System

Abstract

An object of the system according to the embodiment is to develop a new language in which meanings and nuances can be shared and understood in common throughout the world.SOLUTION: A system includes an analysis unit, a generation unit, a creation unit, and a provision unit. The analysis unit analyzes multilingual data and analyzes the meaning and nuance of each language. The generation unit generates grammar and vocabulary of the new language based on the meaning and nuance extracted by the analysis unit. The creation unit creates a grammar document and a dictionary based on the grammar and the vocabulary generated by the generation unit. The providing unit provides an educational material for learning a new language based on the grammar document or the dictionary created by the creating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to share and understand meanings and nuances across multiple languages.

[0005] The system according to the embodiment aims to develop a new language whose meanings and nuances can be shared and understood universally around the world. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, a creation unit, and a provision unit. The analysis unit analyzes multilingual data and analyzes the meanings and nuances of each language. The generation unit generates grammar and vocabulary for a new language based on the meanings and nuances extracted by the analysis unit. The creation unit creates a grammar book and a dictionary based on the grammar and vocabulary generated by the creation unit. The provision unit provides learning materials for learning a new language based on the grammar book and dictionary created by the creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can develop a new language whose meaning and nuances can be shared and understood universally around the world. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to develop a new language whose meanings and nuances can be shared and understood worldwide. In this system, generative AI analyzes existing multilingual data, extracts the meanings and nuances of each language, and generates the grammar and vocabulary of the new language based on the extracted data. For example, generative AI extracts common grammar rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and then designs the basic structure of the new language based on the extracted grammar rules and vocabulary. Furthermore, generative AI creates a grammar book and dictionary for the new language based on the extracted grammar rules and vocabulary, providing learning materials for people around the world to learn the new language. Generative AI also provides tools for translating existing multilingual content into the new language. This allows people who speak different languages ​​to communicate in a common language, promoting international exchange and cooperation. As a result, a system utilizing generative AI can develop a new language whose meanings and nuances can be shared and understood worldwide. For example, using the new language at international conferences and business events can enable smooth communication across language barriers.

[0029] A new language development system according to an embodiment includes an analysis unit, a generation unit, a creation unit, and a provision unit. The analysis unit analyzes multilingual data and extracts the meanings and nuances of each language. Examples of multilingual data include, but are not limited to, English, Spanish, and Chinese. The analysis unit can extract the meanings and nuances of each language using, for example, contextual analysis and sentiment analysis. The analysis unit can also use a generative AI to analyze the grammatical structure and frequency of vocabulary of each language and find common patterns. The generation unit generates grammar and vocabulary for the new language based on the meanings and nuances extracted by the analysis unit. The generation unit extracts common grammatical rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and designs the basic structure of the new language based on the extracted grammatical rules and vocabulary. The generation unit can also use a generative AI to create a grammar book and a dictionary for the new language based on the extracted grammar rules and vocabulary. The creation unit creates a grammar book and a dictionary based on the grammar and vocabulary generated by the generation unit. The creation unit can automatically generate the contents of a grammar book or dictionary using, for example, a generation AI. The provision unit provides learning materials for learning a new language based on the grammar book or dictionary created by the creation unit. The provision unit can provide tools for translating existing multilingual content into a new language using, for example, a generation AI. As a result, the new language development system according to the embodiment can develop a new language whose meanings and nuances can be shared and understood worldwide.

[0030] The analysis unit can analyze the grammatical structure and vocabulary frequency of each language and find common patterns. The analysis unit can analyze grammatical structure by analyzing phrase structure and sentence types, for example. The analysis unit can also analyze vocabulary frequency by analyzing occurrence frequency and co-occurrence frequency. Furthermore, the analysis unit can find common patterns by extracting commonalities between frequently occurring phrases and grammatical rules. In this way, analyzing the grammatical structure and vocabulary frequency of each language and finding common patterns can be useful in creating new languages.

[0031] The generation unit can extract common grammar rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and design the basic structure of a new language based on the extracted grammar rules and vocabulary. The generation unit can analyze the grammar rules and vocabulary of major languages ​​such as English, Spanish, and Chinese, and extract frequently occurring grammar rules and common vocabulary. The generation unit can also design the basic structure of a new language based on the extracted grammar rules and vocabulary. For example, the generation unit can design a basic set of grammar rules and vocabulary. This makes it possible to generate a universal language by extracting common grammar rules and vocabulary from major languages ​​and designing the basic structure of a new language.

[0032] The creation unit can create a grammar book or dictionary for the new language based on the extracted grammar rules and vocabulary. The creation unit can create a grammar book or dictionary based on, for example, the type of grammar or the range of vocabulary. The creation unit can also automatically generate the contents of the grammar book or dictionary using a generative AI. This makes it possible to support the learning of a new language by creating a grammar book or dictionary based on the extracted grammar rules and vocabulary.

[0033] The providing unit can provide a tool for translating existing multilingual content into a new language. The providing unit can provide a tool for translating multilingual content, such as text, audio, or video, into a new language. The providing unit can also use generative AI to set translation algorithms and supported languages. This can promote the spread of new languages ​​by providing a tool for translating existing multilingual content into a new language.

[0034] The analysis unit can improve the accuracy of extracting meanings and nuances based on the cultural background of each language. For example, the analysis unit can extract meanings and nuances by taking into account the historical and social background of each language. The analysis unit can also use generation AI to extract appropriate nuances based on the cultural background. For example, generation AI can analyze honorific expressions in Japanese and extract appropriate nuances based on the cultural background. Generation AI can also analyze poetic expressions in French and extract appropriate nuances based on the cultural background. Furthermore, generation AI can analyze religious expressions in Arabic and extract appropriate nuances based on the cultural background. This enables more accurate extraction of meanings and nuances by taking into account the cultural background of each language.

[0035] The analysis unit can analyze data containing slang and colloquialisms in each language to achieve more natural language understanding. For example, the analysis unit can analyze the types and frequency of use of slang and colloquialisms. The analysis unit can also use a generative AI to analyze datasets containing slang and colloquialisms to achieve more natural language understanding. For example, the generative AI can analyze English slang and extract natural usage in everyday conversations. The generative AI can also analyze Spanish slang and extract regional nuances. Furthermore, the generative AI can analyze Chinese youth slang to reflect the latest language trends. As a result, analyzing datasets containing slang and colloquialisms enables more natural language understanding.

