially by determining the priority of understanding based on the timing of statements. Specific methods and criteria for determining the priority of understanding based on the timing of statements include the use of timing identification methods and priority determination criteria. For example, the understanding unit identifies the timing of statements and determines the priority of understanding based on that timing. This allows the understanding unit to determine the priority of understanding based on the timing of statements.

[0083] The understanding unit can improve the accuracy of its understanding by referring to relevant literature and materials during the understanding process. For example, the understanding unit can refer to relevant literature. The understanding unit can refer to relevant literature using literature referencing techniques. For example, the understanding unit can refer to relevant literature using literature referencing techniques to improve the accuracy of its understanding. The understanding unit can also refer to relevant materials. The understanding unit can refer to relevant materials using material referencing techniques. For example, the understanding unit can refer to relevant materials using material referencing techniques to improve the accuracy of its understanding. This improves the accuracy of understanding by referring to relevant literature and materials. Specific methods and criteria for referring to relevant literature and materials include the use of methods for selecting and referencing literature and materials. For example, the understanding unit can improve the accuracy of its understanding by selecting relevant literature and materials and referring to them. This improves the accuracy of understanding by referring to relevant literature and materials.

[0084] The transformation unit can estimate the user's emotions and adjust the transformation's expression based on the estimated emotions. For example, the transformation unit can estimate the user's emotions using an emotion engine. For example, the transformation unit can estimate emotions from the user's facial expressions and voice using an emotion engine. Furthermore, the transformation unit can adjust the transformation's expression based on the estimated emotions. For example, if the user is tense, the transformation unit can use a gentle expression to alleviate the tension using the emotion engine. Also, if the user is relaxed, the transformation unit can use a soft expression to maintain that relaxed state using the emotion engine. Furthermore, if the user is excited, the transformation unit can use a calm expression to suppress the excitement using the emotion engine. This allows for more appropriate transformations by adjusting the transformation's expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using AI, or not using AI. For example, the conversion unit can input user facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0085] The conversion unit can adjust the level of detail in the conversion based on the frequency of sign language use during the conversion process. For example, the conversion unit considers the frequency of sign language use. The conversion unit can identify the frequency of sign language use using a technique for measuring the frequency of sign language use. For example, the conversion unit identifies the frequency of sign language use using a technique for measuring the frequency of sign language use and adjusts the level of detail in the conversion. This allows for a more accurate conversion by adjusting the level of detail based on the frequency of sign language use. Specific methods and criteria for adjusting the level of detail based on the frequency of sign language use include the use of methods for measuring frequency and criteria for adjusting the level of detail. For example, the conversion unit measures the frequency of sign language use and adjusts the level of detail in the conversion based on that frequency. This allows for adjusting the level of detail in the conversion based on the frequency of sign language use.

[0086] The conversion unit can apply different conversion algorithms depending on the type of sign language during conversion. For example, the conversion unit considers the type of sign language. The conversion unit can identify the type of sign language using a technique for identifying the type of sign language. For example, the conversion unit identifies the type of sign language using a technique for identifying the type of sign language and applies a conversion algorithm. This allows for more accurate conversion by applying the optimal conversion algorithm according to the type of sign language. Specific methods and criteria for applying different conversion algorithms depending on the type of sign language include methods for identifying the type of sign language and the type of algorithm to apply. For example, the conversion unit identifies the type of sign language and applies a conversion algorithm corresponding to that type. This allows for the application of different conversion algorithms depending on the type of sign language.

[0087] The transformation unit can estimate the user's emotions and determine the transformation priority based on the estimated user emotions. For example, the transformation unit can estimate the user's emotions using an emotion engine. For example, the transformation unit can estimate emotions from the user's facial expressions and voice using an emotion engine. Furthermore, the transformation unit can also determine the transformation priority based on the estimated user emotions. The transformation unit can determine the transformation priority based on the emotions estimated using the emotion engine. For example, if the user is tense, the transformation unit can use the emotion engine to prioritize transforming information important to alleviate tension. Also, if the user is relaxed, the transformation unit can use the emotion engine to adjust the information priority to maintain a relaxed state. Furthermore, if the user is excited, the transformation unit can use the emotion engine to determine the information priority to suppress excitement. This allows for the transformation of important information to be prioritized by determining the transformation priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0088] The conversion unit can determine the conversion priority based on the sign language usage during conversion. For example, the conversion unit considers the sign language usage. The conversion unit can identify the sign language usage using a technique for identifying sign language usage. For example, the conversion unit identifies the sign language usage using a technique for identifying sign language usage and determines the conversion priority. This allows important information to be converted preferentially by determining the conversion priority based on the sign language usage. Specific methods and criteria for determining the conversion priority based on the sign language usage include methods for identifying usage and criteria for determining priority. For example, the conversion unit identifies the sign language usage and determines the conversion priority based on that usage. This allows the conversion priority to be determined based on the sign language usage.

