System

Abstract

An object of the system according to the embodiment is to collect real-time information from a local community and provide the information to a user.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The providing unit provides the information analyzed by the analyzing unit to the 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 have not done enough to effectively collect real-time information from local communities and provide it to users, and there is room for improvement.

[0005] The system according to the embodiment aims to collect real-time information from the local community and provide it to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the information to a user based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and provide real-time information from the local community to the user. [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 navigation system according to an embodiment of the present invention incorporates information from the local community in real time and incorporates it into navigation. In this navigation system, a user wears AR glasses, and a generation AI provides information related to the user's area (basic information about the city, tourist attractions, restaurants, shopping centers, etc.). This information is obtained from a database previously trained by the generation AI. Furthermore, the generation AI also collects live information from the local community in real time and feeds it back into the navigation information. For example, this information includes local event information, traffic conditions, and store business status. This information is provided by the generation AI by collecting and analyzing it from social media sites, local news sites, and other sources. The user can visually confirm this information through the AR glasses. For example, when approaching a tourist spot, detailed information about the spot is displayed on the AR glasses. Also, when passing by a restaurant, the restaurant's menu and reviews are displayed. Furthermore, local event information and traffic conditions are also displayed in real time, allowing the user to navigate based on the latest information. This allows the navigation system to efficiently incorporate live information from the local community in real time. This allows the navigation system to plan sightseeing trips based on the latest local information, and local residents to utilize the latest information in their daily lives.

[0029] A navigation system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. For example, the collection unit can collect information from social networking sites (SNS) and news sites. The collection unit can also filter information based on the user's current location information and areas of interest. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the collection unit collects social networking site posts and news articles and prioritizes collecting related information based on the user's current location information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information and identify information related to the user's area. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. The analysis unit can also estimate the user's emotions and adjust the method of expressing the analysis based on the estimated emotions. For example, the analysis unit analyzes the collected information and identifies information related to the user's current location information. The provision unit provides information to the user based on the information analyzed by the analysis unit. For example, the provision unit can provide the user with detailed information about tourist spots. The providing unit can also provide restaurant menus and reviews to the user. Furthermore, the providing unit can also provide local event information and traffic conditions to the user. For example, the providing unit provides detailed information about tourist spots to the user based on the analysis results. This allows the navigation system according to the embodiment to collect, analyze, and provide information to the user, thereby providing the latest information in real time.

[0030] The collection unit may collect information from social media platforms or news websites. For example, the collection unit may collect information from social media platforms such as Twitter (registered trademark), Facebook (registered trademark), and Instagram (registered trademark). The collection unit may also collect information from news websites using RSS feeds or APIs. For example, the collection unit may use Twitter's API to collect posts related to a specific hashtag. The collection unit may also subscribe to the RSS feed of a news website to collect the latest news articles. Furthermore, the collection unit may collect public posts on Facebook to obtain information related to the user's interests. This allows the latest information to be obtained by collecting information from social media platforms and news sites.

[0031] The analysis unit can analyze the collected information and identify information related to the user's area. For example, the analysis unit can analyze the collected information and identify information related to the user's current location information. The analysis unit can also identify the user's area using GPS data or an IP address. For example, the analysis unit can identify event information related to the user's current location from the collected information. The analysis unit can also identify an area based on the user's IP address and extract news articles related to that area. Furthermore, the analysis unit can use the user's GPS data to identify information about nearby tourist spots. This allows the user to be provided with appropriate information by identifying information related to the user's area.

[0032] The providing unit can provide information to the user based on the analysis results. For example, the providing unit can provide the user with detailed information about tourist spots based on the analysis results. The providing unit can also provide the user with restaurant menus and reviews. Furthermore, the providing unit can also provide the user with local event information and traffic conditions. For example, the providing unit can provide the user with detailed information about tourist spots based on the analysis results. The providing unit can also display restaurant menus and reviews based on the analysis results. Furthermore, the providing unit can display local event information and traffic conditions in real time. In this way, by providing information based on the analysis results, appropriate information can be provided to the user.

[0033] The providing unit can provide the user with detailed information about tourist spots. For example, the providing unit can provide the user with detailed information about the history and highlights of the tourist spot. The providing unit can also provide information about the tourist spot's opening hours and admission fees. Furthermore, the providing unit can display reviews and ratings of the tourist spot. For example, the providing unit can provide a detailed explanation of the historical background and highlights of the tourist spot. The providing unit can also display the tourist spot's opening hours and admission fees. Furthermore, the providing unit can display reviews and ratings of the tourist spot by other users. In this way, tourist information can be provided to the user by providing detailed information about the tourist spot.