[0036] The analysis unit can analyze the evolution of the grammatical structure of each language and reflect the latest language trends. The analysis unit can analyze the evolution of grammatical structure by, for example, analyzing the historical changes and regional differences of each language. The analysis unit can also use generation AI to reflect the latest language trends. For example, generation AI can analyze the evolution of English grammatical structure and reflect the latest language trends. Generation AI can also analyze the evolution of Japanese grammatical structure and reflect the latest language trends. Furthermore, generation AI can analyze the evolution of French grammatical structure and reflect the latest language trends. In this way, by analyzing the evolution of grammatical structure, analysis that reflects the latest language trends becomes possible.

[0037] The analysis unit can extract regional nuances by taking into account regional differences in each language. For example, the analysis unit can extract nuances by taking into account the linguistic characteristics and cultural background of each region of each language. The analysis unit can also extract regional nuances using generation AI. For example, the generation AI can analyze regional differences in English and extract the nuanced differences between American English and British English. The generation AI can also analyze regional differences in Spanish and extract the nuanced differences between Spain and Latin America. Furthermore, the generation AI can analyze regional differences in Chinese and extract the nuanced differences between Mandarin and Cantonese. In this way, by taking regional differences into account, regional nuances can be accurately extracted.

[0038] The analysis unit can analyze technical terms and terminology in each language to improve language understanding in a specific field. For example, the analysis unit can analyze technical terms and terminology by analyzing a term list or frequency of use in a specific field. The analysis unit can also use a generative AI to analyze technical terms and terminology in each language to improve language understanding in a specific field. For example, the generative AI can analyze technical terms in the medical field and extract appropriate nuances. The generative AI can also analyze technical terms in the legal field and extract appropriate nuances. Furthermore, the generative AI can analyze technical terms in the IT field and extract appropriate nuances. In this way, analyzing technical terms and terminology improves language understanding in a specific field.

[0039] The analysis unit can analyze the speech data of each language and extract differences in pronunciation and intonation. For example, the analysis unit can analyze speech data by analyzing pronunciation data and intonation data. The analysis unit can also extract differences in pronunciation and intonation using a generation AI. For example, the generation AI can analyze English speech data and extract differences in pronunciation between American English and British English. The generation AI can also analyze Japanese speech data and extract differences in intonation between Kansai dialect and standard Japanese. Furthermore, the generation AI can analyze French speech data and extract differences in pronunciation between regions. This makes it possible to accurately extract differences in pronunciation and intonation by analyzing speech data.

[0040] The generation unit can generate a new language that incorporates the poetic and literary expressions of each language. For example, the generation unit can generate poetic and literary expressions by incorporating poetic forms and literary metaphors. The generation unit can also use a generative AI to generate a new language that incorporates the poetic and literary expressions of each language. For example, the generative AI can generate a new language that incorporates English poetic expressions. The generative AI can also generate a new language that incorporates French literary expressions. Furthermore, the generative AI can generate a new language that incorporates Japanese poetic expressions. This makes it possible to generate a richer new language by incorporating poetic and literary expressions.

[0041] The generation unit can design a new language by taking into account the non-verbal communication of each language. The generation unit can design non-verbal communication by taking into account gestures and facial expressions, for example. The generation unit can also use a generative AI to design a new language by taking into account the non-verbal communication of each language. For example, the generative AI can design a new language by taking into account English gestures. The generative AI can also design a new language by taking into account Japanese facial expressions. Furthermore, the generative AI can design a new language by taking into account Spanish non-verbal communication. This makes it possible to design a new language that is more natural by taking into account non-verbal communication.

[0042] The generation unit can generate grammar and vocabulary that reflect the historical background of each language. For example, the generation unit can generate grammar and vocabulary that reflect the historical background by reflecting historical events and cultural changes. The generation unit can also use a generative AI to generate grammar and vocabulary that reflect the historical background of each language. For example, the generative AI can generate grammar and vocabulary that reflect the historical background of English. The generative AI can also generate grammar and vocabulary that reflect the historical background of French. Furthermore, the generative AI can generate grammar and vocabulary that reflect the historical background of Japanese. This makes it possible to generate new languages ​​with a deeper understanding by reflecting historical background.

[0043] The generation unit can generate a pronunciation guide for a new language based on the audio data of each language. The generation unit can generate a pronunciation guide based on, for example, phonetic symbols or audio samples. The generation unit can also use a generation AI to generate a pronunciation guide for a new language based on the audio data of each language. For example, the generation AI can generate a pronunciation guide for a new language based on English audio data. The generation AI can also generate a pronunciation guide for a new language based on Japanese audio data. Furthermore, the generation AI can generate a pronunciation guide for a new language based on French audio data. In this way, by generating a pronunciation guide based on audio data, it is possible to learn the correct pronunciation of a new language.

[0044] The generation unit can analyze the sign language of each language and generate a sign language version for a new language. The generation unit can analyze the sign language, for example, by analyzing the sign language movements and sign language grammar. The generation unit can also use a generation AI to analyze the sign language of each language and generate a sign language version for a new language. For example, the generation AI can analyze English sign language and generate a sign language version for a new language. The generation AI can also analyze Japanese sign language and generate a sign language version for a new language. Furthermore, the generation AI can analyze French sign language and generate a sign language version for a new language. In this way, by analyzing sign language, a sign language version for a new language can be generated, making it possible to accommodate the hearing impaired.

[0045] The generation unit can design a new language's writing system based on the writing system of each language. The generation unit can design a writing system based on, for example, the alphabet or syllables. The generation unit can also use a generation AI to design a new language's writing system based on the writing system of each language. For example, the generation AI can design a new language's writing system based on the English writing system. The generation AI can also design a new language's writing system based on the Japanese writing system. The generation AI can also design a new language's writing system based on the French writing system. This makes it possible to learn how to write a new language by designing a writing system.

