System

Abstract

An object of a system according to an embodiment is to provide information based on the lifestyle, hobbies, and preferences of a user and to automatically determine and protect risks on the Web.SOLUTION: A system according to an embodiment includes a user understanding unit, a design generation unit, an advertisement integration unit, and a risk determination unit. The user understanding unit uses the generated AI to understand the lifestyle, hobbies, and preferences of the user through interaction with the user. The design generation unit generates an original design layout based on the user's preference understood by the user understanding unit. The advertisement integration unit naturally incorporates an advertisement or a coupon together with information necessary for the design layout generated by the design generation unit. The risk determination unit automatically determines and protects a risk on the Web based on the information provided by the advertisement integration unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had issues such as not providing sufficient information based on users' lifestyles, hobbies, and preferences, and not providing sufficient protection against risks on the Web.

[0005] The system according to the embodiment aims to provide information based on the user's lifestyle, hobbies, and preferences, and to automatically identify and protect against risks on the Web. [Means for solving the problem]

[0006] The system according to the embodiment comprises a user understanding unit, a design generation unit, an advertising integration unit, and a risk discrimination unit. The user understanding unit uses a generation AI to understand a user's lifestyle, hobbies, and preferences through dialogue with the user. The design generation unit generates an original design layout based on the user's preferences understood by the user understanding unit. The advertising integration unit naturally incorporates advertisements and coupons together with the necessary information into the design layout generated by the design generation unit. The risk discrimination unit automatically discerns and protects against risks on the web based on the information provided by the advertising integration unit. [Effects of the Invention]

[0007] The system according to the embodiment provides information based on the user's lifestyle, hobbies, and preferences, and can automatically identify and protect against risks on the Web. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The news service system according to the embodiment of the present invention provides necessary information based on the user's lifestyle, hobbies, and preferences, and the generation AI understands the user's preferences through dialogue and provides information in an original design and layout. This allows the news service system to efficiently provide necessary information based on the user's lifestyle, hobbies, and preferences.

[0029] A news service system according to an embodiment includes a user understanding unit, a design generation unit, an advertising integration unit, and a risk assessment unit. The user understanding unit uses a generation AI to understand a user's lifestyle, hobbies, and preferences through dialogue with the user. For example, when a user provides information such as "I like sports, and I especially watch a lot of soccer news," the generation AI selects the most suitable news for the user based on this information. Furthermore, when a user provides information such as "I like simple designs," the generation AI generates a news layout with a simple design. The design generation unit generates an original design layout based on the user's preferences understood by the user understanding unit. For example, the generation AI provides information in an original design layout based on the user's preferences. The advertising integration unit naturally incorporates advertisements and coupons into the design layout generated by the design generation unit along with the necessary information. For example, when a user provides information such as "I'm looking for a new smartphone," the generation AI provides smartphone-related advertisements and coupons along with news. The risk assessment unit automatically identifies and protects against risks on the web based on the information provided by the advertising integration unit. For example, the generation AI can detect phishing sites and fake news and issue a warning to the user. As a result, the news service system according to the embodiment can provide necessary information based on the user's lifestyle, hobbies, and preferences, naturally incorporate advertisements and coupons, and automatically identify and protect against risks on the Web.

[0030] The user understanding unit can analyze a user's past news browsing history, learn long-term changes in interests, and predict future interests. For example, the user understanding unit analyzes a user's news browsing history over the past year to learn changes in interests. For example, if a user used to browse a lot of sports news but has recently become interested in technology-related news, the unit can learn this change and reflect it in future news provision. The user understanding unit can also use machine learning algorithms to predict future interests based on the user's news browsing history. For example, if a user tends to remain interested in a particular topic, news related to that topic can be provided preferentially. This makes it possible to learn long-term changes in a user's interests and predict future interests.

[0031] The user understanding unit can analyze the content of a user's SNS posts, detect changes in interests in real time, and provide news accordingly. The user understanding unit, for example, analyzes the content of a user's SNS posts and detects changes in interests in real time. For example, if a user has recently posted many technology-related topics, technology-related news can be provided that reflects those interests. The user understanding unit can also use streaming data analysis technology to detect changes in a user's interests based on the content of SNS posts. For example, if a user frequently posts about a particular topic, news related to that topic can be provided preferentially. This allows the content of a user's SNS posts to be analyzed, detect changes in interests in real time, and provide news accordingly.

[0032] The user understanding unit can also simultaneously provide health management and fitness information based on the user's lifestyle. The user understanding unit, for example, analyzes the user's lifestyle data and provides health management and fitness information. For example, if the user is not getting enough exercise, it provides an appropriate fitness plan and health management advice. The user understanding unit can also use data analysis technology to provide health management and fitness information based on the user's lifestyle. For example, if the user is health-conscious, it provides health-related news and advice. This makes it possible to provide health management and fitness information based on the user's lifestyle.

[0033] The user understanding unit can provide event information and community information related to hobbies to promote offline activities. The user understanding unit can, for example, provide event information related to the user's hobbies to promote offline activities. For example, if the user likes music, it can provide information about nearby concerts and live events. The user understanding unit can also use data analysis technology to provide event information and community information based on the user's hobbies. For example, if the user is a member of a community related to a particular hobby, it can provide event information for that community. This can provide event information and community information related to the user's hobbies to promote offline activities.

