System

Abstract

An object of a system according to an embodiment is to enable elderly people to understand new words and foreign words in real time.SOLUTION: A system according to an embodiment includes a translation unit, an interpretation unit, an explanation unit, and a learning unit. The translation unit translates a new word or foreign word in real time. The interpretation unit interprets the language translated by the translation unit in consideration of background knowledge and context. The explanation unit explains the words interpreted by the interpretation unit in friendly words. The learning unit learns the latest words through the Internet connection.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 had the problem of making it difficult for elderly people to understand new words and foreign words in real time.

[0005] The system according to the embodiment aims to enable elderly people to understand new words and foreign words in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a translation unit, an interpretation unit, an explanation unit, and a learning unit. The translation unit translates new words and foreign words in real time. The interpretation unit interprets the words translated by the translation unit, taking into account background knowledge and context. The explanation unit explains the words interpreted by the interpretation unit in friendly language. The learning unit learns the latest words via an internet connection. [Effects of the Invention]

[0007] The system according to the embodiment can enable seniors to understand new words and foreign words in real time. [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) The Language Bridge AI Earphone according to an embodiment of the present invention is a system that translates new words and foreign words that elderly people encounter in their daily lives into easy-to-understand Japanese in real time and provides them in an audible format. As a result, the Language Bridge AI Earphone can translate new words and foreign words that elderly people encounter in their daily lives into easy-to-understand Japanese in real time and provide them in an audible format.

[0029] According to an embodiment, the language-bridge AI earphones include a translation unit, an interpretation unit, an explanation unit, and a learning unit. The translation unit translates new words and foreign words in real time. For example, the translation unit analyzes words heard by a user using speech recognition technology and generates appropriate translations using an instant translation algorithm. The translation unit can also analyze words and phrases heard by a user and generate appropriate translations. For example, if a user hears the word "smartphone," the translation unit translates it as "a type of mobile phone that can access the Internet and use apps." The interpretation unit interprets the words translated by the translation unit by taking into account background knowledge and context. For example, the interpretation unit references past conversation history and related topics and uses a context analysis algorithm to deeply understand the meaning of the words. The interpretation unit can also customize the interpretation results based on the user's interests and provide individually optimized interpretations. For example, if a user hears the word "cloud," the interpretation unit analyzes the context and interprets it as "a technology for storing data on the Internet and using applications." The explanation unit explains the words interpreted by the interpretation unit in user-friendly terms. For example, the explanation unit uses simple language and familiar expressions to provide explanations to the user. The explanation unit can also provide individually optimized explanations by referring to the user's past understanding. For example, the explanation unit can explain the word "influencer" as "a person who influences many people, especially someone who is popular on social media." The learning unit learns the latest vocabulary through an internet connection. For example, the learning unit analyzes online dictionaries and the latest news to quickly learn vocabulary related to new technologies and trends. The learning unit can also provide individually optimized learning content based on the user's past usage history. As a result, the language bridge AI earphones of the embodiment can translate new words and foreign words into easy-to-understand Japanese for the elderly in real time and provide them in an audible format. For example, when a user hears a new word on television or radio, the user can instantly understand its meaning. Furthermore, because the earphones are connected to the internet, they can always keep up with the latest vocabulary. This is expected to improve the quality of life for the elderly.

[0030] The translation unit can analyze words and phrases heard by the user and generate an appropriate translation. For example, the translation unit can analyze words and phrases heard by the user using speech recognition technology and generate an appropriate translation using a real-time translation algorithm. The translation unit can also analyze words and phrases heard by the user and generate an appropriate translation. For example, if the user hears the word "smartphone," the translation unit can translate this as "a type of mobile phone that can use the Internet and apps." This allows the translation unit to analyze words and phrases heard by the user and generate an appropriate translation.

[0031] The interpretation unit can analyze context and gain a deeper understanding of the meaning of words. For example, to analyze context, the interpretation unit refers to past conversation history and related topics and uses a context analysis algorithm to gain a deeper understanding of the meaning of words. The interpretation unit can also customize interpretation results based on the user's interests and provide individually optimized interpretations. For example, if a user hears the word "cloud," the interpretation unit analyzes the context and interprets it as "technology for storing data and using applications on the Internet." This allows the system to analyze context and gain a deeper understanding of the meaning of words.