[0046] The creation unit can reflect the cultural and historical background of each language in the grammar book and dictionary of the new language. For example, the creation unit can reflect the cultural and historical background by reflecting historical events and cultural changes. The creation unit can also use the generation AI to reflect the cultural and historical background of each language in the grammar book and dictionary of the new language. For example, the generation AI can create a grammar book and dictionary that reflects the cultural background of English. The generation AI can also create a grammar book and dictionary that reflects the historical background of Japanese. Furthermore, the generation AI can create a grammar book and dictionary that reflects the cultural background of French. In this way, by reflecting the cultural and historical background, learning with a deeper understanding is possible.

[0047] The creation unit can include slang and colloquialisms of each language in the grammar book and dictionary of the new language. For example, the creation unit can include slang and colloquialisms based on the type of slang and its frequency of use. The creation unit can also use a generation AI to include slang and colloquialisms of each language in the grammar book and dictionary of the new language. For example, the generation AI can create a grammar book and dictionary that includes English slang. The generation AI can also create a grammar book and dictionary that includes Spanish slang. The generation AI can also create a grammar book and dictionary that includes Chinese youth slang. In this way, including slang and colloquialisms enables more natural language understanding.

[0048] The creation unit can include poetic and literary expressions of each language in the grammar book and dictionary of the new language. For example, the creation unit can include poetic and literary expressions based on poetic forms and literary metaphors. The creation unit can also use a generative AI to include poetic and literary expressions of each language in the grammar book and dictionary of the new language. For example, the generative AI can create a grammar book and dictionary that includes English poetic expressions. The generative AI can also create a grammar book and dictionary that includes French literary expressions. The generative AI can also create a grammar book and dictionary that includes Japanese poetic expressions. This enables a richer language understanding by including poetic and literary expressions.

[0049] The creation unit can include technical and specialized terms from each language in the grammar book and dictionary of the new language. For example, the creation unit can include technical and specialized terms based on a term list or frequency of use in a specific field. The creation unit can also use a generative AI to include technical and specialized terms from each language in the grammar book and dictionary of the new language. For example, the generative AI can create a grammar book and dictionary that includes technical terms from the medical field. The generative AI can also create a grammar book and dictionary that includes technical terms from the legal field. The generative AI can also create a grammar book and dictionary that includes technical terms from the IT field. In this way, the inclusion of technical and specialized terms improves language understanding in specific fields.

[0050] The creation unit can include sign language versions of each language in the grammar book or dictionary of the new language. For example, the creation unit can include sign language versions based on sign language actions or sign language grammar. The creation unit can also use a generation AI to include sign language versions of each language in the grammar book or dictionary of the new language. For example, the generation AI can create a grammar book or dictionary that includes English sign language. The generation AI can also create a grammar book or dictionary that includes Japanese sign language. The generation AI can also create a grammar book or dictionary that includes French sign language. In this way, including sign language versions can accommodate people with hearing impairments.

[0051] The creation unit can include audio guides for each language in the grammar book or dictionary of the new language. For example, the creation unit can record audio guides based on phonetic symbols or audio samples. The creation unit can also use the generation AI to include audio guides for each language in the grammar book or dictionary of the new language. For example, the generation AI can create a grammar book or dictionary that includes an English audio guide. The generation AI can also create a grammar book or dictionary that includes a Japanese audio guide. The generation AI can also create a grammar book or dictionary that includes a French audio guide. In this way, including audio guides makes it easier to learn pronunciation.

[0052] The provision unit can reflect the cultural and historical background of each language in the teaching materials for the new language. For example, the provision unit can reflect the cultural and historical background by reflecting historical events and cultural changes. The provision unit can also use the generation AI to reflect the cultural and historical background of each language in the teaching materials for the new language. For example, the generation AI can provide teaching materials that reflect the cultural background of English. The generation AI can also provide teaching materials that reflect the historical background of Japanese. Furthermore, the generation AI can provide teaching materials that reflect the cultural background of French. In this way, by reflecting the cultural and historical background, learning with a deeper understanding is possible.

[0053] The providing unit can include slang and colloquialisms of each language in the teaching materials for the new language. For example, the providing unit can record slang and colloquialisms based on the type of slang and its frequency of use. The providing unit can also use the generating AI to include slang and colloquialisms of each language in the teaching materials for the new language. For example, the generating AI can provide teaching materials including English slang. The generating AI can also provide teaching materials including Spanish slang. The generating AI can still provide teaching materials including Chinese youth slang. In this way, including slang and colloquialisms enables more natural language understanding.

[0054] The providing unit can include poetic and literary expressions of each language in the teaching materials for the new language. For example, the providing unit can include poetic and literary expressions based on poetic forms and literary metaphors. The providing unit can also use the generating AI to include poetic and literary expressions of each language in the teaching materials for the new language. For example, the generating AI can provide teaching materials including English poetic expressions. The generating AI can also provide teaching materials including French literary expressions. The generating AI can still provide teaching materials including Japanese poetic expressions. In this way, the inclusion of poetic and literary expressions enables richer language understanding.

[0055] The provision unit can include technical terms and jargon for each language in the learning materials for the new language. For example, the provision unit can include technical terms and jargon based on a term list or frequency of use in a specific field. The provision unit can also use a generation AI to include technical terms and jargon for each language in the learning materials for the new language. For example, the generation AI can provide learning materials including technical terms in the medical field. The generation AI can also provide learning materials including technical terms in the legal field. The generation AI can also provide learning materials including technical terms in the IT field. In this way, the inclusion of technical terms and jargon improves language comprehension in a specific field.

[0056] The providing unit can include sign language versions of each language in the teaching materials for the new language. For example, the providing unit can record sign language versions based on sign language actions and sign language grammar. The providing unit can also use the generating AI to include sign language versions of each language in the teaching materials for the new language. For example, the generating AI can provide teaching materials including English sign language. The generating AI can also provide teaching materials including Japanese sign language. The generating AI can still provide teaching materials including French sign language. In this way, including sign language versions can accommodate people with hearing impairments.

[0057] The providing unit can include audio guides for each language in the learning materials for the new language. For example, the providing unit can record audio guides based on phonetic symbols or audio samples. The providing unit can also use the generating AI to include audio guides for each language in the learning materials for the new language. For example, the generating AI can provide learning materials including an audio guide in English. The generating AI can also provide learning materials including an audio guide in Japanese. The generating AI can still further provide learning materials including an audio guide in French. In this way, including audio guides makes it easier to learn pronunciation.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can analyze the audio data of each language and extract differences in pronunciation and intonation. For example, the generation AI can analyze English audio data and extract the differences in pronunciation between American English and British English. The generation AI can also analyze Japanese audio data and extract the differences in intonation between Kansai dialect and standard Japanese. Furthermore, the generation AI can analyze French audio data and extract regional differences in pronunciation. This makes it possible to accurately extract differences in pronunciation and intonation by analyzing audio data.