[0034] The design generation unit can analyze device usage status and adjust the optimal layout in real time. The design generation unit, for example, analyzes the user's device usage status and adjusts the optimal layout in real time. For example, if the user is using a smartphone in portrait orientation, it provides a layout optimized for portrait orientation. The design generation unit can also use responsive design technology to adjust the optimal layout in real time based on the device usage status. For example, if the user is using a tablet, it provides a layout optimized for the tablet. This allows the optimal layout to be adjusted in real time based on the user's device usage status.

[0035] The design generation unit can use the eye tracking data to generate a layout that matches the eye movement. The design generation unit, for example, collects the user's eye tracking data and generates a layout that matches the eye movement. For example, if the user looks at a specific part of the screen for a long time, information related to that part is highlighted. The design generation unit can also use an eye analysis algorithm to generate a layout that matches the eye movement based on the eye tracking data. For example, when the user moves their gaze, important information is placed where the user's gaze is. This makes it possible to generate a layout based on the user's eye movement.

[0036] The design generation unit can customize the news read-out function based on preferences. The design generation unit customizes the news read-out function based on, for example, the user's preferences. For example, if the user prefers a calm voice, a calm tone of voice is used. The design generation unit can also use voice synthesis technology to customize the news read-out function based on preferences. For example, if the user prefers a fast reading speed, the reading speed is adjusted. This allows the news read-out function to be customized based on the user's preferences.

[0037] The design generation unit can add interactive graphics and animations based on design preferences. The design generation unit adds interactive graphics based on, for example, the user's design preferences. For example, if the user prefers simple designs, simple interactive graphics are provided. The design generation unit can also use visual effect techniques to add interactive graphics and animations based on the design preferences. For example, if the user prefers dynamic animations, dynamic animations are added. This allows interactive graphics and animations to be added based on the user's design preferences.

[0038] The advertisement integration unit can analyze the purchase history and provide optimal advertisements based on the user's past purchase patterns. The advertisement integration unit, for example, analyzes the user's past purchase history and identifies the purchase pattern. For example, if the user regularly purchases products from a specific brand, advertisements for new products or related products from that brand are provided. The advertisement integration unit can also use data analysis techniques to provide optimal advertisements based on the purchase history. For example, if the user frequently purchases products from a specific category, advertisements related to that category are provided. This makes it possible to provide optimal advertisements based on the user's purchase history.

[0039] The advertisement integrator can use location information to provide advertisements for nearby stores and services. For example, the advertisement integrator acquires the user's location information in real time and provides advertisements for nearby stores and services. For example, if the user is in a shopping mall, sale information for stores in the mall is displayed as an advertisement. The advertisement integrator can also use location information service technology to provide advertisements for nearby stores and services based on the location information. For example, if the user is in a specific area, advertisements for stores and services in that area are provided. This makes it possible to provide advertisements for nearby stores and services based on the user's location information.

[0040] The advertisement integrator can provide reviews and comparison information of products related to hobbies and interests. For example, the advertisement integrator can provide reviews of products related to the user's hobbies and interests. For example, if the user is interested in cameras, the advertisement integrator can display the latest camera reviews. The advertisement integrator can also use data analysis techniques to provide product reviews and comparison information based on hobbies and interests. For example, if the user is interested in products in a specific category, the advertisement integrator can provide reviews and comparison information of products related to that category. This makes it possible to provide reviews and comparison information of products related to the user's hobbies and interests.

[0041] The advertisement integration unit can suggest subscription services tailored to a user's lifestyle. For example, the advertisement integration unit analyzes lifestyle data of the user and suggests the most suitable subscription service. For example, if the user is a movie lover, the advertisement integration unit suggests a subscription to a movie streaming service. The advertisement integration unit can also use data analysis techniques to suggest subscription services based on the user's lifestyle. For example, if the user is health-conscious, the advertisement integration unit suggests health-related subscription services. This makes it possible to suggest subscription services tailored to the user's lifestyle.

[0042] The risk determination unit can analyze browsing history and provide advance warning of high-risk sites. The risk determination unit, for example, analyzes a user's browsing history to identify high-risk sites. For example, if a user has a history of accessing a phishing site in the past, the risk determination unit adds that site to a warning list. The risk determination unit can also use data analysis techniques to provide advance warning of high-risk sites based on browsing history. For example, if a user frequently accesses sites in a specific category, the risk determination unit will provide a warning of high-risk sites related to that category. This allows advance warning of high-risk sites based on the user's browsing history.

[0043] The risk determination unit allows the generation AI to continuously learn in order to improve the accuracy of detecting phishing sites and fake news. The risk determination unit, for example, builds a system in which the generation AI continuously learns in order to improve the accuracy of detecting phishing sites and fake news. For example, it learns the patterns of newly discovered phishing sites and improves detection accuracy. The risk determination unit can also use data collection and analysis techniques to enable the generation AI to continuously learn. For example, it can collect data on phishing sites and fake news reported by users and have the generation AI learn from it. This allows the generation AI to continuously learn, thereby improving the accuracy of detecting phishing sites and fake news.