[0034] The providing unit can provide the user with restaurant menus or reviews. The providing unit can provide the user with, for example, information regarding the names and prices of dishes at the restaurant. The providing unit can also display images and reviews of the dishes at the restaurant. The providing unit can also provide the restaurant's business hours and reservation information. For example, the providing unit can display the restaurant's menu in detail and provide the names and prices of the dishes. The providing unit can also display images of the dishes and reviews by other users. The providing unit can also provide the restaurant's business hours and reservation information. In this way, by providing restaurant menus and reviews, it is possible to provide the user with eating and drinking information.

[0035] The providing unit can provide the user with local event information and traffic conditions. The providing unit can provide the user with, for example, information regarding the name of a local event and the date and time of the event. The providing unit can also provide information regarding the location of the event and how to participate. The providing unit can also display traffic congestion information and the operation status of public transportation. For example, the providing unit can display the name of a local event and the date and time of the event in detail. The providing unit can also provide the location of the event and how to participate. The providing unit can also display traffic congestion information and the operation status of public transportation in real time. In this way, by providing local event information and traffic conditions, the latest information can be provided to the user.

[0036] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit can prioritize collecting related information based on a history of places the user has visited in the past. The collection unit can also select information collection targets based on topics in which the user has shown interest in the past. Furthermore, the collection unit can analyze the user's past behavioral patterns and determine the optimal timing for collecting information. For example, the collection unit analyzes the user's past browsing history and prioritizes collecting related information. The collection unit can also collect related information based on the user's past purchase history. Furthermore, the collection unit can analyze the user's past behavioral patterns and determine the optimal timing for collecting information. In this way, the optimal information collection method can be selected by analyzing the user's past behavioral history.

[0037] When collecting information, the collection unit can perform filtering based on the user's current location information and areas of interest. For example, the collection unit can prioritize collecting information related to the user's current location. The collection unit can also filter and collect information related to the user's areas of interest. Furthermore, the collection unit can combine the user's current location information and areas of interest to collect optimal information. For example, the collection unit prioritizes collecting information about nearby tourist spots based on the user's current GPS data. The collection unit can also filter and collect information related to the user's areas of interest based on the user's past search history. Furthermore, the collection unit can combine the user's current location information and areas of interest to collect optimal information. In this way, highly relevant information can be collected by filtering based on the user's current location information and areas of interest.

[0038] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can use voice recognition technology to collect information. Also, if the user uses text input, the collection unit can use text analysis technology to collect information. Furthermore, if the user uses image input, the collection unit can use image recognition technology to collect information. For example, if the user uses voice input, the collection unit can use voice recognition software to convert the voice into text and collect information. Also, if the user uses text input, the collection unit can use a text analysis algorithm to collect information. Furthermore, if the user uses image input, the collection unit can extract information from the image using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting tourist spot information near the user's current location. The collection unit can also prioritize collecting restaurant information near the user's current location. Furthermore, the collection unit can also prioritize collecting event information near the user's current location. For example, the collection unit prioritizes collecting nearby tourist spot information based on the user's current GPS data. The collection unit can also collect nearby restaurant information based on the user's current location information. Furthermore, the collection unit can collect nearby event information based on the user's current location information. In this way, by collecting highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user.

[0040] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities.

