System

Abstract

To provide a system capable of collecting, translating and personalizing a travel plan and sightseeing information in real time and providing them to a user.SOLUTION: The system comprises a collector, an analyzer, a translator, a personalizer, a travel planning support, an educational content generator, and a provider. The collection unit collects information from a data source. The analysis unit analyzes the collected information. The translation unit translates the analyzed information in a multilingual manner. The personalizer learns the user's behavior patterns or preferences based on the translated information and provides personalized information. The travel plan support unit supports travel planning based on the provided information. An educational content generation part generates educational content related to the climate and history of the tourist resort on the basis of the supported information. The provision unit distributes the generated information to a mobile device or an IoT device of the user in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the drawback of being time-consuming to collect, translate, and personalize travel planning and tourist information, and making it difficult to provide information in real time.

[0005] The system according to the embodiment aims to collect, translate, and personalize travel planning and sightseeing information in real time and provide it to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a translation unit, a personalization unit, a travel planning support unit, an educational content generation unit, and a provision unit. The collection unit collects information from data sources. The analysis unit analyzes the information collected by the collection unit. The translation unit translates the information analyzed by the analysis unit into multiple languages. The personalization unit learns a user's behavioral patterns or preferences based on the information translated by the translation unit and provides personalized information. The travel planning support unit supports travel planning based on the information provided by the personalization unit. The educational content generation unit generates educational content related to the climate and history of tourist destinations based on the information supported by the travel planning support unit. The provision unit delivers the information generated by the educational content generation unit to the user's mobile device or IoT device in real time. [Effects of the Invention]

[0007] The system according to the embodiment can collect, translate, and personalize travel planning and sightseeing information in real time and provide it to users. [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) An AI platform according to an embodiment of the present invention is a system that collects information from data sources, analyzes it with a generation AI, translates it in multiple languages, and provides personalized information based on the user's behavioral patterns and preferences. The AI ​​platform collects information from data sources in real time, analyzes it with a generation AI, and instantly translates it in multiple languages. This translated information includes weather, emergency information, traffic delay information, information on local festivals and specialty dishes, and information that understands local colloquialisms and dialects. Furthermore, the generation AI learns the user's behavioral patterns and preferences and provides personalized information. The system also has a travel planning support function, suggesting sightseeing routes and making restaurant reservations. Finally, the generation AI automatically generates educational content for learning about the climate and history of tourist destinations. For example, the AI ​​platform collects weather information such as current temperature, precipitation probability, and wind speed. For example, emergency information such as disaster information on earthquakes and typhoons is collected. For example, traffic delay information such as train and bus operation status is collected. For example, information on local festivals and specialty dishes is collected, such as the date, time, location, and characteristics of the dishes. For example, the AI ​​platform collects words and expressions used in a specific region, including information on local colloquialisms and dialects. Next, the AI ​​platform uses a generative AI to analyze the collected information and translate it into multiple languages. For example, weather information is translated from Japanese to English, Chinese, French, etc. Emergency information is similarly translated into multiple languages. For example, traffic delay information, local festival information, and specialty food information are similarly translated into multiple languages. For example, information containing local colloquialisms and dialects is also translated into the appropriate language. Next, the AI ​​platform uses a generative AI to learn the user's behavioral patterns and preferences and provide personalized information. For example, it suggests the next tourist spot or restaurant to visit based on data on tourist spots visited by the user and restaurants used by the user. For example, it provides information tailored to the user's preferences. Next, the AI ​​platform provides travel planning support functions, suggesting sightseeing routes and making restaurant reservations. For example, when a user inputs the tourist spots they want to visit, the generative AI suggests the optimal sightseeing route. For example, when a user inputs the restaurant they want to go to, the generative AI makes the reservation.Next, the AI ​​platform automatically generates educational content to help users learn about the culture and customs of tourist destinations. For example, it generates information about the history and culture of tourist destinations and provides it to users. This allows the AI ​​platform to learn about tourist destinations before they visit. This allows the AI ​​platform to translate information collected from data sources into multiple languages ​​and provide personalized information based on users' behavioral patterns and preferences, thereby assisting with travel planning and generating educational content. This allows the AI ​​platform to deliver information in real time to users' mobile devices and IoT devices. For example, information is delivered to devices such as smartphones, tablets, and smartwatches. This allows users to obtain the information they need anytime, anywhere.

[0029] An AI platform according to an embodiment includes a collection unit, an analysis unit, a translation unit, a personalization unit, a travel planning support unit, an educational content generation unit, and a provision unit. The collection unit collects information from data sources. Examples of data sources include, but are not limited to, websites, databases, and APIs. The collection unit collects information including, for example, weather information, emergency information, traffic delay information, information on local festivals and specialty dishes, and local colloquialisms and dialects. For example, the collection unit collects data such as current temperature, probability of precipitation, and wind speed as weather information. The collection unit can also collect disaster information such as earthquakes and typhoons as emergency information. The collection unit can also collect train and bus operation status as traffic delay information. The collection unit can also collect information on local festivals and specialty dishes, such as dates, times, locations, and characteristics of the dishes. The collection unit can also collect words and expressions used in specific regions as information including local colloquialisms and dialects. The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using, for example, statistical analysis, machine learning algorithms, data mining, or the like, but is not limited to these examples. For example, the analysis unit may analyze collected weather information to understand current weather conditions. The analysis unit may also analyze collected emergency information to assess the impact of disasters. The analysis unit may also analyze collected traffic delay information to understand traffic conditions. The analysis unit may also analyze collected information on local festivals and specialty dishes to understand details of the events. The analysis unit may also analyze collected local colloquialisms and dialects to understand regional linguistic characteristics. The translation unit translates the information analyzed by the analysis unit into multiple languages. The translation may be performed using, for example, neural machine translation, rule-based translation, or the like, but is not limited to these examples. For example, the translation unit may translate the collected weather information from Japanese to English, Chinese, French, or the like. The translation unit may also translate the collected emergency information into multiple languages. The translation unit may also translate the collected traffic delay information into multiple languages. The translation department can also translate collected information about local festivals and local specialties into multiple languages.The translation unit can also translate the collected local colloquialisms and dialects into an appropriate language. The personalization unit learns the user's behavioral patterns and preferences based on the information translated by the translation unit and provides personalized information. Learning can be performed, for example, using a machine learning algorithm, a type of dataset, or other methods. For example, the personalization unit can suggest tourist spots and restaurants to visit next based on data on tourist spots visited by the user in the past and restaurants used by the user. The personalization unit can also provide information tailored to the user's preferences. The travel planning support unit supports travel planning based on the information provided by the personalization unit. Support can be performed, for example, by suggesting sightseeing routes and reserving accommodations, for example, but is not limited to these methods. For example, the travel planning support unit can suggest an optimal sightseeing route when the user inputs tourist spots they want to visit. The travel planning support unit can also make reservations when the user inputs a restaurant they want to go to. The educational content generation unit generates educational content related to the climate and history of tourist spots based on the information supported by the travel planning support unit. Generation can be performed, for example, using a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to these methods. For example, the educational content generation unit generates information about the history and culture of a tourist destination and provides it to the user. The educational content generation unit can also generate detailed information about the climate and history of the tourist destination. The providing unit delivers the information generated by the educational content generation unit to the user's mobile device or IoT device in real time. The delivery can be performed, for example, by a method such as a communication protocol used or a delivery delay time, but is not limited to these examples. For example, the providing unit delivers information to devices such as smartphones, tablets, and smartwatches. The providing unit can also deliver information in real time. As a result, the AI ​​platform according to the embodiment can translate information collected from data sources into multiple languages, provide personalized information based on the user's behavioral patterns and preferences, and support travel planning and generate educational content.

[0030] The collection unit can collect information including weather information, emergency information, traffic delay information, information on local festivals and specialty dishes, and local colloquialisms or dialects. For example, the collection unit collects data such as current temperature, probability of precipitation, and wind speed as weather information. The collection unit can also collect disaster information such as earthquakes and typhoons as emergency information. The collection unit can also collect train and bus operation status as traffic delay information. The collection unit can also collect information on local festivals and specialty dishes, such as dates, times, locations, and characteristics of dishes. The collection unit can also collect words and expressions used in specific regions as information including local colloquialisms or dialects. This allows the collection unit to collect a wide variety of information, thereby broadening the range of information provided to users. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input information collected from a data source into a generation AI and have the generation AI analyze and classify the information.

[0031] The translation unit can translate collected information into multiple languages. For example, the translation unit translates collected weather information from Japanese to English, Chinese, French, etc. The translation unit can also translate collected emergency information into multiple languages. The translation unit can also translate collected traffic delay information into multiple languages. The translation unit can also translate collected information about local festivals and specialty foods into multiple languages. The translation unit can also translate collected local colloquialisms and dialects into an appropriate language. This allows the translation unit to translate information into multiple languages, thereby accommodating users who speak different languages. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input collected information into a generation AI and have the generation AI translate the information.

[0032] The personalization unit can learn the user's behavioral patterns and preferences and provide personalized information. For example, the personalization unit can suggest tourist spots and restaurants that the user should visit next based on data on tourist spots visited in the past and restaurants used by the user. The personalization unit can also provide information tailored to the user's preferences. For example, the personalization unit can prioritize providing information about the user's favorite dishes or events that interest the user. In this way, the personalization unit can learn the user's behavioral patterns and preferences and provide more personalized information. Some or all of the above-mentioned processing in the personalization unit may be performed using, or without, a generation AI. For example, the personalization unit can input data on the user's behavioral patterns and preferences into the generation AI and have the generation AI generate personalized information.

