System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting behavioral data of a user; means for transmitting the collected behavioral data to a server; means for analyzing the behavioral data and big data on the server; means for generating personalized information for each user based on an analysis result; means for transmitting the generated information from the server to a terminal of the user; and means for displaying the transmitted information on the terminal.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] In modern society, users are surrounded by a huge amount of information, making it difficult to efficiently select the information they need. This poses a problem: a great deal of time and effort is spent selecting information, hindering efficient information access. The present invention aims to solve this problem of information overload and improve user experience (UX) by providing users with the most appropriate information in real time. [Means for solving the problem]

[0005] The present invention provides a means for collecting user behavioral data and transmitting the collected data to a server for analysis. Specifically, the server uses big data to analyze the behavioral data and generates personalized information for each user based on the results. It also provides a series of means for transmitting and displaying this generated information to the user's device. It also includes a means for collecting feedback data from users and using this data to enable the server to optimize the analysis algorithm. This allows users to efficiently obtain the information they need and significantly reduces the effort required to select and discard information.

[0006] "User behavior data" refers to information related to a user's device operations, including website browsing history, search history, location information, and app usage history.

[0007] "Server" refers to a device or system that operates on a network and receives, stores, analyzes, and transmits data sent by users.

[0008] "Big data" refers to a collection of large amounts of diverse information data, a group of data from which useful insights and patterns can be extracted by applying algorithms such as statistical analysis and machine learning.

[0009] "Analysis" refers to the process of integrating collected behavioral data and big data, using algorithms and machine learning to evaluate users' interests and behavioral patterns, and inferring the most appropriate information.

[0010] "Personalized information" refers to information customized based on a user's individual interests and behavioral patterns, and includes, for example, news articles, coupons, recommended posts on social media, and product information.

[0011] "Terminal" refers to a device that is directly operated by a user, including smartphones, tablets, computers, etc.

[0012] "Feedback data" refers to data that indicates the reactions and actions taken by users in response to information provided, such as clicks, viewing time, subscriptions, and other behavioral information.

[0013] "Analysis algorithm" refers to a set of computational methods or processes used to analyze user behavior patterns and interests based on collected data. [Brief explanation of the drawings]

[0014] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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, a 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), and an APU (Accelerated Processing Unit).

[0018] 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.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] 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), Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention relates to an information provision system that collects user behavior data and uses that data to provide personalized information in real time. The invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display.

[0036] The system mainly consists of the following elements:

[0037] 1. Collection of user behavior data

[0038] The device collects real-time behavioral data such as user website browsing, searches, app usage, and location information, which is temporarily stored on the device and later sent to a server.

[0039] 2. Data transmission

[0040] The device sends the collected behavioral data to a server, where it is encrypted and transmitted over a secure communication channel.

[0041] 3. Receiving and integrating data

[0042] The server receives the data sent from the device, stores it in a database on the server, and analyzes it together with big data.

[0043] 4. Data Analysis

[0044] The server integrates the behavioral data and big data, and analyzes the user's interests and behavioral patterns using machine learning algorithms and statistical analysis. Based on the results of this analysis, it predicts the most appropriate information for each user.

[0045] 5. Generating personalized information

[0046] The server generates personalized information based on the analysis results, including news articles, coupons, recommended social media posts, and product information.

[0047] 6. Transmission of Information

[0048] The server then sends the generated personalized information to the user's device, where it is again encrypted and transmitted over a secure channel.

[0049] 7. Display of Information

[0050] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[0051] Specific examples

[0052] Example 1: Personalizing your news feed

[0053] The device collects the user's news app browsing history and sends it to a server.

[0054] The server analyzes the collected data and big data and determines that the user is interested in economics.

[0055] The server generates the latest news articles on the economy and sends them to the terminal.

[0056] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0057] Example 2: Offering a shopping coupon

[0058] The terminal collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[0059] The server generates relevant coupons for the user based on the analysis results.

[0060] The server transmits the generated coupon information to the terminal.

[0061] The device notifies users of coupons related to electronics, allowing them to purchase specific products at discounted prices.

[0062] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The device monitors user activity, specifically collecting behavioral data such as website browsing history, search history, location information, and app usage history in real time. This data is stored in temporary storage on the device.

[0066] Step 2:

[0067] The device reads the collected behavioral data from temporary storage at a certain timing or after the user has completed their operation. After reading the data, it converts it into a standard communication format and encrypts it as necessary.

[0068] Step 3:

[0069] The device transmits the encrypted behavioral data over the Internet to a server using a secure communication protocol (e.g., HTTPS).

[0070] Step 4:

[0071] The server receives the encrypted data sent from the device, after which the data is decrypted and stored in a secure server.

[0072] Step 5:

[0073] The server integrates the received behavioral data with big data, which includes behavioral data of other users and data obtained from external data sources.

[0074] Step 6:

[0075] The server then analyzes the integrated data using machine learning algorithms and statistical analysis to identify user interests and behavioral patterns and estimate the most appropriate information.

[0076] Step 7:

[0077] Based on the analysis results, the server generates personalized information for each user, including news articles, coupons, recommended social media posts, and product information.

[0078] Step 8:

[0079] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[0080] Step 9:

[0081] The device decrypts the encrypted information it receives, and then stores it on the device for immediate display.

[0082] Step 10:

[0083] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[0084] Step 11:

[0085] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[0086] Step 12:

[0087] The device collects user responses (e.g. clicks, viewing time, subscriptions) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[0088] Step 13:

[0089] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of user behavior patterns and reflects this in the next information provided.

[0090] This series of processing steps allows users to efficiently obtain information that is most suitable for them, and significantly reduces the time and effort required to access information.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Conventional information provision systems often lack the ability to efficiently collect and analyze user behavior data and provide personalized information, resulting in limited improvements to the user experience (UX). Furthermore, they are also inadequate in terms of security and privacy protection, making it difficult to gain user trust.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for generating personalized information for each user based on the analysis results using a machine learning algorithm or statistical analysis, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal via a secure path, thereby enabling efficient collection and analysis of user behavior data and safe provision of personalized information that is valuable to the user.

[0096] "User behavior data" refers to records of a user's usage patterns and behavior, such as operation history and location information, when visiting websites, using search engines, or using applications.

[0097] A "server" is a computer system that receives data sent by a user, analyzes it, generates optimal information, and sends it to the user's terminal.

[0098] "Massive data" refers to a collection of diverse behavioral data collected from a large number of users, a vast amount of information also known as big data.

[0099] "Machine learning algorithms" are algorithms and methodologies that allow computers to learn patterns from data and make predictions and decisions.

[0100] "Statistical analysis" refers to statistical methods for analyzing data and obtaining significant results or patterns.

[0101] "Personalized information" is information (e.g., news articles, coupons, recommended content) that is individually optimized based on each user's interests and behavioral patterns.

[0102] A "secure path" is a communication path in which information is encrypted when transmitted and is protected from unauthorized access or tampering.

[0103] This invention relates to an information provision system that collects user behavior data, analyzes that data, and provides personalized information in real time. This system aims to improve the user experience (UX) by efficiently performing a series of processes: collection, transmission, analysis, generation, and display.

[0104] Hardware and software used

[0105] A device is a device used by a user, such as a smartphone, tablet, or PC, and is used to collect behavioral data in real time, such as website browsing, searches, app usage, and location information.

[0106] The server is a computer system that receives the collected behavioral data, analyzes it, generates the most appropriate information, and sends it to the user's device. The server uses the following software:

[0107] A database management system (e.g., MySQL, PostgreSQL) is used to store and manage behavioral data and large amounts of data.

[0108] Use machine learning frameworks (e.g., TensorFlow and Scikit-learn implemented in Python) to perform data analysis.

[0109] We use encryption technology (e.g., SSL / TLS) to securely manage data transmission.

[0110] System processing overview

[0111] The device collects user behavioral data (e.g., browsing history in news apps, location information, search keywords) in real time and temporarily stores it on the device. The device then encrypts the collected behavioral data and transmits it to a server using a secure communication protocol (e.g., HTTPS).

[0112] The server receives the behavioral data sent from the device and stores it in a database. The stored data is then integrated with the large amount of accumulated data. The server then analyzes the data using machine learning algorithms and statistical analysis to extract the user's interests and behavioral patterns. Based on the analysis results, the server generates personalized information (e.g., the latest economic news, coupons for electronic devices) that is optimal for each user.

[0113] The generated information is re-encrypted by the server and sent to the user's device. The device decodes the received personalized information and displays it through the user interface, allowing the user to access optimized information. In addition, the device collects feedback data on whether the user has confirmed the provided information and sends it back to the server for future analysis.

[0114] Specific examples

[0115] Example 1: Personalizing your news feed

[0116] 1. The device collects the user's news app browsing history and sends it to the server.

[0117] 2. The server analyzes the collected data and large amounts of data to determine that the user is interested in economics.

[0118] 3. The server generates the latest economic news articles and sends them to the device.

[0119] 4. The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0120] Example 2: Offering a shopping coupon

[0121] 1. The device collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[0122] 2. The server generates relevant coupons for the user based on the analysis results.

[0123] 3. The server sends the generated coupon information to the terminal.

[0124] 4. The device notifies the user of electronics-related coupons, allowing the user to purchase certain products at discounted prices.

[0125] Prompt Sentence Examples

[0126] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[0127] Please explain the specific process flow of a system that collects user behavior data, analyzes that data, and provides personalized information in real time. In particular, please describe the specific behavior when a user uses a shopping app.

[0128] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] A user visits a website and initiates an operation. Based on this operation, the device collects user behavior data (e.g., time spent on page, links clicked, search keywords) in real time. The input is the user's behavior, and the output is the collected behavior data. The behavior data is temporarily stored on the device.

[0132] Step 2:

[0133] The device retrieves the temporarily stored behavioral data and encrypts it using an encryption algorithm (e.g., AES encryption). The input is the temporarily stored behavioral data, and the output is the encrypted behavioral data. The encrypted data is then sent to the server using the HTTPS protocol.

[0134] Step 3:

[0135] The server decrypts the encrypted behavioral data it receives. The input is encrypted behavioral data, and the output is decrypted behavioral data. The server checks the integrity of this data to ensure there are no anomalies or missing data. The consistent data is then stored in a database management system.

[0136] Step 4:

[0137] The server combines the collected behavioral data with existing large data sets to generate a dataset for analysis. The input is the decoded behavioral data and large data sets, and the output is the combined dataset. Machine learning algorithms (e.g., TensorFlow models) and statistical analysis are then applied to extract user interests and behavioral patterns.

[0138] Step 5:

[0139] The server generates personalized information for each user based on the analysis results. The input is the analysis results, and the output is personalized information (e.g., the latest news articles, shopping coupons). This information is temporarily stored in the server memory.

[0140] Step 6:

[0141] The server encrypts the generated personalized information and sends it to the user's device via a communication protocol (e.g., HTTPS). The input is personalized information, and the output is encrypted information. A log of data transmission is also recorded.

[0142] Step 7:

[0143] The terminal decrypts the received encrypted personalized information and displays it via a user interface. The input is the encrypted personalized information, and the output is the information displayed on the user interface. The terminal also collects feedback data on whether the user has confirmed the provided information and sends it to the server for use in the next analysis.

[0144] (Application example 1)

[0145] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] Conventional information provision systems have limitations in providing personalized information based on user behavioral data. In particular, in autonomous vehicles, there is a demand for more accurate personalized information by integrating and analyzing not only user behavioral data but also vehicle operation data. The objective of the present invention is to provide an information provision system that utilizes behavioral data and operation data in an integrated manner to improve the user experience in autonomous vehicles.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0148] In this invention, the server includes means for collecting user behavior data and vehicle operation data, means for analyzing the behavior data and large-scale data, and means for generating information optimized for the user based on the analysis results, thereby making it possible to provide the information that the user is most interested in in real time, even inside an autonomous vehicle.

[0149] "User Behavior Data" means data about your activity, browsing history, location, and other usage patterns when you use applications or devices.

[0150] A "server" is a computer system that receives data sent from a client via a network and processes, analyzes, and stores the data.

[0151] "Large-scale data" refers to data that is so large that it is difficult to process using traditional database management tools, and includes both structured and unstructured data.

[0152] "Personalized information" is customized information that is optimized for each individual user and is generated based on the user's behavioral data and driving data.

[0153] "Automobile operation data" refers to data related to the operation of an autonomous vehicle, such as its movement status, speed, route, and operating mode.

[0154] "Feedback data" refers to data such as operation results, evaluations, and opinions provided by users, and is used to improve the system and optimize analysis algorithms.

[0155] "Analytics algorithms" are computational methods or algorithms that process collected data to identify user behavior patterns and interests and generate personalized information.

[0156] The information provision system of the present invention collects user behavior data and vehicle operation data, integrates and analyzes this data, and provides optimized information to users within autonomous vehicles. This system efficiently performs a series of processes, including collection, transmission, analysis, generation, and display, in order to improve the user experience.

[0157] 1. Collecting User Behavior Data:

[0158] The device collects real-time data on the user's in-car activities (e.g., reading, watching videos, listening to music), location information, and vehicle operation data, which are temporarily stored on the device and later transmitted to a server.

[0159] 2. Data transmission:

[0160] The device sends the collected behavioral and operational data to the server. During this process, the data is encrypted and transmitted over a secure communication channel. For encryption, the Fernet library is used, for example.

[0161] 3. Receipt and integration of data:

[0162] The server receives the data sent from the terminal and stores it in a database, where it is analyzed together with large-scale data.

[0163] 4. Data Analysis:

[0164] The server integrates and analyzes behavioral data, operational data, and large-scale data, specifically using machine learning algorithms (such as the K-means algorithm) to identify user interests and behavioral patterns.

[0165] 5. Generating personalized information:

[0166] The server generates personalized information based on the analysis results, including tourist information for the destination, recommended shops along the route, and in-car entertainment suggestions.

[0167] 6. Transmission of Information:

[0168] The generated personalized information is then re-encrypted and sent over a secure channel to the user's device.

[0169] 7. Displaying Information:

[0170] The device displays the received personalized information through a user interface, allowing users to access information optimized for them in real time.

[0171] Examples:

[0172] For example, if a user is reading a book in an autonomous vehicle, the device will collect that behavioral data and send it to the server. The server will analyze the user's past reading history, location information, and current reading content, generate information about nearby bookstores and libraries, and generate related book recommendations, and send them to the device. The device will then display this information to the user, allowing them to obtain appropriate information in the autonomous vehicle.

[0173] Below are some example prompts to input to a generative AI model:

[0174] The system analyzes the user's behavioral data while in the self-driving vehicle (activity: reading, location information: 37.7749,-122.4194, vehicle status: self-driving mode) and provides personalized information based on their current interests.

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1:

[0177] The terminal collects user behavioral data and vehicle operation data in real time. The behavioral data includes user activities such as reading, watching videos, listening to music, and location information. The operation data includes vehicle driving status, speed, route, and operation mode. The input is real-time user activity and operation information, and the output is a structured set of collected behavioral data and operation data.

[0178] Step 2:

[0179] The device encrypts the collected behavioral and operational data and sends it to the server via a secure communication channel. The Fernet library is used to encrypt the data. The input is the behavioral and operational data from step 1, and the output is the encrypted data packet.

[0180] Step 3:

[0181] The server receives the encrypted data sent from the terminal, decrypts it, and stores it in a database. Here, we use the Fernet library to receive and decrypt the encrypted packets and store the data in a database management system (DBMS). The input is the encrypted data packet, and the output is the decrypted raw data.

[0182] Step 4:

[0183] The server analyzes behavioral data, operational data, and large-scale data to identify user interests and behavioral patterns. Specifically, it performs clustering using a machine learning algorithm (e.g., the K-means algorithm). The inputs are the decoded behavioral data, operational data, past history data, and large-scale data, and the output is the clustering results that indicate the user's interests.

[0184] Step 5:

[0185] The server generates personalized information based on the analysis results. For example, if the user likes reading, it generates recommendations for nearby bookstores and related books. The input is the clustering results, and the output is personalized information (e.g., tourist information, route recommendations, in-car entertainment suggestions).

[0186] Step 6:

[0187] The generated personalized information is then encrypted again and sent to the user's device via a secure communication channel. Again, the Fernet library is used for encryption. The input is the generated personalized information, and the output is the encrypted information packet.

[0188] Step 7:

[0189] The terminal decrypts the received encrypted packets and displays the personalized information through a user interface, allowing users to access information optimized for them in real time. The input is the encrypted information packet, and the output is the specific personalized information displayed to the user.

[0190] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0191] The present invention relates to an information provision system that collects user behavioral data and emotional data and provides personalized information based on that data in real time. The present invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display. In this invention, by combining an emotion engine, more advanced personalization based on user emotions becomes possible.