[0041] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also collect information by avoiding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also collect information by avoiding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a brief analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the information. For example, the analysis unit evaluates the importance of information based on the novelty and impact of the information, and performs a detailed analysis of information with high importance. The analysis unit can also perform a brief analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, efficient analysis is possible.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply an analysis algorithm specialized for tourism to tourist spot information. The analysis unit can also apply an analysis algorithm specialized for eating and drinking to restaurant information. The analysis unit can also apply an analysis algorithm specialized for events to event information. For example, the analysis unit can apply an analysis algorithm specialized for tourism to tourist spot information. The analysis unit can also apply an analysis algorithm specialized for eating and drinking to restaurant information. The analysis unit can also apply an analysis algorithm specialized for events to event information. In this way, by applying different analysis algorithms depending on the category of information, highly accurate analysis is possible.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by avoiding analysis results that the user has previously rated poorly. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by avoiding analysis results that the user has previously rated poorly. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0045] During analysis, the analysis unit can determine the analysis priority based on the time when the information was collected. The analysis unit can, for example, prioritize analyzing the latest information. The analysis unit can also lower the priority for analyzing older information. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the information was collected. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also lower the priority for analyzing older information. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the information was collected. In this way, by determining the analysis priority based on the time when the information was collected, the latest information can be analyzed with priority.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize analyzing information related to the user's current location information. The analysis unit can also prioritize analyzing information related to the user's field of interest. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. For example, the analysis unit prioritizes analyzing information related to the user's current location information. The analysis unit can also prioritize analyzing information related to the user's field of interest. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, information that is highly relevant to the user can be prioritized in analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0048] When providing information, the providing unit can select the optimal information providing method by analyzing the user's past behavioral history. For example, the providing unit can preferentially use an information providing method that the user has previously preferred. The providing unit can also select the optimal information providing method based on the user's past behavioral history. Furthermore, the providing unit can also optimize the information providing method by referring to the user's past feedback. For example, the providing unit preferentially uses an information providing method that the user has previously preferred. The providing unit can also select the optimal information providing method based on the user's past behavioral history. Furthermore, the providing unit can also optimize the information providing method by referring to the user's past feedback. In this way, the optimal information providing method can be selected by analyzing the user's past behavioral history.

[0049] The providing unit can customize the provided content based on the user's current living situation when providing information. For example, when the user is traveling, the providing unit can prioritize providing tourist spot and restaurant information. Furthermore, when the user is at work, the providing unit can prioritize providing business-related information. Furthermore, the providing unit can dynamically customize the provided content according to the user's current living situation. For example, when the user is traveling, the providing unit can prioritize providing tourist spot and restaurant information. Furthermore, when the user is at work, the providing unit can prioritize providing business-related information. Furthermore, the providing unit can dynamically customize the provided content according to the user's current living situation. In this way, by customizing the provided content based on the user's current living situation, it is possible to provide useful information to the user.

[0050] The providing unit can improve the providing method by reflecting user feedback when providing information. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically improve the providing method based on user feedback. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically improve the providing method based on user feedback. In this way, the providing method can be optimized by reflecting user feedback.

[0051] When providing information, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, the providing unit can prioritize providing information about tourist spots near the user's current location. The providing unit can also prioritize providing information about restaurants near the user's current location. The providing unit can also prioritize providing information about events near the user's current location. For example, the providing unit can prioritize providing information about nearby tourist spots based on the user's current GPS data. The providing unit can also provide information about nearby restaurants based on the user's current location information. The providing unit can also provide information about nearby events based on the user's current location information. In this way, by selecting the optimal providing method by taking into consideration the user's geographical location information, it is possible to provide useful information to the user.

[0052] When providing information, the providing unit can analyze the user's social media activity and customize the provided content. The providing unit can, for example, provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide information that is highly relevant to the user.

[0053] The providing unit can customize the providing method by reflecting the user's past feedback when providing information. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically customize the providing method based on the user's feedback. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically customize the providing method based on the user's feedback. In this way, the providing method can be optimized by reflecting the user's past feedback.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and calorie consumption, and sends it to the analysis unit. The analysis unit evaluates the user's health condition based on this data and sends it to the provision unit. The provision unit can suggest appropriate tourist spots and activities based on the user's health condition. For example, if the user is tired, it can suggest relaxation spots. If the user is active, it can also suggest hiking trails and sports facilities. This makes it possible to provide optimal navigation based on the user's health condition.

[0056] The navigation system can further include a personalization unit that learns the user's preferences. The personalization unit learns the user's preferences based on the user's past behavioral history and ratings, and sends the learned information to the analysis unit. The analysis unit analyzes this data and prioritizes information that matches the user's preferences, and sends the information to the provision unit. The provision unit can suggest optimal tourist spots and restaurants based on the user's preferences. For example, it can suggest similar restaurants based on ratings of restaurants the user has visited in the past. It can also prioritize the display of events in which the user is interested. This makes it possible to provide navigation that is customized based on the user's preferences.

[0057] The navigation system may further include a safety management unit to ensure the user's safety. The safety management unit collects information on the user's current location and surrounding security conditions and sends it to the analysis unit. The analysis unit evaluates the user's safety based on this data and sends it to the provision unit. The provision unit can suggest routes that avoid dangerous areas to ensure the user's safety. For example, it can suggest routes that avoid areas with poor security at night. It can also provide information on the nearest police station or hospital in an emergency. This allows navigation to be performed while ensuring the user's safety.