[0033] The travel planning support unit can suggest sightseeing routes or make restaurant reservations. For example, when a user inputs tourist spots they want to visit, the travel planning support unit suggests an optimal sightseeing route. The travel planning support unit can also make reservations when a user inputs a restaurant they want to go to. For example, the travel planning support unit suggests an optimal sightseeing route based on the user's preferences and behavioral patterns. The travel planning support unit can also make restaurant reservations according to the user's schedule. In this way, the travel planning support unit supports the user's travel planning by suggesting sightseeing routes and making restaurant reservations. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input user input data into the generation AI and have the generation AI suggest sightseeing routes and make restaurant reservations.

[0034] The educational content generation unit can generate educational content related to the climate and history of a tourist destination. The educational content generation unit can generate, for example, information related to the history and culture of a tourist destination and provide it to a user. The educational content generation unit can also generate detailed information related to the climate and history of a tourist destination. For example, the educational content generation unit can generate information related to historical events and cultural features of a tourist destination. The educational content generation unit can also generate educational content related to the climate and history of a tourist destination and provide it to a user. In this way, the educational content generation unit can generate educational content related to the climate and history of a tourist destination, allowing a user to learn about the tourist destination. Some or all of the above-mentioned processing in the educational content generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the educational content generation unit can input information about the tourist destination into the generation AI and cause the generation AI to generate educational content.

[0035] The providing unit can deliver the generated information to the user's mobile device or IoT device in real time. The providing unit delivers information to devices such as smartphones, tablets, and smartwatches. The providing unit can also deliver information in real time. For example, the providing unit delivers information taking into account the communication protocol used and delivery delay time. The providing unit can also customize the means of providing information depending on the user's device status. For example, if the user is using a smartphone, the providing unit can select an information delivery method that matches the screen size. Also, if the user is using a tablet, the providing unit can select an information delivery method optimized for a large screen. This allows the providing unit to deliver information in real time, allowing the user to always obtain the latest information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the generated information to a generation AI and have the generation AI execute information delivery.

[0036] The collection unit can analyze past collected data and select the optimal information collection method. For example, the collection unit can optimize the type of information to be collected during a specific time period based on past collected data. The collection unit can also prioritize the collection of information that is of most interest to the user based on past collected data. The collection unit can also analyze past collected data and optimize the collection method (API, scraping, etc.). This improves the efficiency and accuracy of information collection by analyzing past data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input past collected data into a generation AI and have the generation AI select the optimal information collection method.

[0037] When collecting information, the collection unit can filter the information based on the user's current location information and areas of interest. For example, the collection unit prioritizes collecting nearby weather information and traffic information based on the user's current location. The collection unit can also filter and collect related information based on the user's areas of interest (e.g., gourmet food, sightseeing). The collection unit can also combine the user's location information and areas of interest to collect optimal information. This allows for filtering information based on the user's location information and areas of interest, thereby providing more relevant information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's location information and areas of interest data into the generation AI and have the generation AI perform information filtering.

[0038] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Also, if the user uses image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting nearby weather information and traffic information based on the user's current location. The collection unit can also prioritize collecting local festival and event information based on the user's location information. The collection unit can also prioritize collecting emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0040] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the user's social media posts and collect information on related events and places. The collection unit can also collect related information based on the user's social media check-in information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0041] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the frequency and method of information collection based on feedback provided by the user in the past. The collection unit can also customize the type of information to be collected by referring to the user's past feedback. The collection unit can also optimize the means of information collection (API, scraping, etc.) by reflecting the user's feedback. This improves the accuracy of information collection by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. The analysis unit can also perform a quick and detailed analysis on urgent information. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a weather data analysis algorithm to weather information. The analysis unit can also apply a disaster information analysis algorithm to emergency information. The analysis unit can also apply a traffic data analysis algorithm to traffic delay information. By applying different analysis algorithms depending on the category of information, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of analysis detail by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also analyze current information by referring to past information. The analysis unit can also give top priority to analyzing emergency information. This allows for determining the priority of analysis based on the time when the information was collected, making it possible to provide faster and more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the information was collected into the generation AI and have the generation AI determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also optimize the order of analysis based on the relevance of the information. This enables more efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high levels of expertise. The analysis unit can also provide analysis results that are concise and easy to understand to a user with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed based on the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0048] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the information. For example, the translation unit provides a detailed translation for important information. The translation unit can also provide a concise translation for general information. The translation unit can also provide a quick and detailed translation for urgent information. By adjusting the level of detail of the translation based on the importance of the information, more appropriate translation results can be provided. Some or all of the above-described processing in the translation unit may be performed using, or without, the generation AI, for example. For example, the translation unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the translation.

[0049] The translation unit can apply different translation algorithms depending on the category of information during translation. For example, the translation unit can apply a weather data translation algorithm to weather information. The translation unit can also apply a disaster information translation algorithm to emergency information. The translation unit can also apply a traffic data translation algorithm to traffic delay information. By applying different translation algorithms depending on the category of information, more accurate translation results can be provided. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the translation unit can input information category data into the generation AI and have the generation AI apply an appropriate translation algorithm.

[0050] During translation, the translation unit can improve the accuracy of the translation by referring to the user's past translation results. The translation unit, for example, optimizes the translation algorithm based on the user's past translation results. The translation unit can also adjust the level of translation detail by referring to the user's past translation results. The translation unit can also improve the accuracy of the translation by reflecting the user's past translation results. In this way, the accuracy of the translation is improved by referring to the user's past translation results. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input the user's past translation result data into the generation AI and have the generation AI improve the accuracy of the translation.

[0051] During translation, the translation unit can determine translation priorities based on when information was collected. For example, the translation unit prioritizes translating the most recent information. The translation unit can also translate current information by referring to past information. The translation unit can also give top priority to translating emergency information. This allows for faster and more appropriate translation results to be provided by determining translation priorities based on when information was collected. Some or all of the above-described processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the translation unit can input information collection time data into the generation AI and have the generation AI determine the translation priorities.

[0052] The translation unit can adjust the order of translation based on the relevance of information during translation. For example, the translation unit prioritizes translation of highly relevant information. The translation unit can also postpone translation of less relevant information. The translation unit can also optimize the order of translation based on the relevance of information. This enables more efficient translation by adjusting the order of translation based on the relevance of information. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, the generation AI, for example. For example, the translation unit can input information relevance data into the generation AI and have the generation AI adjust the order of translation.

[0053] During translation, the translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise. For example, the translation unit can provide a translation result that uses a lot of technical terminology to a user with high level of expertise. The translation unit can also provide a concise and easy-to-understand translation result to a user with low level of expertise. The translation unit can also adjust the way the translation result is expressed based on the user's level of expertise. This makes it possible to provide a translation result that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0054] During personalization, the personalization unit can analyze the user's past behavioral patterns and select the optimal personalization method. For example, the personalization unit can prioritize providing information that is likely to be of interest to the user based on the user's past behavioral patterns. The personalization unit can also analyze the user's past behavioral patterns and determine the optimal timing for providing information. The personalization unit can also customize the method of displaying information by referring to the user's past behavioral patterns. This enables more appropriate personalization by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the personalization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the user's past behavioral pattern data into the generation AI and have the generation AI select the optimal personalization method.

[0055] During personalization, the personalization unit can customize the personalization means based on the user's current living situation. For example, if the user is traveling, the personalization unit can prioritize providing travel-related information. Also, if the user is working, the personalization unit can prioritize providing work-related information. The personalization unit can also customize the way information is displayed based on the user's current living situation. In this way, by customizing the personalization means based on the user's current living situation, more appropriate information can be provided. Some or all of the above-described processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the user's living situation data into the generation AI and cause the generation AI to customize the personalization means.

[0056] During personalization, the personalization unit can improve the personalization method by reflecting user feedback. For example, the personalization unit adjusts the frequency and method of information provision based on user feedback. The personalization unit can also customize the type of information to be provided by referring to user feedback. The personalization unit can also improve the accuracy of personalization by reflecting user feedback. In this way, the accuracy of personalization is improved by reflecting user feedback. Some or all of the above-described processing in the personalization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input user feedback data into the generation AI and cause the generation AI to improve the personalization method.

[0057] During personalization, the personalization unit can select the optimal personalization method by taking into account the user's geographical location information. For example, the personalization unit can prioritize providing information about nearby events and stores based on the user's current location. The personalization unit can also provide information about local landmarks and tourist spots based on the user's location information. The personalization unit can also prioritize providing emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the personalization unit may be performed using, or without, a generation AI. For example, the personalization unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal personalization method.

[0058] During personalization, the personalization unit can analyze the user's social media activity and suggest personalization methods. For example, the personalization unit can analyze the user's social media posts and provide information on related events and places. The personalization unit can also provide relevant information based on the user's social media check-in information. The personalization unit can also provide relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing in the personalization unit may be performed using, or without, a generation AI. For example, the personalization unit can input the user's social media data into the generation AI and have the generation AI suggest personalization methods.