[0060] The generation unit can generate a new language that incorporates the poetic and literary expressions of each language. For example, the generation AI can generate a new language that incorporates English poetic expressions. The generation AI can also generate a new language that incorporates French literary expressions. Furthermore, the generation AI can generate a new language that incorporates Japanese poetic expressions. This makes it possible to generate a new, richer language by incorporating poetic and literary expressions.

[0061] The analysis unit analyzes data containing slang and colloquialisms in each language, enabling more natural language understanding. For example, the generation AI can analyze English slang and extract natural usage in everyday conversation. The generation AI can also analyze Spanish slang and extract regional nuances. Furthermore, the generation AI can analyze Chinese youth slang to reflect the latest language trends. This enables more natural language understanding by analyzing datasets containing slang and colloquialisms.

[0062] The analysis unit can improve the accuracy of extracting meanings and nuances based on the cultural background of each language. For example, the generation AI can analyze honorific expressions in Japanese and extract appropriate nuances based on the cultural background. The generation AI can also analyze poetic expressions in French and extract appropriate nuances based on the cultural background. Furthermore, the generation AI can analyze religious expressions in Arabic and extract appropriate nuances based on the cultural background. This makes it possible to extract more accurate meanings and nuances by taking into account the cultural background of each language.

[0063] The provider can reflect the cultural and historical background of each language in the new language learning materials. For example, the generator AI can provide learning materials that reflect the cultural background of English. The generator AI can also provide learning materials that reflect the historical background of Japanese. The generator AI can also provide learning materials that reflect the cultural background of French. This allows learning with a deeper understanding by reflecting cultural and historical backgrounds.

[0064] The generation unit can design a new language by taking into account the non-verbal communication of each language. For example, the generation AI can design a new language by taking into account English gestures. The generation AI can also design a new language by taking into account Japanese facial expressions. Furthermore, the generation AI can design a new language by taking into account Spanish non-verbal communication. This makes it possible to design a new language that is more natural by taking non-verbal communication into account.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The analysis unit analyzes the multilingual data and extracts the meaning and nuances of each language. Multilingual data includes, but is not limited to, English, Spanish, and Chinese. The analysis unit can extract the meaning and nuances of each language using contextual analysis and sentiment analysis. It can also use generative AI to analyze the grammatical structure and vocabulary frequency of each language and find common patterns. Step 2: The generator generates the grammar and vocabulary of the new language based on the meanings and nuances extracted by the analyzer. For example, the generator extracts common grammar rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and designs the basic structure of the new language based on these. The generator can also use generative AI to create a grammar book or dictionary for the new language based on the extracted grammar rules and vocabulary. Step 3: The creation unit creates a grammar book or dictionary based on the grammar and vocabulary generated by the generation unit. The creation unit can automatically generate the contents of the grammar book or dictionary using generation AI. Step 4: The provider provides learning materials for learning a new language based on the grammar books and dictionaries created by the creator. The provider can also provide tools for translating existing multilingual content into a new language using generative AI.

[0067] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to develop a new language whose meanings and nuances can be shared and understood worldwide. In this system, generative AI analyzes existing multilingual data, extracts the meanings and nuances of each language, and generates the grammar and vocabulary of the new language based on the extracted data. For example, generative AI extracts common grammar rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and then designs the basic structure of the new language based on the extracted grammar rules and vocabulary. Furthermore, generative AI creates a grammar book and dictionary for the new language based on the extracted grammar rules and vocabulary, providing learning materials for people around the world to learn the new language. Generative AI also provides tools for translating existing multilingual content into the new language. This allows people who speak different languages ​​to communicate in a common language, promoting international exchange and cooperation. As a result, a system utilizing generative AI can develop a new language whose meanings and nuances can be shared and understood worldwide. For example, using the new language at international conferences and business events can enable smooth communication across language barriers.

[0068] A new language development system according to an embodiment includes an analysis unit, a generation unit, a creation unit, and a provision unit. The analysis unit analyzes multilingual data and extracts the meanings and nuances of each language. Examples of multilingual data include, but are not limited to, English, Spanish, and Chinese. The analysis unit can extract the meanings and nuances of each language using, for example, contextual analysis and sentiment analysis. The analysis unit can also use a generative AI to analyze the grammatical structure and frequency of vocabulary of each language and find common patterns. The generation unit generates grammar and vocabulary for the new language based on the meanings and nuances extracted by the analysis unit. The generation unit extracts common grammatical rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and designs the basic structure of the new language based on the extracted grammatical rules and vocabulary. The generation unit can also use a generative AI to create a grammar book and a dictionary for the new language based on the extracted grammar rules and vocabulary. The creation unit creates a grammar book and a dictionary based on the grammar and vocabulary generated by the generation unit. The creation unit can automatically generate the contents of a grammar book or dictionary using, for example, a generation AI. The provision unit provides learning materials for learning a new language based on the grammar book or dictionary created by the creation unit. The provision unit can provide tools for translating existing multilingual content into a new language using, for example, a generation AI. As a result, the new language development system according to the embodiment can develop a new language whose meanings and nuances can be shared and understood worldwide.

[0069] The analysis unit can analyze the grammatical structure and vocabulary frequency of each language and find common patterns. The analysis unit can analyze grammatical structure by analyzing phrase structure and sentence types, for example. The analysis unit can also analyze vocabulary frequency by analyzing occurrence frequency and co-occurrence frequency. Furthermore, the analysis unit can find common patterns by extracting commonalities between frequently occurring phrases and grammatical rules. In this way, analyzing the grammatical structure and vocabulary frequency of each language and finding common patterns can be useful in creating new languages.

