system

Abstract

The system according to this embodiment aims to translate in real time into language that is easy for elderly people to understand. [Solution] The system according to the embodiment comprises a collection unit, a translation unit, and a provision unit. The collection unit grasps the user's knowledge level and the words they use. The translation unit translates the incoming words based on the information grasped by the collection unit. The provision unit provides the words translated by the translation unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult for elderly people to understand new words, foreign words, and youth language.

[0005] ​​​​​​​​​​​​​

[0007] The system according to this embodiment can translate in real time into language that is easy for elderly people to understand. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent earphone system according to an embodiment of the present invention is a system that translates new words, loanwords, and slang used in daily life into expressions that are easy for elderly people to understand in real time. This system provides support tailored to the user's everyday language use and level of understanding, enabling them to absorb new knowledge stress-free. For example, a user wears earphones and engages in casual conversation with a conversational AI. Through this conversation, the AI ​​grasps the user's knowledge level and the words they use. Next, the generating AI understands the context of the words received through the earphones and provides the user with an appropriate translation. For example, if a conversation includes new words, loanwords, or slang, the generating AI translates these words into expressions that are easy for elderly people to understand in real time. This mechanism allows elderly people to enjoy free communication in all aspects of their lives without struggling to understand new words or information. Furthermore, deep learning and visual support provide optimal translations tailored to each user, facilitating the absorption of new information. As a result, the AI ​​agent earphone system can provide appropriate translations based on the user's knowledge level and the words they use.

[0029] The AI ​​agent earphone system according to this embodiment comprises a collection unit, a translation unit, and a provision unit. The collection unit grasps the user's level of knowledge and the words they use. For example, the collection unit collects words, technical terms, and slang that the user uses on a daily basis. The collection unit can use test results, self-reports, and past usage history to measure the user's level of knowledge. For example, the collection unit analyzes the frequency of words the user has used in the past to grasp the user's level of knowledge. The collection unit can also identify words that the user finds difficult to understand and collect data to provide appropriate translations. The translation unit translates the words that are received based on the information grasped by the collection unit. The translation unit uses generative AI to understand the context and provide appropriate translations. For example, the translation unit uses generative AI to translate new words, loanwords, and youth slang into expressions that are easy for the elderly to understand in real time. The generative AI can use text generation AI (e.g., LLM) to understand the context of a sentence and generate an appropriate translation. The translation unit can also use generative AI to extract important parts of a sentence and provide a concise translation. The providing unit provides the words translated by the translation unit. The providing unit can provide translations according to the user's walking speed and the orientation of the smartphone. For example, the providing unit adjusts the translation speed according to the user's walking speed. The providing unit can also adjust the way the translation is displayed according to the orientation of the user's smartphone. As a result, the AI ​​agent earphone system according to this embodiment can provide appropriate translations based on the user's knowledge and the words they use.

[0030] The data collection unit understands users' knowledge levels and vocabulary. Specifically, it monitors users' conversations, text messages, and social media posts to collect everyday vocabulary, jargon, and slang. This allows it to understand which words users use frequently and analyze their language habits in detail. Furthermore, to measure users' knowledge levels, the data collection unit collects results from online tests, quizzes, and surveys, and also refers to users' self-reports and past usage history. For example, it analyzes the frequency and comprehension of jargon to determine whether a user has knowledge in a particular field. The data collection unit also monitors the frequency of user responses and questions to identify words that users find difficult to understand, collecting data to provide appropriate translations. This allows the data collection unit to gain a detailed understanding of users' language habits and knowledge levels, helping the translation unit provide more accurate and appropriate translations. In addition, the data collection unit tracks changes in users' language habits and regularly updates the data to ensure that translations are always based on the latest information. For example, when new slang or trendy words emerge, they are quickly collected and provided to the translation unit to ensure users understand the latest language. This allows the data collection unit to grasp the user's language habits and knowledge level in real time, improving the overall accuracy and reliability of the system.

[0031] The translation department translates incoming words based on information gathered by the data collection department. Specifically, the translation department uses generative AI to understand the context and provide appropriate translations. Generative AI can understand the context of a text and generate appropriate translations using text generation AI (e.g., LLM). For example, the translation department can use generative AI to translate new words, loanwords, and slang into expressions that are easy for older people to understand in real time. Generative AI utilizes natural language processing technology to accurately grasp the meaning and intent of a text and generate the most appropriate translation according to the context. The translation department can also use generative AI to extract important parts of a text and provide a concise translation. For example, it can extract important information from long emails or articles and present it in a way that is easy for users to understand. Furthermore, the translation department can adjust the style and tone of the translation according to the user's language habits and knowledge level. For example, it can provide easy-to-understand translations while maintaining specialized expressions for users who frequently use technical terms, and provide translations in simpler language for general users. In this way, the translation department can provide flexible translations that meet user needs and aid user understanding. Furthermore, the translation department can collect user feedback and continuously improve the accuracy and quality of translations. For example, user ratings and comments on translations can be used as training data for the generative AI, improving translation accuracy. This allows the translation department to consistently provide high-quality translations based on the latest information and technology, thereby increasing user satisfaction.