[0059] During personalization, the personalization unit can customize the personalization method by reflecting the user's past feedback. For example, the personalization unit adjusts the frequency and method of information provision based on the user's past feedback. The personalization unit can also customize the type of information to be provided by referring to the user's past feedback. The personalization unit can also improve the accuracy of personalization by reflecting the user's feedback. In this way, the accuracy of personalization is improved by reflecting the user's past feedback. Some or all of the above-described processing in the personalization unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the personalization unit can input user feedback data into the generation AI and cause the generation AI to customize the personalization method.

[0060] When planning a trip, the travel planning support unit can propose an optimal travel plan by referring to the user's past travel history. For example, the travel planning support unit can propose tourist spots that the user may be interested in based on the user's past travel history. The travel planning support unit can also propose an optimal travel route by referring to the user's past travel history. The travel planning support unit can also improve the accuracy of the travel plan by reflecting the user's past travel history. In this way, by referring to the user's past travel history, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's past travel history data into the generation AI and have the generation AI propose an optimal travel plan.

[0061] The travel planning support unit can analyze the user's current living situation and interests when planning a trip and propose an optimal travel route. The travel planning support unit can, for example, propose an optimal travel route based on the user's current living situation. The travel planning support unit can also analyze the user's interests and propose attractive tourist destinations. The travel planning support unit can also propose an optimal travel route by combining the user's living situation and interests. In this way, by analyzing the user's current living situation and interests, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the travel planning support unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the travel planning support unit can input the user's living situation and interest data into the generation AI and have the generation AI propose an optimal travel route.

[0062] The travel planning support unit can improve the travel planning method by reflecting user feedback when planning a trip. For example, the travel planning support unit adjusts the frequency and method of travel planning based on user feedback. The travel planning support unit can also customize the suggested travel route by referring to user feedback. The travel planning support unit can also improve the accuracy of the travel plan by reflecting user feedback. In this way, the accuracy of the travel plan is improved by reflecting user feedback. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input user feedback data into the generation AI and have the generation AI improve the travel planning method.

[0063] The travel planning support unit can propose an optimal travel route by taking into account the user's geographical location information when planning a trip. For example, the travel planning support unit prioritizes proposing nearby tourist attractions based on the user's current location. The travel planning support unit can also propose an optimal travel route based on the user's location information. The travel planning support unit can also propose a travel route that reflects emergency information and disaster information by taking into account the user's location information. This makes it possible to provide a more appropriate travel plan by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the travel planning support unit may be performed using, or without, a generation AI. For example, the travel planning support unit can input the user's location information data into the generation AI and have the generation AI propose an optimal travel route.

[0064] When planning a trip, the travel planning support unit can analyze the user's social media activity and suggest travel plan options. For example, the travel planning support unit can analyze the user's social media posts and suggest related tourist spots and events. The travel planning support unit can also suggest related travel routes based on the user's social media check-in information. The travel planning support unit can also suggest related travel plans by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate travel plans can be provided. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's social media data into the generation AI and have the generation AI suggest travel plan options.

[0065] The travel planning support unit can customize the travel planning method by reflecting the user's past feedback when planning a trip. The travel planning support unit, for example, adjusts the frequency and method of travel planning based on the user's past feedback. The travel planning support unit can also customize the suggested travel route by referring to the user's past feedback. The travel planning support unit can also improve the accuracy of the travel plan by reflecting the user's past feedback. In this way, the accuracy of the travel plan is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's feedback data into the generation AI and have the generation AI customize the travel planning method.

[0066] When generating educational content, the educational content generation unit can generate optimal educational content by referring to the user's past learning history. The educational content generation unit, for example, generates educational content that is likely to interest the user based on the user's past learning history. The educational content generation unit can also suggest an optimal learning route by referring to the user's past learning history. The educational content generation unit can also improve the accuracy of the educational content by reflecting the user's past learning history. This makes it possible to provide more appropriate educational content by referring to the user's past learning history. Some or all of the above-mentioned processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input the user's past learning history data into the generation AI and cause the generation AI to generate optimal educational content.

[0067] When generating educational content, the educational content generation unit can analyze the user's current interests and generate optimal educational content. The educational content generation unit, for example, generates relevant educational content based on the user's current interests. The educational content generation unit can also analyze the user's interests and generate educational content that attracts their interest. The educational content generation unit can also generate optimal educational content by combining the user's interests and concerns. This makes it possible to provide more appropriate educational content by analyzing the user's current interests and concerns. Some or all of the above-described processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input user interest data into the generation AI and cause the generation AI to generate optimal educational content.

[0068] The educational content generation unit can improve the educational content generation method by reflecting user feedback when generating the educational content. For example, the educational content generation unit adjusts the frequency and method of the educational content based on user feedback. The educational content generation unit can also customize the type of educational content to be generated by referring to user feedback. The educational content generation unit can also improve the accuracy of the educational content by reflecting user feedback. In this way, the accuracy of the educational content is improved by reflecting user feedback. Some or all of the above-mentioned processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input user feedback data into the generation AI and cause the generation AI to improve the educational content generation method.

[0069] When generating educational content, the educational content generation unit can generate optimal educational content by taking into account the user's geographical location information. For example, the educational content generation unit generates educational content related to local history and culture based on the user's current location. The educational content generation unit can also generate educational content related to nearby famous places and tourist attractions based on the user's location information. The educational content generation unit can also generate educational content that reflects emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant educational content by taking into account the user's geographical location information. Some or all of the above-described processing in the educational content generation unit may be performed using, or without, a generation AI. For example, the educational content generation unit can input the user's location information data into the generation AI and cause the generation AI to generate optimal educational content.

[0070] When generating educational content, the educational content generation unit can analyze the user's social media activities to generate relevant educational content. For example, the educational content generation unit analyzes the user's social media posts to generate relevant educational content. The educational content generation unit can also generate relevant educational content based on the user's social media check-in information. The educational content generation unit can also generate relevant educational content by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more relevant educational content can be provided. Some or all of the above-described processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input the user's social media data into the generation AI and cause the generation AI to generate relevant educational content.

[0071] When generating educational content, the educational content generation unit can customize the method of generating the educational content by reflecting the user's past feedback. The educational content generation unit, for example, adjusts the frequency and method of the educational content based on the user's past feedback. The educational content generation unit can also customize the type of educational content to be generated by referring to the user's past feedback. The educational content generation unit can also improve the accuracy of the educational content by reflecting the user's past feedback. In this way, the accuracy of the educational content is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the educational content generation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the educational content generation unit can input user feedback data into the generation AI and cause the generation AI to customize the method of generating the educational content.

[0072] When providing information, the providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the providing unit can prioritize providing information that is likely to be of interest to the user based on the user's past usage history. The providing unit can also determine the optimal timing for providing information by referring to the user's past usage history. The providing unit can also improve the accuracy of information provision by reflecting the user's past usage history. In this way, more appropriate information can be provided by referring to the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0073] When providing information, the providing unit can customize the means of providing information based on the user's current device status. For example, if the user is using a smartphone, the providing unit can select an information providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can select an information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select an information providing method that is concise and highly visible. This allows for more appropriate information to be provided by customizing the means of providing information based on the user's current device status. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device status data into the generation AI and cause the generation AI to customize the means of providing information.

[0074] The providing unit can improve the method of providing information by reflecting user feedback when providing information. For example, the providing unit can adjust the frequency and method of providing information based on user feedback. The providing unit can also customize the type of information to be provided by referring to user feedback. The providing unit can also improve the accuracy of information provision by reflecting user feedback. In this way, the accuracy of information provision is improved by reflecting user feedback. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the information provision method.

[0075] When providing information, the providing unit can select the optimal information provision method by taking into account the user's geographical location information. For example, the providing unit can prioritize providing information about nearby events and stores based on the user's current location. The providing unit can also provide information about local landmarks and tourist spots based on the user's location information. The providing unit can also prioritize providing emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal information provision method.

[0076] When providing information, the providing unit can analyze the user's social media activity and suggest means for providing the information. For example, the providing unit can analyze the user's social media posts and provide information on related events and places. The providing unit can also provide relevant information based on the user's social media check-in information. The providing unit can also provide relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to suggest means for providing information.

[0077] When providing information, the providing unit can customize the method of providing information by reflecting the user's past feedback. The providing unit, for example, adjusts the frequency and method of providing information based on the user's past feedback. The providing unit can also customize the type of information to be provided by referring to the user's past feedback. The providing unit can also improve the accuracy of information provision by reflecting the user's feedback. In this way, the accuracy of information provision is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to customize the method of providing information.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The collection unit can analyze past collected data and select the optimal information collection method. For example, it can optimize the type of information to be collected during a specific time period based on past collected data. The collection unit can also prioritize the collection of information that is of most interest to users based on past collected data. The collection unit can also analyze past collected data and optimize the collection method (API, scraping, etc.). This improves the efficiency and accuracy of information collection by analyzing past data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI, for example. For example, the collection unit can input past collected data into the generation AI and have the generation AI select the optimal information collection method.

[0080] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed for important information. The analysis unit can also perform a concise analysis for general information. The analysis unit can also perform a quick and detailed analysis for urgent information. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0081] During translation, the translation unit can apply different translation algorithms depending on the category of information. For example, a weather data translation algorithm can be applied to weather information. The translation unit can also apply a disaster information translation algorithm to emergency information. The translation unit can also apply a traffic data translation algorithm to traffic delay information. By applying different translation algorithms depending on the category of information, more accurate translation results can be provided. Some or all of the above-mentioned processing in the translation unit can be performed using, or without, a generation AI, for example. For example, the translation unit can input information category data into the generation AI and have the generation AI apply an appropriate translation algorithm.

