System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting use state data, a means for collecting information from the Internet, a means for analyzing the collected data and specifying the optimum service selection and the profitable information, a means for providing the optimum information to the user, and a means for collecting feedback from the user and improving the algorithm.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] To maximize economic benefits and streamline their lives, modern users need to properly scrutinize and utilize vast amounts of information. However, information on the Internet is scattered, making it difficult for users to independently find optimal services and deals. Furthermore, with their busy lives, users lack the time and expertise to effectively utilize this information. If this situation is left unresolved, inappropriate choices will not only result in economic losses, but also a decline in the quality of life. Therefore, the purpose of this invention is to provide a system that provides optimal information to help users efficiently build wealth and save money, leading to a richer life. [Means for solving the problem]

[0005] The system according to the present invention solves the above-mentioned problems by including the following means.

[0006] It has a means for collecting usage data from users' smartphones, which allows it to record application usage history, purchase history, search history, etc.

[0007] It has the means to collect and analyze text information, video information, and flyer images from the internet, allowing you to obtain discount information from websites, video platforms, and advertising flyers.

[0008] Based on collected usage data and information on the Internet, the system is equipped with a means to profile users' consumption patterns and preferences, and identify optimal service options and discount information.

[0009] It has a means to provide optimal information to users. Specifically, it includes a function to send information in real time using push notifications and a function to display a list of deals on a user interface.

[0010] It has the means to collect user feedback and use it to improve its algorithms, thereby improving the accuracy and relevance of the information it provides.

[0011] These methods allow users to efficiently obtain the services and deals that are best suited to them, enabling them to build wealth and save money without the need for time or specialized knowledge.

[0012] "Usage data" refers to data that includes information related to a user's behavior and consumption patterns, such as the user's smartphone application usage history, purchase history, and search history.

[0013] "Information on the Internet" refers to information accessible via the Internet, such as websites, video platforms, advertising flyers, etc.

[0014] "Analyze" refers to the process of analyzing collected data and information to derive its meaning and trends.

[0015] "Profiling" refers to a technology that analyzes a user's consumption patterns and preferences to clarify their characteristics and trends.

[0016] "Bargain information" refers to information such as discounts, sales, coupons, and campaigns that allow users to use products and services under economically advantageous conditions.

[0017] "Optimal service selection" refers to the act of selecting services or products that best suit a user's preferences and consumption patterns.

[0018] "Push notifications" refers to the ability to send information or notifications in real time to smartphones and other electronic devices.

[0019] "User interface" refers to the screen and operating means that allow a user to interact with a system.

[0020] "Feedback" refers to information, ratings, and opinions provided by users that are used to improve and adjust the system.

[0021] An "algorithm" refers to a set of computational procedures or processing methods designed to achieve a particular purpose. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] This invention is a system that uses generative AI as a smartphone application. The system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection, with the aim of making users' lives more efficient.

[0044] (Form of data collection)

[0045] The device automatically collects usage data through applications installed on the user's smartphone. This data includes the user's application history, purchase history, search history, and so on. For example, usage records of online shopping apps and product search information within them are stored. The device also collects text information, video information, and flyer images from the Internet. For this purpose, web scraping, video analysis, and image OCR (optical character recognition) technologies are used.

[0046] (Form of data analysis)

[0047] The server performs an integrated analysis of the collected usage data and discount information on the Internet. Specifically, it profiles the user's preferences and interests based on their usage history and consumption patterns. For example, if there is a high interest in a particular brand or category of products, sales information for that brand will be analyzed first. The generation AI also scrutinizes the discount information analyzed and identifies the information that is most suitable for the user.

[0048] (Form of information provision)

[0049] The device then provides the user with the most appropriate information based on the analysis results. This is done using push notifications, which notify users of special offers in real time. The app also has a dashboard that displays a list of special offers. For example, the app can notify users of this week's special sales for brands they are interested in, along with information on how to get additional discounts by using a specific point card.

[0050] (Forms of feedback and algorithmic improvement)

[0051] The user uses the information provided and evaluates the results. For example, they input feedback such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server then analyzes the collected feedback and improves the AI's algorithm. This will enable the provision of even more accurate deals in the future.

[0052] (Example)

[0053] For example, suppose that the history of an online shopping app that User A frequently uses indicates that User A has a high interest in a particular brand B. The device collects information about special sales for Brand B from the Internet using web scraping and analyzes it on the server. As a result of the analysis, it identifies information about a special sale on Brand B's products this weekend. The device sends User A a push notification informing him of this weekend's special sales for Brand B, and also displays the information on the app's dashboard. Furthermore, the device also provides User A with information about stores where additional discounts can be received by using a specific point card, thereby increasing User A's satisfaction.

[0054] In this way, the present invention can maximize the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The device collects user usage data. This is done through applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[0058] Step 2:

[0059] The device collects discount information from the internet. It uses web scraping technology to automatically retrieve sale and coupon information from related websites. It also analyzes advertising videos from video platforms such as YouTube (registered trademark) and social media to extract discount codes and limited campaign information. For flyer images, OCR technology is used to analyze the text information within the image to obtain sale information.

[0060] Step 3:

[0061] The server analyzes the collected usage data. Specifically, it profiles the user's preferences and interests based on the user's past purchase and search history. This profiling can identify the brands and product categories in which the user is particularly interested.

[0062] Step 4:

[0063] The server analyzes the collected information on deals on the Internet, and prioritizes the extraction of information that matches the user's preferences and interests. Based on the results of this analysis, the server identifies the most useful sales and coupon information for the user.

[0064] Step 5:

[0065] The device provides the analysis results to the user. Specifically, it uses push notifications to send users real-time sales and coupon information. It also displays a list of deals on the app's dashboard for easy access. It also provides information on additional discounts that can be received by using a point card at specific stores.

[0066] Step 6:

[0067] The user can use the provided information to take advantage of special sales and coupons. For example, they can do online shopping based on the special sale information they were notified about. When the user actually uses a coupon, the app provides feedback on the results.

[0068] Step 7:

[0069] The device collects feedback from the user, including whether the information provided was useful, whether the sales or coupons were actually used, etc. This feedback is recorded as data that will be used for later analysis.

[0070] Step 8:

[0071] The server analyzes the collected feedback and improves the generative AI algorithm. Based on the feedback, it makes adjustments to improve the accuracy and relevance of the information provided, thereby further improving the quality of information provided to users in the future.

[0072] Through this series of steps, users can efficiently obtain optimal services and deals, and build wealth and save money without spending time or effort.

[0073] Example 1

[0074] 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."

[0075] In today's lifestyles, users face difficulties in finding useful information from the vast amount of information available. Furthermore, existing information services often fail to adequately address users' individual preferences and needs. As a result, users are overwhelmed with unnecessary information, hindering efficient decision-making.

[0076] 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.

[0077] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data using a generative AI model to identify optimal service selections and advantageous information, thereby enabling the provision of personalized information based on each user's preferences and consumption patterns.

[0078] "Usage data" refers to data that includes application usage history, purchase history, search history, etc. when a user uses a smartphone or other device.

[0079] "Means of collecting information from the Internet" refers to technical means for collecting text information, video information, flyer images, etc. on the Internet.

[0080] A "generative AI model" is an artificial intelligence model that learns information from large amounts of data and is used to analyze user preferences and consumption patterns.

[0081] "Analysis" is the process of organizing and analyzing information based on collected data to find specific patterns and relationships.

[0082] "Profiling" is a method of analyzing a user's usage history and consumption patterns to reveal their preferences and interests.

[0083] "OCR technology" is a technology that recognizes characters in an image and extracts them as text data.

[0084] "Video analysis" is a technology that analyzes video data and extracts useful information.

[0085] "Push notification" is a technology that sends information from a server to a user's device in real time.

[0086] "Algorithm improvement" is the process of improving existing algorithms based on feedback data to improve the accuracy of information provided.

[0087] MODE FOR CARRYING OUT THE INVENTION

[0088] This invention is a system that uses an application installed on a user's smartphone to select the best service and provide information on deals from a vast amount of information using a generative AI model. This system involves a series of steps: collecting usage data, collecting information from the Internet, analyzing the data, providing the information, and collecting feedback and improving the algorithm.

[0089] Data collection

[0090] The device launches applications installed on the user's smartphone and collects user usage data. This data includes application usage history, purchase history, search history, etc. For example, if a user uses an online shopping app, the usage record will be collected. In addition, the device uses the following software to collect text information, video information, and flyer images from the Internet:

[0091] Web scraping: Collecting text data from web pages using BeautifulSoup.

[0092] Video analysis: Use OpenCV to extract useful information from videos.

[0093] OCR technology: Uses Tesseract to read characters from images.

[0094] Data analysis

[0095] The server performs an integrated analysis of the collected usage data and online deals. Specifically, it uses a generative AI model (e.g., GPT-4 (registered trademark) or other natural language processing model) to profile users' preferences and interests. The analysis methods include:

[0096] Statistical analysis: Historical usage data is used to statistically analyze consumption patterns.

[0097] Natural language processing: Analyzes collected text data and extracts relevant keywords and phrases.

[0098] Machine learning model: Generative AI models are used to predict user preferences and select the most appropriate information.

[0099] Providing information

[0100] Based on the analysis results, the device provides the user with the most appropriate information in real time. It notifies users of special offers in real time using push notification (e.g., Firebase Cloud Messaging), and also has a function to display a list of special offers on the in-app dashboard. For example, it notifies users of special offers on brands they are interested in, or information on additional discounts they can receive by using a specific point card.

[0101] Feedback and algorithm improvements

[0102] Users provide feedback within the application to evaluate whether the information provided was useful. For example, they answer "yes" or "no" to the question, "Was this sale information useful?" The device collects this feedback data and sends it to the server. The server analyzes the collected feedback data and improves the AI's algorithm, which will enable the provision of even more accurate information in the future.

[0103] Specific examples

[0104] For example, if the history of an online shopping app frequently used by User A indicates a high level of interest in a particular Brand B, the device will collect information about special sales for Brand B from the Internet through web scraping, and the server will analyze it using a generative AI model. As a result, when information is obtained that Brand B's products will be on sale this weekend, the device will provide User A with a push notification informing them of "Brand B's special sales this weekend," and the information will also be displayed on the app's dashboard. Furthermore, information about additional discounts that can be received by using a specific point card will also be provided, improving User A's satisfaction.

[0105] Prompt Sentence Examples

[0106] "Please analyze the information about special offers based on the user's usage history data. In particular, please prioritize extracting information about special sales of Brand B's products and think about the best way to notify the user."

[0107] In this way, the present invention maximizes the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1: Data collection

[0110] The device launches applications installed on the smartphone and collects usage data. This data includes application usage history, purchase history, and search history. For example, when a user uses an online shopping application, the usage record is collected. The input data is application history data, purchase data, and search data. This information is stored in a database as output data. Specifically, the application periodically collects usage history information in the background and stores it in a local database.

[0111] Step 2: Gather information from the internet

[0112] The device collects text information, video information, and flyer images from the Internet. To do this, it uses web scraping (e.g., BeautifulSoup), video analysis (e.g., OpenCV), and image OCR (e.g., Tesseract). The input data is various media content from the Internet. The output data, which includes the collected text information, video analysis results, and OCR results, is stored in a database. Specifically, a scheduled job periodically crawls specific websites and extracts the required information.

[0113] Step 3: Data analysis

[0114] The server performs an integrated analysis of the collected usage data and online deals. It uses a generative AI model (e.g., GPT-4) to profile the user's preferences and interests. The input data is usage history data and collected internet information. The output data is personalized information based on the user's preferences. Specifically, the server processes data batches every night, runs analysis using the generative AI model, and updates the user profile.

[0115] Step 4: Provide information

[0116] The device provides the user with the most appropriate information in real time based on the analysis results. Information is sent in real time using the push notification function (e.g., Firebase Cloud Messaging). Special offers are also displayed on the dashboard within the user's smartphone app. The input data is the analyzed personalized information. The output data is the push notification and the dashboard display content, which are reflected on the user's device. Specifically, the system is set up to send a push notification immediately after the analysis results are obtained.

[0117] Step 5: Gather feedback and improve the algorithm

[0118] The user inputs feedback on the provided information within the application. For example, they provide feedback in response to the question, "Was this sale information useful?" The device collects this feedback and sends it to the server. The input data is the user's feedback information. The feedback data is saved on the server as output data. Specifically, the in-app feedback form collects user ratings and periodically sends them to the server.

[0119] Step 6: Improve the algorithm

[0120] The server analyzes the collected feedback data and improves the algorithm of the generative AI model. The input data is the feedback collected from users. The output data is an improved generative AI model. Specifically, the server periodically retrains the generative AI model using the feedback data to improve the accuracy of the next data analysis.

[0121] This series of steps ensures that information is provided to users in real time and that the algorithm is continually improved.

[0122] (Application example 1)

[0123] 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."

[0124] Conventional smartphone applications were able to provide optimal service selection and discount information by collecting user usage data and information from the Internet. However, they lacked functionality to support the shopping experience in physical stores, making it difficult for users to obtain real-time sales and discount information that can be obtained directly from stores. In addition, there were insufficient means of collecting specific information using image recognition technology. Therefore, there is a need for a method to further improve the user's shopping experience.

[0125] 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.

[0126] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data to identify optimal service selections and discount information. This makes it possible to notify users of sale information in real time and improve their shopping experience. Furthermore, by including a means for extracting text information from images using image recognition technology, a means for displaying collected sale information in list format, and a means for customizing information based on user preferences, it becomes possible to provide more specific and personalized information.

[0127] "Usage data" refers to data such as the application usage history, search history, and purchase history of the user using the smartphone.

[0128] "Collecting information from the Internet" refers to collecting information that is publicly available on the Internet, such as websites, videos, and flyer images.

[0129] "Analyzing collected data" refers to the process of analyzing collected usage data and information on the Internet to clarify user preferences and behavioral patterns.

[0130] "Identifying optimal service selection and discount information" refers to selecting highly useful services and discount information based on the user's profile.

[0131] "Providing information to users" refers to providing useful information to users through notification and display functions based on collected and analyzed data.

[0132] "Collecting feedback and improving the algorithm" refers to the process of adjusting the analysis algorithm based on feedback provided by users, with the aim of providing even more accurate information.

[0133] "Real-time notification of sales information at physical stores" refers to notifying users of sales information at stores they actually visit in real time on their smartphones.

[0134] "Extracting text information using image recognition technology" refers to reading text data from an image using technology such as OCR.

[0135] "Displaying sale information in list format" refers to organizing the analyzed sale information into a list and displaying it in a format that is easy for the user to view.

[0136] "Customizing information" refers to providing personalized information based on the user's individual preferences and behavioral patterns.

[0137] This invention is a system that uses a generative AI model to collect and analyze user usage data and information on the Internet, and provides users with optimal service selection and advantageous information. The main components include a terminal that runs on the user's smartphone, a server that collects and analyzes data, and information sources on the Internet.

[0138] 1. Collection of User Data

[0139] The device collects usage data about the user's smartphone, including application usage history, search history, and purchase history. This data serves as the basis for analyzing user preferences and purchasing habits.

[0140] 2. Collecting information from the Internet

[0141] The server uses web scraping and OCR (optical character recognition) technology to collect sales and discount information from text, videos, and flyer images on the Internet, allowing you to obtain the latest information in real time.

[0142] 3. Data analysis

[0143] The server performs an integrated analysis of the collected usage data and information on the internet. It uses a generative AI model to analyze the user's usage history and consumption patterns to profile their preferences. It also analyzes the collected sales information to identify the best deals for the user.

[0144] 4. Information provision

[0145] The device receives the analysis results from the server and provides users with real-time push notifications about special offers. The app also has a feature that displays a list of special offers on the app's dashboard. For example, special offers for brands or product categories that the user is interested in can be displayed preferentially.

[0146] 5. Gathering feedback and improving the algorithm

[0147] The device collects user feedback and sends it to the server, which analyzes it and refines the algorithm of the generative AI model, thereby improving the accuracy of future deals.

[0148] Hardware and software used

[0149] Hardware: Smartphone (iOS or ANDROID (registered trademark))

[0150] software:

[0151] Programming language: Python 3

[0152] Web scraping libraries: requests, BeautifulSoup

[0153] Image processing library: OpenCV

[0154] OCR library: pytesseract

[0155] Machine learning library: scikit-learn

[0156] Specific examples

[0157] For example, user A's purchase history at a supermarket he frequently visits can be analyzed to determine that he has a high interest in daily necessities and food, especially in products from a particular brand. The server then uses web scraping to obtain images of supermarket flyers containing products from that brand, and uses OCR technology to extract text information. The generative AI model then analyzes the collected data and notifies user A in real time of the supermarket's special sales for this weekend. It also provides information on how to obtain additional discounts by using a point card in addition to the sales information.