[0032] The explanation unit can provide explanations in user-friendly terms. For example, the explanation unit provides explanations to the user using simple language and familiar expressions. The explanation unit can also refer to the user's past understanding and provide individually optimized explanations. For example, the word "influencer" can be explained as "someone who influences many people, especially someone who is popular on social media." This allows explanations to be provided in user-friendly terms.

[0033] The learning unit can learn the latest words via an internet connection. For example, the learning unit analyzes online dictionaries and the latest news to quickly learn words related to new technologies and trends. The learning unit can also provide individually optimized learning content based on the user's past usage history. This allows the user to learn the latest words via an internet connection.

[0034] The translation unit can collect user feedback in real time and improve translation accuracy based on that feedback. The translation unit, for example, provides an interface that allows users to provide feedback on the translation result. For example, a button is provided to allow users to rate whether they are satisfied with the translation result, and the evaluation data is collected. The translation unit can also improve translation accuracy using a machine learning algorithm based on the collected feedback data. This makes it possible to improve translation accuracy based on user feedback.

[0035] The translation unit can provide translations that are individually optimized based on the user's past usage history. For example, the translation unit stores the user's past translation history in a database and provides translations that are individually optimized based on that data. For example, translations that have been frequently used in the past are displayed preferentially. The translation unit can also customize translation results based on the user's preferences. This makes it possible to provide translations that are individually optimized based on the user's past usage history.

[0036] The translation unit can visually explain the translation result using not only text but also images and videos. For example, the translation unit adds a function to visually explain the translation result using not only text but also related images and videos. For example, an image of a smartphone is displayed in the translation result of "smartphone." The translation unit can also provide visual explanations using image generation technology and video editing technology. This allows the translation result to be visually explained using not only text but also images and videos.

[0037] The translation unit can make the translation results available in other languages ​​and develop the earphones as multilingual earphones. The translation unit, for example, builds a multilingual translation system to make the translation results available in other languages. For example, it can support multiple languages, including not only Japanese but also English, French, and Chinese. The translation unit can also provide multilingual translations using a language database. This allows the translation results to be available in other languages ​​and develop the earphones as multilingual earphones.

[0038] The interpretation unit can improve the accuracy of context analysis by referring to the user's past conversation history. The interpretation unit, for example, stores the user's past conversation history in a database and builds a system that performs context analysis based on that data. For example, the interpretation unit understands the current context by referring to the content of the past conversation. The interpretation unit can also improve the accuracy of context analysis by using a machine learning algorithm. This makes it possible to improve the accuracy of context analysis by referring to the user's past conversation history.

[0039] The interpretation unit can provide the results of the context analysis in a visual or tactile manner. For example, the interpretation unit develops smart glasses for visually providing the results of the context analysis. For example, the translation results and interpretation results are displayed on the display of the smart glasses. The interpretation unit can also provide the results of the context analysis tactilely using a haptic feedback device. This allows the results of the context analysis to be provided in a visual or tactile manner.

[0040] The interpretation unit can link the results of the context analysis with other devices, enabling use on multiple devices. The interpretation unit, for example, builds a system that links the results of the context analysis with smartphones and tablets. For example, the translation results and interpretation results are displayed on the screen of the smartphone or tablet. The interpretation unit can also link with other devices using API integration or Bluetooth connection. This allows the results of the context analysis to be linked with other devices, enabling use on multiple devices.

[0041] The explanation unit can refer to the user's past level of understanding and provide individually optimized explanations. For example, the explanation unit stores the user's past level of understanding in a database and builds a system that provides individually optimized explanations based on that data. For example, explanations that were easy to understand in the past are given priority. The explanation unit can also customize the explanation results based on the user's preferences. This makes it possible to refer to the user's past level of understanding and provide individually optimized explanations.