[0082] During personalization, the personalization unit can analyze the user's past behavioral patterns and select the optimal personalization method. For example, the personalization unit can prioritize providing information that is likely to be of interest to the user based on the user's past behavioral patterns. The personalization unit can also analyze the user's past behavioral patterns and determine the optimal timing for providing information. The personalization unit can also customize the method of displaying information by referring to the user's past behavioral patterns. This enables more appropriate personalization by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the personalization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the user's past behavioral pattern data into the generation AI and have the generation AI select the optimal personalization method.

[0083] When planning a trip, the travel planning support unit can suggest an optimal travel plan by referring to the user's past travel history. For example, it can suggest tourist spots that the user may be interested in based on the user's past travel history. The travel planning support unit can also suggest an optimal travel route by referring to the user's past travel history. The travel planning support unit can also improve the accuracy of the travel plan by reflecting the user's past travel history. In this way, by referring to the user's past travel history, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the travel planning support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's past travel history data into the generation AI and have the generation AI suggest an optimal travel plan.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The collection unit collects information from a data source. Examples of data sources include, but are not limited to, websites, databases, and APIs. The collection unit collects information including, for example, weather information, emergency information, traffic delay information, information on local festivals and specialty dishes, and local colloquialisms and dialects. For example, the collection unit collects data such as current temperature, probability of precipitation, and wind speed as weather information. The collection unit can also collect disaster information such as earthquakes and typhoons as emergency information. The collection unit can also collect train and bus operation status as traffic delay information. The collection unit can also collect information on local festivals and specialty dishes, such as dates, times, locations, and characteristics of the dishes. The collection unit can also collect words and expressions used in a specific region as information including local colloquialisms and dialects. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using methods such as, but not limited to, statistical analysis, machine learning algorithms, and data mining. For example, the analysis unit may analyze the collected weather information to understand the current weather conditions. The analysis unit may also analyze the collected emergency information to assess the impact of disasters. The analysis unit may also analyze the collected traffic delay information to understand the traffic situation. The analysis unit may also analyze the collected information on local festivals and specialty dishes to understand the details of the events. The analysis unit may also analyze the collected local colloquialisms and dialects to understand the linguistic characteristics of the region. Step 3: The translation unit translates the information analyzed by the analysis unit into multiple languages. The translation may be performed using, for example, neural machine translation, rule-based translation, or other methods, but is not limited to these examples. For example, the translation unit translates collected weather information from Japanese to English, Chinese, French, or the like. The translation unit may also translate collected emergency information into multiple languages. The translation unit may also translate collected traffic delay information into multiple languages. The translation unit may also translate collected information about local festivals and specialty foods into multiple languages. The translation unit may also translate collected local colloquialisms and dialects into appropriate languages. Step 4: The personalization unit learns the user's behavioral patterns and preferences based on the information translated by the translation unit, and provides personalized information. The learning can be performed, for example, using a machine learning algorithm, a type of data set, or other methods, but is not limited to these examples. For example, the personalization unit can suggest tourist spots and restaurants to visit next based on data on tourist spots visited by the user in the past and restaurants used by the user. The personalization unit can also provide information tailored to the user's preferences. Step 5: The travel planning support unit supports the travel plan based on the information provided by the personalization unit. Support is provided by, for example, but not limited to, methods such as suggesting sightseeing routes and reserving accommodations. For example, the travel planning support unit may suggest an optimal sightseeing route when the user inputs the tourist spots they would like to visit. The travel planning support unit may also make reservations when the user inputs the restaurant they would like to go to. Step 6: The educational content generation unit generates educational content related to the climate and history of the tourist destination based on the information provided by the travel planning support unit. The generation may be performed using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the educational content generation unit generates information related to the history and culture of the tourist destination and provides it to the user. The educational content generation unit can also generate detailed information related to the climate and history of the tourist destination. Step 7: The providing unit distributes the information generated by the educational content generating unit to the user's mobile device or IoT device in real time. The distribution may be performed, for example, depending on the communication protocol used, the distribution delay time, etc., but is not limited to these examples. For example, the providing unit distributes the information to devices such as smartphones, tablets, and smartwatches. The providing unit may also distribute the information in real time.

[0086] (Example 2) An AI platform according to an embodiment of the present invention is a system that collects information from data sources, analyzes it with a generation AI, translates it in multiple languages, and provides personalized information based on the user's behavioral patterns and preferences. The AI ​​platform collects information from data sources in real time, analyzes it with a generation AI, and instantly translates it in multiple languages. This translated information includes weather, emergency information, traffic delay information, information on local festivals and specialty dishes, and information that understands local colloquialisms and dialects. Furthermore, the generation AI learns the user's behavioral patterns and preferences and provides personalized information. The system also has a travel planning support function, suggesting sightseeing routes and making restaurant reservations. Finally, the generation AI automatically generates educational content for learning about the climate and history of tourist destinations. For example, the AI ​​platform collects weather information such as current temperature, precipitation probability, and wind speed. For example, emergency information such as disaster information on earthquakes and typhoons is collected. For example, traffic delay information such as train and bus operation status is collected. For example, information on local festivals and specialty dishes is collected, such as the date, time, location, and characteristics of the dishes. For example, the AI ​​platform collects words and expressions used in a specific region, including information on local colloquialisms and dialects. Next, the AI ​​platform uses a generative AI to analyze the collected information and translate it into multiple languages. For example, weather information is translated from Japanese to English, Chinese, French, etc. Emergency information is similarly translated into multiple languages. For example, traffic delay information, local festival information, and specialty food information are similarly translated into multiple languages. For example, information containing local colloquialisms and dialects is also translated into the appropriate language. Next, the AI ​​platform uses a generative AI to learn the user's behavioral patterns and preferences and provide personalized information. For example, it suggests the next tourist spot or restaurant to visit based on data on tourist spots visited by the user and restaurants used by the user. For example, it provides information tailored to the user's preferences. Next, the AI ​​platform provides travel planning support functions, suggesting sightseeing routes and making restaurant reservations. For example, when a user inputs the tourist spots they want to visit, the generative AI suggests the optimal sightseeing route. For example, when a user inputs the restaurant they want to go to, the generative AI makes the reservation.Next, the AI ​​platform automatically generates educational content to help users learn about the culture and customs of tourist destinations. For example, it generates information about the history and culture of tourist destinations and provides it to users. This allows the AI ​​platform to learn about tourist destinations before they visit. This allows the AI ​​platform to translate information collected from data sources into multiple languages ​​and provide personalized information based on users' behavioral patterns and preferences, thereby assisting with travel planning and generating educational content. This allows the AI ​​platform to deliver information in real time to users' mobile devices and IoT devices. For example, information is delivered to devices such as smartphones, tablets, and smartwatches. This allows users to obtain the information they need anytime, anywhere.

[0087] An AI platform according to an embodiment includes a collection unit, an analysis unit, a translation unit, a personalization unit, a travel planning support unit, an educational content generation unit, and a provision unit. The collection unit collects information from data sources. Examples of data sources include, but are not limited to, websites, databases, and APIs. The collection unit collects information including, for example, weather information, emergency information, traffic delay information, information on local festivals and specialty dishes, and local colloquialisms and dialects. For example, the collection unit collects data such as current temperature, probability of precipitation, and wind speed as weather information. The collection unit can also collect disaster information such as earthquakes and typhoons as emergency information. The collection unit can also collect train and bus operation status as traffic delay information. The collection unit can also collect information on local festivals and specialty dishes, such as dates, times, locations, and characteristics of the dishes. The collection unit can also collect words and expressions used in specific regions as information including local colloquialisms and dialects. The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using, for example, statistical analysis, machine learning algorithms, data mining, or the like, but is not limited to these examples. For example, the analysis unit may analyze collected weather information to understand current weather conditions. The analysis unit may also analyze collected emergency information to assess the impact of disasters. The analysis unit may also analyze collected traffic delay information to understand traffic conditions. The analysis unit may also analyze collected information on local festivals and specialty dishes to understand details of the events. The analysis unit may also analyze collected local colloquialisms and dialects to understand regional linguistic characteristics. The translation unit translates the information analyzed by the analysis unit into multiple languages. The translation may be performed using, for example, neural machine translation, rule-based translation, or the like, but is not limited to these examples. For example, the translation unit may translate the collected weather information from Japanese to English, Chinese, French, or the like. The translation unit may also translate the collected emergency information into multiple languages. The translation unit may also translate the collected traffic delay information into multiple languages. The translation department can also translate collected information about local festivals and local specialties into multiple languages.The translation unit can also translate the collected local colloquialisms and dialects into an appropriate language. The personalization unit learns the user's behavioral patterns and preferences based on the information translated by the translation unit and provides personalized information. Learning can be performed, for example, using a machine learning algorithm, a type of dataset, or other methods. For example, the personalization unit can suggest tourist spots and restaurants to visit next based on data on tourist spots visited by the user in the past and restaurants used by the user. The personalization unit can also provide information tailored to the user's preferences. The travel planning support unit supports travel planning based on the information provided by the personalization unit. Support can be performed, for example, by suggesting sightseeing routes and reserving accommodations, for example, but is not limited to these methods. For example, the travel planning support unit can suggest an optimal sightseeing route when the user inputs tourist spots they want to visit. The travel planning support unit can also make reservations when the user inputs a restaurant they want to go to. The educational content generation unit generates educational content related to the climate and history of tourist spots based on the information supported by the travel planning support unit. Generation can be performed, for example, using a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to these methods. For example, the educational content generation unit generates information about the history and culture of a tourist destination and provides it to the user. The educational content generation unit can also generate detailed information about the climate and history of the tourist destination. The providing unit delivers the information generated by the educational content generation unit to the user's mobile device or IoT device in real time. The delivery can be performed, for example, by a method such as a communication protocol used or a delivery delay time, but is not limited to these examples. For example, the providing unit delivers information to devices such as smartphones, tablets, and smartwatches. The providing unit can also deliver information in real time. As a result, the AI ​​platform according to the embodiment can translate information collected from data sources into multiple languages, provide personalized information based on the user's behavioral patterns and preferences, and support travel planning and generate educational content.