[0044] The risk determination unit can perform security assessment of apps installed on the device and warn of apps that pose a risk. The risk determination unit, for example, performs security assessment of apps installed on the user's device and identifies apps that pose a risk. For example, it adds apps that have previously been reported as having security issues to a warning list. The risk determination unit can also use vulnerability scanning technology to perform security assessment of apps. For example, it checks app permissions and warns of apps that request excessive permissions. This makes it possible to perform security assessment of apps installed on the user's device and warn of apps that pose a risk.

[0045] The risk determination unit can automatically optimize the security settings of the SNS account. For example, the risk determination unit builds a system that automatically optimizes the security settings of the user's SNS account. For example, the risk determination unit checks the strength of the password and suggests a strong password if a weak password is being used. The risk determination unit can also use two-factor authentication technology to optimize the security settings of the SNS account. For example, if the user has not enabled two-factor authentication, the risk determination unit recommends that the user enable it. This allows the security settings of the user's SNS account to be automatically optimized.

[0046] The system can analyze news browsing history and prioritize displaying articles of greatest interest. For example, the system can analyze a user's news browsing history and identify articles of greatest interest. For example, if a user has viewed a lot of sports news in the past, the system can prioritize displaying the latest sports news. The system can also use data analysis techniques to prioritize displaying articles of greatest interest based on news browsing history. For example, if a user has shown a high interest in a particular topic, the system can prioritize displaying articles related to that topic. This allows the system to prioritize displaying articles of greatest interest based on the user's news browsing history.

[0047] The system can also provide relevant video content based on interests. For example, the system provides relevant video content based on a user's interests. For example, if a user is interested in sports news, the system can provide the latest sports highlight videos. The system can also use data analysis techniques to provide relevant video content based on interests. For example, if a user shows a high interest in a particular topic, the system can provide video content related to that topic. This allows the system to provide relevant video content based on a user's interests.

[0048] The system can automatically generate newsletters tailored to a user's lifestyle. For example, the system analyzes a user's lifestyle data and automatically generates a personalized newsletter. For example, if the user is health-conscious, the system provides a newsletter focusing on health-related news. The system can also use data analysis techniques to automatically generate newsletters based on a user's lifestyle. For example, if the user shows a high interest in a particular topic, the system provides a newsletter containing news related to that topic. This allows the system to automatically generate newsletters tailored to the user's lifestyle.

[0049] The system can provide a function for integrating and centrally managing multiple media accounts. For example, the system provides a function for integrating and centrally managing multiple media accounts of a user. For example, if a user has accounts for multiple news sites, the system can enable the user to manage those accounts on a single platform. The system can also provide a dashboard for integrating and centrally managing media accounts. For example, the system enables the user to centrally manage account information for different media. This makes it possible to provide a function for integrating and centrally managing multiple media accounts of a user.

[0050] The system can analyze browsing history and automatically recommend optimal media content. For example, the system can analyze a user's browsing history and automatically recommend optimal media content. For example, if a user has viewed a lot of sports news in the past, the system can recommend the latest sports news. The system can also use data analysis techniques to recommend optimal media content based on the browsing history. For example, if a user has shown a high interest in a particular topic, the system can recommend media content related to that topic. This allows the system to automatically recommend optimal media content based on the user's browsing history.

[0051] The system can provide a customized dashboard based on preferences. For example, the system provides a customized dashboard based on a user's preferences. For example, if a user likes sports news, a dashboard centered on sports news is provided. The system can also use widget placement techniques to provide a customized dashboard based on preferences. For example, if a user likes news in a particular category, widgets related to that category are placed. This makes it possible to provide a customized dashboard based on a user's preferences.

[0052] The system can generate a personalized news feed tailored to a user's lifestyle. For example, the system analyzes a user's lifestyle data to generate a personalized news feed. For example, if the user is health-conscious, the system provides a news feed centered on health-related news. The system can also use data analysis techniques to generate a personalized news feed based on a user's lifestyle. For example, if the user shows a high interest in a particular topic, the system provides a news feed containing news related to that topic. In this way, a personalized news feed tailored to a user's lifestyle can be generated.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The news service system may also include a health management unit that monitors the user's health status and provides health news and advice. For example, if the user uses a fitness tracker, the system may analyze the data and provide exercise news and advice if it detects a lack of exercise. In addition, if the user has set a specific health goal, the system may provide motivational news and success stories according to the user's progress. This allows the system to provide personalized health information based on the user's health status.

[0055] The news service system can also include an educational support unit that analyzes the user's learning history and provides education-related news and content. For example, if the user is studying a specific field, the system can provide the latest research results and news related to that field. Also, if the user has set learning goals, the system can provide learning advice and motivational content according to the user's progress. This allows the system to provide personalized educational information based on the user's learning history.

[0056] The news service system may also include a purchasing support unit that analyzes a user's purchasing history and provides news and advice related to purchases. For example, if a user purchases a specific product, news related to that product and advice on how to use it may be provided. Also, if a user is looking for a new product based on their purchasing history, product information and reviews that meet their needs may be provided. This allows personalized purchasing information to be provided based on the user's purchasing history.

[0057] The news service system may further include a travel support unit that analyzes the user's travel history and provides travel-related news and advice. For example, news and event information related to places the user has visited in the past may be provided. Also, if the user is planning their next travel destination, tourist information and travel advice related to that location may be provided. This allows personalized travel information to be provided based on the user's travel history.