[0042] The explanation unit can customize the explanation based on the user's interests and concerns, and provide the explanation in a more interesting format. For example, the explanation unit stores the user's interests and concerns in a database, and builds a system that customizes the explanation based on that data. For example, explanations related to areas in which the user is interested are provided preferentially. The explanation unit can also provide explanations using storytelling and visual aids. This allows the explanation to be customized based on the user's interests and concerns, and provide the explanation in a more interesting format.

[0043] The explanation unit can provide explanations not only in text but also visually using audio and video. The explanation unit adds a function to provide explanations not only in text but also visually using related audio and video. For example, a video showing how to use a smartphone is displayed in the explanation of "smartphone." The explanation unit can also provide explanations visually using image generation technology or video editing technology. This allows explanations to be provided visually using audio and video rather than just text.

[0044] The explanation unit can also provide explanations in other languages, enabling the earphones to be developed as multilingual earphones. The explanation unit, for example, builds a multilingual translation system to provide explanations in other languages. For example, the explanation unit can support multiple languages, including not only Japanese but also English, French, and Chinese. The explanation unit can also provide explanations in multiple languages ​​using a language database. This allows the explanations to be provided in other languages, enabling the earphones to be developed as multilingual earphones.

[0045] The learning unit can provide individually optimized learning content based on the user's past usage history. For example, the learning unit stores the user's past usage history in a database and builds a system that provides individually optimized learning content based on that data. For example, it prioritizes learning words that have been frequently used in the past. The learning unit can also customize learning content based on the user's preferences. This makes it possible to provide individually optimized learning content based on the user's past usage history.

[0046] The learning unit can customize learning content based on the user's interests and concerns, and provide the learning content in a more engaging format. For example, the learning unit stores the user's interests and concerns in a database and builds a system that customizes learning content based on that data. For example, learning content related to areas of interest to the user can be provided preferentially. The learning unit can also provide learning content using storytelling and visual aids. This allows the learning content to be customized based on the user's interests and concerns, and provided in a more engaging format.

[0047] The learning unit can provide learning results not only as text but also visually using audio and video. For example, the learning unit adds a function to provide learning results not only as text but also visually using related audio and video. For example, the learning result for "smartphone" displays a video showing how to use a smartphone. The learning unit can also provide learning results visually using image generation technology and video editing technology. This allows learning results to be provided visually using audio and video rather than just text.

[0048] The learning unit can adapt the learning results to other languages ​​and develop the earphones as multilingual earphones. The learning unit, for example, builds a multilingual translation system to adapt the learning results to other languages. For example, it can adapt not only Japanese but also multiple languages ​​such as English, French, and Chinese. The learning unit can also provide multilingual learning using a language database. This allows the learning results to be adapted to other languages ​​and developed as multilingual earphones.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The Language Bridge AI earphones can monitor the user's health and encourage them to take breaks at appropriate times. For example, sensors built into the earphones measure the user's heart rate and blood pressure, and if any abnormalities are detected, an alert will be issued urging them to take a break. If the user has been using the earphones for an extended period of time, they can also display reminders to take regular breaks. Furthermore, the earphones can store the user's health data in the cloud and cooperate with medical institutions to support health management.

[0051] The Language Bridge AI Earphones can obtain the user's location information and provide information based on the surrounding environment. For example, if the user is in a tourist spot, they can provide information about the history and tourist attractions of that location. If the user is in a shopping mall, they can also notify the user of sales information and recommended products at nearby stores. They can also provide optimal route guidance based on the user's location information.

[0052] The Word Bridge AI earphones can collect the user's exercise data and provide exercise advice. For example, the built-in acceleration sensor in the earphones measures the user's steps and exercise volume, and if it detects a lack of exercise, it will issue an alert to encourage exercise. It can also propose an individually optimized exercise plan based on the user's exercise data. Furthermore, it can store exercise data in the cloud and link with fitness apps to support exercise management.

[0053] The Kotoba no Kakehashi AI earphones can collect the user's sleep data and provide advice to improve the quality of their sleep. For example, sensors built into the earphones can monitor the user's sleep patterns and suggest improvements if the quality of their sleep declines. They can also suggest individually optimized sleeping environments based on the user's sleep data. Furthermore, they can store sleep data in the cloud and collaborate with medical institutions to support sleep management.