[0032] The service provider delivers the translated text. Specifically, the service provider can provide translations tailored to the user's walking speed and smartphone orientation. For example, the service provider adjusts the translation speed to match the user's walking speed. If the user is walking quickly, it provides short, concise translations; if they are walking slowly, it provides detailed translations. The service provider can also adjust how the translation is displayed according to the user's smartphone orientation. For example, if the smartphone is in portrait mode, it displays the translation in an easy-to-scroll format; if it is in landscape mode, it utilizes the wider screen to display a more detailed translation. Furthermore, the service provider can provide translations in the most optimal way depending on the user's environment and situation. For example, in noisy places, it avoids providing voice translations and prioritizes text display. The service provider can also customize the font size, color, and display position of the translation according to the user's preferences. This allows the service provider to provide translations in the most visually appealing and easy-to-understand format for the user. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its delivery methods. For example, users can provide ratings and comments on the translation display method, and the service provider can use that information to optimize its delivery methods. This allows the service provider to offer users quick and accurate translations, thereby increasing user satisfaction.

[0033] The translation department can understand context using generative AI and provide appropriate translations. For example, the generative AI can translate new words, loanwords, and slang into expressions that are easy for older people to understand in real time. The generative AI can use text generation AI (e.g., LLM) to understand the context of a text and generate an appropriate translation. The translation department can also use the generative AI to extract important parts of a text and provide a concise translation. In this way, the generative AI can understand the context and provide an appropriate translation.

[0034] The translation unit may include a speed adjustment unit that provides translations according to the user's walking speed. For example, the translation unit adjusts the translation speed according to the user's walking speed. The translation unit can measure the user's walking speed using the smartphone's accelerometer and GPS data. For example, if the user is walking fast, the translation unit will provide translations at a rapid speed. Conversely, if the user is walking slowly, the translation unit can provide translations at a slower speed. By providing translations according to the user's walking speed, a more natural translation can be provided.

[0035] The translation delivery unit may include an orientation adjustment unit that provides translations according to the orientation of the user's smartphone. For example, the delivery unit adjusts the display method of the translation according to the orientation of the user's smartphone. The delivery unit can detect the orientation of the smartphone using a gyroscope or accelerometer. For example, if the user is using the smartphone vertically, the delivery unit will provide the translation vertically. Also, if the user is using the smartphone horizontally, the delivery unit can provide the translation horizontally. By providing translations according to the orientation of the user's smartphone, it is possible to provide more user-friendly translations.

[0036] The data collection unit can analyze the user's past conversation history and select the optimal information collection method. For example, the unit can prioritize collecting words that the user has frequently used in the past. It can also avoid words that the user found difficult to understand in the past and collect simpler expressions. Furthermore, the unit can collect information related to topics that the user has shown interest in in the past. In this way, by analyzing the user's past conversation history, the optimal information collection method can be selected.

[0037] The data collection unit can filter data based on the user's current activities and areas of interest. For example, if the user is taking a walk, the unit can collect information about nature and health. If the user is cooking, the unit can also collect information about recipes and ingredients. If the user is reading, the unit can also collect information about relevant books and authors. This allows the system to provide more relevant information by filtering data based on the user's current activities and areas of interest.

[0038] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a tourist area, the unit can collect tourist information for that area. If the user is at home, the unit can also collect information about nearby events. Furthermore, if the user is on a business trip, the unit can collect business information for their destination. By prioritizing the collection of highly relevant information while considering the user's geographical location, the unit can provide more appropriate information.

[0039] The data collection unit can analyze a user's social media activity and collect relevant information during the collection process. For example, the unit can collect posts from accounts that the user follows on social media. The unit can also collect information related to topics that the user has shown interest in on social media. Furthermore, the unit can collect information about the activities of groups that the user participates in on social media. This allows the system to provide relevant information by analyzing the user's social media activity.

[0040] The translation unit can adjust the level of detail in the translation based on the importance of the words. For example, it can translate important words in detail and add supplementary explanations. It can also translate less important words concisely. Furthermore, it can emphasize highly important words. By adjusting the level of detail based on the importance of the words, it can provide a more appropriate translation.

[0041] The translation system can apply different translation algorithms depending on the category of words used during translation. For example, it can apply a specialized translation algorithm to technical terms, a casual translation algorithm to everyday conversations, and a precise translation algorithm to medical terms. By applying different translation algorithms depending on the category of words, it can provide more appropriate translations.

[0042] The translation department can prioritize translations based on word frequency. For example, it will prioritize frequently used words, and postpone less frequently used words. It can also emphasize frequently used words. By prioritizing translations based on word frequency, it can provide more accurate translations.

[0043] The translation unit can adjust the order of translations based on the relevance of words during the translation process. For example, it can translate highly relevant words consecutively, while translating less relevant words with gaps in between. It can also emphasize highly relevant words during translation. This allows for more appropriate translations by adjusting the order of translations based on word relevance.

[0044] The service provider can analyze the user's past translation history to select the most suitable delivery method at the time of delivery. For example, the service provider can prioritize providing translation methods that the user has preferred in the past. The service provider can also select translation methods that the user found easy to understand in the past. Furthermore, the service provider can suggest the most suitable delivery method based on the user's past translation history. In this way, the service provider can select the most suitable delivery method by analyzing the user's past translation history.

