System

Abstract

An object of a system according to an embodiment is to design a new language by aggregating and classifying languages in the whole world.SOLUTION: A system according to an embodiment includes a language data collection unit, a language analysis unit, a language integration unit, and a language design unit. The language data collection part collects language data of the whole world. The language analysis unit analyzes the language data collected by the language data collection unit. The language aggregation unit aggregates and classifies the language data analyzed by the language analysis unit. The linguistic design unit designs a new language based on the linguistic data aggregated and classified by the linguistic aggregation 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 technologies have faced the challenge of effectively aggregating and classifying the world's languages ​​and designing new languages.

[0005] The system according to the embodiment aims to aggregate and classify languages ​​from around the world and to design new languages. [Means for solving the problem]

[0006] The system according to the embodiment includes a language data collection unit, a language analysis unit, a language aggregation unit, and a language design unit. The language data collection unit collects language data from around the world. The language analysis unit analyzes the language data collected by the language data collection unit. The language aggregation unit aggregates and classifies the language data analyzed by the language analysis unit. The language design unit designs a new language based on the language data aggregated and classified by the language aggregation unit. [Effects of the Invention]

[0007] The system according to the embodiment can aggregate and classify languages ​​from around the world and design new languages. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 language development system according to an embodiment of the present invention utilizes the natural language understanding characteristics of generative AI to aggregate and classify languages ​​from around the world and develop new languages ​​based on linguistic principles and comparative analysis between languages. As a result, the language development system aggregates and classifies languages ​​from around the world and develops new languages ​​that are more comprehensive, accurate, and effective based on linguistic principles and comparative analysis between languages.

[0029] A language development system according to an embodiment includes a language data collection unit, a language analysis unit, a language aggregation unit, and a language design unit. The language data collection unit collects language data from around the world. For example, it collects text data, audio data, video data, and the like. The language data collection unit can also collect data from public databases on the Internet and from linguistic research institutes. For example, the language data collection unit collects text data from the Internet using a web crawler. The language analysis unit analyzes the collected language data. For example, it performs morphological analysis, grammatical analysis, semantic analysis, and the like. The language analysis unit can also analyze the characteristics of each language in detail using a generative AI. For example, the generative AI analyzes the language data using a text generation AI (e.g., LLM). The language aggregation unit aggregates and classifies the analyzed language data. For example, it groups words and expressions with the same meaning and finds commonalities between different languages. The language aggregation unit can also perform appropriate classification taking into account the use and cultural background of the language. For example, the language aggregation unit classifies the language data using a generative AI. The language design unit designs a new language based on the aggregated and classified language data. For example, it simplifies grammar, standardizes pronunciation, and clarifies meaning. The language design unit can also use generative AI to design a new language. For example, the generative AI designs a new language based on prompts containing instructions for designing a new language. As a result, the language development system according to the embodiment can develop a new language by collecting, analyzing, aggregating, classifying, and designing language data from around the world. For example, by allowing people who speak different languages ​​to use a common new language, communication barriers can be eliminated and international exchange and cooperation can be promoted. The widespread use of new languages ​​is also expected to have significant effects in the fields of education and business.

[0030] The language data collection unit can collect a wider variety of language data, including regional dialects and slang. For example, the language data collection unit collects language data including regional dialects and slang and inputs it into the generation AI. For example, it collects and analyzes American English slang and Japanese dialects. The language data collection unit can also collect dialect and slang data from public databases on the Internet and from linguistics research institutions. For example, the language data collection unit uses a web crawler to collect dialect and slang data on the Internet. This allows for the collection of a wider variety of language data, including regional dialects and slang.

[0031] The language analysis unit can track the evolution or evolution of a language and also take into account historical language data. For example, the language analysis unit collects historical language data and inputs it into the generative AI to track the evolution or evolution of a language. For example, it analyzes text data of Old English or Old Japanese. The language analysis unit can also analyze the evolution or evolution of a language using historical documents or audio recordings. For example, the language analysis unit uses the generative AI to analyze historical language data. This allows the evolution or evolution of a language to be tracked and historical language data to be taken into account.