[0058] The news service system may also include an exercise support unit that analyzes the user's exercise history and provides exercise-related news and advice. For example, if the user regularly engages in a particular exercise, the system may provide the latest research results and news related to that exercise. Also, if the user wants to start a new exercise, the system may provide advice and success stories related to that exercise. This allows the system to provide personalized exercise information based on the user's exercise history.

[0059] The news service system can also include a reading support unit that analyzes the user's reading history and provides news and advice related to reading. For example, if the user reads many books in a particular genre, the system can provide the latest book information and news related to that genre. Also, if the user is looking for a new book, the system can provide book information and reviews that meet the user's needs. This allows the system to provide personalized reading information based on the user's reading history.

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

[0061] Step 1: The user understanding unit uses generation AI to understand the user's lifestyle, hobbies, and preferences through dialogue with the user. For example, if a user provides information such as "I like sports, and I often watch soccer news," the unit selects the most suitable news for the user based on this information. Also, if a user provides information such as "I like simple designs," the unit generates a news layout with a simple design. Step 2: The design generation unit generates an original design layout based on the user's preferences understood by the user understanding unit. For example, the generation AI provides information in the original design layout based on the user's preferences. Step 3: The ad integration unit seamlessly integrates advertisements and coupons into the design layout generated by the design generation unit along with the necessary information. For example, if a user provides information such as "I'm looking for a new smartphone," smartphone-related advertisements and coupons will be displayed along with news. Step 4: The risk determination unit automatically identifies and protects against web risks based on the information provided by the ad integration unit. For example, the generation AI detects phishing sites and fake news and issues a warning to the user.

[0062] (Example 2) The news service system according to the embodiment of the present invention provides necessary information based on the user's lifestyle, hobbies, and preferences, and the generation AI understands the user's preferences through dialogue and provides information in an original design and layout. This allows the news service system to efficiently provide necessary information based on the user's lifestyle, hobbies, and preferences.

[0063] A news service system according to an embodiment includes a user understanding unit, a design generation unit, an advertising integration unit, and a risk assessment unit. The user understanding unit uses a generation AI to understand a user's lifestyle, hobbies, and preferences through dialogue with the user. For example, when a user provides information such as "I like sports, and I especially watch a lot of soccer news," the generation AI selects the most suitable news for the user based on this information. Furthermore, when a user provides information such as "I like simple designs," the generation AI generates a news layout with a simple design. The design generation unit generates an original design layout based on the user's preferences understood by the user understanding unit. For example, the generation AI provides information in an original design layout based on the user's preferences. The advertising integration unit naturally incorporates advertisements and coupons into the design layout generated by the design generation unit along with the necessary information. For example, when a user provides information such as "I'm looking for a new smartphone," the generation AI provides smartphone-related advertisements and coupons along with news. The risk assessment unit automatically identifies and protects against risks on the web based on the information provided by the advertising integration unit. For example, the generation AI can detect phishing sites and fake news and issue a warning to the user. As a result, the news service system according to the embodiment can provide necessary information based on the user's lifestyle, hobbies, and preferences, naturally incorporate advertisements and coupons, and automatically identify and protect against risks on the Web.

[0064] The user understanding unit can infer emotions from the user's voice and facial expressions and adjust the content of the news based on the emotions. For example, while the user is viewing the news, the user understanding unit uses a camera and microphone to analyze the user's voice and facial expressions in real time to infer emotions. For example, if the user is smiling while watching the news, the unit determines that emotion to be positive and prioritizes displaying similarly positive news. Furthermore, if the user has a sad expression, the unit can determine that emotion to be negative and provide relaxing news. This allows the content of the news to be adjusted based on the user's emotions.

[0065] The user understanding unit can analyze a user's past news browsing history, learn long-term changes in interests, and predict future interests. For example, the user understanding unit analyzes a user's news browsing history over the past year to learn changes in interests. For example, if a user used to browse a lot of sports news but has recently become interested in technology-related news, the unit can learn this change and reflect it in future news provision. The user understanding unit can also use machine learning algorithms to predict future interests based on the user's news browsing history. For example, if a user tends to remain interested in a particular topic, news related to that topic can be provided preferentially. This makes it possible to learn long-term changes in a user's interests and predict future interests.

[0066] The user understanding unit can analyze the content of a user's SNS posts, detect changes in interests in real time, and provide news accordingly. The user understanding unit, for example, analyzes the content of a user's SNS posts and detects changes in interests in real time. For example, if a user has recently posted many technology-related topics, technology-related news can be provided that reflects those interests. The user understanding unit can also use streaming data analysis technology to detect changes in a user's interests based on the content of SNS posts. For example, if a user frequently posts about a particular topic, news related to that topic can be provided preferentially. This allows the content of a user's SNS posts to be analyzed, detect changes in interests in real time, and provide news accordingly.

[0067] The user understanding unit can also simultaneously provide health management and fitness information based on the user's lifestyle. The user understanding unit, for example, analyzes the user's lifestyle data and provides health management and fitness information. For example, if the user is not getting enough exercise, it provides an appropriate fitness plan and health management advice. The user understanding unit can also use data analysis technology to provide health management and fitness information based on the user's lifestyle. For example, if the user is health-conscious, it provides health-related news and advice. This makes it possible to provide health management and fitness information based on the user's lifestyle.

