system

Abstract

The system according to the embodiment aims to provide access to information across language barriers. [Solution] A system according to an embodiment includes a reception unit, a translation unit, a search unit, a retranslation unit, and a provision unit. The reception unit receives input of search words. The translation unit translates the search words received by the reception unit into multiple languages. The search unit performs searches on search engines in each language using the search words translated by the translation unit. The retranslation unit retranslates the search results obtained by the search unit. The provision unit provides the user with the search results retranslated by the retranslation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult to search for information in different languages, making it difficult to access information across language barriers.

[0005] The system according to the embodiment aims to provide access to information across language barriers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a translation unit, a search unit, a retranslation unit, and a provision unit. The reception unit receives input of search words. The translation unit translates the search words received by the reception unit into multiple languages. The search unit performs searches on search engines in each language using the search words translated by the translation unit. The retranslation unit retranslates the search results obtained by the search unit. The provision unit provides the search results retranslated by the retranslation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment allows access to information across language barriers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A multilingual search engine according to an embodiment of the present invention is a system that enables access to information across language barriers by translating search terms into multiple languages ​​and performing searches. In a multilingual search engine, a generation AI translates a search term entered by a user into multiple languages, performs a search on each language's search engine, and then translates the obtained search results back into the user's language and provides them. For example, in a multilingual search engine, a user enters a search term such as "latest technology news." The search term is input to a generation AI, which translates the search term into English, Chinese, French, etc. Next, the translated search terms are used to search on each language's search engine. For example, an English search term is used to search on an English search engine, and a Chinese search term is used to search on a Chinese search engine. This results in search results in each language. Next, the generation AI translates the search results back into the user's language. For example, English and Chinese search results are translated into Japanese. Finally, the translated search results are provided to the user. This allows users to access information from around the world without being limited to their own language. This allows the multilingual search engine to access information across language barriers. For example, even a user who only speaks Japanese can access information in English and Chinese. Additionally, English-speaking users can access information in Japanese and French, allowing them to obtain more information and broaden their knowledge.

[0029] A multilingual search engine according to an embodiment includes a reception unit, a translation unit, a search unit, a retranslation unit, and a provision unit. The reception unit receives search terms entered by a user. For example, the user enters a search term such as "latest technology news." The translation unit uses a generation AI to translate the search terms received by the reception unit into multiple languages. For example, the generation AI translates the search terms into English, Chinese, French, etc. The generation AI performs accurate translations taking into account the grammar and vocabulary of each language. The search unit performs searches on search engines in each language using the search terms translated by the translation unit. For example, an English search term is used to search an English search engine, and a Chinese search term is used to search a Chinese search engine. This results in search results in each language. The retranslation unit retranslates the search results obtained by the search unit. For example, the generation AI translates English and Chinese search results into Japanese. The generation AI performs accurate translations taking into account the context of the search results. The provision unit provides the search results retranslated by the retranslation unit to the user. For example, the user can view the search results in their own language. As a result, the multilingual search engine according to the embodiment can translate search words into multiple languages ​​and perform searches, thereby making it possible to access information beyond language barriers.

[0030] The translation unit can translate search terms based on the grammar and vocabulary of each language. For example, the translation unit uses generation AI to translate search terms into English, Chinese, French, etc. The generation AI takes into account the grammar and vocabulary of each language to perform accurate translations. For example, the generation AI takes into account grammar rules and technical terms when translating. The generation AI can also take into account slang and regional expressions when translating. This allows for accurate translations by taking into account the grammar and vocabulary of each language.

[0031] The retranslation unit can perform retranslation based on the context of the search results. The retranslation unit, for example, uses a generation AI to retranslate the search results into the user's language. The generation AI performs accurate translation by taking into account the context of the search results. For example, the generation AI performs retranslation by taking into account the surrounding sentences and related topics. The generation AI can also select appropriate expressions depending on the content of the search results and perform retranslation. This enables accurate retranslation by taking into account the context of the search results.

[0032] The providing unit may have a function that allows a user to customize search results. The providing unit provides, for example, a function that allows a user to customize search results. For example, a user can change the display order of search results or filter them according to specific conditions. The providing unit may also provide a function that allows a user to save or share search results. This allows a user to customize search results and obtain more relevant information.

[0033] The providing unit may have a filtering function that displays only information in a specific language or region. The providing unit provides, for example, a filtering function that displays only information in a specific language or region. For example, a user can select a specific language or region to display only information related to that language or region. The providing unit may also provide a function that performs automatic filtering by allowing the user to set in advance the languages ​​and regions in which the user is interested. This allows the user to quickly access the information they need by displaying only information in a specific language or region.

[0034] The search unit may have a ranking function that filters search results and arranges them in order of usefulness to the user. For example, the search unit provides a ranking function that filters search results and arranges them in order of usefulness to the user. For example, the search unit ranks search results based on user ratings, click counts, relevance, etc. The search unit may also provide optimal search results by taking into account the user's past search history and areas of interest. In this way, by filtering and ranking search results, the user can quickly access useful information.

[0035] The reception unit can analyze the user's past search history and suggest an appropriate method for inputting search words. The reception unit, for example, analyzes the user's past search history and suggests the optimal method for inputting search words. For example, search words that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest search words that will be used in a specific time period from the user's past search history. In this way, it is possible to suggest the optimal input method for the user by analyzing the past search history.

[0036] The reception unit can provide a suggestion function based on the user's areas of interest when a search word is entered. The reception unit provides a suggestion function based on the user's areas of interest when a search word is entered, for example. For example, the reception unit can suggest related search words based on topics recently searched by the user. It can also suggest trending search words based on the user's areas of interest. It can also suggest optimal search words by combining the user's past search history and current areas of interest. In this way, by providing a suggestion function based on the user's areas of interest, it is possible to suggest more appropriate search words.

[0037] The reception unit can select an appropriate input means depending on the user's input method when entering a search word. For example, when entering a search word, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the search word is entered using voice recognition technology. Also, if the user selects text input, the search word can be entered using a keyboard or touch panel. Also, if the user selects image input, the search word can be entered using image recognition technology. This improves input convenience by selecting the optimal input means depending on the user's input method.

