System

Abstract

An object of the system according to the embodiment is to understand a user's language, a culture of a destination, and a local language and to provide navigation guidance based on the understanding.SOLUTION: A system includes an analysis unit, an understanding unit, a generation unit, a provision unit, and a collection unit. The analysis unit analyzes a language of a user. The understanding unit understands the culture of the destination and the language of the region based on the information analyzed by the analysis unit. The generation unit generates navigation guidance based on the information understood by the understanding unit. The providing unit provides the user with the navigation guidance generated by the generating unit. The collection unit collects information related to an area of a user.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 do not provide navigation guidance that takes into account the user's language, the destination's culture, or local dialect, and there is room for improvement.

[0005] The system according to the embodiment aims to understand the user's language, the culture of the destination, and the local dialect, and to provide navigation guidance based on that. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an understanding unit, a generation unit, a provision unit, and a collection unit. The analysis unit analyzes the user's language. The understanding unit understands the culture and local dialect of the destination based on the information analyzed by the analysis unit. The generation unit generates navigation guidance based on the information understood by the understanding unit. The provision unit provides the user with the navigation guidance generated by the generation unit. The collection unit collects information related to the user's area. [Effects of the Invention]

[0007] The system according to the embodiment can understand the user's language, the culture of the destination, and the local dialect, and provide navigation guidance based on that. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A real-time navigation system according to an embodiment of the present invention analyzes a user's language, understands the culture and local dialect of the destination, and generates and provides navigation guidance based on that. The real-time navigation system analyzes the user's language, understands the culture and local dialect of the destination, and generates and provides navigation guidance based on that. For example, in a real-time navigation system, a user inputs a destination, such as "I want to go to Tokyo Tower." This information is input to a generation AI. The generation AI then analyzes the input information and understands the user's language. The generation AI analyzes the user's language and understands the culture and local dialect of the destination. For example, if a user inputs "I want to go to Tokyo Tower" in English, the generation AI analyzes the English and understands the culture and local dialect of Tokyo. The generation AI then generates navigation guidance. The generation AI generates navigation guidance based on the user's language and the culture and local dialect of the destination. For example, the generation AI generates navigation guidance such as "To get to Tokyo Tower, first take the subway, then walk." The generated navigation guidance is provided to the user. This navigation guidance includes information related to the destination (basic information about the city, tourist attractions, restaurants, shopping centers, etc.), as well as information on local culture, customs, and language. For example, the generation AI provides information such as, "There are popular restaurants and shopping centers near Tokyo Tower. You can also learn about Tokyo's culture and customs." This allows the user to have a deeper experience. This allows the real-time navigation system to analyze the user's language, understand the destination's culture and local dialect, and generate and provide navigation guidance based on that. For example, users can obtain detailed information about their destination and deepen their understanding of local culture and customs. For example, when visiting Tokyo Tower, users can learn about nearby tourist attractions and restaurants, and also learn about Tokyo's culture and customs.

[0029] A real-time navigation system according to an embodiment includes an analysis unit, an understanding unit, a generation unit, a provision unit, and a collection unit. The analysis unit analyzes a user's language. The user's language may include, but is not limited to, speech, text, and gestures. The analysis unit may analyze the user's language using, for example, natural language processing technology. The analysis unit may also analyze voice input using speech recognition technology. For example, the analysis unit may convert voice input into text data and analyze the text data. The analysis unit may also analyze gesture input using image analysis technology. For example, the analysis unit may analyze gestures captured by a camera and understand their meaning. The understanding unit may understand the culture and local dialect of a destination based on the information analyzed by the analysis unit. For example, the understanding unit may understand the culture and local dialect of the destination using dictionary-based translation technology. The understanding unit may also understand the culture and local dialect of the destination using a machine learning model. For example, the understanding unit may use a machine learning model to learn the meaning of the culture and local dialect of the destination and deepen its understanding. Furthermore, the understanding unit can use the generation AI to understand the culture and local dialect of the destination. For example, the understanding unit inputs the culture and local dialect of the destination into the generation AI and analyzes its meaning. The generation unit generates navigation guidance based on the information understood by the understanding unit. The generation unit generates navigation guidance using, for example, the generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit inputs a prompt such as "To get to Tokyo Tower, first take the subway and then walk" to the generation AI to generate navigation guidance. The generation unit can also use the generation AI to generate navigation guidance including information related to the destination (basic information about the city, tourist spots, restaurants, shopping centers, etc.). For example, the generation unit inputs a prompt such as "There are popular restaurants and shopping centers near Tokyo Tower. You can also learn about Tokyo's culture and customs" to the generation AI to generate navigation guidance. The provision unit provides the navigation guidance generated by the generation unit to the user.The providing unit provides navigation guidance in the form of, for example, voice guidance, text guidance, map display, or the like. For example, the providing unit provides voice guidance using speech synthesis technology. The providing unit can also provide text guidance using text display technology. Furthermore, the providing unit can display navigation guidance on a map using map display technology. The collecting unit collects information related to the user's area. The collecting unit collects, for example, geographic information, tourist information, traffic information, and the like. For example, the collecting unit collects geographic information from an online database. The collecting unit can also collect tourist information from a tourist information website. Furthermore, the collecting unit can collect traffic information from a traffic information providing service. This allows the real-time navigation system according to the embodiment to analyze the user's language, understand the culture and local dialect of the destination, and generate and provide navigation guidance based on the understanding. For example, the user can obtain detailed information about the destination and deepen their understanding of local culture and customs. For example, when visiting Tokyo Tower, the user can learn about nearby tourist attractions and restaurants and learn about Tokyo's culture and customs.

[0030] The analysis unit can analyze the user's past language usage history and select the optimal analysis method. The analysis unit selects the optimal analysis method based on, for example, language patterns frequently used by the user in the past. For example, the analysis unit can analyze the user's chat log and identify frequently used language patterns. The analysis unit can also prioritize analysis of a specific language style based on the user's past language usage history. For example, the analysis unit can analyze the user's voice history and identify a specific language style. The analysis unit can also select an analysis method taking into account technical terms and slang used by the user in the past. For example, the analysis unit can analyze the user's text messages and identify technical terms and slang. This improves analysis accuracy by selecting the optimal analysis method based on the user's past language usage history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past language usage history into a generation AI and have the generation AI select the optimal analysis method.

[0031] During language analysis, the analysis unit can filter the analysis results by taking into account the user's current situation and intentions. For example, if the user is traveling, the analysis unit prioritizes analyzing travel-related information. For example, the analysis unit acquires the user's location information and determines that the user is traveling. Furthermore, if the user is in a business meeting, the analysis unit can prioritize analyzing business-related information. For example, the analysis unit acquires the user's schedule information and determines that the user is in a business meeting. Furthermore, if the user is in an emergency, the analysis unit can prioritize analyzing information necessary for emergency response. For example, the analysis unit acquires the user's behavioral history and determines that the user is in an emergency. This provides more relevant analysis results by taking into account the user's current situation and intentions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's location information and schedule information into the generation AI and have the generation AI filter the analysis results.

[0032] During language analysis, the analysis unit can select the optimal analysis method depending on the user's input method. For example, if the user uses voice input, the analysis unit performs language analysis using voice recognition technology. For example, the analysis unit converts the voice input into text data and analyzes the text data. Furthermore, if the user uses text input, the analysis unit can also perform language analysis using natural language processing technology. For example, the analysis unit analyzes the text data and understands its meaning. Furthermore, if the user uses image input, the analysis unit can also perform language analysis using image recognition technology. For example, the analysis unit analyzes images taken with a camera and understands their meaning. This improves analysis accuracy by selecting the optimal analysis method depending on the user's input method. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice input data to a generation AI and have the generation AI analyze the voice data.

[0033] During language analysis, the analysis unit can prioritize analyzing highly relevant language by taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit prioritizes analyzing the language or dialect of that region. For example, the analysis unit acquires the user's GPS data and determines that the user is in a specific region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing information related to the language and culture of the travel destination. For example, the analysis unit uses a location information service to determine that the user is traveling. Furthermore, if the user is in their hometown, the analysis unit can prioritize analyzing language related to local news and events. For example, the analysis unit determines that the user is in their hometown based on the user's location information. This allows for providing highly relevant analysis results by taking the user's geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's location information data into a generation AI and cause the generation AI to analyze highly relevant language.

[0034] During language analysis, the analysis unit can analyze the user's social media activities and analyze related languages. The analysis unit, for example, analyzes related languages ​​based on the language and topics used by the user on social media. For example, the analysis unit analyzes the content of the user's social media posts and identifies the language and topic used. The analysis unit can also analyze the content of the user's social media posts and prioritize analysis of related languages. For example, the analysis unit identifies related languages ​​based on the content of the user's social media posts. The analysis unit can also analyze related languages ​​with reference to the activities of the user's friends on social media. For example, the analysis unit analyzes the social media activities of the user's friends and identifies related languages. In this way, by analyzing the user's social media activities, highly relevant analysis results are provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media post data into a generation AI and cause the generation AI to analyze related languages.