[0032] The language data collection unit can collect not only text data but also non-verbal communication data such as gestures or facial expressions. For example, when collecting language data, the language data collection unit collects not only text data but also non-verbal communication data such as gestures and facial expressions. For example, video data is analyzed to extract gestures and facial expressions. The language data collection unit can also collect non-verbal communication data from public databases on the Internet or from linguistic research institutions. For example, the language data collection unit uses a web crawler to collect non-verbal communication data on the Internet. This makes it possible to collect not only text data but also non-verbal communication data such as gestures and facial expressions.

[0033] The language analysis unit can compare language data from different cultural spheres and perform language analysis taking cultural background into consideration. The language analysis unit, for example, collects language data from different cultural spheres and performs language analysis taking cultural background into consideration. For example, it analyzes language data from different cultural spheres such as Asia, Europe, and Africa. The language analysis unit can also perform language analysis taking cultural background into consideration using generative AI. For example, the language analysis unit analyzes language data from different cultural spheres using generative AI. This makes it possible to compare language data from different cultural spheres and perform language analysis taking cultural background into consideration.

[0034] The language aggregation unit can prioritize aggregation of highly practical languages, taking into account the frequency of use or prevalence of the language. The language aggregation unit, for example, builds a system that prioritizes aggregation of highly practical languages, taking into account the frequency of use and prevalence of the language. For example, it prioritizes aggregation of highly used languages ​​such as English and Chinese. The language aggregation unit also aggregates languages ​​based on the frequency of use and prevalence of the language. For example, it prioritizes aggregation of highly used languages. This allows it to prioritize aggregation of highly practical languages, taking into account the frequency of use and prevalence of the language.

[0035] The language aggregating unit can also take into consideration the phonological system or rhythm of the language and prioritize classification of the phonetically pleasant language. The language aggregating unit, for example, builds a system that takes into consideration the phonological system and rhythm of the language and prioritizes classification of phonetically pleasant languages. For example, languages ​​with a well-organized phonological system are prioritized for aggregation. The language aggregating unit also classifies phonetically pleasant languages. For example, languages ​​with a well-organized phonological system are prioritized for classification. This allows for prioritize classification of phonetically pleasant languages ​​by taking into consideration the phonological system and rhythm of the language.

[0036] The language aggregation unit can attempt to fuse different languages ​​and develop a new hybrid language. The language aggregation unit, for example, builds a system that attempts to fuse different languages ​​and develop a new hybrid language. For example, it designs a new language that combines elements of English and Japanese. The language aggregation unit also develops a new language by integrating the features of different languages. For example, it designs a new language that combines the grammar and vocabulary of English and Japanese. This makes it possible to attempt to fuse different languages ​​and develop a new hybrid language.

[0037] The language design unit can adopt the grammar or vocabulary that is intuitively easy to understand, taking into account the ease of learning the language. For example, when designing a new language, the language design unit builds a system that adopts grammar and vocabulary that are intuitively easy to understand, taking into account the ease of learning the language. For example, simple grammar rules and frequently used vocabulary are preferentially adopted. The language design unit also selects grammar and vocabulary taking into account the ease of learning the language. For example, simple grammar and easy-to-remember vocabulary are selected. This allows for the adoption of grammar and vocabulary that are intuitively easy to understand, taking into account the ease of learning the language.

[0038] The language design unit can incorporate aesthetic elements of different languages ​​to enhance artistic value. For example, when designing a new language, the language design unit builds a system that incorporates aesthetic elements of different languages ​​to enhance artistic value. For example, it adopts grammar and vocabulary that reflect poetic expression and rhythm. The language design unit also designs languages ​​taking aesthetic elements into consideration. For example, it selects grammar and vocabulary that incorporate poetic expression and rhythm. This allows the aesthetic elements of different languages ​​to be incorporated and enhance artistic value.

[0039] The language design unit can integrate the technical terms from different fields of expertise to facilitate the technical communication. For example, in designing a new language, the language design unit integrates technical terms from different fields of expertise to build a system that facilitates technical communication. For example, the language design unit designs a new language that integrates technical terms from medicine and law. The language design unit also designs a new language by integrating technical terms. For example, the language design unit designs a new language that combines technical terms from medicine and law. This makes it possible to integrate technical terms from different fields of expertise and facilitate technical communication.