[0058] The navigation system can further analyze the user's past behavior history and select the optimal information collection method. The collection unit analyzes the user's past behavior history and selects the optimal information collection method. For example, it can prioritize collection of related information based on the history of places the user has visited in the past. It can also select information collection targets based on topics in which the user has shown interest in the past. It can also analyze the user's past behavior patterns and determine the optimal timing for collecting information. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history.

[0059] The navigation system can further perform filtering based on the user's current location information and areas of interest. The collection unit performs filtering based on the user's current location information and areas of interest. For example, it can prioritize collection of information related to the user's current location. It can also filter and collect information related to areas in which the user is interested. It can also combine the user's current location information and areas of interest to collect optimal information. In this way, highly relevant information can be collected by filtering based on the user's current location information and areas of interest.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects information. The collection unit can collect information from, for example, social networking sites and news sites. The collection unit can also filter information based on the user's current location information and areas of interest. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the collection unit collects social networking site posts and news articles, and prioritizes collecting related information based on the user's current location information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information and identify information related to the user's area. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, the analysis unit can analyze the collected information and identify information related to the user's current location information. Step 3: The providing unit provides information to the user based on the information analyzed by the analyzing unit. For example, the providing unit can provide the user with detailed information about tourist spots. The providing unit can also provide the user with restaurant menus and reviews. Furthermore, the providing unit can also provide the user with local event information and traffic conditions. For example, the providing unit provides the user with detailed information about tourist spots based on the analysis results.

[0062] (Example 2) A navigation system according to an embodiment of the present invention incorporates information from the local community in real time and incorporates it into navigation. In this navigation system, a user wears AR glasses, and a generation AI provides information related to the user's area (basic information about the city, tourist attractions, restaurants, shopping centers, etc.). This information is obtained from a database previously trained by the generation AI. Furthermore, the generation AI also collects live information from the local community in real time and feeds it back into the navigation information. For example, this information includes local event information, traffic conditions, and store business status. This information is provided by the generation AI by collecting and analyzing it from social media sites, local news sites, and other sources. The user can visually confirm this information through the AR glasses. For example, when approaching a tourist spot, detailed information about the spot is displayed on the AR glasses. Also, when passing by a restaurant, the restaurant's menu and reviews are displayed. Furthermore, local event information and traffic conditions are also displayed in real time, allowing the user to navigate based on the latest information. This allows the navigation system to efficiently incorporate live information from the local community in real time. This allows the navigation system to plan sightseeing trips based on the latest local information, and local residents to utilize the latest information in their daily lives.

[0063] A navigation system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information. For example, the collection unit can collect information from social networking sites (SNS) and news sites. The collection unit can also filter information based on the user's current location information and areas of interest. The collection unit can also estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the collection unit collects social networking site posts and news articles and prioritizes collecting related information based on the user's current location information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information and identify information related to the user's area. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. The analysis unit can also estimate the user's emotions and adjust the method of expressing the analysis based on the estimated emotions. For example, the analysis unit analyzes the collected information and identifies information related to the user's current location information. The provision unit provides information to the user based on the information analyzed by the analysis unit. For example, the provision unit can provide the user with detailed information about tourist spots. The providing unit can also provide restaurant menus and reviews to the user. Furthermore, the providing unit can also provide local event information and traffic conditions to the user. For example, the providing unit provides detailed information about tourist spots to the user based on the analysis results. This allows the navigation system according to the embodiment to collect, analyze, and provide information to the user, thereby providing the latest information in real time.

[0064] The collection unit may collect information from social media platforms or news websites. For example, the collection unit may collect information from social media platforms such as Twitter, Facebook, and Instagram. The collection unit may also collect information from news websites using RSS feeds or APIs. For example, the collection unit may use Twitter's API to collect posts related to a specific hashtag. The collection unit may also subscribe to the RSS feed of a news website to collect the latest news articles. Furthermore, the collection unit may collect public posts on Facebook to obtain information related to the user's interests. This allows the latest information to be obtained by collecting information from social media platforms and news sites.

[0065] The analysis unit can analyze the collected information and identify information related to the user's area. For example, the analysis unit can analyze the collected information and identify information related to the user's current location information. The analysis unit can also identify the user's area using GPS data or an IP address. For example, the analysis unit can identify event information related to the user's current location from the collected information. The analysis unit can also identify an area based on the user's IP address and extract news articles related to that area. Furthermore, the analysis unit can use the user's GPS data to identify information about nearby tourist spots. This allows the user to be provided with appropriate information by identifying information related to the user's area.

