system

Abstract

The system according to the embodiment aims to efficiently collect and provide useful information based on location information. [Solution] A system according to an embodiment includes an acquisition unit, a collection unit, an analysis unit, and a provision unit. The acquisition unit acquires location information. The collection unit collects information from social networking sites and websites based on the location information acquired by the acquisition unit. The analysis unit analyzes the information collected by the collection unit. The provision unit provides information based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been sufficient in efficiently collecting and providing useful information from social networking sites and websites based on location information, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and provide useful information based on location information. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a collection unit, an analysis unit, and a provision unit. The acquisition unit acquires location information. The collection unit collects information from SNSs and websites based on the location information acquired by the acquisition unit. The analysis unit analyzes the information collected by the collection unit. The provision unit provides information based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and provide useful information based on location information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information provision system according to an embodiment of the present invention links location information and centrally searches, collects, and provides information on recommended travel destinations and special offers from various social networking sites and websites. This information provision system allows users to search for information such as flyers and coupons, as well as limited-time sales and time-limited offers within their daily living area. For example, searching for "meat" can provide nearby special offers. Specifically, the system comprises the following steps: First, location information is acquired. Next, information is collected from social networking sites and websites. The collected information is analyzed and information is provided based on the analysis results. This system allows users to efficiently obtain relevant information. To protect user privacy, the system also obtains consent and anonymizes information. For example, the information provision system obtains a user's current location and collects relevant information from social networking sites and websites based on the location information. The collected information is analyzed by an analysis unit, and optimal information is provided based on the user's search keywords and location information. Furthermore, to protect user privacy, a consent acquisition unit obtains the user's consent, and an anonymization unit anonymizes the information. This allows the information provision system to efficiently collect, analyze, and provide information based on the user's location information.

[0029] An information provision system according to an embodiment includes an acquisition unit, a collection unit, an analysis unit, and a provision unit. The acquisition unit acquires user location information. The location information includes, but is not limited to, GPS information, Wi-Fi location information, and cell tower location information. The acquisition unit acquires the user's current location using, for example, a GPS sensor. The acquisition unit can also acquire location information using Wi-Fi access point information. The acquisition unit can also identify the user's location using cell tower location information. The collection unit collects information from social networking sites (SNS) and websites based on the location information acquired by the acquisition unit. The collection unit can collect information from websites using, for example, scraping technology. The collection unit can also collect information from social networking sites using APIs. The collection unit can also collect the latest information using RSS feeds. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze text data using, for example, natural language processing technology. The analysis unit can also evaluate the relevance of information using a machine learning algorithm. The analysis unit can also classify information using clustering technology. The providing unit provides information based on the information analyzed by the analyzing unit. The providing unit provides related information based on, for example, a user's search keywords. The providing unit can also provide information in real time when location information is updated. Furthermore, the providing unit includes a search bar and a display of recommended information as a user interface. This allows the information providing system according to the embodiment to efficiently collect, analyze, and provide information based on the user's location information.

[0030] The collection unit can collect information using scraping or APIs. For example, the collection unit collects information from websites using scraping technology. For example, the collection unit can analyze the HTML of a webpage using Python's BeautifulSoup library and extract necessary information. The collection unit can also collect information from dynamic webpages using Selenium. Furthermore, the collection unit can perform large-scale web scraping using the Scrapy framework. The collection unit can also collect information from social media using APIs. For example, the collection unit can collect tweets using the X (formerly Twitter®) API. The collection unit can also collect posts using the Facebook® Graph API. Furthermore, the collection unit can collect photos and comments using the Instagram® API. This allows the collection unit to efficiently collect information by using scraping or APIs.

[0031] The providing unit can provide information based on a user's search keywords. The providing unit provides related information based on, for example, the search keywords entered by the user. For example, if a user searches for "meat," the providing unit can provide sale information at nearby restaurants and supermarkets. Furthermore, if a user searches for "sightseeing," the providing unit can provide information about tourist spots around the current location. Furthermore, if a user searches for "events," the providing unit can provide information about events being held nearby. The providing unit can also provide related information based on past search history. For example, the providing unit can prioritize related information based on keywords searched for by the user in the past. Furthermore, the providing unit can provide information related to a specific time period from the user's search history. Furthermore, the providing unit can analyze the user's search history and prioritize information related to specific events or places. This allows the providing unit to provide highly relevant information based on the user's search keywords.

[0032] The providing unit can provide information in real time when the location information is updated. For example, the providing unit provides the latest information every time the user's location information is updated. For example, the providing unit provides recommended spots and discount information around the user's current location every time the user moves. The providing unit can also provide information related to a specific area in real time when the user enters the area. Furthermore, the providing unit can also provide information related to a specific location when the user leaves the location. The providing unit uses a low-latency communication protocol to provide the information in real time. For example, the providing unit can provide the information in real time using WebSocket. The providing unit can also provide the information efficiently using HTTP / 2. Furthermore, the providing unit can provide the information with low latency using MQTT. This allows the providing unit to provide the latest information in real time every time the location information is updated.

[0033] The providing unit may include a search bar and a display of recommended information as a user interface. The providing unit may include, for example, a search bar to enable a user to easily search for information. For example, the providing unit may provide a search bar with an autocomplete function. The providing unit may also provide a search bar with a function to display a search history. Furthermore, the providing unit may also provide a search bar with a filtering option. The providing unit may include a function to display recommended information to a user. For example, the providing unit may display information relevant to a user using a recommendation algorithm. The providing unit may also include a function to customize the display format of the information. Furthermore, the providing unit may include a function to set the update frequency of the information. In this way, the providing unit may enable a user to easily search for and view information.