[0040] The language design department can incorporate visual symbols or icons to develop a language that is visually easy to understand. For example, in designing a new language, the language design department builds a system that incorporates visual symbols and icons to develop a language that is visually easy to understand. For example, the language design department adopts grammar and vocabulary that use symbols and icons. The language design department also designs a language that is visually easy to understand. For example, the language design department selects grammar and vocabulary that use symbols and icons. This allows the development of a language that is visually easy to understand by incorporating visual symbols and icons.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The language data collection unit can collect user voice data and convert it into text data using voice recognition technology. For example, the language data collection unit can collect user voice using a smartphone or microphone and convert it into text data using voice recognition technology. The language data collection unit can also take into account different accents and pronunciation variations when collecting voice data. For example, the language data collection unit can collect and analyze differences in pronunciation between American English and British English. This allows the language data collection unit to collect user voice data and convert it into text data using voice recognition technology.

[0043] The language data collection unit can collect data from the user's input device and analyze the user's input pattern. For example, input data from a keyboard or touch screen is collected and the user's input pattern is analyzed. The language data collection unit can also analyze the user's input speed and frequency of typos to identify the user's input pattern. For example, the language data collection unit can analyze the user's input speed and frequency of typos to identify the user's input pattern. This makes it possible to collect data from the user's input device and analyze the user's input pattern.

[0044] The language data collection unit can collect user gaze data and identify language data that the user focuses on using gaze tracking technology. For example, the language data collection unit can collect user gaze data using a camera and identify language data that the user focuses on using gaze tracking technology. The language data collection unit can also analyze the user gaze data and identify language data that the user is interested in. For example, the language data collection unit can analyze the user gaze data and identify language data that the user is interested in. In this way, the language data collection unit can collect user gaze data and identify language data that the user focuses on using gaze tracking technology.

[0045] The language data collection unit can collect biometric data of the user and monitor the health condition. For example, biometric data such as heart rate and blood pressure is collected to monitor the user's health condition. The language data collection unit can also filter the language data based on the user's health condition. For example, if the user's health condition is deteriorating, language data that reduces stress is preferentially provided. This makes it possible to collect the user's biometric data and monitor the health condition.

[0046] The language aggregation unit can analyze the user's usage history and prioritize aggregation of frequently used language expressions. For example, the language aggregation unit can analyze the user's usage history and identify frequently used language expressions. The language aggregation unit can also aggregate language expressions based on the user's usage history. For example, the language aggregation unit can prioritize aggregation of frequently used language expressions. This allows the user's usage history to be analyzed and the language aggregation unit can prioritize aggregation of frequently used language expressions.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The language data collection unit collects language data from around the world. For example, it collects text data, audio data, video data, etc. It can also collect data from public databases on the Internet and from linguistic research institutions. Specifically, it uses a web crawler to collect text data from the Internet. Step 2: The language analysis unit analyzes the collected language data. For example, it performs morphological analysis, grammatical analysis, semantic analysis, etc. It can also use generative AI to analyze the characteristics of each language in detail. Specifically, it analyzes the language data using text generation AI (e.g., LLM). Step 3: The language aggregation unit aggregates and classifies the analyzed language data. For example, it groups words and expressions with the same meaning and finds commonalities between different languages. It can also perform appropriate classification taking into account the language's usage and cultural background. Specifically, it uses generative AI to classify the language data. Step 4: The language designer designs a new language based on the aggregated and categorized language data. For example, they simplify grammar, standardize pronunciation, and clarify meaning. Generative AI can also be used to design a new language. Specifically, they design a new language based on prompts containing instructions for designing the new language.

[0049] (Example 2) A language development system according to an embodiment of the present invention utilizes the natural language understanding characteristics of generative AI to aggregate and classify languages ​​from around the world and develop new languages ​​based on linguistic principles and comparative analysis between languages. As a result, the language development system aggregates and classifies languages ​​from around the world and develops new languages ​​that are more comprehensive, accurate, and effective based on linguistic principles and comparative analysis between languages.