[0045] The service provider can customize the delivery method based on the user's current activity at the time of delivery. For example, if the user is exercising, the service provider can provide a concise and easy-to-read translation. If the user is taking a break, the service provider can also provide a detailed translation. Furthermore, if the user is in a meeting, the service provider can provide a concise translation that gets to the point. This allows for the provision of more appropriate translations by customizing the delivery method based on the user's current activity.

[0046] The service provider can customize the delivery method based on the user's current activity at the time of delivery. For example, if the user is exercising, the service provider can provide a concise and easy-to-read translation. If the user is taking a break, the service provider can also provide a detailed translation. Furthermore, if the user is in a meeting, the service provider can provide a concise translation that gets to the point. This allows for the provision of more appropriate translations by customizing the delivery method based on the user's current activity.

[0047] The service provider can select the most appropriate delivery method at the time of delivery, taking into account the user's geographical location. For example, if the user is in a tourist area, the service provider can prioritize providing information about that area. If the user is at home, the service provider can also prioritize providing information about the nearby area. Furthermore, if the user is on a business trip, the service provider can prioritize providing information about their business trip destination. By selecting the most appropriate delivery method considering the user's geographical location, the service provider can provide more accurate translations.

[0048] The service provider can analyze the user's social media activity and propose a suitable delivery method at the time of delivery. For example, the service provider can prioritize providing information on accounts the user follows on social media. The service provider can also provide information related to topics the user has shown interest in on social media. Furthermore, the service provider can provide information on groups the user participates in on social media. By analyzing the user's social media activity, the service provider can propose a more appropriate delivery method.

[0049] The speed adjustment unit can analyze the user's walking pattern and select the optimal speed during speed adjustment. For example, if the user is walking fast, the speed adjustment unit will provide translation at a rapid speed. If the user is walking slowly, the speed adjustment unit can also provide translation at a slow speed. Furthermore, if the user is standing still, the speed adjustment unit can provide translation at a normal speed. In this way, by analyzing the user's walking pattern, the system can provide translation at the optimal speed.

[0050] The speed adjustment unit can select the optimal speed by considering the user's geographical location information during speed adjustment. For example, if the user is in a crowded place, the speed adjustment unit will provide translation at a slow speed. If the user is in a spacious area, the speed adjustment unit can also provide translation at a normal speed. Furthermore, if the user is in a quiet place, the speed adjustment unit can provide translation at a moderate speed. By selecting the optimal speed while considering the user's geographical location information, a more appropriate translation can be provided.

[0051] The orientation adjustment unit can analyze the user's smartphone usage patterns and select the optimal orientation during orientation adjustment. For example, if the user is using the smartphone vertically, the orientation adjustment unit will provide translation in portrait orientation. If the user is using the smartphone horizontally, the orientation adjustment unit can also provide translation in landscape orientation. Furthermore, if the user frequently rotates the smartphone, the orientation adjustment unit can appropriately adjust the orientation and provide translation. In this way, by analyzing the user's smartphone usage patterns, the unit can provide translation in the optimal orientation.

[0052] The orientation adjustment unit can select the optimal orientation by considering the user's device information during orientation adjustment. For example, if the user is using a smartphone, the orientation adjustment unit will provide translations according to the smartphone's orientation. If the user is using a tablet, the orientation adjustment unit can also provide translations according to the tablet's orientation. Furthermore, if the user is using a smartwatch, the orientation adjustment unit can also provide translations according to the smartwatch's orientation. By selecting the optimal orientation while considering the user's device information, it is possible to provide more appropriate translations.

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

[0054] The data collection unit can analyze the user's past conversation history and select the optimal information collection method. For example, it can prioritize collecting words that the user has frequently used in the past. It can also avoid words that the user found difficult to understand in the past and collect simpler expressions. Furthermore, it can collect information related to topics that the user has shown interest in in the past. In this way, by analyzing the user's past conversation history, the optimal information collection method can be selected.

[0055] The data collection unit can filter data based on the user's current activities and areas of interest. For example, if the user is taking a walk, it can collect information about nature and health. If the user is cooking, it can collect information about recipes and ingredients. If the user is reading, it can collect information about relevant books and authors. By filtering data based on the user's current activities and areas of interest, the system can provide more relevant information.

[0056] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a tourist area, it can collect tourist information for that area. If the user is at home, it can also collect information about nearby events. Furthermore, if the user is on a business trip, it can collect business information for their destination. By prioritizing the collection of highly relevant information while considering the user's geographical location, the system can provide more appropriate information.

[0057] The data collection unit analyzes the user's social media activity during collection and can collect relevant information. For example, it can collect posts from accounts the user follows on social media. It can also collect information related to topics the user has shown interest in on social media. Furthermore, it can collect information about the activities of groups the user participates in on social media. This allows the system to provide relevant information by analyzing the user's social media activity.

[0058] The translation department can adjust the level of detail in translations based on the importance of the words. For example, important words can be translated in detail with supplementary explanations, while less important words can be translated concisely. Furthermore, highly important words can be emphasized. This allows for more accurate translations by adjusting the level of detail based on the importance of the words.