[0035] The analysis unit can customize the analysis method by reflecting the user's past feedback during language analysis. The analysis unit adjusts the analysis method, for example, based on feedback provided by the user in the past. For example, the analysis unit analyzes the user's survey results and adjusts the analysis method. The analysis unit can also preferentially use a specific analysis method based on the user's past feedback. For example, the analysis unit analyzes the user's evaluation comments and identifies a specific analysis method. The analysis unit can also improve analysis accuracy by reflecting the user's feedback. For example, the analysis unit adjusts the analysis algorithm based on the user's feedback. In this way, analysis accuracy is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0036] The understanding unit can improve the accuracy of understanding when understanding the culture and local dialect by taking into account the historical background of the destination. For example, the understanding unit more accurately understands the culture and local dialect based on the historical background of the destination. For example, the understanding unit prioritizes understanding words related to historical events and people of the destination. The understanding unit can also accurately understand the nuances and meanings of words by taking into account the historical background of the destination. For example, the understanding unit identifies the meanings and nuances of words based on the historical background of the destination. In this way, by taking the historical background of the destination into consideration, the accuracy of understanding the culture and local dialect is improved. Some or all of the above-mentioned processing in the understanding unit may be performed using, or without, AI. For example, the understanding unit can input historical background data of the destination into the generation AI and cause the generation AI to understand the culture and local dialect.

[0037] The understanding unit can improve the accuracy of understanding when understanding cultures and local dialects by referring to the user's past travel history. The understanding unit improves the accuracy of understanding, for example, based on the language and culture of places the user has visited in the past. For example, the understanding unit analyzes the user's past travel history to identify the language and culture of places visited. The understanding unit can also prioritize understanding the language and culture of a specific region based on the user's past travel history. For example, the understanding unit identifies the language and culture of a specific region based on the user's travel history. The understanding unit can also accurately understand the meanings and nuances of words by referring to the user's travel history. For example, the understanding unit identifies the meanings and nuances of words based on the user's travel history. In this way, the accuracy of understanding cultures and local dialects is improved by referring to the user's past travel history. Some or all of the above-described processing in the understanding unit may be performed using, or without, AI. For example, the understanding unit can input the user's past travel history data into the generation AI and cause the generation AI to understand the culture and local dialects.

[0038] When understanding cultures and local dialects, the understanding unit can customize the understanding means by taking into account the user's current interests. For example, the understanding unit prioritizes understanding of words and cultures related to topics in which the user is currently interested. For example, the understanding unit analyzes the user's search history to identify topics in which the user is currently interested. The understanding unit can also prioritize understanding of specific words and cultures based on the user's current interests. For example, the understanding unit analyzes the user's browsing history to identify current interests. The understanding unit can also customize the understanding means by taking into account the user's interests. For example, the understanding unit analyzes the user's social media activity to identify interests. This allows for providing more relevant understanding results by taking into account the user's current interests. Some or all of the above-described processing in the understanding unit may be performed using, or without, AI. For example, the understanding unit can input the user's search history data into the generation AI and have the generation AI customize the understanding means.

[0039] When understanding culture and local dialects, the understanding unit can prioritize understanding highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific region, the understanding unit prioritizes understanding the language and dialect of that region. For example, the understanding unit acquires the user's GPS data and determines that the user is in a specific region. Furthermore, if the user is traveling, the understanding unit can prioritize understanding information related to the language and culture of the travel destination. For example, the understanding unit uses the user's location information service to determine that the user is traveling. Furthermore, if the user is in their hometown, the understanding unit can prioritize understanding words related to local news and events. For example, the understanding unit determines that the user is in their hometown based on the user's location information. This allows for providing highly relevant understanding results by taking the user's geographical location information into consideration. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without AI. For example, the understanding unit can input the user's location information data into the generation AI and cause the generation AI to understand highly relevant information.

[0040] The understanding unit can analyze the user's social media activities and understand related information when understanding cultures and local dialects. The understanding unit can understand related information based on, for example, the words and topics used by the user on social media. For example, the understanding unit can analyze the user's social media posts and identify the words and topics used. The understanding unit can also analyze the user's social media posts and prioritize understanding related words. For example, the understanding unit can identify related words based on the user's social media posts. The understanding unit can also understand related words by referring to the activities of the user's friends on social media. For example, the understanding unit can analyze the social media activities of the user's friends and identify related words. In this way, by analyzing the user's social media activities, highly relevant understanding results can be provided. Some or all of the above-mentioned processing in the understanding unit can be performed using, for example, AI, or without AI. For example, the understanding unit can input the user's social media post data into a generation AI and cause the generation AI to understand the related information.

[0041] The understanding unit can customize the understanding method by reflecting the user's past feedback when understanding a culture or local dialect. The understanding unit, for example, adjusts the understanding method based on feedback provided by the user in the past. For example, the understanding unit analyzes the user's survey results and adjusts the understanding method. The understanding unit can also preferentially use a specific understanding method based on the user's past feedback. For example, the understanding unit analyzes the user's evaluation comments and identifies a specific understanding method. The understanding unit can also improve understanding accuracy by reflecting the user's feedback. For example, the understanding unit adjusts the understanding algorithm based on the user's feedback. In this way, understanding accuracy is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input user feedback data into the generation AI and cause the generation AI to customize the understanding method.

[0042] When generating navigation guidance, the generation unit can adjust the level of detail of the guidance based on the importance of the destination. For example, if the destination is a tourist attraction, the generation unit generates detailed guidance. For example, the generation unit collects tourist information about the destination and generates detailed guidance. The generation unit can also generate quick and concise guidance if the destination is a business meeting location. For example, the generation unit collects business information about the destination and generates quick and concise guidance. The generation unit can also generate guidance that emphasizes the shortest route if the destination is an emergency evacuation site. For example, the generation unit collects emergency information about the destination and generates guidance that emphasizes the shortest route. In this way, by adjusting the level of detail of the guidance based on the importance of the destination, more appropriate guidance is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input destination information to the generation AI and cause the generation AI to adjust the level of detail of the guidance.

[0043] When generating navigation guidance, the generation unit can apply different generation algorithms depending on the category of the destination. For example, if the destination is a tourist spot, the generation unit generates guidance including tourist information. For example, the generation unit collects information about tourist spots and generates guidance including tourist information. Furthermore, if the destination is a restaurant, the generation unit can generate guidance including menus and business hours. For example, the generation unit collects information about restaurants and generates guidance including menus and business hours. Furthermore, if the destination is a shopping center, the generation unit can generate guidance including store information and sale information. For example, the generation unit collects information about shopping centers and generates guidance including store information and sale information. In this way, by applying different generation algorithms depending on the category of the destination, more appropriate guidance can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input destination category information to the generation AI and cause the generation AI to apply the generation algorithm.

[0044] When generating navigation guidance, the generation unit can improve the accuracy of the generation by referring to the user's past navigation results. The generation unit improves the accuracy of the generation, for example, based on the results of navigation guidance used by the user in the past. For example, the generation unit analyzes the user's past navigation results and improves the accuracy. The generation unit can also preferentially generate specific routes and information from the user's past navigation results. For example, the generation unit identifies specific routes and information based on the user's past navigation results. The generation unit can also improve the accuracy of the navigation guidance by reflecting user feedback. For example, the generation unit adjusts the generation algorithm based on user feedback. In this way, the accuracy of the generation is improved by referring to the user's past navigation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past navigation result data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0045] When generating navigation guidance, the generation unit can determine the priority of guidance based on the submission date of the destination. For example, if the destination is an emergency evacuation site, the generation unit generates guidance with the highest priority. For example, the generation unit collects emergency information about the destination and generates guidance with the highest priority. The generation unit can also quickly generate guidance if the destination is a business meeting location. For example, the generation unit collects business information about the destination and quickly generates guidance. The generation unit can also generate detailed guidance if the destination is a tourist spot. For example, the generation unit collects tourist information about the destination and generates detailed guidance. This allows for more appropriate guidance to be provided by determining the priority of guidance based on the submission date of the destination. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about the submission date of the destination into the generation AI and have the generation AI determine the priority of guidance.

[0046] When generating navigation guidance, the generation unit can adjust the order of guidance based on the relevance of destinations. For example, when there are multiple destinations, the generation unit generates guidance in order of relevance. For example, the generation unit collects information related to the destinations and generates guidance in order of relevance. Furthermore, when the destination is a tourist spot, the generation unit can generate guidance in an order that follows a tourist route. For example, the generation unit collects information about tourist spots and generates guidance in an order that follows a tourist route. Furthermore, when the destination is a shopping center, the generation unit can adjust the order of guidance based on the relative positions of the stores. For example, the generation unit collects information about the shopping center and adjusts the order of guidance based on the relative positions of the stores. In this way, by adjusting the order of guidance based on the relevance of the destinations, more appropriate guidance is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information related to the destination into the generation AI and cause the generation AI to adjust the order of guidance.