[0050] A language development system according to an embodiment includes a language data collection unit, a language analysis unit, a language aggregation unit, and a language design unit. The language data collection unit collects language data from around the world. For example, it collects text data, audio data, video data, and the like. The language data collection unit can also collect data from public databases on the Internet and from linguistic research institutes. For example, the language data collection unit collects text data from the Internet using a web crawler. The language analysis unit analyzes the collected language data. For example, it performs morphological analysis, grammatical analysis, semantic analysis, and the like. The language analysis unit can also analyze the characteristics of each language in detail using a generative AI. For example, the generative AI analyzes the language data using a text generation AI (e.g., LLM). The language aggregation unit aggregates and classifies the analyzed language data. For example, it groups words and expressions with the same meaning and finds commonalities between different languages. The language aggregation unit can also perform appropriate classification taking into account the use and cultural background of the language. For example, the language aggregation unit classifies the language data using a generative AI. The language design unit designs a new language based on the aggregated and classified language data. For example, it simplifies grammar, standardizes pronunciation, and clarifies meaning. The language design unit can also use generative AI to design a new language. For example, the generative AI designs a new language based on prompts containing instructions for designing a new language. As a result, the language development system according to the embodiment can develop a new language by collecting, analyzing, aggregating, classifying, and designing language data from around the world. For example, by allowing people who speak different languages ​​to use a common new language, communication barriers can be eliminated and international exchange and cooperation can be promoted. The widespread use of new languages ​​is also expected to have significant effects in the fields of education and business.

[0051] The language analysis unit can analyze emotional expressions in each language and filter language data based on the intensity and type of emotion using an emotion estimation function. The language analysis unit, for example, collects text data in each language and analyzes the intensity and type of emotion using the emotion estimation function. For example, it extracts emotional expressions such as "happy" in English and "delighted" in Japanese and calculates an emotion score. The language analysis unit also filters language data based on the intensity and type of emotion. For example, it prioritizes analysis of data with a high emotion score. This allows for analyzing emotional expressions and filtering language data based on the intensity and type of emotion.

[0052] The language data collection unit can collect a wider variety of language data, including regional dialects and slang. For example, the language data collection unit collects language data including regional dialects and slang and inputs it into the generation AI. For example, it collects and analyzes American English slang and Japanese dialects. The language data collection unit can also collect dialect and slang data from public databases on the Internet and from linguistics research institutions. For example, the language data collection unit uses a web crawler to collect dialect and slang data on the Internet. This allows for the collection of a wider variety of language data, including regional dialects and slang.

[0053] The language analysis unit can track the evolution or evolution of a language and also take into account historical language data. For example, the language analysis unit collects historical language data and inputs it into the generative AI to track the evolution or evolution of a language. For example, it analyzes text data of Old English or Old Japanese. The language analysis unit can also analyze the evolution or evolution of a language using historical documents or audio recordings. For example, the language analysis unit uses the generative AI to analyze historical language data. This allows the evolution or evolution of a language to be tracked and historical language data to be taken into account.

[0054] The language data collection unit can collect not only text data but also non-verbal communication data such as gestures or facial expressions. For example, when collecting language data, the language data collection unit collects not only text data but also non-verbal communication data such as gestures and facial expressions. For example, video data is analyzed to extract gestures and facial expressions. The language data collection unit can also collect non-verbal communication data from public databases on the Internet or from linguistic research institutions. For example, the language data collection unit uses a web crawler to collect non-verbal communication data on the Internet. This makes it possible to collect not only text data but also non-verbal communication data such as gestures and facial expressions.

[0055] The language analysis unit can compare language data from different cultural spheres and perform language analysis taking cultural background into consideration. The language analysis unit, for example, collects language data from different cultural spheres and performs language analysis taking cultural background into consideration. For example, it analyzes language data from different cultural spheres such as Asia, Europe, and Africa. The language analysis unit can also perform language analysis taking cultural background into consideration using generative AI. For example, the language analysis unit analyzes language data from different cultural spheres using generative AI. This makes it possible to compare language data from different cultural spheres and perform language analysis taking cultural background into consideration.

[0056] The language analysis unit can use the emotion estimation function to monitor the user's emotional response to collected language data in real time and prioritize analysis of the data that elicits positive emotions. For example, the language analysis unit can use the emotion estimation function to develop a system that monitors the user's emotional response to collected language data in real time. For example, the language analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The language analysis unit also prioritizes analysis of data that elicits positive emotions. For example, data with a high emotion score is prioritized for analysis. This makes it possible to monitor the user's emotional response in real time and prioritize analysis of data that elicits positive emotions.