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

[0060] Step 1: The data collection unit understands the user's knowledge level and vocabulary. The unit collects words, jargon, and slang that the user uses on a daily basis. Furthermore, the unit uses test results, self-reports, and past usage history to measure the user's knowledge level. For example, the unit analyzes the frequency of words the user has used in the past to understand the user's knowledge level. The unit can also collect data to identify words that the user has difficulty understanding and provide appropriate translations. Step 2: The translation unit translates the incoming words based on the information gathered by the data collection unit. The translation unit uses generative AI to understand the context and provide an appropriate translation. For example, the translation unit uses generative AI to translate new words, loanwords, and slang into expressions that are easy for older people to understand in real time. The generative AI can use text generation AI (e.g., LLM) to understand the context of a sentence and generate an appropriate translation. The translation unit can also use generative AI to extract the important parts of a sentence and provide a concise translation. Step 3: The provider unit provides the words translated by the translation unit. The provider unit can provide translations according to the user's walking speed and smartphone orientation. For example, the provider unit adjusts the translation speed to match the user's walking speed. The provider unit can also adjust how the translation is displayed according to the user's smartphone orientation.

[0061] (Example of form 2) The AI ​​agent earphone system according to an embodiment of the present invention is a system that translates new words, loanwords, and slang used in daily life into expressions that are easy for elderly people to understand in real time. This system provides support tailored to the user's everyday language use and level of understanding, enabling them to absorb new knowledge stress-free. For example, a user wears earphones and engages in casual conversation with a conversational AI. Through this conversation, the AI ​​grasps the user's knowledge level and the words they use. Next, the generating AI understands the context of the words received through the earphones and provides the user with an appropriate translation. For example, if a conversation includes new words, loanwords, or slang, the generating AI translates these words into expressions that are easy for elderly people to understand in real time. This mechanism allows elderly people to enjoy free communication in all aspects of their lives without struggling to understand new words or information. Furthermore, deep learning and visual support provide optimal translations tailored to each user, facilitating the absorption of new information. As a result, the AI ​​agent earphone system can provide appropriate translations based on the user's knowledge level and the words they use.

[0062] The AI ​​agent earphone system according to this embodiment comprises a collection unit, a translation unit, and a provision unit. The collection unit grasps the user's level of knowledge and the words they use. For example, the collection unit collects words, technical terms, and slang that the user uses on a daily basis. The collection unit can use test results, self-reports, and past usage history to measure the user's level of knowledge. For example, the collection unit analyzes the frequency of words the user has used in the past to grasp the user's level of knowledge. The collection unit can also identify words that the user finds difficult to understand and collect data to provide appropriate translations. The translation unit translates the words that are received based on the information grasped by the collection unit. The translation unit uses generative AI to understand the context and provide appropriate translations. For example, the translation unit uses generative AI to translate new words, loanwords, and youth slang into expressions that are easy for the elderly to understand in real time. The generative AI can use text generation AI (e.g., LLM) to understand the context of a sentence and generate an appropriate translation. The translation unit can also use generative AI to extract important parts of a sentence and provide a concise translation. The providing unit provides the words translated by the translation unit. The providing unit can provide translations according to the user's walking speed and the orientation of the smartphone. For example, the providing unit adjusts the translation speed according to the user's walking speed. The providing unit can also adjust the way the translation is displayed according to the orientation of the user's smartphone. As a result, the AI ​​agent earphone system according to this embodiment can provide appropriate translations based on the user's knowledge and the words they use.

[0063] The data collection unit understands users' knowledge levels and vocabulary. Specifically, it monitors users' conversations, text messages, and social media posts to collect everyday vocabulary, jargon, and slang. This allows it to understand which words users use frequently and analyze their language habits in detail. Furthermore, to measure users' knowledge levels, the data collection unit collects results from online tests, quizzes, and surveys, and also refers to users' self-reports and past usage history. For example, it analyzes the frequency and comprehension of jargon to determine whether a user has knowledge in a particular field. The data collection unit also monitors the frequency of user responses and questions to identify words that users find difficult to understand, collecting data to provide appropriate translations. This allows the data collection unit to gain a detailed understanding of users' language habits and knowledge levels, helping the translation unit provide more accurate and appropriate translations. In addition, the data collection unit tracks changes in users' language habits and regularly updates the data to ensure that translations are always based on the latest information. For example, when new slang or trendy words emerge, they are quickly collected and provided to the translation unit to ensure users understand the latest language. This allows the data collection unit to grasp the user's language habits and knowledge level in real time, improving the overall accuracy and reliability of the system.

[0064] The translation department translates incoming words based on information gathered by the data collection department. Specifically, the translation department uses generative AI to understand the context and provide appropriate translations. Generative AI can understand the context of a text and generate appropriate translations using text generation AI (e.g., LLM). For example, the translation department can use generative AI to translate new words, loanwords, and slang into expressions that are easy for older people to understand in real time. Generative AI utilizes natural language processing technology to accurately grasp the meaning and intent of a text and generate the most appropriate translation according to the context. The translation department can also use generative AI to extract important parts of a text and provide a concise translation. For example, it can extract important information from long emails or articles and present it in a way that is easy for users to understand. Furthermore, the translation department can adjust the style and tone of the translation according to the user's language habits and knowledge level. For example, it can provide easy-to-understand translations while maintaining specialized expressions for users who frequently use technical terms, and provide translations in simpler language for general users. In this way, the translation department can provide flexible translations that meet user needs and aid user understanding. Furthermore, the translation department can collect user feedback and continuously improve the accuracy and quality of translations. For example, user ratings and comments on translations can be used as training data for the generative AI, improving translation accuracy. This allows the translation department to consistently provide high-quality translations based on the latest information and technology, thereby increasing user satisfaction.