[0088] The collection unit can collect information including weather information, emergency information, traffic delay information, information on local festivals and specialty dishes, and local colloquialisms or dialects. For example, the collection unit collects data such as current temperature, probability of precipitation, and wind speed as weather information. The collection unit can also collect disaster information such as earthquakes and typhoons as emergency information. The collection unit can also collect train and bus operation status as traffic delay information. The collection unit can also collect information on local festivals and specialty dishes, such as dates, times, locations, and characteristics of dishes. The collection unit can also collect words and expressions used in specific regions as information including local colloquialisms or dialects. This allows the collection unit to collect a wide variety of information, thereby broadening the range of information provided to users. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input information collected from a data source into a generation AI and have the generation AI analyze and classify the information.

[0089] The translation unit can translate collected information into multiple languages. For example, the translation unit translates collected weather information from Japanese to English, Chinese, French, etc. The translation unit can also translate collected emergency information into multiple languages. The translation unit can also translate collected traffic delay information into multiple languages. The translation unit can also translate collected information about local festivals and specialty foods into multiple languages. The translation unit can also translate collected local colloquialisms and dialects into an appropriate language. This allows the translation unit to translate information into multiple languages, thereby accommodating users who speak different languages. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input collected information into a generation AI and have the generation AI translate the information.

[0090] The personalization unit can learn the user's behavioral patterns and preferences and provide personalized information. For example, the personalization unit can suggest tourist spots and restaurants that the user should visit next based on data on tourist spots visited in the past and restaurants used by the user. The personalization unit can also provide information tailored to the user's preferences. For example, the personalization unit can prioritize providing information about the user's favorite dishes or events that interest the user. In this way, the personalization unit can learn the user's behavioral patterns and preferences and provide more personalized information. Some or all of the above-mentioned processing in the personalization unit may be performed using, or without, a generation AI. For example, the personalization unit can input data on the user's behavioral patterns and preferences into the generation AI and have the generation AI generate personalized information.

[0091] The travel planning support unit can suggest sightseeing routes or make restaurant reservations. For example, when a user inputs tourist spots they want to visit, the travel planning support unit suggests an optimal sightseeing route. The travel planning support unit can also make reservations when a user inputs a restaurant they want to go to. For example, the travel planning support unit suggests an optimal sightseeing route based on the user's preferences and behavioral patterns. The travel planning support unit can also make restaurant reservations according to the user's schedule. In this way, the travel planning support unit supports the user's travel planning by suggesting sightseeing routes and making restaurant reservations. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input user input data into the generation AI and have the generation AI suggest sightseeing routes and make restaurant reservations.

[0092] The educational content generation unit can generate educational content related to the climate and history of a tourist destination. The educational content generation unit can generate, for example, information related to the history and culture of a tourist destination and provide it to a user. The educational content generation unit can also generate detailed information related to the climate and history of a tourist destination. For example, the educational content generation unit can generate information related to historical events and cultural features of a tourist destination. The educational content generation unit can also generate educational content related to the climate and history of a tourist destination and provide it to a user. In this way, the educational content generation unit can generate educational content related to the climate and history of a tourist destination, allowing a user to learn about the tourist destination. Some or all of the above-mentioned processing in the educational content generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the educational content generation unit can input information about the tourist destination into the generation AI and cause the generation AI to generate educational content.

[0093] The providing unit can deliver the generated information to the user's mobile device or IoT device in real time. The providing unit delivers information to devices such as smartphones, tablets, and smartwatches. The providing unit can also deliver information in real time. For example, the providing unit delivers information taking into account the communication protocol used and delivery delay time. The providing unit can also customize the means of providing information depending on the user's device status. For example, if the user is using a smartphone, the providing unit can select an information delivery method that matches the screen size. Also, if the user is using a tablet, the providing unit can select an information delivery method optimized for a large screen. This allows the providing unit to deliver information in real time, allowing the user to always obtain the latest information. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the generated information to a generation AI and have the generation AI execute information delivery.

[0094] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of information collection and collects only important information. Furthermore, when the user is relaxed, the collection unit can increase the frequency of information collection and collect detailed information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting important information in real time. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0095] The collection unit can analyze past collected data and select the optimal information collection method. For example, the collection unit can optimize the type of information to be collected during a specific time period based on past collected data. The collection unit can also prioritize the collection of information that is of most interest to the user based on past collected data. The collection unit can also analyze past collected data and optimize the collection method (API, scraping, etc.). This improves the efficiency and accuracy of information collection by analyzing past data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input past collected data into a generation AI and have the generation AI select the optimal information collection method.

[0096] When collecting information, the collection unit can filter the information based on the user's current location information and areas of interest. For example, the collection unit prioritizes collecting nearby weather information and traffic information based on the user's current location. The collection unit can also filter and collect related information based on the user's areas of interest (e.g., gourmet food, sightseeing). The collection unit can also combine the user's location information and areas of interest to collect optimal information. This allows for filtering information based on the user's location information and areas of interest, thereby providing more relevant information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's location information and areas of interest data into the generation AI and have the generation AI perform information filtering.

[0097] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Also, if the user uses image input, the collection unit can collect information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's input data into the generation AI and have the generation AI select the optimal collection means.

[0098] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting emergency information or important information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting information related to entertainment and hobbies. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting information needed in real time. This allows for more appropriate information to be provided by determining the priority of information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0099] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting nearby weather information and traffic information based on the user's current location. The collection unit can also prioritize collecting local festival and event information based on the user's location information. The collection unit can also prioritize collecting emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0100] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the user's social media posts and collect information on related events and places. The collection unit can also collect related information based on the user's social media check-in information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0101] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit adjusts the frequency and method of information collection based on feedback provided by the user in the past. The collection unit can also customize the type of information to be collected by referring to the user's past feedback. The collection unit can also optimize the means of information collection (API, scraping, etc.) by reflecting the user's feedback. This improves the accuracy of information collection by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0102] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0103] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on important information. The analysis unit can also perform a concise analysis on general information. The analysis unit can also perform a quick and detailed analysis on urgent information. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0104] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a weather data analysis algorithm to weather information. The analysis unit can also apply a disaster information analysis algorithm to emergency information. The analysis unit can also apply a traffic data analysis algorithm to traffic delay information. By applying different analysis algorithms depending on the category of information, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply an appropriate analysis algorithm.

[0105] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of analysis detail by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by reflecting the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0106] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0107] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analyzing the most recent information. The analysis unit can also analyze current information by referring to past information. The analysis unit can also give top priority to analyzing emergency information. This allows for determining the priority of analysis based on the time when the information was collected, making it possible to provide faster and more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the information was collected into the generation AI and have the generation AI determine the priority of analysis.

[0108] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also optimize the order of analysis based on the relevance of the information. This enables more efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0109] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terminology to a user with high levels of expertise. The analysis unit can also provide analysis results that are concise and easy to understand to a user with low levels of expertise. The analysis unit can also adjust the way the analysis results are expressed based on the user's level of expertise. This makes it possible to provide analysis results that are easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0110] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is nervous, the translation unit can provide a simple, highly visible translation result. Furthermore, if the user is relaxed, the translation unit can provide a detailed translation result. Furthermore, if the user is in a hurry, the translation unit can provide a concise translation result that focuses on the main points. This allows for adjusting the translation expression according to the user's emotions, thereby providing a more appropriate translation result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0111] During translation, the translation unit can adjust the level of detail of the translation based on the importance of the information. For example, the translation unit provides a detailed translation for important information. The translation unit can also provide a concise translation for general information. The translation unit can also provide a quick and detailed translation for urgent information. By adjusting the level of detail of the translation based on the importance of the information, more appropriate translation results can be provided. Some or all of the above-described processing in the translation unit may be performed using, or without, the generation AI, for example. For example, the translation unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the translation.

[0112] The translation unit can apply different translation algorithms depending on the category of information during translation. For example, the translation unit can apply a weather data translation algorithm to weather information. The translation unit can also apply a disaster information translation algorithm to emergency information. The translation unit can also apply a traffic data translation algorithm to traffic delay information. By applying different translation algorithms depending on the category of information, more accurate translation results can be provided. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the translation unit can input information category data into the generation AI and have the generation AI apply an appropriate translation algorithm.

[0113] During translation, the translation unit can improve the accuracy of the translation by referring to the user's past translation results. The translation unit, for example, optimizes the translation algorithm based on the user's past translation results. The translation unit can also adjust the level of translation detail by referring to the user's past translation results. The translation unit can also improve the accuracy of the translation by reflecting the user's past translation results. In this way, the accuracy of the translation is improved by referring to the user's past translation results. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit can input the user's past translation result data into the generation AI and have the generation AI improve the accuracy of the translation.