[0047] When generating navigation guidance, the generation unit can adjust the use of technical terminology in the guidance according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates detailed guidance including technical terminology. For example, the generation unit collects the user's occupational information and identifies the user's level of expertise. Furthermore, if the user is a beginner, the generation unit can generate guidance that explains the user in simple terms. For example, the generation unit collects the user's learning history and identifies the user as a beginner. Furthermore, the generation unit can adjust the use of appropriate technical terminology based on the user's past usage history. For example, the generation unit analyzes the user's usage history and identifies appropriate technical terminology. This allows for more appropriate guidance to be provided by adjusting the use of technical terminology in the guidance according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's occupational information and learning history data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] When providing navigation guidance, the providing unit can select the optimal delivery method by referring to the user's past operation history. The providing unit selects the optimal method, for example, based on delivery methods used by the user in the past. For example, the providing unit analyzes the user's app usage history to identify the optimal delivery method. The providing unit can also preferentially use a specific delivery method based on the user's past operation history. For example, the providing unit analyzes the user's operation log to identify the specific delivery method. The providing unit can also customize the delivery method by reflecting user feedback. For example, the providing unit adjusts the delivery method based on the user's feedback. In this way, the optimal delivery method is selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to a generation AI and cause the generation AI to select the optimal delivery method.

[0049] The providing unit can customize the content to be provided according to the user's current task when providing navigation guidance. For example, if the user is traveling, the providing unit can prioritize providing travel-related information. For example, the providing unit can acquire the user's schedule information and identify that the user is traveling. Furthermore, if the user is in a business meeting, the providing unit can prioritize providing business-related information. For example, the providing unit can acquire the user's calendar information and identify that the user is in a business meeting. Furthermore, if the user is in an emergency, the providing unit can prioritize providing information necessary for emergency response. For example, the providing unit can acquire the user's behavior history and identify that the user is in an emergency. This allows the content to be customized according to the user's current task, thereby providing more appropriate guidance. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's schedule information into a generating AI and cause the generating AI to customize the content to be provided.

[0050] When providing navigation guidance, the providing unit can select the optimal providing means by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit acquires the user's device information and determines that the user is using a smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit acquires the user's device information and determines that the user is using a tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit acquires the user's device information and determines that the user is using a smartwatch. In this way, the optimal providing means is selected by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing means.

[0051] When providing navigation guidance, the providing unit can prioritize providing highly relevant guidance by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit prioritizes providing information about that area. For example, the providing unit acquires the user's GPS data and determines that the user is in a specific area. Furthermore, if the user is traveling, the providing unit can prioritize providing information about the user's travel destination. For example, the providing unit uses a location information service to determine that the user is traveling. Furthermore, if the user is in their local area, the providing unit can prioritize providing information related to local news and events. For example, the providing unit determines that the user is in their local area based on the user's location information. In this way, highly relevant guidance is provided by taking the user's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to a generation AI and cause the generation AI to provide highly relevant guidance.

[0052] The providing unit can analyze the user's social media activity and provide related guidance when providing navigation guidance. The providing unit, for example, provides information about places where the user has checked in on social media. For example, the providing unit analyzes the user's social media posts to identify the checked-in places. The providing unit can also analyze the user's social media posts to provide information about related tourist spots and stores. For example, the providing unit identifies related tourist spots and stores based on the user's social media posts. The providing unit can also provide information about related places and events based on the activities of the user's friends on social media. For example, the providing unit analyzes the social media activities of the user's friends to identify related places and events. In this way, highly relevant guidance is provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media post data into a generating AI and cause the generating AI to provide related guidance.

[0053] The providing unit can customize the provision method by reflecting the user's past feedback when providing navigation guidance. The providing unit, for example, adjusts the provision method based on feedback provided by the user in the past. For example, the providing unit analyzes the user's survey results and adjusts the provision method. The providing unit can also preferentially use a specific provision method based on the user's past feedback. For example, the providing unit analyzes the user's evaluation comments and identifies a specific provision method. The providing unit can also improve provision accuracy by reflecting the user's feedback. For example, the providing unit adjusts the provision algorithm based on the user's feedback. In this way, the provision method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data to a generation AI and cause the generation AI to customize the provision method.

[0054] When collecting area information, the collection unit can optimize the collection algorithm by referring to past collected data. The collection unit, for example, selects an optimal collection algorithm based on the past collected data. For example, the collection unit analyzes the past collected data and identifies an optimal collection algorithm. The collection unit can also preferentially collect specific area information from the past collected data. For example, the collection unit identifies specific area information based on the past collected data. The collection unit can also analyze the past collected data and optimize the collection algorithm. For example, the collection unit adjusts the collection algorithm based on the past collected data. In this way, the collection algorithm is optimized by referring to the past collected data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past collected data to a generation AI and cause the generation AI to optimize the collection algorithm.

[0055] The collection unit can update the collected data by reflecting user feedback when collecting area information. The collection unit updates the collected data based on, for example, feedback provided by the user. For example, the collection unit analyzes user survey results and updates the collected data. The collection unit can also preferentially collect specific area information based on user feedback. For example, the collection unit analyzes user evaluation comments and identifies specific area information. The collection unit can also improve collection accuracy by reflecting user feedback. For example, the collection unit adjusts the collection algorithm based on user feedback. In this way, the collected data is updated by reflecting user feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs user feedback data to a generation AI and causes the generation AI to update the collected data.

[0056] When collecting area information, the collection unit can customize the collected content taking into account the user's current interests. For example, the collection unit prioritizes collecting area information related to topics in which the user is currently interested. For example, the collection unit analyzes the user's search history to identify topics in which the user is currently interested. The collection unit can also prioritize collecting specific area information based on the user's current interests. For example, the collection unit analyzes the user's browsing history to identify current interests. The collection unit can also customize the collected content taking into account the user's interests. For example, the collection unit analyzes the user's social media activity to identify interests. This allows the user's current interests to be taken into account, thereby providing more relevant area information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's search history data into a generation AI and have the generation AI customize the collected content.

[0057] When collecting area information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting information about that area. For example, the collection unit acquires the user's GPS data and identifies that the user is in a specific area. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about the user's travel destination. For example, the collection unit uses a location information service to identify that the user is traveling. Furthermore, if the user is in their local area, the collection unit can prioritize collecting information related to local news and events. For example, the collection unit identifies that the user is in their local area based on the user's location information. This provides highly relevant area information by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into a generation AI and cause the generation AI to collect highly relevant information.

[0058] When collecting area information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information about places where the user has checked in on social media. For example, the collection unit analyzes the user's social media posts and identifies the checked-in places. The collection unit can also analyze the user's social media posts and collect information about related tourist spots and stores. For example, the collection unit identifies related tourist spots and stores based on the user's social media posts. The collection unit can also collect information about related places and events based on the activities of the user's friends on social media. For example, the collection unit analyzes the social media activities of the user's friends and identifies related places and events. In this way, highly relevant area information is provided by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media post data into a generation AI and cause the generation AI to collect related information.

[0059] When collecting area information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. For example, the collection unit analyzes the user's survey results and adjusts the collection method. The collection unit can also preferentially use a specific collection method based on the user's past feedback. For example, the collection unit analyzes the user's evaluation comments and identifies a specific collection method. The collection unit can also improve collection accuracy by reflecting the user's feedback. For example, the collection unit adjusts the collection algorithm based on the user's feedback. In this way, the collection method is customized 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, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] In addition to analyzing the user's language, the analysis unit can monitor the user's health condition and reflect it in the analysis results. For example, the analysis unit can measure the user's heart rate and blood pressure using sensors to evaluate the user's health condition. The analysis unit can also analyze the user's sleep patterns and estimate the user's fatigue level. Furthermore, the analysis unit can evaluate the user's nutritional status by taking into account the user's dietary history. This makes it possible to provide more appropriate navigation guidance based on the user's health condition.

[0062] The analysis unit can analyze the user's past language usage history as well as the user's hobbies and interests to select the optimal analysis method. For example, the analysis unit can analyze the user's music playback history to identify their favorite genres. The analysis unit can also analyze the user's reading history to identify topics of interest. Furthermore, the analysis unit can analyze the user's movie viewing history to identify their favorite movie genres. This makes it possible to provide more relevant analysis results based on the user's hobbies and interests.

[0063] During language analysis, the analysis unit can filter the analysis results by taking into account the user's current activity level. For example, if the user is exercising, information related to exercise is prioritized for analysis. For example, the analysis unit acquires the user's exercise data and identifies that the user is exercising. Furthermore, if the user is resting, information related to relaxation can be prioritized for analysis. For example, the analysis unit acquires the user's heart rate data and identifies that the user is resting. Furthermore, if the user is working, information related to work can be prioritized for analysis. This makes it possible to provide more relevant analysis results according to the user's current activity level.

[0064] During language analysis, the analysis unit can select the optimal analysis means depending on the type of device used by the user as well as the user's input method. For example, if the user is using a smartphone, the analysis unit selects an analysis means optimized for the smartphone. For example, the analysis unit analyzes touch input on the smartphone. Furthermore, if the user is using a tablet, the analysis unit can also select an analysis means optimized for the tablet. For example, the analysis unit analyzes stylus input on the tablet. Furthermore, if the user is using a smartwatch, the analysis unit can also select an analysis means optimized for the smartwatch. This makes it possible to provide more appropriate analysis results depending on the type of device used by the user.