[0054] The Language Bridge AI earphones can collect the user's dietary data and provide healthy eating advice. For example, sensors built into the earphones can monitor the user's diet and suggest improvements if there is a nutritional imbalance. They can also propose individually optimized meal plans based on the user's dietary data. Furthermore, they can store dietary data in the cloud and work with nutritionists to support dietary management.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The translation unit translates new words and foreign words in real time. For example, it uses speech recognition technology to analyze what the user hears and then uses an instant translation algorithm to generate an appropriate translation. It can also analyze words and phrases the user hears and generate an appropriate translation. Step 2: The interpretation unit interprets the words translated by the translation unit, taking into account background knowledge and context. For example, it references past conversation history and related topics and uses context analysis algorithms to gain a deeper understanding of the meaning of the words. It can also customize the interpretation results based on the user's interests and concerns, providing an individually optimized interpretation. Step 3: The explanation unit explains the words interpreted by the interpretation unit in friendly terms. For example, it uses simple language and familiar expressions to explain to the user. It can also refer to the user's past understanding and provide individually optimized explanations. Step 4: The learning module learns the latest vocabulary via an internet connection. For example, it analyzes online dictionaries and the latest news to quickly learn vocabulary related to new technologies and trends. It can also provide personalized learning content based on the user's past usage history.

[0057] (Example 2) The Language Bridge AI Earphone according to an embodiment of the present invention is a system that translates new words and foreign words that elderly people encounter in their daily lives into easy-to-understand Japanese in real time and provides them in an audible format. As a result, the Language Bridge AI Earphone can translate new words and foreign words that elderly people encounter in their daily lives into easy-to-understand Japanese in real time and provide them in an audible format.

[0058] According to an embodiment, the language-bridge AI earphones include a translation unit, an interpretation unit, an explanation unit, and a learning unit. The translation unit translates new words and foreign words in real time. For example, the translation unit analyzes words heard by a user using speech recognition technology and generates appropriate translations using an instant translation algorithm. The translation unit can also analyze words and phrases heard by a user and generate appropriate translations. For example, if a user hears the word "smartphone," the translation unit translates it as "a type of mobile phone that can access the Internet and use apps." The interpretation unit interprets the words translated by the translation unit by taking into account background knowledge and context. For example, the interpretation unit references past conversation history and related topics and uses a context analysis algorithm to deeply understand the meaning of the words. The interpretation unit can also customize the interpretation results based on the user's interests and provide individually optimized interpretations. For example, if a user hears the word "cloud," the interpretation unit analyzes the context and interprets it as "a technology for storing data on the Internet and using applications." The explanation unit explains the words interpreted by the interpretation unit in user-friendly terms. For example, the explanation unit uses simple language and familiar expressions to provide explanations to the user. The explanation unit can also provide individually optimized explanations by referring to the user's past understanding. For example, the explanation unit can explain the word "influencer" as "a person who influences many people, especially someone who is popular on social media." The learning unit learns the latest vocabulary through an internet connection. For example, the learning unit analyzes online dictionaries and the latest news to quickly learn vocabulary related to new technologies and trends. The learning unit can also provide individually optimized learning content based on the user's past usage history. As a result, the language bridge AI earphones of the embodiment can translate new words and foreign words into easy-to-understand Japanese for the elderly in real time and provide them in an audible format. For example, when a user hears a new word on television or radio, the user can instantly understand its meaning. Furthermore, because the earphones are connected to the internet, they can always keep up with the latest vocabulary. This is expected to improve the quality of life for the elderly.

[0059] The translation unit can analyze words and phrases heard by the user and generate an appropriate translation. For example, the translation unit can analyze words and phrases heard by the user using speech recognition technology and generate an appropriate translation using a real-time translation algorithm. The translation unit can also analyze words and phrases heard by the user and generate an appropriate translation. For example, if the user hears the word "smartphone," the translation unit can translate this as "a type of mobile phone that can use the Internet and apps." This allows the translation unit to analyze words and phrases heard by the user and generate an appropriate translation.