[0158] Prompt Sentence Examples

[0159] "We will analyze User A's past purchases and search history to gather information on deals from the internet. We will then build a system that will provide users with push notifications about sales on specific products. This system will collect and analyze data using web scraping, OCR technology, and machine learning."

[0160] As described above, this invention is a system that can utilize user data and generative AI models to greatly improve the shopping experience in physical stores.

[0161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0162] Step 1:

[0163] User Data Collection

[0164] The device collects usage data from the user's smartphone. Specifically, application usage history, search history, purchase history, etc. are automatically acquired. This input data includes information on products and brands that the user is interested in. This data is collected and sent to the server.

[0165] Step 2:

[0166] Gathering information from the internet

[0167] The server uses web scraping technology to collect sale information on the Internet. Specifically, it accesses specific URLs, analyzes HTML, and extracts the necessary information. It also uses OCR technology to extract text information from flyer images. This allows it to obtain text and image information as input data and store it in a database.

[0168] Step 3:

[0169] Data analysis

[0170] The server performs an integrated analysis of the collected usage data and information on the Internet. A generative AI model is used to profile the user's preferences and consumption patterns. In this step, the user's history data and sales information are used as input data, and optimal service selection and discount information are output. Specifically, a clustering algorithm (e.g., KMeans) is used to identify user groups with similar preferences.

[0171] Step 4:

[0172] Providing information

[0173] The device receives the analysis results from the server and provides the user with the most appropriate information. Specifically, it notifies the user of sale information in real time using the push notification function. It also displays a list of sale information on the app's dashboard. In this step, the analysis results are used as input, and the output is notification or display to the user.

[0174] Step 5:

[0175] Feedback collection and algorithm improvement

[0176] The device collects feedback from users and sends it to the server. Specifically, it obtains feedback data such as whether the sale information was useful and whether the coupon was used. Based on this input data, the server improves the algorithm of the generative AI model. Improvements to the algorithm will improve the accuracy of information provided from the next time onwards.

[0177] This series of processing steps allows users to maximize their shopping experience in a physical store, which will greatly improve user convenience and satisfaction.

[0178] 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.

[0179] This invention is a system that uses generative AI and an emotion engine as a smartphone application. This system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides information according to the user's emotional state.

[0180] (Form of data collection)

[0181] The device automatically collects usage data through applications installed on the user's smartphone. This data includes application usage history, purchase history, search history, etc. Web scraping, video analysis, and image OCR (optical character recognition) technologies are also used to collect text information, video information, and flyer images from the Internet.

[0182] (Forms of emotion recognition)

[0183] The device uses an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, when a user enters a text comment into an app, the content is analyzed to determine the user's current state of mind. The device also analyzes the tone and tempo of the voice data and facial expressions from image data.

[0184] (Forms of data and sentiment analysis)

[0185] The server performs an integrated analysis of the collected usage data and emotion recognition results. Specifically, it identifies optimal service selection and discount information based on the user's current emotional state in addition to their past usage history and consumption patterns. For example, if the user is feeling stressed, it prioritizes relaxation services and entertainment-related sales information. Also, if the user is excited, it provides information that encourages action that leads to immediate purchase.

[0186] (Form of information provision)

[0187] The device provides the user with the most appropriate information based on the analysis results. It uses the push notification function to send sale and coupon information in real time. It also displays a list of discount information on the in-app dashboard for easy access by the user. It also provides information on additional discounts that can be received by using a point card at specific stores. By providing information based on emotion recognition results, it is possible to provide information at the appropriate time that meets the user's needs.

[0188] (Forms of feedback and algorithmic improvement)

[0189] The user uses the information provided and evaluates the results. For example, they can enter feedback in the app, such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server analyzes the feedback and improves the accuracy of the generative AI algorithm and emotion engine. This will enable the provision of even more accurate information in the future.

[0190] (Example)

[0191] For example, let's say that the history of an online shopping app that User A frequently uses indicates that he or she has a high interest in a particular brand, Brand B. The device then detects User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device evaluates the results within the app. The device collects this feedback and sends it to the server.

[0192] In this way, the present invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and discount information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[0193] The processing flow will be explained below.

[0194] Step 1:

[0195] The device collects user usage data. This is done by applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is continuously recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[0196] Step 2:

[0197] The device uses web scraping technology to collect discount information from the Internet. This technology automatically retrieves sale and coupon information from related websites. It also analyzes video advertisements from platforms such as YouTube and social media to extract discount codes and limited campaign information. Furthermore, OCR technology is used on flyer images to extract sale information from the images as text.

[0198] Step 3:

[0199] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, it analyzes text comments entered by the user into the app to recognize emotions such as joy, sadness, and stress. It also recognizes emotions by analyzing facial expressions and tone of voice from voice data during calls and image data captured by the camera.

[0200] Step 4:

[0201] The server performs analysis based on the collected usage data and emotion recognition results. Specifically, it profiles the user's preferences and interests, taking into account their current emotional state as well as their purchase and search history. Based on this profiling, it identifies optimal service selections and deals. For example, if the user is feeling stressed, it will prioritize analyzing special sale information for relaxation services.

[0202] Step 5:

[0203] The server analyzes the discount information collected from the Internet and extracts information that matches the user's preferences and emotional state. In particular, if the user's emotional state is important, the server will prioritize the selection of sale information and coupon information that matches the user's feelings.

[0204] Step 6:

[0205] The device provides the user with the most appropriate information based on the analysis results. Information is sent in real time using push notifications. A list of special offers is also displayed on the in-app dashboard for easy access by the user. Information on additional discounts that can be obtained by using a point card is also provided at the same time. User satisfaction is improved by providing appropriate information at a time that matches the user's emotions based on the results of the emotion engine.

[0206] Step 7:

[0207] Users can utilize the information provided to them to take advantage of sales and coupons. For example, they can do online shopping based on sales information received via push notifications. After making a purchase, if the user actually uses the coupon, they can enter the results as feedback within the app.

[0208] Step 8:

[0209] The device collects feedback from users and sends it to the server, including data on whether the information provided was useful and whether special offers or coupons were used.

[0210] Step 9:

[0211] The server analyzes the feedback and uses it as data to improve the generative AI algorithm and emotion engine. Based on the feedback, adjustments can be made to improve the accuracy and relevance of the information provided, further improving the quality of future information provided.

[0212] In this way, through a series of steps, the system of the present invention comprehensively analyzes the user's usage data and emotions, and provides optimal services and discount information in real time, thereby improving the user's quality of life.

[0213] Example 2

[0214] 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."

[0215] In modern society, users are exposed to a vast amount of information, making it difficult to find information and services that are truly useful to them. Furthermore, conventional information provision systems do not provide information that takes into account the user's emotional state, making it difficult to provide services that are in line with the user's current psychological state. Because information provision is limited to information based on usage history and search history, it is difficult to maximize user satisfaction.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0217] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data and identifying optimal service selections and advantageous information, means for recognizing the emotional state of the user, means for providing optimal information to the user, and means for collecting feedback from the user and improving the algorithm. This makes it possible to provide information that takes into account the emotional state of the user in addition to their usage history and consumption patterns, thereby maximizing user satisfaction.

[0218] "Usage data" refers to data relating to a user's behavioral patterns, such as the user's application usage history, purchase history, and search history.

[0219] "Means of collecting information from the Internet" refers to techniques for obtaining text, video, and image information from the Internet using web scraping, video analysis, image OCR (optical character recognition) technology, etc.

[0220] "Means for analyzing collected data and identifying optimal service selection and advantageous information" refers to a means for analyzing collected usage data and emotion recognition results using generative AI models, etc., to select optimal services and useful information for users.

[0221] The "means for recognizing the user's emotional state" is an emotion engine technology for analyzing text data, voice data, and image data to identify the user's emotional state.

[0222] "Means of providing optimal information to users" refers to technology that provides the most appropriate information to users based on analysis results through push notifications, in-app dashboards, etc.

[0223] "Means for collecting feedback from users and improving the algorithm" refers to means for collecting evaluations and feedback given by users on the information provided, and using this information to improve the accuracy of the analysis model and emotion engine.

[0224] A "generative AI model" is an artificial intelligence technology that learns specific patterns and characteristics from large amounts of data and makes recommendations and predictions for new data.

[0225] A "prompt sentence" is an input sentence given to a generative AI model to obtain a specific output.

[0226] This invention is a system that uses a generative AI model and an emotion recognition engine as a smartphone application. This system collects user usage data and analyzes text, video, and image information on the Internet to provide valuable information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides information according to the user's emotions.

[0227] Specifically, the following hardware and software are used.

[0228] 1. Data Collection

[0229] Device: A dedicated application installed on a smartphone

[0230] Collection methods: Web scraping, video analysis, image OCR (Optical Character Recognition)

[0231] Data content: application usage history, purchase history, search history, etc.

[0232] 2. Emotion recognition

[0233] Device: Emotion engine installed on smartphone

[0234] Analysis method: Analysis of text data, audio data, and image data

[0235] Technology: Natural language processing, speech analysis, facial expression analysis

[0236] 3. Integrated Data Analysis

[0237] Server: High-Performance Computing Server

[0238] Analysis method: Integrated analysis of usage data and emotion recognition results using a generated AI model

[0239] Deliverables: Optimal service selection and deals for users

[0240] 4. Information provision

[0241] Device: Smartphone

[0242] Delivery method: Push notification, in-app dashboard

[0243] Information content: Special sale information, coupon information, relaxation service information

[0244] 5. Gathering feedback and improving the algorithm

[0245] Users: Provide feedback through a smartphone application

[0246] Device: Collect user feedback

[0247] Server: Analyze feedback data to improve the accuracy of generative AI and emotion engine

[0248] Specific examples

[0249] For example, if the history of an online shopping app that User A frequently uses indicates that he or she is highly interested in a particular brand B, the device can further detect User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device can rate the results within the app. The device collects this feedback and sends it to the server.

[0250] Prompt Sentence Examples

[0251] "Design a system to provide optimal services and special offers to a specific user based on their emotional state and past app usage history. The system will require the user to enter text indicating their emotional state within the app, and past purchase and usage history will also be analyzed. The goal is to improve user satisfaction by providing information according to their emotional state."

[0252] This invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and advantageous information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[0253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0254] Step 1:

[0255] Data collection

[0256] The device automatically collects usage data through applications installed on the user's smartphone. Input data includes application usage history, purchase history, search history, etc. Specifically, the device saves a log of each user's application operation. It also uses web scraping technology to obtain product price information and reviews from specific websites and saves them on the device. Furthermore, it uses OCR technology to extract text information from flyer images taken by the user. This data is then saved on the device for use in subsequent analysis steps.

[0257] Step 2:

[0258] emotion recognition

[0259] The device uses an emotion engine to recognize the user's emotional state. Input data includes the user's text data, voice data, and image data. Specifically, when the user enters a text comment into the app, the device analyzes the content using natural language processing technology to determine the user's emotional state. It also analyzes the tone and tempo of the voice data recorded by the user within the app, and analyzes facial expressions from image data. These results are stored on the device as emotion recognition data.

[0260] Step 3:

[0261] Integrated Data Analysis

[0262] The server performs an integrated analysis of the usage data and emotion recognition results sent from the device. The input data includes the user's application usage history, purchase history, search history, and emotion recognition data. Specifically, the server inputs this data into a generative AI model to identify optimal service selections and deals based on past usage history, consumption patterns, and current emotional state. For example, if the user is feeling stressed, it will prioritize the extraction of relaxation services and entertainment-related sales information. The analysis results are stored on the server for use in subsequent information provision steps.

[0263] Step 4:

[0264] Providing information

[0265] The device provides the user with the most appropriate information based on the analysis results sent from the server. The input data includes the recommended information sent from the server. Specifically, the device receives the analysis results and notifies the user via push notification. The app's dashboard also displays the latest sales information and a list of coupons. This allows the user to easily access the information and prompts them to take a specific action (for example, clicking the "Buy Now" button).

[0266] Step 5:

[0267] Feedback collection and algorithm improvement

[0268] The user uses the provided information and evaluates the results within the app. The input data includes the user's feedback on the provided information. Specifically, the user enters evaluations within the app, such as "This sale information was useful" or "I used the coupon." The device collects this feedback data and uploads it to the server. The server analyzes the collected feedback data and compares the output of the emotion engine with actual user feedback. This readjusts the parameters of the generative AI model and emotion engine, improving the accuracy of the recommended information.

[0269] (Application example 2)

[0270] 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."

[0271] The purpose of this invention is to provide a new method for analyzing a user's usage behavior and emotional state in a smartphone application to make appropriate recommendations for food delivery services. Conventional food delivery services generally suggest menu items simply based on a user's ordering history and preferences, but by taking the user's emotional state into account, more personalized recommendations become possible. This technology is expected to improve user satisfaction and increase the frequency of service use.

[0272] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0273] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data to identify optimal service selections and advantageous information, means for analyzing user emotion data to make optimal recommendations, and means for proposing recommended menus based on the user's emotional state in a food delivery service, thereby making it possible to provide optimal food delivery options that match the user's mood and emotional state.

[0274] definition statement

[0275] "Usage data" refers to information about the actions and operations a user performs through an application. Specifically, it includes application usage history, purchase history, search history, etc.

[0276] "Means of collecting information from the Internet" refers to technical means of obtaining useful information for users through web scraping, APIs, etc. Examples include text information, video information, flyer images, etc.

[0277] "Means for analyzing collected data and identifying optimal service selections and advantageous information" refers to a function that statistically or algorithmically analyzes collected data and determines the information that is most useful to the user.

[0278] "Means of providing optimal information to users" refers to technology that presents useful information through notifications and screen displays on users' smartphones and devices.

[0279] "Means of collecting feedback from users and improving the algorithm" refers to a function that collects user evaluations and results of the information provided as data and uses this data to improve the accuracy and performance of the system.

[0280] "Means for analyzing user emotional data and making optimal recommendations" refers to technology that estimates a user's emotional state from text data, voice data, and image data, and makes appropriate recommendations based on that.

[0281] "A means for suggesting recommended menu items based on the user's emotional state in a food delivery service" is a function that selects and presents menu items that correspond to the user's stress and mood state in a food delivery service.

[0282] MODE FOR CARRYING OUT THE INVENTION

[0283] The system of the present invention operates mainly through an application installed on a smartphone, analyzes user usage data and emotion data, and provides optimal recommendations for food delivery services. Specific embodiments for implementing the present invention are described below.

[0284] System Configuration

[0285] server:

[0286] 1. The server has a means for collecting usage data, specifically a database system for safely storing and analyzing data such as user application usage history, purchase history, and search history.

[0287] 2. It has the means to collect information from the internet. It has the ability to collect text information, video information, flyer images, etc. using web scraping and APIs.

[0288] 3. It has the means to analyze the collected data and identify the optimal service selection and advantageous information. The various collected data is processed using statistical analysis and machine learning algorithms to extract the optimal services and information for the user.

[0289] 4. It has a means to analyze the user's emotional data and make optimal recommendations. It uses an emotion engine that analyzes the user's emotional state from text data, voice data, and image data.

[0290] 5. A food delivery service has a means to suggest menu recommendations based on the user's emotional state. Using a generative AI model, menu suggestions are made that match the user's emotional state.

[0291] Device:

[0292] 1. It has the means to provide users with the most appropriate information. It has the function of sending recommended information to users' smartphones via push notifications, etc.

[0293] 2. It has a means to collect feedback from users and improve the algorithm. It has a function that allows users to input their evaluations and results of the information provided within the app and send them to the server.

[0294] Hardware and software used

[0295] 1. Hardware: Cloud servers, smartphones

[0296] 2. Software: Natural language processing libraries (e.g., TextBlob), image recognition libraries (e.g., pytesseract), database systems, web scraping tools (e.g., BeautifulSoup)

[0297] Specific examples of processing

[0298] The user inputs text or voice via the application. If the user inputs something like "I feel really tired today," the device analyzes this as text data and uses an emotion engine to determine the user's emotional state. If the current emotional state is determined to be "stressed," the server recommends a menu of foods that have a relaxation effect to the user based on the collected usage data and emotion data. This recommendation information is sent to the user via a push notification from the device. For example, in response to a prompt such as "What menu items do you recommend for relieving stress?" the server might provide information such as "Our special herbal tea and relaxing salad are currently on special offer."