[0038] The reception unit can prioritize suggesting highly relevant search words based on the user's geographical location information when the user inputs a search word. For example, the reception unit prioritizes suggesting highly relevant search words by taking into account the user's geographical location information when the user inputs a search word. For example, the reception unit can suggest search words including nearby information based on the user's current location. Also, if the user is traveling, it can suggest search words including information about the travel destination. Also, if the user is interested in a specific area, it can prioritize displaying search words related to that area. In this way, it is possible to suggest highly relevant search words by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when a search term is entered and suggest related search terms. For example, when a search term is entered, the reception unit can analyze the user's social media activity and suggest related search terms. For example, the reception unit can suggest search terms based on topics that the user frequently mentions on social media. It can also suggest related search terms by taking into account the activity of the user's friends on social media. It can also analyze the content of the user's social media posts and suggest related search terms. In this way, it is possible to suggest related search terms by analyzing social media activity.

[0040] The reception unit can customize the input method based on the user's past feedback when entering a search word. For example, the reception unit customizes the input method by reflecting the user's past feedback when entering a search word. For example, the reception unit preferentially suggests an input method that the user has used favorably in the past. The reception unit can also provide an optimal input interface based on the user's past feedback. The reception unit can also suggest a different input method, avoiding an input method that the user has been dissatisfied with in the past. In this way, the reception unit can provide the user with the optimal input method by reflecting past feedback.

[0041] The translation unit can adjust the level of detail of the translation based on the importance of the search word during translation. The translation unit, for example, uses a generation AI to adjust the level of detail of the translation based on the importance of the search word during translation. For example, the generation AI provides a detailed translation for search words with high importance. The generation AI can also provide a concise translation for search words with low importance. The generation AI can also provide a translation with an appropriate level of detail depending on the context of the search word. In this way, by adjusting the level of detail of the translation based on the importance of the search word, an appropriate translation can be provided.

[0042] The translation unit can apply different translation algorithms based on the category of the search word during translation. The translation unit can use, for example, generative AI to apply different translation algorithms based on the category of the search word during translation. For example, an algorithm that accurately translates technical terms can be applied to technology-related search words. An algorithm that uses casual expressions can also be applied to entertainment-related search words. An algorithm that provides accurate and reliable translations can also be applied to medical-related search words. This improves translation accuracy by applying an appropriate translation algorithm depending on the category of the search word.

[0043] The translation unit can improve the accuracy of translation based on the user's past translation results during translation. The translation unit, for example, uses a generation AI to improve the accuracy of translation by referring to the user's past translation results during translation. For example, the generation AI refers to the translation style that the user has preferred in the past and performs a similar translation. The generation AI can also avoid translation results that the user was dissatisfied with in the past and perform an improved translation. The generation AI can also analyze the user's past translation history and perform the optimal translation. In this way, the accuracy of the translation is improved by referring to past translation results.

[0044] The translation unit can determine the priority of translations based on when the search words were submitted during translation. The translation unit, for example, uses a generation AI to determine the priority of translations based on when the search words were submitted during translation. For example, the most recent search words are translated first. Older search words can also be translated later. Translations can also be performed with appropriate priority depending on when they were submitted. In this way, by determining the priority of translations based on when the search words were submitted, the latest information can be provided preferentially.

[0045] The translation unit can adjust the order of translations based on the relevance of search words during translation. The translation unit can use, for example, generation AI to adjust the order of translations based on the relevance of search words during translation. For example, highly relevant search words can be translated first. Also, search words with low relevance can be translated later. Furthermore, translations can be performed in an appropriate order depending on the context of the search words. In this way, by adjusting the order of translations based on the relevance of search words, highly relevant information can be provided preferentially.

[0046] The translation unit can adjust the use of technical terminology in the translation based on the user's level of expertise during translation. The translation unit, for example, uses a generation AI to adjust the use of technical terminology in the translation based on the user's level of expertise during translation. For example, if the user is an expert, the generation AI can provide a translation that makes heavy use of technical terminology. Alternatively, if the user is a beginner, the generation AI can provide a translation that explains things in simple terms. The use of appropriate technical terminology can also be adjusted based on the user's level of expertise. In this way, an appropriate translation can be provided by adjusting the use of technical terminology based on the user's level of expertise.

[0047] The search unit can improve search accuracy based on the interrelationships between search words during a search. For example, the search unit can improve search accuracy by considering the interrelationships between search words during a search. For example, the search unit can analyze the interrelationships between search words and preferentially display highly relevant search results. It can also provide appropriate search results by considering the context of the search words. It can also display optimal search results based on the interrelationships between search words. In this way, the search accuracy is improved by considering the interrelationships between search words.

[0048] The search unit can perform a search based on the attribute information of the person who submitted the search word. For example, the search unit can perform a search taking into account the attribute information of the person who submitted the search word. For example, the search unit can provide appropriate search results based on the age of the person who submitted the search word. It can also provide highly relevant search results based on the occupation of the person who submitted the search word. It can also provide optimal search results based on the interests and concerns of the person who submitted the search word. In this way, more appropriate search results can be provided by taking into account the attribute information of the person who submitted the search word.

[0049] The search unit can weight the search based on the frequency of submission of the search word during the search. For example, the search unit weights the search based on the frequency of submission of the search word during the search. For example, search results for search words that are submitted more frequently can be displayed preferentially. Search results for search words that are submitted less frequently can also be displayed later. Search results can also be displayed with appropriate weighting according to the frequency of submission. In this way, by weighting based on the frequency of submission of the search word, more appropriate search results can be provided.

[0050] The search unit can perform a search based on the geographical distribution of search words when searching. For example, the search unit performs a search taking into account the geographical distribution of search words when searching. For example, the search unit analyzes the geographical distribution of search words to provide highly relevant search results. It can also display appropriate search results based on the geographical distribution. It can also provide optimal search results by taking into account the geographical distribution of search words. In this way, more appropriate search results can be provided by taking into account the geographical distribution of search words.