[0060] The interpretation unit can analyze context and gain a deeper understanding of the meaning of words. For example, to analyze context, the interpretation unit refers to past conversation history and related topics and uses a context analysis algorithm to gain a deeper understanding of the meaning of words. The interpretation unit can also customize interpretation results based on the user's interests and provide individually optimized interpretations. For example, if a user hears the word "cloud," the interpretation unit analyzes the context and interprets it as "technology for storing data and using applications on the Internet." This allows the system to analyze context and gain a deeper understanding of the meaning of words.

[0061] The explanation unit can provide explanations in user-friendly terms. For example, the explanation unit provides explanations to the user using simple language and familiar expressions. The explanation unit can also refer to the user's past understanding and provide individually optimized explanations. For example, the word "influencer" can be explained as "someone who influences many people, especially someone who is popular on social media." This allows explanations to be provided in user-friendly terms.

[0062] The learning unit can learn the latest words via an internet connection. For example, the learning unit analyzes online dictionaries and the latest news to quickly learn words related to new technologies and trends. The learning unit can also provide individually optimized learning content based on the user's past usage history. This allows the user to learn the latest words via an internet connection.

[0063] The translation unit can collect user feedback in real time and improve translation accuracy based on that feedback. The translation unit, for example, provides an interface that allows users to provide feedback on the translation result. For example, a button is provided to allow users to rate whether they are satisfied with the translation result, and the evaluation data is collected. The translation unit can also improve translation accuracy using a machine learning algorithm based on the collected feedback data. This makes it possible to improve translation accuracy based on user feedback.

[0064] The translation unit can provide translations that are individually optimized based on the user's past usage history. For example, the translation unit stores the user's past translation history in a database and provides translations that are individually optimized based on that data. For example, translations that have been frequently used in the past are displayed preferentially. The translation unit can also customize translation results based on the user's preferences. This makes it possible to provide translations that are individually optimized based on the user's past usage history.

[0065] The translation unit can use the emotion estimation function to analyze the emotions a user feels when hearing new words and provide a translation that elicits positive emotions. The translation unit, for example, uses the emotion estimation function to analyze the emotions a user feels when hearing new words in real time. For example, the translation unit analyzes the user's facial expressions and tone of voice to calculate an emotion score. The translation unit can also provide a translation that elicits positive emotions based on the emotion score. This makes it possible to analyze the emotions a user feels when hearing new words and provide a translation that elicits positive emotions.

[0066] The translation unit can visually explain the translation result using not only text but also images and videos. For example, the translation unit adds a function to visually explain the translation result using not only text but also related images and videos. For example, an image of a smartphone is displayed in the translation result of "smartphone." The translation unit can also provide visual explanations using image generation technology and video editing technology. This allows the translation result to be visually explained using not only text but also images and videos.

[0067] The translation unit can make the translation results available in other languages ​​and develop the earphones as multilingual earphones. The translation unit, for example, builds a multilingual translation system to make the translation results available in other languages. For example, it can support multiple languages, including not only Japanese but also English, French, and Chinese. The translation unit can also provide multilingual translations using a language database. This allows the translation results to be available in other languages ​​and develop the earphones as multilingual earphones.

[0068] The translation unit can use the emotion estimation function to monitor in real time the emotions of a user when hearing a new word and provide a translation that corresponds to the emotion. For example, the translation unit develops a system that uses the emotion estimation function to monitor in real time the emotions of a user when hearing a new word. For example, the translation unit analyzes the user's facial expressions and tone of voice to calculate an emotion score. The translation unit can also provide a translation that corresponds to the emotion based on the emotion score. This allows the translation unit to monitor in real time the emotions of a user when hearing a new word and provide a translation that corresponds to the emotion.

[0069] The interpretation unit can improve the accuracy of context analysis by referring to the user's past conversation history. The interpretation unit, for example, stores the user's past conversation history in a database and builds a system that performs context analysis based on that data. For example, the interpretation unit understands the current context by referring to the content of the past conversation. The interpretation unit can also improve the accuracy of context analysis by using a machine learning algorithm. This makes it possible to improve the accuracy of context analysis by referring to the user's past conversation history.