[0065] During language analysis, the analysis unit can prioritize highly relevant languages ​​by taking into account the user's geographical location information as well as the user's movement history. For example, the analysis unit can prioritize analyzing the language and dialect of places the user has visited in the past. For example, the analysis unit can acquire the user's movement history data and identify places visited in the past. The analysis unit can also prioritize analyzing the language and dialect of places the user frequently visits. For example, the analysis unit can analyze the user's movement patterns and identify frequently visited places. Furthermore, when the user visits a new place, the analysis unit can prioritize analyzing the language and dialect of that place. This makes it possible to provide more relevant analysis results based on the user's movement history.

[0066] During language analysis, the analysis unit can analyze the user's online shopping history in addition to the user's social media activities and analyze related languages. For example, the analysis unit analyzes related languages ​​based on the language and topics used by the user when shopping online. For example, the analysis unit analyzes the user's online shopping history and identifies the language and topic used. The analysis unit can also analyze the user's purchase history and prioritize analysis of related languages. For example, the analysis unit identifies related languages ​​based on the user's purchase history. Furthermore, the analysis unit can analyze the user's review history and analyze related languages. In this way, by analyzing the user's online shopping history, it is possible to provide highly relevant analysis results.

[0067] During language analysis, the analysis unit can customize the analysis method by reflecting the user's real-time feedback in addition to the user's past feedback. For example, the analysis method is adjusted based on feedback provided by the user in real time. For example, the analysis unit analyzes the user's real-time evaluation comments and adjusts the analysis method. It is also possible to preferentially use a specific analysis method based on the user's real-time feedback. For example, the analysis unit identifies a specific analysis method based on the user's real-time feedback. It is also possible to improve the analysis accuracy by reflecting the user's real-time feedback. In this way, the analysis accuracy can be improved by reflecting the user's real-time feedback.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The analysis unit analyzes the user's language. The user's language includes voice, text, and gestures. The analysis unit analyzes these inputs using natural language processing technology, voice recognition technology, and image analysis technology. For example, it converts voice input into text data and analyzes that text data. It also analyzes gestures captured by a camera and understands their meaning. Step 2: The understanding unit understands the destination's culture and local language based on the information analyzed by the analysis unit. The understanding unit uses dictionary-based translation technology, machine learning models, and generative AI to understand the destination's culture and local language. For example, it uses machine learning models to learn the meanings of the destination's culture and local language and deepen its understanding. Step 3: The generation unit generates navigation guidance based on the information understood by the understanding unit. The generation unit generates navigation guidance using a generation AI. For example, the generation AI receives a prompt such as "To get to Tokyo Tower, first take the subway and then walk," and generates navigation guidance. The generation unit also generates navigation guidance that includes information related to the destination. Step 4: The providing unit provides the navigation guidance generated by the generating unit to the user. The providing unit provides the navigation guidance in the form of voice guidance, text guidance, map display, etc. For example, the providing unit provides voice guidance using voice synthesis technology, provides text guidance using text display technology, and displays navigation guidance on a map using map display technology. Step 5: The collection unit collects information related to the user's area. The collection unit collects geographic information, tourist information, traffic information, etc. For example, the collection unit collects geographic information from databases on the Internet, tourist information from tourist information sites, and traffic information from traffic information services.

[0070] (Example 2) A real-time navigation system according to an embodiment of the present invention analyzes a user's language, understands the culture and local dialect of the destination, and generates and provides navigation guidance based on that. The real-time navigation system analyzes the user's language, understands the culture and local dialect of the destination, and generates and provides navigation guidance based on that. For example, in a real-time navigation system, a user inputs a destination, such as "I want to go to Tokyo Tower." This information is input to a generation AI. The generation AI then analyzes the input information and understands the user's language. The generation AI analyzes the user's language and understands the culture and local dialect of the destination. For example, if a user inputs "I want to go to Tokyo Tower" in English, the generation AI analyzes the English and understands the culture and local dialect of Tokyo. The generation AI then generates navigation guidance. The generation AI generates navigation guidance based on the user's language and the culture and local dialect of the destination. For example, the generation AI generates navigation guidance such as "To get to Tokyo Tower, first take the subway, then walk." The generated navigation guidance is provided to the user. This navigation guidance includes information related to the destination (basic information about the city, tourist attractions, restaurants, shopping centers, etc.), as well as information on local culture, customs, and language. For example, the generation AI provides information such as, "There are popular restaurants and shopping centers near Tokyo Tower. You can also learn about Tokyo's culture and customs." This allows the user to have a deeper experience. This allows the real-time navigation system to analyze the user's language, understand the destination's culture and local dialect, and generate and provide navigation guidance based on that. For example, users can obtain detailed information about their destination and deepen their understanding of local culture and customs. For example, when visiting Tokyo Tower, users can learn about nearby tourist attractions and restaurants, and also learn about Tokyo's culture and customs.

[0071] A real-time navigation system according to an embodiment includes an analysis unit, an understanding unit, a generation unit, a provision unit, and a collection unit. The analysis unit analyzes a user's language. The user's language may include, but is not limited to, speech, text, and gestures. The analysis unit may analyze the user's language using, for example, natural language processing technology. The analysis unit may also analyze voice input using speech recognition technology. For example, the analysis unit may convert voice input into text data and analyze the text data. The analysis unit may also analyze gesture input using image analysis technology. For example, the analysis unit may analyze gestures captured by a camera and understand their meaning. The understanding unit may understand the culture and local dialect of a destination based on the information analyzed by the analysis unit. For example, the understanding unit may understand the culture and local dialect of the destination using dictionary-based translation technology. The understanding unit may also understand the culture and local dialect of the destination using a machine learning model. For example, the understanding unit may use a machine learning model to learn the meaning of the culture and local dialect of the destination and deepen its understanding. Furthermore, the understanding unit can use the generation AI to understand the culture and local dialect of the destination. For example, the understanding unit inputs the culture and local dialect of the destination into the generation AI and analyzes its meaning. The generation unit generates navigation guidance based on the information understood by the understanding unit. The generation unit generates navigation guidance using, for example, the generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit inputs a prompt such as "To get to Tokyo Tower, first take the subway and then walk" to the generation AI to generate navigation guidance. The generation unit can also use the generation AI to generate navigation guidance including information related to the destination (basic information about the city, tourist spots, restaurants, shopping centers, etc.). For example, the generation unit inputs a prompt such as "There are popular restaurants and shopping centers near Tokyo Tower. You can also learn about Tokyo's culture and customs" to the generation AI to generate navigation guidance. The provision unit provides the navigation guidance generated by the generation unit to the user.The providing unit provides navigation guidance in the form of, for example, voice guidance, text guidance, map display, or the like. For example, the providing unit provides voice guidance using speech synthesis technology. The providing unit can also provide text guidance using text display technology. Furthermore, the providing unit can display navigation guidance on a map using map display technology. The collecting unit collects information related to the user's area. The collecting unit collects, for example, geographic information, tourist information, traffic information, and the like. For example, the collecting unit collects geographic information from an online database. The collecting unit can also collect tourist information from a tourist information website. Furthermore, the collecting unit can collect traffic information from a traffic information providing service. This allows the real-time navigation system according to the embodiment to analyze the user's language, understand the culture and local dialect of the destination, and generate and provide navigation guidance based on the understanding. For example, the user can obtain detailed information about the destination and deepen their understanding of local culture and customs. For example, when visiting Tokyo Tower, the user can learn about nearby tourist attractions and restaurants and learn about Tokyo's culture and customs.

[0072] The analysis unit estimates the user's emotions and adjusts the accuracy of language analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit adjusts to perform concise and clear language analysis. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on changes in facial expression and adjusts the accuracy of language analysis. Furthermore, if the user is relaxed, the analysis unit adjusts to perform detailed language analysis. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. The analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the accuracy of language analysis. Furthermore, if the user is in a hurry, the analysis unit adjusts the accuracy of analysis to provide results quickly. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the accuracy of language analysis. This allows for adjusting the accuracy of language analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input image data of the user captured by a camera into the generative AI and cause the generative AI to estimate the user's emotions.

[0073] The analysis unit can analyze the user's past language usage history and select the optimal analysis method. The analysis unit selects the optimal analysis method based on, for example, language patterns frequently used by the user in the past. For example, the analysis unit can analyze the user's chat log and identify frequently used language patterns. The analysis unit can also prioritize analysis of a specific language style based on the user's past language usage history. For example, the analysis unit can analyze the user's voice history and identify a specific language style. The analysis unit can also select an analysis method taking into account technical terms and slang used by the user in the past. For example, the analysis unit can analyze the user's text messages and identify technical terms and slang. This improves analysis accuracy by selecting the optimal analysis method based on the user's past language usage history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's past language usage history into a generation AI and have the generation AI select the optimal analysis method.