[0034] The device may be provided with a consent acquisition unit and an anonymization unit for privacy protection. The consent acquisition unit, for example, provides a screen for acquiring user consent. For example, the consent acquisition unit displays a screen that explains the consent details to the user in detail. The consent acquisition unit also has a function for recording the user's consent. Furthermore, the consent acquisition unit also has a function for updating or canceling the consent. The anonymization unit, for example, uses technology for anonymizing user information. For example, the anonymization unit uses data masking technology to hide the user's personal information. The anonymization unit can also anonymize user information using pseudo-anonymization technology. Furthermore, the anonymization unit also has a function for setting the level of anonymization. Thus, by providing a consent acquisition unit and an anonymization unit for privacy protection, it is possible to provide information while protecting the user's privacy.

[0035] The acquisition unit can analyze the user's past movement history and select the optimal location information acquisition method. The acquisition unit, for example, uses a machine learning algorithm to analyze the user's past movement history. For example, the acquisition unit analyzes patterns of places the user has visited in the past and acquires location information from frequently visited places. The acquisition unit can also acquire location information for a specific time period from the user's movement history. Furthermore, the acquisition unit can preferentially acquire location information related to specific events or places based on the user's past movement history. This allows the acquisition unit to select the optimal location information acquisition method based on the user's past movement history.

[0036] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity status. The acquisition unit, for example, uses a sensor to acquire the user's current activity status. For example, the acquisition unit can detect whether the user is walking using an acceleration sensor. The acquisition unit can also detect whether the user is driving a car using a GPS sensor. Furthermore, the acquisition unit can detect whether the user is using public transportation using a Wi-Fi sensor. The acquisition unit filters the location information based on the user's current activity status. For example, if the user is walking, the acquisition unit can prioritize acquiring location information of nearby recommended spots. Furthermore, if the user is driving a car, the acquisition unit can prioritize acquiring location information of parking lots and drive-throughs. Furthermore, if the user is using public transportation, the acquisition unit can prioritize acquiring information near stations and bus stops. This allows the acquisition unit to filter location information according to the user's current activity status.

[0037] When acquiring location information, the acquisition unit can select the optimal acquisition method taking into account the user's device information. The acquisition unit, for example, uses a device sensor to acquire the user's device information. For example, if the user is using a smartphone, the acquisition unit can acquire location information using GPS. Also, if the user is using a tablet, the acquisition unit can acquire location information using Wi-Fi or Bluetooth. Furthermore, if the user is using a smartwatch, the acquisition unit can acquire location information in low power mode. This allows the acquisition unit to select the optimal location information acquisition method based on the user's device information.

[0038] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related location information. The acquisition unit, for example, uses a social media API to analyze the user's social media activity. For example, the acquisition unit can prioritize acquiring location information near places where the user has checked in on the social media. The acquisition unit can also acquire location information of recommended spots from accounts the user follows on the social media. Furthermore, the acquisition unit can acquire location information related to posts the user has shared on the social media. This allows the acquisition unit to acquire related location information based on the user's social media activity.

[0039] During collection, the collection unit can analyze the user's past search history and select the optimal information collection method. The collection unit, for example, uses a machine learning algorithm to analyze the user's past search history. For example, the collection unit prioritizes collecting related information based on keywords the user has previously searched for. The collection unit can also collect information related to a specific time period from the user's past search history. Furthermore, the collection unit can analyze the user's past search history and prioritize collecting information related to a specific event or location. This allows the collection unit to select the optimal information collection method based on the user's past search history.

[0040] The collection unit may perform filtering based on the user's current areas of interest at the time of collection. For example, the collection unit may analyze the user's behavioral data to obtain the user's current areas of interest. For example, the collection unit may preferentially collect information related to topics in which the user is currently interested. The collection unit may also preferentially collect information related to events in which the user is currently participating. Furthermore, the collection unit may preferentially collect information related to accounts the user is currently following. This allows the collection unit to filter information according to the user's current areas of interest.

[0041] During collection, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses a GPS sensor to acquire the user's geographical location information. For example, the collection unit prioritizes collecting information near the user's current location. The collection unit can also prioritize collecting information related to places the user plans to visit. Furthermore, the collection unit can also prioritize collecting information near places the user has visited in the past. This allows the collection unit to prioritize collecting highly relevant information based on the user's geographical location information.

[0042] During collection, the collection unit can analyze the user's social media activity and collect related information. The collection unit, for example, uses a social media API to analyze the user's social media activity. For example, the collection unit can prioritize collecting information related to places the user has checked in to on the social media. The collection unit can also collect recommended information from accounts the user follows on the social media. Furthermore, the collection unit can collect information related to posts the user has shared on the social media. This allows the collection unit to collect related information based on the user's social media activity.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, uses a machine learning algorithm to evaluate the importance of the collected information. For example, the analysis unit evaluates the reliability and relevance of the information and performs a detailed analysis of information with high importance. The analysis unit can also perform a simplified analysis of information with low importance to provide information that focuses on the main points. Furthermore, the analysis unit can analyze information with medium importance at an appropriate level of detail to provide balanced information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected information.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit uses clustering technology to classify the category of information. For example, the analysis unit applies a tourism algorithm to information about tourist spots for analysis. The analysis unit can also apply a gourmet algorithm to information about restaurants for analysis. Furthermore, the analysis unit can also apply a shopping algorithm to information about shopping for analysis. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of information.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit uses, for example, a timestamp to evaluate the time of submission of information. For example, the analysis unit can prioritize analysis of the most recent information and provide it promptly. The analysis unit can also lower the priority of analysis of old information and provide it as needed. Furthermore, the analysis unit can analyze information of medium newness with moderate priority and provide balanced information. This allows the analysis unit to determine the priority of analysis based on the time of submission of information.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit, for example, analyzes co-occurrence relationships and correlations to evaluate the relevance of information. For example, the analysis unit prioritizes analysis of information with high relevance and provides it quickly. The analysis unit can also postpone the analysis order of information with low relevance and provide it as needed. Furthermore, the analysis unit can analyze information with medium relevance in an appropriate order and provide balanced information. This allows the analysis unit to adjust the order of analysis based on the relevance of information.