[0070] The generation unit can extract common grammar rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and design the basic structure of a new language based on the extracted grammar rules and vocabulary. The generation unit can analyze the grammar rules and vocabulary of major languages ​​such as English, Spanish, and Chinese, and extract frequently occurring grammar rules and common vocabulary. The generation unit can also design the basic structure of a new language based on the extracted grammar rules and vocabulary. For example, the generation unit can design a basic set of grammar rules and vocabulary. This makes it possible to generate a universal language by extracting common grammar rules and vocabulary from major languages ​​and designing the basic structure of a new language.

[0071] The creation unit can create a grammar book or dictionary for the new language based on the extracted grammar rules and vocabulary. The creation unit can create a grammar book or dictionary based on, for example, the type of grammar or the range of vocabulary. The creation unit can also automatically generate the contents of the grammar book or dictionary using a generative AI. This makes it possible to support the learning of a new language by creating a grammar book or dictionary based on the extracted grammar rules and vocabulary.

[0072] The providing unit can provide a tool for translating existing multilingual content into a new language. The providing unit can provide a tool for translating multilingual content, such as text, audio, or video, into a new language. The providing unit can also use generative AI to set translation algorithms and supported languages. This can promote the spread of new languages ​​by providing a tool for translating existing multilingual content into a new language.

[0073] The analysis unit can estimate the user's emotions and determine the order of analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The analysis unit can also adjust the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can adjust the analysis priority to prioritize analysis of simple grammatical structures and frequently occurring vocabulary. Alternatively, if the user is relaxed, the generation AI can prioritize analysis of complex grammatical structures and technical terms. Furthermore, if the user is excited, the generation AI can prioritize analysis of creative expressions in new languages ​​and poetic expressions. This allows analysis to be tailored to the user's state by adjusting the analysis priority based on the user's emotions.

[0074] The analysis unit can improve the accuracy of extracting meanings and nuances based on the cultural background of each language. For example, the analysis unit can extract meanings and nuances by taking into account the historical and social background of each language. The analysis unit can also use generation AI to extract appropriate nuances based on the cultural background. For example, generation AI can analyze honorific expressions in Japanese and extract appropriate nuances based on the cultural background. Generation AI can also analyze poetic expressions in French and extract appropriate nuances based on the cultural background. Furthermore, generation AI can analyze religious expressions in Arabic and extract appropriate nuances based on the cultural background. This enables more accurate extraction of meanings and nuances by taking into account the cultural background of each language.

[0075] The analysis unit can analyze data containing slang and colloquialisms in each language to achieve more natural language understanding. For example, the analysis unit can analyze the types and frequency of use of slang and colloquialisms. The analysis unit can also use a generative AI to analyze datasets containing slang and colloquialisms to achieve more natural language understanding. For example, the generative AI can analyze English slang and extract natural usage in everyday conversations. The generative AI can also analyze Spanish slang and extract regional nuances. Furthermore, the generative AI can analyze Chinese youth slang to reflect the latest language trends. As a result, analyzing datasets containing slang and colloquialisms enables more natural language understanding.

[0076] The analysis unit can analyze the evolution of the grammatical structure of each language and reflect the latest language trends. The analysis unit can analyze the evolution of grammatical structure by, for example, analyzing the historical changes and regional differences of each language. The analysis unit can also use generation AI to reflect the latest language trends. For example, generation AI can analyze the evolution of English grammatical structure and reflect the latest language trends. Generation AI can also analyze the evolution of Japanese grammatical structure and reflect the latest language trends. Furthermore, generation AI can analyze the evolution of French grammatical structure and reflect the latest language trends. In this way, by analyzing the evolution of grammatical structure, analysis that reflects the latest language trends becomes possible.

[0077] The analysis unit can estimate the user's emotions and determine the display format of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The analysis unit can also adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple, highly visible display method. If the user is relaxed, the generation AI can provide a display method that includes detailed information. Furthermore, if the user is excited, the generation AI can provide a visually stimulating display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide a display that is easy for the user to view.

[0078] The analysis unit can extract regional nuances by taking into account regional differences in each language. For example, the analysis unit can extract nuances by taking into account the linguistic characteristics and cultural background of each region of each language. The analysis unit can also extract regional nuances using generation AI. For example, the generation AI can analyze regional differences in English and extract the nuanced differences between American English and British English. The generation AI can also analyze regional differences in Spanish and extract the nuanced differences between Spain and Latin America. Furthermore, the generation AI can analyze regional differences in Chinese and extract the nuanced differences between Mandarin and Cantonese. In this way, by taking regional differences into account, regional nuances can be accurately extracted.

[0079] The analysis unit can analyze technical terms and terminology in each language to improve language understanding in a specific field. For example, the analysis unit can analyze technical terms and terminology by analyzing a term list or frequency of use in a specific field. The analysis unit can also use a generative AI to analyze technical terms and terminology in each language to improve language understanding in a specific field. For example, the generative AI can analyze technical terms in the medical field and extract appropriate nuances. The generative AI can also analyze technical terms in the legal field and extract appropriate nuances. Furthermore, the generative AI can analyze technical terms in the IT field and extract appropriate nuances. In this way, analyzing technical terms and terminology improves language understanding in a specific field.

[0080] The analysis unit can analyze the speech data of each language and extract differences in pronunciation and intonation. For example, the analysis unit can analyze speech data by analyzing pronunciation data and intonation data. The analysis unit can also extract differences in pronunciation and intonation using a generation AI. For example, the generation AI can analyze English speech data and extract differences in pronunciation between American English and British English. The generation AI can also analyze Japanese speech data and extract differences in intonation between Kansai dialect and standard Japanese. Furthermore, the generation AI can analyze French speech data and extract differences in pronunciation between regions. This makes it possible to accurately extract differences in pronunciation and intonation by analyzing speech data.

[0081] The generation unit can estimate the user's emotions and determine the order of grammar and vocabulary to be generated based on the estimated user emotions. The generation unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The generation unit can also determine the priority of grammar and vocabulary to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can prioritize simple grammar and frequently used vocabulary. Also, if the user is relaxed, the generation AI can prioritize complex grammar and technical terms. Furthermore, if the user is excited, the generation AI can prioritize creative and poetic expressions. In this way, by determining the priority of grammar and vocabulary based on the user's emotions, it is possible to generate language that is suitable for the user.