[0192] The system mainly consists of the following elements:

[0193] 1. Collecting user behavioral and emotional data

[0194] The device collects real-time behavioral data, such as user website browsing, searches, app usage, and location information. It also collects user emotional data using an emotion engine. Emotional data is obtained, for example, through facial recognition technology and voice analysis. This data is stored in temporary storage on the device.

[0195] 2. Data transmission

[0196] The device sends the collected behavioral and emotional data to a server, where it is encrypted and transmitted over a secure communication channel.

[0197] 3. Receiving and integrating data

[0198] The server receives the encrypted data sent from the device. After receiving the data, it is decrypted and stored in a secure server. The behavioral data and emotional data are integrated and analyzed together with big data.

[0199] 4. Data Analysis

[0200] The server integrates behavioral data, emotional data, and big data, and uses machine learning algorithms and statistical analysis to analyze users' interests, emotional states, and behavioral patterns. Based on the results of this analysis, it predicts the most appropriate information for each user.

[0201] 5. Generating personalized information

[0202] The server generates personalized information based on the analysis results, including news articles, coupons, recommended posts on social media, product information, etc. By utilizing emotion data, the server can provide information that fits the user's current mood and emotional state.

[0203] 6. Transmission of Information

[0204] The server then transmits the generated personalized information to the user's device, again using a secure communication protocol.

[0205] 7. Display of Information

[0206] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[0207] Specific examples

[0208] Example 1: Personalizing your news feed based on your emotional state

[0209] The device collects the user's news app browsing history and emotional data and sends it to a server.

[0210] The server analyzes the collected data and big data to determine that the user is interested in economics and that current sentiment is calm.

[0211] The server generates the latest news articles on the economy and sends them to the terminal.

[0212] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0213] Example 2: Providing shopping coupons based on emotional state

[0214] The device collects the user's purchasing history and current emotional data (e.g., excited) and sends it to the server.

[0215] Based on the analysis results, the server generates coupons for exciting products (e.g., gadgets) for the user.

[0216] The server transmits the generated coupon information to the terminal.

[0217] The device will notify the user of the coupon, allowing them to purchase specific products at a discounted price.

[0218] This system allows users to efficiently obtain information that is appropriate for their emotional state, significantly reducing the time and effort required to access information. Furthermore, the system continues to improve based on feedback data, ensuring that optimal information is always provided.

[0219] The processing flow will be explained below.

[0220] Step 1:

[0221] The device monitors user activity. Specifically, it collects behavioral data in real time, such as website browsing history, search history, location information, and app usage history. It also uses an emotion engine to recognize the user's face and analyze their voice to collect emotional data. This data is then stored in temporary storage.

[0222] Step 2:

[0223] The device reads the collected behavioral and emotional data from temporary storage at a certain timing or after the user has completed their operation. The read data is converted into a data format and encrypted as necessary.

[0224] Step 3:

[0225] The device transmits encrypted behavioral and emotional data to a server over the internet using a secure communication protocol (e.g., HTTPS).

[0226] Step 4:

[0227] The server receives the encrypted data sent from the device, decrypts it, and stores it in a secure database on the server.

[0228] Step 5:

[0229] The server integrates the received behavioral and emotional data with big data, which includes behavioral data of other users and data obtained from external data sources.

[0230] Step 6:

[0231] The server analyzes the integrated data using machine learning algorithms and statistical analysis to identify the user's interests, emotional state, and behavioral patterns. It also takes into account the user's emotional data to reflect their current mood and emotional state in the analysis.

[0232] Step 7:

[0233] The server generates personalized information for each user based on the analysis results. This information includes news articles, coupons, recommended posts on social media, product information, etc. By using emotion data, it is possible to provide information that fits the user's current emotional state.

[0234] Step 8:

[0235] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[0236] Step 9:

[0237] The device decrypts the encrypted information it receives, and then stores it locally for immediate viewing.

[0238] Step 10:

[0239] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[0240] Step 11:

[0241] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[0242] Step 12:

[0243] The device collects user responses (e.g., clicks, viewing time, subscription behavior) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[0244] Step 13:

[0245] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of the user's behavioral patterns and emotional state and reflects this in the next information provided.

[0246] This series of processing steps allows users to efficiently obtain information that is appropriate for their emotional state, significantly improving the information access experience. The system is continuously improved based on feedback data, ensuring that optimal information is always provided.

[0247] Example 2

[0248] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0249] Conventional systems provide information based solely on user behavior data, making it difficult to provide information appropriate to the user's emotional state or current interests. Furthermore, they do not efficiently utilize user feedback, resulting in a decline in the quality of the information provided and the accuracy of personalization. Furthermore, the difficulty of collecting and analyzing emotional data limits the user experience.

[0250] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and large-scale data on the server, means for generating personalized information for each user based on the analysis results, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal. By using not only the user's behavioral data but also their emotional data, it is possible to provide personalized information suited to the user's current emotional state and interests. Furthermore, optimizing the analysis algorithm based on feedback data improves the quality and accuracy of information provided.

[0251] "Behavioral data" refers to information including user operation history and location information when browsing websites, searching, or using apps.

[0252] "Emotion data" is information that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[0253] "Big data" refers to large and diverse datasets collected from a wide variety of data sources.

[0254] "Personalized information" is information that is tailored to a user's interests and emotional state, generated based on the user's individual behavioral and emotional data.

[0255] "Feedback Data" refers to information, including ratings and opinions, obtained when users respond to information provided.

[0256] "Collection means" refers to devices or programs that acquire user behavioral data and emotional data and store them in temporary storage.

[0257] "Transmission means" refers to a device or program that has the function of encrypting collected data and transmitting it to a server via a secure communication protocol.

[0258] "Analysis means" refers to devices or programs that use machine learning algorithms and statistical analysis to analyze users' interests and emotional states based on data acquired on the server.

[0259] "Generation means" refers to a device or program that has the function of generating optimal information for the user based on the analysis results.

[0260] "Display means" refers to a device or program for displaying the transmitted information on the terminal in a format that is easy for the user to view.

[0261] This invention relates to an information provision system that collects user behavioral and emotional data and provides personalized information based on that data in real time. The aim is to improve the user experience (UX) by efficiently performing a series of processes.

[0262] Hardware and Software

[0263] The device will be equipped with sensors to collect website browsing history, search history, app usage records, and real-time location information, as well as a camera and microphone that will use facial recognition and voice analysis to collect emotional data. The data will be stored in temporary storage.

[0264] The server has the following functions:

[0265] 1. Data Reception: Encrypted communication protocols (e.g., HTTPS) to securely receive collected behavioral and emotional data from devices.

[0266] 2. Data analysis: Utilizing machine learning libraries such as Python's Scikit-learn and TensorFlow, we analyze behavioral data, emotional data, and large-scale data.

[0267] 3. Generating personalized information: Using natural language processing (NLP) techniques and text generation models (e.g., GPT-3), we generate content that is best suited to the user.

[0268] 4. Sending information: The generated information is sent to the terminal in real time via WebSocket.

[0269] System Functionality Description

[0270] The device collects user behavioral and emotional data and stores it in temporary storage, which is periodically encrypted and sent to a server using the HTTPS protocol.

[0271] The server decrypts the received data and stores it in a secure database. It then integrates and analyzes behavioral, emotional, and large-scale data. It uses Python libraries to identify the user's interests and emotional state. Based on the analysis results, it uses natural language processing technology to generate personalized content. The generated content is then sent to the device using a secure communication protocol (such as WebSocket).

[0272] The device displays the received personalized information through a user interface (e.g., React Native, Swift), allowing users to access information optimized for them.

[0273] Specific examples

[0274] 1. Personalize your news feed based on your emotional state:

[0275] The device collects the user's news app browsing history and sentiment data and stores it in temporary storage.

[0276] The device encrypts the collected data and sends it to the server via HTTPS.

[0277] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[0278] The server uses Python's Scikit-learn to analyze the user's interests and emotional state.

[0279] The server uses GPT-3 to generate economic news articles appropriate for the user.

[0280] The server generates news articles and sends them to the device via HTTPS.

[0281] The device displays the received articles using React Native, and the user can view economic news.

[0282] 2. Providing shopping coupons based on emotional state:

[0283] The device collects the user's purchasing history and current emotional data and stores it in temporary storage.

[0284] The device encrypts the collected data and sends it to the server via HTTPS.

[0285] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[0286] The server uses TensorFlow to analyze users' excitement levels and purchasing patterns.

[0287] The server generates coupons for exciting products (gadgets).

[0288] The coupon information generated by the server is sent to the terminal via WebSocket.

[0289] The device notifies the user of the coupon via push notification and displays the coupon information using React Native, allowing the user to purchase the product at a discounted price.

[0290] Example prompts to input to the generative AI model

[0291] Describe a system that collects user behavioral and emotional data, analyzes that data, and provides personalized information in real time. The system uses facial recognition technology and voice analysis to obtain emotional data, integrates it with large-scale data, and analyzes it using machine learning algorithms. Specific examples include providing a news feed and shopping coupons based on the user's emotional state.

[0292] In this way, by integrating and analyzing user behavior and emotional data and providing personalized information, it is possible to achieve an advanced user experience.

[0293] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0294] Step 1: Collect data

[0295] The device collects user behavioral data (website browsing history, search history, app usage records, location information, etc.) and emotional data (facial expressions and tone of voice) in real time. At this time, the device stores the collected data in temporary storage. The input is the user's behavior and emotions, and the output is the data stored in temporary storage. For example, website browsing history can be recorded through a browser extension, and emotional data can be obtained using the camera and microphone.

[0296] Step 2: Sending data

[0297] The device encrypts the behavioral and emotional data stored in temporary storage and sends it to the server using a secure communication protocol (such as HTTPS). The input is the data stored in temporary storage, and the output is the encrypted data sent to the server. The device sends data periodically (e.g., every 5 minutes) and uses a backoff algorithm that takes into account data size and connection quality.

[0298] Step 3: Receiving and consolidating data

[0299] The server receives and decrypts the encrypted data sent from the device. The received data is stored in a secure database. The input is encrypted behavioral and emotional data, and the output is the decrypted data stored in the database. The server then goes through a data cleansing process to fill in missing values ​​and detect outliers.

[0300] Step 4: Analyze the data

[0301] The server uses machine learning algorithms and statistical analysis to analyze the user's interests, emotional state, and behavioral patterns based on the integrated data. The input is the integrated data, and the output is the analysis results. Specifically, it uses Python's Scikit-learn and TensorFlow to group user behavior and emotional patterns using a clustering algorithm (e.g., K-means).

[0302] Step 5: Generate personalization information

[0303] The server generates personalized information (news articles, coupons, recommended posts on social media, product information, etc.) based on the analysis results. The input is the analysis results, and the output is the generated personalized information. For example, natural language processing (NLP) techniques or text generation models (e.g., GPT-3) can be used to generate content that matches the user's emotional state.

[0304] Step 6: Submit your information

[0305] The server sends the generated personalized information to the user's device. The input is the generated personalized information, and the output is the information sent to the device. This transmission uses a secure communication protocol (e.g., HTTPS, WebSocket).

[0306] Step 7: Viewing information

[0307] The device displays the received personalized information through a user interface. The input is the received information, and the output is the information displayed to the user. For example, a UI framework such as React Native or Swift can be used to display the information in a visually easy-to-understand format, and push notifications can be used to notify the user as needed.

[0308] (Application example 2)

[0309] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0310] Conventional personalization systems provide information based solely on user behavioral data and are unable to consider the user's emotional state. This makes it difficult for users to obtain information that matches their emotions at any given time, making it impossible to provide an optimal user experience. Furthermore, providing real-time personalized information to improve the user experience is also insufficient.

[0311] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and big data on the server and determining the user's interests and emotional state, means for generating information and coupons personalized for each user based on the analysis results, means for transmitting the generated information and coupons from the server to the user's terminal, and means for displaying the transmitted information and coupons on the terminal. This makes it possible to provide optimal personalized information and coupons according to the user's emotional state in real time.

[0312] "User behavioral data" refers to data about a series of actions generated when a user uses a device, such as website browsing history, search history, app usage history, and location information.

[0313] "Emotion data" refers to data about a user's emotional state (e.g., joy, sadness, anger, surprise, etc.) obtained using facial recognition technology or voice analysis technology.

[0314] A "server" is a computer system that receives, analyzes, stores, and distributes data over a network.

[0315] "Big data" refers to large, diverse, and rapidly generated data sets that are difficult to process using traditional database management tools.

[0316] "Personalized information" refers to content such as news articles, coupons, and recommended product information that is optimized for each user based on their behavioral and emotional data.

[0317] A "coupon" is an electronic certificate that allows a user to purchase a particular product or service at a discounted price.

[0318] A "terminal" is a device that is directly operated by a user, such as a smartphone or a personal computer.

[0319] "Feedback Data" refers to data provided by users, such as their opinions, impressions, and usage status, which is used to improve and optimize the system.

[0320] "Analysis algorithms" are mathematical and statistical methods for analyzing behavioral data, emotional data, and big data to extract useful patterns and trends.

[0321] The system for realizing this invention operates based on the following procedure. The system starts by collecting user behavioral data and emotional data and sending them to a server. The collected behavioral data includes the user's website browsing history, app usage history, product search history, location information, etc. The collection of emotional data uses facial recognition technology and voice analysis technology (e.g., DeepFace or OpenCV).

[0322] After receiving the collected behavioral and emotional data, the server integrates this data with big data and analyzes it. The analysis uses machine learning algorithms and statistical analysis methods to determine the user's interests and emotional state. For example, if the user expresses "joy" or is determined to be interested in "fashion items," the server generates a list of product recommendations and coupons that are optimal for the user.

[0323] The generated personalized information and coupons are then sent from the server to the user's device (e.g., smartphone) using a secure communication protocol. Finally, the device displays the received information in a user interface, allowing the user to access the optimized information.

[0324] For example, when a user captures their face using their smartphone camera, the data is analyzed by an emotion analysis engine. If the analysis result indicates "joy," a coupon for the latest fashion items is generated based on the user's past purchase history and interests. This coupon is displayed on the user's smartphone in real time.

[0325] An example prompt for a generative AI model is:

[0326] "For users whose current emotional state is joy, generate discount offers on fashion items that you think they might be interested in based on their past purchase history."

[0327] This system improves the shopping experience by providing users with personalized information in real time that is tailored to their emotional state, and the accuracy of the information provided continues to improve as the system is continuously optimized based on feedback data.

[0328] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0329] Step 1:

[0330] The user captures their face using the device's camera. The device then analyzes the facial image using facial recognition technology (e.g., DeepFace and OpenCV) and extracts emotional data. The facial image is the input, and the extracted emotional data (e.g., "happiness" or "sadness") is the output.

[0331] Step 2:

[0332] The device collects behavioral data (website browsing history, search history, app usage history, location information, etc.) and the emotion data extracted in step 1. This collected data is the input.

[0333] Step 3:

[0334] The device encrypts the collected behavioral data and emotional data and transmits them to the server using a secure communication path. The behavioral data and emotional data are then transferred to the server. The collected data is the input, and the data sent to the server is the output.

[0335] Step 4:

[0336] The server decrypts and securely stores the received behavioral and emotional data. This stored data is the input, and the preparation for analysis is the output.

[0337] Step 5:

[0338] The server integrates the behavioral data, emotional data, and big data, and analyzes the user's interests and emotional state using machine learning algorithms and statistical analysis methods. This analysis involves calculations based on the input data (behavioral data, emotional data, big data), and the user's interest determination and emotional state are obtained as outputs.

[0339] Step 6:

[0340] The server generates personalized information and coupons for each user based on the analysis results of step 5. For example, if the user is in a "joy" emotional state, a discount coupon for the latest fashion items is generated. The analysis results are the input, and the generated personalized information and coupons are the output.

[0341] Step 7:

[0342] The server encrypts and transmits the generated personalized information and coupons to the user's terminal using a secure communication protocol, with the generated information and coupons being the input and data transmission to the terminal being the output.

[0343] Step 8:

[0344] The terminal displays the received personalized information and coupons through a user interface, allowing the user to access the transmitted information in real time. The received data is the input, and the display of the information to the user is the output.

[0345] Through these processing steps, the system can provide an optimal personalized shopping experience based on the user's emotional state and behavioral data. An example of a prompt for analysis and information generation using a generative AI model is, "For a user whose current emotional state is joy, please generate discount information on fashion items that you think they might be interested in based on their past purchase history."

[0346] 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.

[0347] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0348] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0349] [Second embodiment]

[0350] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0351] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0352] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0353] 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.

[0354] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0355] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0356] 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.

[0357] 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.

[0358] 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 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.

[0359] 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.