[0070] The interpretation unit can use the emotion estimation function to analyze what emotion a user has in a specific context and provide an interpretation according to that emotion. The interpretation unit, for example, develops a system that uses the emotion estimation function to analyze in real time what emotion a user has in a specific context. For example, the interpretation unit analyzes the user's facial expression and tone of voice to calculate an emotion score. The interpretation unit can also provide an interpretation according to the emotion based on the emotion score. This makes it possible to analyze what emotion a user has in a specific context and provide an interpretation according to that emotion.

[0071] The interpretation unit can provide the results of the context analysis in a visual or tactile manner. For example, the interpretation unit develops smart glasses for visually providing the results of the context analysis. For example, the translation results and interpretation results are displayed on the display of the smart glasses. The interpretation unit can also provide the results of the context analysis tactilely using a haptic feedback device. This allows the results of the context analysis to be provided in a visual or tactile manner.

[0072] The interpretation unit can link the results of the context analysis with other devices, enabling use on multiple devices. The interpretation unit, for example, builds a system that links the results of the context analysis with smartphones and tablets. For example, the translation results and interpretation results are displayed on the screen of the smartphone or tablet. The interpretation unit can also link with other devices using API integration or Bluetooth connection. This allows the results of the context analysis to be linked with other devices, enabling use on multiple devices.

[0073] The interpretation unit can use the emotion estimation function to monitor in real time what emotions a user feels in a specific context and provide an interpretation according to that emotion. The interpretation unit, for example, develops a system that uses the emotion estimation function to monitor in real time what emotions a user feels in a specific context. For example, the interpretation unit analyzes the user's facial expressions and tone of voice to calculate an emotion score. The interpretation unit can also provide an interpretation according to the emotion based on the emotion score. This makes it possible to monitor in real time what emotions a user feels in a specific context and provide an interpretation according to that emotion.

[0074] The explanation unit can refer to the user's past level of understanding and provide individually optimized explanations. For example, the explanation unit stores the user's past level of understanding in a database and builds a system that provides individually optimized explanations based on that data. For example, explanations that were easy to understand in the past are given priority. The explanation unit can also customize the explanation results based on the user's preferences. This makes it possible to refer to the user's past level of understanding and provide individually optimized explanations.

[0075] The explanation unit can customize the explanation based on the user's interests and concerns, and provide the explanation in a more interesting format. For example, the explanation unit stores the user's interests and concerns in a database, and builds a system that customizes the explanation based on that data. For example, explanations related to areas in which the user is interested are provided preferentially. The explanation unit can also provide explanations using storytelling and visual aids. This allows the explanation to be customized based on the user's interests and concerns, and provide the explanation in a more interesting format.

[0076] The explanation unit can use the emotion estimation function to analyze the emotions felt by the user when listening to an explanation and provide an explanation that elicits positive emotions. The explanation unit, for example, develops a system that uses the emotion estimation function to analyze the emotions felt by the user when listening to an explanation in real time. For example, the explanation unit analyzes the user's facial expressions and tone of voice to calculate an emotion score. The explanation unit can also provide an explanation that elicits positive emotions based on the emotion score. This makes it possible to analyze the emotions felt by the user when listening to an explanation and provide an explanation that elicits positive emotions.

[0077] The explanation unit can provide explanations not only in text but also visually using audio and video. The explanation unit adds a function to provide explanations not only in text but also visually using related audio and video. For example, a video showing how to use a smartphone is displayed in the explanation of "smartphone." The explanation unit can also provide explanations visually using image generation technology or video editing technology. This allows explanations to be provided visually using audio and video rather than just text.

[0078] The explanation unit can also provide explanations in other languages, enabling the earphones to be developed as multilingual earphones. The explanation unit, for example, builds a multilingual translation system to provide explanations in other languages. For example, the explanation unit can support multiple languages, including not only Japanese but also English, French, and Chinese. The explanation unit can also provide explanations in multiple languages ​​using a language database. This allows the explanations to be provided in other languages, enabling the earphones to be developed as multilingual earphones.