[0299] In this way, the present invention improves user satisfaction in food delivery services by analyzing the user's emotional state and providing personalized recommendations. Furthermore, it is possible to improve the algorithm based on user feedback and provide more accurate information.

[0300] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0301] System program processing flow

[0302] Step 1:

[0303] The device collects user usage data, including application usage history, purchase history, and search history. The input data is the user activity log, and the output data is saved in a database.

[0304] Step 2:

[0305] The device collects information from the Internet, using web scraping or APIs to obtain content such as text, video, and flyer images. The input data is the URL of the web page or the API endpoint, and the output data is the information obtained from the scraping or API.

[0306] Step 3:

[0307] The server analyzes the collected data. Specifically, it uses statistical analysis and machine learning algorithms to identify the best services and deals for users. The input data is usage data and information collected from the Internet, and the output data is recommendation information as a result of the analysis.

[0308] Step 4:

[0309] The device collects the user's emotional input (e.g., text, voice, image). The input data is text or voice data entered by the user, which is converted into text data and sent to the emotion engine for analysis. The output data is the emotional state resulting from the analysis.

[0310] Step 5:

[0311] The server uses a generative AI model to make optimal recommendations based on the emotional state analyzed by the emotion engine. The input data is the emotional state, usage data, and information from the internet, and the generative AI model operates based on this to generate a recommendation menu as output data.

[0312] Step 6:

[0313] The device provides the user with the recommendation information sent from the server. Specifically, the information is presented through push notifications or in-app displays. The input data is the recommendation information sent from the server, and the output data is the notification or push message displayed to the user.

[0314] Step 7:

[0315] Users input feedback on the provided recommendation information within the app. The input data is feedback information, which is collected by the application and sent to the server to become output data.

[0316] Step 8:

[0317] The server analyzes the collected feedback and uses it to improve the algorithm: the input data is the user feedback, and the output data is the improved algorithm and the new recommendation model generated based on it.

[0318] At each step, we manage in detail how servers and devices collect, analyze, and provide data, aiming to improve user satisfaction and optimize services.

[0319] 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.

[0320] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0321] 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.

[0322] [Second embodiment]

[0323] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0324] 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.

[0325] 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).

[0326] 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.

[0327] 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.

[0328] 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).

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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."

[0335] This invention is a system that uses generative AI as a smartphone application. The system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection, with the aim of making users' lives more efficient.

[0336] (Form of data collection)

[0337] The device automatically collects usage data through applications installed on the user's smartphone. This data includes the user's application history, purchase history, search history, and so on. For example, usage records of online shopping apps and product search information within them are stored. The device also collects text information, video information, and flyer images from the Internet. For this purpose, web scraping, video analysis, and image OCR (optical character recognition) technologies are used.

[0338] (Form of data analysis)

[0339] The server performs an integrated analysis of the collected usage data and discount information on the Internet. Specifically, it profiles the user's preferences and interests based on their usage history and consumption patterns. For example, if there is a high interest in a particular brand or category of products, sales information for that brand will be analyzed first. The generation AI also scrutinizes the discount information analyzed and identifies the information that is most suitable for the user.

[0340] (Form of information provision)

[0341] The device then provides the user with the most appropriate information based on the analysis results. This is done using push notifications, which notify users of special offers in real time. The app also has a dashboard that displays a list of special offers. For example, the app can notify users of this week's special sales for brands they are interested in, along with information on how to get additional discounts by using a specific point card.

[0342] (Forms of feedback and algorithmic improvement)

[0343] The user uses the information provided and evaluates the results. For example, they input feedback such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server then analyzes the collected feedback and improves the AI's algorithm. This will enable the provision of even more accurate deals in the future.

[0344] (Example)

[0345] For example, suppose that the history of an online shopping app that User A frequently uses indicates that User A has a high interest in a particular brand B. The device collects information about special sales for Brand B from the Internet using web scraping and analyzes it on the server. As a result of the analysis, it identifies information about a special sale on Brand B's products this weekend. The device sends User A a push notification informing him of this weekend's special sales for Brand B, and also displays the information on the app's dashboard. Furthermore, the device also provides User A with information about stores where additional discounts can be received by using a specific point card, thereby increasing User A's satisfaction.

[0346] In this way, the present invention can maximize the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0347] The processing flow will be explained below.

[0348] Step 1:

[0349] The device collects user usage data. This is done through applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[0350] Step 2:

[0351] The device collects discount information from the internet. It uses web scraping technology to automatically retrieve sale and coupon information from related websites. It also analyzes advertising videos from video platforms such as YouTube and social media to extract discount codes and limited campaign information. For flyer images, OCR technology is used to analyze the text information within the image to obtain sale information.

[0352] Step 3:

[0353] The server analyzes the collected usage data. Specifically, it profiles the user's preferences and interests based on the user's past purchase and search history. This profiling can identify the brands and product categories in which the user is particularly interested.

[0354] Step 4:

[0355] The server analyzes the collected information on deals on the Internet, and prioritizes the extraction of information that matches the user's preferences and interests. Based on the results of this analysis, the server identifies the most useful sales and coupon information for the user.

[0356] Step 5:

[0357] The device provides the analysis results to the user. Specifically, it uses push notifications to send users real-time sales and coupon information. It also displays a list of deals on the app's dashboard for easy access. It also provides information on additional discounts that can be received by using a point card at specific stores.

[0358] Step 6:

[0359] The user can use the provided information to take advantage of special sales and coupons. For example, they can do online shopping based on the special sale information they were notified about. When the user actually uses a coupon, the app provides feedback on the results.

[0360] Step 7:

[0361] The device collects feedback from the user, including whether the information provided was useful, whether the sales or coupons were actually used, etc. This feedback is recorded as data that will be used for later analysis.

[0362] Step 8:

[0363] The server analyzes the collected feedback and improves the generative AI algorithm. Based on the feedback, it makes adjustments to improve the accuracy and relevance of the information provided, thereby further improving the quality of information provided to users in the future.

[0364] Through this series of steps, users can efficiently obtain optimal services and deals, and build wealth and save money without spending time or effort.

[0365] Example 1

[0366] 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."

[0367] In today's lifestyles, users face difficulties in finding useful information from the vast amount of information available. Furthermore, existing information services often fail to adequately address users' individual preferences and needs. As a result, users are overwhelmed with unnecessary information, hindering efficient decision-making.

[0368] 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.

[0369] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data using a generative AI model to identify optimal service selections and advantageous information, thereby enabling the provision of personalized information based on each user's preferences and consumption patterns.

[0370] "Usage data" refers to data that includes application usage history, purchase history, search history, etc. when a user uses a smartphone or other device.

[0371] "Means of collecting information from the Internet" refers to technical means for collecting text information, video information, flyer images, etc. on the Internet.

[0372] A "generative AI model" is an artificial intelligence model that learns information from large amounts of data and is used to analyze user preferences and consumption patterns.

[0373] "Analysis" is the process of organizing and analyzing information based on collected data to find specific patterns and relationships.

[0374] "Profiling" is a method of analyzing a user's usage history and consumption patterns to reveal their preferences and interests.

[0375] "OCR technology" is a technology that recognizes characters in an image and extracts them as text data.

[0376] "Video analysis" is a technology that analyzes video data and extracts useful information.

[0377] "Push notification" is a technology that sends information from a server to a user's device in real time.

[0378] "Algorithm improvement" is the process of improving existing algorithms based on feedback data to improve the accuracy of information provided.

[0379] MODE FOR CARRYING OUT THE INVENTION

[0380] This invention is a system that uses an application installed on a user's smartphone to select the best service and provide information on deals from a vast amount of information using a generative AI model. This system involves a series of steps: collecting usage data, collecting information from the Internet, analyzing the data, providing the information, and collecting feedback and improving the algorithm.

[0381] Data collection

[0382] The device launches applications installed on the user's smartphone and collects user usage data. This data includes application usage history, purchase history, search history, etc. For example, if a user uses an online shopping app, the usage record will be collected. In addition, the device uses the following software to collect text information, video information, and flyer images from the Internet:

[0383] Web scraping: Collecting text data from web pages using BeautifulSoup.

[0384] Video analysis: Use OpenCV to extract useful information from videos.

[0385] OCR technology: Uses Tesseract to read characters from images.

[0386] Data analysis

[0387] The server performs an integrated analysis of the collected usage data and online deals. Specifically, it uses a generative AI model (e.g., GPT-4 or other natural language processing model) to profile users' preferences and interests. The analysis methods include:

[0388] Statistical analysis: Historical usage data is used to statistically analyze consumption patterns.

[0389] Natural language processing: Analyzes collected text data and extracts relevant keywords and phrases.

[0390] Machine learning model: Generative AI models are used to predict user preferences and select the most appropriate information.

[0391] Providing information

[0392] Based on the analysis results, the device provides the user with the most appropriate information in real time. It notifies users of special offers in real time using push notification (e.g., Firebase Cloud Messaging), and also has a function to display a list of special offers on the in-app dashboard. For example, it notifies users of special offers on brands they are interested in, or information on additional discounts they can receive by using a specific point card.

[0393] Feedback and algorithm improvements

[0394] Users provide feedback within the application to evaluate whether the information provided was useful. For example, they answer "yes" or "no" to the question, "Was this sale information useful?" The device collects this feedback data and sends it to the server. The server analyzes the collected feedback data and improves the AI's algorithm, which will enable the provision of even more accurate information in the future.

[0395] Specific examples

[0396] For example, if the history of an online shopping app frequently used by User A indicates a high level of interest in a particular Brand B, the device will collect information about special sales for Brand B from the Internet through web scraping, and the server will analyze it using a generative AI model. As a result, when information is obtained that Brand B's products will be on sale this weekend, the device will provide User A with a push notification informing them of "Brand B's special sales this weekend," and the information will also be displayed on the app's dashboard. Furthermore, information about additional discounts that can be received by using a specific point card will also be provided, improving User A's satisfaction.

[0397] Prompt Sentence Examples

[0398] "Please analyze the information about special offers based on the user's usage history data. In particular, please prioritize extracting information about special sales of Brand B's products and think about the best way to notify the user."

[0399] In this way, the present invention maximizes the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0401] Step 1: Data collection

[0402] The device launches applications installed on the smartphone and collects usage data. This data includes application usage history, purchase history, and search history. For example, when a user uses an online shopping application, the usage record is collected. The input data is application history data, purchase data, and search data. This information is stored in a database as output data. Specifically, the application periodically collects usage history information in the background and stores it in a local database.

[0403] Step 2: Gather information from the internet

[0404] The device collects text information, video information, and flyer images from the Internet. To do this, it uses web scraping (e.g., BeautifulSoup), video analysis (e.g., OpenCV), and image OCR (e.g., Tesseract). The input data is various media content from the Internet. The output data, which includes the collected text information, video analysis results, and OCR results, is stored in a database. Specifically, a scheduled job periodically crawls specific websites and extracts the required information.

[0405] Step 3: Data analysis

[0406] The server performs an integrated analysis of the collected usage data and online deals. It uses a generative AI model (e.g., GPT-4) to profile the user's preferences and interests. The input data is usage history data and collected internet information. The output data is personalized information based on the user's preferences. Specifically, the server processes data batches every night, runs analysis using the generative AI model, and updates the user profile.

[0407] Step 4: Provide information

[0408] The device provides the user with the most appropriate information in real time based on the analysis results. Information is sent in real time using the push notification function (e.g., Firebase Cloud Messaging). Special offers are also displayed on the dashboard within the user's smartphone app. The input data is the analyzed personalized information. The output data is the push notification and the dashboard display content, which are reflected on the user's device. Specifically, the system is set up to send a push notification immediately after the analysis results are obtained.

[0409] Step 5: Gather feedback and improve the algorithm

[0410] The user inputs feedback on the provided information within the application. For example, they provide feedback in response to the question, "Was this sale information useful?" The device collects this feedback and sends it to the server. The input data is the user's feedback information. The feedback data is saved on the server as output data. Specifically, the in-app feedback form collects user ratings and periodically sends them to the server.

[0411] Step 6: Improve the algorithm

[0412] The server analyzes the collected feedback data and improves the algorithm of the generative AI model. The input data is the feedback collected from users. The output data is an improved generative AI model. Specifically, the server periodically retrains the generative AI model using the feedback data to improve the accuracy of the next data analysis.

[0413] This series of steps ensures that information is provided to users in real time and that the algorithm is continually improved.

[0414] (Application example 1)

[0415] 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."

[0416] Conventional smartphone applications were able to provide optimal service selection and discount information by collecting user usage data and information from the Internet. However, they lacked functionality to support the shopping experience in physical stores, making it difficult for users to obtain real-time sales and discount information that can be obtained directly from stores. In addition, there were insufficient means of collecting specific information using image recognition technology. Therefore, there is a need for a method to further improve the user's shopping experience.

[0417] 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.

[0418] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data to identify optimal service selections and discount information. This makes it possible to notify users of sale information in real time and improve their shopping experience. Furthermore, by including a means for extracting text information from images using image recognition technology, a means for displaying collected sale information in list format, and a means for customizing information based on user preferences, it becomes possible to provide more specific and personalized information.

[0419] "Usage data" refers to data such as the application usage history, search history, and purchase history of the user using the smartphone.

[0420] "Collecting information from the Internet" refers to collecting information that is publicly available on the Internet, such as websites, videos, and flyer images.

[0421] "Analyzing collected data" refers to the process of analyzing collected usage data and information on the Internet to clarify user preferences and behavioral patterns.

[0422] "Identifying optimal service selection and discount information" refers to selecting highly useful services and discount information based on the user's profile.

[0423] "Providing information to users" refers to providing useful information to users through notification and display functions based on collected and analyzed data.

[0424] "Collecting feedback and improving the algorithm" refers to the process of adjusting the analysis algorithm based on feedback provided by users, with the aim of providing even more accurate information.

[0425] "Real-time notification of sales information at physical stores" refers to notifying users of sales information at stores they actually visit in real time on their smartphones.

[0426] "Extracting text information using image recognition technology" refers to reading text data from an image using technology such as OCR.

[0427] "Displaying sale information in list format" refers to organizing the analyzed sale information into a list and displaying it in a format that is easy for the user to view.

[0428] "Customizing information" refers to providing personalized information based on the user's individual preferences and behavioral patterns.

[0429] This invention is a system that uses a generative AI model to collect and analyze user usage data and information on the Internet, and provides users with optimal service selection and advantageous information. The main components include a terminal that runs on the user's smartphone, a server that collects and analyzes data, and information sources on the Internet.

[0430] 1. Collection of User Data

[0431] The device collects usage data about the user's smartphone, including application usage history, search history, and purchase history. This data serves as the basis for analyzing user preferences and purchasing habits.

[0432] 2. Collecting information from the Internet

[0433] The server uses web scraping and OCR (optical character recognition) technology to collect sales and discount information from text, videos, and flyer images on the Internet, allowing you to obtain the latest information in real time.

[0434] 3. Data analysis

[0435] The server performs an integrated analysis of the collected usage data and information on the internet. It uses a generative AI model to analyze the user's usage history and consumption patterns to profile their preferences. It also analyzes the collected sales information to identify the best deals for the user.

[0436] 4. Information provision

[0437] The device receives the analysis results from the server and provides users with real-time push notifications about special offers. The app also has a feature that displays a list of special offers on the app's dashboard. For example, special offers for brands or product categories that the user is interested in can be displayed preferentially.

[0438] 5. Gathering feedback and improving the algorithm

[0439] The device collects user feedback and sends it to the server, which analyzes it and refines the algorithm of the generative AI model, thereby improving the accuracy of future deals.

[0440] Hardware and software used

[0441] Hardware: Smartphone (iOS or Android)

[0442] software:

[0443] Programming language: Python 3

[0444] Web scraping libraries: requests, BeautifulSoup

[0445] Image processing library: OpenCV

[0446] OCR library: pytesseract

[0447] Machine learning library: scikit-learn

[0448] Specific examples

[0449] For example, user A's purchase history at a supermarket he frequently visits can be analyzed to determine that he has a high interest in daily necessities and food, especially in products from a particular brand. The server then uses web scraping to obtain images of supermarket flyers containing products from that brand, and uses OCR technology to extract text information. The generative AI model then analyzes the collected data and notifies user A in real time of the supermarket's special sales for this weekend. It also provides information on how to obtain additional discounts by using a point card in addition to the sales information.