[0089] The conversion unit can improve the accuracy of the conversion by referring to relevant sign language dictionaries during the conversion process. For example, the conversion unit can refer to relevant sign language dictionaries. The conversion unit can refer to relevant sign language dictionaries using sign language dictionary referencing technology. For example, the conversion unit can improve the accuracy of the conversion by referring to relevant sign language dictionaries using sign language dictionary referencing technology. This improves the accuracy of the conversion by referring to relevant sign language dictionaries. Specific methods and criteria for referring to relevant sign language dictionaries include methods for selecting sign language dictionaries and using referencing methods. For example, the conversion unit can improve the accuracy of the conversion by selecting relevant sign language dictionaries and referring to them. This improves the accuracy of the conversion by referring to relevant sign language dictionaries.

[0090] The service provider can estimate the user's emotions and adjust the display method of the service based on the estimated user emotions. For example, the service provider can estimate the user's emotions using an emotion engine. For example, the service provider can estimate emotions from the user's facial expressions and voice using an emotion engine. The service provider can also adjust the display method of the service based on the estimated user emotions. The service provider can adjust the display method of the service based on the emotions estimated using an emotion engine. For example, if the user is tense, the service provider can use an emotion engine to use a calm display method to alleviate the tension. Also, if the user is relaxed, the service provider can use an emotion engine to use a soft display method to maintain a relaxed state. Furthermore, if the user is excited, the service provider can use an emotion engine to use a calm display method to suppress the excitement. This allows for more appropriate display by adjusting the display method of the service based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0091] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can refer to the user's past usage history. The service provider can refer to the user's past usage history using usage history referencing technology. For example, the service provider can refer to the user's past usage history using usage history referencing technology and select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history. Specific methods and criteria for referring to the user's past usage history include the use of methods for collecting and referencing usage history. For example, the service provider can collect the user's past usage history and refer to it to select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history.

[0092] The service provider can estimate the user's emotions and determine the priority of its offerings based on those emotions. For example, the service provider can estimate the user's emotions using an emotion engine. For example, the service provider can estimate emotions from the user's facial expressions and voice using an emotion engine. Furthermore, the service provider can also determine the priority of its offerings based on the estimated user emotions. The service provider can determine the priority of its offerings based on the emotions estimated using an emotion engine. For example, if the user is tense, the service provider can use the emotion engine to prioritize providing important information to alleviate the tension. Also, if the user is relaxed, the service provider can use the emotion engine to adjust the priority of information to maintain that relaxed state. Furthermore, if the user is excited, the service provider can use the emotion engine to determine the priority of information to calm the excitement. This allows for the prioritization of important information by determining the priority of offerings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0093] The service provider can select the optimal service delivery method by considering the user's device information at the time of delivery. For example, the service provider can consider the user's device information. The service provider can acquire the user's device information using device information acquisition technology. For example, the service provider can acquire the user's device information using device information acquisition technology and select the optimal service delivery method. This allows the service provider to select the optimal service delivery method by considering the user's device information. Specific methods and criteria for considering the user's device information include the use of device information acquisition and consideration methods. For example, the service provider can acquire the user's device information and select the optimal service delivery method based on that information. This allows the service provider to select the optimal service delivery method by considering the user's device information.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The analysis unit can analyze the user's speech rate and provide that information to the comprehension unit. For example, if the user speaks quickly, the analysis unit analyzes the speech rate and provides it to the comprehension unit. Based on the provided speech rate, the comprehension unit can adjust how it understands the context. For example, if the user speaks quickly, the comprehension unit improves the accuracy of its understanding to avoid missing important information. Also, if the user speaks slowly, the comprehension unit can understand the context in more detail. This enables appropriate understanding according to the user's speech rate.

[0096] The analysis unit can analyze the volume of the user's speech and provide that information to the understanding unit. For example, if the user speaks loudly, the analysis unit analyzes the volume and provides it to the understanding unit. Based on the provided volume, the understanding unit can adjust how it understands the context. For example, if the user speaks loudly, the understanding unit will emphasize and understand important information. Also, if the user speaks softly, the understanding unit can understand the context more carefully. This enables appropriate understanding according to the volume of the user's speech.

[0097] The analysis unit can analyze the accent of the user's speech and provide that information to the understanding unit. For example, if the user speaks with a specific accent, the analysis unit can analyze that accent and provide it to the understanding unit. The understanding unit can then adjust how it understands the context based on the provided accent. For example, if the user speaks with a specific accent, the understanding unit will understand the context in a way that is appropriate for that accent. Furthermore, in order to accurately understand the speech of users with different accents, the understanding unit can take accent differences into account when understanding the context. This enables appropriate understanding according to the accent of the user's speech.