[0068] The user understanding unit can provide event information and community information related to hobbies to promote offline activities. The user understanding unit can, for example, provide event information related to the user's hobbies to promote offline activities. For example, if the user likes music, it can provide information about nearby concerts and live events. The user understanding unit can also use data analysis technology to provide event information and community information based on the user's hobbies. For example, if the user is a member of a community related to a particular hobby, it can provide event information for that community. This can provide event information and community information related to the user's hobbies to promote offline activities.

[0069] The user understanding unit can use the emotion estimation function to provide relaxing content when the user is feeling stressed. The user understanding unit, for example, analyzes the user's emotional state in real time and provides relaxing content when the user is feeling stressed. For example, if it is determined that the user is feeling stressed, it provides relaxing music or a meditation guide. The user understanding unit can also use an algorithm to provide relaxing content when the user is feeling stressed using the emotion estimation function. For example, it analyzes the user's voice and facial expressions and selects relaxing content. This makes it possible to provide relaxing content when the user is feeling stressed.

[0070] The design generation unit can analyze device usage status and adjust the optimal layout in real time. The design generation unit, for example, analyzes the user's device usage status and adjusts the optimal layout in real time. For example, if the user is using a smartphone in portrait orientation, it provides a layout optimized for portrait orientation. The design generation unit can also use responsive design technology to adjust the optimal layout in real time based on the device usage status. For example, if the user is using a tablet, it provides a layout optimized for the tablet. This allows the optimal layout to be adjusted in real time based on the user's device usage status.

[0071] The design generation unit can use the eye tracking data to generate a layout that matches the eye movement. The design generation unit, for example, collects the user's eye tracking data and generates a layout that matches the eye movement. For example, if the user looks at a specific part of the screen for a long time, information related to that part is highlighted. The design generation unit can also use an eye analysis algorithm to generate a layout that matches the eye movement based on the eye tracking data. For example, when the user moves their gaze, important information is placed where the user's gaze is. This makes it possible to generate a layout based on the user's eye movement.

[0072] The design generation unit can dynamically change color tones and fonts depending on the emotional state. For example, the design generation unit analyzes the user's emotional state in real time and dynamically changes color tones and fonts. For example, if the user is relaxed, soft color tones and easy-to-read fonts are used. The design generation unit can also use a color palette and font style change algorithm to dynamically change color tones and fonts based on the emotional state. For example, if the user is stressed, calm color tones and simple fonts are used. In this way, color tones and fonts can be dynamically changed based on the user's emotional state.

[0073] The design generation unit can customize the news read-out function based on preferences. The design generation unit customizes the news read-out function based on, for example, the user's preferences. For example, if the user prefers a calm voice, a calm tone of voice is used. The design generation unit can also use voice synthesis technology to customize the news read-out function based on preferences. For example, if the user prefers a fast reading speed, the reading speed is adjusted. This allows the news read-out function to be customized based on the user's preferences.

[0074] The design generation unit can add interactive graphics and animations based on design preferences. The design generation unit adds interactive graphics based on, for example, the user's design preferences. For example, if the user prefers simple designs, simple interactive graphics are provided. The design generation unit can also use visual effect techniques to add interactive graphics and animations based on the design preferences. For example, if the user prefers dynamic animations, dynamic animations are added. This allows interactive graphics and animations to be added based on the user's design preferences.

[0075] The advertisement integration unit can analyze the purchase history and provide optimal advertisements based on the user's past purchase patterns. The advertisement integration unit, for example, analyzes the user's past purchase history and identifies the purchase pattern. For example, if the user regularly purchases products from a specific brand, advertisements for new products or related products from that brand are provided. The advertisement integration unit can also use data analysis techniques to provide optimal advertisements based on the purchase history. For example, if the user frequently purchases products from a specific category, advertisements related to that category are provided. This makes it possible to provide optimal advertisements based on the user's purchase history.

[0076] The advertisement integrator can use location information to provide advertisements for nearby stores and services. For example, the advertisement integrator acquires the user's location information in real time and provides advertisements for nearby stores and services. For example, if the user is in a shopping mall, sale information for stores in the mall is displayed as an advertisement. The advertisement integrator can also use location information service technology to provide advertisements for nearby stores and services based on the location information. For example, if the user is in a specific area, advertisements for stores and services in that area are provided. This makes it possible to provide advertisements for nearby stores and services based on the user's location information.

[0077] The advertisement integration unit can select advertisements that elicit positive emotions according to the emotional state. For example, the advertisement integration unit analyzes the user's emotional state in real time and selects advertisements that elicit positive emotions. For example, if the user is relaxed, advertisements for relaxing travel destinations are displayed. The advertisement integration unit can also use an algorithm to select advertisements that elicit positive emotions based on the emotional state. For example, the advertisement integration unit analyzes the user's voice and facial expressions and selects advertisements that elicit positive emotions. In this way, advertisements that elicit positive emotions can be selected based on the user's emotional state.