[0074] During language analysis, the analysis unit can filter the analysis results by taking into account the user's current situation and intentions. For example, if the user is traveling, the analysis unit prioritizes analyzing travel-related information. For example, the analysis unit acquires the user's location information and determines that the user is traveling. Furthermore, if the user is in a business meeting, the analysis unit can prioritize analyzing business-related information. For example, the analysis unit acquires the user's schedule information and determines that the user is in a business meeting. Furthermore, if the user is in an emergency, the analysis unit can prioritize analyzing information necessary for emergency response. For example, the analysis unit acquires the user's behavioral history and determines that the user is in an emergency. This provides more relevant analysis results by taking into account the user's current situation and intentions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the user's location information and schedule information into the generation AI and have the generation AI filter the analysis results.

[0075] During language analysis, the analysis unit can select the optimal analysis method depending on the user's input method. For example, if the user uses voice input, the analysis unit performs language analysis using voice recognition technology. For example, the analysis unit converts the voice input into text data and analyzes the text data. Furthermore, if the user uses text input, the analysis unit can also perform language analysis using natural language processing technology. For example, the analysis unit analyzes the text data and understands its meaning. Furthermore, if the user uses image input, the analysis unit can also perform language analysis using image recognition technology. For example, the analysis unit analyzes images taken with a camera and understands their meaning. This improves analysis accuracy by selecting the optimal analysis method depending on the user's input method. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice input data to a generation AI and have the generation AI analyze the voice data.

[0076] The analysis unit can estimate the user's emotions and determine the priority of languages ​​to analyze based on the estimated user emotions. For example, if the user is excited, the analysis unit prioritizes analyzing languages ​​related to emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit calculates an emotion score based on changes in facial expressions and determines the priority of languages ​​to analyze. Furthermore, if the user is calm, the analysis unit prioritizes analyzing languages ​​containing detailed information. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. The analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of languages ​​to analyze. Furthermore, if the user is feeling anxious, the analysis unit prioritizes analyzing languages ​​that give a sense of security. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on fluctuations in heart rate and determines the priority of languages ​​to analyze. This allows for determining the priority of the languages ​​to be analyzed according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may 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 may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input image data of the user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] During language analysis, the analysis unit can prioritize analyzing highly relevant language by taking into account the user's geographical location information. For example, if the user is in a specific region, the analysis unit prioritizes analyzing the language or dialect of that region. For example, the analysis unit acquires the user's GPS data and determines that the user is in a specific region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing information related to the language and culture of the travel destination. For example, the analysis unit uses a location information service to determine that the user is traveling. Furthermore, if the user is in their hometown, the analysis unit can prioritize analyzing language related to local news and events. For example, the analysis unit determines that the user is in their hometown based on the user's location information. This allows for providing highly relevant analysis results by taking the user's geographical location information into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's location information data into a generation AI and cause the generation AI to analyze highly relevant language.

[0078] During language analysis, the analysis unit can analyze the user's social media activities and analyze related languages. The analysis unit, for example, analyzes related languages ​​based on the language and topics used by the user on social media. For example, the analysis unit analyzes the content of the user's social media posts and identifies the language and topic used. The analysis unit can also analyze the content of the user's social media posts and prioritize analysis of related languages. For example, the analysis unit identifies related languages ​​based on the content of the user's social media posts. The analysis unit can also analyze related languages ​​with reference to the activities of the user's friends on social media. For example, the analysis unit analyzes the social media activities of the user's friends and identifies related languages. In this way, by analyzing the user's social media activities, highly relevant analysis results are provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media post data into a generation AI and cause the generation AI to analyze related languages.

[0079] The analysis unit can customize the analysis method by reflecting the user's past feedback during language analysis. The analysis unit adjusts the analysis method, for example, based on feedback provided by the user in the past. For example, the analysis unit analyzes the user's survey results and adjusts the analysis method. The analysis unit can also preferentially use a specific analysis method based on the user's past feedback. For example, the analysis unit analyzes the user's evaluation comments and identifies a specific analysis method. The analysis unit can also improve analysis accuracy by reflecting the user's feedback. For example, the analysis unit adjusts the analysis algorithm based on the user's feedback. In this way, analysis accuracy is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's feedback data into the generation AI and cause the generation AI to customize the analysis method.

[0080] The understanding unit can estimate the user's emotions and adjust the cultural and regional dialect understanding method based on the estimated user emotions. For example, if the user is excited, the understanding unit prioritizes understanding cultural and regional dialects related to the emotion. For example, the understanding unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The understanding unit calculates an emotion score based on changes in facial expression and adjusts the cultural and regional dialect understanding method. Furthermore, if the user is relaxed, the understanding unit understands detailed cultural and regional dialects. For example, the understanding unit records the user's voice and estimates the emotion using voice analysis technology. The understanding unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the cultural and regional dialect understanding method. Furthermore, if the user is anxious, the understanding unit prioritizes understanding cultural and regional dialects that provide a sense of security. For example, the understanding unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The understanding unit calculates an emotion score based on heart rate fluctuations and adjusts the understanding method for the culture and local dialect. This adjusts the understanding method for the culture and local dialect according to the user's emotions, thereby providing a more appropriate understanding result. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the understanding unit may input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0081] The understanding unit can improve the accuracy of understanding when understanding the culture and local dialect by taking into account the historical background of the destination. For example, the understanding unit more accurately understands the culture and local dialect based on the historical background of the destination. For example, the understanding unit prioritizes understanding words related to historical events and people of the destination. The understanding unit can also accurately understand the nuances and meanings of words by taking into account the historical background of the destination. For example, the understanding unit identifies the meanings and nuances of words based on the historical background of the destination. In this way, by taking the historical background of the destination into consideration, the accuracy of understanding the culture and local dialect is improved. Some or all of the above-mentioned processing in the understanding unit may be performed using, or without, AI. For example, the understanding unit can input historical background data of the destination into the generation AI and cause the generation AI to understand the culture and local dialect.

[0082] The understanding unit can improve the accuracy of understanding when understanding cultures and local dialects by referring to the user's past travel history. The understanding unit improves the accuracy of understanding, for example, based on the language and culture of places the user has visited in the past. For example, the understanding unit analyzes the user's past travel history to identify the language and culture of places visited. The understanding unit can also prioritize understanding the language and culture of a specific region based on the user's past travel history. For example, the understanding unit identifies the language and culture of a specific region based on the user's travel history. The understanding unit can also accurately understand the meanings and nuances of words by referring to the user's travel history. For example, the understanding unit identifies the meanings and nuances of words based on the user's travel history. In this way, the accuracy of understanding cultures and local dialects is improved by referring to the user's past travel history. Some or all of the above-described processing in the understanding unit may be performed using, or without, AI. For example, the understanding unit can input the user's past travel history data into the generation AI and cause the generation AI to understand the culture and local dialects.

[0083] When understanding cultures and local dialects, the understanding unit can customize the understanding means by taking into account the user's current interests. For example, the understanding unit prioritizes understanding of words and cultures related to topics in which the user is currently interested. For example, the understanding unit analyzes the user's search history to identify topics in which the user is currently interested. The understanding unit can also prioritize understanding of specific words and cultures based on the user's current interests. For example, the understanding unit analyzes the user's browsing history to identify current interests. The understanding unit can also customize the understanding means by taking into account the user's interests. For example, the understanding unit analyzes the user's social media activity to identify interests. This allows for providing more relevant understanding results by taking into account the user's current interests. Some or all of the above-described processing in the understanding unit may be performed using, or without, AI. For example, the understanding unit can input the user's search history data into the generation AI and have the generation AI customize the understanding means.

[0084] The understanding unit can estimate the user's emotions and determine the priority of cultures and regional dialects to understand based on the estimated user emotions. For example, if the user is excited, the understanding unit prioritizes understanding cultures and regional dialects related to the emotion. For example, the understanding unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The understanding unit calculates an emotion score based on changes in facial expression and determines the priority of cultures and regional dialects to understand. Furthermore, if the user is relaxed, the understanding unit understands detailed cultures and regional dialects. For example, the understanding unit records the user's voice and estimates the emotion using voice analysis technology. The understanding unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of cultures and regional dialects to understand. Furthermore, if the user is anxious, the understanding unit prioritizes understanding cultures and regional dialects that provide a sense of security. For example, the understanding unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The understanding unit calculates an emotion score based on heart rate fluctuations and determines the priority of cultures and regional dialects to be understood. This determines the priority of cultures and regional dialects according to the user's emotions, thereby providing more appropriate understanding results. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit may input image data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0085] When understanding culture and local dialects, the understanding unit can prioritize understanding highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific region, the understanding unit prioritizes understanding the language and dialect of that region. For example, the understanding unit acquires the user's GPS data and determines that the user is in a specific region. Furthermore, if the user is traveling, the understanding unit can prioritize understanding information related to the language and culture of the travel destination. For example, the understanding unit uses the user's location information service to determine that the user is traveling. Furthermore, if the user is in their hometown, the understanding unit can prioritize understanding words related to local news and events. For example, the understanding unit determines that the user is in their hometown based on the user's location information. This allows for providing highly relevant understanding results by taking the user's geographical location information into consideration. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without AI. For example, the understanding unit can input the user's location information data into the generation AI and cause the generation AI to understand highly relevant information.