[0065] The service provider delivers the translated text. Specifically, the service provider can provide translations tailored to the user's walking speed and smartphone orientation. For example, the service provider adjusts the translation speed to match the user's walking speed. If the user is walking quickly, it provides short, concise translations; if they are walking slowly, it provides detailed translations. The service provider can also adjust how the translation is displayed according to the user's smartphone orientation. For example, if the smartphone is in portrait mode, it displays the translation in an easy-to-scroll format; if it is in landscape mode, it utilizes the wider screen to display a more detailed translation. Furthermore, the service provider can provide translations in the most optimal way depending on the user's environment and situation. For example, in noisy places, it avoids providing voice translations and prioritizes text display. The service provider can also customize the font size, color, and display position of the translation according to the user's preferences. This allows the service provider to provide translations in the most visually appealing and easy-to-understand format for the user. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its delivery methods. For example, users can provide ratings and comments on the translation display method, and the service provider can use that information to optimize its delivery methods. This allows the service provider to offer users quick and accurate translations, thereby increasing user satisfaction.

[0066] The translation department can understand context using generative AI and provide appropriate translations. For example, the generative AI can translate new words, loanwords, and slang into expressions that are easy for older people to understand in real time. The generative AI can use text generation AI (e.g., LLM) to understand the context of a text and generate an appropriate translation. The translation department can also use the generative AI to extract important parts of a text and provide a concise translation. In this way, the generative AI can understand the context and provide an appropriate translation.

[0067] The translation unit may include a speed adjustment unit that provides translations according to the user's walking speed. For example, the translation unit adjusts the translation speed according to the user's walking speed. The translation unit can measure the user's walking speed using the smartphone's accelerometer and GPS data. For example, if the user is walking fast, the translation unit will provide translations at a rapid speed. Conversely, if the user is walking slowly, the translation unit can provide translations at a slower speed. By providing translations according to the user's walking speed, a more natural translation can be provided.

[0068] The translation delivery unit may include an orientation adjustment unit that provides translations according to the orientation of the user's smartphone. For example, the delivery unit adjusts the display method of the translation according to the orientation of the user's smartphone. The delivery unit can detect the orientation of the smartphone using a gyroscope or accelerometer. For example, if the user is using the smartphone vertically, the delivery unit will provide the translation vertically. Also, if the user is using the smartphone horizontally, the delivery unit can provide the translation horizontally. By providing translations according to the orientation of the user's smartphone, it is possible to provide more user-friendly translations.

[0069] The data collection unit can estimate the user's emotions and adjust the type of information it collects based on those emotions. For example, if the user is stressed, the unit will prioritize collecting information that helps them relax. If the user is excited, the unit can also collect interesting new words and trending information. If the user is depressed, the unit can also collect information that includes words of encouragement and comfort. By adjusting the type of information collected based on the user's emotions, the system can provide more relevant information. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0070] The data collection unit can analyze the user's past conversation history and select the optimal information collection method. For example, the unit can prioritize collecting words that the user has frequently used in the past. It can also avoid words that the user found difficult to understand in the past and collect simpler expressions. Furthermore, the unit can collect information related to topics that the user has shown interest in in the past. In this way, by analyzing the user's past conversation history, the optimal information collection method can be selected.

[0071] The data collection unit can filter data based on the user's current activities and areas of interest. For example, if the user is taking a walk, the unit can collect information about nature and health. If the user is cooking, the unit can also collect information about recipes and ingredients. If the user is reading, the unit can also collect information about relevant books and authors. This allows the system to provide more relevant information by filtering data based on the user's current activities and areas of interest.

[0072] The data collection unit can estimate the user's emotions and prioritize the information to collect based on those emotions. For example, if the user is tired, the data collection unit will prioritize collecting information that promotes relaxation. If the user is excited, the data collection unit may also prioritize collecting stimulating information. Furthermore, if the user is calm, the data collection unit may prioritize collecting information that is helpful for learning. This allows for the provision of more appropriate information by prioritizing the information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a tourist area, the unit can collect tourist information for that area. If the user is at home, the unit can also collect information about nearby events. Furthermore, if the user is on a business trip, the unit can collect business information for their destination. By prioritizing the collection of highly relevant information while considering the user's geographical location, the unit can provide more appropriate information.

[0074] The data collection unit can analyze a user's social media activity and collect relevant information during the collection process. For example, the unit can collect posts from accounts that the user follows on social media. The unit can also collect information related to topics that the user has shown interest in on social media. Furthermore, the unit can collect information about the activities of groups that the user participates in on social media. This allows the system to provide relevant information by analyzing the user's social media activity.

[0075] The translation unit can estimate the user's emotions and adjust the translation's expression based on that estimation. For example, if the user is relaxed, the translation unit will use softer language. If the user is in a hurry, the translation unit can use concise language. Furthermore, if the user is excited, the translation unit can use more energetic language. This allows for more appropriate translations by adjusting the translation's expression based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The translation unit can adjust the level of detail in the translation based on the importance of the words. For example, it can translate important words in detail and add supplementary explanations. It can also translate less important words concisely. Furthermore, it can emphasize highly important words. By adjusting the level of detail based on the importance of the words, it can provide a more appropriate translation.