[0047] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The providing unit, for example, uses a machine learning algorithm to evaluate the importance of the information. For example, the providing unit evaluates the reliability and relevance of the information and provides detailed information for information with high importance. The providing unit can also provide simplified information for information with low importance. Furthermore, the providing unit can provide information with an appropriate level of detail for information with medium importance. This allows the providing unit to adjust the level of detail of the information provided based on the importance of the information.

[0048] The providing unit can apply different providing algorithms depending on the category of information when providing the information. The providing unit uses, for example, a clustering technique to classify the category of information. For example, the providing unit can apply a tourism algorithm to provide information about tourist spots. The providing unit can also apply a gourmet algorithm to provide information about restaurants. Furthermore, the providing unit can also apply a shopping algorithm to provide information about shopping. This allows the providing unit to apply the optimal providing algorithm depending on the category of information.

[0049] The providing unit can determine the priority of provision based on the time of submission of the information at the time of provision. The providing unit uses, for example, a timestamp to evaluate the time of submission of the information. For example, the providing unit provides the latest information with priority. The providing unit can also lower the priority of provision of old information and provide it as needed. Furthermore, the providing unit can provide information of medium newness with moderate priority. This allows the providing unit to determine the priority of provision based on the time of submission of the information.

[0050] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. The providing unit, for example, analyzes co-occurrence relationships and correlations to evaluate the relevance of the information. For example, the providing unit provides information with high relevance preferentially. The providing unit can also postpone the order of providing information with low relevance and provide it as needed. Furthermore, the providing unit can provide information with medium relevance in an appropriate order to provide balanced information. This allows the providing unit to adjust the order of providing information based on the relevance of the information.

[0051] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by analyzing the user's past consent history. The consent acquisition unit, for example, uses a machine learning algorithm to analyze the user's past consent history. For example, the consent acquisition unit preferentially suggests methods to which the user has previously given consent. The consent acquisition unit can also obtain consent for a specific time period based on the user's past consent history. Furthermore, the consent acquisition unit can also preferentially obtain consent related to a specific event or location based on the user's past consent history. This allows the consent acquisition unit to select the optimal consent acquisition method based on the user's past consent history.

[0052] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by taking into account the user's device information. The consent acquisition unit, for example, uses a device sensor to obtain the user's device information. For example, if the user is using a smartphone, the consent acquisition unit obtains consent through a touch operation. Furthermore, if the user is using a tablet, the consent acquisition unit can provide a consent acquisition method optimized for a large screen. Furthermore, if the user is using a smartwatch, the consent acquisition unit can provide a simple and highly visible consent acquisition method. This allows the consent acquisition unit to select the optimal consent acquisition method based on the user's device information.

[0053] During anonymization, the anonymization unit can analyze the user's past data usage history and select the optimal anonymization method. The anonymization unit, for example, uses a machine learning algorithm to analyze the user's past data usage history. For example, the anonymization unit analyzes patterns of data used by the user in the past and selects the optimal anonymization method. The anonymization unit can also perform anonymization during a specific time period based on the user's past data usage history. Furthermore, the anonymization unit can prioritize anonymization related to specific events or locations based on the user's past data usage history. This allows the anonymization unit to select the optimal anonymization method based on the user's past data usage history.

[0054] The anonymization unit can select the optimal anonymization method during anonymization by taking into account the user's device information. The anonymization unit, for example, uses a device sensor to acquire the user's device information. For example, if the user is using a smartphone, the anonymization unit performs anonymization by touch operation. Furthermore, if the user is using a tablet, the anonymization unit can provide an anonymization method optimized for a large screen. Furthermore, if the user is using a smartwatch, the anonymization unit can provide a simple and highly visible anonymization method. This allows the anonymization unit to select the optimal anonymization method based on the user's device information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When acquiring user location information, the acquisition unit can select the optimal acquisition method taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, a location information acquisition method with low power consumption can be selected. Also, when the remaining battery level is sufficient, a location information acquisition method with high accuracy can be selected. Furthermore, when the remaining battery level is medium, a balanced location information acquisition method can be selected. This allows the acquisition unit to select the optimal location information acquisition method according to the remaining battery level of the user's device.

[0057] The collection unit can analyze the user's past search history and prioritize collection of information related to a specific time period. For example, related information can be collected based on keywords that the user previously searched for during a specific time period. Also, if the user searches for information related to a specific event or location, that information can be prioritized. Furthermore, information related to a specific topic can be prioritized based on the user's past search history. This allows the collection unit to select the optimal information collection method based on the user's past search history.

[0058] The providing unit can adjust the information providing method based on the user's current activity status. For example, if the user is walking, the information can be provided by voice. If the user is driving a car, the information can be provided visually. Furthermore, if the user is using public transportation, the information can be provided by text message. This allows the providing unit to select the optimal information providing method according to the user's current activity status.

[0059] The providing unit can adjust the information display method based on the screen size of the user's device. For example, if the user is using a smartphone, a compact display format can be provided. Also, if the user is using a tablet, a display format optimized for a large screen can be provided. Furthermore, if the user is using a desktop, detailed information can be displayed. This allows the providing unit to select the optimal information display method according to the screen size of the user's device.