[0051] The search unit can improve the accuracy of a search based on literature related to the search word during a search. For example, the search unit can improve the accuracy of a search by referring to literature related to the search word during a search. For example, the search unit can refer to literature related to the search word and provide appropriate search results. The accuracy of the search results can also be improved based on the related literature. The search unit can also analyze literature related to the search word and provide optimal search results. In this way, the accuracy of the search can be improved by referring to related literature.

[0052] The search unit can perform a search based on the market value of the search word when searching. For example, the search unit can perform a search taking into account the market value of the search word when searching. For example, search results for search words with high market value can be displayed preferentially. Search results for search words with low market value can also be displayed later. Appropriate search results can also be provided depending on market value. In this way, more appropriate search results can be provided by taking market value into consideration.

[0053] The retranslation unit can adjust the level of detail of the retranslation based on the importance of the search results during retranslation. The retranslation unit, for example, uses a generation AI to adjust the level of detail of the retranslation based on the importance of the search results during retranslation. For example, the generation AI performs a detailed retranslation of search results with high importance. The generation AI can also perform a concise retranslation of search results with low importance. The generation AI can also perform a retranslation with an appropriate level of detail depending on the context of the search results. In this way, by adjusting the level of detail of the retranslation based on the importance of the search results, an appropriate retranslation can be provided.

[0054] The re-translation unit can apply different re-translation algorithms based on the category of the search result during re-translation. The re-translation unit can use, for example, generation AI to apply different re-translation algorithms based on the category of the search result during re-translation. For example, an algorithm that accurately re-translates technical terms can be applied to technology-related search results. An algorithm that uses casual expressions can also be applied to entertainment-related search results. An algorithm that performs accurate and reliable re-translation can also be applied to medical-related search results. In this way, the accuracy of re-translation is improved by applying an appropriate re-translation algorithm depending on the category of the search result.

[0055] The re-translation unit can improve the accuracy of re-translation based on the user's past re-translation results during re-translation. The re-translation unit, for example, uses a generation AI to improve the accuracy of re-translation by referring to the user's past re-translation results during re-translation. For example, the generation AI refers to the re-translation style that the user has preferred in the past and performs a similar re-translation. The generation AI can also perform an improved re-translation by avoiding re-translation results that the user was dissatisfied with in the past. The generation AI can also analyze the user's past re-translation history and perform the optimal re-translation. In this way, the accuracy of re-translation is improved by referring to the past re-translation results.

[0056] The re-translation unit can determine the priority of re-translation based on the time of submission of search results during re-translation. The re-translation unit, for example, uses generation AI to determine the priority of re-translation based on the time of submission of search results during re-translation. For example, the most recent search results are re-translated with priority. Older search results can also be re-translated at a later date. Re-translation can also be performed with appropriate priority depending on the time of submission. In this way, by determining the priority of re-translation based on the time of submission of search results, the latest information can be provided preferentially.

[0057] The re-translation unit can adjust the order of re-translation based on the relevance of the search results during re-translation. The re-translation unit adjusts the order of re-translation based on the relevance of the search results during re-translation, for example, using a generation AI. For example, highly relevant search results can be re-translated preferentially. Also, less relevant search results can be re-translated later. Also, re-translation can be performed in an appropriate order depending on the context of the search results. In this way, by adjusting the order of re-translation based on the relevance of the search results, highly relevant information can be provided preferentially.

[0058] The retranslation unit can adjust the use of technical terminology in the retranslation based on the user's level of expertise during retranslation. The retranslation unit, for example, uses a generation AI to adjust the use of technical terminology in the retranslation based on the user's level of expertise during retranslation. For example, if the user is an expert, the generation AI can perform a retranslation that makes heavy use of technical terminology. Also, if the user is a beginner, the generation AI can perform a retranslation that explains things in simpler terms. The use of appropriate technical terminology can also be adjusted based on the user's level of expertise. In this way, an appropriate retranslation can be provided by adjusting the use of technical terminology based on the user's level of expertise.

[0059] The providing unit can select the optimal display method based on the user's past usage history of search results when providing the results. For example, the providing unit selects the optimal display method by referring to the user's past usage history of search results when providing the results. For example, the providing unit preferentially provides a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past usage history. It can also provide a different display method, avoiding a display method that the user has been dissatisfied with in the past. In this way, the optimal display method can be provided to the user by referring to the past usage history.

[0060] The providing unit can customize the display content based on the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. For example, when the user is at work, relevant information can be displayed with priority. Also, when the user is on vacation, information that helps relaxation can be displayed with priority. Also, the optimal display content can be provided according to the user's current task. In this way, by customizing the display content according to the current task, useful information can be provided to the user.

[0061] The providing unit can improve the display method based on user feedback when providing the display. For example, the providing unit improves the display method by reflecting user feedback when providing the display. For example, when a user provides feedback on the provided display method, the providing unit improves the display method based on that feedback. The providing unit can also analyze user feedback and provide an optimal display method. The providing unit can also continuously improve the display method by referring to past user feedback. In this way, the display method can be continuously improved by reflecting feedback.

[0062] The providing unit can select the optimal display method based on the user's geographical location information at the time of providing. For example, the providing unit selects the optimal display method taking into account the user's geographical location information at the time of providing. For example, based on the user's current location, search results including nearby information can be preferentially displayed. Also, if the user is traveling, search results including information about the travel destination can be preferentially displayed. Also, if the user is interested in a specific area, search results related to that area can be preferentially displayed. In this way, by taking into account the geographical location information, it is possible to provide information that is highly relevant to the user.

[0063] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, the optimal display method for the user can be provided by taking into consideration the device information.