[0077] The translation system can apply different translation algorithms depending on the category of words used during translation. For example, it can apply a specialized translation algorithm to technical terms, a casual translation algorithm to everyday conversations, and a precise translation algorithm to medical terms. By applying different translation algorithms depending on the category of words, it can provide more appropriate translations.

[0078] The translation unit can estimate the user's emotions and adjust the translation length based on the estimated emotions. For example, if the user is in a hurry, the translation unit will provide a short, concise translation. If the user is relaxed, the translation unit may provide a longer translation with more detailed explanations. Furthermore, if the user is excited, the translation unit may provide a translation with visually stimulating effects. By adjusting the translation length based on the user's emotions, a more appropriate translation can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The translation department can prioritize translations based on word frequency. For example, it will prioritize frequently used words, and postpone less frequently used words. It can also emphasize frequently used words. By prioritizing translations based on word frequency, it can provide more accurate translations.

[0080] The translation unit can adjust the order of translations based on the relevance of words during the translation process. For example, it can translate highly relevant words consecutively, while translating less relevant words with gaps in between. It can also emphasize highly relevant words during translation. This allows for more appropriate translations by adjusting the order of translations based on word relevance.

[0081] The translation system can estimate the user's emotions and adjust the translation method based on the estimated emotions. For example, if the user is relaxed, the system will provide a soft translation. If the user is in a hurry, the system can provide a quick and concise translation. If the user is excited, the system can provide a lively translation. By adjusting the translation method based on the user's emotions, a more appropriate translation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The service provider can analyze the user's past translation history to select the most suitable delivery method at the time of delivery. For example, the service provider can prioritize providing translation methods that the user has preferred in the past. The service provider can also select translation methods that the user found easy to understand in the past. Furthermore, the service provider can suggest the most suitable delivery method based on the user's past translation history. In this way, the service provider can select the most suitable delivery method by analyzing the user's past translation history.

[0083] The service provider can customize the delivery method based on the user's current activity at the time of delivery. For example, if the user is exercising, the service provider can provide a concise and easy-to-read translation. If the user is taking a break, the service provider can also provide a detailed translation. Furthermore, if the user is in a meeting, the service provider can provide a concise translation that gets to the point. This allows for the provision of more appropriate translations by customizing the delivery method based on the user's current activity.

[0084] The service provider can customize the delivery method based on the user's current activity at the time of delivery. For example, if the user is exercising, the service provider can provide a concise and easy-to-read translation. If the user is taking a break, the service provider can also provide a detailed translation. Furthermore, if the user is in a meeting, the service provider can provide a concise translation that gets to the point. This allows for the provision of more appropriate translations by customizing the delivery method based on the user's current activity.

[0085] The service provider can estimate the user's emotions and determine the priority of translations to provide based on those emotions. For example, if the user is in a hurry, the service provider will prioritize important translations. If the user is relaxed, the service provider may also prioritize detailed translations. Furthermore, if the user is excited, the service provider may prioritize interesting translations. This allows for the provision of more appropriate translations by prioritizing translations based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The service provider can select the most appropriate delivery method at the time of delivery, taking into account the user's geographical location. For example, if the user is in a tourist area, the service provider can prioritize providing information about that area. If the user is at home, the service provider can also prioritize providing information about the nearby area. Furthermore, if the user is on a business trip, the service provider can prioritize providing information about their business trip destination. By selecting the most appropriate delivery method considering the user's geographical location, the service provider can provide more accurate translations.

[0087] The service provider can analyze the user's social media activity and propose a suitable delivery method at the time of delivery. For example, the service provider can prioritize providing information on accounts the user follows on social media. The service provider can also provide information related to topics the user has shown interest in on social media. Furthermore, the service provider can provide information on groups the user participates in on social media. By analyzing the user's social media activity, the service provider can propose a more appropriate delivery method.

[0088] The speed adjustment unit can estimate the user's emotions and adjust the translation speed based on the estimated emotions. For example, if the user is relaxed, the speed adjustment unit will provide a slow translation. If the user is in a hurry, the speed adjustment unit can also provide a fast translation. Furthermore, if the user is excited, the speed adjustment unit can provide a moderate translation. In this way, by adjusting the translation speed based on the user's emotions, a more appropriate translation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The speed adjustment unit can analyze the user's walking pattern and select the optimal speed during speed adjustment. For example, if the user is walking fast, the speed adjustment unit will provide translation at a rapid speed. If the user is walking slowly, the speed adjustment unit can also provide translation at a slow speed. Furthermore, if the user is standing still, the speed adjustment unit can provide translation at a normal speed. In this way, by analyzing the user's walking pattern, the system can provide translation at the optimal speed.