[0079] The explanation unit can use the emotion estimation function to monitor the emotion of the user when listening to an explanation in real time and provide an explanation that corresponds to the emotion. For example, the explanation unit develops a system that uses the emotion estimation function to monitor the emotion of the user when listening to an explanation in real time. For example, the explanation unit analyzes the user's facial expression and tone of voice to calculate an emotion score. The explanation unit can also provide an explanation that corresponds to the emotion based on the emotion score. This makes it possible to monitor the emotion of the user when listening to an explanation in real time and provide an explanation that corresponds to the emotion.

[0080] The learning unit can provide individually optimized learning content based on the user's past usage history. For example, the learning unit stores the user's past usage history in a database and builds a system that provides individually optimized learning content based on that data. For example, it prioritizes learning words that have been frequently used in the past. The learning unit can also customize learning content based on the user's preferences. This makes it possible to provide individually optimized learning content based on the user's past usage history.

[0081] The learning unit can customize learning content based on the user's interests and concerns, and provide the learning content in a more engaging format. For example, the learning unit stores the user's interests and concerns in a database and builds a system that customizes learning content based on that data. For example, learning content related to areas of interest to the user can be provided preferentially. The learning unit can also provide learning content using storytelling and visual aids. This allows the learning content to be customized based on the user's interests and concerns, and provided in a more engaging format.

[0082] The learning unit can use the emotion estimation function to analyze the emotions of a user when learning new words and provide learning content that elicits positive emotions. The learning unit, for example, develops a system that uses the emotion estimation function to analyze the emotions of a user when learning new words in real time. For example, the learning unit analyzes the user's facial expressions and voice tone to calculate an emotion score. The learning unit can also provide learning content that elicits positive emotions based on the emotion score. This makes it possible to analyze the emotions of a user when learning new words and provide learning content that elicits positive emotions.

[0083] The learning unit can provide learning results not only as text but also visually using audio and video. For example, the learning unit adds a function to provide learning results not only as text but also visually using related audio and video. For example, the learning result for "smartphone" displays a video showing how to use a smartphone. The learning unit can also provide learning results visually using image generation technology and video editing technology. This allows learning results to be provided visually using audio and video rather than just text.

[0084] The learning unit can adapt the learning results to other languages ​​and develop the earphones as multilingual earphones. The learning unit, for example, builds a multilingual translation system to adapt the learning results to other languages. For example, it can adapt not only Japanese but also multiple languages ​​such as English, French, and Chinese. The learning unit can also provide multilingual learning using a language database. This allows the learning results to be adapted to other languages ​​and developed as multilingual earphones.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The Language Bridge AI earphones can monitor the user's health and encourage them to take breaks at appropriate times. For example, sensors built into the earphones measure the user's heart rate and blood pressure, and if any abnormalities are detected, an alert will be issued urging them to take a break. If the user has been using the earphones for an extended period of time, they can also display reminders to take regular breaks. Furthermore, the earphones can store the user's health data in the cloud and cooperate with medical institutions to support health management.

[0087] The Kotoba Bridge AI earphones can estimate the user's emotions and provide relaxation music to reduce stress. For example, if it estimates that the user is feeling stressed, it will automatically play relaxation music. It can also provide meditation guides and deep breathing instructions depending on the user's emotions. It can also analyze the user's emotional data, identify the cause of stress, and suggest countermeasures.

[0088] The Language Bridge AI Earphones can obtain the user's location information and provide information based on the surrounding environment. For example, if the user is in a tourist spot, they can provide information about the history and tourist attractions of that location. If the user is in a shopping mall, they can also notify the user of sales information and recommended products at nearby stores. They can also provide optimal route guidance based on the user's location information.

[0089] The Language Bridge AI earphones can estimate the user's emotions and provide news and content that reflects their emotions. For example, if the user is estimated to be relaxed, they can provide positive news and entertainment content. If the user is estimated to be sad, they can provide encouraging messages and inspiring stories. Furthermore, they can analyze the user's emotional data and suggest individually optimized content.