[0064] The providing unit can make the display content multilingual based on the user's language setting when providing the information. The providing unit, for example, makes the display content multilingual based on the user's language setting when providing the information. For example, the providing unit automatically sets the language of the search results based on the language setting of the user's device. In addition, if the user uses multiple languages, a language switching function can be provided. In addition, if the user selects a specific language, search results can be provided in that language. In this way, by providing multilingual support according to the language setting, it is possible to provide information that is easy for the user to understand.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The reception unit can analyze the user's past search history and suggest appropriate methods for inputting search words. For example, it can automatically display search words that the user has frequently input in the past as candidates. It can also prioritize suggestions for input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest search words that will be used during a specific time period based on the user's past search history. In this way, by analyzing the user's past search history, it can suggest the optimal input method for the user.

[0067] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the search word. For example, the generation AI can provide a detailed translation for search words with high importance. The generation AI can also provide a concise translation for search words with low importance. The generation AI can also provide a translation with an appropriate level of detail depending on the context of the search word. This allows the system to provide an appropriate translation by adjusting the level of detail of the translation based on the importance of the search word.

[0068] The search unit can improve search accuracy based on the interrelationships between search words during a search. For example, it can analyze the interrelationships between search words and prioritize the display of highly relevant search results. It can also provide appropriate search results by taking into account the context of the search words. It can also display optimal search results based on the interrelationships between search words. In this way, the search accuracy is improved by taking into account the interrelationships between search words.

[0069] When providing the search results, the providing unit can select the optimal display method based on the user's past usage history of search results. For example, the providing unit can provide preferentially the display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past usage history. The providing unit can also provide a different display method, avoiding a display method that the user has been dissatisfied with in the past. In this way, the optimal display method can be provided to the user by referring to the past usage history.

[0070] When providing the display, the providing unit can select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, the optimal display method for the user can be provided by taking into consideration the device information.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The reception unit receives a search term entered by a user. For example, the user enters a search term such as "latest technology news." Step 2: The translation unit uses the generation AI to translate the search words received by the reception unit into multiple languages. For example, the generation AI translates the search words into English, Chinese, French, etc. The generation AI takes into account the grammar and vocabulary of each language to perform accurate translations. Step 3: The search unit searches the search engines in each language using the search words translated by the translation unit. For example, the search unit searches an English search engine using an English search word, and searches a Chinese search engine using a Chinese search word. This provides search results in each language. Step 4: The re-translation unit retranslates the search results obtained by the search unit. For example, the generation AI translates English or Chinese search results into Japanese. The generation AI takes into account the context of the search results to perform an accurate translation. Step 5: The providing unit provides the search results retranslated by the retranslation unit to the user. For example, the user can check the search results in their own language.

[0073] (Example 2) A multilingual search engine according to an embodiment of the present invention is a system that enables access to information across language barriers by translating search terms into multiple languages ​​and performing searches. In a multilingual search engine, a generation AI translates a search term entered by a user into multiple languages, performs a search on each language's search engine, and then translates the obtained search results back into the user's language and provides them. For example, in a multilingual search engine, a user enters a search term such as "latest technology news." The search term is input to a generation AI, which translates the search term into English, Chinese, French, etc. Next, the translated search terms are used to search on each language's search engine. For example, an English search term is used to search on an English search engine, and a Chinese search term is used to search on a Chinese search engine. This results in search results in each language. Next, the generation AI translates the search results back into the user's language. For example, English and Chinese search results are translated into Japanese. Finally, the translated search results are provided to the user. This allows users to access information from around the world without being limited to their own language. This allows the multilingual search engine to access information across language barriers. For example, even a user who only speaks Japanese can access information in English and Chinese. Additionally, English-speaking users can access information in Japanese and French, allowing them to obtain more information and broaden their knowledge.

[0074] A multilingual search engine according to an embodiment includes a reception unit, a translation unit, a search unit, a retranslation unit, and a provision unit. The reception unit receives search terms entered by a user. For example, the user enters a search term such as "latest technology news." The translation unit uses a generation AI to translate the search terms received by the reception unit into multiple languages. For example, the generation AI translates the search terms into English, Chinese, French, etc. The generation AI performs accurate translations taking into account the grammar and vocabulary of each language. The search unit performs searches on search engines in each language using the search terms translated by the translation unit. For example, an English search term is used to search an English search engine, and a Chinese search term is used to search a Chinese search engine. This results in search results in each language. The retranslation unit retranslates the search results obtained by the search unit. For example, the generation AI translates English and Chinese search results into Japanese. The generation AI performs accurate translations taking into account the context of the search results. The provision unit provides the search results retranslated by the retranslation unit to the user. For example, the user can view the search results in their own language. As a result, the multilingual search engine according to the embodiment can translate search words into multiple languages ​​and perform searches, thereby making it possible to access information beyond language barriers.

[0075] The translation unit can translate search terms based on the grammar and vocabulary of each language. For example, the translation unit uses generation AI to translate search terms into English, Chinese, French, etc. The generation AI takes into account the grammar and vocabulary of each language to perform accurate translations. For example, the generation AI takes into account grammar rules and technical terms when translating. The generation AI can also take into account slang and regional expressions when translating. This allows for accurate translations by taking into account the grammar and vocabulary of each language.

[0076] The retranslation unit can perform retranslation based on the context of the search results. The retranslation unit, for example, uses a generation AI to retranslate the search results into the user's language. The generation AI performs accurate translation by taking into account the context of the search results. For example, the generation AI performs retranslation by taking into account the surrounding sentences and related topics. The generation AI can also select appropriate expressions depending on the content of the search results and perform retranslation. This enables accurate retranslation by taking into account the context of the search results.

[0077] The providing unit may have a function that allows a user to customize search results. The providing unit provides, for example, a function that allows a user to customize search results. For example, a user can change the display order of search results or filter them according to specific conditions. The providing unit may also provide a function that allows a user to save or share search results. This allows a user to customize search results and obtain more relevant information.

[0078] The providing unit may have a filtering function that displays only information in a specific language or region. The providing unit provides, for example, a filtering function that displays only information in a specific language or region. For example, a user can select a specific language or region to display only information related to that language or region. The providing unit may also provide a function that performs automatic filtering by allowing the user to set in advance the languages ​​and regions in which the user is interested. This allows the user to quickly access the information they need by displaying only information in a specific language or region.