[0090] The speed adjustment unit can estimate the user's emotions and determine the frequency of speed adjustments based on the estimated emotions. For example, if the user is in a hurry, the speed adjustment unit will adjust the speed more frequently. If the user is relaxed, the speed adjustment unit can also reduce the frequency of speed adjustments. Furthermore, if the user is excited, the speed adjustment unit can adjust the speed at a moderate frequency. This allows for more appropriate translations by determining the frequency of speed adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The speed adjustment unit can select the optimal speed by considering the user's geographical location information during speed adjustment. For example, if the user is in a crowded place, the speed adjustment unit will provide translation at a slow speed. If the user is in a spacious area, the speed adjustment unit can also provide translation at a normal speed. Furthermore, if the user is in a quiet place, the speed adjustment unit can provide translation at a moderate speed. By selecting the optimal speed while considering the user's geographical location information, a more appropriate translation can be provided.

[0092] The orientation adjustment unit can estimate the user's emotions and adjust the translation orientation based on the estimated emotions. For example, if the user is relaxed, the orientation adjustment unit will provide the translation in a natural orientation. If the user is in a hurry, the orientation adjustment unit can also quickly adjust the orientation and provide the translation. Furthermore, if the user is excited, the orientation adjustment unit can moderately adjust the orientation and provide the translation. In this way, by adjusting the translation orientation based on the user's emotions, a more appropriate translation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The orientation adjustment unit can analyze the user's smartphone usage patterns and select the optimal orientation during orientation adjustment. For example, if the user is using the smartphone vertically, the orientation adjustment unit will provide translation in portrait orientation. If the user is using the smartphone horizontally, the orientation adjustment unit can also provide translation in landscape orientation. Furthermore, if the user frequently rotates the smartphone, the orientation adjustment unit can appropriately adjust the orientation and provide translation. In this way, by analyzing the user's smartphone usage patterns, the unit can provide translation in the optimal orientation.

[0094] The orientation adjustment unit can estimate the user's emotions and determine the frequency of orientation adjustments based on the estimated emotions. For example, if the user is in a hurry, the orientation adjustment unit will adjust the orientation frequently. If the user is relaxed, the orientation adjustment unit can also reduce the frequency of orientation adjustments. Furthermore, if the user is excited, the orientation adjustment unit can adjust the orientation at a moderate frequency. This allows for more appropriate translations by determining the frequency of orientation adjustments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The orientation adjustment unit can select the optimal orientation by considering the user's device information during orientation adjustment. For example, if the user is using a smartphone, the orientation adjustment unit will provide translations according to the smartphone's orientation. If the user is using a tablet, the orientation adjustment unit can also provide translations according to the tablet's orientation. Furthermore, if the user is using a smartwatch, the orientation adjustment unit can also provide translations according to the smartwatch's orientation. By selecting the optimal orientation while considering the user's device information, it is possible to provide more appropriate translations.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can estimate the user's emotions and adjust the type of information collected based on those emotions. For example, if the user is stressed, it will prioritize collecting information that helps them relax. If the user is excited, it can also collect information on new words and trends that might interest them. If the user is depressed, it can also collect information that includes words of encouragement and comfort. By adjusting the type of information collected based on the user's emotions, it can provide more relevant information.

[0098] The translation unit can estimate the user's emotions and adjust the translation's expression based on that estimation. For example, if the user is relaxed, it will use softer language. If the user is in a hurry, it can use concise language. If the user is excited, it can use more energetic language. By adjusting the translation's expression based on the user's emotions, it can provide more appropriate translations.

[0099] The translation system can estimate the user's emotions and adjust the translation method based on that estimation. For example, if the user is relaxed, it can provide a soft voice. If the user is in a hurry, it can provide a quick and concise translation. If the user is excited, it can provide a lively voice. By adjusting the translation method based on the user's emotions, it can provide a more appropriate translation.

[0100] The speed adjustment unit can estimate the user's emotions and adjust the translation speed based on those emotions. For example, if the user is relaxed, it can provide a slow translation. If the user is in a hurry, it can provide a fast translation. If the user is excited, it can provide a moderate translation. By adjusting the translation speed based on the user's emotions, it is possible to provide a more appropriate translation.

[0101] The orientation adjustment unit can estimate the user's emotions and adjust the translation orientation based on those emotions. For example, if the user is relaxed, it will provide the translation in a natural orientation. If the user is in a hurry, it can quickly adjust the orientation and provide the translation. If the user is excited, it can adjust the orientation appropriately and provide the translation. By adjusting the translation orientation based on the user's emotions, it is possible to provide a more appropriate translation.

[0102] The data collection unit can analyze the user's past conversation history and select the optimal information collection method. For example, it can prioritize collecting words that the user has frequently used in the past. It can also avoid words that the user found difficult to understand in the past and collect simpler expressions. Furthermore, it can collect information related to topics that the user has shown interest in in the past. In this way, by analyzing the user's past conversation history, the optimal information collection method can be selected.

[0103] The data collection unit can filter data based on the user's current activities and areas of interest. For example, if the user is taking a walk, it can collect information about nature and health. If the user is cooking, it can collect information about recipes and ingredients. If the user is reading, it can collect information about relevant books and authors. By filtering data based on the user's current activities and areas of interest, the system can provide more relevant information.

[0104] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location during the collection process. For example, if the user is in a tourist area, it can collect tourist information for that area. If the user is at home, it can also collect information about nearby events. Furthermore, if the user is on a business trip, it can collect business information for their destination. By prioritizing the collection of highly relevant information while considering the user's geographical location, the system can provide more appropriate information.