[0114] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. For example, if the user is in a hurry, the translation unit can provide a short, concise translation result. If the user is relaxed, the translation unit can also provide a detailed translation result. If the user is excited, the translation unit can also provide a translation result with a visually stimulating effect. This allows for adjusting the length of the translation according to the user's emotions, thereby providing a more appropriate translation result. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the translation.

[0115] During translation, the translation unit can determine translation priorities based on when information was collected. For example, the translation unit prioritizes translating the most recent information. The translation unit can also translate current information by referring to past information. The translation unit can also give top priority to translating emergency information. This allows for faster and more appropriate translation results to be provided by determining translation priorities based on when information was collected. Some or all of the above-described processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the translation unit can input information collection time data into the generation AI and have the generation AI determine the translation priorities.

[0116] The translation unit can adjust the order of translation based on the relevance of information during translation. For example, the translation unit prioritizes translation of highly relevant information. The translation unit can also postpone translation of less relevant information. The translation unit can also optimize the order of translation based on the relevance of information. This enables more efficient translation by adjusting the order of translation based on the relevance of information. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, the generation AI, for example. For example, the translation unit can input information relevance data into the generation AI and have the generation AI adjust the order of translation.

[0117] During translation, the translation unit can adjust the use of technical terminology in the translation according to the user's level of expertise. For example, the translation unit can provide a translation result that uses a lot of technical terminology to a user with high level of expertise. The translation unit can also provide a concise and easy-to-understand translation result to a user with low level of expertise. The translation unit can also adjust the way the translation result is expressed based on the user's level of expertise. This makes it possible to provide a translation result that is easier to understand by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0118] The personalization unit can estimate the user's emotions and adjust the personalization method based on the estimated user emotions. For example, if the user is feeling stressed, the personalization unit can prioritize providing relaxing information. Furthermore, if the user is relaxed, the personalization unit can also provide interesting information. Furthermore, if the user is in a hurry, the personalization unit can quickly provide necessary information. By adjusting the personalization method according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the personalization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the personalization unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the personalization method.

[0119] During personalization, the personalization unit can analyze the user's past behavioral patterns and select the optimal personalization method. For example, the personalization unit can prioritize providing information that is likely to be of interest to the user based on the user's past behavioral patterns. The personalization unit can also analyze the user's past behavioral patterns and determine the optimal timing for providing information. The personalization unit can also customize the method of displaying information by referring to the user's past behavioral patterns. This enables more appropriate personalization by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the personalization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the user's past behavioral pattern data into the generation AI and have the generation AI select the optimal personalization method.

[0120] During personalization, the personalization unit can customize the personalization means based on the user's current living situation. For example, if the user is traveling, the personalization unit can prioritize providing travel-related information. Also, if the user is working, the personalization unit can prioritize providing work-related information. The personalization unit can also customize the way information is displayed based on the user's current living situation. In this way, by customizing the personalization means based on the user's current living situation, more appropriate information can be provided. Some or all of the above-described processing in the personalization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the user's living situation data into the generation AI and cause the generation AI to customize the personalization means.

[0121] During personalization, the personalization unit can improve the personalization method by reflecting user feedback. For example, the personalization unit adjusts the frequency and method of information provision based on user feedback. The personalization unit can also customize the type of information to be provided by referring to user feedback. The personalization unit can also improve the accuracy of personalization by reflecting user feedback. In this way, the accuracy of personalization is improved by reflecting user feedback. Some or all of the above-described processing in the personalization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input user feedback data into the generation AI and cause the generation AI to improve the personalization method.

[0122] The personalization unit can estimate the user's emotions and determine personalization priorities based on the estimated user emotions. For example, if the user is feeling stressed, the personalization unit can prioritize providing relaxing information. Furthermore, if the user is relaxed, the personalization unit can also prioritize providing interesting information. Furthermore, if the user is in a hurry, the personalization unit can quickly provide necessary information. By determining personalization priorities according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the personalization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the personalization unit can input the user's emotion data into the generation AI and have the generation AI determine the personalization priorities.

[0123] During personalization, the personalization unit can select the optimal personalization method by taking into account the user's geographical location information. For example, the personalization unit can prioritize providing information about nearby events and stores based on the user's current location. The personalization unit can also provide information about local landmarks and tourist spots based on the user's location information. The personalization unit can also prioritize providing emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the personalization unit may be performed using, or without, a generation AI. For example, the personalization unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal personalization method.

[0124] During personalization, the personalization unit can analyze the user's social media activity and suggest personalization methods. For example, the personalization unit can analyze the user's social media posts and provide information on related events and places. The personalization unit can also provide relevant information based on the user's social media check-in information. The personalization unit can also provide relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing in the personalization unit may be performed using, or without, a generation AI. For example, the personalization unit can input the user's social media data into the generation AI and have the generation AI suggest personalization methods.

[0125] During personalization, the personalization unit can customize the personalization method by reflecting the user's past feedback. For example, the personalization unit adjusts the frequency and method of information provision based on the user's past feedback. The personalization unit can also customize the type of information to be provided by referring to the user's past feedback. The personalization unit can also improve the accuracy of personalization by reflecting the user's feedback. In this way, the accuracy of personalization is improved by reflecting the user's past feedback. Some or all of the above-described processing in the personalization unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the personalization unit can input user feedback data into the generation AI and cause the generation AI to customize the personalization method.

[0126] The travel planning support unit can estimate the user's emotions and adjust the method of proposing a travel plan based on the estimated user's emotions. For example, if the user is feeling stressed, the travel planning support unit can suggest a relaxing travel plan. Furthermore, if the user is relaxed, the travel planning support unit can suggest an active travel plan. Furthermore, if the user is in a hurry, the travel planning support unit can suggest a travel plan that can be quickly planned. This allows for adjusting the method of proposing a travel plan according to the user's emotions, thereby providing a more appropriate travel plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the travel planning support unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the travel planning support unit can input the user's emotion data into the generation AI and have the generation AI adjust the method of proposing a travel plan.

[0127] When planning a trip, the travel planning support unit can propose an optimal travel plan by referring to the user's past travel history. For example, the travel planning support unit can propose tourist spots that the user may be interested in based on the user's past travel history. The travel planning support unit can also propose an optimal travel route by referring to the user's past travel history. The travel planning support unit can also improve the accuracy of the travel plan by reflecting the user's past travel history. In this way, by referring to the user's past travel history, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's past travel history data into the generation AI and have the generation AI propose an optimal travel plan.

[0128] The travel planning support unit can analyze the user's current living situation and interests when planning a trip and propose an optimal travel route. The travel planning support unit can, for example, propose an optimal travel route based on the user's current living situation. The travel planning support unit can also analyze the user's interests and propose attractive tourist destinations. The travel planning support unit can also propose an optimal travel route by combining the user's living situation and interests. In this way, by analyzing the user's current living situation and interests, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the travel planning support unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the travel planning support unit can input the user's living situation and interest data into the generation AI and have the generation AI propose an optimal travel route.

[0129] The travel planning support unit can improve the travel planning method by reflecting user feedback when planning a trip. For example, the travel planning support unit adjusts the frequency and method of travel planning based on user feedback. The travel planning support unit can also customize the suggested travel route by referring to user feedback. The travel planning support unit can also improve the accuracy of the travel plan by reflecting user feedback. In this way, the accuracy of the travel plan is improved by reflecting user feedback. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input user feedback data into the generation AI and have the generation AI improve the travel planning method.

[0130] The travel planning support unit can estimate the user's emotions and prioritize travel plans based on the estimated user emotions. For example, if the user is feeling stressed, the travel planning support unit can prioritize relaxing travel plans. Furthermore, if the user is relaxed, the travel planning support unit can prioritize active travel plans. Furthermore, if the user is in a hurry, the travel planning support unit can prioritize travel plans that can be planned quickly. This allows for more appropriate travel plans to be provided by prioritizing travel plans based on the user's emotions. Estimation of emotions is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the travel planning support unit can be performed using, for example, the generation AI, or without the generation AI. For example, the travel planning support unit can input the user's emotion data into the generation AI and have the generation AI determine the priorities of the travel plans.

[0131] The travel planning support unit can propose an optimal travel route by taking into account the user's geographical location information when planning a trip. For example, the travel planning support unit prioritizes proposing nearby tourist attractions based on the user's current location. The travel planning support unit can also propose an optimal travel route based on the user's location information. The travel planning support unit can also propose a travel route that reflects emergency information and disaster information by taking into account the user's location information. This makes it possible to provide a more appropriate travel plan by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the travel planning support unit may be performed using, or without, a generation AI. For example, the travel planning support unit can input the user's location information data into the generation AI and have the generation AI propose an optimal travel route.

[0132] When planning a trip, the travel planning support unit can analyze the user's social media activity and suggest travel plan options. For example, the travel planning support unit can analyze the user's social media posts and suggest related tourist spots and events. The travel planning support unit can also suggest related travel routes based on the user's social media check-in information. The travel planning support unit can also suggest related travel plans by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate travel plans can be provided. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's social media data into the generation AI and have the generation AI suggest travel plan options.