[0079] The search unit may have a ranking function that filters search results and arranges them in order of usefulness to the user. For example, the search unit provides a ranking function that filters search results and arranges them in order of usefulness to the user. For example, the search unit ranks search results based on user ratings, click counts, relevance, etc. The search unit may also provide optimal search results by taking into account the user's past search history and areas of interest. In this way, by filtering and ranking search results, the user can quickly access useful information.

[0080] The reception unit can estimate the user's emotions and adjust the search word input interface based on the emotions. For example, the reception unit can estimate the user's emotions and adjust the search word input interface based on the emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Alternatively, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of search words. This improves user convenience by adjusting the input interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The reception unit can analyze the user's past search history and suggest an appropriate method for inputting search words. The reception unit, for example, analyzes the user's past search history and suggests the optimal method for inputting search words. For example, search words that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest search words that will be used in a specific time period from the user's past search history. In this way, it is possible to suggest the optimal input method for the user by analyzing the past search history.

[0082] The reception unit can provide a suggestion function based on the user's areas of interest when a search word is entered. The reception unit provides a suggestion function based on the user's areas of interest when a search word is entered, for example. For example, the reception unit can suggest related search words based on topics recently searched by the user. It can also suggest trending search words based on the user's areas of interest. It can also suggest optimal search words by combining the user's past search history and current areas of interest. In this way, by providing a suggestion function based on the user's areas of interest, it is possible to suggest more appropriate search words.

[0083] The reception unit can select an appropriate input means depending on the user's input method when entering a search word. For example, when entering a search word, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the search word is entered using voice recognition technology. Also, if the user selects text input, the search word can be entered using a keyboard or touch panel. Also, if the user selects image input, the search word can be entered using image recognition technology. This improves input convenience by selecting the optimal input means depending on the user's input method.

[0084] The reception unit can estimate the user's emotions and prioritize the input search terms based on the emotions. The reception unit, for example, estimates the user's emotions and prioritizes the input search terms based on the emotions. For example, if the user is excited, highly relevant search terms can be displayed preferentially. Also, if the user is relaxed, a wide range of search terms can be suggested. Also, if the user is stressed, simple and intuitive search terms can be displayed preferentially. In this way, by prioritizing search terms based on the user's emotions, more appropriate search results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The reception unit can prioritize suggesting highly relevant search words based on the user's geographical location information when the user inputs a search word. For example, the reception unit prioritizes suggesting highly relevant search words by taking into account the user's geographical location information when the user inputs a search word. For example, the reception unit can suggest search words including nearby information based on the user's current location. Also, if the user is traveling, it can suggest search words including information about the travel destination. Also, if the user is interested in a specific area, it can prioritize displaying search words related to that area. In this way, it is possible to suggest highly relevant search words by taking into account the user's geographical location information.

[0086] The reception unit can analyze the user's social media activity when a search term is entered and suggest related search terms. For example, when a search term is entered, the reception unit can analyze the user's social media activity and suggest related search terms. For example, the reception unit can suggest search terms based on topics that the user frequently mentions on social media. It can also suggest related search terms by taking into account the activity of the user's friends on social media. It can also analyze the content of the user's social media posts and suggest related search terms. In this way, it is possible to suggest related search terms by analyzing social media activity.

[0087] The reception unit can customize the input method based on the user's past feedback when entering a search word. For example, the reception unit customizes the input method by reflecting the user's past feedback when entering a search word. For example, the reception unit preferentially suggests an input method that the user has used favorably in the past. The reception unit can also provide an optimal input interface based on the user's past feedback. The reception unit can also suggest a different input method, avoiding an input method that the user has been dissatisfied with in the past. In this way, the reception unit can provide the user with the optimal input method by reflecting past feedback.

[0088] The translation unit can estimate the user's emotions and adjust the translation expression based on those emotions. The translation unit, for example, uses a generation AI to estimate the user's emotions and adjust the translation expression based on those emotions. For example, if the user is relaxed, the generation AI can provide a natural and fluent translation. If the user is in a hurry, the generation AI can provide a concise and to-the-point translation. If the user is excited, the generation AI can provide a translation that includes visually appealing expressions. This allows for adjusting the translation expression based on the user's emotions, thereby providing a more appropriate translation. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] The translation unit can adjust the level of detail of the translation based on the importance of the search word during translation. The translation unit, for example, uses a generation AI to adjust the level of detail of the translation based on the importance of the search word during translation. For example, the generation AI provides a detailed translation for search words with high importance. The generation AI can also provide a concise translation for search words with low importance. The generation AI can also provide a translation with an appropriate level of detail depending on the context of the search word. In this way, by adjusting the level of detail of the translation based on the importance of the search word, an appropriate translation can be provided.

[0090] The translation unit can apply different translation algorithms based on the category of the search word during translation. The translation unit can use, for example, generative AI to apply different translation algorithms based on the category of the search word during translation. For example, an algorithm that accurately translates technical terms can be applied to technology-related search words. An algorithm that uses casual expressions can also be applied to entertainment-related search words. An algorithm that provides accurate and reliable translations can also be applied to medical-related search words. This improves translation accuracy by applying an appropriate translation algorithm depending on the category of the search word.

[0091] The translation unit can improve the accuracy of translation based on the user's past translation results during translation. The translation unit, for example, uses a generation AI to improve the accuracy of translation by referring to the user's past translation results during translation. For example, the generation AI refers to the translation style that the user has preferred in the past and performs a similar translation. The generation AI can also avoid translation results that the user was dissatisfied with in the past and perform an improved translation. The generation AI can also analyze the user's past translation history and perform the optimal translation. In this way, the accuracy of the translation is improved by referring to past translation results.