[0105] The data collection unit analyzes the user's social media activity during collection and can collect relevant information. For example, it can collect posts from accounts the user follows on social media. It can also collect information related to topics the user has shown interest in on social media. Furthermore, it can collect information about the activities of groups the user participates in on social media. This allows the system to provide relevant information by analyzing the user's social media activity.

[0106] The translation department can adjust the level of detail in translations based on the importance of the words. For example, important words can be translated in detail with supplementary explanations, while less important words can be translated concisely. Furthermore, highly important words can be emphasized. This allows for more accurate translations by adjusting the level of detail based on the importance of the words.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit understands the user's knowledge level and vocabulary. The unit collects words, jargon, and slang that the user uses on a daily basis. Furthermore, the unit uses test results, self-reports, and past usage history to measure the user's knowledge level. For example, the unit analyzes the frequency of words the user has used in the past to understand the user's knowledge level. The unit can also collect data to identify words that the user has difficulty understanding and provide appropriate translations. Step 2: The translation unit translates the incoming words based on the information gathered by the data collection unit. The translation unit uses generative AI to understand the context and provide an appropriate translation. For example, the translation unit uses generative AI to translate new words, loanwords, and slang into expressions that are easy for older people to understand in real time. The generative AI can use text generation AI (e.g., LLM) to understand the context of a sentence and generate an appropriate translation. The translation unit can also use generative AI to extract the important parts of a sentence and provide a concise translation. Step 3: The provider unit provides the words translated by the translation unit. The provider unit can provide translations according to the user's walking speed and smartphone orientation. For example, the provider unit adjusts the translation speed to match the user's walking speed. The provider unit can also adjust how the translation is displayed according to the user's smartphone orientation.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, translation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's words using the microphone 38B of the smart device 14 and the control unit 46A understands the user's knowledge level and the words they use. The translation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and uses a generation AI to understand the context and provide an appropriate translation. The provision unit provides the translated words to the user using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, translation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's words using the microphone 238 of the smart glasses 214 and the control unit 46A understands the user's knowledge level and the words they use. The translation unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and uses generation AI to understand the context and provide an appropriate translation. The provision unit provides the translated words to the user using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, translation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's words using the microphone 238 of the headset terminal 314 and the control unit 46A understands the user's knowledge level and the words they use. The translation unit is implemented in the identification processing unit 290 of the data processing unit 12, which uses a generation AI to understand the context and provide an appropriate translation. The provision unit provides the translated words to the user using the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, translation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's words using the microphone 238 of the robot 414 and the control unit 46A grasps the user's knowledge level and the words they use. The translation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which uses a generation AI to understand the context and provide an appropriate translation. The provision unit provides the translated words to the user using, for example, the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection unit that understands the user's level of knowledge and the words they use, Based on the information gathered by the aforementioned collection unit, a translation unit translates the incoming words, A providing unit that provides the words translated by the aforementioned translation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned translation department, Generative AI understands context and provides appropriate translations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It features a speed adjustment unit that provides translations according to the user's walking speed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It features an orientation adjustment unit that provides translation based on the orientation of the user's smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Analyze the user's past conversation history to select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned translation department, During translation, adjust the level of detail based on the importance of each word. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned translation department, During translation, different translation algorithms are applied depending on the word category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned translation department, During translation, prioritize translations based on word frequency. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the words. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We estimate the user's emotions and adjust the translation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the service, the system analyzes the user's past translation history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, customize the delivery method based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, customize the delivery method based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of translations to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The speed adjustment unit is It estimates the user's emotions and adjusts the translation speed based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The speed adjustment unit is When adjusting the speed, the system analyzes the user's walking pattern to select the optimal speed. The system described in Appendix 2, characterized by the features described herein. (Note 26) The speed adjustment unit is The system estimates the user's emotions and determines the frequency of speed adjustments based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The speed adjustment unit is When adjusting the speed, the system selects the optimal speed by taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 28) The orientation adjustment unit is, It estimates the user's emotions and adjusts the translation direction based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The orientation adjustment unit is, When adjusting the orientation, the system analyzes the user's smartphone usage patterns to select the optimal orientation. The system described in Appendix 3, characterized by the features described herein. (Note 30) The orientation adjustment unit is, The system estimates the user's emotions and determines the frequency of orientation adjustments based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The orientation adjustment unit is, When adjusting the orientation, the optimal orientation is selected by considering the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that understands the user's level of knowledge and the words they use, Based on the information gathered by the aforementioned collection unit, a translation unit translates the incoming words, A providing unit that provides the words translated by the aforementioned translation unit, Equipped with A system characterized by the following features.

2. The aforementioned translation department, Generative AI understands the context and provides appropriate translations. The system according to feature 1.

3. The aforementioned supply unit is, It features a speed adjustment unit that provides translations according to the user's walking speed. The system according to feature 1.

4. The aforementioned supply unit is, It features an orientation adjustment unit that provides translation based on the orientation of the user's smartphone. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of information collected based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze the user's past conversation history to select the optimal information gathering method. The system according to feature 1.

7. The aforementioned collection unit is During data collection, filtering is performed based on the user's current activity and areas of interest. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system according to feature 1.

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