[0098] The analysis unit can analyze the rhythm of the user's speech and provide that information to the understanding unit. For example, if the user speaks with a specific rhythm, the analysis unit can analyze that rhythm and provide it to the understanding unit. The understanding unit can then adjust its contextual understanding based on the provided rhythm. For example, if the user speaks with a specific rhythm, the understanding unit will perform contextual understanding according to that rhythm. Furthermore, in order to accurately understand the speech of users with different rhythms, the understanding unit can take rhythmic differences into account when understanding context. This enables appropriate understanding according to the rhythm of the user's speech.

[0099] The analysis unit can analyze the intonation of the user's speech and provide that information to the understanding unit. For example, if the user speaks with a specific intonation, the analysis unit can analyze that intonation and provide it to the understanding unit. The understanding unit can then adjust its contextual understanding based on the provided intonation. For example, if the user speaks with a specific intonation, the understanding unit will perform contextual understanding according to that intonation. Furthermore, in order to accurately understand the speech of users with different intonations, the understanding unit can consider the differences in intonation when understanding the context. This enables appropriate understanding according to the intonation of the user's speech.

[0100] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can use the emotion engine to adjust the timing of the analysis to alleviate the tension. If the user is relaxed, it can also use the emotion engine to adjust the timing of the analysis to maintain that relaxed state. Furthermore, if the user is excited, it can use the emotion engine to adjust the timing of the analysis to suppress the excitement. By adjusting the timing of the analysis based on the user's emotions, more appropriate analysis becomes possible.

[0101] The understanding unit can estimate the user's emotions and adjust the depth of understanding based on those emotions. For example, if the user is tense, the understanding unit can use the emotion engine to adjust the depth of understanding to alleviate the tension. Similarly, if the user is relaxed, it can use the emotion engine to adjust the depth of understanding to maintain that relaxed state. Furthermore, if the user is excited, it can use the emotion engine to adjust the depth of understanding to suppress the excitement. This allows for a more appropriate understanding by adjusting the depth of understanding based on the user's emotions.

[0102] The translation unit can estimate the user's emotions and adjust the translation speed based on the estimated emotions. For example, if the user is tense, the translation unit can use the emotion engine to adjust the translation speed to alleviate the tension. If the user is relaxed, it can also use the emotion engine to adjust the translation speed to maintain a relaxed state. Furthermore, if the user is excited, it can use the emotion engine to adjust the translation speed to suppress the excitement. By adjusting the translation speed based on the user's emotions, more appropriate translations become possible.

[0103] The delivery unit can estimate the user's emotions and adjust the timing of delivery based on those emotions. For example, if the user is tense, the delivery unit can use the emotion engine to adjust the timing of delivery to alleviate the tension. If the user is relaxed, it can also use the emotion engine to adjust the timing of delivery to maintain that relaxed state. Furthermore, if the user is excited, it can use the emotion engine to adjust the timing of delivery to calm the excitement. By adjusting the timing of delivery based on the user's emotions, more appropriate delivery becomes possible.