[0360] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0361] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0362] This invention relates to an information provision system that collects user behavior data and uses that data to provide personalized information in real time. The invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display.

[0363] The system mainly consists of the following elements:

[0364] 1. Collection of user behavior data

[0365] The device collects real-time behavioral data such as user website browsing, searches, app usage, and location information, which is temporarily stored on the device and later sent to a server.

[0366] 2. Data transmission

[0367] The device sends the collected behavioral data to a server, where it is encrypted and transmitted over a secure communication channel.

[0368] 3. Receiving and integrating data

[0369] The server receives the data sent from the device, stores it in a database on the server, and analyzes it together with big data.

[0370] 4. Data Analysis

[0371] The server integrates the behavioral data and big data, and analyzes the user's interests and behavioral patterns using machine learning algorithms and statistical analysis. Based on the results of this analysis, it predicts the most appropriate information for each user.

[0372] 5. Generating personalized information

[0373] The server generates personalized information based on the analysis results, including news articles, coupons, recommended social media posts, and product information.

[0374] 6. Transmission of Information

[0375] The server then sends the generated personalized information to the user's device, where it is again encrypted and transmitted over a secure channel.

[0376] 7. Display of Information

[0377] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[0378] Specific examples

[0379] Example 1: Personalizing your news feed

[0380] The device collects the user's news app browsing history and sends it to a server.

[0381] The server analyzes the collected data and big data and determines that the user is interested in economics.

[0382] The server generates the latest news articles on the economy and sends them to the terminal.

[0383] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0384] Example 2: Offering a shopping coupon

[0385] The terminal collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[0386] The server generates relevant coupons for the user based on the analysis results.

[0387] The server transmits the generated coupon information to the terminal.

[0388] The device notifies users of coupons related to electronics, allowing them to purchase specific products at discounted prices.

[0389] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[0390] The processing flow will be explained below.

[0391] Step 1:

[0392] The device monitors user activity, specifically collecting behavioral data such as website browsing history, search history, location information, and app usage history in real time. This data is stored in temporary storage on the device.

[0393] Step 2:

[0394] The device reads the collected behavioral data from temporary storage at a certain timing or after the user has completed their operation. After reading the data, it converts it into a standard communication format and encrypts it as necessary.

[0395] Step 3:

[0396] The device transmits the encrypted behavioral data over the Internet to a server using a secure communication protocol (e.g., HTTPS).

[0397] Step 4:

[0398] The server receives the encrypted data sent from the device, after which the data is decrypted and stored in a secure server.

[0399] Step 5:

[0400] The server integrates the received behavioral data with big data, which includes behavioral data of other users and data obtained from external data sources.

[0401] Step 6:

[0402] The server then analyzes the integrated data using machine learning algorithms and statistical analysis to identify user interests and behavioral patterns and estimate the most appropriate information.

[0403] Step 7:

[0404] Based on the analysis results, the server generates personalized information for each user, including news articles, coupons, recommended social media posts, and product information.

[0405] Step 8:

[0406] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[0407] Step 9:

[0408] The device decrypts the encrypted information it receives, and then stores it on the device for immediate display.

[0409] Step 10:

[0410] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[0411] Step 11:

[0412] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[0413] Step 12:

[0414] The device collects user responses (e.g. clicks, viewing time, subscriptions) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[0415] Step 13:

[0416] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of user behavior patterns and reflects this in the next information provided.

[0417] This series of processing steps allows users to efficiently obtain information that is most suitable for them, and significantly reduces the time and effort required to access information.

[0418] Example 1

[0419] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0420] Conventional information provision systems often lack the ability to efficiently collect and analyze user behavior data and provide personalized information, resulting in limited improvements to the user experience (UX). Furthermore, they are also inadequate in terms of security and privacy protection, making it difficult to gain user trust.

[0421] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0422] In this invention, the server includes means for generating personalized information for each user based on the analysis results using a machine learning algorithm or statistical analysis, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal via a secure path, thereby enabling efficient collection and analysis of user behavior data and safe provision of personalized information that is valuable to the user.

[0423] "User behavior data" refers to records of a user's usage patterns and behavior, such as operation history and location information, when visiting websites, using search engines, or using applications.

[0424] A "server" is a computer system that receives data sent by a user, analyzes it, generates optimal information, and sends it to the user's terminal.

[0425] "Massive data" refers to a collection of diverse behavioral data collected from a large number of users, a vast amount of information also known as big data.

[0426] "Machine learning algorithms" are algorithms and methodologies that allow computers to learn patterns from data and make predictions and decisions.

[0427] "Statistical analysis" refers to statistical methods for analyzing data and obtaining significant results or patterns.

[0428] "Personalized information" is information (e.g., news articles, coupons, recommended content) that is individually optimized based on each user's interests and behavioral patterns.

[0429] A "secure path" is a communication path in which information is encrypted when transmitted and is protected from unauthorized access or tampering.

[0430] This invention relates to an information provision system that collects user behavior data, analyzes that data, and provides personalized information in real time. This system aims to improve the user experience (UX) by efficiently performing a series of processes: collection, transmission, analysis, generation, and display.

[0431] Hardware and software used

[0432] A device is a device used by a user, such as a smartphone, tablet, or PC, and is used to collect behavioral data in real time, such as website browsing, searches, app usage, and location information.

[0433] The server is a computer system that receives the collected behavioral data, analyzes it, generates the most appropriate information, and sends it to the user's device. The server uses the following software:

[0434] A database management system (e.g., MySQL, PostgreSQL) is used to store and manage behavioral data and large amounts of data.

[0435] Use machine learning frameworks (e.g., TensorFlow and Scikit-learn implemented in Python) to perform data analysis.

[0436] We use encryption technology (e.g., SSL / TLS) to securely manage data transmission.

[0437] System processing overview

[0438] The device collects user behavioral data (e.g., browsing history in news apps, location information, search keywords) in real time and temporarily stores it on the device. The device then encrypts the collected behavioral data and transmits it to a server using a secure communication protocol (e.g., HTTPS).

[0439] The server receives the behavioral data sent from the device and stores it in a database. The stored data is then integrated with the large amount of accumulated data. The server then analyzes the data using machine learning algorithms and statistical analysis to extract the user's interests and behavioral patterns. Based on the analysis results, the server generates personalized information (e.g., the latest economic news, coupons for electronic devices) that is optimal for each user.

[0440] The generated information is re-encrypted by the server and sent to the user's device. The device decodes the received personalized information and displays it through the user interface, allowing the user to access optimized information. In addition, the device collects feedback data on whether the user has confirmed the provided information and sends it back to the server for future analysis.

[0441] Specific examples

[0442] Example 1: Personalizing your news feed

[0443] 1. The device collects the user's news app browsing history and sends it to the server.

[0444] 2. The server analyzes the collected data and large amounts of data to determine that the user is interested in economics.

[0445] 3. The server generates the latest economic news articles and sends them to the device.

[0446] 4. The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0447] Example 2: Offering a shopping coupon

[0448] 1. The device collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[0449] 2. The server generates relevant coupons for the user based on the analysis results.

[0450] 3. The server sends the generated coupon information to the terminal.

[0451] 4. The device notifies the user of electronics-related coupons, allowing the user to purchase certain products at discounted prices.

[0452] Prompt Sentence Examples

[0453] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[0454] Please explain the specific process flow of a system that collects user behavior data, analyzes that data, and provides personalized information in real time. In particular, please describe the specific behavior when a user uses a shopping app.

[0455] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[0456] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0457] Step 1:

[0458] A user visits a website and initiates an operation. Based on this operation, the device collects user behavior data (e.g., time spent on page, links clicked, search keywords) in real time. The input is the user's behavior, and the output is the collected behavior data. The behavior data is temporarily stored on the device.

[0459] Step 2:

[0460] The device retrieves the temporarily stored behavioral data and encrypts it using an encryption algorithm (e.g., AES encryption). The input is the temporarily stored behavioral data, and the output is the encrypted behavioral data. The encrypted data is then sent to the server using the HTTPS protocol.

[0461] Step 3:

[0462] The server decrypts the encrypted behavioral data it receives. The input is encrypted behavioral data, and the output is decrypted behavioral data. The server checks the integrity of this data to ensure there are no anomalies or missing data. The consistent data is then stored in a database management system.

[0463] Step 4:

[0464] The server combines the collected behavioral data with existing large data sets to generate a dataset for analysis. The input is the decoded behavioral data and large data sets, and the output is the combined dataset. Machine learning algorithms (e.g., TensorFlow models) and statistical analysis are then applied to extract user interests and behavioral patterns.

[0465] Step 5:

[0466] The server generates personalized information for each user based on the analysis results. The input is the analysis results, and the output is personalized information (e.g., the latest news articles, shopping coupons). This information is temporarily stored in the server memory.

[0467] Step 6:

[0468] The server encrypts the generated personalized information and sends it to the user's device via a communication protocol (e.g., HTTPS). The input is personalized information, and the output is encrypted information. A log of data transmission is also recorded.

[0469] Step 7:

[0470] The terminal decrypts the received encrypted personalized information and displays it via a user interface. The input is the encrypted personalized information, and the output is the information displayed on the user interface. The terminal also collects feedback data on whether the user has confirmed the provided information and sends it to the server for use in the next analysis.

[0471] (Application example 1)

[0472] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0473] Conventional information provision systems have limitations in providing personalized information based on user behavioral data. In particular, in autonomous vehicles, there is a demand for more accurate personalized information by integrating and analyzing not only user behavioral data but also vehicle operation data. The objective of the present invention is to provide an information provision system that utilizes behavioral data and operation data in an integrated manner to improve the user experience in autonomous vehicles.

[0474] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0475] In this invention, the server includes means for collecting user behavior data and vehicle operation data, means for analyzing the behavior data and large-scale data, and means for generating information optimized for the user based on the analysis results, thereby making it possible to provide the information that the user is most interested in in real time, even inside an autonomous vehicle.

[0476] "User Behavior Data" means data about your activity, browsing history, location, and other usage patterns when you use applications or devices.

[0477] A "server" is a computer system that receives data sent from a client via a network and processes, analyzes, and stores the data.

[0478] "Large-scale data" refers to data that is so large that it is difficult to process using traditional database management tools, and includes both structured and unstructured data.

[0479] "Personalized information" is customized information that is optimized for each individual user and is generated based on the user's behavioral data and driving data.

[0480] "Automobile operation data" refers to data related to the operation of an autonomous vehicle, such as its movement status, speed, route, and operating mode.

[0481] "Feedback data" refers to data such as operation results, evaluations, and opinions provided by users, and is used to improve the system and optimize analysis algorithms.

[0482] "Analytics algorithms" are computational methods or algorithms that process collected data to identify user behavior patterns and interests and generate personalized information.

[0483] The information provision system of the present invention collects user behavior data and vehicle operation data, integrates and analyzes this data, and provides optimized information to users within autonomous vehicles. This system efficiently performs a series of processes, including collection, transmission, analysis, generation, and display, in order to improve the user experience.

[0484] 1. Collecting User Behavior Data:

[0485] The device collects real-time data on the user's in-car activities (e.g., reading, watching videos, listening to music), location information, and vehicle operation data, which are temporarily stored on the device and later transmitted to a server.

[0486] 2. Data transmission:

[0487] The device sends the collected behavioral and operational data to the server. During this process, the data is encrypted and transmitted over a secure communication channel. For encryption, the Fernet library is used, for example.

[0488] 3. Receipt and integration of data:

[0489] The server receives the data sent from the terminal and stores it in a database, where it is analyzed together with large-scale data.

[0490] 4. Data Analysis:

[0491] The server integrates and analyzes behavioral data, operational data, and large-scale data, specifically using machine learning algorithms (such as the K-means algorithm) to identify user interests and behavioral patterns.

[0492] 5. Generating personalized information:

[0493] The server generates personalized information based on the analysis results, including tourist information for the destination, recommended shops along the route, and in-car entertainment suggestions.

[0494] 6. Transmission of Information:

[0495] The generated personalized information is then re-encrypted and sent over a secure channel to the user's device.

[0496] 7. Displaying Information:

[0497] The device displays the received personalized information through a user interface, allowing users to access information optimized for them in real time.

[0498] Examples:

[0499] For example, if a user is reading a book in an autonomous vehicle, the device will collect that behavioral data and send it to the server. The server will analyze the user's past reading history, location information, and current reading content, generate information about nearby bookstores and libraries, and generate related book recommendations, and send them to the device. The device will then display this information to the user, allowing them to obtain appropriate information in the autonomous vehicle.

[0500] Below are some example prompts to input to a generative AI model:

[0501] The system analyzes the user's behavioral data while in the self-driving vehicle (activity: reading, location information: 37.7749,-122.4194, vehicle status: self-driving mode) and provides personalized information based on their current interests.

[0502] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0503] Step 1:

[0504] The terminal collects user behavioral data and vehicle operation data in real time. The behavioral data includes user activities such as reading, watching videos, listening to music, and location information. The operation data includes vehicle driving status, speed, route, and operation mode. The input is real-time user activity and operation information, and the output is a structured set of collected behavioral data and operation data.

[0505] Step 2:

[0506] The device encrypts the collected behavioral and operational data and sends it to the server via a secure communication channel. The Fernet library is used to encrypt the data. The input is the behavioral and operational data from step 1, and the output is the encrypted data packet.

[0507] Step 3:

[0508] The server receives the encrypted data sent from the terminal, decrypts it, and stores it in a database. Here, we use the Fernet library to receive and decrypt the encrypted packets and store the data in a database management system (DBMS). The input is the encrypted data packet, and the output is the decrypted raw data.

[0509] Step 4:

[0510] The server analyzes behavioral data, operational data, and large-scale data to identify user interests and behavioral patterns. Specifically, it performs clustering using a machine learning algorithm (e.g., the K-means algorithm). The inputs are the decoded behavioral data, operational data, past history data, and large-scale data, and the output is the clustering results that indicate the user's interests.

[0511] Step 5:

[0512] The server generates personalized information based on the analysis results. For example, if the user likes reading, it generates recommendations for nearby bookstores and related books. The input is the clustering results, and the output is personalized information (e.g., tourist information, route recommendations, in-car entertainment suggestions).

[0513] Step 6:

[0514] The generated personalized information is then encrypted again and sent to the user's device via a secure communication channel. Again, the Fernet library is used for encryption. The input is the generated personalized information, and the output is the encrypted information packet.

[0515] Step 7:

[0516] The terminal decrypts the received encrypted packets and displays the personalized information through a user interface, allowing users to access information optimized for them in real time. The input is the encrypted information packet, and the output is the specific personalized information displayed to the user.

[0517] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0518] The present invention relates to an information provision system that collects user behavioral data and emotional data and provides personalized information based on that data in real time. The present invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display. In this invention, by combining an emotion engine, more advanced personalization based on user emotions becomes possible.

[0519] The system mainly consists of the following elements:

[0520] 1. Collecting user behavioral and emotional data

[0521] The device collects real-time behavioral data, such as user website browsing, searches, app usage, and location information. It also collects user emotional data using an emotion engine. Emotional data is obtained, for example, through facial recognition technology and voice analysis. This data is stored in temporary storage on the device.

[0522] 2. Data transmission

[0523] The device sends the collected behavioral and emotional data to a server, where it is encrypted and transmitted over a secure communication channel.

[0524] 3. Receiving and integrating data

[0525] The server receives the encrypted data sent from the device. After receiving the data, it is decrypted and stored in a secure server. The behavioral data and emotional data are integrated and analyzed together with big data.

[0526] 4. Data Analysis

[0527] The server integrates behavioral data, emotional data, and big data, and uses machine learning algorithms and statistical analysis to analyze users' interests, emotional states, and behavioral patterns. Based on the results of this analysis, it predicts the most appropriate information for each user.

[0528] 5. Generating personalized information

[0529] The server generates personalized information based on the analysis results, including news articles, coupons, recommended posts on social media, product information, etc. By utilizing emotion data, the server can provide information that fits the user's current mood and emotional state.

[0530] 6. Transmission of Information

[0531] The server then transmits the generated personalized information to the user's device, again using a secure communication protocol.

[0532] 7. Display of Information

[0533] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[0534] Specific examples

[0535] Example 1: Personalizing your news feed based on your emotional state

[0536] The device collects the user's news app browsing history and emotional data and sends it to a server.

[0537] The server analyzes the collected data and big data to determine that the user is interested in economics and that current sentiment is calm.

[0538] The server generates the latest news articles on the economy and sends them to the terminal.

[0539] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0540] Example 2: Providing shopping coupons based on emotional state

[0541] The device collects the user's purchasing history and current emotional data (e.g., excited) and sends it to the server.

[0542] Based on the analysis results, the server generates coupons for exciting products (e.g., gadgets) for the user.