[0450] Prompt Sentence Examples

[0451] "We will analyze User A's past purchases and search history to gather information on deals from the internet. We will then build a system that will provide users with push notifications about sales on specific products. This system will collect and analyze data using web scraping, OCR technology, and machine learning."

[0452] As described above, this invention is a system that can utilize user data and generative AI models to greatly improve the shopping experience in physical stores.

[0453] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0454] Step 1:

[0455] User Data Collection

[0456] The device collects usage data from the user's smartphone. Specifically, application usage history, search history, purchase history, etc. are automatically acquired. This input data includes information on products and brands that the user is interested in. This data is collected and sent to the server.

[0457] Step 2:

[0458] Gathering information from the internet

[0459] The server uses web scraping technology to collect sale information on the Internet. Specifically, it accesses specific URLs, analyzes HTML, and extracts the necessary information. It also uses OCR technology to extract text information from flyer images. This allows it to obtain text and image information as input data and store it in a database.

[0460] Step 3:

[0461] Data analysis

[0462] The server performs an integrated analysis of the collected usage data and information on the Internet. A generative AI model is used to profile the user's preferences and consumption patterns. In this step, the user's history data and sales information are used as input data, and optimal service selection and discount information are output. Specifically, a clustering algorithm (e.g., KMeans) is used to identify user groups with similar preferences.

[0463] Step 4:

[0464] Providing information

[0465] The device receives the analysis results from the server and provides the user with the most appropriate information. Specifically, it notifies the user of sale information in real time using the push notification function. It also displays a list of sale information on the app's dashboard. In this step, the analysis results are used as input, and the output is notification or display to the user.

[0466] Step 5:

[0467] Feedback collection and algorithm improvement

[0468] The device collects feedback from users and sends it to the server. Specifically, it obtains feedback data such as whether the sale information was useful and whether the coupon was used. Based on this input data, the server improves the algorithm of the generative AI model. Improvements to the algorithm will improve the accuracy of information provided from the next time onwards.

[0469] This series of processing steps allows users to maximize their shopping experience in a physical store, which will greatly improve user convenience and satisfaction.

[0470] 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.

[0471] This invention is a system that uses generative AI and an emotion engine as a smartphone application. This system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides information according to the user's emotional state.

[0472] (Form of data collection)

[0473] The device automatically collects usage data through applications installed on the user's smartphone. This data includes application usage history, purchase history, search history, etc. Web scraping, video analysis, and image OCR (optical character recognition) technologies are also used to collect text information, video information, and flyer images from the Internet.

[0474] (Forms of emotion recognition)

[0475] The device uses an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, when a user enters a text comment into an app, the content is analyzed to determine the user's current state of mind. The device also analyzes the tone and tempo of the voice data and facial expressions from image data.

[0476] (Forms of data and sentiment analysis)

[0477] The server performs an integrated analysis of the collected usage data and emotion recognition results. Specifically, it identifies optimal service selection and discount information based on the user's current emotional state in addition to their past usage history and consumption patterns. For example, if the user is feeling stressed, it prioritizes relaxation services and entertainment-related sales information. Also, if the user is excited, it provides information that encourages action that leads to immediate purchase.

[0478] (Form of information provision)

[0479] The device provides the user with the most appropriate information based on the analysis results. It uses the push notification function to send sale and coupon information in real time. It also displays a list of discount information on the in-app dashboard for easy access by the user. It also provides information on additional discounts that can be received by using a point card at specific stores. By providing information based on emotion recognition results, it is possible to provide information at the appropriate time that meets the user's needs.

[0480] (Forms of feedback and algorithmic improvement)

[0481] The user uses the information provided and evaluates the results. For example, they can enter feedback in the app, such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server analyzes the feedback and improves the accuracy of the generative AI algorithm and emotion engine. This will enable the provision of even more accurate information in the future.

[0482] (Example)

[0483] For example, let's say that the history of an online shopping app that User A frequently uses indicates that he or she has a high interest in a particular brand, Brand B. The device then detects User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device evaluates the results within the app. The device collects this feedback and sends it to the server.

[0484] In this way, the present invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and discount information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[0485] The processing flow will be explained below.

[0486] Step 1:

[0487] The device collects user usage data. This is done by applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is continuously recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[0488] Step 2:

[0489] The device uses web scraping technology to collect discount information from the Internet. This technology automatically retrieves sale and coupon information from related websites. It also analyzes video advertisements from platforms such as YouTube and social media to extract discount codes and limited campaign information. Furthermore, OCR technology is used on flyer images to extract sale information from the images as text.

[0490] Step 3:

[0491] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, it analyzes text comments entered by the user into the app to recognize emotions such as joy, sadness, and stress. It also recognizes emotions by analyzing facial expressions and tone of voice from voice data during calls and image data captured by the camera.

[0492] Step 4:

[0493] The server performs analysis based on the collected usage data and emotion recognition results. Specifically, it profiles the user's preferences and interests, taking into account their current emotional state as well as their purchase and search history. Based on this profiling, it identifies optimal service selections and deals. For example, if the user is feeling stressed, it will prioritize analyzing special sale information for relaxation services.

[0494] Step 5:

[0495] The server analyzes the discount information collected from the Internet and extracts information that matches the user's preferences and emotional state. In particular, if the user's emotional state is important, the server will prioritize the selection of sale information and coupon information that matches the user's feelings.

[0496] Step 6:

[0497] The device provides the user with the most appropriate information based on the analysis results. Information is sent in real time using push notifications. A list of special offers is also displayed on the in-app dashboard for easy access by the user. Information on additional discounts that can be obtained by using a point card is also provided at the same time. User satisfaction is improved by providing appropriate information at a time that matches the user's emotions based on the results of the emotion engine.

[0498] Step 7:

[0499] Users can utilize the information provided to them to take advantage of sales and coupons. For example, they can do online shopping based on sales information received via push notifications. After making a purchase, if the user actually uses the coupon, they can enter the results as feedback within the app.

[0500] Step 8:

[0501] The device collects feedback from users and sends it to the server, including data on whether the information provided was useful and whether special offers or coupons were used.

[0502] Step 9:

[0503] The server analyzes the feedback and uses it as data to improve the generative AI algorithm and emotion engine. Based on the feedback, adjustments can be made to improve the accuracy and relevance of the information provided, further improving the quality of future information provided.

[0504] In this way, through a series of steps, the system of the present invention comprehensively analyzes the user's usage data and emotions, and provides optimal services and discount information in real time, thereby improving the user's quality of life.

[0505] Example 2

[0506] 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."

[0507] In modern society, users are exposed to a vast amount of information, making it difficult to find information and services that are truly useful to them. Furthermore, conventional information provision systems do not provide information that takes into account the user's emotional state, making it difficult to provide services that are in line with the user's current psychological state. Because information provision is limited to information based on usage history and search history, it is difficult to maximize user satisfaction.

[0508] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0509] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data and identifying optimal service selections and advantageous information, means for recognizing the emotional state of the user, means for providing optimal information to the user, and means for collecting feedback from the user and improving the algorithm. This makes it possible to provide information that takes into account the emotional state of the user in addition to their usage history and consumption patterns, thereby maximizing user satisfaction.

[0510] "Usage data" refers to data relating to a user's behavioral patterns, such as the user's application usage history, purchase history, and search history.

[0511] "Means of collecting information from the Internet" refers to techniques for obtaining text, video, and image information from the Internet using web scraping, video analysis, image OCR (optical character recognition) technology, etc.

[0512] "Means for analyzing collected data and identifying optimal service selection and advantageous information" refers to a means for analyzing collected usage data and emotion recognition results using generative AI models, etc., to select optimal services and useful information for users.

[0513] The "means for recognizing the user's emotional state" is an emotion engine technology for analyzing text data, voice data, and image data to identify the user's emotional state.

[0514] "Means of providing optimal information to users" refers to technology that provides the most appropriate information to users based on analysis results through push notifications, in-app dashboards, etc.

[0515] "Means for collecting feedback from users and improving the algorithm" refers to means for collecting evaluations and feedback given by users on the information provided, and using this information to improve the accuracy of the analysis model and emotion engine.

[0516] A "generative AI model" is an artificial intelligence technology that learns specific patterns and characteristics from large amounts of data and makes recommendations and predictions for new data.

[0517] A "prompt sentence" is an input sentence given to a generative AI model to obtain a specific output.

[0518] This invention is a system that uses a generative AI model and an emotion recognition engine as a smartphone application. This system collects user usage data and analyzes text, video, and image information on the Internet to provide valuable information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides information according to the user's emotions.

[0519] Specifically, the following hardware and software are used.

[0520] 1. Data Collection

[0521] Device: A dedicated application installed on a smartphone

[0522] Collection methods: Web scraping, video analysis, image OCR (Optical Character Recognition)

[0523] Data content: application usage history, purchase history, search history, etc.

[0524] 2. Emotion recognition

[0525] Device: Emotion engine installed on smartphone

[0526] Analysis method: Analysis of text data, audio data, and image data

[0527] Technology: Natural language processing, speech analysis, facial expression analysis

[0528] 3. Integrated Data Analysis

[0529] Server: High-Performance Computing Server

[0530] Analysis method: Integrated analysis of usage data and emotion recognition results using a generated AI model

[0531] Deliverables: Optimal service selection and deals for users

[0532] 4. Information provision

[0533] Device: Smartphone

[0534] Delivery method: Push notification, in-app dashboard

[0535] Information content: Special sale information, coupon information, relaxation service information

[0536] 5. Gathering feedback and improving the algorithm

[0537] Users: Provide feedback through a smartphone application

[0538] Device: Collect user feedback

[0539] Server: Analyze feedback data to improve the accuracy of generative AI and emotion engine

[0540] Specific examples

[0541] For example, if the history of an online shopping app that User A frequently uses indicates that he or she is highly interested in a particular brand B, the device can further detect User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device can rate the results within the app. The device collects this feedback and sends it to the server.

[0542] Prompt Sentence Examples

[0543] "Design a system to provide optimal services and special offers to a specific user based on their emotional state and past app usage history. The system will require the user to enter text indicating their emotional state within the app, and past purchase and usage history will also be analyzed. The goal is to improve user satisfaction by providing information according to their emotional state."

[0544] This invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and advantageous information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[0545] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0546] Step 1:

[0547] Data collection

[0548] The device automatically collects usage data through applications installed on the user's smartphone. Input data includes application usage history, purchase history, search history, etc. Specifically, the device saves a log of each user's application operation. It also uses web scraping technology to obtain product price information and reviews from specific websites and saves them on the device. Furthermore, it uses OCR technology to extract text information from flyer images taken by the user. This data is then saved on the device for use in subsequent analysis steps.

[0549] Step 2:

[0550] emotion recognition

[0551] The device uses an emotion engine to recognize the user's emotional state. Input data includes the user's text data, voice data, and image data. Specifically, when the user enters a text comment into the app, the device analyzes the content using natural language processing technology to determine the user's emotional state. It also analyzes the tone and tempo of the voice data recorded by the user within the app, and analyzes facial expressions from image data. These results are stored on the device as emotion recognition data.

[0552] Step 3:

[0553] Integrated Data Analysis

[0554] The server performs an integrated analysis of the usage data and emotion recognition results sent from the device. The input data includes the user's application usage history, purchase history, search history, and emotion recognition data. Specifically, the server inputs this data into a generative AI model to identify optimal service selections and deals based on past usage history, consumption patterns, and current emotional state. For example, if the user is feeling stressed, it will prioritize the extraction of relaxation services and entertainment-related sales information. The analysis results are stored on the server for use in subsequent information provision steps.

[0555] Step 4:

[0556] Providing information

[0557] The device provides the user with the most appropriate information based on the analysis results sent from the server. The input data includes the recommended information sent from the server. Specifically, the device receives the analysis results and notifies the user via push notification. The app's dashboard also displays the latest sales information and a list of coupons. This allows the user to easily access the information and prompts them to take a specific action (for example, clicking the "Buy Now" button).

[0558] Step 5:

[0559] Feedback collection and algorithm improvement

[0560] The user uses the provided information and evaluates the results within the app. The input data includes the user's feedback on the provided information. Specifically, the user enters evaluations within the app, such as "This sale information was useful" or "I used the coupon." The device collects this feedback data and uploads it to the server. The server analyzes the collected feedback data and compares the output of the emotion engine with actual user feedback. This readjusts the parameters of the generative AI model and emotion engine, improving the accuracy of the recommended information.

[0561] (Application example 2)

[0562] 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."

[0563] The purpose of this invention is to provide a new method for analyzing a user's usage behavior and emotional state in a smartphone application to make appropriate recommendations for food delivery services. Conventional food delivery services generally suggest menu items simply based on a user's ordering history and preferences, but by taking the user's emotional state into account, more personalized recommendations become possible. This technology is expected to improve user satisfaction and increase the frequency of service use.

[0564] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0565] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data to identify optimal service selections and advantageous information, means for analyzing user emotion data to make optimal recommendations, and means for proposing recommended menus based on the user's emotional state in a food delivery service, thereby making it possible to provide optimal food delivery options that match the user's mood and emotional state.

[0566] definition statement

[0567] "Usage data" refers to information about the actions and operations a user performs through an application. Specifically, it includes application usage history, purchase history, search history, etc.

[0568] "Means of collecting information from the Internet" refers to technical means of obtaining useful information for users through web scraping, APIs, etc. Examples include text information, video information, flyer images, etc.

[0569] "Means for analyzing collected data and identifying optimal service selections and advantageous information" refers to a function that statistically or algorithmically analyzes collected data and determines the information that is most useful to the user.

[0570] "Means of providing optimal information to users" refers to technology that presents useful information through notifications and screen displays on users' smartphones and devices.

[0571] "Means of collecting feedback from users and improving the algorithm" refers to a function that collects user evaluations and results of the information provided as data and uses this data to improve the accuracy and performance of the system.

[0572] "Means for analyzing user emotional data and making optimal recommendations" refers to technology that estimates a user's emotional state from text data, voice data, and image data, and makes appropriate recommendations based on that.

[0573] "A means for suggesting recommended menu items based on the user's emotional state in a food delivery service" is a function that selects and presents menu items that correspond to the user's stress and mood state in a food delivery service.

[0574] MODE FOR CARRYING OUT THE INVENTION

[0575] The system of the present invention operates mainly through an application installed on a smartphone, analyzes user usage data and emotion data, and provides optimal recommendations for food delivery services. Specific embodiments for implementing the present invention are described below.

[0576] System Configuration

[0577] server:

[0578] 1. The server has a means for collecting usage data, specifically a database system for safely storing and analyzing data such as user application usage history, purchase history, and search history.

[0579] 2. It has the means to collect information from the internet. It has the ability to collect text information, video information, flyer images, etc. using web scraping and APIs.

[0580] 3. It has the means to analyze the collected data and identify the optimal service selection and advantageous information. The various collected data is processed using statistical analysis and machine learning algorithms to extract the optimal services and information for the user.

[0581] 4. It has a means to analyze the user's emotional data and make optimal recommendations. It uses an emotion engine that analyzes the user's emotional state from text data, voice data, and image data.

[0582] 5. A food delivery service has a means to suggest menu recommendations based on the user's emotional state. Using a generative AI model, menu suggestions are made that match the user's emotional state.

[0583] Device:

[0584] 1. It has the means to provide users with the most appropriate information. It has the function of sending recommended information to users' smartphones via push notifications, etc.

[0585] 2. It has a means to collect feedback from users and improve the algorithm. It has a function that allows users to input their evaluations and results of the information provided within the app and send them to the server.

[0586] Hardware and software used

[0587] 1. Hardware: Cloud servers, smartphones

[0588] 2. Software: Natural language processing libraries (e.g., TextBlob), image recognition libraries (e.g., pytesseract), database systems, web scraping tools (e.g., BeautifulSoup)

[0589] Specific examples of processing

[0590] The user inputs text or voice via the application. If the user inputs something like "I feel really tired today," the device analyzes this as text data and uses an emotion engine to determine the user's emotional state. If the current emotional state is determined to be "stressed," the server recommends a menu of foods that have a relaxation effect to the user based on the collected usage data and emotion data. This recommendation information is sent to the user via a push notification from the device. For example, in response to a prompt such as "What menu items do you recommend for relieving stress?" the server might provide information such as "Our special herbal tea and relaxing salad are currently on special offer."