[0066] The providing unit can provide information to the user based on the analysis results. For example, the providing unit can provide the user with detailed information about tourist spots based on the analysis results. The providing unit can also provide the user with restaurant menus and reviews. Furthermore, the providing unit can also provide the user with local event information and traffic conditions. For example, the providing unit can provide the user with detailed information about tourist spots based on the analysis results. The providing unit can also display restaurant menus and reviews based on the analysis results. Furthermore, the providing unit can display local event information and traffic conditions in real time. In this way, by providing information based on the analysis results, appropriate information can be provided to the user.

[0067] The providing unit can provide the user with detailed information about tourist spots. For example, the providing unit can provide the user with detailed information about the history and highlights of the tourist spot. The providing unit can also provide information about the tourist spot's opening hours and admission fees. Furthermore, the providing unit can display reviews and ratings of the tourist spot. For example, the providing unit can provide a detailed explanation of the historical background and highlights of the tourist spot. The providing unit can also display the tourist spot's opening hours and admission fees. Furthermore, the providing unit can display reviews and ratings of the tourist spot by other users. In this way, tourist information can be provided to the user by providing detailed information about the tourist spot.

[0068] The providing unit can provide the user with restaurant menus or reviews. The providing unit can provide the user with, for example, information regarding the names and prices of dishes at the restaurant. The providing unit can also display images and reviews of the dishes at the restaurant. The providing unit can also provide the restaurant's business hours and reservation information. For example, the providing unit can display the restaurant's menu in detail and provide the names and prices of the dishes. The providing unit can also display images of the dishes and reviews by other users. The providing unit can also provide the restaurant's business hours and reservation information. In this way, by providing restaurant menus and reviews, it is possible to provide the user with eating and drinking information.

[0069] The providing unit can provide the user with local event information and traffic conditions. The providing unit can provide the user with, for example, information regarding the name of a local event and the date and time of the event. The providing unit can also provide information regarding the location of the event and how to participate. The providing unit can also display traffic congestion information and the operation status of public transportation. For example, the providing unit can display the name of a local event and the date and time of the event in detail. The providing unit can also provide the location of the event and how to participate. The providing unit can also display traffic congestion information and the operation status of public transportation in real time. In this way, by providing local event information and traffic conditions, the latest information can be provided to the user.

[0070] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit can, for example, analyze the user's facial expressions and voice to estimate emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal AI. The collection unit can reduce the frequency of information collection and collect only important information when the user is feeling stressed. The collection unit can also increase the frequency of information collection and collect detailed information when the user is relaxed. Furthermore, the collection unit can prioritize collecting important information in real time when the user is in a hurry. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. This allows the timing of information collection to be adjusted based on the user's emotions, thereby collecting information at a more appropriate time.

[0071] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. For example, the collection unit can prioritize collecting related information based on a history of places the user has visited in the past. The collection unit can also select information collection targets based on topics in which the user has shown interest in the past. Furthermore, the collection unit can analyze the user's past behavioral patterns and determine the optimal timing for collecting information. For example, the collection unit analyzes the user's past browsing history and prioritizes collecting related information. The collection unit can also collect related information based on the user's past purchase history. Furthermore, the collection unit can analyze the user's past behavioral patterns and determine the optimal timing for collecting information. In this way, the optimal information collection method can be selected by analyzing the user's past behavioral history.

[0072] When collecting information, the collection unit can perform filtering based on the user's current location information and areas of interest. For example, the collection unit can prioritize collecting information related to the user's current location. The collection unit can also filter and collect information related to the user's areas of interest. Furthermore, the collection unit can combine the user's current location information and areas of interest to collect optimal information. For example, the collection unit prioritizes collecting information about nearby tourist spots based on the user's current GPS data. The collection unit can also filter and collect information related to the user's areas of interest based on the user's past search history. Furthermore, the collection unit can combine the user's current location information and areas of interest to collect optimal information. In this way, highly relevant information can be collected by filtering based on the user's current location information and areas of interest.

[0073] When collecting information, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can use voice recognition technology to collect information. Also, if the user uses text input, the collection unit can use text analysis technology to collect information. Furthermore, if the user uses image input, the collection unit can use image recognition technology to collect information. For example, if the user uses voice input, the collection unit can use voice recognition software to convert the voice into text and collect information. Also, if the user uses text input, the collection unit can use a text analysis algorithm to collect information. Furthermore, if the user uses image input, the collection unit can extract information from the image using image recognition technology. This allows information to be collected efficiently by selecting the optimal collection means depending on the user's input method.