[0086] The understanding unit can analyze the user's social media activities and understand related information when understanding cultures and local dialects. The understanding unit can understand related information based on, for example, the words and topics used by the user on social media. For example, the understanding unit can analyze the user's social media posts and identify the words and topics used. The understanding unit can also analyze the user's social media posts and prioritize understanding related words. For example, the understanding unit can identify related words based on the user's social media posts. The understanding unit can also understand related words by referring to the activities of the user's friends on social media. For example, the understanding unit can analyze the social media activities of the user's friends and identify related words. In this way, by analyzing the user's social media activities, highly relevant understanding results can be provided. Some or all of the above-mentioned processing in the understanding unit can be performed using, for example, AI, or without AI. For example, the understanding unit can input the user's social media post data into a generation AI and cause the generation AI to understand the related information.

[0087] The understanding unit can customize the understanding method by reflecting the user's past feedback when understanding a culture or local dialect. The understanding unit, for example, adjusts the understanding method based on feedback provided by the user in the past. For example, the understanding unit analyzes the user's survey results and adjusts the understanding method. The understanding unit can also preferentially use a specific understanding method based on the user's past feedback. For example, the understanding unit analyzes the user's evaluation comments and identifies a specific understanding method. The understanding unit can also improve understanding accuracy by reflecting the user's feedback. For example, the understanding unit adjusts the understanding algorithm based on the user's feedback. In this way, understanding accuracy is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the understanding unit may be performed using AI, for example, or may be performed without using AI. For example, the understanding unit can input user feedback data into the generation AI and cause the generation AI to customize the understanding method.

[0088] The generation unit can estimate the user's emotion and adjust the presentation method of the navigation guidance based on the estimated user emotion. For example, if the user is relaxed, the generation unit generates navigation guidance that proceeds at a leisurely pace. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on changes in the facial expression and adjusts the presentation method of the navigation guidance. Furthermore, if the user is in a hurry, the generation unit generates navigation guidance that emphasizes the shortest route. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. The generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the presentation method of the navigation guidance. Furthermore, if the user is excited, the generation unit generates navigation guidance that adds visually stimulating effects. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on fluctuations in the heart rate and adjusts the presentation method of the navigation guidance. This allows for more appropriate guidance to be provided by adjusting the way navigation guidance is presented according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit may input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0089] When generating navigation guidance, the generation unit can adjust the level of detail of the guidance based on the importance of the destination. For example, if the destination is a tourist attraction, the generation unit generates detailed guidance. For example, the generation unit collects tourist information about the destination and generates detailed guidance. The generation unit can also generate quick and concise guidance if the destination is a business meeting location. For example, the generation unit collects business information about the destination and generates quick and concise guidance. The generation unit can also generate guidance that emphasizes the shortest route if the destination is an emergency evacuation site. For example, the generation unit collects emergency information about the destination and generates guidance that emphasizes the shortest route. In this way, by adjusting the level of detail of the guidance based on the importance of the destination, more appropriate guidance is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input destination information to the generation AI and cause the generation AI to adjust the level of detail of the guidance.

[0090] When generating navigation guidance, the generation unit can apply different generation algorithms depending on the category of the destination. For example, if the destination is a tourist spot, the generation unit generates guidance including tourist information. For example, the generation unit collects information about tourist spots and generates guidance including tourist information. Furthermore, if the destination is a restaurant, the generation unit can generate guidance including menus and business hours. For example, the generation unit collects information about restaurants and generates guidance including menus and business hours. Furthermore, if the destination is a shopping center, the generation unit can generate guidance including store information and sale information. For example, the generation unit collects information about shopping centers and generates guidance including store information and sale information. In this way, by applying different generation algorithms depending on the category of the destination, more appropriate guidance can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input destination category information to the generation AI and cause the generation AI to apply the generation algorithm.

[0091] When generating navigation guidance, the generation unit can improve the accuracy of the generation by referring to the user's past navigation results. The generation unit improves the accuracy of the generation, for example, based on the results of navigation guidance used by the user in the past. For example, the generation unit analyzes the user's past navigation results and improves the accuracy. The generation unit can also preferentially generate specific routes and information from the user's past navigation results. For example, the generation unit identifies specific routes and information based on the user's past navigation results. The generation unit can also improve the accuracy of the navigation guidance by reflecting user feedback. For example, the generation unit adjusts the generation algorithm based on user feedback. In this way, the accuracy of the generation is improved by referring to the user's past navigation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past navigation result data into the generation AI and cause the generation AI to improve the accuracy of the generation.

[0092] The generation unit can estimate the user's emotion and adjust the length of the navigation guidance based on the estimated user emotion. For example, if the user is in a hurry, the generation unit generates short and to-the-point navigation guidance. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on changes in the facial expression and adjusts the length of the navigation guidance. Furthermore, if the user is relaxed, the generation unit generates longer navigation guidance that includes detailed explanations. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. The generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the navigation guidance. Furthermore, if the user is excited, the generation unit generates navigation guidance that adds visually stimulating effects. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on fluctuations in the heart rate and adjusts the length of the navigation guidance. This allows the length of the navigation guidance to be adjusted according to the user's emotions, thereby providing more appropriate guidance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may 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 generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit may input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0093] When generating navigation guidance, the generation unit can determine the priority of guidance based on the submission date of the destination. For example, if the destination is an emergency evacuation site, the generation unit generates guidance with the highest priority. For example, the generation unit collects emergency information about the destination and generates guidance with the highest priority. The generation unit can also quickly generate guidance if the destination is a business meeting location. For example, the generation unit collects business information about the destination and quickly generates guidance. The generation unit can also generate detailed guidance if the destination is a tourist spot. For example, the generation unit collects tourist information about the destination and generates detailed guidance. This allows for more appropriate guidance to be provided by determining the priority of guidance based on the submission date of the destination. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information about the submission date of the destination into the generation AI and have the generation AI determine the priority of guidance.

[0094] When generating navigation guidance, the generation unit can adjust the order of guidance based on the relevance of destinations. For example, when there are multiple destinations, the generation unit generates guidance in order of relevance. For example, the generation unit collects information related to the destinations and generates guidance in order of relevance. Furthermore, when the destination is a tourist spot, the generation unit can generate guidance in an order that follows a tourist route. For example, the generation unit collects information about tourist spots and generates guidance in an order that follows a tourist route. Furthermore, when the destination is a shopping center, the generation unit can adjust the order of guidance based on the relative positions of the stores. For example, the generation unit collects information about the shopping center and adjusts the order of guidance based on the relative positions of the stores. In this way, by adjusting the order of guidance based on the relevance of the destinations, more appropriate guidance is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input information related to the destination into the generation AI and cause the generation AI to adjust the order of guidance.

[0095] When generating navigation guidance, the generation unit can adjust the use of technical terminology in the guidance according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates detailed guidance including technical terminology. For example, the generation unit collects the user's occupational information and identifies the user's level of expertise. Furthermore, if the user is a beginner, the generation unit can generate guidance that explains the user in simple terms. For example, the generation unit collects the user's learning history and identifies the user as a beginner. Furthermore, the generation unit can adjust the use of appropriate technical terminology based on the user's past usage history. For example, the generation unit analyzes the user's usage history and identifies appropriate technical terminology. This allows for more appropriate guidance to be provided by adjusting the use of technical terminology in the guidance according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's occupational information and learning history data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The providing unit can estimate the user's emotions and adjust the method of providing navigation guidance based on the estimated user emotions. For example, if the user is nervous, the providing unit provides guidance in a calm voice. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on changes in the facial expression and adjusts the method of providing navigation guidance. Furthermore, if the user is relaxed, the providing unit provides guidance in a cheerful voice. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. The providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method of providing navigation guidance. Furthermore, if the user is in a hurry, the providing unit provides quick and concise guidance. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on fluctuations in the heart rate and adjusts the method of providing navigation guidance. In this way, the method of providing navigation guidance is adjusted according to the user's emotions, thereby providing more appropriate guidance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0097] When providing navigation guidance, the providing unit can select the optimal delivery method by referring to the user's past operation history. The providing unit selects the optimal method, for example, based on delivery methods used by the user in the past. For example, the providing unit analyzes the user's app usage history to identify the optimal delivery method. The providing unit can also preferentially use a specific delivery method based on the user's past operation history. For example, the providing unit analyzes the user's operation log to identify the specific delivery method. The providing unit can also customize the delivery method by reflecting user feedback. For example, the providing unit adjusts the delivery method based on the user's feedback. In this way, the optimal delivery method is selected by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's operation history data to a generation AI and cause the generation AI to select the optimal delivery method.

[0098] The providing unit can customize the content to be provided according to the user's current task when providing navigation guidance. For example, if the user is traveling, the providing unit can prioritize providing travel-related information. For example, the providing unit can acquire the user's schedule information and identify that the user is traveling. Furthermore, if the user is in a business meeting, the providing unit can prioritize providing business-related information. For example, the providing unit can acquire the user's calendar information and identify that the user is in a business meeting. Furthermore, if the user is in an emergency, the providing unit can prioritize providing information necessary for emergency response. For example, the providing unit can acquire the user's behavior history and identify that the user is in an emergency. This allows the content to be customized according to the user's current task, thereby providing more appropriate guidance. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's schedule information into a generating AI and cause the generating AI to customize the content to be provided.