[0591] In this way, the present invention improves user satisfaction in food delivery services by analyzing the user's emotional state and providing personalized recommendations. Furthermore, it is possible to improve the algorithm based on user feedback and provide more accurate information.

[0592] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0593] System program processing flow

[0594] Step 1:

[0595] The device collects user usage data, including application usage history, purchase history, and search history. The input data is the user activity log, and the output data is saved in a database.

[0596] Step 2:

[0597] The device collects information from the Internet, using web scraping or APIs to obtain content such as text, video, and flyer images. The input data is the URL of the web page or the API endpoint, and the output data is the information obtained from the scraping or API.

[0598] Step 3:

[0599] The server analyzes the collected data. Specifically, it uses statistical analysis and machine learning algorithms to identify the best services and deals for users. The input data is usage data and information collected from the Internet, and the output data is recommendation information as a result of the analysis.

[0600] Step 4:

[0601] The device collects the user's emotional input (e.g., text, voice, image). The input data is text or voice data entered by the user, which is converted into text data and sent to the emotion engine for analysis. The output data is the emotional state resulting from the analysis.

[0602] Step 5:

[0603] The server uses a generative AI model to make optimal recommendations based on the emotional state analyzed by the emotion engine. The input data is the emotional state, usage data, and information from the internet, and the generative AI model operates based on this to generate a recommendation menu as output data.

[0604] Step 6:

[0605] The device provides the user with the recommendation information sent from the server. Specifically, the information is presented through push notifications or in-app displays. The input data is the recommendation information sent from the server, and the output data is the notification or push message displayed to the user.

[0606] Step 7:

[0607] Users input feedback on the provided recommendation information within the app. The input data is feedback information, which is collected by the application and sent to the server to become output data.

[0608] Step 8:

[0609] The server analyzes the collected feedback and uses it to improve the algorithm: the input data is the user feedback, and the output data is the improved algorithm and the new recommendation model generated based on it.

[0610] At each step, we manage in detail how servers and devices collect, analyze, and provide data, aiming to improve user satisfaction and optimize services.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] [Third embodiment]

[0615] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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."

[0627] This invention is a system that uses generative AI as a smartphone application. The system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection, with the aim of making users' lives more efficient.

[0628] (Form of data collection)

[0629] The device automatically collects usage data through applications installed on the user's smartphone. This data includes the user's application history, purchase history, search history, and so on. For example, usage records of online shopping apps and product search information within them are stored. The device also collects text information, video information, and flyer images from the Internet. For this purpose, web scraping, video analysis, and image OCR (optical character recognition) technologies are used.

[0630] (Form of data analysis)

[0631] The server performs an integrated analysis of the collected usage data and discount information on the Internet. Specifically, it profiles the user's preferences and interests based on their usage history and consumption patterns. For example, if there is a high interest in a particular brand or category of products, sales information for that brand will be analyzed first. The generation AI also scrutinizes the discount information analyzed and identifies the information that is most suitable for the user.

[0632] (Form of information provision)

[0633] The device then provides the user with the most appropriate information based on the analysis results. This is done using push notifications, which notify users of special offers in real time. The app also has a dashboard that displays a list of special offers. For example, the app can notify users of this week's special sales for brands they are interested in, along with information on how to get additional discounts by using a specific point card.

[0634] (Forms of feedback and algorithmic improvement)

[0635] The user uses the information provided and evaluates the results. For example, they input feedback such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server then analyzes the collected feedback and improves the AI's algorithm. This will enable the provision of even more accurate deals in the future.

[0636] (Example)

[0637] For example, suppose that the history of an online shopping app that User A frequently uses indicates that User A has a high interest in a particular brand B. The device collects information about special sales for Brand B from the Internet using web scraping and analyzes it on the server. As a result of the analysis, it identifies information about a special sale on Brand B's products this weekend. The device sends User A a push notification informing him of this weekend's special sales for Brand B, and also displays the information on the app's dashboard. Furthermore, the device also provides User A with information about stores where additional discounts can be received by using a specific point card, thereby increasing User A's satisfaction.

[0638] In this way, the present invention can maximize the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0639] The processing flow will be explained below.

[0640] Step 1:

[0641] The device collects user usage data. This is done through applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[0642] Step 2:

[0643] The device collects discount information from the internet. It uses web scraping technology to automatically retrieve sale and coupon information from related websites. It also analyzes advertising videos from video platforms such as YouTube and social media to extract discount codes and limited campaign information. For flyer images, OCR technology is used to analyze the text information within the image to obtain sale information.

[0644] Step 3:

[0645] The server analyzes the collected usage data. Specifically, it profiles the user's preferences and interests based on the user's past purchase and search history. This profiling can identify the brands and product categories in which the user is particularly interested.

[0646] Step 4:

[0647] The server analyzes the collected information on deals on the Internet, and prioritizes the extraction of information that matches the user's preferences and interests. Based on the results of this analysis, the server identifies the most useful sales and coupon information for the user.

[0648] Step 5:

[0649] The device provides the analysis results to the user. Specifically, it uses push notifications to send users real-time sales and coupon information. It also displays a list of deals on the app's dashboard for easy access. It also provides information on additional discounts that can be received by using a point card at specific stores.

[0650] Step 6:

[0651] The user can use the provided information to take advantage of special sales and coupons. For example, they can do online shopping based on the special sale information they were notified about. When the user actually uses a coupon, the app provides feedback on the results.

[0652] Step 7:

[0653] The device collects feedback from the user, including whether the information provided was useful, whether the sales or coupons were actually used, etc. This feedback is recorded as data that will be used for later analysis.

[0654] Step 8:

[0655] The server analyzes the collected feedback and improves the generative AI algorithm. Based on the feedback, it makes adjustments to improve the accuracy and relevance of the information provided, thereby further improving the quality of information provided to users in the future.

[0656] Through this series of steps, users can efficiently obtain optimal services and deals, and build wealth and save money without spending time or effort.

[0657] Example 1

[0658] 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."

[0659] In today's lifestyles, users face difficulties in finding useful information from the vast amount of information available. Furthermore, existing information services often fail to adequately address users' individual preferences and needs. As a result, users are overwhelmed with unnecessary information, hindering efficient decision-making.

[0660] 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.

[0661] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data using a generative AI model to identify optimal service selections and advantageous information, thereby enabling the provision of personalized information based on each user's preferences and consumption patterns.

[0662] "Usage data" refers to data that includes application usage history, purchase history, search history, etc. when a user uses a smartphone or other device.

[0663] "Means of collecting information from the Internet" refers to technical means for collecting text information, video information, flyer images, etc. on the Internet.

[0664] A "generative AI model" is an artificial intelligence model that learns information from large amounts of data and is used to analyze user preferences and consumption patterns.

[0665] "Analysis" is the process of organizing and analyzing information based on collected data to find specific patterns and relationships.

[0666] "Profiling" is a method of analyzing a user's usage history and consumption patterns to reveal their preferences and interests.

[0667] "OCR technology" is a technology that recognizes characters in an image and extracts them as text data.

[0668] "Video analysis" is a technology that analyzes video data and extracts useful information.

[0669] "Push notification" is a technology that sends information from a server to a user's device in real time.

[0670] "Algorithm improvement" is the process of improving existing algorithms based on feedback data to improve the accuracy of information provided.

[0671] MODE FOR CARRYING OUT THE INVENTION

[0672] This invention is a system that uses an application installed on a user's smartphone to select the best service and provide information on deals from a vast amount of information using a generative AI model. This system involves a series of steps: collecting usage data, collecting information from the Internet, analyzing the data, providing the information, and collecting feedback and improving the algorithm.

[0673] Data collection

[0674] The device launches applications installed on the user's smartphone and collects user usage data. This data includes application usage history, purchase history, search history, etc. For example, if a user uses an online shopping app, the usage record will be collected. In addition, the device uses the following software to collect text information, video information, and flyer images from the Internet:

[0675] Web scraping: Collecting text data from web pages using BeautifulSoup.

[0676] Video analysis: Use OpenCV to extract useful information from videos.

[0677] OCR technology: Uses Tesseract to read characters from images.

[0678] Data analysis

[0679] The server performs an integrated analysis of the collected usage data and online deals. Specifically, it uses a generative AI model (e.g., GPT-4 or other natural language processing model) to profile users' preferences and interests. The analysis methods include:

[0680] Statistical analysis: Historical usage data is used to statistically analyze consumption patterns.

[0681] Natural language processing: Analyzes collected text data and extracts relevant keywords and phrases.

[0682] Machine learning model: Generative AI models are used to predict user preferences and select the most appropriate information.

[0683] Providing information

[0684] Based on the analysis results, the device provides the user with the most appropriate information in real time. It notifies users of special offers in real time using push notification (e.g., Firebase Cloud Messaging), and also has a function to display a list of special offers on the in-app dashboard. For example, it notifies users of special offers on brands they are interested in, or information on additional discounts they can receive by using a specific point card.

[0685] Feedback and algorithm improvements

[0686] Users provide feedback within the application to evaluate whether the information provided was useful. For example, they answer "yes" or "no" to the question, "Was this sale information useful?" The device collects this feedback data and sends it to the server. The server analyzes the collected feedback data and improves the AI's algorithm, which will enable the provision of even more accurate information in the future.

[0687] Specific examples

[0688] For example, if the history of an online shopping app frequently used by User A indicates a high level of interest in a particular Brand B, the device will collect information about special sales for Brand B from the Internet through web scraping, and the server will analyze it using a generative AI model. As a result, when information is obtained that Brand B's products will be on sale this weekend, the device will provide User A with a push notification informing them of "Brand B's special sales this weekend," and the information will also be displayed on the app's dashboard. Furthermore, information about additional discounts that can be received by using a specific point card will also be provided, improving User A's satisfaction.

[0689] Prompt Sentence Examples

[0690] "Please analyze the information about special offers based on the user's usage history data. In particular, please prioritize extracting information about special sales of Brand B's products and think about the best way to notify the user."

[0691] In this way, the present invention maximizes the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0692] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0693] Step 1: Data collection

[0694] The device launches applications installed on the smartphone and collects usage data. This data includes application usage history, purchase history, and search history. For example, when a user uses an online shopping application, the usage record is collected. The input data is application history data, purchase data, and search data. This information is stored in a database as output data. Specifically, the application periodically collects usage history information in the background and stores it in a local database.

[0695] Step 2: Gather information from the internet

[0696] The device collects text information, video information, and flyer images from the Internet. To do this, it uses web scraping (e.g., BeautifulSoup), video analysis (e.g., OpenCV), and image OCR (e.g., Tesseract). The input data is various media content from the Internet. The output data, which includes the collected text information, video analysis results, and OCR results, is stored in a database. Specifically, a scheduled job periodically crawls specific websites and extracts the required information.

[0697] Step 3: Data analysis

[0698] The server performs an integrated analysis of the collected usage data and online deals. It uses a generative AI model (e.g., GPT-4) to profile the user's preferences and interests. The input data is usage history data and collected internet information. The output data is personalized information based on the user's preferences. Specifically, the server processes data batches every night, runs analysis using the generative AI model, and updates the user profile.

[0699] Step 4: Provide information

[0700] The device provides the user with the most appropriate information in real time based on the analysis results. Information is sent in real time using the push notification function (e.g., Firebase Cloud Messaging). Special offers are also displayed on the dashboard within the user's smartphone app. The input data is the analyzed personalized information. The output data is the push notification and the dashboard display content, which are reflected on the user's device. Specifically, the system is set up to send a push notification immediately after the analysis results are obtained.

[0701] Step 5: Gather feedback and improve the algorithm

[0702] The user inputs feedback on the provided information within the application. For example, they provide feedback in response to the question, "Was this sale information useful?" The device collects this feedback and sends it to the server. The input data is the user's feedback information. The feedback data is saved on the server as output data. Specifically, the in-app feedback form collects user ratings and periodically sends them to the server.

[0703] Step 6: Improve the algorithm

[0704] The server analyzes the collected feedback data and improves the algorithm of the generative AI model. The input data is the feedback collected from users. The output data is an improved generative AI model. Specifically, the server periodically retrains the generative AI model using the feedback data to improve the accuracy of the next data analysis.

[0705] This series of steps ensures that information is provided to users in real time and that the algorithm is continually improved.

[0706] (Application example 1)

[0707] 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."

[0708] Conventional smartphone applications were able to provide optimal service selection and discount information by collecting user usage data and information from the Internet. However, they lacked functionality to support the shopping experience in physical stores, making it difficult for users to obtain real-time sales and discount information that can be obtained directly from stores. In addition, there were insufficient means of collecting specific information using image recognition technology. Therefore, there is a need for a method to further improve the user's shopping experience.

[0709] 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.

[0710] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data to identify optimal service selections and discount information. This makes it possible to notify users of sale information in real time and improve their shopping experience. Furthermore, by including a means for extracting text information from images using image recognition technology, a means for displaying collected sale information in list format, and a means for customizing information based on user preferences, it becomes possible to provide more specific and personalized information.

[0711] "Usage data" refers to data such as the application usage history, search history, and purchase history of the user using the smartphone.

[0712] "Collecting information from the Internet" refers to collecting information that is publicly available on the Internet, such as websites, videos, and flyer images.

[0713] "Analyzing collected data" refers to the process of analyzing collected usage data and information on the Internet to clarify user preferences and behavioral patterns.

[0714] "Identifying optimal service selection and discount information" refers to selecting highly useful services and discount information based on the user's profile.

[0715] "Providing information to users" refers to providing useful information to users through notification and display functions based on collected and analyzed data.

[0716] "Collecting feedback and improving the algorithm" refers to the process of adjusting the analysis algorithm based on feedback provided by users, with the aim of providing even more accurate information.

[0717] "Real-time notification of sales information at physical stores" refers to notifying users of sales information at stores they actually visit in real time on their smartphones.

[0718] "Extracting text information using image recognition technology" refers to reading text data from an image using technology such as OCR.

[0719] "Displaying sale information in list format" refers to organizing the analyzed sale information into a list and displaying it in a format that is easy for the user to view.

[0720] "Customizing information" refers to providing personalized information based on the user's individual preferences and behavioral patterns.

[0721] This invention is a system that uses a generative AI model to collect and analyze user usage data and information on the Internet, and provides users with optimal service selection and advantageous information. The main components include a terminal that runs on the user's smartphone, a server that collects and analyzes data, and information sources on the Internet.

[0722] 1. Collection of User Data

[0723] The device collects usage data about the user's smartphone, including application usage history, search history, and purchase history. This data serves as the basis for analyzing user preferences and purchasing habits.

[0724] 2. Collecting information from the Internet

[0725] The server uses web scraping and OCR (optical character recognition) technology to collect sales and discount information from text, videos, and flyer images on the Internet, allowing you to obtain the latest information in real time.

[0726] 3. Data analysis

[0727] The server performs an integrated analysis of the collected usage data and information on the internet. It uses a generative AI model to analyze the user's usage history and consumption patterns to profile their preferences. It also analyzes the collected sales information to identify the best deals for the user.

[0728] 4. Information provision

[0729] The device receives the analysis results from the server and provides users with real-time push notifications about special offers. The app also has a feature that displays a list of special offers on the app's dashboard. For example, special offers for brands or product categories that the user is interested in can be displayed preferentially.

[0730] 5. Gathering feedback and improving the algorithm

[0731] The device collects user feedback and sends it to the server, which analyzes it and refines the algorithm of the generative AI model, thereby improving the accuracy of future deals.

[0732] Hardware and software used

[0733] Hardware: Smartphone (iOS or Android)

[0734] software:

[0735] Programming language: Python 3

[0736] Web scraping libraries: requests, BeautifulSoup

[0737] Image processing library: OpenCV

[0738] OCR library: pytesseract

[0739] Machine learning library: scikit-learn

[0740] Specific examples

[0741] For example, user A's purchase history at a supermarket he frequently visits can be analyzed to determine that he has a high interest in daily necessities and food, especially in products from a particular brand. The server then uses web scraping to obtain images of supermarket flyers containing products from that brand, and uses OCR technology to extract text information. The generative AI model then analyzes the collected data and notifies user A in real time of the supermarket's special sales for this weekend. It also provides information on how to obtain additional discounts by using a point card in addition to the sales information.