[0057] The language aggregation unit can use the emotion estimation function to consider emotional value and preferentially classify the linguistic expressions having positive emotions. For example, the language aggregation unit uses the emotion estimation function to build a system that considers emotional value in language aggregation and classification. For example, it preferentially classifies linguistic expressions with high positive emotion scores. The language aggregation unit also classifies linguistic expressions taking emotional value into consideration. For example, it preferentially classifies linguistic expressions with high emotion scores. This allows for preferential classification of linguistic expressions having positive emotions taking emotional value into consideration.

[0058] The language aggregation unit can prioritize aggregation of highly practical languages, taking into account the frequency of use or prevalence of the language. The language aggregation unit, for example, builds a system that prioritizes aggregation of highly practical languages, taking into account the frequency of use and prevalence of the language. For example, it prioritizes aggregation of highly used languages ​​such as English and Chinese. The language aggregation unit also aggregates languages ​​based on the frequency of use and prevalence of the language. For example, it prioritizes aggregation of highly used languages. This allows it to prioritize aggregation of highly practical languages, taking into account the frequency of use and prevalence of the language.

[0059] The language aggregating unit can also take into consideration the phonological system or rhythm of the language and prioritize classification of the phonetically pleasant language. The language aggregating unit, for example, builds a system that takes into consideration the phonological system and rhythm of the language and prioritizes classification of phonetically pleasant languages. For example, languages ​​with a well-organized phonological system are prioritized for aggregation. The language aggregating unit also classifies phonetically pleasant languages. For example, languages ​​with a well-organized phonological system are prioritized for classification. This allows for prioritize classification of phonetically pleasant languages ​​by taking into consideration the phonological system and rhythm of the language.

[0060] The language aggregation unit can attempt to fuse different languages ​​and develop a new hybrid language. The language aggregation unit, for example, builds a system that attempts to fuse different languages ​​and develop a new hybrid language. For example, it designs a new language that combines elements of English and Japanese. The language aggregation unit also develops a new language by integrating the features of different languages. For example, it designs a new language that combines the grammar and vocabulary of English and Japanese. This makes it possible to attempt to fuse different languages ​​and develop a new hybrid language.

[0061] The language aggregation unit can use an emotion estimation function to analyze the user's emotional response to the classified languages ​​and identify the languages ​​that are likely to be emotionally relatable. The language aggregation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional response to the classified languages. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The language aggregation unit also identifies languages ​​that are likely to be emotionally relatable. For example, it identifies languages ​​with high emotion scores. This makes it possible to analyze the user's emotional response to the classified languages ​​and identify languages ​​that are likely to be emotionally relatable.

[0062] The language design unit can use the emotion estimation function to incorporate the language elements that evoke the most positive emotions in the user. For example, when designing a new language, the language design unit uses the emotion estimation function to build a system that incorporates language elements that evoke the most positive emotions in the user. For example, words and expressions with high emotion scores are preferentially adopted. The language design unit also uses the emotion estimation function to select language elements that elicit positive emotions. For example, language elements with high emotion scores are selected. This makes it possible to incorporate language elements that evoke the most positive emotions in the user.

[0063] The language design unit can adopt the grammar or vocabulary that is intuitively easy to understand, taking into account the ease of learning the language. For example, when designing a new language, the language design unit builds a system that adopts grammar and vocabulary that are intuitively easy to understand, taking into account the ease of learning the language. For example, simple grammar rules and frequently used vocabulary are preferentially adopted. The language design unit also selects grammar and vocabulary taking into account the ease of learning the language. For example, simple grammar and easy-to-remember vocabulary are selected. This allows for the adoption of grammar and vocabulary that are intuitively easy to understand, taking into account the ease of learning the language.

[0064] The language design unit can incorporate aesthetic elements of different languages ​​to enhance artistic value. For example, when designing a new language, the language design unit builds a system that incorporates aesthetic elements of different languages ​​to enhance artistic value. For example, it adopts grammar and vocabulary that reflect poetic expression and rhythm. The language design unit also designs languages ​​taking aesthetic elements into consideration. For example, it selects grammar and vocabulary that incorporate poetic expression and rhythm. This allows the aesthetic elements of different languages ​​to be incorporated and enhance artistic value.