[0082] The generation unit can generate a new language that incorporates the poetic and literary expressions of each language. For example, the generation unit can generate poetic and literary expressions by incorporating poetic forms and literary metaphors. The generation unit can also use a generative AI to generate a new language that incorporates the poetic and literary expressions of each language. For example, the generative AI can generate a new language that incorporates English poetic expressions. The generative AI can also generate a new language that incorporates French literary expressions. Furthermore, the generative AI can generate a new language that incorporates Japanese poetic expressions. This makes it possible to generate a richer new language by incorporating poetic and literary expressions.

[0083] The generation unit can design a new language by taking into account the non-verbal communication of each language. The generation unit can design non-verbal communication by taking into account gestures and facial expressions, for example. The generation unit can also use a generative AI to design a new language by taking into account the non-verbal communication of each language. For example, the generative AI can design a new language by taking into account English gestures. The generative AI can also design a new language by taking into account Japanese facial expressions. Furthermore, the generative AI can design a new language by taking into account Spanish non-verbal communication. This makes it possible to design a new language that is more natural by taking into account non-verbal communication.

[0084] The generation unit can generate grammar and vocabulary that reflect the historical background of each language. For example, the generation unit can generate grammar and vocabulary that reflect the historical background by reflecting historical events and cultural changes. The generation unit can also use a generative AI to generate grammar and vocabulary that reflect the historical background of each language. For example, the generative AI can generate grammar and vocabulary that reflect the historical background of English. The generative AI can also generate grammar and vocabulary that reflect the historical background of French. Furthermore, the generative AI can generate grammar and vocabulary that reflect the historical background of Japanese. This makes it possible to generate new languages ​​with a deeper understanding by reflecting historical background.

[0085] The generation unit can estimate the user's emotions and determine the grammar and vocabulary expression format to be generated based on the estimated user's emotions. The generation unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The generation unit can also adjust the grammar and vocabulary expression format to be generated based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide a simple and highly visible expression format. If the user is relaxed, the generation AI can provide an expression format that includes detailed information. Furthermore, if the user is excited, the generation AI can provide a visually stimulating expression format. This makes it possible to generate language that is suitable for the user by adjusting the expression format based on the user's emotions.

[0086] The generation unit can generate a pronunciation guide for a new language based on the audio data of each language. The generation unit can generate a pronunciation guide based on, for example, phonetic symbols or audio samples. The generation unit can also use a generation AI to generate a pronunciation guide for a new language based on the audio data of each language. For example, the generation AI can generate a pronunciation guide for a new language based on English audio data. The generation AI can also generate a pronunciation guide for a new language based on Japanese audio data. Furthermore, the generation AI can generate a pronunciation guide for a new language based on French audio data. In this way, by generating a pronunciation guide based on audio data, it is possible to learn the correct pronunciation of a new language.

[0087] The generation unit can analyze the sign language of each language and generate a sign language version for a new language. The generation unit can analyze the sign language, for example, by analyzing the sign language movements and sign language grammar. The generation unit can also use a generation AI to analyze the sign language of each language and generate a sign language version for a new language. For example, the generation AI can analyze English sign language and generate a sign language version for a new language. The generation AI can also analyze Japanese sign language and generate a sign language version for a new language. Furthermore, the generation AI can analyze French sign language and generate a sign language version for a new language. In this way, by analyzing sign language, a sign language version for a new language can be generated, making it possible to accommodate the hearing impaired.

[0088] The generation unit can design a new language's writing system based on the writing system of each language. The generation unit can design a writing system based on, for example, the alphabet or syllables. The generation unit can also use a generation AI to design a new language's writing system based on the writing system of each language. For example, the generation AI can design a new language's writing system based on the English writing system. The generation AI can also design a new language's writing system based on the Japanese writing system. The generation AI can also design a new language's writing system based on the French writing system. This makes it possible to learn how to write a new language by designing a writing system.

[0089] The creation unit can estimate the user's emotions and determine the contents of the grammar book or dictionary based on the estimated user emotions. The creation unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The creation unit can also adjust the contents of the grammar book or dictionary based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple, highly visible grammar book or dictionary. Alternatively, if the user is relaxed, the generation AI can provide a grammar book or dictionary with detailed information. Furthermore, if the user is excited, the generation AI can provide a visually stimulating grammar book or dictionary. This allows learning that is tailored to the user by adjusting the contents of the grammar book or dictionary based on the user's emotions.

[0090] The creation unit can reflect the cultural and historical background of each language in the grammar book and dictionary of the new language. For example, the creation unit can reflect the cultural and historical background by reflecting historical events and cultural changes. The creation unit can also use the generation AI to reflect the cultural and historical background of each language in the grammar book and dictionary of the new language. For example, the generation AI can create a grammar book and dictionary that reflects the cultural background of English. The generation AI can also create a grammar book and dictionary that reflects the historical background of Japanese. Furthermore, the generation AI can create a grammar book and dictionary that reflects the cultural background of French. In this way, by reflecting the cultural and historical background, learning with a deeper understanding is possible.

[0091] The creation unit can include slang and colloquialisms of each language in the grammar book and dictionary of the new language. For example, the creation unit can include slang and colloquialisms based on the type of slang and its frequency of use. The creation unit can also use a generation AI to include slang and colloquialisms of each language in the grammar book and dictionary of the new language. For example, the generation AI can create a grammar book and dictionary that includes English slang. The generation AI can also create a grammar book and dictionary that includes Spanish slang. The generation AI can also create a grammar book and dictionary that includes Chinese youth slang. In this way, including slang and colloquialisms enables more natural language understanding.

[0092] The creation unit can include poetic and literary expressions of each language in the grammar book and dictionary of the new language. For example, the creation unit can include poetic and literary expressions based on poetic forms and literary metaphors. The creation unit can also use a generative AI to include poetic and literary expressions of each language in the grammar book and dictionary of the new language. For example, the generative AI can create a grammar book and dictionary that includes English poetic expressions. The generative AI can also create a grammar book and dictionary that includes French literary expressions. The generative AI can also create a grammar book and dictionary that includes Japanese poetic expressions. This enables a richer language understanding by including poetic and literary expressions.