[0742] Prompt Sentence Examples

[0743] "We will analyze User A's past purchases and search history to gather information on deals from the internet. We will then build a system that will provide users with push notifications about sales on specific products. This system will collect and analyze data using web scraping, OCR technology, and machine learning."

[0744] As described above, this invention is a system that can utilize user data and generative AI models to greatly improve the shopping experience in physical stores.

[0745] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0746] Step 1:

[0747] User Data Collection

[0748] The device collects usage data from the user's smartphone. Specifically, application usage history, search history, purchase history, etc. are automatically acquired. This input data includes information on products and brands that the user is interested in. This data is collected and sent to the server.

[0749] Step 2:

[0750] Gathering information from the internet

[0751] The server uses web scraping technology to collect sale information on the Internet. Specifically, it accesses specific URLs, analyzes HTML, and extracts the necessary information. It also uses OCR technology to extract text information from flyer images. This allows it to obtain text and image information as input data and store it in a database.

[0752] Step 3:

[0753] Data analysis

[0754] The server performs an integrated analysis of the collected usage data and information on the Internet. A generative AI model is used to profile the user's preferences and consumption patterns. In this step, the user's history data and sales information are used as input data, and optimal service selection and discount information are output. Specifically, a clustering algorithm (e.g., KMeans) is used to identify user groups with similar preferences.

[0755] Step 4:

[0756] Providing information

[0757] The device receives the analysis results from the server and provides the user with the most appropriate information. Specifically, it notifies the user of sale information in real time using the push notification function. It also displays a list of sale information on the app's dashboard. In this step, the analysis results are used as input, and the output is notification or display to the user.

[0758] Step 5:

[0759] Feedback collection and algorithm improvement

[0760] The device collects feedback from users and sends it to the server. Specifically, it obtains feedback data such as whether the sale information was useful and whether the coupon was used. Based on this input data, the server improves the algorithm of the generative AI model. Improvements to the algorithm will improve the accuracy of information provided from the next time onwards.

[0761] This series of processing steps allows users to maximize their shopping experience in a physical store, which will greatly improve user convenience and satisfaction.

[0762] 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.

[0763] This invention is a system that uses generative AI and an emotion engine as a smartphone application. This system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides information according to the user's emotional state.

[0764] (Form of data collection)

[0765] The device automatically collects usage data through applications installed on the user's smartphone. This data includes application usage history, purchase history, search history, etc. Web scraping, video analysis, and image OCR (optical character recognition) technologies are also used to collect text information, video information, and flyer images from the Internet.

[0766] (Forms of emotion recognition)

[0767] The device uses an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, when a user enters a text comment into an app, the content is analyzed to determine the user's current state of mind. The device also analyzes the tone and tempo of the voice data and facial expressions from image data.

[0768] (Forms of data and sentiment analysis)

[0769] The server performs an integrated analysis of the collected usage data and emotion recognition results. Specifically, it identifies optimal service selection and discount information based on the user's current emotional state in addition to their past usage history and consumption patterns. For example, if the user is feeling stressed, it prioritizes relaxation services and entertainment-related sales information. Also, if the user is excited, it provides information that encourages action that leads to immediate purchase.

[0770] (Form of information provision)

[0771] The device provides the user with the most appropriate information based on the analysis results. It uses the push notification function to send sale and coupon information in real time. It also displays a list of discount information on the in-app dashboard for easy access by the user. It also provides information on additional discounts that can be received by using a point card at specific stores. By providing information based on emotion recognition results, it is possible to provide information at the appropriate time that meets the user's needs.

[0772] (Forms of feedback and algorithmic improvement)

[0773] The user uses the information provided and evaluates the results. For example, they can enter feedback in the app, such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server analyzes the feedback and improves the accuracy of the generative AI algorithm and emotion engine. This will enable the provision of even more accurate information in the future.

[0774] (Example)

[0775] For example, let's say that the history of an online shopping app that User A frequently uses indicates that he or she has a high interest in a particular brand, Brand B. The device then detects User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device evaluates the results within the app. The device collects this feedback and sends it to the server.

[0776] In this way, the present invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and discount information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[0777] The processing flow will be explained below.

[0778] Step 1:

[0779] The device collects user usage data. This is done by applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is continuously recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[0780] Step 2:

[0781] The device uses web scraping technology to collect discount information from the Internet. This technology automatically retrieves sale and coupon information from related websites. It also analyzes video advertisements from platforms such as YouTube and social media to extract discount codes and limited campaign information. Furthermore, OCR technology is used on flyer images to extract sale information from the images as text.

[0782] Step 3:

[0783] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, it analyzes text comments entered by the user into the app to recognize emotions such as joy, sadness, and stress. It also recognizes emotions by analyzing facial expressions and tone of voice from voice data during calls and image data captured by the camera.

[0784] Step 4:

[0785] The server performs analysis based on the collected usage data and emotion recognition results. Specifically, it profiles the user's preferences and interests, taking into account their current emotional state as well as their purchase and search history. Based on this profiling, it identifies optimal service selections and deals. For example, if the user is feeling stressed, it will prioritize analyzing special sale information for relaxation services.

[0786] Step 5:

[0787] The server analyzes the discount information collected from the Internet and extracts information that matches the user's preferences and emotional state. In particular, if the user's emotional state is important, the server will prioritize the selection of sale information and coupon information that matches the user's feelings.

[0788] Step 6:

[0789] The device provides the user with the most appropriate information based on the analysis results. Information is sent in real time using push notifications. A list of special offers is also displayed on the in-app dashboard for easy access by the user. Information on additional discounts that can be obtained by using a point card is also provided at the same time. User satisfaction is improved by providing appropriate information at a time that matches the user's emotions based on the results of the emotion engine.

[0790] Step 7:

[0791] Users can utilize the information provided to them to take advantage of sales and coupons. For example, they can do online shopping based on sales information received via push notifications. After making a purchase, if the user actually uses the coupon, they can enter the results as feedback within the app.

[0792] Step 8:

[0793] The device collects feedback from users and sends it to the server, including data on whether the information provided was useful and whether special offers or coupons were used.

[0794] Step 9:

[0795] The server analyzes the feedback and uses it as data to improve the generative AI algorithm and emotion engine. Based on the feedback, adjustments can be made to improve the accuracy and relevance of the information provided, further improving the quality of future information provided.

[0796] In this way, through a series of steps, the system of the present invention comprehensively analyzes the user's usage data and emotions, and provides optimal services and discount information in real time, thereby improving the user's quality of life.

[0797] Example 2

[0798] 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."

[0799] In modern society, users are exposed to a vast amount of information, making it difficult to find information and services that are truly useful to them. Furthermore, conventional information provision systems do not provide information that takes into account the user's emotional state, making it difficult to provide services that are in line with the user's current psychological state. Because information provision is limited to information based on usage history and search history, it is difficult to maximize user satisfaction.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0801] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data and identifying optimal service selections and advantageous information, means for recognizing the emotional state of the user, means for providing optimal information to the user, and means for collecting feedback from the user and improving the algorithm. This makes it possible to provide information that takes into account the emotional state of the user in addition to their usage history and consumption patterns, thereby maximizing user satisfaction.

[0802] "Usage data" refers to data relating to a user's behavioral patterns, such as the user's application usage history, purchase history, and search history.

[0803] "Means of collecting information from the Internet" refers to techniques for obtaining text, video, and image information from the Internet using web scraping, video analysis, image OCR (optical character recognition) technology, etc.

[0804] "Means for analyzing collected data and identifying optimal service selection and advantageous information" refers to a means for analyzing collected usage data and emotion recognition results using generative AI models, etc., to select optimal services and useful information for users.

[0805] The "means for recognizing the user's emotional state" is an emotion engine technology for analyzing text data, voice data, and image data to identify the user's emotional state.

[0806] "Means of providing optimal information to users" refers to technology that provides the most appropriate information to users based on analysis results through push notifications, in-app dashboards, etc.

[0807] "Means for collecting feedback from users and improving the algorithm" refers to means for collecting evaluations and feedback given by users on the information provided, and using this information to improve the accuracy of the analysis model and emotion engine.

[0808] A "generative AI model" is an artificial intelligence technology that learns specific patterns and characteristics from large amounts of data and makes recommendations and predictions for new data.

[0809] A "prompt sentence" is an input sentence given to a generative AI model to obtain a specific output.

[0810] This invention is a system that uses a generative AI model and an emotion recognition engine as a smartphone application. This system collects user usage data and analyzes text, video, and image information on the Internet to provide valuable information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides information according to the user's emotions.

[0811] Specifically, the following hardware and software are used.

[0812] 1. Data Collection

[0813] Device: A dedicated application installed on a smartphone

[0814] Collection methods: Web scraping, video analysis, image OCR (Optical Character Recognition)

[0815] Data content: application usage history, purchase history, search history, etc.

[0816] 2. Emotion recognition

[0817] Device: Emotion engine installed on smartphone

[0818] Analysis method: Analysis of text data, audio data, and image data

[0819] Technology: Natural language processing, speech analysis, facial expression analysis

[0820] 3. Integrated Data Analysis

[0821] Server: High-Performance Computing Server

[0822] Analysis method: Integrated analysis of usage data and emotion recognition results using a generated AI model

[0823] Deliverables: Optimal service selection and deals for users

[0824] 4. Information provision

[0825] Device: Smartphone

[0826] Delivery method: Push notification, in-app dashboard

[0827] Information content: Special sale information, coupon information, relaxation service information

[0828] 5. Gathering feedback and improving the algorithm

[0829] Users: Provide feedback through a smartphone application

[0830] Device: Collect user feedback

[0831] Server: Analyze feedback data to improve the accuracy of generative AI and emotion engine

[0832] Specific examples

[0833] For example, if the history of an online shopping app that User A frequently uses indicates that he or she is highly interested in a particular brand B, the device can further detect User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device can rate the results within the app. The device collects this feedback and sends it to the server.

[0834] Prompt Sentence Examples

[0835] "Design a system to provide optimal services and special offers to a specific user based on their emotional state and past app usage history. The system will require the user to enter text indicating their emotional state within the app, and past purchase and usage history will also be analyzed. The goal is to improve user satisfaction by providing information according to their emotional state."

[0836] This invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and advantageous information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[0837] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0838] Step 1:

[0839] Data collection

[0840] The device automatically collects usage data through applications installed on the user's smartphone. Input data includes application usage history, purchase history, search history, etc. Specifically, the device saves a log of each user's application operation. It also uses web scraping technology to obtain product price information and reviews from specific websites and saves them on the device. Furthermore, it uses OCR technology to extract text information from flyer images taken by the user. This data is then saved on the device for use in subsequent analysis steps.

[0841] Step 2:

[0842] emotion recognition

[0843] The device uses an emotion engine to recognize the user's emotional state. Input data includes the user's text data, voice data, and image data. Specifically, when the user enters a text comment into the app, the device analyzes the content using natural language processing technology to determine the user's emotional state. It also analyzes the tone and tempo of the voice data recorded by the user within the app, and analyzes facial expressions from image data. These results are stored on the device as emotion recognition data.

[0844] Step 3:

[0845] Integrated Data Analysis

[0846] The server performs an integrated analysis of the usage data and emotion recognition results sent from the device. The input data includes the user's application usage history, purchase history, search history, and emotion recognition data. Specifically, the server inputs this data into a generative AI model to identify optimal service selections and deals based on past usage history, consumption patterns, and current emotional state. For example, if the user is feeling stressed, it will prioritize the extraction of relaxation services and entertainment-related sales information. The analysis results are stored on the server for use in subsequent information provision steps.

[0847] Step 4:

[0848] Providing information

[0849] The device provides the user with the most appropriate information based on the analysis results sent from the server. The input data includes the recommended information sent from the server. Specifically, the device receives the analysis results and notifies the user via push notification. The app's dashboard also displays the latest sales information and a list of coupons. This allows the user to easily access the information and prompts them to take a specific action (for example, clicking the "Buy Now" button).

[0850] Step 5:

[0851] Feedback collection and algorithm improvement

[0852] The user uses the provided information and evaluates the results within the app. The input data includes the user's feedback on the provided information. Specifically, the user enters evaluations within the app, such as "This sale information was useful" or "I used the coupon." The device collects this feedback data and uploads it to the server. The server analyzes the collected feedback data and compares the output of the emotion engine with actual user feedback. This readjusts the parameters of the generative AI model and emotion engine, improving the accuracy of the recommended information.

[0853] (Application example 2)

[0854] 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."

[0855] The purpose of this invention is to provide a new method for analyzing a user's usage behavior and emotional state in a smartphone application to make appropriate recommendations for food delivery services. Conventional food delivery services generally suggest menu items simply based on a user's ordering history and preferences, but by taking the user's emotional state into account, more personalized recommendations become possible. This technology is expected to improve user satisfaction and increase the frequency of service use.

[0856] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0857] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data to identify optimal service selections and advantageous information, means for analyzing user emotion data to make optimal recommendations, and means for proposing recommended menus based on the user's emotional state in a food delivery service, thereby making it possible to provide optimal food delivery options that match the user's mood and emotional state.

[0858] definition statement

[0859] "Usage data" refers to information about the actions and operations a user performs through an application. Specifically, it includes application usage history, purchase history, search history, etc.

[0860] "Means of collecting information from the Internet" refers to technical means of obtaining useful information for users through web scraping, APIs, etc. Examples include text information, video information, flyer images, etc.

[0861] "Means for analyzing collected data and identifying optimal service selections and advantageous information" refers to a function that statistically or algorithmically analyzes collected data and determines the information that is most useful to the user.

[0862] "Means of providing optimal information to users" refers to technology that presents useful information through notifications and screen displays on users' smartphones and devices.

[0863] "Means of collecting feedback from users and improving the algorithm" refers to a function that collects user evaluations and results of the information provided as data and uses this data to improve the accuracy and performance of the system.

[0864] "Means for analyzing user emotional data and making optimal recommendations" refers to technology that estimates a user's emotional state from text data, voice data, and image data, and makes appropriate recommendations based on that.

[0865] "A means for suggesting recommended menu items based on the user's emotional state in a food delivery service" is a function that selects and presents menu items that correspond to the user's stress and mood state in a food delivery service.

[0866] MODE FOR CARRYING OUT THE INVENTION

[0867] The system of the present invention operates mainly through an application installed on a smartphone, analyzes user usage data and emotion data, and provides optimal recommendations for food delivery services. Specific embodiments for implementing the present invention are described below.

[0868] System Configuration

[0869] server:

[0870] 1. The server has a means for collecting usage data, specifically a database system for safely storing and analyzing data such as user application usage history, purchase history, and search history.

[0871] 2. It has the means to collect information from the internet. It has the ability to collect text information, video information, flyer images, etc. using web scraping and APIs.

[0872] 3. It has the means to analyze the collected data and identify the optimal service selection and advantageous information. The various collected data is processed using statistical analysis and machine learning algorithms to extract the optimal services and information for the user.

[0873] 4. It has a means to analyze the user's emotional data and make optimal recommendations. It uses an emotion engine that analyzes the user's emotional state from text data, voice data, and image data.

[0874] 5. A food delivery service has a means to suggest menu recommendations based on the user's emotional state. Using a generative AI model, menu suggestions are made that match the user's emotional state.

[0875] Device:

[0876] 1. It has the means to provide users with the most appropriate information. It has the function of sending recommended information to users' smartphones via push notifications, etc.

[0877] 2. It has a means to collect feedback from users and improve the algorithm. It has a function that allows users to input their evaluations and results of the information provided within the app and send them to the server.

[0878] Hardware and software used

[0879] 1. Hardware: Cloud servers, smartphones

[0880] 2. Software: Natural language processing libraries (e.g., TextBlob), image recognition libraries (e.g., pytesseract), database systems, web scraping tools (e.g., BeautifulSoup)

[0881] Specific examples of processing

[0882] The user inputs text or voice via the application. If the user inputs something like "I feel really tired today," the device analyzes this as text data and uses an emotion engine to determine the user's emotional state. If the current emotional state is determined to be "stressed," the server recommends a menu of foods that have a relaxation effect to the user based on the collected usage data and emotion data. This recommendation information is sent to the user via a push notification from the device. For example, in response to a prompt such as "What menu items do you recommend for relieving stress?" the server might provide information such as "Our special herbal tea and relaxing salad are currently on special offer."

[0883] In this way, the present invention improves user satisfaction in food delivery services by analyzing the user's emotional state and providing personalized recommendations. Furthermore, it is possible to improve the algorithm based on user feedback and provide more accurate information.