[0060] The providing unit can adjust the information providing method based on the user's internet connection status. For example, if the internet connection is unstable, offline information can be provided. Alternatively, if the internet connection is stable, real-time updated information can be provided. Furthermore, if the internet connection is slow, information can be provided in a lightweight data format. This allows the providing unit to select the optimal information providing method depending on the user's internet connection status.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The acquisition unit acquires user location information. The location information includes, for example, GPS information, Wi-Fi location information, and cell tower location information. The acquisition unit identifies the user's current location using the GPS sensor, Wi-Fi access point information, and cell tower location information. Step 2: The collection unit collects information from social media and websites based on the location information acquired by the acquisition unit. The collection unit collects information using scraping technology, APIs, and RSS feeds. Step 3: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using natural language processing technology, machine learning algorithms, and clustering technology, assessing relevance, and classifying it. Step 4: The providing unit provides information based on the information analyzed by the analyzing unit. The providing unit provides related information based on the user's search keywords and provides information in real time when the location information is updated. The providing unit also provides a user interface with a search bar and display of recommended information.

[0063] (Example 2) An information provision system according to an embodiment of the present invention links location information and centrally searches, collects, and provides information on recommended travel destinations and special offers from various social networking sites and websites. This information provision system allows users to search for information such as flyers and coupons, as well as limited-time sales and time-limited offers within their daily living area. For example, searching for "meat" can provide nearby special offers. Specifically, the system comprises the following steps: First, location information is acquired. Next, information is collected from social networking sites and websites. The collected information is analyzed and information is provided based on the analysis results. This system allows users to efficiently obtain relevant information. To protect user privacy, the system also obtains consent and anonymizes information. For example, the information provision system obtains a user's current location and collects relevant information from social networking sites and websites based on the location information. The collected information is analyzed by an analysis unit, and optimal information is provided based on the user's search keywords and location information. Furthermore, to protect user privacy, a consent acquisition unit obtains the user's consent, and an anonymization unit anonymizes the information. This allows the information provision system to efficiently collect, analyze, and provide information based on the user's location information.

[0064] An information provision system according to an embodiment includes an acquisition unit, a collection unit, an analysis unit, and a provision unit. The acquisition unit acquires user location information. The location information includes, but is not limited to, GPS information, Wi-Fi location information, and cell tower location information. The acquisition unit acquires the user's current location using, for example, a GPS sensor. The acquisition unit can also acquire location information using Wi-Fi access point information. The acquisition unit can also identify the user's location using cell tower location information. The collection unit collects information from social networking sites (SNS) and websites based on the location information acquired by the acquisition unit. The collection unit can collect information from websites using, for example, scraping technology. The collection unit can also collect information from social networking sites using APIs. The collection unit can also collect the latest information using RSS feeds. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze text data using, for example, natural language processing technology. The analysis unit can also evaluate the relevance of information using a machine learning algorithm. The analysis unit can also classify information using clustering technology. The providing unit provides information based on the information analyzed by the analyzing unit. The providing unit provides related information based on, for example, a user's search keywords. The providing unit can also provide information in real time when location information is updated. Furthermore, the providing unit includes a search bar and a display of recommended information as a user interface. This allows the information providing system according to the embodiment to efficiently collect, analyze, and provide information based on the user's location information.

[0065] The collection unit can collect information using scraping or APIs. For example, the collection unit collects information from websites using scraping technology. For example, the collection unit can analyze the HTML of a webpage using Python's BeautifulSoup library and extract the necessary information. The collection unit can also collect information from dynamic webpages using Selenium. Furthermore, the collection unit can perform large-scale web scraping using the Scrapy framework. The collection unit can also collect information from social media using APIs. For example, the collection unit can collect tweets using the X (formerly Twitter) API. The collection unit can also collect posts using the Facebook Graph API. Furthermore, the collection unit can collect photos and comments using the Instagram API. This allows the collection unit to efficiently collect information by using scraping or APIs.

[0066] The providing unit can provide information based on a user's search keywords. The providing unit provides related information based on, for example, the search keywords entered by the user. For example, if a user searches for "meat," the providing unit can provide sale information at nearby restaurants and supermarkets. Furthermore, if a user searches for "sightseeing," the providing unit can provide information about tourist spots around the current location. Furthermore, if a user searches for "events," the providing unit can provide information about events being held nearby. The providing unit can also provide related information based on past search history. For example, the providing unit can prioritize related information based on keywords searched for by the user in the past. Furthermore, the providing unit can provide information related to a specific time period from the user's search history. Furthermore, the providing unit can analyze the user's search history and prioritize information related to specific events or places. This allows the providing unit to provide highly relevant information based on the user's search keywords.

[0067] The providing unit can provide information in real time when the location information is updated. For example, the providing unit provides the latest information every time the user's location information is updated. For example, the providing unit provides recommended spots and discount information around the user's current location every time the user moves. The providing unit can also provide information related to a specific area in real time when the user enters the area. Furthermore, the providing unit can also provide information related to a specific location when the user leaves the location. The providing unit uses a low-latency communication protocol to provide the information in real time. For example, the providing unit can provide the information in real time using WebSocket. The providing unit can also provide the information efficiently using HTTP / 2. Furthermore, the providing unit can provide the information with low latency using MQTT. This allows the providing unit to provide the latest information in real time every time the location information is updated.

[0068] The providing unit may include a search bar and a display of recommended information as a user interface. The providing unit may include, for example, a search bar to enable a user to easily search for information. For example, the providing unit may provide a search bar with an autocomplete function. The providing unit may also provide a search bar with a function to display a search history. Furthermore, the providing unit may also provide a search bar with a filtering option. The providing unit may include a function to display recommended information to a user. For example, the providing unit may display information relevant to a user using a recommendation algorithm. The providing unit may also include a function to customize the display format of the information. Furthermore, the providing unit may include a function to set the update frequency of the information. In this way, the providing unit may enable a user to easily search for and view information.