[0093] The creation unit can estimate the user's emotions and determine the layout of a grammar book or dictionary based on the estimated user's emotions. The creation unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The creation unit can also adjust the layout of the grammar book or dictionary based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide a simple, highly visible layout. If the user is relaxed, the generation AI can provide a layout that includes detailed information. Furthermore, if the user is excited, the generation AI can provide a visually stimulating layout. This allows for learning that is suited to the user by adjusting the layout based on the user's emotions.

[0094] The creation unit can include technical and specialized terms from each language in the grammar book and dictionary of the new language. For example, the creation unit can include technical and specialized terms based on a term list or frequency of use in a specific field. The creation unit can also use a generative AI to include technical and specialized terms from each language in the grammar book and dictionary of the new language. For example, the generative AI can create a grammar book and dictionary that includes technical terms from the medical field. The generative AI can also create a grammar book and dictionary that includes technical terms from the legal field. The generative AI can also create a grammar book and dictionary that includes technical terms from the IT field. In this way, the inclusion of technical and specialized terms improves language understanding in specific fields.

[0095] The creation unit can include sign language versions of each language in the grammar book or dictionary of the new language. For example, the creation unit can include sign language versions based on sign language actions or sign language grammar. The creation unit can also use a generation AI to include sign language versions of each language in the grammar book or dictionary of the new language. For example, the generation AI can create a grammar book or dictionary that includes English sign language. The generation AI can also create a grammar book or dictionary that includes Japanese sign language. The generation AI can also create a grammar book or dictionary that includes French sign language. In this way, including sign language versions can accommodate people with hearing impairments.

[0096] The creation unit can include audio guides for each language in the grammar book or dictionary of the new language. For example, the creation unit can record audio guides based on phonetic symbols or audio samples. The creation unit can also use the generation AI to include audio guides for each language in the grammar book or dictionary of the new language. For example, the generation AI can create a grammar book or dictionary that includes an English audio guide. The generation AI can also create a grammar book or dictionary that includes a Japanese audio guide. The generation AI can also create a grammar book or dictionary that includes a French audio guide. In this way, including audio guides makes it easier to learn pronunciation.

[0097] The providing unit can estimate the user's emotions and determine the content of the learning materials based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The providing unit can also adjust the content of the learning materials based on the estimated user's emotions. For example, if the user is feeling stressed, the generating AI can provide learning materials that are simple and highly visible. Also, if the user is relaxed, the generating AI can provide learning materials that include detailed information. Furthermore, if the user is excited, the generating AI can provide visually stimulating learning materials. In this way, adjusting the content of the learning materials based on the user's emotions enables learning that is suited to the user.

[0098] The provision unit can reflect the cultural and historical background of each language in the teaching materials for the new language. For example, the provision unit can reflect the cultural and historical background by reflecting historical events and cultural changes. The provision unit can also use the generation AI to reflect the cultural and historical background of each language in the teaching materials for the new language. For example, the generation AI can provide teaching materials that reflect the cultural background of English. The generation AI can also provide teaching materials that reflect the historical background of Japanese. Furthermore, the generation AI can provide teaching materials that reflect the cultural background of French. In this way, by reflecting the cultural and historical background, learning with a deeper understanding is possible.

[0099] The providing unit can include slang and colloquialisms of each language in the teaching materials for the new language. For example, the providing unit can record slang and colloquialisms based on the type of slang and its frequency of use. The providing unit can also use the generating AI to include slang and colloquialisms of each language in the teaching materials for the new language. For example, the generating AI can provide teaching materials including English slang. The generating AI can also provide teaching materials including Spanish slang. The generating AI can still provide teaching materials including Chinese youth slang. In this way, including slang and colloquialisms enables more natural language understanding.

[0100] The providing unit can include poetic and literary expressions of each language in the teaching materials for the new language. For example, the providing unit can include poetic and literary expressions based on poetic forms and literary metaphors. The providing unit can also use the generating AI to include poetic and literary expressions of each language in the teaching materials for the new language. For example, the generating AI can provide teaching materials including English poetic expressions. The generating AI can also provide teaching materials including French literary expressions. The generating AI can still provide teaching materials including Japanese poetic expressions. In this way, the inclusion of poetic and literary expressions enables richer language understanding.

[0101] The providing unit can estimate the user's emotions and determine the format of the teaching materials based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The providing unit can also adjust the format of the teaching materials based on the estimated user's emotions. For example, if the user is feeling stressed, the generating AI can provide teaching materials in a simple, highly visible format. If the user is relaxed, the generating AI can provide teaching materials in a format that includes detailed information. Furthermore, if the user is excited, the generating AI can provide teaching materials in a visually stimulating format. In this way, adjusting the format of the teaching materials based on the user's emotions enables learning that is suitable for the user.

[0102] The provision unit can include technical terms and jargon for each language in the learning materials for the new language. For example, the provision unit can include technical terms and jargon based on a term list or frequency of use in a specific field. The provision unit can also use a generation AI to include technical terms and jargon for each language in the learning materials for the new language. For example, the generation AI can provide learning materials including technical terms in the medical field. The generation AI can also provide learning materials including technical terms in the legal field. The generation AI can also provide learning materials including technical terms in the IT field. In this way, the inclusion of technical terms and jargon improves language comprehension in a specific field.

[0103] The providing unit can include sign language versions of each language in the teaching materials for the new language. For example, the providing unit can record sign language versions based on sign language actions and sign language grammar. The providing unit can also use the generating AI to include sign language versions of each language in the teaching materials for the new language. For example, the generating AI can provide teaching materials including English sign language. The generating AI can also provide teaching materials including Japanese sign language. The generating AI can still provide teaching materials including French sign language. In this way, including sign language versions can accommodate people with hearing impairments.

[0104] The providing unit can include audio guides for each language in the learning materials for the new language. For example, the providing unit can record audio guides based on phonetic symbols or audio samples. The providing unit can also use the generating AI to include audio guides for each language in the learning materials for the new language. For example, the generating AI can provide learning materials including an audio guide in English. The generating AI can also provide learning materials including an audio guide in Japanese. The generating AI can still further provide learning materials including an audio guide in French. In this way, including audio guides makes it easier to learn pronunciation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, creation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, creation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, creation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, creation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the creation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The analysis unit can analyze the audio data of each language and extract differences in pronunciation and intonation. For example, the generation AI can analyze English audio data and extract the differences in pronunciation between American English and British English. The generation AI can also analyze Japanese audio data and extract the differences in intonation between Kansai dialect and standard Japanese. Furthermore, the generation AI can analyze French audio data and extract regional differences in pronunciation. This makes it possible to accurately extract differences in pronunciation and intonation by analyzing audio data.