[0543] The server transmits the generated coupon information to the terminal.

[0544] The device will notify the user of the coupon, allowing them to purchase specific products at a discounted price.

[0545] This system allows users to efficiently obtain information that is appropriate for their emotional state, significantly reducing the time and effort required to access information. Furthermore, the system continues to improve based on feedback data, ensuring that optimal information is always provided.

[0546] The processing flow will be explained below.

[0547] Step 1:

[0548] The device monitors user activity. Specifically, it collects behavioral data in real time, such as website browsing history, search history, location information, and app usage history. It also uses an emotion engine to recognize the user's face and analyze their voice to collect emotional data. This data is then stored in temporary storage.

[0549] Step 2:

[0550] The device reads the collected behavioral and emotional data from temporary storage at a certain timing or after the user has completed their operation. The read data is converted into a data format and encrypted as necessary.

[0551] Step 3:

[0552] The device transmits encrypted behavioral and emotional data to a server over the internet using a secure communication protocol (e.g., HTTPS).

[0553] Step 4:

[0554] The server receives the encrypted data sent from the device, decrypts it, and stores it in a secure database on the server.

[0555] Step 5:

[0556] The server integrates the received behavioral and emotional data with big data, which includes behavioral data of other users and data obtained from external data sources.

[0557] Step 6:

[0558] The server analyzes the integrated data using machine learning algorithms and statistical analysis to identify the user's interests, emotional state, and behavioral patterns. It also takes into account the user's emotional data to reflect their current mood and emotional state in the analysis.

[0559] Step 7:

[0560] The server generates personalized information for each user based on the analysis results. This information includes news articles, coupons, recommended posts on social media, product information, etc. By using emotion data, it is possible to provide information that fits the user's current emotional state.

[0561] Step 8:

[0562] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[0563] Step 9:

[0564] The device decrypts the encrypted information it receives, and then stores it locally for immediate viewing.

[0565] Step 10:

[0566] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[0567] Step 11:

[0568] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[0569] Step 12:

[0570] The device collects user responses (e.g., clicks, viewing time, subscription behavior) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[0571] Step 13:

[0572] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of the user's behavioral patterns and emotional state and reflects this in the next information provided.

[0573] This series of processing steps allows users to efficiently obtain information that is appropriate for their emotional state, significantly improving the information access experience. The system is continuously improved based on feedback data, ensuring that optimal information is always provided.

[0574] Example 2

[0575] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0576] Conventional systems provide information based solely on user behavior data, making it difficult to provide information appropriate to the user's emotional state or current interests. Furthermore, they do not efficiently utilize user feedback, resulting in a decline in the quality of the information provided and the accuracy of personalization. Furthermore, the difficulty of collecting and analyzing emotional data limits the user experience.

[0577] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and large-scale data on the server, means for generating personalized information for each user based on the analysis results, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal. By using not only the user's behavioral data but also their emotional data, it is possible to provide personalized information suited to the user's current emotional state and interests. Furthermore, optimizing the analysis algorithm based on feedback data improves the quality and accuracy of information provided.

[0578] "Behavioral data" refers to information including user operation history and location information when browsing websites, searching, or using apps.

[0579] "Emotion data" is information that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[0580] "Big data" refers to large and diverse datasets collected from a wide variety of data sources.

[0581] "Personalized information" is information that is tailored to a user's interests and emotional state, generated based on the user's individual behavioral and emotional data.

[0582] "Feedback Data" refers to information, including ratings and opinions, obtained when users respond to information provided.

[0583] "Collection means" refers to devices or programs that acquire user behavioral data and emotional data and store them in temporary storage.

[0584] "Transmission means" refers to a device or program that has the function of encrypting collected data and transmitting it to a server via a secure communication protocol.

[0585] "Analysis means" refers to devices or programs that use machine learning algorithms and statistical analysis to analyze users' interests and emotional states based on data acquired on the server.

[0586] "Generation means" refers to a device or program that has the function of generating optimal information for the user based on the analysis results.

[0587] "Display means" refers to a device or program for displaying the transmitted information on the terminal in a format that is easy for the user to view.

[0588] This invention relates to an information provision system that collects user behavioral and emotional data and provides personalized information based on that data in real time. The aim is to improve the user experience (UX) by efficiently performing a series of processes.

[0589] Hardware and Software

[0590] The device will be equipped with sensors to collect website browsing history, search history, app usage records, and real-time location information, as well as a camera and microphone that will use facial recognition and voice analysis to collect emotional data. The data will be stored in temporary storage.

[0591] The server has the following functions:

[0592] 1. Data Reception: Encrypted communication protocols (e.g., HTTPS) to securely receive collected behavioral and emotional data from devices.

[0593] 2. Data analysis: Utilizing machine learning libraries such as Python's Scikit-learn and TensorFlow, we analyze behavioral data, emotional data, and large-scale data.

[0594] 3. Generating personalized information: Using natural language processing (NLP) techniques and text generation models (e.g., GPT-3), we generate content that is best suited to the user.

[0595] 4. Sending information: The generated information is sent to the terminal in real time via WebSocket.

[0596] System Functionality Description

[0597] The device collects user behavioral and emotional data and stores it in temporary storage, which is periodically encrypted and sent to a server using the HTTPS protocol.

[0598] The server decrypts the received data and stores it in a secure database. It then integrates and analyzes behavioral, emotional, and large-scale data. It uses Python libraries to identify the user's interests and emotional state. Based on the analysis results, it uses natural language processing technology to generate personalized content. The generated content is then sent to the device using a secure communication protocol (such as WebSocket).

[0599] The device displays the received personalized information through a user interface (e.g., React Native, Swift), allowing users to access information optimized for them.

[0600] Specific examples

[0601] 1. Personalize your news feed based on your emotional state:

[0602] The device collects the user's news app browsing history and sentiment data and stores it in temporary storage.

[0603] The device encrypts the collected data and sends it to the server via HTTPS.

[0604] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[0605] The server uses Python's Scikit-learn to analyze the user's interests and emotional state.

[0606] The server uses GPT-3 to generate economic news articles appropriate for the user.

[0607] The server generates news articles and sends them to the device via HTTPS.

[0608] The device displays the received articles using React Native, and the user can view economic news.

[0609] 2. Providing shopping coupons based on emotional state:

[0610] The device collects the user's purchasing history and current emotional data and stores it in temporary storage.

[0611] The device encrypts the collected data and sends it to the server via HTTPS.

[0612] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[0613] The server uses TensorFlow to analyze users' excitement levels and purchasing patterns.

[0614] The server generates coupons for exciting products (gadgets).

[0615] The coupon information generated by the server is sent to the terminal via WebSocket.

[0616] The device notifies the user of the coupon via push notification and displays the coupon information using React Native, allowing the user to purchase the product at a discounted price.

[0617] Example prompts to input to the generative AI model

[0618] Describe a system that collects user behavioral and emotional data, analyzes that data, and provides personalized information in real time. The system uses facial recognition technology and voice analysis to obtain emotional data, integrates it with large-scale data, and analyzes it using machine learning algorithms. Specific examples include providing a news feed and shopping coupons based on the user's emotional state.

[0619] In this way, by integrating and analyzing user behavior and emotional data and providing personalized information, it is possible to achieve an advanced user experience.

[0620] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0621] Step 1: Collect data

[0622] The device collects user behavioral data (website browsing history, search history, app usage records, location information, etc.) and emotional data (facial expressions and tone of voice) in real time. At this time, the device stores the collected data in temporary storage. The input is the user's behavior and emotions, and the output is the data stored in temporary storage. For example, website browsing history can be recorded through a browser extension, and emotional data can be obtained using the camera and microphone.

[0623] Step 2: Sending data

[0624] The device encrypts the behavioral and emotional data stored in temporary storage and sends it to the server using a secure communication protocol (such as HTTPS). The input is the data stored in temporary storage, and the output is the encrypted data sent to the server. The device sends data periodically (e.g., every 5 minutes) and uses a backoff algorithm that takes into account data size and connection quality.

[0625] Step 3: Receiving and consolidating data

[0626] The server receives and decrypts the encrypted data sent from the device. The received data is stored in a secure database. The input is encrypted behavioral and emotional data, and the output is the decrypted data stored in the database. The server then goes through a data cleansing process to fill in missing values ​​and detect outliers.

[0627] Step 4: Analyze the data

[0628] The server uses machine learning algorithms and statistical analysis to analyze the user's interests, emotional state, and behavioral patterns based on the integrated data. The input is the integrated data, and the output is the analysis results. Specifically, it uses Python's Scikit-learn and TensorFlow to group user behavior and emotional patterns using a clustering algorithm (e.g., K-means).

[0629] Step 5: Generate personalization information

[0630] The server generates personalized information (news articles, coupons, recommended posts on social media, product information, etc.) based on the analysis results. The input is the analysis results, and the output is the generated personalized information. For example, natural language processing (NLP) techniques or text generation models (e.g., GPT-3) can be used to generate content that matches the user's emotional state.

[0631] Step 6: Submit your information

[0632] The server sends the generated personalized information to the user's device. The input is the generated personalized information, and the output is the information sent to the device. This transmission uses a secure communication protocol (e.g., HTTPS, WebSocket).

[0633] Step 7: Viewing information

[0634] The device displays the received personalized information through a user interface. The input is the received information, and the output is the information displayed to the user. For example, a UI framework such as React Native or Swift can be used to display the information in a visually easy-to-understand format, and push notifications can be used to notify the user as needed.

[0635] (Application example 2)

[0636] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0637] Conventional personalization systems provide information based solely on user behavioral data and are unable to consider the user's emotional state. This makes it difficult for users to obtain information that matches their emotions at any given time, making it impossible to provide an optimal user experience. Furthermore, providing real-time personalized information to improve the user experience is also insufficient.

[0638] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and big data on the server and determining the user's interests and emotional state, means for generating information and coupons personalized for each user based on the analysis results, means for transmitting the generated information and coupons from the server to the user's terminal, and means for displaying the transmitted information and coupons on the terminal. This makes it possible to provide optimal personalized information and coupons according to the user's emotional state in real time.

[0639] "User behavioral data" refers to data about a series of actions generated when a user uses a device, such as website browsing history, search history, app usage history, and location information.

[0640] "Emotion data" refers to data about a user's emotional state (e.g., joy, sadness, anger, surprise, etc.) obtained using facial recognition technology or voice analysis technology.

[0641] A "server" is a computer system that receives, analyzes, stores, and distributes data over a network.

[0642] "Big data" refers to large, diverse, and rapidly generated data sets that are difficult to process using traditional database management tools.

[0643] "Personalized information" refers to content such as news articles, coupons, and recommended product information that is optimized for each user based on their behavioral and emotional data.

[0644] A "coupon" is an electronic certificate that allows a user to purchase a particular product or service at a discounted price.

[0645] A "terminal" is a device that is directly operated by a user, such as a smartphone or a personal computer.

[0646] "Feedback Data" refers to data provided by users, such as their opinions, impressions, and usage status, which is used to improve and optimize the system.

[0647] "Analysis algorithms" are mathematical and statistical methods for analyzing behavioral data, emotional data, and big data to extract useful patterns and trends.

[0648] The system for realizing this invention operates based on the following procedure. The system starts by collecting user behavioral data and emotional data and sending them to a server. The collected behavioral data includes the user's website browsing history, app usage history, product search history, location information, etc. The collection of emotional data uses facial recognition technology and voice analysis technology (e.g., DeepFace or OpenCV).

[0649] After receiving the collected behavioral and emotional data, the server integrates this data with big data and analyzes it. The analysis uses machine learning algorithms and statistical analysis methods to determine the user's interests and emotional state. For example, if the user expresses "joy" or is determined to be interested in "fashion items," the server generates a list of product recommendations and coupons that are optimal for the user.

[0650] The generated personalized information and coupons are then sent from the server to the user's device (e.g., smartphone) using a secure communication protocol. Finally, the device displays the received information in a user interface, allowing the user to access the optimized information.

[0651] For example, when a user captures their face using their smartphone camera, the data is analyzed by an emotion analysis engine. If the analysis result indicates "joy," a coupon for the latest fashion items is generated based on the user's past purchase history and interests. This coupon is displayed on the user's smartphone in real time.

[0652] An example prompt for a generative AI model is:

[0653] "For users whose current emotional state is joy, generate discount offers on fashion items that you think they might be interested in based on their past purchase history."

[0654] This system improves the shopping experience by providing users with personalized information in real time that is tailored to their emotional state, and the accuracy of the information provided continues to improve as the system is continuously optimized based on feedback data.

[0655] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0656] Step 1:

[0657] The user captures their face using the device's camera. The device then analyzes the facial image using facial recognition technology (e.g., DeepFace and OpenCV) and extracts emotional data. The facial image is the input, and the extracted emotional data (e.g., "happiness" or "sadness") is the output.

[0658] Step 2:

[0659] The device collects behavioral data (website browsing history, search history, app usage history, location information, etc.) and the emotion data extracted in step 1. This collected data is the input.

[0660] Step 3:

[0661] The device encrypts the collected behavioral data and emotional data and transmits them to the server using a secure communication path. The behavioral data and emotional data are then transferred to the server. The collected data is the input, and the data sent to the server is the output.

[0662] Step 4:

[0663] The server decrypts and securely stores the received behavioral and emotional data. This stored data is the input, and the preparation for analysis is the output.

[0664] Step 5:

[0665] The server integrates the behavioral data, emotional data, and big data, and analyzes the user's interests and emotional state using machine learning algorithms and statistical analysis methods. This analysis involves calculations based on the input data (behavioral data, emotional data, big data), and the user's interest determination and emotional state are obtained as outputs.

[0666] Step 6:

[0667] The server generates personalized information and coupons for each user based on the analysis results of step 5. For example, if the user is in a "joy" emotional state, a discount coupon for the latest fashion items is generated. The analysis results are the input, and the generated personalized information and coupons are the output.

[0668] Step 7:

[0669] The server encrypts and transmits the generated personalized information and coupons to the user's terminal using a secure communication protocol, with the generated information and coupons being the input and data transmission to the terminal being the output.

[0670] Step 8:

[0671] The terminal displays the received personalized information and coupons through a user interface, allowing the user to access the transmitted information in real time. The received data is the input, and the display of the information to the user is the output.

[0672] Through these processing steps, the system can provide an optimal personalized shopping experience based on the user's emotional state and behavioral data. An example of a prompt for analysis and information generation using a generative AI model is, "For a user whose current emotional state is joy, please generate discount information on fashion items that you think they might be interested in based on their past purchase history."

[0673] 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.

[0674] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0675] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0676] [Third embodiment]

[0677] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0678] 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.

[0679] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0680] 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.

[0681] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0682] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0683] 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.

[0684] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0685] 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 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.

[0686] 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.

[0687] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0688] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0689] This invention relates to an information provision system that collects user behavior data and uses that data to provide personalized information in real time. The invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display.

[0690] The system mainly consists of the following elements:

[0691] 1. Collection of user behavior data

[0692] The device collects real-time behavioral data such as user website browsing, searches, app usage, and location information, which is temporarily stored on the device and later sent to a server.

[0693] 2. Data transmission

[0694] The device sends the collected behavioral data to a server, where it is encrypted and transmitted over a secure communication channel.

[0695] 3. Receiving and integrating data

[0696] The server receives the data sent from the device, stores it in a database on the server, and analyzes it together with big data.

[0697] 4. Data Analysis

[0698] The server integrates the behavioral data and big data, and analyzes the user's interests and behavioral patterns using machine learning algorithms and statistical analysis. Based on the results of this analysis, it predicts the most appropriate information for each user.

[0699] 5. Generating personalized information

[0700] The server generates personalized information based on the analysis results, including news articles, coupons, recommended social media posts, and product information.

[0701] 6. Transmission of Information

[0702] The server then sends the generated personalized information to the user's device, where it is again encrypted and transmitted over a secure channel.

[0703] 7. Display of Information

[0704] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[0705] Specific examples

[0706] Example 1: Personalizing your news feed

[0707] The device collects the user's news app browsing history and sends it to a server.

[0708] The server analyzes the collected data and big data and determines that the user is interested in economics.

[0709] The server generates the latest news articles on the economy and sends them to the terminal.

[0710] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0711] Example 2: Offering a shopping coupon

[0712] The terminal collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[0713] The server generates relevant coupons for the user based on the analysis results.

[0714] The server transmits the generated coupon information to the terminal.

[0715] The device notifies users of coupons related to electronics, allowing them to purchase specific products at discounted prices.

[0716] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[0717] The processing flow will be explained below.

[0718] Step 1:

[0719] The device monitors user activity, specifically collecting behavioral data such as website browsing history, search history, location information, and app usage history in real time. This data is stored in temporary storage on the device.