[0074] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information that helps the user relax. Furthermore, if the user is excited, the collection unit can prioritize collecting information that piques the user's interest. Furthermore, if the user is tired, the collection unit can prioritize collecting useful information. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the emotion using voice analysis technology. In this way, by determining the priority of information to be collected based on the user's emotions, more appropriate information can be collected.

[0075] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting tourist spot information near the user's current location. The collection unit can also prioritize collecting restaurant information near the user's current location. Furthermore, the collection unit can also prioritize collecting event information near the user's current location. For example, the collection unit prioritizes collecting nearby tourist spot information based on the user's current GPS data. The collection unit can also collect nearby restaurant information based on the user's current location information. Furthermore, the collection unit can collect nearby event information based on the user's current location information. In this way, by collecting highly relevant information by taking into account the user's geographical location information, it is possible to provide useful information to the user.

[0076] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. For example, the collection unit collects information about places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected by analyzing the user's social media activities.

[0077] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also collect information by avoiding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. For example, the collection unit can preferentially collect information from information sources that the user has previously rated highly. The collection unit can also collect information by avoiding information sources that the user has previously rated poorly. Furthermore, the collection unit can optimize the collection method based on the user's past feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0078] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that focus on the main points. Furthermore, if the user is excited, the analysis unit can also provide visually stimulating analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. In this way, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit can perform a detailed analysis of information with high importance. The analysis unit can also perform a brief analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the information. For example, the analysis unit evaluates the importance of information based on the novelty and impact of the information, and performs a detailed analysis of information with high importance. The analysis unit can also perform a brief analysis of information with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the information. In this way, by adjusting the level of detail of the analysis based on the importance of the information, efficient analysis is possible.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply an analysis algorithm specialized for tourism to tourist spot information. The analysis unit can also apply an analysis algorithm specialized for eating and drinking to restaurant information. The analysis unit can also apply an analysis algorithm specialized for events to event information. For example, the analysis unit can apply an analysis algorithm specialized for tourism to tourist spot information. The analysis unit can also apply an analysis algorithm specialized for eating and drinking to restaurant information. The analysis unit can also apply an analysis algorithm specialized for events to event information. In this way, by applying different analysis algorithms depending on the category of information, highly accurate analysis is possible.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by avoiding analysis results that the user has previously rated poorly. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. The analysis unit can also improve the accuracy of the analysis by avoiding analysis results that the user has previously rated poorly. Furthermore, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. In this way, the analysis length can be adjusted based on the user's emotions, thereby providing the optimal analysis result for the user.

[0083] During analysis, the analysis unit can determine the analysis priority based on the time when the information was collected. The analysis unit can, for example, prioritize analyzing the latest information. The analysis unit can also lower the priority for analyzing older information. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the information was collected. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also lower the priority for analyzing older information. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the information was collected. In this way, by determining the analysis priority based on the time when the information was collected, the latest information can be analyzed with priority.

[0084] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit can prioritize analyzing information related to the user's current location information. The analysis unit can also prioritize analyzing information related to the user's field of interest. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. For example, the analysis unit prioritizes analyzing information related to the user's current location information. The analysis unit can also prioritize analyzing information related to the user's field of interest. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the information. In this way, by adjusting the order of analysis based on the relevance of the information, information that is highly relevant to the user can be prioritized in analysis.

[0085] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results that are concise and easy to understand. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0086] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide information in a simple and highly visible manner. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. In this way, by adjusting the method of providing information based on the user's emotions, it is possible to provide information in a manner that is optimal for the user.

[0087] When providing information, the providing unit can select the optimal information providing method by analyzing the user's past behavioral history. For example, the providing unit can preferentially use an information providing method that the user has previously preferred. The providing unit can also select the optimal information providing method based on the user's past behavioral history. Furthermore, the providing unit can also optimize the information providing method by referring to the user's past feedback. For example, the providing unit preferentially uses an information providing method that the user has previously preferred. The providing unit can also select the optimal information providing method based on the user's past behavioral history. Furthermore, the providing unit can also optimize the information providing method by referring to the user's past feedback. In this way, the optimal information providing method can be selected by analyzing the user's past behavioral history.