[0099] When providing navigation guidance, the providing unit can select the optimal providing means by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit acquires the user's device information and determines that the user is using a smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit acquires the user's device information and determines that the user is using a tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit acquires the user's device information and determines that the user is using a smartwatch. In this way, the optimal providing means is selected by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal providing means.

[0100] The providing unit can estimate the user's emotions and adjust the timing of providing navigation guidance based on the estimated user emotions. For example, if the user is nervous, the providing unit provides guidance earlier. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on changes in the facial expression and adjusts the timing of providing the navigation guidance. Furthermore, if the user is relaxed, the providing unit provides guidance at an appropriate timing. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. The providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of providing the navigation guidance. Furthermore, if the user is in a hurry, the providing unit provides guidance quickly. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The providing unit calculates an emotion score based on fluctuations in the heart rate and adjusts the timing of providing the navigation guidance. In this way, the timing of providing the navigation guidance is adjusted according to the user's emotions, thereby providing more appropriate guidance. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0101] When providing navigation guidance, the providing unit can prioritize providing highly relevant guidance by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit prioritizes providing information about that area. For example, the providing unit acquires the user's GPS data and determines that the user is in a specific area. Furthermore, if the user is traveling, the providing unit can prioritize providing information about the user's travel destination. For example, the providing unit uses a location information service to determine that the user is traveling. Furthermore, if the user is in their local area, the providing unit can prioritize providing information related to local news and events. For example, the providing unit determines that the user is in their local area based on the user's location information. In this way, highly relevant guidance is provided by taking the user's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's location information data to a generation AI and cause the generation AI to provide highly relevant guidance.

[0102] The providing unit can analyze the user's social media activity and provide related guidance when providing navigation guidance. The providing unit, for example, provides information about places where the user has checked in on social media. For example, the providing unit analyzes the user's social media posts to identify the checked-in places. The providing unit can also analyze the user's social media posts to provide information about related tourist spots and stores. For example, the providing unit identifies related tourist spots and stores based on the user's social media posts. The providing unit can also provide information about related places and events based on the activities of the user's friends on social media. For example, the providing unit analyzes the social media activities of the user's friends to identify related places and events. In this way, highly relevant guidance is provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media post data into a generating AI and cause the generating AI to provide related guidance.

[0103] The providing unit can customize the provision method by reflecting the user's past feedback when providing navigation guidance. The providing unit, for example, adjusts the provision method based on feedback provided by the user in the past. For example, the providing unit analyzes the user's survey results and adjusts the provision method. The providing unit can also preferentially use a specific provision method based on the user's past feedback. For example, the providing unit analyzes the user's evaluation comments and identifies a specific provision method. The providing unit can also improve provision accuracy by reflecting the user's feedback. For example, the providing unit adjusts the provision algorithm based on the user's feedback. In this way, the provision method is customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data to a generation AI and cause the generation AI to customize the provision method.

[0104] The collection unit can estimate the user's emotion and adjust the area information collection method based on the estimated user emotion. For example, when the user is excited, the collection unit prioritizes collecting area information related to the emotion. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on changes in the facial expression and adjusts the area information collection method. Furthermore, when the user is relaxed, the collection unit collects detailed area information. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. The collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the area information collection method. Furthermore, when the user is feeling anxious, the collection unit prioritizes collecting area information that gives the user a sense of security. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on fluctuations in the heart rate and adjusts the area information collection method. This allows for more appropriate information to be provided by adjusting the area information collection method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0105] When collecting area information, the collection unit can optimize the collection algorithm by referring to past collected data. The collection unit, for example, selects an optimal collection algorithm based on the past collected data. For example, the collection unit analyzes the past collected data and identifies an optimal collection algorithm. The collection unit can also preferentially collect specific area information from the past collected data. For example, the collection unit identifies specific area information based on the past collected data. The collection unit can also analyze the past collected data and optimize the collection algorithm. For example, the collection unit adjusts the collection algorithm based on the past collected data. In this way, the collection algorithm is optimized by referring to the past collected data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the past collected data to a generation AI and cause the generation AI to optimize the collection algorithm.

[0106] The collection unit can update the collected data by reflecting user feedback when collecting area information. The collection unit updates the collected data based on, for example, feedback provided by the user. For example, the collection unit analyzes user survey results and updates the collected data. The collection unit can also preferentially collect specific area information based on user feedback. For example, the collection unit analyzes user evaluation comments and identifies specific area information. The collection unit can also improve collection accuracy by reflecting user feedback. For example, the collection unit adjusts the collection algorithm based on user feedback. In this way, the collected data is updated by reflecting user feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs user feedback data to a generation AI and causes the generation AI to update the collected data.

[0107] When collecting area information, the collection unit can customize the collected content taking into account the user's current interests. For example, the collection unit prioritizes collecting area information related to topics in which the user is currently interested. For example, the collection unit analyzes the user's search history to identify topics in which the user is currently interested. The collection unit can also prioritize collecting specific area information based on the user's current interests. For example, the collection unit analyzes the user's browsing history to identify current interests. The collection unit can also customize the collected content taking into account the user's interests. For example, the collection unit analyzes the user's social media activity to identify interests. This allows the user's current interests to be taken into account, thereby providing more relevant area information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's search history data into a generation AI and have the generation AI customize the collected content.

[0108] The collection unit can estimate the user's emotions and adjust the frequency of collecting area information based on the estimated user emotions. For example, when the user is excited, the collection unit frequently collects area information. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on changes in the facial expression and adjusts the frequency of collecting area information. Furthermore, when the user is relaxed, the collection unit collects area information at an appropriate frequency. For example, the collection unit records the user's voice and estimates the emotion using voice analysis technology. The collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the frequency of collecting area information. Furthermore, when the user is feeling anxious, the collection unit quickly collects area information. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The collection unit calculates an emotion score based on fluctuations in heart rate and adjusts the frequency of collecting area information. In this way, by adjusting the frequency of collecting area information according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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, an AI, or may be performed without using an AI. For example, the collection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0109] When collecting area information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit prioritizes collecting information about that area. For example, the collection unit acquires the user's GPS data and identifies that the user is in a specific area. Furthermore, if the user is traveling, the collection unit can prioritize collecting information about the user's travel destination. For example, the collection unit uses a location information service to identify that the user is traveling. Furthermore, if the user is in their local area, the collection unit can prioritize collecting information related to local news and events. For example, the collection unit identifies that the user is in their local area based on the user's location information. This provides highly relevant area information by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's location information data into a generation AI and cause the generation AI to collect highly relevant information.

[0110] When collecting area information, the collection unit can analyze the user's social media activities and collect related information. The collection unit, for example, collects information about places where the user has checked in on social media. For example, the collection unit analyzes the user's social media posts and identifies the checked-in places. The collection unit can also analyze the user's social media posts and collect information about related tourist spots and stores. For example, the collection unit identifies related tourist spots and stores based on the user's social media posts. The collection unit can also collect information about related places and events based on the activities of the user's friends on social media. For example, the collection unit analyzes the social media activities of the user's friends and identifies related places and events. In this way, highly relevant area information is provided by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media post data into a generation AI and cause the generation AI to collect related information.

[0111] When collecting area information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. For example, the collection unit analyzes the user's survey results and adjusts the collection method. The collection unit can also preferentially use a specific collection method based on the user's past feedback. For example, the collection unit analyzes the user's evaluation comments and identifies a specific collection method. The collection unit can also improve collection accuracy by reflecting the user's feedback. For example, the collection unit adjusts the collection algorithm based on the user's feedback. In this way, the collection method is customized 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, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the collection method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, understanding unit, generation unit, provision unit, and collection unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit can analyze the user's language using the camera 42 or microphone 38B of the smart device 14. The understanding unit can understand the culture and local dialect of the destination by the specific processing unit 290 of the data processing device 12. The generation unit can generate navigation guidance by the specific processing unit 290 of the data processing device 12. The provision unit can provide the navigation guidance generated by the control unit 46A of the smart device 14 to the user. The collection unit can collect geographic information and tourist information by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, understanding unit, generation unit, provision unit, and collection unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit can analyze the user's language using the camera 42 or microphone 238 of the smart glasses 214. The understanding unit can understand the culture and local dialect of the destination by the specific processing unit 290 of the data processing device 12. The generation unit can generate navigation guidance by the specific processing unit 290 of the data processing device 12. The provision unit can provide the navigation guidance generated by the control unit 46A of the smart glasses 214 to the user. The collection unit can collect geographic information and tourist information by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, understanding unit, generation unit, provision unit, and collection unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit can analyze the user's language using the camera 42 or the microphone 238 of the headset type terminal 314. The understanding unit can understand the culture and local dialect of the destination by the specific processing unit 290 of the data processing device 12. The generation unit can generate navigation guidance by the specific processing unit 290 of the data processing device 12. The provision unit can provide the user with the navigation guidance generated by the control unit 46A of the headset type terminal 314. The collection unit can collect geographic information and tourist information by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, understanding unit, generation unit, provision unit, and collection unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the analysis unit can analyze the user's language using the camera 42 or microphone 238 of the robot 414. The understanding unit can understand the culture and local dialect of the destination by the specific processing unit 290 of the data processing device 12. The generation unit can generate navigation guidance by the specific processing unit 290 of the data processing device 12. The provision unit can provide the navigation guidance generated by the control unit 46A of the robot 414 to the user. The collection unit can collect geographical information and tourist information by the specific processing unit 290 of the data processing device 12.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] In addition to analyzing the user's language, the analysis unit can monitor the user's health condition and reflect it in the analysis results. For example, the analysis unit can measure the user's heart rate and blood pressure using sensors to evaluate the user's health condition. The analysis unit can also analyze the user's sleep patterns and estimate the user's fatigue level. Furthermore, the analysis unit can evaluate the user's nutritional status by taking into account the user's dietary history. This makes it possible to provide more appropriate navigation guidance based on the user's health condition.