[0720] Step 2:

[0721] The device reads the collected behavioral data from temporary storage at a certain timing or after the user has completed their operation. After reading the data, it converts it into a standard communication format and encrypts it as necessary.

[0722] Step 3:

[0723] The device transmits the encrypted behavioral data over the Internet to a server using a secure communication protocol (e.g., HTTPS).

[0724] Step 4:

[0725] The server receives the encrypted data sent from the device, after which the data is decrypted and stored in a secure server.

[0726] Step 5:

[0727] The server integrates the received behavioral data with big data, which includes behavioral data of other users and data obtained from external data sources.

[0728] Step 6:

[0729] The server then analyzes the integrated data using machine learning algorithms and statistical analysis to identify user interests and behavioral patterns and estimate the most appropriate information.

[0730] Step 7:

[0731] Based on the analysis results, the server generates personalized information for each user, including news articles, coupons, recommended social media posts, and product information.

[0732] Step 8:

[0733] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[0734] Step 9:

[0735] The device decrypts the encrypted information it receives, and then stores it on the device for immediate display.

[0736] Step 10:

[0737] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[0738] Step 11:

[0739] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[0740] Step 12:

[0741] The device collects user responses (e.g. clicks, viewing time, subscriptions) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[0742] Step 13:

[0743] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of user behavior patterns and reflects this in the next information provided.

[0744] This series of processing steps allows users to efficiently obtain information that is most suitable for them, and significantly reduces the time and effort required to access information.

[0745] Example 1

[0746] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0747] Conventional information provision systems often lack the ability to efficiently collect and analyze user behavior data and provide personalized information, resulting in limited improvements to the user experience (UX). Furthermore, they are also inadequate in terms of security and privacy protection, making it difficult to gain user trust.

[0748] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0749] In this invention, the server includes means for generating personalized information for each user based on the analysis results using a machine learning algorithm or statistical analysis, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal via a secure path, thereby enabling efficient collection and analysis of user behavior data and safe provision of personalized information that is valuable to the user.

[0750] "User behavior data" refers to records of a user's usage patterns and behavior, such as operation history and location information, when visiting websites, using search engines, or using applications.

[0751] A "server" is a computer system that receives data sent by a user, analyzes it, generates optimal information, and sends it to the user's terminal.

[0752] "Massive data" refers to a collection of diverse behavioral data collected from a large number of users, a vast amount of information also known as big data.

[0753] "Machine learning algorithms" are algorithms and methodologies that allow computers to learn patterns from data and make predictions and decisions.

[0754] "Statistical analysis" refers to statistical methods for analyzing data and obtaining significant results or patterns.

[0755] "Personalized information" is information (e.g., news articles, coupons, recommended content) that is individually optimized based on each user's interests and behavioral patterns.

[0756] A "secure path" is a communication path in which information is encrypted when transmitted and is protected from unauthorized access or tampering.

[0757] This invention relates to an information provision system that collects user behavior data, analyzes that data, and provides personalized information in real time. This system aims to improve the user experience (UX) by efficiently performing a series of processes: collection, transmission, analysis, generation, and display.

[0758] Hardware and software used

[0759] A device is a device used by a user, such as a smartphone, tablet, or PC, and is used to collect behavioral data in real time, such as website browsing, searches, app usage, and location information.

[0760] The server is a computer system that receives the collected behavioral data, analyzes it, generates the most appropriate information, and sends it to the user's device. The server uses the following software:

[0761] A database management system (e.g., MySQL, PostgreSQL) is used to store and manage behavioral data and large amounts of data.

[0762] Use machine learning frameworks (e.g., TensorFlow and Scikit-learn implemented in Python) to perform data analysis.

[0763] We use encryption technology (e.g., SSL / TLS) to securely manage data transmission.

[0764] System processing overview

[0765] The device collects user behavioral data (e.g., browsing history in news apps, location information, search keywords) in real time and temporarily stores it on the device. The device then encrypts the collected behavioral data and transmits it to a server using a secure communication protocol (e.g., HTTPS).

[0766] The server receives the behavioral data sent from the device and stores it in a database. The stored data is then integrated with the large amount of accumulated data. The server then analyzes the data using machine learning algorithms and statistical analysis to extract the user's interests and behavioral patterns. Based on the analysis results, the server generates personalized information (e.g., the latest economic news, coupons for electronic devices) that is optimal for each user.

[0767] The generated information is re-encrypted by the server and sent to the user's device. The device decodes the received personalized information and displays it through the user interface, allowing the user to access optimized information. In addition, the device collects feedback data on whether the user has confirmed the provided information and sends it back to the server for future analysis.

[0768] Specific examples

[0769] Example 1: Personalizing your news feed

[0770] 1. The device collects the user's news app browsing history and sends it to the server.

[0771] 2. The server analyzes the collected data and large amounts of data to determine that the user is interested in economics.

[0772] 3. The server generates the latest economic news articles and sends them to the device.

[0773] 4. The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0774] Example 2: Offering a shopping coupon

[0775] 1. The device collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[0776] 2. The server generates relevant coupons for the user based on the analysis results.

[0777] 3. The server sends the generated coupon information to the terminal.

[0778] 4. The device notifies the user of electronics-related coupons, allowing the user to purchase certain products at discounted prices.

[0779] Prompt Sentence Examples

[0780] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[0781] Please explain the specific process flow of a system that collects user behavior data, analyzes that data, and provides personalized information in real time. In particular, please describe the specific behavior when a user uses a shopping app.

[0782] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[0783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0784] Step 1:

[0785] A user visits a website and initiates an operation. Based on this operation, the device collects user behavior data (e.g., time spent on page, links clicked, search keywords) in real time. The input is the user's behavior, and the output is the collected behavior data. The behavior data is temporarily stored on the device.

[0786] Step 2:

[0787] The device retrieves the temporarily stored behavioral data and encrypts it using an encryption algorithm (e.g., AES encryption). The input is the temporarily stored behavioral data, and the output is the encrypted behavioral data. The encrypted data is then sent to the server using the HTTPS protocol.

[0788] Step 3:

[0789] The server decrypts the encrypted behavioral data it receives. The input is encrypted behavioral data, and the output is decrypted behavioral data. The server checks the integrity of this data to ensure there are no anomalies or missing data. The consistent data is then stored in a database management system.

[0790] Step 4:

[0791] The server combines the collected behavioral data with existing large data sets to generate a dataset for analysis. The input is the decoded behavioral data and large data sets, and the output is the combined dataset. Machine learning algorithms (e.g., TensorFlow models) and statistical analysis are then applied to extract user interests and behavioral patterns.

[0792] Step 5:

[0793] The server generates personalized information for each user based on the analysis results. The input is the analysis results, and the output is personalized information (e.g., the latest news articles, shopping coupons). This information is temporarily stored in the server memory.

[0794] Step 6:

[0795] The server encrypts the generated personalized information and sends it to the user's device via a communication protocol (e.g., HTTPS). The input is personalized information, and the output is encrypted information. A log of data transmission is also recorded.

[0796] Step 7:

[0797] The terminal decrypts the received encrypted personalized information and displays it via a user interface. The input is the encrypted personalized information, and the output is the information displayed on the user interface. The terminal also collects feedback data on whether the user has confirmed the provided information and sends it to the server for use in the next analysis.

[0798] (Application example 1)

[0799] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0800] Conventional information provision systems have limitations in providing personalized information based on user behavioral data. In particular, in autonomous vehicles, there is a demand for more accurate personalized information by integrating and analyzing not only user behavioral data but also vehicle operation data. The objective of the present invention is to provide an information provision system that utilizes behavioral data and operation data in an integrated manner to improve the user experience in autonomous vehicles.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0802] In this invention, the server includes means for collecting user behavior data and vehicle operation data, means for analyzing the behavior data and large-scale data, and means for generating information optimized for the user based on the analysis results, thereby making it possible to provide the information that the user is most interested in in real time, even inside an autonomous vehicle.

[0803] "User Behavior Data" means data about your activity, browsing history, location, and other usage patterns when you use applications or devices.

[0804] A "server" is a computer system that receives data sent from a client via a network and processes, analyzes, and stores the data.

[0805] "Large-scale data" refers to data that is so large that it is difficult to process using traditional database management tools, and includes both structured and unstructured data.

[0806] "Personalized information" is customized information that is optimized for each individual user and is generated based on the user's behavioral data and driving data.

[0807] "Automobile operation data" refers to data related to the operation of an autonomous vehicle, such as its movement status, speed, route, and operating mode.

[0808] "Feedback data" refers to data such as operation results, evaluations, and opinions provided by users, and is used to improve the system and optimize analysis algorithms.

[0809] "Analytics algorithms" are computational methods or algorithms that process collected data to identify user behavior patterns and interests and generate personalized information.

[0810] The information provision system of the present invention collects user behavior data and vehicle operation data, integrates and analyzes this data, and provides optimized information to users within autonomous vehicles. This system efficiently performs a series of processes, including collection, transmission, analysis, generation, and display, in order to improve the user experience.

[0811] 1. Collecting User Behavior Data:

[0812] The device collects real-time data on the user's in-car activities (e.g., reading, watching videos, listening to music), location information, and vehicle operation data, which are temporarily stored on the device and later transmitted to a server.

[0813] 2. Data transmission:

[0814] The device sends the collected behavioral and operational data to the server. During this process, the data is encrypted and transmitted over a secure communication channel. For encryption, the Fernet library is used, for example.

[0815] 3. Receipt and integration of data:

[0816] The server receives the data sent from the terminal and stores it in a database, where it is analyzed together with large-scale data.

[0817] 4. Data Analysis:

[0818] The server integrates and analyzes behavioral data, operational data, and large-scale data, specifically using machine learning algorithms (such as the K-means algorithm) to identify user interests and behavioral patterns.

[0819] 5. Generating personalized information:

[0820] The server generates personalized information based on the analysis results, including tourist information for the destination, recommended shops along the route, and in-car entertainment suggestions.

[0821] 6. Transmission of Information:

[0822] The generated personalized information is then re-encrypted and sent over a secure channel to the user's device.

[0823] 7. Displaying Information:

[0824] The device displays the received personalized information through a user interface, allowing users to access information optimized for them in real time.

[0825] Examples:

[0826] For example, if a user is reading a book in an autonomous vehicle, the device will collect that behavioral data and send it to the server. The server will analyze the user's past reading history, location information, and current reading content, generate information about nearby bookstores and libraries, and generate related book recommendations, and send them to the device. The device will then display this information to the user, allowing them to obtain appropriate information in the autonomous vehicle.

[0827] Below are some example prompts to input to a generative AI model:

[0828] The system analyzes the user's behavioral data while in the self-driving vehicle (activity: reading, location information: 37.7749,-122.4194, vehicle status: self-driving mode) and provides personalized information based on their current interests.

[0829] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0830] Step 1:

[0831] The terminal collects user behavioral data and vehicle operation data in real time. The behavioral data includes user activities such as reading, watching videos, listening to music, and location information. The operation data includes vehicle driving status, speed, route, and operation mode. The input is real-time user activity and operation information, and the output is a structured set of collected behavioral data and operation data.

[0832] Step 2:

[0833] The device encrypts the collected behavioral and operational data and sends it to the server via a secure communication channel. The Fernet library is used to encrypt the data. The input is the behavioral and operational data from step 1, and the output is the encrypted data packet.

[0834] Step 3:

[0835] The server receives the encrypted data sent from the terminal, decrypts it, and stores it in a database. Here, we use the Fernet library to receive and decrypt the encrypted packets and store the data in a database management system (DBMS). The input is the encrypted data packet, and the output is the decrypted raw data.

[0836] Step 4:

[0837] The server analyzes behavioral data, operational data, and large-scale data to identify user interests and behavioral patterns. Specifically, it performs clustering using a machine learning algorithm (e.g., the K-means algorithm). The inputs are the decoded behavioral data, operational data, past history data, and large-scale data, and the output is the clustering results that indicate the user's interests.

[0838] Step 5:

[0839] The server generates personalized information based on the analysis results. For example, if the user likes reading, it generates recommendations for nearby bookstores and related books. The input is the clustering results, and the output is personalized information (e.g., tourist information, route recommendations, in-car entertainment suggestions).

[0840] Step 6:

[0841] The generated personalized information is then encrypted again and sent to the user's device via a secure communication channel. Again, the Fernet library is used for encryption. The input is the generated personalized information, and the output is the encrypted information packet.

[0842] Step 7:

[0843] The terminal decrypts the received encrypted packets and displays the personalized information through a user interface, allowing users to access information optimized for them in real time. The input is the encrypted information packet, and the output is the specific personalized information displayed to the user.

[0844] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0845] The present invention relates to an information provision system that collects user behavioral data and emotional data and provides personalized information based on that data in real time. The present invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display. In this invention, by combining an emotion engine, more advanced personalization based on user emotions becomes possible.

[0846] The system mainly consists of the following elements:

[0847] 1. Collecting user behavioral and emotional data

[0848] The device collects real-time behavioral data, such as user website browsing, searches, app usage, and location information. It also collects user emotional data using an emotion engine. Emotional data is obtained, for example, through facial recognition technology and voice analysis. This data is stored in temporary storage on the device.

[0849] 2. Data transmission

[0850] The device sends the collected behavioral and emotional data to a server, where it is encrypted and transmitted over a secure communication channel.

[0851] 3. Receiving and integrating data

[0852] The server receives the encrypted data sent from the device. After receiving the data, it is decrypted and stored in a secure server. The behavioral data and emotional data are integrated and analyzed together with big data.

[0853] 4. Data Analysis

[0854] The server integrates behavioral data, emotional data, and big data, and uses machine learning algorithms and statistical analysis to analyze users' interests, emotional states, and behavioral patterns. Based on the results of this analysis, it predicts the most appropriate information for each user.

[0855] 5. Generating personalized information

[0856] The server generates personalized information based on the analysis results, including news articles, coupons, recommended posts on social media, product information, etc. By utilizing emotion data, the server can provide information that fits the user's current mood and emotional state.

[0857] 6. Transmission of Information

[0858] The server then transmits the generated personalized information to the user's device, again using a secure communication protocol.

[0859] 7. Display of Information

[0860] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[0861] Specific examples

[0862] Example 1: Personalizing your news feed based on your emotional state

[0863] The device collects the user's news app browsing history and emotional data and sends it to a server.

[0864] The server analyzes the collected data and big data to determine that the user is interested in economics and that current sentiment is calm.

[0865] The server generates the latest news articles on the economy and sends them to the terminal.

[0866] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[0867] Example 2: Providing shopping coupons based on emotional state

[0868] The device collects the user's purchasing history and current emotional data (e.g., excited) and sends it to the server.

[0869] Based on the analysis results, the server generates coupons for exciting products (e.g., gadgets) for the user.

[0870] The server transmits the generated coupon information to the terminal.

[0871] The device will notify the user of the coupon, allowing them to purchase specific products at a discounted price.

[0872] This system allows users to efficiently obtain information that is appropriate for their emotional state, significantly reducing the time and effort required to access information. Furthermore, the system continues to improve based on feedback data, ensuring that optimal information is always provided.

[0873] The processing flow will be explained below.

[0874] Step 1:

[0875] The device monitors user activity. Specifically, it collects behavioral data in real time, such as website browsing history, search history, location information, and app usage history. It also uses an emotion engine to recognize the user's face and analyze their voice to collect emotional data. This data is then stored in temporary storage.

[0876] Step 2:

[0877] The device reads the collected behavioral and emotional data from temporary storage at a certain timing or after the user has completed their operation. The read data is converted into a data format and encrypted as necessary.

[0878] Step 3:

[0879] The device transmits encrypted behavioral and emotional data to a server over the internet using a secure communication protocol (e.g., HTTPS).

[0880] Step 4:

[0881] The server receives the encrypted data sent from the device, decrypts it, and stores it in a secure database on the server.

[0882] Step 5:

[0883] The server integrates the received behavioral and emotional data with big data, which includes behavioral data of other users and data obtained from external data sources.

[0884] Step 6:

[0885] The server analyzes the integrated data using machine learning algorithms and statistical analysis to identify the user's interests, emotional state, and behavioral patterns. It also takes into account the user's emotional data to reflect their current mood and emotional state in the analysis.

[0886] Step 7:

[0887] The server generates personalized information for each user based on the analysis results. This information includes news articles, coupons, recommended posts on social media, product information, etc. By using emotion data, it is possible to provide information that fits the user's current emotional state.

[0888] Step 8:

[0889] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[0890] Step 9:

[0891] The device decrypts the encrypted information it receives, and then stores it locally for immediate viewing.