[0065] The language design unit can integrate the technical terms from different fields of expertise to facilitate the technical communication. For example, in designing a new language, the language design unit integrates technical terms from different fields of expertise to build a system that facilitates technical communication. For example, the language design unit designs a new language that integrates technical terms from medicine and law. The language design unit also designs a new language by integrating technical terms. For example, the language design unit designs a new language that combines technical terms from medicine and law. This makes it possible to integrate technical terms from different fields of expertise and facilitate technical communication.

[0066] The language design department can incorporate visual symbols or icons to develop a language that is visually easy to understand. For example, in designing a new language, the language design department builds a system that incorporates visual symbols and icons to develop a language that is visually easy to understand. For example, the language design department adopts grammar and vocabulary that use symbols and icons. The language design department also designs a language that is visually easy to understand. For example, the language design department selects grammar and vocabulary that use symbols and icons. This allows the development of a language that is visually easy to understand by incorporating visual symbols and icons.

[0067] The language design department can use the emotion estimation function to monitor the user's emotional response to the new language design in real time, and continuously improve the optimal language design. The language design department, for example, uses the emotion estimation function to develop a system that monitors the user's emotional response to the new language design in real time. For example, the language design department analyzes the user's facial expressions and voice and calculates an emotion score. The language design department also continuously improves the optimal language design. For example, the language design is improved based on user feedback. This allows the user's emotional response to the new language design to be monitored in real time, and the optimal language design to be continuously improved.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The language data collection unit can collect user voice data and convert it into text data using voice recognition technology. For example, the language data collection unit can collect user voice using a smartphone or microphone and convert it into text data using voice recognition technology. The language data collection unit can also take into account different accents and pronunciation variations when collecting voice data. For example, the language data collection unit can collect and analyze differences in pronunciation between American English and British English. This allows the language data collection unit to collect user voice data and convert it into text data using voice recognition technology.

[0070] The language analysis unit can estimate the user's emotions, identify language expressions that make the user feel stressed based on the estimated emotions, and filter to avoid these. For example, language expressions that make the user feel stressed can be identified and filtered to avoid these. The language analysis unit can also use the emotion estimation function to preferentially analyze language expressions that make the user feel relaxed. For example, language expressions that make the user feel relaxed can be preferentially analyzed. This makes it possible to estimate the user's emotions and filter to avoid language expressions that make the user feel stressed.

[0071] The language data collection unit can collect data from the user's input device and analyze the user's input pattern. For example, input data from a keyboard or touch screen is collected and the user's input pattern is analyzed. The language data collection unit can also analyze the user's input speed and frequency of typos to identify the user's input pattern. For example, the language data collection unit can analyze the user's input speed and frequency of typos to identify the user's input pattern. This makes it possible to collect data from the user's input device and analyze the user's input pattern.

[0072] The language analysis unit can estimate the user's emotions, identify language topics in which the user is interested based on the estimated emotions, and analyze these preferentially. For example, the language analysis unit can identify language topics in which the user is interested and analyze these preferentially. The language analysis unit can also use the emotion estimation function to preferentially analyze language topics in which the user is interested. For example, the language analysis unit can preferentially analyze language topics in which the user is interested. This makes it possible to estimate the user's emotions, identify language topics in which the user is interested, and analyze these preferentially.

[0073] The language data collection unit can collect user gaze data and identify language data that the user focuses on using gaze tracking technology. For example, the language data collection unit can collect user gaze data using a camera and identify language data that the user focuses on using gaze tracking technology. The language data collection unit can also analyze the user gaze data and identify language data that the user is interested in. For example, the language data collection unit can analyze the user gaze data and identify language data that the user is interested in. In this way, the language data collection unit can collect user gaze data and identify language data that the user focuses on using gaze tracking technology.