[0078] The advertisement integrator can provide reviews and comparison information of products related to hobbies and interests. For example, the advertisement integrator can provide reviews of products related to the user's hobbies and interests. For example, if the user is interested in cameras, the advertisement integrator can display the latest camera reviews. The advertisement integrator can also use data analysis techniques to provide product reviews and comparison information based on hobbies and interests. For example, if the user is interested in products in a specific category, the advertisement integrator can provide reviews and comparison information of products related to that category. This makes it possible to provide reviews and comparison information of products related to the user's hobbies and interests.

[0079] The advertisement integration unit can suggest subscription services tailored to a user's lifestyle. For example, the advertisement integration unit analyzes lifestyle data of the user and suggests the most suitable subscription service. For example, if the user is a movie lover, the advertisement integration unit suggests a subscription to a movie streaming service. The advertisement integration unit can also use data analysis techniques to suggest subscription services based on the user's lifestyle. For example, if the user is health-conscious, the advertisement integration unit suggests health-related subscription services. This makes it possible to suggest subscription services tailored to the user's lifestyle.

[0080] The ad integration unit can use the emotion estimation function to provide advertisements that are likely to interest the user in real time. The ad integration unit, for example, uses the emotion estimation function to provide advertisements that are likely to interest the user in real time. For example, if the user has positive emotions, an advertisement that matches that emotion is displayed. The ad integration unit can also use the emotion estimation function to use an algorithm for providing advertisements that are likely to interest the user. For example, the advertisement integration unit can analyze the user's voice and facial expressions to select advertisements that are likely to interest the user. This makes it possible to provide advertisements that are likely to interest the user in real time.

[0081] The risk determination unit can analyze browsing history and provide advance warning of high-risk sites. The risk determination unit, for example, analyzes a user's browsing history to identify high-risk sites. For example, if a user has a history of accessing a phishing site in the past, the risk determination unit adds that site to a warning list. The risk determination unit can also use data analysis techniques to provide advance warning of high-risk sites based on browsing history. For example, if a user frequently accesses sites in a specific category, the risk determination unit will provide a warning of high-risk sites related to that category. This allows advance warning of high-risk sites based on the user's browsing history.

[0082] The risk determination unit allows the generation AI to continuously learn in order to improve the accuracy of detecting phishing sites and fake news. The risk determination unit, for example, builds a system in which the generation AI continuously learns in order to improve the accuracy of detecting phishing sites and fake news. For example, it learns the patterns of newly discovered phishing sites and improves detection accuracy. The risk determination unit can also use data collection and analysis techniques to enable the generation AI to continuously learn. For example, it can collect data on phishing sites and fake news reported by users and have the generation AI learn from it. This allows the generation AI to continuously learn, thereby improving the accuracy of detecting phishing sites and fake news.

[0083] The risk determination unit can analyze the user's emotional state and filter out high-risk information if the user is feeling stressed. The risk determination unit, for example, analyzes the user's emotional state in real time and filters out high-risk information if the user is feeling stressed. For example, if it is determined that the user is feeling stressed, phishing sites and fake news will not be displayed. The risk determination unit can also use an algorithm to filter out high-risk information based on the user's emotional state. For example, it analyzes the user's voice and facial expressions to filter out high-risk information. This makes it possible to filter out high-risk information if the user is feeling stressed.

[0084] The risk determination unit can perform security assessment of apps installed on the device and warn of apps that pose a risk. The risk determination unit, for example, performs security assessment of apps installed on the user's device and identifies apps that pose a risk. For example, it adds apps that have previously been reported as having security issues to a warning list. The risk determination unit can also use vulnerability scanning technology to perform security assessment of apps. For example, it checks app permissions and warns of apps that request excessive permissions. This makes it possible to perform security assessment of apps installed on the user's device and warn of apps that pose a risk.

[0085] The risk determination unit can automatically optimize the security settings of the SNS account. For example, the risk determination unit builds a system that automatically optimizes the security settings of the user's SNS account. For example, the risk determination unit checks the strength of the password and suggests a strong password if a weak password is being used. The risk determination unit can also use two-factor authentication technology to optimize the security settings of the SNS account. For example, if the user has not enabled two-factor authentication, the risk determination unit recommends that the user enable it. This allows the security settings of the user's SNS account to be automatically optimized.

[0086] The risk determination unit can use the emotion estimation function to provide information that gives a sense of security when the user is feeling anxious. The risk determination unit, for example, uses the emotion estimation function to provide information that gives a sense of security when the user is feeling anxious. For example, if it is determined that the user is feeling anxious, it displays reassuring news or content. The risk determination unit can also use the emotion estimation function to use an algorithm for providing information that gives a sense of security when the user is feeling anxious. For example, it analyzes the user's voice and facial expressions and selects information that gives a sense of security. This makes it possible to provide information that gives a sense of security when the user is feeling anxious.

[0087] The system can analyze news browsing history and prioritize displaying articles of greatest interest. For example, the system can analyze a user's news browsing history and identify articles of greatest interest. For example, if a user has viewed a lot of sports news in the past, the system can prioritize displaying the latest sports news. The system can also use data analysis techniques to prioritize displaying articles of greatest interest based on news browsing history. For example, if a user has shown a high interest in a particular topic, the system can prioritize displaying articles related to that topic. This allows the system to prioritize displaying articles of greatest interest based on the user's news browsing history.