[0069] The device may be provided with a consent acquisition unit and an anonymization unit for privacy protection. The consent acquisition unit, for example, provides a screen for acquiring user consent. For example, the consent acquisition unit displays a screen that explains the consent details to the user in detail. The consent acquisition unit also has a function for recording the user's consent. Furthermore, the consent acquisition unit also has a function for updating or canceling the consent. The anonymization unit, for example, uses technology for anonymizing user information. For example, the anonymization unit uses data masking technology to hide the user's personal information. The anonymization unit can also anonymize user information using pseudo-anonymization technology. Furthermore, the anonymization unit also has a function for setting the level of anonymization. Thus, by providing a consent acquisition unit and an anonymization unit for privacy protection, it is possible to provide information while protecting the user's privacy.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring location information based on the estimated user emotions. The acquisition unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The acquisition unit adjusts the timing of acquiring location information based on the estimated user emotions. For example, when the user is relaxed, the acquisition unit periodically acquires location information and provides recommended travel spots. When the user is in a hurry, the acquisition unit can increase the frequency of acquiring location information and provide real-time discount information. Furthermore, when the user is feeling stressed, the acquisition unit can reduce the frequency of acquiring location information and reduce notifications. This allows the acquisition unit to adjust the timing of acquiring location information according to the user's emotions.

[0071] The acquisition unit can analyze the user's past movement history and select the optimal location information acquisition method. The acquisition unit, for example, uses a machine learning algorithm to analyze the user's past movement history. For example, the acquisition unit analyzes patterns of places the user has visited in the past and acquires location information from frequently visited places. The acquisition unit can also acquire location information for a specific time period from the user's movement history. Furthermore, the acquisition unit can preferentially acquire location information related to specific events or places based on the user's past movement history. This allows the acquisition unit to select the optimal location information acquisition method based on the user's past movement history.

[0072] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity status. The acquisition unit, for example, uses a sensor to acquire the user's current activity status. For example, the acquisition unit can detect whether the user is walking using an acceleration sensor. The acquisition unit can also detect whether the user is driving a car using a GPS sensor. Furthermore, the acquisition unit can detect whether the user is using public transportation using a Wi-Fi sensor. The acquisition unit filters the location information based on the user's current activity status. For example, if the user is walking, the acquisition unit can prioritize acquiring location information of nearby recommended spots. Furthermore, if the user is driving a car, the acquisition unit can prioritize acquiring location information of parking lots and drive-throughs. Furthermore, if the user is using public transportation, the acquisition unit can prioritize acquiring information near stations and bus stops. This allows the acquisition unit to filter location information according to the user's current activity status.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user's emotions. The acquisition unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The acquisition unit determines the priority of location information to be acquired based on the estimated user's emotions. For example, if the user is having fun, the acquisition unit can prioritize acquiring location information of tourist spots and leisure facilities. If the user is tired, the acquisition unit can prioritize acquiring location information of cafes and rest areas. If the user is hungry, the acquisition unit can prioritize acquiring location information of restaurants and food courts. This allows the acquisition unit to determine the priority of location information to be acquired according to the user's emotions.

[0074] When acquiring location information, the acquisition unit can select the optimal acquisition method taking into account the user's device information. The acquisition unit, for example, uses a device sensor to acquire the user's device information. For example, if the user is using a smartphone, the acquisition unit can acquire location information using GPS. Also, if the user is using a tablet, the acquisition unit can acquire location information using Wi-Fi or Bluetooth. Furthermore, if the user is using a smartwatch, the acquisition unit can acquire location information in low power mode. This allows the acquisition unit to select the optimal location information acquisition method based on the user's device information.

[0075] When acquiring location information, the acquisition unit can analyze the user's social media activity and acquire related location information. The acquisition unit, for example, uses a social media API to analyze the user's social media activity. For example, the acquisition unit can prioritize acquiring location information near places where the user has checked in on the social media. The acquisition unit can also acquire location information of recommended spots from accounts the user follows on the social media. Furthermore, the acquisition unit can acquire location information related to posts the user has shared on the social media. This allows the acquisition unit to acquire related location information based on the user's social media activity.

[0076] 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, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The collection unit adjusts the timing of information collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit periodically collects information and provides recommended travel spots. When the user is in a hurry, the collection unit can increase the frequency of information collection and provide real-time discount information. Furthermore, when the user is feeling stressed, the collection unit can reduce the frequency of information collection and reduce notifications. This allows the collection unit to adjust the timing of information collection according to the user's emotions.

[0077] During collection, the collection unit can analyze the user's past search history and select the optimal information collection method. The collection unit, for example, uses a machine learning algorithm to analyze the user's past search history. For example, the collection unit prioritizes collecting related information based on keywords the user has previously searched for. The collection unit can also collect information related to a specific time period from the user's past search history. Furthermore, the collection unit can analyze the user's past search history and prioritize collecting information related to a specific event or location. This allows the collection unit to select the optimal information collection method based on the user's past search history.

[0078] The collection unit may perform filtering based on the user's current areas of interest at the time of collection. For example, the collection unit may analyze the user's behavioral data to obtain the user's current areas of interest. For example, the collection unit may preferentially collect information related to topics in which the user is currently interested. The collection unit may also preferentially collect information related to events in which the user is currently participating. Furthermore, the collection unit may preferentially collect information related to accounts the user is currently following. This allows the collection unit to filter information according to the user's current areas of interest.