[0892] Step 10:

[0893] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[0894] Step 11:

[0895] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[0896] Step 12:

[0897] The device collects user responses (e.g., clicks, viewing time, subscription behavior) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[0898] Step 13:

[0899] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of the user's behavioral patterns and emotional state and reflects this in the next information provided.

[0900] This series of processing steps allows users to efficiently obtain information that is appropriate for their emotional state, significantly improving the information access experience. The system is continuously improved based on feedback data, ensuring that optimal information is always provided.

[0901] Example 2

[0902] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0903] Conventional systems provide information based solely on user behavior data, making it difficult to provide information appropriate to the user's emotional state or current interests. Furthermore, they do not efficiently utilize user feedback, resulting in a decline in the quality of the information provided and the accuracy of personalization. Furthermore, the difficulty of collecting and analyzing emotional data limits the user experience.

[0904] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and large-scale data on the server, means for generating personalized information for each user based on the analysis results, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal. By using not only the user's behavioral data but also their emotional data, it is possible to provide personalized information suited to the user's current emotional state and interests. Furthermore, optimizing the analysis algorithm based on feedback data improves the quality and accuracy of information provided.

[0905] "Behavioral data" refers to information including user operation history and location information when browsing websites, searching, or using apps.

[0906] "Emotion data" is information that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[0907] "Big data" refers to large and diverse datasets collected from a wide variety of data sources.

[0908] "Personalized information" is information that is tailored to a user's interests and emotional state, generated based on the user's individual behavioral and emotional data.

[0909] "Feedback Data" refers to information, including ratings and opinions, obtained when users respond to information provided.

[0910] "Collection means" refers to devices or programs that acquire user behavioral data and emotional data and store them in temporary storage.

[0911] "Transmission means" refers to a device or program that has the function of encrypting collected data and transmitting it to a server via a secure communication protocol.

[0912] "Analysis means" refers to devices or programs that use machine learning algorithms and statistical analysis to analyze users' interests and emotional states based on data acquired on the server.

[0913] "Generation means" refers to a device or program that has the function of generating optimal information for the user based on the analysis results.

[0914] "Display means" refers to a device or program for displaying the transmitted information on the terminal in a format that is easy for the user to view.

[0915] This invention relates to an information provision system that collects user behavioral and emotional data and provides personalized information based on that data in real time. The aim is to improve the user experience (UX) by efficiently performing a series of processes.

[0916] Hardware and Software

[0917] The device will be equipped with sensors to collect website browsing history, search history, app usage records, and real-time location information, as well as a camera and microphone that will use facial recognition and voice analysis to collect emotional data. The data will be stored in temporary storage.

[0918] The server has the following functions:

[0919] 1. Data Reception: Encrypted communication protocols (e.g., HTTPS) to securely receive collected behavioral and emotional data from devices.

[0920] 2. Data analysis: Utilizing machine learning libraries such as Python's Scikit-learn and TensorFlow, we analyze behavioral data, emotional data, and large-scale data.

[0921] 3. Generating personalized information: Using natural language processing (NLP) techniques and text generation models (e.g., GPT-3), we generate content that is best suited to the user.

[0922] 4. Sending information: The generated information is sent to the terminal in real time via WebSocket.

[0923] System Functionality Description

[0924] The device collects user behavioral and emotional data and stores it in temporary storage, which is periodically encrypted and sent to a server using the HTTPS protocol.

[0925] The server decrypts the received data and stores it in a secure database. It then integrates and analyzes behavioral, emotional, and large-scale data. It uses Python libraries to identify the user's interests and emotional state. Based on the analysis results, it uses natural language processing technology to generate personalized content. The generated content is then sent to the device using a secure communication protocol (such as WebSocket).

[0926] The device displays the received personalized information through a user interface (e.g., React Native, Swift), allowing users to access information optimized for them.

[0927] Specific examples

[0928] 1. Personalize your news feed based on your emotional state:

[0929] The device collects the user's news app browsing history and sentiment data and stores it in temporary storage.

[0930] The device encrypts the collected data and sends it to the server via HTTPS.

[0931] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[0932] The server uses Python's Scikit-learn to analyze the user's interests and emotional state.

[0933] The server uses GPT-3 to generate economic news articles appropriate for the user.

[0934] The server generates news articles and sends them to the device via HTTPS.

[0935] The device displays the received articles using React Native, and the user can view economic news.

[0936] 2. Providing shopping coupons based on emotional state:

[0937] The device collects the user's purchasing history and current emotional data and stores it in temporary storage.

[0938] The device encrypts the collected data and sends it to the server via HTTPS.

[0939] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[0940] The server uses TensorFlow to analyze users' excitement levels and purchasing patterns.

[0941] The server generates coupons for exciting products (gadgets).

[0942] The coupon information generated by the server is sent to the terminal via WebSocket.

[0943] The device notifies the user of the coupon via push notification and displays the coupon information using React Native, allowing the user to purchase the product at a discounted price.

[0944] Example prompts to input to the generative AI model

[0945] Describe a system that collects user behavioral and emotional data, analyzes that data, and provides personalized information in real time. The system uses facial recognition technology and voice analysis to obtain emotional data, integrates it with large-scale data, and analyzes it using machine learning algorithms. Specific examples include providing a news feed and shopping coupons based on the user's emotional state.

[0946] In this way, by integrating and analyzing user behavior and emotional data and providing personalized information, it is possible to achieve an advanced user experience.

[0947] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0948] Step 1: Collect data

[0949] The device collects user behavioral data (website browsing history, search history, app usage records, location information, etc.) and emotional data (facial expressions and tone of voice) in real time. At this time, the device stores the collected data in temporary storage. The input is the user's behavior and emotions, and the output is the data stored in temporary storage. For example, website browsing history can be recorded through a browser extension, and emotional data can be obtained using the camera and microphone.

[0950] Step 2: Sending data

[0951] The device encrypts the behavioral and emotional data stored in temporary storage and sends it to the server using a secure communication protocol (such as HTTPS). The input is the data stored in temporary storage, and the output is the encrypted data sent to the server. The device sends data periodically (e.g., every 5 minutes) and uses a backoff algorithm that takes into account data size and connection quality.

[0952] Step 3: Receiving and consolidating data

[0953] The server receives and decrypts the encrypted data sent from the device. The received data is stored in a secure database. The input is encrypted behavioral and emotional data, and the output is the decrypted data stored in the database. The server then goes through a data cleansing process to fill in missing values ​​and detect outliers.

[0954] Step 4: Analyze the data

[0955] The server uses machine learning algorithms and statistical analysis to analyze the user's interests, emotional state, and behavioral patterns based on the integrated data. The input is the integrated data, and the output is the analysis results. Specifically, it uses Python's Scikit-learn and TensorFlow to group user behavior and emotional patterns using a clustering algorithm (e.g., K-means).

[0956] Step 5: Generate personalization information

[0957] The server generates personalized information (news articles, coupons, recommended posts on social media, product information, etc.) based on the analysis results. The input is the analysis results, and the output is the generated personalized information. For example, natural language processing (NLP) techniques or text generation models (e.g., GPT-3) can be used to generate content that matches the user's emotional state.

[0958] Step 6: Submit your information

[0959] The server sends the generated personalized information to the user's device. The input is the generated personalized information, and the output is the information sent to the device. This transmission uses a secure communication protocol (e.g., HTTPS, WebSocket).

[0960] Step 7: Viewing information

[0961] The device displays the received personalized information through a user interface. The input is the received information, and the output is the information displayed to the user. For example, a UI framework such as React Native or Swift can be used to display the information in a visually easy-to-understand format, and push notifications can be used to notify the user as needed.

[0962] (Application example 2)

[0963] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0964] Conventional personalization systems provide information based solely on user behavioral data and are unable to consider the user's emotional state. This makes it difficult for users to obtain information that matches their emotions at any given time, making it impossible to provide an optimal user experience. Furthermore, providing real-time personalized information to improve the user experience is also insufficient.

[0965] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and big data on the server and determining the user's interests and emotional state, means for generating information and coupons personalized for each user based on the analysis results, means for transmitting the generated information and coupons from the server to the user's terminal, and means for displaying the transmitted information and coupons on the terminal. This makes it possible to provide optimal personalized information and coupons according to the user's emotional state in real time.

[0966] "User behavioral data" refers to data about a series of actions generated when a user uses a device, such as website browsing history, search history, app usage history, and location information.

[0967] "Emotion data" refers to data about a user's emotional state (e.g., joy, sadness, anger, surprise, etc.) obtained using facial recognition technology or voice analysis technology.

[0968] A "server" is a computer system that receives, analyzes, stores, and distributes data over a network.

[0969] "Big data" refers to large, diverse, and rapidly generated data sets that are difficult to process using traditional database management tools.

[0970] "Personalized information" refers to content such as news articles, coupons, and recommended product information that is optimized for each user based on their behavioral and emotional data.

[0971] A "coupon" is an electronic certificate that allows a user to purchase a particular product or service at a discounted price.

[0972] A "terminal" is a device that is directly operated by a user, such as a smartphone or a personal computer.

[0973] "Feedback Data" refers to data provided by users, such as their opinions, impressions, and usage status, which is used to improve and optimize the system.

[0974] "Analysis algorithms" are mathematical and statistical methods for analyzing behavioral data, emotional data, and big data to extract useful patterns and trends.

[0975] The system for realizing this invention operates based on the following procedure. The system starts by collecting user behavioral data and emotional data and sending them to a server. The collected behavioral data includes the user's website browsing history, app usage history, product search history, location information, etc. The collection of emotional data uses facial recognition technology and voice analysis technology (e.g., DeepFace or OpenCV).

[0976] After receiving the collected behavioral and emotional data, the server integrates this data with big data and analyzes it. The analysis uses machine learning algorithms and statistical analysis methods to determine the user's interests and emotional state. For example, if the user expresses "joy" or is determined to be interested in "fashion items," the server generates a list of product recommendations and coupons that are optimal for the user.

[0977] The generated personalized information and coupons are then sent from the server to the user's device (e.g., smartphone) using a secure communication protocol. Finally, the device displays the received information in a user interface, allowing the user to access the optimized information.

[0978] For example, when a user captures their face using their smartphone camera, the data is analyzed by an emotion analysis engine. If the analysis result indicates "joy," a coupon for the latest fashion items is generated based on the user's past purchase history and interests. This coupon is displayed on the user's smartphone in real time.

[0979] An example prompt for a generative AI model is:

[0980] "For users whose current emotional state is joy, generate discount offers on fashion items that you think they might be interested in based on their past purchase history."

[0981] This system improves the shopping experience by providing users with personalized information in real time that is tailored to their emotional state, and the accuracy of the information provided continues to improve as the system is continuously optimized based on feedback data.

[0982] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0983] Step 1:

[0984] The user captures their face using the device's camera. The device then analyzes the facial image using facial recognition technology (e.g., DeepFace and OpenCV) and extracts emotional data. The facial image is the input, and the extracted emotional data (e.g., "happiness" or "sadness") is the output.

[0985] Step 2:

[0986] The device collects behavioral data (website browsing history, search history, app usage history, location information, etc.) and the emotion data extracted in step 1. This collected data is the input.

[0987] Step 3:

[0988] The device encrypts the collected behavioral data and emotional data and transmits them to the server using a secure communication path. The behavioral data and emotional data are then transferred to the server. The collected data is the input, and the data sent to the server is the output.

[0989] Step 4:

[0990] The server decrypts and securely stores the received behavioral and emotional data. This stored data is the input, and the preparation for analysis is the output.

[0991] Step 5:

[0992] The server integrates the behavioral data, emotional data, and big data, and analyzes the user's interests and emotional state using machine learning algorithms and statistical analysis methods. This analysis involves calculations based on the input data (behavioral data, emotional data, big data), and the user's interest determination and emotional state are obtained as outputs.

[0993] Step 6:

[0994] The server generates personalized information and coupons for each user based on the analysis results of step 5. For example, if the user is in a "joy" emotional state, a discount coupon for the latest fashion items is generated. The analysis results are the input, and the generated personalized information and coupons are the output.

[0995] Step 7:

[0996] The server encrypts and transmits the generated personalized information and coupons to the user's terminal using a secure communication protocol, with the generated information and coupons being the input and data transmission to the terminal being the output.

[0997] Step 8:

[0998] The terminal displays the received personalized information and coupons through a user interface, allowing the user to access the transmitted information in real time. The received data is the input, and the display of the information to the user is the output.

[0999] Through these processing steps, the system can provide an optimal personalized shopping experience based on the user's emotional state and behavioral data. An example of a prompt for analysis and information generation using a generative AI model is, "For a user whose current emotional state is joy, please generate discount information on fashion items that you think they might be interested in based on their past purchase history."

[1000] 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.

[1001] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1002] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1003] [Fourth embodiment]

[1004] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1005] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1006] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1007] 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.

[1008] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1009] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1010] 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.

[1011] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1012] 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.

[1013] 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 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.

[1014] 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.

[1015] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1016] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1017] This invention relates to an information provision system that collects user behavior data and uses that data to provide personalized information in real time. The invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display.

[1018] The system mainly consists of the following elements:

[1019] 1. Collection of user behavior data

[1020] The device collects real-time behavioral data such as user website browsing, searches, app usage, and location information, which is temporarily stored on the device and later sent to a server.

[1021] 2. Data transmission

[1022] The device sends the collected behavioral data to a server, where it is encrypted and transmitted over a secure communication channel.

[1023] 3. Receiving and integrating data

[1024] The server receives the data sent from the device, stores it in a database on the server, and analyzes it together with big data.

[1025] 4. Data Analysis

[1026] The server integrates the behavioral data and big data, and analyzes the user's interests and behavioral patterns using machine learning algorithms and statistical analysis. Based on the results of this analysis, it predicts the most appropriate information for each user.

[1027] 5. Generating personalized information

[1028] The server generates personalized information based on the analysis results, including news articles, coupons, recommended social media posts, and product information.

[1029] 6. Transmission of Information

[1030] The server then sends the generated personalized information to the user's device, where it is again encrypted and transmitted over a secure channel.

[1031] 7. Display of Information

[1032] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[1033] Specific examples

[1034] Example 1: Personalizing your news feed

[1035] The device collects the user's news app browsing history and sends it to a server.

[1036] The server analyzes the collected data and big data and determines that the user is interested in economics.

[1037] The server generates the latest news articles on the economy and sends them to the terminal.

[1038] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[1039] Example 2: Offering a shopping coupon

[1040] The terminal collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[1041] The server generates relevant coupons for the user based on the analysis results.

[1042] The server transmits the generated coupon information to the terminal.

[1043] The device notifies users of coupons related to electronics, allowing them to purchase specific products at discounted prices.

[1044] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[1045] The processing flow will be explained below.

[1046] Step 1:

[1047] The device monitors user activity, specifically collecting behavioral data such as website browsing history, search history, location information, and app usage history in real time. This data is stored in temporary storage on the device.

[1048] Step 2:

[1049] The device reads the collected behavioral data from temporary storage at a certain timing or after the user has completed their operation. After reading the data, it converts it into a standard communication format and encrypts it as necessary.

[1050] Step 3:

[1051] The device transmits the encrypted behavioral data over the Internet to a server using a secure communication protocol (e.g., HTTPS).

[1052] Step 4:

[1053] The server receives the encrypted data sent from the device, after which the data is decrypted and stored in a secure server.

[1054] Step 5:

[1055] The server integrates the received behavioral data with big data, which includes behavioral data of other users and data obtained from external data sources.

[1056] Step 6:

[1057] The server then analyzes the integrated data using machine learning algorithms and statistical analysis to identify user interests and behavioral patterns and estimate the most appropriate information.

[1058] Step 7:

[1059] Based on the analysis results, the server generates personalized information for each user, including news articles, coupons, recommended social media posts, and product information.

[1060] Step 8:

[1061] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[1062] Step 9:

[1063] The device decrypts the encrypted information it receives, and then stores it on the device for immediate display.

[1064] Step 10:

[1065] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[1066] Step 11:

[1067] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[1068] Step 12:

[1069] The device collects user responses (e.g. clicks, viewing time, subscriptions) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[1070] Step 13:

[1071] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of user behavior patterns and reflects this in the next information provided.

[1072] This series of processing steps allows users to efficiently obtain information that is most suitable for them, and significantly reduces the time and effort required to access information.

[1073] Example 1

[1074] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1075] Conventional information provision systems often lack the ability to efficiently collect and analyze user behavior data and provide personalized information, resulting in limited improvements to the user experience (UX). Furthermore, they are also inadequate in terms of security and privacy protection, making it difficult to gain user trust.

[1076] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1077] In this invention, the server includes means for generating personalized information for each user based on the analysis results using a machine learning algorithm or statistical analysis, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal via a secure path, thereby enabling efficient collection and analysis of user behavior data and safe provision of personalized information that is valuable to the user.