[0107] The generation unit can generate a new language that incorporates the poetic and literary expressions of each language. For example, the generation AI can generate a new language that incorporates English poetic expressions. The generation AI can also generate a new language that incorporates French literary expressions. Furthermore, the generation AI can generate a new language that incorporates Japanese poetic expressions. This makes it possible to generate a new, richer language by incorporating poetic and literary expressions.

[0108] The providing unit can estimate the user's emotions and determine the content of the learning materials based on the estimated user emotions. For example, if the user is feeling stressed, the generating AI can provide learning materials that are simple and highly visible. If the user is relaxed, the generating AI can provide learning materials that include detailed information. Furthermore, if the user is excited, the generating AI can provide learning materials that are visually stimulating. This allows learning that is appropriate for the user to be achieved by adjusting the content of the learning materials based on the user's emotions.

[0109] The analysis unit analyzes data containing slang and colloquialisms in each language, enabling more natural language understanding. For example, the generation AI can analyze English slang and extract natural usage in everyday conversation. The generation AI can also analyze Spanish slang and extract regional nuances. Furthermore, the generation AI can analyze Chinese youth slang to reflect the latest language trends. This enables more natural language understanding by analyzing datasets containing slang and colloquialisms.

[0110] The generation unit can estimate the user's emotions and determine the order of grammar and vocabulary to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can prioritize simple grammar and frequently used vocabulary. Alternatively, if the user is relaxed, the generation AI can prioritize complex grammar and technical terms. Furthermore, if the user is excited, the generation AI can prioritize creative and poetic expressions. This allows for language generation that is suited to the user by determining the priority of grammar and vocabulary based on the user's emotions.

[0111] The analysis unit can improve the accuracy of extracting meanings and nuances based on the cultural background of each language. For example, the generation AI can analyze honorific expressions in Japanese and extract appropriate nuances based on the cultural background. The generation AI can also analyze poetic expressions in French and extract appropriate nuances based on the cultural background. Furthermore, the generation AI can analyze religious expressions in Arabic and extract appropriate nuances based on the cultural background. This makes it possible to extract more accurate meanings and nuances by taking into account the cultural background of each language.

[0112] The provider can reflect the cultural and historical background of each language in the new language learning materials. For example, the generator AI can provide learning materials that reflect the cultural background of English. The generator AI can also provide learning materials that reflect the historical background of Japanese. The generator AI can also provide learning materials that reflect the cultural background of French. This allows learning with a deeper understanding by reflecting cultural and historical backgrounds.

[0113] The analysis unit can estimate the user's emotions and determine the order of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can adjust the analysis priority and prioritize analysis of simple grammatical structures and frequently occurring vocabulary. Alternatively, if the user is relaxed, the generation AI can prioritize analysis of complex grammatical structures and technical terms. Furthermore, if the user is excited, the generation AI can prioritize analysis of creative expressions in new languages ​​and poetic expressions. This allows analysis to be tailored to the user's state by adjusting the analysis priority based on the user's emotions.

[0114] The generation unit can design a new language by taking into account the non-verbal communication of each language. For example, the generation AI can design a new language by taking into account English gestures. The generation AI can also design a new language by taking into account Japanese facial expressions. Furthermore, the generation AI can design a new language by taking into account Spanish non-verbal communication. This makes it possible to design a new language that is more natural by taking non-verbal communication into account.

[0115] The providing unit can estimate the user's emotions and determine the format of the learning materials based on the estimated user's emotions. For example, if the user is feeling stressed, the generating AI can provide learning materials in a simple, highly visible format. If the user is relaxed, the generating AI can provide learning materials in a format that includes detailed information. Furthermore, if the user is excited, the generating AI can provide learning materials in a visually stimulating format. This allows learning that is suitable for the user by adjusting the format of the learning materials based on the user's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The analysis unit analyzes the multilingual data and extracts the meaning and nuances of each language. Multilingual data includes, but is not limited to, English, Spanish, and Chinese. The analysis unit can extract the meaning and nuances of each language using contextual analysis and sentiment analysis. It can also use generative AI to analyze the grammatical structure and vocabulary frequency of each language and find common patterns. Step 2: The generator generates the grammar and vocabulary of the new language based on the meanings and nuances extracted by the analyzer. For example, the generator extracts common grammar rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and designs the basic structure of the new language based on these. The generator can also use generative AI to create a grammar book or dictionary for the new language based on the extracted grammar rules and vocabulary. Step 3: The creation unit creates a grammar book or dictionary based on the grammar and vocabulary generated by the generation unit. The creation unit can automatically generate the contents of the grammar book or dictionary using generation AI. Step 4: The provider provides learning materials for learning a new language based on the grammar books and dictionaries created by the creator. The provider can also provide tools for translating existing multilingual content into a new language using generative AI.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes multilingual data and analyzes the meaning and nuance of each language; a generation unit that generates a grammar and vocabulary of a new language based on the meanings and nuances extracted by the analysis unit; a creation unit that creates a grammar book or a dictionary based on the grammar and vocabulary generated by the creation unit; a providing unit that provides learning materials for learning a new language based on the grammar book and dictionary created by the creating unit. A system characterized by:

2. The analysis unit Analyze the grammatical structure and vocabulary frequency of each language to find common patterns 2. The system of claim 1.

3. The generation unit Extracting common grammar rules and vocabulary from major languages ​​such as English, Spanish, and Chinese, and using them to design the basic structure of a new language 2. The system of claim 1.

4. The creation unit Create a grammar book or dictionary for a new language based on the extracted grammar rules and vocabulary 2. The system of claim 1.

5. The providing unit Providing tools to translate existing multilingual content into new languages 2. The system of claim 1.

6. The analysis unit Estimate the user's emotions and determine the order of analysis based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit Improve the accuracy of extracting meaning and nuance based on the cultural background of each language 2. The system of claim 1.

8. The analysis unit Analyzing data containing slang and colloquialisms from various languages ​​to achieve more natural language understanding 2. The system of claim 1.

Images