[0079] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. The collection unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The collection unit determines the priority of information to be collected based on the estimated user's emotions. For example, the collection unit can prioritize collecting information about tourist spots and leisure facilities when the user is having fun. Furthermore, the collection unit can prioritize collecting information about cafes and rest areas when the user is tired. Furthermore, the collection unit can prioritize collecting information about restaurants and food courts when the user is hungry. This allows the collection unit to determine the priority of information to be collected according to the user's emotions.

[0080] During collection, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit, for example, uses a GPS sensor to acquire the user's geographical location information. For example, the collection unit prioritizes collecting information near the user's current location. The collection unit can also prioritize collecting information related to places the user plans to visit. Furthermore, the collection unit can also prioritize collecting information near places the user has visited in the past. This allows the collection unit to prioritize collecting highly relevant information based on the user's geographical location information.

[0081] During collection, the collection unit can analyze the user's social media activity and collect related information. The collection unit, for example, uses a social media API to analyze the user's social media activity. For example, the collection unit can prioritize collecting information related to places the user has checked in to on the social media. The collection unit can also collect recommended information from accounts the user follows on the social media. Furthermore, the collection unit can collect information related to posts the user has shared on the social media. This allows the collection unit to collect related information based on the user's social media activity.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The analysis unit adjusts the analysis method based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly relevant information. If the user is in a hurry, the analysis unit can perform a quick analysis and provide information that focuses on the main points. Furthermore, if the user is feeling stressed, the analysis unit can reduce the frequency of analysis and reduce notifications. This allows the analysis unit to adjust the analysis method according to the user's emotions.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, uses a machine learning algorithm to evaluate the importance of the collected information. For example, the analysis unit evaluates the reliability and relevance of the information and performs a detailed analysis on information with high importance. The analysis unit can also perform a simplified analysis on information with low importance and provide information that focuses on the main points. Furthermore, the analysis unit can analyze information with medium importance at an appropriate level of detail and provide balanced information. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the collected information.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit uses clustering technology to classify the category of information. For example, the analysis unit applies a tourism algorithm to information about tourist spots for analysis. The analysis unit can also apply a gourmet algorithm to information about restaurants for analysis. Furthermore, the analysis unit can also apply a shopping algorithm to information about shopping for analysis. This allows the analysis unit to apply the optimal analysis algorithm depending on the category of information.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The analysis unit adjusts the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. Furthermore, the analysis unit can display analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can display simple, highly visible analysis results when the user is feeling stressed. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of information. The analysis unit uses, for example, a timestamp to evaluate the time of submission of information. For example, the analysis unit can prioritize analysis of the most recent information and provide it promptly. The analysis unit can also lower the priority of analysis of old information and provide it as needed. Furthermore, the analysis unit can analyze information of medium newness with moderate priority and provide balanced information. This allows the analysis unit to determine the priority of analysis based on the time of submission of information.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of information. The analysis unit, for example, analyzes co-occurrence relationships and correlations to evaluate the relevance of information. For example, the analysis unit prioritizes analysis of information with high relevance and provides it quickly. The analysis unit can also postpone the analysis order of information with low relevance and provide it as needed. Furthermore, the analysis unit can analyze information with medium relevance in an appropriate order and provide balanced information. This allows the analysis unit to adjust the order of analysis based on the relevance of information.

[0088] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. The providing unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The providing unit adjusts the presentation method of the information to be provided based on the estimated user's emotions. For example, the providing unit can provide detailed information when the user is relaxed. Furthermore, the providing unit can provide information that focuses on the main points when the user is in a hurry. Furthermore, the providing unit can provide simple, highly visible information when the user is feeling stressed. This allows the providing unit to adjust the presentation method of the information to be provided according to the user's emotions.

[0089] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. The providing unit, for example, uses a machine learning algorithm to evaluate the importance of the information. For example, the providing unit evaluates the reliability and relevance of the information and provides detailed information for information with high importance. The providing unit can also provide simplified information for information with low importance. Furthermore, the providing unit can provide information with an appropriate level of detail for information with medium importance. This allows the providing unit to adjust the level of detail of the information provided based on the importance of the information.

[0090] The providing unit can apply different providing algorithms depending on the category of information when providing the information. The providing unit uses, for example, a clustering technique to classify the category of information. For example, the providing unit can apply a tourism algorithm to provide information about tourist spots. The providing unit can also apply a gourmet algorithm to provide information about restaurants. Furthermore, the providing unit can also apply a shopping algorithm to provide information about shopping. This allows the providing unit to apply the optimal providing algorithm depending on the category of information.

[0091] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user's emotion. The providing unit, for example, uses an emotion analysis algorithm to estimate the user's emotion. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using facial expression recognition technology. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion. The providing unit adjusts the length of the information to be provided based on the estimated user's emotion. For example, the providing unit can provide short, to-the-point information when the user is in a hurry. The providing unit can also provide longer information with detailed explanations when the user is relaxed. Furthermore, the providing unit can provide simple, highly visible, short information when the user is feeling stressed. This allows the providing unit to adjust the length of the information to be provided according to the user's emotion.

[0092] The providing unit can determine the priority of provision based on the time of submission of the information at the time of provision. The providing unit uses, for example, a timestamp to evaluate the time of submission of the information. For example, the providing unit provides the latest information with priority. The providing unit can also lower the priority of provision of old information and provide it as needed. Furthermore, the providing unit can provide information of medium newness with moderate priority. This allows the providing unit to determine the priority of provision based on the time of submission of the information.