[1078] "User behavior data" refers to records of a user's usage patterns and behavior, such as operation history and location information, when visiting websites, using search engines, or using applications.

[1079] A "server" is a computer system that receives data sent by a user, analyzes it, generates optimal information, and sends it to the user's terminal.

[1080] "Massive data" refers to a collection of diverse behavioral data collected from a large number of users, a vast amount of information also known as big data.

[1081] "Machine learning algorithms" are algorithms and methodologies that allow computers to learn patterns from data and make predictions and decisions.

[1082] "Statistical analysis" refers to statistical methods for analyzing data and obtaining significant results or patterns.

[1083] "Personalized information" is information (e.g., news articles, coupons, recommended content) that is individually optimized based on each user's interests and behavioral patterns.

[1084] A "secure path" is a communication path in which information is encrypted when transmitted and is protected from unauthorized access or tampering.

[1085] This invention relates to an information provision system that collects user behavior data, analyzes that data, and provides personalized information in real time. This system aims to improve the user experience (UX) by efficiently performing a series of processes: collection, transmission, analysis, generation, and display.

[1086] Hardware and software used

[1087] A device is a device used by a user, such as a smartphone, tablet, or PC, and is used to collect behavioral data in real time, such as website browsing, searches, app usage, and location information.

[1088] The server is a computer system that receives the collected behavioral data, analyzes it, generates the most appropriate information, and sends it to the user's device. The server uses the following software:

[1089] A database management system (e.g., MySQL, PostgreSQL) is used to store and manage behavioral data and large amounts of data.

[1090] Use machine learning frameworks (e.g., TensorFlow and Scikit-learn implemented in Python) to perform data analysis.

[1091] We use encryption technology (e.g., SSL / TLS) to securely manage data transmission.

[1092] System processing overview

[1093] The device collects user behavioral data (e.g., browsing history in news apps, location information, search keywords) in real time and temporarily stores it on the device. The device then encrypts the collected behavioral data and transmits it to a server using a secure communication protocol (e.g., HTTPS).

[1094] The server receives the behavioral data sent from the device and stores it in a database. The stored data is then integrated with the large amount of accumulated data. The server then analyzes the data using machine learning algorithms and statistical analysis to extract the user's interests and behavioral patterns. Based on the analysis results, the server generates personalized information (e.g., the latest economic news, coupons for electronic devices) that is optimal for each user.

[1095] The generated information is re-encrypted by the server and sent to the user's device. The device decodes the received personalized information and displays it through the user interface, allowing the user to access optimized information. In addition, the device collects feedback data on whether the user has confirmed the provided information and sends it back to the server for future analysis.

[1096] Specific examples

[1097] Example 1: Personalizing your news feed

[1098] 1. The device collects the user's news app browsing history and sends it to the server.

[1099] 2. The server analyzes the collected data and large amounts of data to determine that the user is interested in economics.

[1100] 3. The server generates the latest economic news articles and sends them to the device.

[1101] 4. The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[1102] Example 2: Offering a shopping coupon

[1103] 1. The device collects the user's purchasing history (e.g., electronic devices) and sends it to the server.

[1104] 2. The server generates relevant coupons for the user based on the analysis results.

[1105] 3. The server sends the generated coupon information to the terminal.

[1106] 4. The device notifies the user of electronics-related coupons, allowing the user to purchase certain products at discounted prices.

[1107] Prompt Sentence Examples

[1108] Below are some example prompts to input to a generative AI model (e.g., GPT-3):

[1109] Please explain the specific process flow of a system that collects user behavior data, analyzes that data, and provides personalized information in real time. In particular, please describe the specific behavior when a user uses a shopping app.

[1110] This system allows users to efficiently access the information that is most useful to them without being overwhelmed by information overload. Furthermore, the system continues to improve based on feedback data, ensuring that the most appropriate information is always provided. This process significantly improves the user experience.

[1111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1112] Step 1:

[1113] A user visits a website and initiates an operation. Based on this operation, the device collects user behavior data (e.g., time spent on page, links clicked, search keywords) in real time. The input is the user's behavior, and the output is the collected behavior data. The behavior data is temporarily stored on the device.

[1114] Step 2:

[1115] The device retrieves the temporarily stored behavioral data and encrypts it using an encryption algorithm (e.g., AES encryption). The input is the temporarily stored behavioral data, and the output is the encrypted behavioral data. The encrypted data is then sent to the server using the HTTPS protocol.

[1116] Step 3:

[1117] The server decrypts the encrypted behavioral data it receives. The input is encrypted behavioral data, and the output is decrypted behavioral data. The server checks the integrity of this data to ensure there are no anomalies or missing data. The consistent data is then stored in a database management system.

[1118] Step 4:

[1119] The server combines the collected behavioral data with existing large data sets to generate a dataset for analysis. The input is the decoded behavioral data and large data sets, and the output is the combined dataset. Machine learning algorithms (e.g., TensorFlow models) and statistical analysis are then applied to extract user interests and behavioral patterns.

[1120] Step 5:

[1121] The server generates personalized information for each user based on the analysis results. The input is the analysis results, and the output is personalized information (e.g., the latest news articles, shopping coupons). This information is temporarily stored in the server memory.

[1122] Step 6:

[1123] The server encrypts the generated personalized information and sends it to the user's device via a communication protocol (e.g., HTTPS). The input is personalized information, and the output is encrypted information. A log of data transmission is also recorded.

[1124] Step 7:

[1125] The terminal decrypts the received encrypted personalized information and displays it via a user interface. The input is the encrypted personalized information, and the output is the information displayed on the user interface. The terminal also collects feedback data on whether the user has confirmed the provided information and sends it to the server for use in the next analysis.

[1126] (Application example 1)

[1127] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1128] Conventional information provision systems have limitations in providing personalized information based on user behavioral data. In particular, in autonomous vehicles, there is a demand for more accurate personalized information by integrating and analyzing not only user behavioral data but also vehicle operation data. The objective of the present invention is to provide an information provision system that utilizes behavioral data and operation data in an integrated manner to improve the user experience in autonomous vehicles.

[1129] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1130] In this invention, the server includes means for collecting user behavior data and vehicle operation data, means for analyzing the behavior data and large-scale data, and means for generating information optimized for the user based on the analysis results, thereby making it possible to provide the information that the user is most interested in in real time, even inside an autonomous vehicle.

[1131] "User Behavior Data" means data about your activity, browsing history, location, and other usage patterns when you use applications or devices.

[1132] A "server" is a computer system that receives data sent from a client via a network and processes, analyzes, and stores the data.

[1133] "Large-scale data" refers to data that is so large that it is difficult to process using traditional database management tools, and includes both structured and unstructured data.

[1134] "Personalized information" is customized information that is optimized for each individual user and is generated based on the user's behavioral data and driving data.

[1135] "Automobile operation data" refers to data related to the operation of an autonomous vehicle, such as its movement status, speed, route, and operating mode.

[1136] "Feedback data" refers to data such as operation results, evaluations, and opinions provided by users, and is used to improve the system and optimize analysis algorithms.

[1137] "Analytics algorithms" are computational methods or algorithms that process collected data to identify user behavior patterns and interests and generate personalized information.

[1138] The information provision system of the present invention collects user behavior data and vehicle operation data, integrates and analyzes this data, and provides optimized information to users within autonomous vehicles. This system efficiently performs a series of processes, including collection, transmission, analysis, generation, and display, in order to improve the user experience.

[1139] 1. Collecting User Behavior Data:

[1140] The device collects real-time data on the user's in-car activities (e.g., reading, watching videos, listening to music), location information, and vehicle operation data, which are temporarily stored on the device and later transmitted to a server.

[1141] 2. Data transmission:

[1142] The device sends the collected behavioral and operational data to the server. During this process, the data is encrypted and transmitted over a secure communication channel. For encryption, the Fernet library is used, for example.

[1143] 3. Receipt and integration of data:

[1144] The server receives the data sent from the terminal and stores it in a database, where it is analyzed together with large-scale data.

[1145] 4. Data Analysis:

[1146] The server integrates and analyzes behavioral data, operational data, and large-scale data, specifically using machine learning algorithms (such as the K-means algorithm) to identify user interests and behavioral patterns.

[1147] 5. Generating personalized information:

[1148] The server generates personalized information based on the analysis results, including tourist information for the destination, recommended shops along the route, and in-car entertainment suggestions.

[1149] 6. Transmission of Information:

[1150] The generated personalized information is then re-encrypted and sent over a secure channel to the user's device.

[1151] 7. Displaying Information:

[1152] The device displays the received personalized information through a user interface, allowing users to access information optimized for them in real time.

[1153] Examples:

[1154] For example, if a user is reading a book in an autonomous vehicle, the device will collect that behavioral data and send it to the server. The server will analyze the user's past reading history, location information, and current reading content, generate information about nearby bookstores and libraries, and generate related book recommendations, and send them to the device. The device will then display this information to the user, allowing them to obtain appropriate information in the autonomous vehicle.

[1155] Below are some example prompts to input to a generative AI model:

[1156] The system analyzes the user's behavioral data while in the self-driving vehicle (activity: reading, location information: 37.7749,-122.4194, vehicle status: self-driving mode) and provides personalized information based on their current interests.

[1157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1158] Step 1:

[1159] The terminal collects user behavioral data and vehicle operation data in real time. The behavioral data includes user activities such as reading, watching videos, listening to music, and location information. The operation data includes vehicle driving status, speed, route, and operation mode. The input is real-time user activity and operation information, and the output is a structured set of collected behavioral data and operation data.

[1160] Step 2:

[1161] The device encrypts the collected behavioral and operational data and sends it to the server via a secure communication channel. The Fernet library is used to encrypt the data. The input is the behavioral and operational data from step 1, and the output is the encrypted data packet.

[1162] Step 3:

[1163] The server receives the encrypted data sent from the terminal, decrypts it, and stores it in a database. Here, we use the Fernet library to receive and decrypt the encrypted packets and store the data in a database management system (DBMS). The input is the encrypted data packet, and the output is the decrypted raw data.

[1164] Step 4:

[1165] The server analyzes behavioral data, operational data, and large-scale data to identify user interests and behavioral patterns. Specifically, it performs clustering using a machine learning algorithm (e.g., the K-means algorithm). The inputs are the decoded behavioral data, operational data, past history data, and large-scale data, and the output is the clustering results that indicate the user's interests.

[1166] Step 5:

[1167] The server generates personalized information based on the analysis results. For example, if the user likes reading, it generates recommendations for nearby bookstores and related books. The input is the clustering results, and the output is personalized information (e.g., tourist information, route recommendations, in-car entertainment suggestions).

[1168] Step 6:

[1169] The generated personalized information is then encrypted again and sent to the user's device via a secure communication channel. Again, the Fernet library is used for encryption. The input is the generated personalized information, and the output is the encrypted information packet.

[1170] Step 7:

[1171] The terminal decrypts the received encrypted packets and displays the personalized information through a user interface, allowing users to access information optimized for them in real time. The input is the encrypted information packet, and the output is the specific personalized information displayed to the user.

[1172] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1173] The present invention relates to an information provision system that collects user behavioral data and emotional data and provides personalized information based on that data in real time. The present invention aims to improve the user experience (UX) by efficiently performing a series of processes including collection, transmission, analysis, generation, and display. In this invention, by combining an emotion engine, more advanced personalization based on user emotions becomes possible.

[1174] The system mainly consists of the following elements:

[1175] 1. Collecting user behavioral and emotional data

[1176] The device collects real-time behavioral data, such as user website browsing, searches, app usage, and location information. It also collects user emotional data using an emotion engine. Emotional data is obtained, for example, through facial recognition technology and voice analysis. This data is stored in temporary storage on the device.

[1177] 2. Data transmission

[1178] The device sends the collected behavioral and emotional data to a server, where it is encrypted and transmitted over a secure communication channel.

[1179] 3. Receiving and integrating data

[1180] The server receives the encrypted data sent from the device. After receiving the data, it is decrypted and stored in a secure server. The behavioral data and emotional data are integrated and analyzed together with big data.

[1181] 4. Data Analysis

[1182] The server integrates behavioral data, emotional data, and big data, and uses machine learning algorithms and statistical analysis to analyze users' interests, emotional states, and behavioral patterns. Based on the results of this analysis, it predicts the most appropriate information for each user.

[1183] 5. Generating personalized information

[1184] The server generates personalized information based on the analysis results, including news articles, coupons, recommended posts on social media, product information, etc. By utilizing emotion data, the server can provide information that fits the user's current mood and emotional state.

[1185] 6. Transmission of Information

[1186] The server then transmits the generated personalized information to the user's device, again using a secure communication protocol.

[1187] 7. Display of Information

[1188] The device displays the received personalized information through a user interface, allowing users to access information optimized for them.

[1189] Specific examples

[1190] Example 1: Personalizing your news feed based on your emotional state

[1191] The device collects the user's news app browsing history and emotional data and sends it to a server.

[1192] The server analyzes the collected data and big data to determine that the user is interested in economics and that current sentiment is calm.

[1193] The server generates the latest news articles on the economy and sends them to the terminal.

[1194] The device presents the generated news feed to the user, allowing the user to access the latest economic information.

[1195] Example 2: Providing shopping coupons based on emotional state

[1196] The device collects the user's purchasing history and current emotional data (e.g., excited) and sends it to the server.

[1197] Based on the analysis results, the server generates coupons for exciting products (e.g., gadgets) for the user.

[1198] The server transmits the generated coupon information to the terminal.

[1199] The device will notify the user of the coupon, allowing them to purchase specific products at a discounted price.

[1200] This system allows users to efficiently obtain information that is appropriate for their emotional state, significantly reducing the time and effort required to access information. Furthermore, the system continues to improve based on feedback data, ensuring that optimal information is always provided.

[1201] The processing flow will be explained below.

[1202] Step 1:

[1203] The device monitors user activity. Specifically, it collects behavioral data in real time, such as website browsing history, search history, location information, and app usage history. It also uses an emotion engine to recognize the user's face and analyze their voice to collect emotional data. This data is then stored in temporary storage.

[1204] Step 2:

[1205] The device reads the collected behavioral and emotional data from temporary storage at a certain timing or after the user has completed their operation. The read data is converted into a data format and encrypted as necessary.

[1206] Step 3:

[1207] The device transmits encrypted behavioral and emotional data to a server over the internet using a secure communication protocol (e.g., HTTPS).

[1208] Step 4:

[1209] The server receives the encrypted data sent from the device, decrypts it, and stores it in a secure database on the server.

[1210] Step 5:

[1211] The server integrates the received behavioral and emotional data with big data, which includes behavioral data of other users and data obtained from external data sources.

[1212] Step 6:

[1213] The server analyzes the integrated data using machine learning algorithms and statistical analysis to identify the user's interests, emotional state, and behavioral patterns. It also takes into account the user's emotional data to reflect their current mood and emotional state in the analysis.

[1214] Step 7:

[1215] The server generates personalized information for each user based on the analysis results. This information includes news articles, coupons, recommended posts on social media, product information, etc. By using emotion data, it is possible to provide information that fits the user's current emotional state.

[1216] Step 8:

[1217] The server encrypts the generated personalized information and transmits it to the corresponding user's device, also using a secure communication protocol.

[1218] Step 9:

[1219] The device decrypts the encrypted information it receives, and then stores it locally for immediate viewing.

[1220] Step 10:

[1221] The device displays the received personalized information in the user interface, including in various formats such as news apps, shopping apps, and social networking apps.

[1222] Step 11:

[1223] The user takes action based on the presented information (e.g., read a news article or redeem a coupon), and the device continues to monitor the user's response.

[1224] Step 12:

[1225] The device collects user responses (e.g., clicks, viewing time, subscription behavior) and sends this feedback data to the server. The feedback data is also encrypted and sent securely.

[1226] Step 13:

[1227] The server analyzes the received feedback data and uses it to improve the accuracy of the analysis algorithm and optimize the system. Specifically, it gains a more detailed understanding of the user's behavioral patterns and emotional state and reflects this in the next information provided.

[1228] This series of processing steps allows users to efficiently obtain information that is appropriate for their emotional state, significantly improving the information access experience. The system is continuously improved based on feedback data, ensuring that optimal information is always provided.

[1229] Example 2

[1230] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1231] Conventional systems provide information based solely on user behavior data, making it difficult to provide information appropriate to the user's emotional state or current interests. Furthermore, they do not efficiently utilize user feedback, resulting in a decline in the quality of the information provided and the accuracy of personalization. Furthermore, the difficulty of collecting and analyzing emotional data limits the user experience.