[0090] The Word Bridge AI earphones can collect the user's exercise data and provide exercise advice. For example, the built-in acceleration sensor in the earphones measures the user's steps and exercise volume, and if it detects a lack of exercise, it will issue an alert to encourage exercise. It can also propose an individually optimized exercise plan based on the user's exercise data. Furthermore, it can store exercise data in the cloud and link with fitness apps to support exercise management.

[0091] The Language Bridge AI Earphones can estimate the user's emotions and provide learning content that matches their emotions. For example, if it estimates that the user is concentrating, it can provide more difficult learning content. If it estimates that the user is tired, it can provide relaxing learning content. It can also analyze the user's emotional data and provide feedback according to their learning progress.

[0092] The Kotoba no Kakehashi AI earphones can collect the user's sleep data and provide advice to improve the quality of their sleep. For example, sensors built into the earphones can monitor the user's sleep patterns and suggest improvements if the quality of their sleep declines. They can also suggest individually optimized sleeping environments based on the user's sleep data. Furthermore, they can store sleep data in the cloud and collaborate with medical institutions to support sleep management.

[0093] The Language Bridge AI Earphones can estimate the user's emotions and provide feedback according to their emotions. For example, if it estimates that the user is happy, it will provide positive feedback. If it estimates that the user is feeling anxious, it can provide reassuring feedback. It can also analyze the user's emotional data and provide feedback according to changes in their emotions.

[0094] The Language Bridge AI earphones can collect the user's dietary data and provide healthy eating advice. For example, sensors built into the earphones can monitor the user's diet and suggest improvements if there is a nutritional imbalance. They can also propose individually optimized meal plans based on the user's dietary data. Furthermore, they can store dietary data in the cloud and work with nutritionists to support dietary management.

[0095] The Language Bridge AI Earphones can estimate the user's emotions and provide reminders based on those emotions. For example, if it estimates that the user is feeling stressed, it can provide a reminder to relax. If it estimates that the user is concentrating, it can also provide a reminder for an important task. Furthermore, it can analyze the user's emotional data and provide reminders based on changes in emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The translation unit translates new words and foreign words in real time. For example, it uses speech recognition technology to analyze what the user hears and then uses an instant translation algorithm to generate an appropriate translation. It can also analyze words and phrases the user hears and generate an appropriate translation. Step 2: The interpretation unit interprets the words translated by the translation unit, taking into account background knowledge and context. For example, it references past conversation history and related topics and uses context analysis algorithms to gain a deeper understanding of the meaning of the words. It can also customize the interpretation results based on the user's interests and concerns, providing an individually optimized interpretation. Step 3: The explanation unit explains the words interpreted by the interpretation unit in friendly terms. For example, it uses simple language and familiar expressions to explain to the user. It can also refer to the user's past understanding and provide individually optimized explanations. Step 4: The learning module learns the latest vocabulary via an internet connection. For example, it analyzes online dictionaries and the latest news to quickly learn vocabulary related to new technologies and trends. It can also provide personalized learning content based on the user's past usage history.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0134] 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.

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

[0136] 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).

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 translation department that translates new words and foreign words in real time, an interpretation unit that interprets the words translated by the translation unit in consideration of background knowledge and context; an explanation unit that explains the words interpreted by the interpretation unit in friendly terms; a learning unit that learns the latest words through an internet connection; A system characterized by:

2. The translation unit Explain translation results visually using images and videos in addition to text 2. The system of claim 1.

3. The interpretation unit Refer to the user's past conversation history to improve the accuracy of contextual analysis 2. The system of claim 1.

4. The explanation section Refer to the user's past understanding and provide an individually optimized explanation 2. The system of claim 1.

5. The learning unit Provides personalized learning content based on the user's past usage history 2. The system of claim 1.

6. The translation unit Analyzing the emotions a user feels when hearing new words and providing translations that evoke positive emotions 2. The system of claim 1.

7. The interpretation unit Analyzing the emotions that a user feels in a particular context and providing the interpretation according to the emotions 2. The system of claim 1.

8. The explanation section Analyzing the emotions felt by the user when listening to the explanation, and providing the explanation that elicits positive emotions 2. The system of claim 1.

Images