[0093] The providing unit can adjust the order of providing information based on the relevance of the information when providing the information. The providing unit, for example, analyzes co-occurrence relationships and correlations to evaluate the relevance of the information. For example, the providing unit provides information with high relevance preferentially. The providing unit can also postpone the order of providing information with low relevance and provide it as needed. Furthermore, the providing unit can provide information with medium relevance in an appropriate order to provide balanced information. This allows the providing unit to adjust the order of providing information based on the relevance of the information.

[0094] The consent acquisition unit can estimate the user's emotions and adjust the timing of consent acquisition based on the estimated user's emotions. The consent acquisition unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the consent acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The consent acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the consent acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The consent acquisition unit adjusts the timing of consent acquisition based on the estimated user's emotions. For example, the consent acquisition unit flexibly adjusts the timing of consent acquisition when the user is relaxed. The consent acquisition unit can also quickly acquire consent when the user is in a hurry. Furthermore, the consent acquisition unit can delay the timing of consent acquisition when the user is feeling stressed. This allows the consent acquisition unit to adjust the timing of consent acquisition according to the user's emotions.

[0095] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by analyzing the user's past consent history. The consent acquisition unit, for example, uses a machine learning algorithm to analyze the user's past consent history. For example, the consent acquisition unit preferentially suggests methods to which the user has previously given consent. The consent acquisition unit can also obtain consent for a specific time period based on the user's past consent history. Furthermore, the consent acquisition unit can also preferentially obtain consent related to a specific event or location based on the user's past consent history. This allows the consent acquisition unit to select the optimal consent acquisition method based on the user's past consent history.

[0096] The consent acquisition unit can estimate the user's emotions and determine the priority of consent acquisition based on the estimated user's emotions. The consent acquisition unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the consent acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The consent acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the consent acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The consent acquisition unit determines the priority of consent acquisition based on the estimated user's emotions. For example, the consent acquisition unit flexibly adjusts the priority of consent acquisition when the user is relaxed. The consent acquisition unit can also quickly obtain consent when the user is in a hurry. Furthermore, the consent acquisition unit can delay the priority of consent acquisition when the user is feeling stressed. This allows the consent acquisition unit to determine the priority of consent acquisition according to the user's emotions.

[0097] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by taking into account the user's device information. The consent acquisition unit, for example, uses a device sensor to obtain the user's device information. For example, if the user is using a smartphone, the consent acquisition unit obtains consent through a touch operation. Furthermore, if the user is using a tablet, the consent acquisition unit can provide a consent acquisition method optimized for a large screen. Furthermore, if the user is using a smartwatch, the consent acquisition unit can provide a simple and highly visible consent acquisition method. This allows the consent acquisition unit to select the optimal consent acquisition method based on the user's device information.

[0098] The anonymization unit can estimate the user's emotions and adjust the anonymization method based on the estimated user's emotions. The anonymization unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the anonymization unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The anonymization unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the anonymization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The anonymization unit adjusts the anonymization method based on the estimated user's emotions. For example, the anonymization unit can perform detailed anonymization to protect the user's privacy when the user is relaxed. The anonymization unit can also perform quick anonymization to protect the minimum necessary information when the user is in a hurry. Furthermore, the anonymization unit can reduce the frequency of anonymization and reduce notifications when the user is feeling stressed. This allows the anonymization unit to adjust the anonymization method according to the user's emotions.

[0099] During anonymization, the anonymization unit can analyze the user's past data usage history and select the optimal anonymization method. The anonymization unit, for example, uses a machine learning algorithm to analyze the user's past data usage history. For example, the anonymization unit analyzes patterns of data used by the user in the past and selects the optimal anonymization method. The anonymization unit can also perform anonymization during a specific time period based on the user's past data usage history. Furthermore, the anonymization unit can prioritize anonymization related to specific events or locations based on the user's past data usage history. This allows the anonymization unit to select the optimal anonymization method based on the user's past data usage history.

[0100] The anonymization unit can estimate the user's emotions and determine the priority of anonymization based on the estimated user's emotions. The anonymization unit, for example, uses an emotion analysis algorithm to estimate the user's emotions. For example, the anonymization unit can capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. The anonymization unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the anonymization unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions. The anonymization unit determines the priority of anonymization based on the estimated user's emotions. For example, the anonymization unit flexibly adjusts the priority of anonymization when the user is relaxed. The anonymization unit can also quickly perform anonymization when the user is in a hurry. Furthermore, the anonymization unit can delay the priority of anonymization when the user is stressed. This allows the anonymization unit to determine the priority of anonymization according to the user's emotions.

[0101] The anonymization unit can select the optimal anonymization method during anonymization by taking into account the user's device information. The anonymization unit, for example, uses a device sensor to acquire the user's device information. For example, if the user is using a smartphone, the anonymization unit performs anonymization by touch operation. Furthermore, if the user is using a tablet, the anonymization unit can provide an anonymization method optimized for a large screen. Furthermore, if the user is using a smartwatch, the anonymization unit can provide a simple and highly visible anonymization method. This allows the anonymization unit to select the optimal anonymization method based on the user's device information. === Hard Collateral 1-1 === Each of the multiple elements including the above-described acquisition unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the user's current location using a GPS sensor or a Wi-Fi module of the smart device 14. The collection unit collects information from SNSs and websites, for example, by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides information based on the analysis results by the control unit 46A of the smart device 14, for example. === Hard Collateral 1-2 === Each of the multiple elements including the above-described acquisition unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires the user's current location using a GPS sensor or a Wi-Fi module of the smart glasses 214. The collection unit collects information from SNSs and websites, for example, by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides information based on the analysis results by the control unit 46A of the smart glasses 214, for example. === Hard Collateral 1-3 === Each of the multiple elements including the above-described acquisition unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires the user's current location using a GPS sensor or Wi-Fi module of the headset type terminal 314. The collection unit collects information from SNSs and websites, for example, by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides information based on the analysis results by the control unit 46A of the headset type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, 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 acquisition unit acquires the user's current location using a GPS sensor or Wi-Fi module of the robot 414. The collection unit collects information from SNSs and websites, for example, by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12, for example. The provision unit provides information based on the analysis results by the control unit 46A of the robot 414, for example.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] When acquiring user location information, the acquisition unit can select the optimal acquisition method taking into account the remaining battery level of the user's device. For example, when the remaining battery level is low, a location information acquisition method with low power consumption can be selected. Also, when the remaining battery level is sufficient, a location information acquisition method with high accuracy can be selected. Furthermore, when the remaining battery level is medium, a balanced location information acquisition method can be selected. This allows the acquisition unit to select the optimal location information acquisition method according to the remaining battery level of the user's device.