[0884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0885] System program processing flow

[0886] Step 1:

[0887] The device collects user usage data, including application usage history, purchase history, and search history. The input data is the user activity log, and the output data is saved in a database.

[0888] Step 2:

[0889] The device collects information from the Internet, using web scraping or APIs to obtain content such as text, video, and flyer images. The input data is the URL of the web page or the API endpoint, and the output data is the information obtained from the scraping or API.

[0890] Step 3:

[0891] The server analyzes the collected data. Specifically, it uses statistical analysis and machine learning algorithms to identify the best services and deals for users. The input data is usage data and information collected from the Internet, and the output data is recommendation information as a result of the analysis.

[0892] Step 4:

[0893] The device collects the user's emotional input (e.g., text, voice, image). The input data is text or voice data entered by the user, which is converted into text data and sent to the emotion engine for analysis. The output data is the emotional state resulting from the analysis.

[0894] Step 5:

[0895] The server uses a generative AI model to make optimal recommendations based on the emotional state analyzed by the emotion engine. The input data is the emotional state, usage data, and information from the internet, and the generative AI model operates based on this to generate a recommendation menu as output data.

[0896] Step 6:

[0897] The device provides the user with the recommendation information sent from the server. Specifically, the information is presented through push notifications or in-app displays. The input data is the recommendation information sent from the server, and the output data is the notification or push message displayed to the user.

[0898] Step 7:

[0899] Users input feedback on the provided recommendation information within the app. The input data is feedback information, which is collected by the application and sent to the server to become output data.

[0900] Step 8:

[0901] The server analyzes the collected feedback and uses it to improve the algorithm: the input data is the user feedback, and the output data is the improved algorithm and the new recommendation model generated based on it.

[0902] At each step, we manage in detail how servers and devices collect, analyze, and provide data, aiming to improve user satisfaction and optimize services.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] [Fourth embodiment]

[0907] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0908] 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.

[0909] 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).

[0910] 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.

[0911] 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.

[0912] 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).

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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."

[0920] This invention is a system that uses generative AI as a smartphone application. The system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection, with the aim of making users' lives more efficient.

[0921] (Form of data collection)

[0922] The device automatically collects usage data through applications installed on the user's smartphone. This data includes the user's application history, purchase history, search history, and so on. For example, usage records of online shopping apps and product search information within them are stored. The device also collects text information, video information, and flyer images from the Internet. For this purpose, web scraping, video analysis, and image OCR (optical character recognition) technologies are used.

[0923] (Form of data analysis)

[0924] The server performs an integrated analysis of the collected usage data and discount information on the Internet. Specifically, it profiles the user's preferences and interests based on their usage history and consumption patterns. For example, if there is a high interest in a particular brand or category of products, sales information for that brand will be analyzed first. The generation AI also scrutinizes the discount information analyzed and identifies the information that is most suitable for the user.

[0925] (Form of information provision)

[0926] The device then provides the user with the most appropriate information based on the analysis results. This is done using push notifications, which notify users of special offers in real time. The app also has a dashboard that displays a list of special offers. For example, the app can notify users of this week's special sales for brands they are interested in, along with information on how to get additional discounts by using a specific point card.

[0927] (Forms of feedback and algorithmic improvement)

[0928] The user uses the information provided and evaluates the results. For example, they input feedback such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server then analyzes the collected feedback and improves the AI's algorithm. This will enable the provision of even more accurate deals in the future.

[0929] (Example)

[0930] For example, suppose that the history of an online shopping app that User A frequently uses indicates that User A has a high interest in a particular brand B. The device collects information about special sales for Brand B from the Internet using web scraping and analyzes it on the server. As a result of the analysis, it identifies information about a special sale on Brand B's products this weekend. The device sends User A a push notification informing him of this weekend's special sales for Brand B, and also displays the information on the app's dashboard. Furthermore, the device also provides User A with information about stores where additional discounts can be received by using a specific point card, thereby increasing User A's satisfaction.

[0931] In this way, the present invention can maximize the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0932] The processing flow will be explained below.

[0933] Step 1:

[0934] The device collects user usage data. This is done through applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[0935] Step 2:

[0936] The device collects discount information from the internet. It uses web scraping technology to automatically retrieve sale and coupon information from related websites. It also analyzes advertising videos from video platforms such as YouTube and social media to extract discount codes and limited campaign information. For flyer images, OCR technology is used to analyze the text information within the image to obtain sale information.

[0937] Step 3:

[0938] The server analyzes the collected usage data. Specifically, it profiles the user's preferences and interests based on the user's past purchase and search history. This profiling can identify the brands and product categories in which the user is particularly interested.

[0939] Step 4:

[0940] The server analyzes the collected information on deals on the Internet, and prioritizes the extraction of information that matches the user's preferences and interests. Based on the results of this analysis, the server identifies the most useful sales and coupon information for the user.

[0941] Step 5:

[0942] The device provides the analysis results to the user. Specifically, it uses push notifications to send users real-time sales and coupon information. It also displays a list of deals on the app's dashboard for easy access. It also provides information on additional discounts that can be received by using a point card at specific stores.

[0943] Step 6:

[0944] The user can use the provided information to take advantage of special sales and coupons. For example, they can do online shopping based on the special sale information they were notified about. When the user actually uses a coupon, the app provides feedback on the results.

[0945] Step 7:

[0946] The device collects feedback from the user, including whether the information provided was useful, whether the sales or coupons were actually used, etc. This feedback is recorded as data that will be used for later analysis.

[0947] Step 8:

[0948] The server analyzes the collected feedback and improves the generative AI algorithm. Based on the feedback, it makes adjustments to improve the accuracy and relevance of the information provided, thereby further improving the quality of information provided to users in the future.

[0949] Through this series of steps, users can efficiently obtain optimal services and deals, and build wealth and save money without spending time or effort.

[0950] Example 1

[0951] 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."

[0952] In today's lifestyles, users face difficulties in finding useful information from the vast amount of information available. Furthermore, existing information services often fail to adequately address users' individual preferences and needs. As a result, users are overwhelmed with unnecessary information, hindering efficient decision-making.

[0953] 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.

[0954] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data using a generative AI model to identify optimal service selections and advantageous information, thereby enabling the provision of personalized information based on each user's preferences and consumption patterns.

[0955] "Usage data" refers to data that includes application usage history, purchase history, search history, etc. when a user uses a smartphone or other device.

[0956] "Means of collecting information from the Internet" refers to technical means for collecting text information, video information, flyer images, etc. on the Internet.

[0957] A "generative AI model" is an artificial intelligence model that learns information from large amounts of data and is used to analyze user preferences and consumption patterns.

[0958] "Analysis" is the process of organizing and analyzing information based on collected data to find specific patterns and relationships.

[0959] "Profiling" is a method of analyzing a user's usage history and consumption patterns to reveal their preferences and interests.

[0960] "OCR technology" is a technology that recognizes characters in an image and extracts them as text data.

[0961] "Video analysis" is a technology that analyzes video data and extracts useful information.

[0962] "Push notification" is a technology that sends information from a server to a user's device in real time.

[0963] "Algorithm improvement" is the process of improving existing algorithms based on feedback data to improve the accuracy of information provided.

[0964] MODE FOR CARRYING OUT THE INVENTION

[0965] This invention is a system that uses an application installed on a user's smartphone to select the best service and provide information on deals from a vast amount of information using a generative AI model. This system involves a series of steps: collecting usage data, collecting information from the Internet, analyzing the data, providing the information, and collecting feedback and improving the algorithm.

[0966] Data collection

[0967] The device launches applications installed on the user's smartphone and collects user usage data. This data includes application usage history, purchase history, search history, etc. For example, if a user uses an online shopping app, the usage record will be collected. In addition, the device uses the following software to collect text information, video information, and flyer images from the Internet:

[0968] Web scraping: Collecting text data from web pages using BeautifulSoup.

[0969] Video analysis: Use OpenCV to extract useful information from videos.

[0970] OCR technology: Uses Tesseract to read characters from images.

[0971] Data analysis

[0972] The server performs an integrated analysis of the collected usage data and online deals. Specifically, it uses a generative AI model (e.g., GPT-4 or other natural language processing model) to profile users' preferences and interests. The analysis methods include:

[0973] Statistical analysis: Historical usage data is used to statistically analyze consumption patterns.

[0974] Natural language processing: Analyzes collected text data and extracts relevant keywords and phrases.

[0975] Machine learning model: Generative AI models are used to predict user preferences and select the most appropriate information.

[0976] Providing information

[0977] Based on the analysis results, the device provides the user with the most appropriate information in real time. It notifies users of special offers in real time using push notification (e.g., Firebase Cloud Messaging), and also has a function to display a list of special offers on the in-app dashboard. For example, it notifies users of special offers on brands they are interested in, or information on additional discounts they can receive by using a specific point card.

[0978] Feedback and algorithm improvements

[0979] Users provide feedback within the application to evaluate whether the information provided was useful. For example, they answer "yes" or "no" to the question, "Was this sale information useful?" The device collects this feedback data and sends it to the server. The server analyzes the collected feedback data and improves the AI's algorithm, which will enable the provision of even more accurate information in the future.

[0980] Specific examples

[0981] For example, if the history of an online shopping app frequently used by User A indicates a high level of interest in a particular Brand B, the device will collect information about special sales for Brand B from the Internet through web scraping, and the server will analyze it using a generative AI model. As a result, when information is obtained that Brand B's products will be on sale this weekend, the device will provide User A with a push notification informing them of "Brand B's special sales this weekend," and the information will also be displayed on the app's dashboard. Furthermore, information about additional discounts that can be received by using a specific point card will also be provided, improving User A's satisfaction.

[0982] Prompt Sentence Examples

[0983] "Please analyze the information about special offers based on the user's usage history data. In particular, please prioritize extracting information about special sales of Brand B's products and think about the best way to notify the user."

[0984] In this way, the present invention maximizes the efficiency of users' lives and their economic benefits by collecting and analyzing user usage data and providing optimal services and discount information.

[0985] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0986] Step 1: Data collection

[0987] The device launches applications installed on the smartphone and collects usage data. This data includes application usage history, purchase history, and search history. For example, when a user uses an online shopping application, the usage record is collected. The input data is application history data, purchase data, and search data. This information is stored in a database as output data. Specifically, the application periodically collects usage history information in the background and stores it in a local database.

[0988] Step 2: Gather information from the internet

[0989] The device collects text information, video information, and flyer images from the Internet. To do this, it uses web scraping (e.g., BeautifulSoup), video analysis (e.g., OpenCV), and image OCR (e.g., Tesseract). The input data is various media content from the Internet. The output data, which includes the collected text information, video analysis results, and OCR results, is stored in a database. Specifically, a scheduled job periodically crawls specific websites and extracts the required information.

[0990] Step 3: Data analysis

[0991] The server performs an integrated analysis of the collected usage data and online deals. It uses a generative AI model (e.g., GPT-4) to profile the user's preferences and interests. The input data is usage history data and collected internet information. The output data is personalized information based on the user's preferences. Specifically, the server processes data batches every night, runs analysis using the generative AI model, and updates the user profile.

[0992] Step 4: Provide information

[0993] The device provides the user with the most appropriate information in real time based on the analysis results. Information is sent in real time using the push notification function (e.g., Firebase Cloud Messaging). Special offers are also displayed on the dashboard within the user's smartphone app. The input data is the analyzed personalized information. The output data is the push notification and the dashboard display content, which are reflected on the user's device. Specifically, the system is set up to send a push notification immediately after the analysis results are obtained.

[0994] Step 5: Gather feedback and improve the algorithm

[0995] The user inputs feedback on the provided information within the application. For example, they provide feedback in response to the question, "Was this sale information useful?" The device collects this feedback and sends it to the server. The input data is the user's feedback information. The feedback data is saved on the server as output data. Specifically, the in-app feedback form collects user ratings and periodically sends them to the server.

[0996] Step 6: Improve the algorithm

[0997] The server analyzes the collected feedback data and improves the algorithm of the generative AI model. The input data is the feedback collected from users. The output data is an improved generative AI model. Specifically, the server periodically retrains the generative AI model using the feedback data to improve the accuracy of the next data analysis.

[0998] This series of steps ensures that information is provided to users in real time and that the algorithm is continually improved.

[0999] (Application example 1)

[1000] 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."

[1001] Conventional smartphone applications were able to provide optimal service selection and discount information by collecting user usage data and information from the Internet. However, they lacked functionality to support the shopping experience in physical stores, making it difficult for users to obtain real-time sales and discount information that can be obtained directly from stores. In addition, there were insufficient means of collecting specific information using image recognition technology. Therefore, there is a need for a method to further improve the user's shopping experience.

[1002] 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.

[1003] In this invention, the server includes a means for collecting usage data, a means for collecting information from the Internet, and a means for analyzing the collected data to identify optimal service selections and discount information. This makes it possible to notify users of sale information in real time and improve their shopping experience. Furthermore, by including a means for extracting text information from images using image recognition technology, a means for displaying collected sale information in list format, and a means for customizing information based on user preferences, it becomes possible to provide more specific and personalized information.

[1004] "Usage data" refers to data such as the application usage history, search history, and purchase history of the user using the smartphone.

[1005] "Collecting information from the Internet" refers to collecting information that is publicly available on the Internet, such as websites, videos, and flyer images.

[1006] "Analyzing collected data" refers to the process of analyzing collected usage data and information on the Internet to clarify user preferences and behavioral patterns.

[1007] "Identifying optimal service selection and discount information" refers to selecting highly useful services and discount information based on the user's profile.

[1008] "Providing information to users" refers to providing useful information to users through notification and display functions based on collected and analyzed data.

[1009] "Collecting feedback and improving the algorithm" refers to the process of adjusting the analysis algorithm based on feedback provided by users, with the aim of providing even more accurate information.

[1010] "Real-time notification of sales information at physical stores" refers to notifying users of sales information at stores they actually visit in real time on their smartphones.

[1011] "Extracting text information using image recognition technology" refers to reading text data from an image using technology such as OCR.

[1012] "Displaying sale information in list format" refers to organizing the analyzed sale information into a list and displaying it in a format that is easy for the user to view.

[1013] "Customizing information" refers to providing personalized information based on the user's individual preferences and behavioral patterns.

[1014] This invention is a system that uses a generative AI model to collect and analyze user usage data and information on the Internet, and provides users with optimal service selection and advantageous information. The main components include a terminal that runs on the user's smartphone, a server that collects and analyzes data, and information sources on the Internet.

[1015] 1. Collection of User Data

[1016] The device collects usage data about the user's smartphone, including application usage history, search history, and purchase history. This data serves as the basis for analyzing user preferences and purchasing habits.

[1017] 2. Collecting information from the Internet

[1018] The server uses web scraping and OCR (optical character recognition) technology to collect sales and discount information from text, videos, and flyer images on the Internet, allowing you to obtain the latest information in real time.

[1019] 3. Data analysis

[1020] The server performs an integrated analysis of the collected usage data and information on the internet. It uses a generative AI model to analyze the user's usage history and consumption patterns to profile their preferences. It also analyzes the collected sales information to identify the best deals for the user.

[1021] 4. Information provision

[1022] The device receives the analysis results from the server and provides users with real-time push notifications about special offers. The app also has a feature that displays a list of special offers on the app's dashboard. For example, special offers for brands or product categories that the user is interested in can be displayed preferentially.

[1023] 5. Gathering feedback and improving the algorithm

[1024] The device collects user feedback and sends it to the server, which analyzes it and refines the algorithm of the generative AI model, thereby improving the accuracy of future deals.

[1025] Hardware and software used

[1026] Hardware: Smartphone (iOS or Android)

[1027] software:

[1028] Programming language: Python 3

[1029] Web scraping libraries: requests, BeautifulSoup

[1030] Image processing library: OpenCV

[1031] OCR library: pytesseract

[1032] Machine learning library: scikit-learn

[1033] Specific examples

[1034] For example, user A's purchase history at a supermarket he frequently visits can be analyzed to determine that he has a high interest in daily necessities and food, especially in products from a particular brand. The server then uses web scraping to obtain images of supermarket flyers containing products from that brand, and uses OCR technology to extract text information. The generative AI model then analyzes the collected data and notifies user A in real time of the supermarket's special sales for this weekend. It also provides information on how to obtain additional discounts by using a point card in addition to the sales information.