[0074] The language analysis unit can estimate the user's emotion and, based on the estimated emotion, prioritize analysis of linguistic expressions that convey positive emotions to the user. For example, it identifies linguistic expressions that convey positive emotions to the user and analyzes them preferentially. The language analysis unit can also use the emotion estimation function to prioritize analysis of linguistic expressions that convey positive emotions to the user. For example, it prioritizes analysis of linguistic expressions that convey positive emotions to the user. This makes it possible to estimate the user's emotion and prioritize analysis of linguistic expressions that convey positive emotions to the user.

[0075] The language data collection unit can collect biometric data of the user and monitor the health condition. For example, biometric data such as heart rate and blood pressure is collected to monitor the user's health condition. The language data collection unit can also filter the language data based on the user's health condition. For example, if the user's health condition is deteriorating, language data that reduces stress is preferentially provided. This makes it possible to collect the user's biometric data and monitor the health condition.

[0076] The language aggregation unit can estimate the user's emotions and, based on the estimated emotions, prioritize classify language expressions that the user can easily empathize with. For example, it identifies language expressions that the user can easily empathize with and prioritizes classifying them. The language aggregation unit can also use the emotion estimation function to prioritize classify language expressions that the user can easily empathize with. For example, it prioritizes classifying language expressions that the user can easily empathize with. This makes it possible to estimate the user's emotions and prioritize classify language expressions that the user can easily empathize with.

[0077] The language aggregation unit can analyze the user's usage history and prioritize aggregation of frequently used language expressions. For example, the language aggregation unit can analyze the user's usage history and identify frequently used language expressions. The language aggregation unit can also aggregate language expressions based on the user's usage history. For example, the language aggregation unit can prioritize aggregation of frequently used language expressions. This allows the user's usage history to be analyzed and the language aggregation unit can prioritize aggregation of frequently used language expressions.

[0078] The language design unit can estimate the user's emotions and incorporate language elements that make the user most relaxed based on the estimated emotions. For example, language elements that make the user relaxed can be identified and incorporated into the design of a new language. The language design unit can also use the emotion estimation function to preferentially adopt language elements that make the user relaxed. For example, language elements that make the user relaxed can be preferentially adopted. This makes it possible to estimate the user's emotions and incorporate language elements that make the user most relaxed.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The language data collection unit collects language data from around the world. For example, it collects text data, audio data, video data, etc. It can also collect data from public databases on the Internet and from linguistic research institutions. Specifically, it uses a web crawler to collect text data from the Internet. Step 2: The language analysis unit analyzes the collected language data. For example, it performs morphological analysis, grammatical analysis, semantic analysis, etc. It can also use generative AI to analyze the characteristics of each language in detail. Specifically, it analyzes the language data using text generation AI (e.g., LLM). Step 3: The language aggregation unit aggregates and classifies the analyzed language data. For example, it groups words and expressions with the same meaning and finds commonalities between different languages. It can also perform appropriate classification taking into account the language's usage and cultural background. Specifically, it uses generative AI to classify the language data. Step 4: The language designer designs a new language based on the aggregated and categorized language data. For example, they simplify grammar, standardize pronunciation, and clarify meaning. Generative AI can also be used to design a new language. Specifically, they design a new language based on prompts containing instructions for designing the new language.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A language data collection department that collects language data from all over the world; a language analysis unit that analyzes the language data collected by the language data collection unit; a language aggregation unit that aggregates and classifies the language data analyzed by the language analysis unit; a language design unit that designs a new language based on the language data aggregated and classified by the language aggregation unit. A system characterized by:

2. The language data collection unit Collect more diverse language data, including regional dialects or slang.

2. The system of claim 1.

3. The language analysis unit Tracking the evolution or change of a language and taking into account historical data on the language 2. The system of claim 1.

4. The language aggregation unit Taking into account the phonological system or rhythm of the language, prioritize the classification of said language that is phonetically pleasing.

2. The system of claim 1.

5. The language design unit Incorporate the language elements that users associate with the most positive feelings 2. The system of claim 1.

6. The language analysis unit Analyze the emotional expressions in each language and filter the language data based on the intensity and type of emotion.

2. The system of claim 1.

7. The language aggregation unit Considering emotional value, prioritize classification of linguistic expressions with positive emotions 2. The system of claim 1.

8. The language design unit Monitor users' emotional reactions to new language designs in real time and continuously refine the optimal language design.

2. The system of claim 1.

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