[0088] The providing unit can customize the provided content based on the user's current living situation when providing information. For example, when the user is traveling, the providing unit can prioritize providing tourist spot and restaurant information. Furthermore, when the user is at work, the providing unit can prioritize providing business-related information. Furthermore, the providing unit can dynamically customize the provided content according to the user's current living situation. For example, when the user is traveling, the providing unit can prioritize providing tourist spot and restaurant information. Furthermore, when the user is at work, the providing unit can prioritize providing business-related information. Furthermore, the providing unit can dynamically customize the provided content according to the user's current living situation. In this way, by customizing the provided content based on the user's current living situation, it is possible to provide useful information to the user.

[0089] The providing unit can improve the providing method by reflecting user feedback when providing information. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically improve the providing method based on user feedback. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically improve the providing method based on user feedback. In this way, the providing method can be optimized by reflecting user feedback.

[0090] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing information. Furthermore, if the user is excited, the providing unit can prioritize providing interesting information. Furthermore, if the user is tired, the providing unit can prioritize providing useful information. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. In this way, by determining the priority of information to be provided based on the user's emotions, it is possible to prioritize providing information that is useful to the user.

[0091] When providing information, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, the providing unit can prioritize providing information about tourist spots near the user's current location. The providing unit can also prioritize providing information about restaurants near the user's current location. The providing unit can also prioritize providing information about events near the user's current location. For example, the providing unit can prioritize providing information about nearby tourist spots based on the user's current GPS data. The providing unit can also provide information about nearby restaurants based on the user's current location information. The providing unit can also provide information about nearby events based on the user's current location information. In this way, by selecting the optimal providing method by taking into consideration the user's geographical location information, it is possible to provide useful information to the user.

[0092] When providing information, the providing unit can analyze the user's social media activity and customize the provided content. The providing unit can, for example, provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. For example, the providing unit provides information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide information that is highly relevant to the user.

[0093] The providing unit can customize the providing method by reflecting the user's past feedback when providing information. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically customize the providing method based on the user's feedback. For example, the providing unit can preferentially use a providing method that the user has previously rated highly. The providing unit can also provide information by avoiding a providing method that the user has previously rated poorly. Furthermore, the providing unit can dynamically customize the providing method based on the user's feedback. In this way, the providing method can be optimized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and estimate the emotion using the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the information collected by the identification processing unit 290 of the data processing device 12 and identify information related to the user's area. For example, the provision unit can provide the user with detailed information about tourist spots based on the analysis results using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and estimate the emotion using the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the information collected by the identification processing unit 290 of the data processing device 12 and identify information related to the user's area. For example, the provision unit can provide the user with detailed information about tourist spots based on the analysis results using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the headset type terminal 314, and estimate the emotion using the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the information collected by the identification processing unit 290 of the data processing device 12 and identify information related to the user's area. For example, the provision unit can provide the user with detailed information about tourist spots based on the analysis results using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and estimate the emotion using the identification processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the information collected by the identification processing unit 290 of the data processing device 12 and identify information related to the user's area. For example, the provision unit can provide the user with detailed information about tourist spots based on the analysis results using the control unit 46A of the robot 414.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and calorie consumption, and sends it to the analysis unit. The analysis unit evaluates the user's health condition based on this data and sends it to the provision unit. The provision unit can suggest appropriate tourist spots and activities based on the user's health condition. For example, if the user is tired, it can suggest relaxation spots. If the user is active, it can also suggest hiking trails and sports facilities. This makes it possible to provide optimal navigation based on the user's health condition.

[0096] The navigation system can further include a personalization unit that learns the user's preferences. The personalization unit learns the user's preferences based on the user's past behavioral history and ratings, and sends the learned information to the analysis unit. The analysis unit analyzes this data and prioritizes information that matches the user's preferences, and sends the information to the provision unit. The provision unit can suggest optimal tourist spots and restaurants based on the user's preferences. For example, it can suggest similar restaurants based on ratings of restaurants the user has visited in the past. It can also prioritize the display of events in which the user is interested. This makes it possible to provide navigation that is customized based on the user's preferences.

[0097] The navigation system may further include a safety management unit to ensure the user's safety. The safety management unit collects information on the user's current location and surrounding security conditions and sends it to the analysis unit. The analysis unit evaluates the user's safety based on this data and sends it to the provision unit. The provision unit can suggest routes that avoid dangerous areas to ensure the user's safety. For example, it can suggest routes that avoid areas with poor security at night. It can also provide information on the nearest police station or hospital in an emergency. This allows navigation to be performed while ensuring the user's safety.