[0104] The delivery unit can estimate the user's emotions and adjust the delivery format based on the estimated emotions. For example, if the user is tense, the delivery unit can use the emotion engine to provide information in a gentle format to alleviate the tension. If the user is relaxed, it can also use the emotion engine to provide information in a soft format to maintain that relaxed state. Furthermore, if the user is excited, it can use the emotion engine to provide information in a calm format to suppress the excitement. This allows for more appropriate delivery by adjusting the delivery format based on the user's emotions.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The analysis unit analyzes speech, text, and gestures. The analysis unit analyzes the user's spoken language and sign language in real time and converts speech into text data using speech recognition technology. It also captures sign language with a camera and converts it into text data using sign language recognition technology. Furthermore, it captures gestures with a camera and analyzes them using gesture recognition technology. Step 2: The understanding unit understands the context and emotions based on the information analyzed by the analysis unit. The understanding unit understands the jargon and cultural background-based expressions used in business meetings and understands the context using natural language processing techniques. It also understands emotions using sentiment analysis techniques. Step 3: The conversion unit converts sign language into text and speech based on the context and emotions understood by the understanding unit. The conversion unit converts sign language into text using sign language recognition technology and converts sign language into speech using speech synthesis technology. Step 4: The providing unit provides the information converted by the conversion unit. The providing unit displays the converted information using a display and outputs the converted information as sound using a speaker.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the analysis unit, understanding unit, conversion unit, and providing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes the user's speech or sign language using the camera 42 and microphone 38B of the smart device 14 and converts it into text data using the control unit 46A. The understanding unit is implemented in the specific processing unit 290 of the data processing unit 12 and understands the context and sentiment of the analyzed text data. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12 and converts the sign language into text and speech based on the understood context and sentiment. The providing unit provides the converted information to the user using the display 40A and speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the analysis unit, understanding unit, conversion unit, and providing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes the user's speech or sign language using the camera 42 and microphone 238 of the smart glasses 214 and converts it into text data using the control unit 46A. The understanding unit is implemented in the specific processing unit 290 of the data processing unit 12 and understands the context and sentiment of the analyzed text data. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12 and converts the sign language into text and speech based on the understood context and sentiment. The providing unit provides the converted information to the user using the display and speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the analysis unit, understanding unit, conversion unit, and providing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes the user's speech or sign language using the camera 42 and microphone 238 of the headset terminal 314 and converts it into text data using the control unit 46A. The understanding unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and understands the context and sentiment of the analyzed text data. The conversion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and converts the sign language into text and speech based on the understood context and sentiment. The providing unit provides the converted information to the user using, for example, the display 343 and speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the analysis unit, understanding unit, conversion unit, and providing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit analyzes the user's speech or sign language using the camera 42 and microphone 238 of the robot 414 and converts it into text data by the control unit 46A. The understanding unit is implemented in the specific processing unit 290 of the data processing unit 12 and understands the context and sentiment of the analyzed text data. The conversion unit is implemented in the specific processing unit 290 of the data processing unit 12 and converts the sign language into text and speech based on the understood context and sentiment. The providing unit provides the converted information to the user using the display and speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) An analysis unit that analyzes speech, text, and gestures, An understanding unit that understands context and emotions based on the information analyzed by the aforementioned analysis unit, A conversion unit that converts sign language into written text and sounds based on the context and emotions understood by the aforementioned understanding unit, The system comprises a providing unit that provides the information converted by the conversion unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzes the language spoken by the user and their sign language in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system described in Appendix 1 is characterized in that the understanding unit understands specialized terminology and expressions based on cultural background used in business meetings. (Note 4) The conversion unit is Convert sign language into text or speech The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the converted information to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system as described in Appendix 1, characterized in that the analysis unit analyzes gestures and supports communication through body language. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The system described in Appendix 1 is characterized in that the analysis unit analyzes the user's past speech history and selects an appropriate analysis method. (Note 9) The system according to Appendix 1, characterized in that the analysis unit performs filtering based on the user's current situation and areas of interest during analysis. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and determines the priority of information to analyze based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The system described in Appendix 1, characterized in that the analysis unit prioritizes analyzing information that is highly relevant based on the user's geographical location information during analysis. (Note 12) The aforementioned analysis unit, During the analysis, the user's social media activity is analyzed, and relevant information is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned understanding unit is, It estimates the user's emotions and adjusts how it understands the context based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned understanding unit is, When comprehending, adjust the level of detail based on the frequency of use of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned understanding unit is, When understanding, different understanding algorithms are applied depending on the cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned understanding unit is, It estimates the user's emotions and determines the priority of understanding based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned understanding unit is, When understanding, prioritize understanding based on the timing of the statements. The system described in Appendix 1, characterized by the features described herein. (Note 18) The system described in Appendix 1 is characterized in that the understanding unit improves the accuracy of understanding by referring to relevant literature and materials during the understanding process. (Note 19) The conversion unit is It estimates the user's emotions and adjusts the way the transformation is expressed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The conversion unit is During conversion, adjust the level of detail based on the frequency of sign language usage. The system described in Appendix 1, characterized by the features described herein. (Note 21) The conversion unit is During conversion, different conversion algorithms are applied depending on the type of sign language. The system described in Appendix 1, characterized by the features described herein. (Note 22) The conversion unit is It estimates the user's emotions and determines the priority of conversions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The conversion unit is During conversion, the conversion priority is determined based on the usage of sign language. The system described in Appendix 1, characterized by the features described herein. (Note 24) The conversion unit is During conversion, the system references relevant sign language dictionaries to improve conversion accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The system described in Appendix 1, characterized in that the provisioning unit selects an appropriate provisioning method by referring to the user's past usage history at the time of provision. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes speech, text, and gestures, An understanding unit that understands context and emotions based on the information analyzed by the aforementioned analysis unit, A conversion unit that converts sign language into written text and sounds based on the context and emotions understood by the aforementioned understanding unit, The system comprises a providing unit that provides the information converted by the conversion unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyzes the language spoken by the user and their sign language in real time. The system according to feature 1.

3. The system according to claim 1, characterized in that the understanding unit understands specialized terminology and expressions based on cultural background used in business meetings.

4. The conversion unit is Convert sign language into text or speech The system according to feature 1.

5. The aforementioned supply unit is, Provide the converted information to the user. The system according to feature 1.

6. The system according to claim 1, characterized in that the analysis unit analyzes gestures and supports communication using body language.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

8. The system according to claim 1, characterized in that the analysis unit analyzes the user's past speech history and selects an appropriate analysis method.

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