[0092] The translation unit can estimate the user's emotions and adjust the length of the translation based on those emotions. The translation unit, for example, uses a generation AI to estimate the user's emotions and adjust the length of the translation based on those emotions. For example, if the user is in a hurry, the generation AI can provide a short, to-the-point translation. If the user is relaxed, the generation AI can provide a longer translation with detailed explanations. If the user is excited, the generation AI can provide a translation with visually stimulating effects. This allows for adjusting the length of the translation based on the user's emotions, thereby providing a more appropriate translation. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The translation unit can determine the priority of translations based on when the search words were submitted during translation. The translation unit, for example, uses a generation AI to determine the priority of translations based on when the search words were submitted during translation. For example, the most recent search words are translated first. Older search words can also be translated later. Translations can also be performed with appropriate priority depending on when they were submitted. In this way, by determining the priority of translations based on when the search words were submitted, the latest information can be provided preferentially.

[0094] The translation unit can adjust the order of translations based on the relevance of search words during translation. The translation unit can use, for example, generation AI to adjust the order of translations based on the relevance of search words during translation. For example, highly relevant search words can be translated first. Also, search words with low relevance can be translated later. Furthermore, translations can be performed in an appropriate order depending on the context of the search words. In this way, by adjusting the order of translations based on the relevance of search words, highly relevant information can be provided preferentially.

[0095] The translation unit can adjust the use of technical terminology in the translation based on the user's level of expertise during translation. The translation unit, for example, uses a generation AI to adjust the use of technical terminology in the translation based on the user's level of expertise during translation. For example, if the user is an expert, the generation AI can provide a translation that makes heavy use of technical terminology. Alternatively, if the user is a beginner, the generation AI can provide a translation that explains things in simple terms. The use of appropriate technical terminology can also be adjusted based on the user's level of expertise. In this way, an appropriate translation can be provided by adjusting the use of technical terminology based on the user's level of expertise.

[0096] The search unit can estimate the user's emotions and adjust the display method of search results based on the emotions. The search unit, for example, estimates the user's emotions and adjusts the display method of search results based on the emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of search results based on the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0097] The search unit can improve search accuracy based on the interrelationships between search words during a search. For example, the search unit can improve search accuracy by considering the interrelationships between search words during a search. For example, the search unit can analyze the interrelationships between search words and preferentially display highly relevant search results. It can also provide appropriate search results by considering the context of the search words. It can also display optimal search results based on the interrelationships between search words. In this way, the search accuracy is improved by considering the interrelationships between search words.

[0098] The search unit can perform a search based on the attribute information of the person who submitted the search word. For example, the search unit can perform a search taking into account the attribute information of the person who submitted the search word. For example, the search unit can provide appropriate search results based on the age of the person who submitted the search word. It can also provide highly relevant search results based on the occupation of the person who submitted the search word. It can also provide optimal search results based on the interests and concerns of the person who submitted the search word. In this way, more appropriate search results can be provided by taking into account the attribute information of the person who submitted the search word.

[0099] The search unit can weight the search based on the frequency of submission of the search word during the search. For example, the search unit weights the search based on the frequency of submission of the search word during the search. For example, search results for search words that are submitted more frequently can be displayed preferentially. Search results for search words that are submitted less frequently can also be displayed later. Search results can also be displayed with appropriate weighting according to the frequency of submission. In this way, by weighting based on the frequency of submission of the search word, more appropriate search results can be provided.

[0100] The search unit can estimate the user's emotions and adjust the display order of search results based on the emotions. The search unit, for example, estimates the user's emotions and adjusts the display order of search results based on the emotions. For example, if the user is excited, highly relevant search results can be displayed preferentially. Also, if the user is relaxed, a wide range of search results can be displayed. Also, if the user is stressed, simple and intuitive search results can be displayed preferentially. In this way, by adjusting the display order of search results based on the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The search unit can perform a search based on the geographical distribution of search words when searching. For example, the search unit performs a search taking into account the geographical distribution of search words when searching. For example, the search unit analyzes the geographical distribution of search words to provide highly relevant search results. It can also display appropriate search results based on the geographical distribution. It can also provide optimal search results by taking into account the geographical distribution of search words. In this way, more appropriate search results can be provided by taking into account the geographical distribution of search words.

[0102] The search unit can improve the accuracy of a search based on literature related to the search word during a search. For example, the search unit can improve the accuracy of a search by referring to literature related to the search word during a search. For example, the search unit can refer to literature related to the search word and provide appropriate search results. The accuracy of the search results can also be improved based on the related literature. The search unit can also analyze literature related to the search word and provide optimal search results. In this way, the accuracy of the search can be improved by referring to related literature.

[0103] The search unit can perform a search based on the market value of the search word when searching. For example, the search unit can perform a search taking into account the market value of the search word when searching. For example, search results for search words with high market value can be displayed preferentially. Search results for search words with low market value can also be displayed later. Appropriate search results can also be provided depending on market value. In this way, more appropriate search results can be provided by taking market value into consideration.

[0104] The retranslation unit can estimate the user's emotions and adjust the expression style of the retranslation based on the emotions. The retranslation unit, for example, uses a generation AI to estimate the user's emotions and adjust the expression style of the retranslation based on the emotions. For example, if the user is relaxed, the generation AI can provide a natural and fluent retranslation. If the user is in a hurry, the generation AI can provide a concise and to-the-point retranslation. If the user is excited, the generation AI can provide a retranslation that includes visually appealing expressions. This allows for adjusting the expression style of the retranslation based on the user's emotions, thereby providing a more appropriate retranslation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The retranslation unit can adjust the level of detail of the retranslation based on the importance of the search results during retranslation. The retranslation unit, for example, uses a generation AI to adjust the level of detail of the retranslation based on the importance of the search results during retranslation. For example, the generation AI performs a detailed retranslation of search results with high importance. The generation AI can also perform a concise retranslation of search results with low importance. The generation AI can also perform a retranslation with an appropriate level of detail depending on the context of the search results. In this way, by adjusting the level of detail of the retranslation based on the importance of the search results, an appropriate retranslation can be provided.

[0106] The re-translation unit can apply different re-translation algorithms based on the category of the search result during re-translation. The re-translation unit can use, for example, generation AI to apply different re-translation algorithms based on the category of the search result during re-translation. For example, an algorithm that accurately re-translates technical terms can be applied to technology-related search results. An algorithm that uses casual expressions can also be applied to entertainment-related search results. An algorithm that performs accurate and reliable re-translation can also be applied to medical-related search results. In this way, the accuracy of re-translation is improved by applying an appropriate re-translation algorithm depending on the category of the search result.