[0114] The analysis unit can estimate the user's emotions and adjust the tone and style of the navigation guidance based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide guidance in a gentle tone that relaxes the user. If the user is excited, the analysis unit can provide guidance in an energetic tone. Furthermore, if the user is feeling anxious, the analysis unit can provide guidance in a calm tone that gives a sense of security. This makes it possible to provide appropriate navigation guidance according to the user's emotions.

[0115] The analysis unit can analyze the user's past language usage history as well as the user's hobbies and interests to select the optimal analysis method. For example, the analysis unit can analyze the user's music playback history to identify their favorite genres. The analysis unit can also analyze the user's reading history to identify topics of interest. Furthermore, the analysis unit can analyze the user's movie viewing history to identify their favorite movie genres. This makes it possible to provide more relevant analysis results based on the user's hobbies and interests.

[0116] During language analysis, the analysis unit can filter the analysis results by taking into account the user's current activity level. For example, if the user is exercising, information related to exercise is prioritized for analysis. For example, the analysis unit acquires the user's exercise data and identifies that the user is exercising. Furthermore, if the user is resting, information related to relaxation can be prioritized for analysis. For example, the analysis unit acquires the user's heart rate data and identifies that the user is resting. Furthermore, if the user is working, information related to work can be prioritized for analysis. This makes it possible to provide more relevant analysis results according to the user's current activity level.

[0117] During language analysis, the analysis unit can select the optimal analysis means depending on the type of device used by the user as well as the user's input method. For example, if the user is using a smartphone, the analysis unit selects an analysis means optimized for the smartphone. For example, the analysis unit analyzes touch input on the smartphone. Furthermore, if the user is using a tablet, the analysis unit can also select an analysis means optimized for the tablet. For example, the analysis unit analyzes stylus input on the tablet. Furthermore, if the user is using a smartwatch, the analysis unit can also select an analysis means optimized for the smartwatch. This makes it possible to provide more appropriate analysis results depending on the type of device used by the user.

[0118] The analysis unit can estimate the user's emotions and determine the priority of languages ​​to analyze based on the estimated user emotions. For example, if the user is excited, language related to emotions is prioritized for analysis. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on changes in facial expression and determines the priority of languages ​​to analyze. Also, if the user is calm, language containing detailed information is prioritized for analysis. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. The analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of languages ​​to analyze. Also, if the user is feeling anxious, language that gives a sense of security is prioritized for analysis. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. The analysis unit calculates an emotion score based on heart rate fluctuations and determines the priority of languages ​​to analyze. This allows the system to determine the priority of the languages ​​to be analyzed according to the user's emotions, thereby providing more appropriate analysis results.

[0119] During language analysis, the analysis unit can prioritize highly relevant languages ​​by taking into account the user's geographical location information as well as the user's movement history. For example, the analysis unit can prioritize analyzing the language and dialect of places the user has visited in the past. For example, the analysis unit can acquire the user's movement history data and identify places visited in the past. The analysis unit can also prioritize analyzing the language and dialect of places the user frequently visits. For example, the analysis unit can analyze the user's movement patterns and identify frequently visited places. Furthermore, when the user visits a new place, the analysis unit can prioritize analyzing the language and dialect of that place. This makes it possible to provide more relevant analysis results based on the user's movement history.

[0120] During language analysis, the analysis unit can analyze the user's online shopping history in addition to the user's social media activities and analyze related languages. For example, the analysis unit analyzes related languages ​​based on the language and topics used by the user when shopping online. For example, the analysis unit analyzes the user's online shopping history and identifies the language and topic used. The analysis unit can also analyze the user's purchase history and prioritize analysis of related languages. For example, the analysis unit identifies related languages ​​based on the user's purchase history. Furthermore, the analysis unit can analyze the user's review history and analyze related languages. In this way, by analyzing the user's online shopping history, it is possible to provide highly relevant analysis results.

[0121] During language analysis, the analysis unit can customize the analysis method by reflecting the user's real-time feedback in addition to the user's past feedback. For example, the analysis method is adjusted based on feedback provided by the user in real time. For example, the analysis unit analyzes the user's real-time evaluation comments and adjusts the analysis method. It is also possible to preferentially use a specific analysis method based on the user's real-time feedback. For example, the analysis unit identifies a specific analysis method based on the user's real-time feedback. It is also possible to improve the analysis accuracy by reflecting the user's real-time feedback. In this way, the analysis accuracy can be improved by reflecting the user's real-time feedback.

[0122] The understanding unit can estimate the user's emotions and adjust the method of understanding the culture and local dialect based on the estimated user emotions. For example, if the user is excited, it prioritizes understanding the culture and local dialect related to the emotion. For example, the understanding unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The understanding unit calculates an emotion score based on changes in facial expression and adjusts the method of understanding the culture and local dialect. Also, if the user is relaxed, it understands the culture and local dialect in detail. For example, the understanding unit records the user's voice and estimates the emotion using voice analysis technology. The understanding unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method of understanding the culture and local dialect. Also, if the user is anxious, it prioritizes understanding the culture and local dialect that gives a sense of security. For example, the understanding unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The understanding unit calculates an emotion score based on fluctuations in heart rate and adjusts the method of understanding the culture and local dialect. This allows the system to provide more appropriate understanding results by adjusting the understanding method for culture and local dialects according to the user's feelings.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The analysis unit analyzes the user's language. The user's language includes voice, text, and gestures. The analysis unit analyzes these inputs using natural language processing technology, voice recognition technology, and image analysis technology. For example, it converts voice input into text data and analyzes that text data. It also analyzes gestures captured by a camera and understands their meaning. Step 2: The understanding unit understands the destination's culture and local language based on the information analyzed by the analysis unit. The understanding unit uses dictionary-based translation technology, machine learning models, and generative AI to understand the destination's culture and local language. For example, it uses machine learning models to learn the meanings of the destination's culture and local language and deepen its understanding. Step 3: The generation unit generates navigation guidance based on the information understood by the understanding unit. The generation unit generates navigation guidance using a generation AI. For example, the generation AI receives a prompt such as "To get to Tokyo Tower, first take the subway and then walk," and generates navigation guidance. The generation unit also generates navigation guidance that includes information related to the destination. Step 4: The providing unit provides the navigation guidance generated by the generating unit to the user. The providing unit provides the navigation guidance in the form of voice guidance, text guidance, map display, etc. For example, the providing unit provides voice guidance using voice synthesis technology, provides text guidance using text display technology, and displays navigation guidance on a map using map display technology. Step 5: The collection unit collects information related to the user's area. The collection unit collects geographic information, tourist information, traffic information, etc. For example, the collection unit collects geographic information from databases on the Internet, tourist information from tourist information sites, and traffic information from traffic information services.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 AI 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 AI 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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 AI 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.

[0176] 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.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] [Explanation of symbols]

[0197] 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. an analysis unit that analyzes the language of a user; an understanding unit that understands the culture and local language of the destination based on the information analyzed by the analysis unit; a generating unit that generates a navigation guide based on the information understood by the understanding unit; a providing unit that provides the navigation guidance generated by the generating unit to a user; a collection unit that collects information related to the user's area; A system characterized by:

2. The analysis unit Estimate the user's emotions and adjust the accuracy of language analysis based on the estimated user emotions.

2. The system of claim 1.

3. The analysis unit Analyze the user's past language usage history and select the most appropriate analysis method 2. The system of claim 1.

4. The analysis unit When analyzing language, filter the analysis results by taking into account the user's current situation and intent.

2. The system of claim 1.

5. The analysis unit When analyzing language, select the optimal analysis method depending on the user's input method.

2. The system of claim 1.

6. The analysis unit Estimate the user's emotions and prioritize the languages ​​to analyze based on the estimated user emotions.

2. The system of claim 1.

7. The analysis unit When analyzing language, the system takes into account the user's geographic location information and prioritizes highly relevant language.

2. The system of claim 1.

8. The analysis unit During language analysis, the system analyzes the user's social media activity and analyzes related language.

2. The system of claim 1.

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