[0088] The system can analyze the emotional state and prioritize displaying articles that elicit positive emotions. For example, the system can analyze the user's emotional state in real time and prioritize displaying articles that elicit positive emotions. For example, if the user is relaxed, the system can display relaxing news articles. The system can also use an algorithm to prioritize displaying articles that elicit positive emotions based on the emotional state. For example, the system can analyze the user's voice and facial expressions and select articles that elicit positive emotions. This allows articles that elicit positive emotions to be prioritized and displayed based on the user's emotional state.

[0089] The system can also provide relevant video content based on interests. For example, the system provides relevant video content based on a user's interests. For example, if a user is interested in sports news, the system can provide the latest sports highlight videos. The system can also use data analysis techniques to provide relevant video content based on interests. For example, if a user shows a high interest in a particular topic, the system can provide video content related to that topic. This allows the system to provide relevant video content based on a user's interests.

[0090] The system can automatically generate newsletters tailored to a user's lifestyle. For example, the system analyzes a user's lifestyle data and automatically generates a personalized newsletter. For example, if the user is health-conscious, the system provides a newsletter focusing on health-related news. The system can also use data analysis techniques to automatically generate newsletters based on a user's lifestyle. For example, if the user shows a high interest in a particular topic, the system provides a newsletter containing news related to that topic. This allows the system to automatically generate newsletters tailored to the user's lifestyle.

[0091] The system can use the emotion estimation function to suggest articles that are likely to interest the user in real time. For example, the system uses the emotion estimation function to suggest articles that are likely to interest the user in real time. For example, if the user has positive emotions, articles that match those emotions are displayed. The system can also use the emotion estimation function to use an algorithm to suggest articles that are likely to interest the user. For example, the system can analyze the user's voice and facial expressions to select articles that are likely to interest the user. In this way, the emotion estimation function can be used to suggest articles that are likely to interest the user in real time.

[0092] The system can provide a function for integrating and centrally managing multiple media accounts. For example, the system provides a function for integrating and centrally managing multiple media accounts of a user. For example, if a user has accounts for multiple news sites, the system can enable the user to manage those accounts on a single platform. The system can also provide a dashboard for integrating and centrally managing media accounts. For example, the system enables the user to centrally manage account information for different media. This makes it possible to provide a function for integrating and centrally managing multiple media accounts of a user.

[0093] The system can analyze browsing history and automatically recommend optimal media content. For example, the system can analyze a user's browsing history and automatically recommend optimal media content. For example, if a user has viewed a lot of sports news in the past, the system can recommend the latest sports news. The system can also use data analysis techniques to recommend optimal media content based on the browsing history. For example, if a user has shown a high interest in a particular topic, the system can recommend media content related to that topic. This allows the system to automatically recommend optimal media content based on the user's browsing history.

[0094] The system can analyze the user's emotional state and prioritize displaying media content that elicits positive emotions. For example, the system can analyze the user's emotional state in real time and prioritize displaying media content that elicits positive emotions. For example, if the user is relaxed, the system can display relaxing news articles. The system can also use an algorithm to prioritize displaying media content that elicits positive emotions based on the user's emotional state. For example, the system can analyze the user's voice and facial expressions to select media content that elicits positive emotions. This allows the system to prioritize displaying media content that elicits positive emotions based on the user's emotional state.

[0095] The system can provide a customized dashboard based on preferences. For example, the system provides a customized dashboard based on a user's preferences. For example, if a user likes sports news, a dashboard centered on sports news is provided. The system can also use widget placement techniques to provide a customized dashboard based on preferences. For example, if a user likes news in a particular category, widgets related to that category are placed. This makes it possible to provide a customized dashboard based on a user's preferences.

[0096] The system can generate a personalized news feed tailored to a user's lifestyle. For example, the system analyzes a user's lifestyle data to generate a personalized news feed. For example, if the user is health-conscious, the system provides a news feed centered on health-related news. The system can also use data analysis techniques to generate a personalized news feed based on a user's lifestyle. For example, if the user shows a high interest in a particular topic, the system provides a news feed containing news related to that topic. In this way, a personalized news feed tailored to a user's lifestyle can be generated.

[0097] The system can use the emotion estimation function to provide media content that helps the user relax. For example, the system can use the emotion estimation function to provide media content that helps the user relax. For example, if the user is feeling stressed, the system can provide relaxing music or a meditation guide. The system can also use an algorithm to provide media content that helps the user relax using the emotion estimation function. For example, the system can analyze the user's voice and facial expressions to select media content that helps the user relax. In this way, the system can use the emotion estimation function to provide media content that helps the user relax.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The news service system may also include a health management unit that monitors the user's health status and provides health news and advice. For example, if the user uses a fitness tracker, the system may analyze the data and provide exercise news and advice if it detects a lack of exercise. In addition, if the user has set a specific health goal, the system may provide motivational news and success stories according to the user's progress. This allows the system to provide personalized health information based on the user's health status.

[0100] The user understanding unit can estimate the user's emotions and dynamically change the order in which news is displayed based on the estimated emotions. For example, if the user has positive emotions, entertainment and positive news can be displayed preferentially. On the other hand, if the user has negative emotions, relaxing news and mood-lightening content can be displayed preferentially. Furthermore, the tone and expression of the news can be adjusted according to the user's emotions, allowing for more appropriate information to be provided. This makes it possible to dynamically change the order and content of news displayed based on the user's emotions.