[0107] The re-translation unit can improve the accuracy of re-translation based on the user's past re-translation results during re-translation. The re-translation unit, for example, uses a generation AI to improve the accuracy of re-translation by referring to the user's past re-translation results during re-translation. For example, the generation AI refers to the re-translation style that the user has preferred in the past and performs a similar re-translation. The generation AI can also perform an improved re-translation by avoiding re-translation results that the user was dissatisfied with in the past. The generation AI can also analyze the user's past re-translation history and perform the optimal re-translation. In this way, the accuracy of re-translation is improved by referring to the past re-translation results.

[0108] The retranslation unit can estimate the user's emotions and adjust the length of the retranslation based on the emotions. The retranslation unit, for example, uses a generation AI to estimate the user's emotions and adjust the length of the retranslation based on the emotions. For example, if the user is in a hurry, the generation AI can provide a short, to-the-point retranslation. If the user is relaxed, the generation AI can provide a longer retranslation that includes detailed explanations. If the user is excited, the generation AI can provide a retranslation that adds visually stimulating effects. This allows for adjusting the length of the retranslation based on the user's emotions, thereby providing a more appropriate retranslation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The re-translation unit can determine the priority of re-translation based on the time of submission of search results during re-translation. The re-translation unit, for example, uses generation AI to determine the priority of re-translation based on the time of submission of search results during re-translation. For example, the most recent search results are re-translated with priority. Older search results can also be re-translated at a later date. Re-translation can also be performed with appropriate priority depending on the time of submission. In this way, by determining the priority of re-translation based on the time of submission of search results, the latest information can be provided preferentially.

[0110] The re-translation unit can adjust the order of re-translation based on the relevance of the search results during re-translation. The re-translation unit adjusts the order of re-translation based on the relevance of the search results during re-translation, for example, using a generation AI. For example, highly relevant search results can be re-translated preferentially. Also, less relevant search results can be re-translated later. Also, re-translation can be performed in an appropriate order depending on the context of the search results. In this way, by adjusting the order of re-translation based on the relevance of the search results, highly relevant information can be provided preferentially.

[0111] The retranslation unit can adjust the use of technical terminology in the retranslation based on the user's level of expertise during retranslation. The retranslation unit, for example, uses a generation AI to adjust the use of technical terminology in the retranslation based on the user's level of expertise during retranslation. For example, if the user is an expert, the generation AI can perform a retranslation that makes heavy use of technical terminology. Also, if the user is a beginner, the generation AI can perform a retranslation that explains things in simpler terms. The use of appropriate technical terminology can also be adjusted based on the user's level of expertise. In this way, an appropriate retranslation can be provided by adjusting the use of technical terminology based on the user's level of expertise.

[0112] The providing unit can estimate the user's emotions and adjust the display method of the search results to be provided based on the emotions. The providing unit, for example, estimates the user's emotions and adjusts the display method of the search results to be provided based on the emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the search results based on the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The providing unit can select the optimal display method based on the user's past usage history of search results when providing the results. For example, the providing unit selects the optimal display method by referring to the user's past usage history of search results when providing the results. For example, the providing unit preferentially provides a display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past usage history. It can also provide a different display method, avoiding a display method that the user has been dissatisfied with in the past. In this way, the optimal display method can be provided to the user by referring to the past usage history.

[0114] The providing unit can customize the display content based on the user's current task when providing the information. For example, the providing unit customizes the display content according to the user's current task when providing the information. For example, when the user is at work, relevant information can be displayed with priority. Also, when the user is on vacation, information that helps relaxation can be displayed with priority. Also, the optimal display content can be provided according to the user's current task. In this way, by customizing the display content according to the current task, useful information can be provided to the user.

[0115] The providing unit can improve the display method based on user feedback when providing the display. For example, the providing unit improves the display method by reflecting user feedback when providing the display. For example, when a user provides feedback on the provided display method, the providing unit improves the display method based on that feedback. The providing unit can also analyze user feedback and provide an optimal display method. The providing unit can also continuously improve the display method by referring to past user feedback. In this way, the display method can be continuously improved by reflecting feedback.

[0116] The providing unit can estimate the user's emotions and determine the priority of search results to be provided based on the emotions. The providing unit, for example, estimates the user's emotions and determines the priority of search results to be provided based on the emotions. For example, if the user is excited, highly relevant search results can be displayed preferentially. Also, if the user is relaxed, a wide range of search results can be displayed. Also, if the user is stressed, simple and intuitive search results can be displayed preferentially. In this way, by determining the priority of search results based on the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0117] The providing unit can select the optimal display method based on the user's geographical location information at the time of providing. For example, the providing unit selects the optimal display method taking into account the user's geographical location information at the time of providing. For example, based on the user's current location, search results including nearby information can be preferentially displayed. Also, if the user is traveling, search results including information about the travel destination can be preferentially displayed. Also, if the user is interested in a specific area, search results related to that area can be preferentially displayed. In this way, by taking into account the geographical location information, it is possible to provide information that is highly relevant to the user.

[0118] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, the optimal display method for the user can be provided by taking into consideration the device information.