[0104] The collection unit can analyze the user's past search history and prioritize collection of information related to a specific time period. For example, related information can be collected based on keywords that the user previously searched for during a specific time period. Also, if the user searches for information related to a specific event or location, that information can be prioritized. Furthermore, information related to a specific topic can be prioritized based on the user's past search history. This allows the collection unit to select the optimal information collection method based on the user's past search history.

[0105] The providing unit can adjust the information providing method based on the user's current activity status. For example, if the user is walking, the information can be provided by voice. If the user is driving a car, the information can be provided visually. Furthermore, if the user is using public transportation, the information can be provided by text message. This allows the providing unit to select the optimal information providing method according to the user's current activity status.

[0106] The providing unit can adjust the information display method based on the screen size of the user's device. For example, if the user is using a smartphone, a compact display format can be provided. Also, if the user is using a tablet, a display format optimized for a large screen can be provided. Furthermore, if the user is using a desktop, detailed information can be displayed. This allows the providing unit to select the optimal information display method according to the screen size of the user's device.

[0107] The providing unit can adjust the information providing method based on the user's internet connection status. For example, if the internet connection is unstable, offline information can be provided. Alternatively, if the internet connection is stable, real-time updated information can be provided. Furthermore, if the internet connection is slow, information can be provided in a lightweight data format. This allows the providing unit to select the optimal information providing method depending on the user's internet connection status.

[0108] The acquisition unit can estimate the user's emotions and adjust the frequency of acquiring location information based on the estimated user's emotions. For example, if the user is relaxed, the frequency of acquiring location information can be set low. If the user is in a hurry, the frequency of acquiring location information can be set high. Furthermore, if the user is feeling stressed, the frequency of acquiring location information can be set to a medium level. This allows the acquisition unit to adjust the frequency of acquiring location information according to the user's emotions.

[0109] The collection unit can estimate the user's emotions and determine the priority of information collection based on the estimated user's emotions. For example, if the user is having fun, information about tourist spots and leisure facilities can be collected with priority. Also, if the user is tired, information about cafes and rest areas can be collected with priority. Furthermore, if the user is hungry, information about restaurants and food courts can be collected with priority. In this way, the collection unit can determine the priority of information collection according to the user's emotions.

[0110] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a simplified analysis can be performed. Furthermore, if the user is feeling stressed, an analysis can be performed with an appropriate level of detail. This allows the analysis unit to adjust the level of detail of the analysis according to the user's emotions.

[0111] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is relaxed, detailed information can be provided. If the user is in a hurry, information that focuses on the main points can be provided. Furthermore, if the user is feeling stressed, simple, highly visible information can be provided. This allows the providing unit to adjust the format of the information to be provided depending on the user's emotions.

[0112] The consent acquisition unit can estimate the user's emotions and adjust the consent acquisition method based on the estimated user's emotions. For example, if the user is relaxed, a consent acquisition method including detailed explanations can be provided. If the user is in a hurry, a concise consent acquisition method can be provided. Furthermore, if the user is stressed, a highly visible consent acquisition method can be provided. This allows the consent acquisition unit to adjust the consent acquisition method according to the user's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The acquisition unit acquires user location information. The location information includes, for example, GPS information, Wi-Fi location information, and cell tower location information. The acquisition unit identifies the user's current location using the GPS sensor, Wi-Fi access point information, and cell tower location information. Step 2: The collection unit collects information from social media and websites based on the location information acquired by the acquisition unit. The collection unit collects information using scraping technology, APIs, and RSS feeds. Step 3: The analysis unit analyzes the information collected by the collection unit. The analysis unit analyzes the information using natural language processing technology, machine learning algorithms, and clustering technology, assessing relevance, and classifying it. Step 4: The providing unit provides information based on the information analyzed by the analyzing unit. The providing unit provides related information based on the user's search keywords and provides information in real time when the location information is updated. The providing unit also provides a user interface with a search bar and display of recommended information.

[0115] 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.

[0116] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires location information; a collection unit that collects information from SNS and websites based on the location information acquired by the acquisition unit; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides information based on the information analyzed by the analyzing unit; Equipped with A system characterized by:

2. The collecting unit Collecting information using scraping or APIs The system of claim 1 .

3. The providing unit Providing information based on user search keywords The system of claim 1 .

4. The providing unit Provides real-time information when location information is updated The system of claim 1 .

5. The providing unit The user interface includes a search bar and display of recommended information. The system of claim 1 .

6. Equipped with consent acquisition and anonymization functions for privacy protection The system of claim 1 .

7. The acquisition unit Estimates the user's emotions and adjusts the timing of acquiring location information based on the estimated user emotions. The system of claim 1 .

8. The acquisition unit Analyze the user's past movement history and select the optimal method for obtaining location information The system of claim 1 .

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