[0133] The travel planning support unit can customize the travel planning method by reflecting the user's past feedback when planning a trip. The travel planning support unit, for example, adjusts the frequency and method of travel planning based on the user's past feedback. The travel planning support unit can also customize the suggested travel route by referring to the user's past feedback. The travel planning support unit can also improve the accuracy of the travel plan by reflecting the user's past feedback. In this way, the accuracy of the travel plan is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the travel planning support unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's feedback data into the generation AI and have the generation AI customize the travel planning method.

[0134] The educational content generation unit can estimate the user's emotions and adjust the educational content generation method based on the estimated user's emotions. For example, when the user is relaxed, the educational content generation unit generates detailed educational content. Furthermore, when the user is in a hurry, the educational content generation unit can generate concise educational content that focuses on the main points. Furthermore, when the user is excited, the educational content generation unit can generate educational content with visually stimulating effects. This allows for adjusting the educational content generation method according to the user's emotions to provide more appropriate educational content. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the educational content generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the educational content generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the educational content generation method.

[0135] When generating educational content, the educational content generation unit can generate optimal educational content by referring to the user's past learning history. The educational content generation unit, for example, generates educational content that is likely to interest the user based on the user's past learning history. The educational content generation unit can also suggest an optimal learning route by referring to the user's past learning history. The educational content generation unit can also improve the accuracy of the educational content by reflecting the user's past learning history. This makes it possible to provide more appropriate educational content by referring to the user's past learning history. Some or all of the above-mentioned processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input the user's past learning history data into the generation AI and cause the generation AI to generate optimal educational content.

[0136] When generating educational content, the educational content generation unit can analyze the user's current interests and generate optimal educational content. The educational content generation unit, for example, generates relevant educational content based on the user's current interests. The educational content generation unit can also analyze the user's interests and generate educational content that attracts their interest. The educational content generation unit can also generate optimal educational content by combining the user's interests and concerns. This makes it possible to provide more appropriate educational content by analyzing the user's current interests and concerns. Some or all of the above-described processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input user interest data into the generation AI and cause the generation AI to generate optimal educational content.

[0137] The educational content generation unit can improve the educational content generation method by reflecting user feedback when generating the educational content. For example, the educational content generation unit adjusts the frequency and method of the educational content based on user feedback. The educational content generation unit can also customize the type of educational content to be generated by referring to user feedback. The educational content generation unit can also improve the accuracy of the educational content by reflecting user feedback. In this way, the accuracy of the educational content is improved by reflecting user feedback. Some or all of the above-mentioned processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input user feedback data into the generation AI and cause the generation AI to improve the educational content generation method.

[0138] The educational content generation unit can estimate the user's emotions and prioritize educational content based on the estimated user emotions. For example, when the user is relaxed, the educational content generation unit can prioritize detailed educational content. Furthermore, when the user is in a hurry, the educational content generation unit can prioritize concise educational content that focuses on the main points. Furthermore, when the user is excited, the educational content generation unit can prioritize educational content that adds visually stimulating effects. This allows for more appropriate educational content to be provided by prioritizing educational content according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the educational content generation unit can be performed using, for example, the generation AI. For example, the educational content generation unit can input user emotion data into the generation AI and have the generation AI determine the priority of educational content.

[0139] When generating educational content, the educational content generation unit can generate optimal educational content by taking into account the user's geographical location information. For example, the educational content generation unit generates educational content related to local history and culture based on the user's current location. The educational content generation unit can also generate educational content related to nearby famous places and tourist attractions based on the user's location information. The educational content generation unit can also generate educational content that reflects emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant educational content by taking into account the user's geographical location information. Some or all of the above-described processing in the educational content generation unit may be performed using, or without, a generation AI. For example, the educational content generation unit can input the user's location information data into the generation AI and cause the generation AI to generate optimal educational content.

[0140] When generating educational content, the educational content generation unit can analyze the user's social media activities to generate relevant educational content. For example, the educational content generation unit analyzes the user's social media posts to generate relevant educational content. The educational content generation unit can also generate relevant educational content based on the user's social media check-in information. The educational content generation unit can also generate relevant educational content by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more relevant educational content can be provided. Some or all of the above-described processing in the educational content generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the educational content generation unit can input the user's social media data into the generation AI and cause the generation AI to generate relevant educational content.

[0141] When generating educational content, the educational content generation unit can customize the method of generating the educational content by reflecting the user's past feedback. The educational content generation unit, for example, adjusts the frequency and method of the educational content based on the user's past feedback. The educational content generation unit can also customize the type of educational content to be generated by referring to the user's past feedback. The educational content generation unit can also improve the accuracy of the educational content by reflecting the user's past feedback. In this way, the accuracy of the educational content is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the educational content generation unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the educational content generation unit can input user feedback data into the generation AI and cause the generation AI to customize the method of generating the educational content.

[0142] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can select a simple, highly visible information provision method. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can select a concise information provision method that focuses on the main points. This allows for more appropriate information to be provided by adjusting the information provision method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information provision method.

[0143] When providing information, the providing unit can select the optimal information providing method by referring to the user's past usage history. For example, the providing unit can prioritize providing information that is likely to be of interest to the user based on the user's past usage history. The providing unit can also determine the optimal timing for providing information by referring to the user's past usage history. The providing unit can also improve the accuracy of information provision by reflecting the user's past usage history. In this way, more appropriate information can be provided by referring to the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal information providing method.

[0144] When providing information, the providing unit can customize the means of providing information based on the user's current device status. For example, if the user is using a smartphone, the providing unit can select an information providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can select an information providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can select an information providing method that is concise and highly visible. This allows for more appropriate information to be provided by customizing the means of providing information based on the user's current device status. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device status data into the generation AI and cause the generation AI to customize the means of providing information.

[0145] The providing unit can improve the method of providing information by reflecting user feedback when providing information. For example, the providing unit can adjust the frequency and method of providing information based on user feedback. The providing unit can also customize the type of information to be provided by referring to user feedback. The providing unit can also improve the accuracy of information provision by reflecting user feedback. In this way, the accuracy of information provision is improved by reflecting user feedback. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the information provision method.

[0146] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing interesting information. Furthermore, when the user is in a hurry, the providing unit can quickly provide necessary information. This allows for more appropriate information to be provided by determining the priority of information provision according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information provision.

[0147] When providing information, the providing unit can select the optimal information provision method by taking into account the user's geographical location information. For example, the providing unit can prioritize providing information about nearby events and stores based on the user's current location. The providing unit can also provide information about local landmarks and tourist spots based on the user's location information. The providing unit can also prioritize providing emergency information and disaster information by taking into account the user's location information. This makes it possible to provide more relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal information provision method.

[0148] When providing information, the providing unit can analyze the user's social media activity and suggest means for providing the information. For example, the providing unit can analyze the user's social media posts and provide information on related events and places. The providing unit can also provide relevant information based on the user's social media check-in information. The providing unit can also provide relevant information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's social media data into the generation AI and cause the generation AI to suggest means for providing information.

[0149] When providing information, the providing unit can customize the method of providing information by reflecting the user's past feedback. The providing unit, for example, adjusts the frequency and method of providing information based on the user's past feedback. The providing unit can also customize the type of information to be provided by referring to the user's past feedback. The providing unit can also improve the accuracy of information provision by reflecting the user's feedback. In this way, the accuracy of information provision is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to customize the method of providing information. === Hard Collateral 1-1 === For example, the collection unit can collect information from a data source using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit can analyze the collected information using the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate information into multiple languages ​​using the specific processing unit 290 of the data processing device 12. For example, the personalization unit can learn a user's behavioral patterns and preferences using the specific processing unit 290 of the data processing device 12 and provide personalized information using the specific processing unit 290 of the data processing device 12. For example, the travel planning support unit can support travel planning using the specific processing unit 290 of the data processing device 12. For example, the educational content generation unit can generate educational content related to the climate and history of tourist destinations using the specific processing unit 290 of the data processing device 12. For example, the provision unit can distribute information generated by the control unit 46A of the smart device 14 to the user's mobile device or IoT device in real time. === Hard Collateral 1-2 === For example, the collection unit can collect information from a data source using the camera 42 or microphone 238 of the smart glasses 214. For example, the analysis unit can analyze the collected information using the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate information into multiple languages ​​using the specific processing unit 290 of the data processing device 12. For example, the personalization unit can learn a user's behavioral patterns and preferences using the specific processing unit 290 of the data processing device 12 and provide personalized information using the specific processing unit 290 of the data processing device 12. For example, the travel planning support unit can support travel planning using the specific processing unit 290 of the data processing device 12. For example, the educational content generation unit can generate educational content related to the climate and history of tourist destinations using the specific processing unit 290 of the data processing device 12. For example, the provision unit can deliver the information generated by the control unit 46A of the smart glasses 214 to a user's mobile device or IoT device in real time. === Hard Collateral 1-3 === For example, the collection unit can collect information from a data source using the camera 42 or microphone 238 of the headset-type terminal 314. For example, the analysis unit can analyze the collected information using the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate information into multiple languages ​​using the specific processing unit 290 of the data processing device 12. For example, the personalization unit can learn the user's behavioral patterns and preferences using the specific processing unit 290 of the data processing device 12 and provide personalized information using the specific processing unit 290 of the data processing device 12. For example, the travel planning support unit can support travel planning using the specific processing unit 290 of the data processing device 12. For example, the educational content generation unit can generate educational content related to the climate and history of tourist destinations using the specific processing unit 290 of the data processing device 12. For example, the provision unit can distribute the information generated by the control unit 46A of the headset-type terminal 314 to the user's mobile device or IoT device in real time. === Hard Collateral 1-4 === For example, the collection unit can collect information from a data source using the camera 42 or microphone 238 of the robot 414. For example, the analysis unit can analyze the collected information using the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate information into multiple languages ​​using the specific processing unit 290 of the data processing device 12. For example, the personalization unit can learn a user's behavioral patterns and preferences using the specific processing unit 290 of the data processing device 12 and provide personalized information using the specific processing unit 290 of the data processing device 12. For example, the travel planning support unit can support travel planning using the specific processing unit 290 of the data processing device 12. For example, the educational content generation unit can generate educational content related to the climate and history of tourist destinations using the specific processing unit 290 of the data processing device 12. For example, the provision unit can distribute the information generated by the control unit 46A of the robot 414 to a user's mobile device or IoT device in real time.