[0119] The providing unit can make the display content multilingual based on the user's language setting when providing the information. The providing unit, for example, makes the display content multilingual based on the user's language setting when providing the information. For example, the providing unit automatically sets the language of the search results based on the language setting of the user's device. In addition, if the user uses multiple languages, a language switching function can be provided. In addition, if the user selects a specific language, search results can be provided in that language. In this way, by providing multilingual support according to the language setting, it is possible to provide information that is easy for the user to understand. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, translation unit, search unit, retranslation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives search words entered by a user. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the search words into multiple languages ​​using a generation AI. The search unit is realized by the specific processing unit 290 of the data processing device 12 and performs searches on search engines of each language using the translated search words. The retranslation unit is realized by the specific processing unit 290 of the data processing device 12 and retranslates the search results. The provision unit is realized by the output device 40 of the smart device 14 and provides the retranslated search results to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, translation unit, search unit, retranslation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives search words entered by a user. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the search words into multiple languages ​​using a generation AI. The search unit is realized by the specific processing unit 290 of the data processing device 12 and performs searches on search engines in each language using the translated search words. The retranslation unit is realized by the specific processing unit 290 of the data processing device 12 and retranslates the search results. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the retranslated search results to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, translation unit, search unit, retranslation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives search words entered by a user. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the search words into multiple languages ​​using a generation AI. The search unit is realized by the specific processing unit 290 of the data processing device 12 and performs searches on search engines in each language using the translated search words. The retranslation unit is realized by the specific processing unit 290 of the data processing device 12 and retranslates the search results. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the retranslated search results to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, translation unit, search unit, retranslation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives search words entered by a user. The translation unit is realized by the specific processing unit 290 of the data processing device 12 and translates the search words into multiple languages ​​using a generation AI. The search unit is realized by the specific processing unit 290 of the data processing device 12 and performs searches on search engines in each language using the translated search words. The retranslation unit is realized by the specific processing unit 290 of the data processing device 12 and retranslates the search results. The provision unit is realized by the speaker 240 of the robot 414 and provides the retranslated search results to the user.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The reception unit can estimate the user's emotions and adjust the search word input interface based on the emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Alternatively, if the user is in a hurry, voice input can be prioritized to enable quick input of search words. In this way, user convenience can be improved by adjusting the input interface according to the user's emotions.

[0122] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is relaxed, the generation AI can provide a natural and fluent translation. If the user is in a hurry, the generation AI can provide a concise and to-the-point translation. If the user is excited, the generation AI can provide a translation that includes visually appealing expressions. This allows the system to provide more appropriate translations by adjusting the translation's expression based on the user's emotions.

[0123] The retranslation unit can estimate the user's emotions and adjust the way the retranslation is expressed based on those emotions. For example, if the user is relaxed, the generation AI can provide a natural and fluent retranslation. If the user is in a hurry, the generation AI can provide a concise and to-the-point retranslation. If the user is excited, the generation AI can provide a retranslation that includes visually appealing expressions. This allows the system to provide a more appropriate retranslation by adjusting the way the retranslation is expressed based on the user's emotions.

[0124] The search unit can estimate the user's emotions and adjust the display method of search results based on those emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of search results based on the user's emotions, more appropriate information can be provided.

[0125] The providing unit can estimate the user's emotions and determine the priority of search results to be provided based on the emotions. For example, if the user is excited, highly relevant search results can be displayed with priority. Also, if the user is relaxed, a wide range of search results can be displayed with priority. Also, if the user is stressed, simple and intuitive search results can be displayed with priority. In this way, by determining the priority of search results based on the user's emotions, more appropriate information can be provided.

[0126] The reception unit can analyze the user's past search history and suggest appropriate methods for inputting search words. For example, it can automatically display search words that the user has frequently input in the past as candidates. It can also prioritize suggestions for input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest search words that will be used during a specific time period based on the user's past search history. In this way, by analyzing the user's past search history, it can suggest the optimal input method for the user.

[0127] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the search word. For example, the generation AI can provide a detailed translation for search words with high importance. The generation AI can also provide a concise translation for search words with low importance. The generation AI can also provide a translation with an appropriate level of detail depending on the context of the search word. This allows the system to provide an appropriate translation by adjusting the level of detail of the translation based on the importance of the search word.

[0128] The search unit can improve search accuracy based on the interrelationships between search words during a search. For example, it can analyze the interrelationships between search words and prioritize the display of highly relevant search results. It can also provide appropriate search results by taking into account the context of the search words. It can also display optimal search results based on the interrelationships between search words. In this way, the search accuracy is improved by taking into account the interrelationships between search words.

[0129] When providing the search results, the providing unit can select the optimal display method based on the user's past usage history of search results. For example, the providing unit can provide preferentially the display method that the user has used favorably in the past. The providing unit can also suggest the optimal display method based on the user's past usage history. The providing unit can also provide a different display method, avoiding a display method that the user has been dissatisfied with in the past. In this way, the optimal display method can be provided to the user by referring to the past usage history.

[0130] When providing the display, the providing unit can select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, a display method that matches the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Also, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, the optimal display method for the user can be provided by taking into consideration the device information.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The reception unit receives a search term entered by a user. For example, the user enters a search term such as "latest technology news." Step 2: The translation unit uses the generation AI to translate the search words received by the reception unit into multiple languages. For example, the generation AI translates the search words into English, Chinese, French, etc. The generation AI takes into account the grammar and vocabulary of each language to perform accurate translations. Step 3: The search unit searches the search engines in each language using the search words translated by the translation unit. For example, the search unit searches an English search engine using an English search word, and searches a Chinese search engine using a Chinese search word. This provides search results in each language. Step 4: The re-translation unit retranslates the search results obtained by the search unit. For example, the generation AI translates English or Chinese search results into Japanese. The generation AI takes into account the context of the search results to perform an accurate translation. Step 5: The providing unit provides the search results retranslated by the retranslation unit to the user. For example, the user can check the search results in their own language.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a search word; a translation unit that translates the search words received by the reception unit into multiple languages; a search unit that performs a search on a search engine in each language using the search words translated by the translation unit; a re-translation unit that re-translates the search results obtained by the search unit; a providing unit that provides the search results retranslated by the retranslation unit to a user. A system characterized by:

2. The translation unit Translate search terms based on the grammar and vocabulary of each language 2. The system of claim 1.

3. The re-translation unit Retranslating search results based on their context 2. The system of claim 1.

4. The providing unit Allows users to customize search results 2. The system of claim 1.

5. The providing unit Filtering functionality to display only information in a specific language or region 2. The system of claim 1.

6. The search unit It has a ranking function that filters search results and arranges them in order of usefulness for the user.

2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the search word input interface based on those emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past search history and suggests appropriate ways to enter search terms 2. The system of claim 1.

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