[0098] The navigation system can further estimate the user's emotions and adjust the method of providing information based on the estimated emotions. The providing unit estimates the user's emotions and adjusts the method of providing information based on the estimated emotions. For example, if the user is nervous, information can be provided in a simple, highly visible manner. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that focuses on the main points can be provided. In this way, by adjusting the method of providing information based on the user's emotions, information can be provided in the most optimal manner for the user.

[0099] The navigation system can further estimate the user's emotions and determine the priority of information to be provided based on the estimated emotions. The providing unit estimates the user's emotions and determines the priority of information to be provided based on the estimated emotions. For example, if the user is feeling stressed, information that helps the user relax can be provided preferentially. Also, if the user is excited, information that piques the user's interest can be provided preferentially. Furthermore, if the user is tired, useful information can be provided preferentially. In this way, by determining the priority of information to be provided based on the user's emotions, it is possible to provide information that is useful to the user preferentially.

[0100] The navigation system can further estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. The analysis unit estimates the user's emotions and adjusts the way the analysis is presented based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, visually stimulating analysis results can be provided. In this way, by adjusting the way the analysis is presented based on the user's emotions, analysis results that are easy for the user to understand can be provided.

[0101] The navigation system can further estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. The collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is feeling stressed, the frequency of information collection can be reduced and only important information can be collected. Also, if the user is relaxed, the frequency of information collection can be increased and detailed information can be collected. Furthermore, if the user is in a hurry, important information can be collected preferentially in real time. In this way, by adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time.

[0102] The navigation system can further estimate the user's emotions and determine the priority of information to be collected based on the estimated emotions. The collection unit estimates the user's emotions and determines the priority of information to be collected based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize collecting information that helps the user relax. Also, if the user is excited, it can prioritize collecting information that piques the user's interest. Furthermore, if the user is tired, it can prioritize collecting useful information. In this way, by determining the priority of information to be collected based on the user's emotions, it is possible to collect more appropriate information.

[0103] The navigation system can further analyze the user's past behavior history and select the optimal information collection method. The collection unit analyzes the user's past behavior history and selects the optimal information collection method. For example, it can prioritize collection of related information based on the history of places the user has visited in the past. It can also select information collection targets based on topics in which the user has shown interest in the past. It can also analyze the user's past behavior patterns and determine the optimal timing for collecting information. In this way, the optimal information collection method can be selected by analyzing the user's past behavior history.

[0104] The navigation system can further perform filtering based on the user's current location information and areas of interest. The collection unit performs filtering based on the user's current location information and areas of interest. For example, it can prioritize collection of information related to the user's current location. It can also filter and collect information related to areas in which the user is interested. It can also combine the user's current location information and areas of interest to collect optimal information. In this way, highly relevant information can be collected by filtering based on the user's current location information and areas of interest.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The collection unit collects information. The collection unit can collect information from, for example, social networking sites and news sites. The collection unit can also filter information based on the user's current location information and areas of interest. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the collection unit collects social networking site posts and news articles, and prioritizes collecting related information based on the user's current location information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information and identify information related to the user's area. The analysis unit can also adjust the level of detail of the analysis based on the importance of the information. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, the analysis unit can analyze the collected information and identify information related to the user's current location information. Step 3: The providing unit provides information to the user based on the information analyzed by the analyzing unit. For example, the providing unit can provide the user with detailed information about tourist spots. The providing unit can also provide the user with restaurant menus and reviews. Furthermore, the providing unit can also provide the user with local event information and traffic conditions. For example, the providing unit provides the user with detailed information about tourist spots based on the analysis results.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides information to a user based on the information analyzed by the analyzing unit; Equipped with A system characterized by:

2. The collecting unit Collect information from social media platforms or news websites 2. The system of claim 1.

3. The analysis unit Analyze the collected information to identify information relevant to your area 2. The system of claim 1.

4. The providing unit Providing information to users based on analysis results 2. The system of claim 1.

5. The providing unit Providing users with detailed information about tourist attractions 2. The system of claim 1.

6. The providing unit Providing restaurant menus or reviews to users 2. The system of claim 1.

7. The providing unit Providing users with local event information and traffic conditions 2. The system of claim 1.

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

2. The system of claim 1.

9. The collecting unit Analyze the user's past behavior history and select the optimal information collection method 2. The system of claim 1.

10. The collecting unit When collecting information, it filters it based on the user's current location and areas of interest.

2. The system of claim 1.

Images