[0150] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0151] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of information collection and collect only important information. Furthermore, if the user is relaxed, the collection unit can increase the frequency of information collection and collect detailed information. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important information in real time. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of information collection.

[0152] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a concise analysis result that focuses on the main points if the user is in a hurry. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation of the analysis.

[0153] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is nervous, the translation unit can provide a simple, highly visible translation result. If the user is relaxed, the translation unit can provide a detailed translation result. If the user is in a hurry, the translation unit can provide a concise translation result that focuses on the main points. This allows for adjusting the translation expression according to the user's emotions, resulting in a more appropriate translation result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0154] The personalization unit can estimate the user's emotions and adjust the personalization method based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize providing relaxing information. Furthermore, if the user is relaxed, the personalization unit can also provide interesting information. Furthermore, if the user is in a hurry, the personalization unit can quickly provide necessary information. By adjusting the personalization method according to the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the personalization unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the personalization unit can input the user's emotion data into the generation AI and have the generation AI adjust the personalization method.

[0155] The travel planning support unit can estimate the user's emotions and adjust the method of proposing a travel plan based on the estimated user's emotions. For example, if the user is feeling stressed, the travel planning support unit can suggest a relaxing travel plan. Furthermore, if the user is relaxed, the travel planning support unit can suggest an active travel plan. Furthermore, if the user is in a hurry, the travel planning support unit can suggest a travel plan that allows for quick planning. This allows for adjusting the method of proposing a travel plan according to the user's emotions, thereby providing a more appropriate travel plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the travel planning support unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the travel planning support unit can input the user's emotion data into the generation AI and have the generation AI adjust the method of proposing a travel plan.

[0156] The collection unit can analyze past collected data and select the optimal information collection method. For example, it can optimize the type of information to be collected during a specific time period based on past collected data. The collection unit can also prioritize the collection of information that is of most interest to users based on past collected data. The collection unit can also analyze past collected data and optimize the collection method (API, scraping, etc.). This improves the efficiency and accuracy of information collection by analyzing past data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI, for example. For example, the collection unit can input past collected data into the generation AI and have the generation AI select the optimal information collection method.

[0157] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, a detailed analysis is performed for important information. The analysis unit can also perform a concise analysis for general information. The analysis unit can also perform a quick and detailed analysis for urgent information. By adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information importance data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0158] During translation, the translation unit can apply different translation algorithms depending on the category of information. For example, a weather data translation algorithm can be applied to weather information. The translation unit can also apply a disaster information translation algorithm to emergency information. The translation unit can also apply a traffic data translation algorithm to traffic delay information. By applying different translation algorithms depending on the category of information, more accurate translation results can be provided. Some or all of the above-mentioned processing in the translation unit can be performed using, or without, a generation AI, for example. For example, the translation unit can input information category data into the generation AI and have the generation AI apply an appropriate translation algorithm.

[0159] During personalization, the personalization unit can analyze the user's past behavioral patterns and select the optimal personalization method. For example, the personalization unit can prioritize providing information that is likely to be of interest to the user based on the user's past behavioral patterns. The personalization unit can also analyze the user's past behavioral patterns and determine the optimal timing for providing information. The personalization unit can also customize the method of displaying information by referring to the user's past behavioral patterns. This enables more appropriate personalization by analyzing the user's past behavioral patterns. Some or all of the above-described processing in the personalization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the personalization unit can input the user's past behavioral pattern data into the generation AI and have the generation AI select the optimal personalization method.

[0160] When planning a trip, the travel planning support unit can suggest an optimal travel plan by referring to the user's past travel history. For example, it can suggest tourist spots that the user may be interested in based on the user's past travel history. The travel planning support unit can also suggest an optimal travel route by referring to the user's past travel history. The travel planning support unit can also improve the accuracy of the travel plan by reflecting the user's past travel history. In this way, by referring to the user's past travel history, a more appropriate travel plan can be provided. Some or all of the above-mentioned processing in the travel planning support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the travel planning support unit can input the user's past travel history data into the generation AI and have the generation AI suggest an optimal travel plan.

[0161] The processing flow of the second embodiment will be briefly explained below.

[0162] Step 1: The collection unit collects information from a data source. Examples of data sources include, but are not limited to, websites, databases, and APIs. The collection unit collects information including, for example, weather information, emergency information, traffic delay information, information on local festivals and specialty dishes, and local colloquialisms and dialects. For example, the collection unit collects data such as current temperature, probability of precipitation, and wind speed as weather information. The collection unit can also collect disaster information such as earthquakes and typhoons as emergency information. The collection unit can also collect train and bus operation status as traffic delay information. The collection unit can also collect information on local festivals and specialty dishes, such as dates, times, locations, and characteristics of the dishes. The collection unit can also collect words and expressions used in a specific region as information including local colloquialisms and dialects. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis may be performed using methods such as, but not limited to, statistical analysis, machine learning algorithms, and data mining. For example, the analysis unit may analyze the collected weather information to understand the current weather conditions. The analysis unit may also analyze the collected emergency information to assess the impact of disasters. The analysis unit may also analyze the collected traffic delay information to understand the traffic situation. The analysis unit may also analyze the collected information on local festivals and specialty dishes to understand the details of the events. The analysis unit may also analyze the collected local colloquialisms and dialects to understand the linguistic characteristics of the region. Step 3: The translation unit translates the information analyzed by the analysis unit into multiple languages. The translation may be performed using, for example, neural machine translation, rule-based translation, or other methods, but is not limited to these examples. For example, the translation unit translates collected weather information from Japanese to English, Chinese, French, or the like. The translation unit may also translate collected emergency information into multiple languages. The translation unit may also translate collected traffic delay information into multiple languages. The translation unit may also translate collected information about local festivals and specialty foods into multiple languages. The translation unit may also translate collected local colloquialisms and dialects into appropriate languages. Step 4: The personalization unit learns the user's behavioral patterns and preferences based on the information translated by the translation unit, and provides personalized information. The learning can be performed, for example, using a machine learning algorithm, a type of data set, or other methods, but is not limited to these examples. For example, the personalization unit can suggest tourist spots and restaurants to visit next based on data on tourist spots visited by the user in the past and restaurants used by the user. The personalization unit can also provide information tailored to the user's preferences. Step 5: The travel planning support unit supports the travel plan based on the information provided by the personalization unit. Support is provided by, for example, but not limited to, methods such as suggesting sightseeing routes and reserving accommodations. For example, the travel planning support unit may suggest an optimal sightseeing route when the user inputs the tourist spots they would like to visit. The travel planning support unit may also make reservations when the user inputs the restaurant they would like to go to. Step 6: The educational content generation unit generates educational content related to the climate and history of the tourist destination based on the information provided by the travel planning support unit. The generation may be performed using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the educational content generation unit generates information related to the history and culture of the tourist destination and provides it to the user. The educational content generation unit can also generate detailed information related to the climate and history of the tourist destination. Step 7: The providing unit distributes the information generated by the educational content generating unit to the user's mobile device or IoT device in real time. The distribution may be performed, for example, depending on the communication protocol used, the distribution delay time, etc., but is not limited to these examples. For example, the providing unit distributes the information to devices such as smartphones, tablets, and smartwatches. The providing unit may also distribute the information in real time.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0199] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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."

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] [Explanation of symbols]

[0235] 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 collection unit for collecting information from data sources; an analysis unit that analyzes the information collected by the collection unit; a translation unit that translates the information analyzed by the analysis unit into multiple languages; a personalization unit that learns the user's behavioral patterns or preferences based on the information translated by the translation unit and provides personalized information; a travel planning support unit that supports travel planning based on the information provided by the personalization unit; an educational content generation unit that generates educational content related to the climate and history of tourist destinations based on the information supported by the travel planning support unit; a providing unit that delivers the information generated by the educational content generating unit to a user's mobile device or IoT device in real time. A system characterized by:

2. The collecting unit Collect information including weather information, emergency information, traffic delay information, information about local festivals and local specialties, and local colloquialisms or dialects 2. The system of claim 1.

3. The translation unit Translate collected information into multiple languages 2. The system of claim 1.

4. The personalization unit Learn user behavior patterns and preferences to provide personalized information 2. The system of claim 1.

5. The travel planning support unit Suggest sightseeing routes or make restaurant reservations 2. The system of claim 1.

6. The educational content generation unit Generate educational content about the culture and history of tourist destinations 2. The system of claim 1.

7. The providing unit Deliver generated information to users' mobile and IoT devices in real time 2. The system of claim 1.

8. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

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