[1232] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and large-scale data on the server, means for generating personalized information for each user based on the analysis results, means for transmitting the generated information from the server to the user's terminal, and means for displaying the transmitted information on the terminal. By using not only the user's behavioral data but also their emotional data, it is possible to provide personalized information suited to the user's current emotional state and interests. Furthermore, optimizing the analysis algorithm based on feedback data improves the quality and accuracy of information provided.

[1233] "Behavioral data" refers to information including user operation history and location information when browsing websites, searching, or using apps.

[1234] "Emotion data" is information that indicates the emotional state of a user, obtained by analyzing the user's facial expressions, tone of voice, etc.

[1235] "Big data" refers to large and diverse datasets collected from a wide variety of data sources.

[1236] "Personalized information" is information that is tailored to a user's interests and emotional state, generated based on the user's individual behavioral and emotional data.

[1237] "Feedback Data" refers to information, including ratings and opinions, obtained when users respond to information provided.

[1238] "Collection means" refers to devices or programs that acquire user behavioral data and emotional data and store them in temporary storage.

[1239] "Transmission means" refers to a device or program that has the function of encrypting collected data and transmitting it to a server via a secure communication protocol.

[1240] "Analysis means" refers to devices or programs that use machine learning algorithms and statistical analysis to analyze users' interests and emotional states based on data acquired on the server.

[1241] "Generation means" refers to a device or program that has the function of generating optimal information for the user based on the analysis results.

[1242] "Display means" refers to a device or program for displaying the transmitted information on the terminal in a format that is easy for the user to view.

[1243] This invention relates to an information provision system that collects user behavioral and emotional data and provides personalized information based on that data in real time. The aim is to improve the user experience (UX) by efficiently performing a series of processes.

[1244] Hardware and Software

[1245] The device will be equipped with sensors to collect website browsing history, search history, app usage records, and real-time location information, as well as a camera and microphone that will use facial recognition and voice analysis to collect emotional data. The data will be stored in temporary storage.

[1246] The server has the following functions:

[1247] 1. Data Reception: Encrypted communication protocols (e.g., HTTPS) to securely receive collected behavioral and emotional data from devices.

[1248] 2. Data analysis: Utilizing machine learning libraries such as Python's Scikit-learn and TensorFlow, we analyze behavioral data, emotional data, and large-scale data.

[1249] 3. Generating personalized information: Using natural language processing (NLP) techniques and text generation models (e.g., GPT-3), we generate content that is best suited to the user.

[1250] 4. Sending information: The generated information is sent to the terminal in real time via WebSocket.

[1251] System Functionality Description

[1252] The device collects user behavioral and emotional data and stores it in temporary storage, which is periodically encrypted and sent to a server using the HTTPS protocol.

[1253] The server decrypts the received data and stores it in a secure database. It then integrates and analyzes behavioral, emotional, and large-scale data. It uses Python libraries to identify the user's interests and emotional state. Based on the analysis results, it uses natural language processing technology to generate personalized content. The generated content is then sent to the device using a secure communication protocol (such as WebSocket).

[1254] The device displays the received personalized information through a user interface (e.g., React Native, Swift), allowing users to access information optimized for them.

[1255] Specific examples

[1256] 1. Personalize your news feed based on your emotional state:

[1257] The device collects the user's news app browsing history and sentiment data and stores it in temporary storage.

[1258] The device encrypts the collected data and sends it to the server via HTTPS.

[1259] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[1260] The server uses Python's Scikit-learn to analyze the user's interests and emotional state.

[1261] The server uses GPT-3 to generate economic news articles appropriate for the user.

[1262] The server generates news articles and sends them to the device via HTTPS.

[1263] The device displays the received articles using React Native, and the user can view economic news.

[1264] 2. Providing shopping coupons based on emotional state:

[1265] The device collects the user's purchasing history and current emotional data and stores it in temporary storage.

[1266] The device encrypts the collected data and sends it to the server via HTTPS.

[1267] The server receives the encrypted data, decrypts it, stores it in a database, and integrates it.

[1268] The server uses TensorFlow to analyze users' excitement levels and purchasing patterns.

[1269] The server generates coupons for exciting products (gadgets).

[1270] The coupon information generated by the server is sent to the terminal via WebSocket.

[1271] The device notifies the user of the coupon via push notification and displays the coupon information using React Native, allowing the user to purchase the product at a discounted price.

[1272] Example prompts to input to the generative AI model

[1273] Describe a system that collects user behavioral and emotional data, analyzes that data, and provides personalized information in real time. The system uses facial recognition technology and voice analysis to obtain emotional data, integrates it with large-scale data, and analyzes it using machine learning algorithms. Specific examples include providing a news feed and shopping coupons based on the user's emotional state.

[1274] In this way, by integrating and analyzing user behavior and emotional data and providing personalized information, it is possible to achieve an advanced user experience.

[1275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1276] Step 1: Collect data

[1277] The device collects user behavioral data (website browsing history, search history, app usage records, location information, etc.) and emotional data (facial expressions and tone of voice) in real time. At this time, the device stores the collected data in temporary storage. The input is the user's behavior and emotions, and the output is the data stored in temporary storage. For example, website browsing history can be recorded through a browser extension, and emotional data can be obtained using the camera and microphone.

[1278] Step 2: Sending data

[1279] The device encrypts the behavioral and emotional data stored in temporary storage and sends it to the server using a secure communication protocol (such as HTTPS). The input is the data stored in temporary storage, and the output is the encrypted data sent to the server. The device sends data periodically (e.g., every 5 minutes) and uses a backoff algorithm that takes into account data size and connection quality.

[1280] Step 3: Receiving and consolidating data

[1281] The server receives and decrypts the encrypted data sent from the device. The received data is stored in a secure database. The input is encrypted behavioral and emotional data, and the output is the decrypted data stored in the database. The server then goes through a data cleansing process to fill in missing values ​​and detect outliers.

[1282] Step 4: Analyze the data

[1283] The server uses machine learning algorithms and statistical analysis to analyze the user's interests, emotional state, and behavioral patterns based on the integrated data. The input is the integrated data, and the output is the analysis results. Specifically, it uses Python's Scikit-learn and TensorFlow to group user behavior and emotional patterns using a clustering algorithm (e.g., K-means).

[1284] Step 5: Generate personalization information

[1285] The server generates personalized information (news articles, coupons, recommended posts on social media, product information, etc.) based on the analysis results. The input is the analysis results, and the output is the generated personalized information. For example, natural language processing (NLP) techniques or text generation models (e.g., GPT-3) can be used to generate content that matches the user's emotional state.

[1286] Step 6: Submit your information

[1287] The server sends the generated personalized information to the user's device. The input is the generated personalized information, and the output is the information sent to the device. This transmission uses a secure communication protocol (e.g., HTTPS, WebSocket).

[1288] Step 7: Viewing information

[1289] The device displays the received personalized information through a user interface. The input is the received information, and the output is the information displayed to the user. For example, a UI framework such as React Native or Swift can be used to display the information in a visually easy-to-understand format, and push notifications can be used to notify the user as needed.

[1290] (Application example 2)

[1291] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1292] Conventional personalization systems provide information based solely on user behavioral data and are unable to consider the user's emotional state. This makes it difficult for users to obtain information that matches their emotions at any given time, making it impossible to provide an optimal user experience. Furthermore, providing real-time personalized information to improve the user experience is also insufficient.

[1293] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data and emotional data, means for transmitting the collected behavioral data and emotional data to the server, means for analyzing the behavioral data, emotional data, and big data on the server and determining the user's interests and emotional state, means for generating information and coupons personalized for each user based on the analysis results, means for transmitting the generated information and coupons from the server to the user's terminal, and means for displaying the transmitted information and coupons on the terminal. This makes it possible to provide optimal personalized information and coupons according to the user's emotional state in real time.

[1294] "User behavioral data" refers to data about a series of actions generated when a user uses a device, such as website browsing history, search history, app usage history, and location information.

[1295] "Emotion data" refers to data about a user's emotional state (e.g., joy, sadness, anger, surprise, etc.) obtained using facial recognition technology or voice analysis technology.

[1296] A "server" is a computer system that receives, analyzes, stores, and distributes data over a network.

[1297] "Big data" refers to large, diverse, and rapidly generated data sets that are difficult to process using traditional database management tools.

[1298] "Personalized information" refers to content such as news articles, coupons, and recommended product information that is optimized for each user based on their behavioral and emotional data.

[1299] A "coupon" is an electronic certificate that allows a user to purchase a particular product or service at a discounted price.

[1300] A "terminal" is a device that is directly operated by a user, such as a smartphone or a personal computer.

[1301] "Feedback Data" refers to data provided by users, such as their opinions, impressions, and usage status, which is used to improve and optimize the system.

[1302] "Analysis algorithms" are mathematical and statistical methods for analyzing behavioral data, emotional data, and big data to extract useful patterns and trends.

[1303] The system for realizing this invention operates based on the following procedure. The system starts by collecting user behavioral data and emotional data and sending them to a server. The collected behavioral data includes the user's website browsing history, app usage history, product search history, location information, etc. The collection of emotional data uses facial recognition technology and voice analysis technology (e.g., DeepFace or OpenCV).

[1304] After receiving the collected behavioral and emotional data, the server integrates this data with big data and analyzes it. The analysis uses machine learning algorithms and statistical analysis methods to determine the user's interests and emotional state. For example, if the user expresses "joy" or is determined to be interested in "fashion items," the server generates a list of product recommendations and coupons that are optimal for the user.

[1305] The generated personalized information and coupons are then sent from the server to the user's device (e.g., smartphone) using a secure communication protocol. Finally, the device displays the received information in a user interface, allowing the user to access the optimized information.

[1306] For example, when a user captures their face using their smartphone camera, the data is analyzed by an emotion analysis engine. If the analysis result indicates "joy," a coupon for the latest fashion items is generated based on the user's past purchase history and interests. This coupon is displayed on the user's smartphone in real time.

[1307] An example prompt for a generative AI model is:

[1308] "For users whose current emotional state is joy, generate discount offers on fashion items that you think they might be interested in based on their past purchase history."

[1309] This system improves the shopping experience by providing users with personalized information in real time that is tailored to their emotional state, and the accuracy of the information provided continues to improve as the system is continuously optimized based on feedback data.

[1310] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1311] Step 1:

[1312] The user captures their face using the device's camera. The device then analyzes the facial image using facial recognition technology (e.g., DeepFace and OpenCV) and extracts emotional data. The facial image is the input, and the extracted emotional data (e.g., "happiness" or "sadness") is the output.

[1313] Step 2:

[1314] The device collects behavioral data (website browsing history, search history, app usage history, location information, etc.) and the emotion data extracted in step 1. This collected data is the input.

[1315] Step 3:

[1316] The device encrypts the collected behavioral data and emotional data and transmits them to the server using a secure communication path. The behavioral data and emotional data are then transferred to the server. The collected data is the input, and the data sent to the server is the output.

[1317] Step 4:

[1318] The server decrypts and securely stores the received behavioral and emotional data. This stored data is the input, and the preparation for analysis is the output.

[1319] Step 5:

[1320] The server integrates the behavioral data, emotional data, and big data, and analyzes the user's interests and emotional state using machine learning algorithms and statistical analysis methods. This analysis involves calculations based on the input data (behavioral data, emotional data, big data), and the user's interest determination and emotional state are obtained as outputs.

[1321] Step 6:

[1322] The server generates personalized information and coupons for each user based on the analysis results of step 5. For example, if the user is in a "joy" emotional state, a discount coupon for the latest fashion items is generated. The analysis results are the input, and the generated personalized information and coupons are the output.

[1323] Step 7:

[1324] The server encrypts and transmits the generated personalized information and coupons to the user's terminal using a secure communication protocol, with the generated information and coupons being the input and data transmission to the terminal being the output.

[1325] Step 8:

[1326] The terminal displays the received personalized information and coupons through a user interface, allowing the user to access the transmitted information in real time. The received data is the input, and the display of the information to the user is the output.

[1327] Through these processing steps, the system can provide an optimal personalized shopping experience based on the user's emotional state and behavioral data. An example of a prompt for analysis and information generation using a generative AI model is, "For a user whose current emotional state is joy, please generate discount information on fashion items that you think they might be interested in based on their past purchase history."

[1328] 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.

[1329] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1330] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1331] 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.

[1332] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1333] 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.

[1334] 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).

[1335] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1336] 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."

[1337] 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.

[1338] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1339] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1340] 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.

[1341] 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.

[1342] 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.

[1343] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1344] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] The following is further disclosed regarding the above embodiment.

[1350] (Claim 1)

[1351] means of collecting user behavior data;

[1352] means for transmitting the collected behavioral data to a server;

[1353] A means of analyzing behavioral data and big data on the server;

[1354] A means for generating personalized information for each user based on the analysis results;

[1355] A means for transmitting the generated information from the server to the user's terminal;

[1356] means for displaying the transmitted information on the terminal;

[1357] A system including:

[1358] (Claim 2)

[1359] 10. The system of claim 1, further comprising means for collecting and transmitting feedback data from users to the server.

[1360] (Claim 3)

[1361] 10. The system of claim 1, wherein the server further comprises means for utilizing the feedback data to optimize the analysis algorithm.

[1362] "Example 1"

[1363] (Claim 1)

[1364] means of collecting user behavior data;

[1365] means for transmitting the collected behavioral data to a server;

[1366] A means for analyzing behavioral data and large amounts of data on a server;

[1367] A means for generating personalized information for each user based on the analysis results using machine learning algorithms and statistical analysis; and

[1368] A means for transmitting the generated information from the server to the user's terminal;

[1369] means for displaying the transmitted information on the terminal through a secure channel;

[1370] A system including:

[1371] (Claim 2)

[1372] 10. The system of claim 1, further comprising means for collecting and transmitting feedback data from users to the server.

[1373] (Claim 3)

[1374] 10. The system of claim 1, wherein the server further comprises means for utilizing the feedback data to optimize the analysis algorithm.

[1375] "Application Example 1"

[1376] (Claim 1)

[1377] means of collecting user behavior data;

[1378] means for transmitting the collected behavioral data to a server;

[1379] A means for analyzing behavioral data and large-scale data on a server;

[1380] A means for generating personalized information for each user based on the analysis results;

[1381] A means for transmitting the generated information from the server to the user's terminal;

[1382] means for displaying the transmitted information on the terminal;

[1383] A means for collecting vehicle operation data;

[1384] means for generating information relevant to a user based on vehicle operation data;

[1385] A system including:

[1386] (Claim 2)

[1387] 10. The system of claim 1, further comprising means for collecting and transmitting feedback data from users to the server.

[1388] (Claim 3)

[1389] 10. The system of claim 1, wherein the server further comprises means for utilizing the feedback data to optimize the analysis algorithm.

[1390] "Example 2: Combining Emotion Engines"

[1391] (Claim 1)

[1392] a means for collecting user behavioral and emotional data;

[1393] means for transmitting the collected behavioral data and emotion data to a server;

[1394] A means for analyzing behavioral data, emotion data, and large-scale data on a server;

[1395] A means for generating personalized information for each user based on the analysis results;

[1396] A means for transmitting the generated information from the server to the user's terminal;

[1397] means for displaying the transmitted information on the terminal;

[1398] A system including:

[1399] (Claim 2)

[1400] 10. The system of claim 1, further comprising means for collecting and transmitting feedback data from users to the server.

[1401] (Claim 3)

[1402] 10. The system of claim 1, wherein the server further comprises means for utilizing the feedback data to optimize the analysis algorithm.

[1403] "Application example 2 when combining emotion engines"

[1404] (Claim 1)

[1405] a means for collecting user behavioral and emotional data;

[1406] means for transmitting the collected behavioral data and emotion data to a server;

[1407] A means for analyzing the behavioral data, emotional data, and big data on the server to determine the user's interests and emotional state;

[1408] A means for generating personalized information and coupons for each user based on the analysis results;

[1409] means for transmitting the generated information and coupons from the server to the user's terminal;

[1410] means for displaying the transmitted information and coupons on the terminal;

[1411] A system including:

[1412] (Claim 2)

[1413] 10. The system of claim 1, further comprising means for collecting and transmitting feedback data from users to the server.

[1414] (Claim 3)

[1415] 10. The system of claim 1, wherein the server further comprises means for utilizing the feedback data to optimize the analysis algorithm. [Explanation of symbols]

[1416] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means of collecting user behavior data; means for transmitting the collected behavioral data to a server; A means of analyzing behavioral data and big data on the server; A means for generating personalized information for each user based on the analysis results; A means for transmitting the generated information from the server to the user's terminal; means for displaying the transmitted information on the terminal; A system including:

2. 10. The system of claim 1, further comprising means for collecting and transmitting feedback data from users to the server.

3. The system of claim 1 , wherein the server further comprises means for utilizing the feedback data to optimize the analysis algorithm.

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