[0101] The news service system can also include an educational support unit that analyzes the user's learning history and provides education-related news and content. For example, if the user is studying a specific field, the system can provide the latest research results and news related to that field. Also, if the user has set learning goals, the system can provide learning advice and motivational content according to the user's progress. This allows the system to provide personalized educational information based on the user's learning history.

[0102] The user understanding unit can estimate the user's emotions and adjust the display content of advertisements based on the estimated emotions. For example, if the user is relaxed, advertisements for relaxing travel destinations can be displayed. Also, if the user is feeling stressed, advertisements for products and services that help relieve stress can be displayed. Furthermore, the design and message of advertisements can be adjusted according to the user's emotions, making it possible to provide more effective advertisements. This makes it possible to dynamically adjust the display content of advertisements based on the user's emotions.

[0103] The news service system may also include a purchasing support unit that analyzes a user's purchasing history and provides news and advice related to purchases. For example, if a user purchases a specific product, news related to that product and advice on how to use it may be provided. Also, if a user is looking for a new product based on their purchasing history, product information and reviews that meet their needs may be provided. This allows personalized purchasing information to be provided based on the user's purchasing history.

[0104] The user understanding unit can estimate the user's emotions and adjust the timing of news delivery based on the estimated emotions. For example, it can deliver relaxing news when the user is relaxed. It can also deliver news that can be read in a short time when the user is busy. Furthermore, it can adjust the frequency of news delivery according to the user's emotions to avoid providing excessive information. This makes it possible to dynamically adjust the timing and frequency of news delivery based on the user's emotions.

[0105] The news service system may further include a travel support unit that analyzes the user's travel history and provides travel-related news and advice. For example, news and event information related to places the user has visited in the past may be provided. Also, if the user is planning their next travel destination, tourist information and travel advice related to that location may be provided. This allows personalized travel information to be provided based on the user's travel history.

[0106] The design generation unit can estimate the user's emotions and dynamically change the design and layout of the news based on the estimated emotions. For example, if the user is relaxed, soft colors and a simple layout can be used. Alternatively, if the user is stressed, subdued colors and easy-to-read fonts can be used. Furthermore, the design elements of the news can be adjusted according to the user's emotions to provide a more comfortable browsing experience. This allows the design and layout of the news to be dynamically changed based on the user's emotions.

[0107] The news service system may also include an exercise support unit that analyzes the user's exercise history and provides exercise-related news and advice. For example, if the user regularly engages in a particular exercise, the system may provide the latest research results and news related to that exercise. Also, if the user wants to start a new exercise, the system may provide advice and success stories related to that exercise. This allows the system to provide personalized exercise information based on the user's exercise history.

[0108] The news service system can also include a reading support unit that analyzes the user's reading history and provides news and advice related to reading. For example, if the user reads many books in a particular genre, the system can provide the latest book information and news related to that genre. Also, if the user is looking for a new book, the system can provide book information and reviews that meet the user's needs. This allows the system to provide personalized reading information based on the user's reading history.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The user understanding unit uses generation AI to understand the user's lifestyle, hobbies, and preferences through dialogue with the user. For example, if a user provides information such as "I like sports, and I often watch soccer news," the unit selects the most suitable news for the user based on this information. Also, if a user provides information such as "I like simple designs," the unit generates a news layout with a simple design. Step 2: The design generation unit generates an original design layout based on the user's preferences understood by the user understanding unit. For example, the generation AI provides information in the original design layout based on the user's preferences. Step 3: The ad integration unit seamlessly integrates advertisements and coupons into the design layout generated by the design generation unit along with the necessary information. For example, if a user provides information such as "I'm looking for a new smartphone," smartphone-related advertisements and coupons will be displayed along with news. Step 4: The risk determination unit automatically identifies and protects against web risks based on the information provided by the ad integration unit. For example, the generation AI detects phishing sites and fake news and issues a warning to the user.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The 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.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0155] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A user understanding unit uses generative AI to understand the user's lifestyle, hobbies, and preferences through dialogue with the user. a design generation unit that generates an original design / layout based on the user's preferences understood by the user understanding unit; an advertisement integration unit that naturally incorporates advertisements and coupons into the design layout generated by the design generation unit along with necessary information; a risk determination unit that automatically determines and protects against risks on the Web based on the information provided by the advertisement integration unit. A system characterized by:

2. The user understanding unit Estimates emotions from the user's voice and facial expressions, and adjusts news content based on those emotions.

2. The system of claim 1.

3. The user understanding unit Based on the lifestyle, health management and fitness information is also provided.

2. The system of claim 1.

4. The design generation unit Analyzes user device usage and adjusts the optimal layout in real time 2. The system of claim 1.

5. The advertisement integration unit Analyze users' purchasing history and provide optimal advertisements based on their past purchasing patterns 2. The system of claim 1.

6. The risk determination unit Analyzes user browsing history and warns users about high-risk sites in advance 2. The system of claim 1.

7. The system comprises: Analyzes the user's emotional state and prioritizes displaying articles that evoke positive emotions 2. The system of claim 1.

8. The system comprises: Analyzes the user's emotional state and prioritizes displaying media content that elicits positive emotions 2. The system of claim 1.

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