[1035] Prompt Sentence Examples

[1036] "We will analyze User A's past purchases and search history to gather information on deals from the internet. We will then build a system that will provide users with push notifications about sales on specific products. This system will collect and analyze data using web scraping, OCR technology, and machine learning."

[1037] As described above, this invention is a system that can utilize user data and generative AI models to greatly improve the shopping experience in physical stores.

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Step 1:

[1040] User Data Collection

[1041] The device collects usage data from the user's smartphone. Specifically, application usage history, search history, purchase history, etc. are automatically acquired. This input data includes information on products and brands that the user is interested in. This data is collected and sent to the server.

[1042] Step 2:

[1043] Gathering information from the internet

[1044] The server uses web scraping technology to collect sale information on the Internet. Specifically, it accesses specific URLs, analyzes HTML, and extracts the necessary information. It also uses OCR technology to extract text information from flyer images. This allows it to obtain text and image information as input data and store it in a database.

[1045] Step 3:

[1046] Data analysis

[1047] The server performs an integrated analysis of the collected usage data and information on the Internet. A generative AI model is used to profile the user's preferences and consumption patterns. In this step, the user's history data and sales information are used as input data, and optimal service selection and discount information are output. Specifically, a clustering algorithm (e.g., KMeans) is used to identify user groups with similar preferences.

[1048] Step 4:

[1049] Providing information

[1050] The device receives the analysis results from the server and provides the user with the most appropriate information. Specifically, it notifies the user of sale information in real time using the push notification function. It also displays a list of sale information on the app's dashboard. In this step, the analysis results are used as input, and the output is notification or display to the user.

[1051] Step 5:

[1052] Feedback collection and algorithm improvement

[1053] The device collects feedback from users and sends it to the server. Specifically, it obtains feedback data such as whether the sale information was useful and whether the coupon was used. Based on this input data, the server improves the algorithm of the generative AI model. Improvements to the algorithm will improve the accuracy of information provided from the next time onwards.

[1054] This series of processing steps allows users to maximize their shopping experience in a physical store, which will greatly improve user convenience and satisfaction.

[1055] 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.

[1056] This invention is a system that uses generative AI and an emotion engine as a smartphone application. This system collects user usage data and analyzes text information, video information, and flyer images on the Internet to provide discount information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotions, it provides information according to the user's emotional state.

[1057] (Form of data collection)

[1058] The device automatically collects usage data through applications installed on the user's smartphone. This data includes application usage history, purchase history, search history, etc. Web scraping, video analysis, and image OCR (optical character recognition) technologies are also used to collect text information, video information, and flyer images from the Internet.

[1059] (Forms of emotion recognition)

[1060] The device uses an emotion engine to recognize the user's emotions. This emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, when a user enters a text comment into an app, the content is analyzed to determine the user's current state of mind. The device also analyzes the tone and tempo of the voice data and facial expressions from image data.

[1061] (Forms of data and sentiment analysis)

[1062] The server performs an integrated analysis of the collected usage data and emotion recognition results. Specifically, it identifies optimal service selection and discount information based on the user's current emotional state in addition to their past usage history and consumption patterns. For example, if the user is feeling stressed, it prioritizes relaxation services and entertainment-related sales information. Also, if the user is excited, it provides information that encourages action that leads to immediate purchase.

[1063] (Form of information provision)

[1064] The device provides the user with the most appropriate information based on the analysis results. It uses the push notification function to send sale and coupon information in real time. It also displays a list of discount information on the in-app dashboard for easy access by the user. It also provides information on additional discounts that can be received by using a point card at specific stores. By providing information based on emotion recognition results, it is possible to provide information at the appropriate time that meets the user's needs.

[1065] (Forms of feedback and algorithmic improvement)

[1066] The user uses the information provided and evaluates the results. For example, they can enter feedback in the app, such as whether the sale information was useful or whether the coupon was used. The device collects this feedback and sends it to the server. The server analyzes the feedback and improves the accuracy of the generative AI algorithm and emotion engine. This will enable the provision of even more accurate information in the future.

[1067] (Example)

[1068] For example, let's say that the history of an online shopping app that User A frequently uses indicates that he or she has a high interest in a particular brand, Brand B. The device then detects User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device evaluates the results within the app. The device collects this feedback and sends it to the server.

[1069] In this way, the present invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and discount information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[1070] The processing flow will be explained below.

[1071] Step 1:

[1072] The device collects user usage data. This is done by applications installed on the smartphone. Specifically, information such as the usage history of each application, purchase history, and search history is continuously recorded as a log. Wi-Fi connection status and location information are also collected to understand usage in more detail.

[1073] Step 2:

[1074] The device uses web scraping technology to collect discount information from the Internet. This technology automatically retrieves sale and coupon information from related websites. It also analyzes video advertisements from platforms such as YouTube and social media to extract discount codes and limited campaign information. Furthermore, OCR technology is used on flyer images to extract sale information from the images as text.

[1075] Step 3:

[1076] The device uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's text data, voice data, and image data to determine the user's emotional state. For example, it analyzes text comments entered by the user into the app to recognize emotions such as joy, sadness, and stress. It also recognizes emotions by analyzing facial expressions and tone of voice from voice data during calls and image data captured by the camera.

[1077] Step 4:

[1078] The server performs analysis based on the collected usage data and emotion recognition results. Specifically, it profiles the user's preferences and interests, taking into account their current emotional state as well as their purchase and search history. Based on this profiling, it identifies optimal service selections and deals. For example, if the user is feeling stressed, it will prioritize analyzing special sale information for relaxation services.

[1079] Step 5:

[1080] The server analyzes the discount information collected from the Internet and extracts information that matches the user's preferences and emotional state. In particular, if the user's emotional state is important, the server will prioritize the selection of sale information and coupon information that matches the user's feelings.

[1081] Step 6:

[1082] The device provides the user with the most appropriate information based on the analysis results. Information is sent in real time using push notifications. A list of special offers is also displayed on the in-app dashboard for easy access by the user. Information on additional discounts that can be obtained by using a point card is also provided at the same time. User satisfaction is improved by providing appropriate information at a time that matches the user's emotions based on the results of the emotion engine.

[1083] Step 7:

[1084] Users can utilize the information provided to them to take advantage of sales and coupons. For example, they can do online shopping based on sales information received via push notifications. After making a purchase, if the user actually uses the coupon, they can enter the results as feedback within the app.

[1085] Step 8:

[1086] The device collects feedback from users and sends it to the server, including data on whether the information provided was useful and whether special offers or coupons were used.

[1087] Step 9:

[1088] The server analyzes the feedback and uses it as data to improve the generative AI algorithm and emotion engine. Based on the feedback, adjustments can be made to improve the accuracy and relevance of the information provided, further improving the quality of future information provided.

[1089] In this way, through a series of steps, the system of the present invention comprehensively analyzes the user's usage data and emotions, and provides optimal services and discount information in real time, thereby improving the user's quality of life.

[1090] Example 2

[1091] 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."

[1092] In modern society, users are exposed to a vast amount of information, making it difficult to find information and services that are truly useful to them. Furthermore, conventional information provision systems do not provide information that takes into account the user's emotional state, making it difficult to provide services that are in line with the user's current psychological state. Because information provision is limited to information based on usage history and search history, it is difficult to maximize user satisfaction.

[1093] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1094] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data and identifying optimal service selections and advantageous information, means for recognizing the emotional state of the user, means for providing optimal information to the user, and means for collecting feedback from the user and improving the algorithm. This makes it possible to provide information that takes into account the emotional state of the user in addition to their usage history and consumption patterns, thereby maximizing user satisfaction.

[1095] "Usage data" refers to data relating to a user's behavioral patterns, such as the user's application usage history, purchase history, and search history.

[1096] "Means of collecting information from the Internet" refers to techniques for obtaining text, video, and image information from the Internet using web scraping, video analysis, image OCR (optical character recognition) technology, etc.

[1097] "Means for analyzing collected data and identifying optimal service selection and advantageous information" refers to a means for analyzing collected usage data and emotion recognition results using generative AI models, etc., to select optimal services and useful information for users.

[1098] The "means for recognizing the user's emotional state" is an emotion engine technology for analyzing text data, voice data, and image data to identify the user's emotional state.

[1099] "Means of providing optimal information to users" refers to technology that provides the most appropriate information to users based on analysis results through push notifications, in-app dashboards, etc.

[1100] "Means for collecting feedback from users and improving the algorithm" refers to means for collecting evaluations and feedback given by users on the information provided, and using this information to improve the accuracy of the analysis model and emotion engine.

[1101] A "generative AI model" is an artificial intelligence technology that learns specific patterns and characteristics from large amounts of data and makes recommendations and predictions for new data.

[1102] A "prompt sentence" is an input sentence given to a generative AI model to obtain a specific output.

[1103] This invention is a system that uses a generative AI model and an emotion recognition engine as a smartphone application. This system collects user usage data and analyzes text, video, and image information on the Internet to provide valuable information and optimal service selection. In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides information according to the user's emotions.

[1104] Specifically, the following hardware and software are used.

[1105] 1. Data Collection

[1106] Device: A dedicated application installed on a smartphone

[1107] Collection methods: Web scraping, video analysis, image OCR (Optical Character Recognition)

[1108] Data content: application usage history, purchase history, search history, etc.

[1109] 2. Emotion recognition

[1110] Device: Emotion engine installed on smartphone

[1111] Analysis method: Analysis of text data, audio data, and image data

[1112] Technology: Natural language processing, speech analysis, facial expression analysis

[1113] 3. Integrated Data Analysis

[1114] Server: High-Performance Computing Server

[1115] Analysis method: Integrated analysis of usage data and emotion recognition results using a generated AI model

[1116] Deliverables: Optimal service selection and deals for users

[1117] 4. Information provision

[1118] Device: Smartphone

[1119] Delivery method: Push notification, in-app dashboard

[1120] Information content: Special sale information, coupon information, relaxation service information

[1121] 5. Gathering feedback and improving the algorithm

[1122] Users: Provide feedback through a smartphone application

[1123] Device: Collect user feedback

[1124] Server: Analyze feedback data to improve the accuracy of generative AI and emotion engine

[1125] Specific examples

[1126] For example, if the history of an online shopping app that User A frequently uses indicates that he or she is highly interested in a particular brand B, the device can further detect User A's irritated emotions from the text input within the app. The server, taking into account User A's emotional state, prioritizes identifying information about special sales on relaxation products from Brand B. The device provides this information to User A via a push notification and also displays it on the app's dashboard. If User A uses the provided information and actually purchases the product, the device can rate the results within the app. The device collects this feedback and sends it to the server.

[1127] Prompt Sentence Examples

[1128] "Design a system to provide optimal services and special offers to a specific user based on their emotional state and past app usage history. The system will require the user to enter text indicating their emotional state within the app, and past purchase and usage history will also be analyzed. The goal is to improve user satisfaction by providing information according to their emotional state."

[1129] This invention comprehensively analyzes a user's usage data and emotions, and provides optimal services and advantageous information, thereby improving the efficiency of users' lives and maximizing their economic benefits.

[1130] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] Data collection

[1133] The device automatically collects usage data through applications installed on the user's smartphone. Input data includes application usage history, purchase history, search history, etc. Specifically, the device saves a log of each user's application operation. It also uses web scraping technology to obtain product price information and reviews from specific websites and saves them on the device. Furthermore, it uses OCR technology to extract text information from flyer images taken by the user. This data is then saved on the device for use in subsequent analysis steps.

[1134] Step 2:

[1135] emotion recognition

[1136] The device uses an emotion engine to recognize the user's emotional state. Input data includes the user's text data, voice data, and image data. Specifically, when the user enters a text comment into the app, the device analyzes the content using natural language processing technology to determine the user's emotional state. It also analyzes the tone and tempo of the voice data recorded by the user within the app, and analyzes facial expressions from image data. These results are stored on the device as emotion recognition data.

[1137] Step 3:

[1138] Integrated Data Analysis

[1139] The server performs an integrated analysis of the usage data and emotion recognition results sent from the device. The input data includes the user's application usage history, purchase history, search history, and emotion recognition data. Specifically, the server inputs this data into a generative AI model to identify optimal service selections and deals based on past usage history, consumption patterns, and current emotional state. For example, if the user is feeling stressed, it will prioritize the extraction of relaxation services and entertainment-related sales information. The analysis results are stored on the server for use in subsequent information provision steps.

[1140] Step 4:

[1141] Providing information

[1142] The device provides the user with the most appropriate information based on the analysis results sent from the server. The input data includes the recommended information sent from the server. Specifically, the device receives the analysis results and notifies the user via push notification. The app's dashboard also displays the latest sales information and a list of coupons. This allows the user to easily access the information and prompts them to take a specific action (for example, clicking the "Buy Now" button).

[1143] Step 5:

[1144] Feedback collection and algorithm improvement

[1145] The user uses the provided information and evaluates the results within the app. The input data includes the user's feedback on the provided information. Specifically, the user enters evaluations within the app, such as "This sale information was useful" or "I used the coupon." The device collects this feedback data and uploads it to the server. The server analyzes the collected feedback data and compares the output of the emotion engine with actual user feedback. This readjusts the parameters of the generative AI model and emotion engine, improving the accuracy of the recommended information.

[1146] (Application example 2)

[1147] 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."

[1148] The purpose of this invention is to provide a new method for analyzing a user's usage behavior and emotional state in a smartphone application to make appropriate recommendations for food delivery services. Conventional food delivery services generally suggest menu items simply based on a user's ordering history and preferences, but by taking the user's emotional state into account, more personalized recommendations become possible. This technology is expected to improve user satisfaction and increase the frequency of service use.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1150] In this invention, the server includes means for collecting usage data, means for collecting information from the Internet, means for analyzing the collected data to identify optimal service selections and advantageous information, means for analyzing user emotion data to make optimal recommendations, and means for proposing recommended menus based on the user's emotional state in a food delivery service, thereby making it possible to provide optimal food delivery options that match the user's mood and emotional state.

[1151] definition statement

[1152] "Usage data" refers to information about the actions and operations a user performs through an application. Specifically, it includes application usage history, purchase history, search history, etc.

[1153] "Means of collecting information from the Internet" refers to technical means of obtaining useful information for users through web scraping, APIs, etc. Examples include text information, video information, flyer images, etc.

[1154] "Means for analyzing collected data and identifying optimal service selections and advantageous information" refers to a function that statistically or algorithmically analyzes collected data and determines the information that is most useful to the user.

[1155] "Means of providing optimal information to users" refers to technology that presents useful information through notifications and screen displays on users' smartphones and devices.

[1156] "Means of collecting feedback from users and improving the algorithm" refers to a function that collects user evaluations and results of the information provided as data and uses this data to improve the accuracy and performance of the system.

[1157] "Means for analyzing user emotional data and making optimal recommendations" refers to technology that estimates a user's emotional state from text data, voice data, and image data, and makes appropriate recommendations based on that.

[1158] "A means for suggesting recommended menu items based on the user's emotional state in a food delivery service" is a function that selects and presents menu items that correspond to the user's stress and mood state in a food delivery service.

[1159] MODE FOR CARRYING OUT THE INVENTION

[1160] The system of the present invention operates mainly through an application installed on a smartphone, analyzes user usage data and emotion data, and provides optimal recommendations for food delivery services. Specific embodiments for implementing the present invention are described below.

[1161] System Configuration

[1162] server:

[1163] 1. The server has a means for collecting usage data, specifically a database system for safely storing and analyzing data such as user application usage history, purchase history, and search history.

[1164] 2. It has the means to collect information from the internet. It has the ability to collect text information, video information, flyer images, etc. using web scraping and APIs.

[1165] 3. It has the means to analyze the collected data and identify the optimal service selection and advantageous information. The various collected data is processed using statistical analysis and machine learning algorithms to extract the optimal services and information for the user.

[1166] 4. It has a means to analyze the user's emotional data and make optimal recommendations. It uses an emotion engine that analyzes the user's emotional state from text data, voice data, and image data.

[1167] 5. A food delivery service has a means to suggest menu recommendations based on the user's emotional state. Using a generative AI model, menu suggestions are made that match the user's emotional state.

[1168] Device:

[1169] 1. It has the means to provide users with the most appropriate information. It has the function of sending recommended information to users' smartphones via push notifications, etc.

[1170] 2. It has a means to collect feedback from users and improve the algorithm. It has a function that allows users to input their evaluations and results of the information provided within the app and send them to the server.

[1171] Hardware and software used

[1172] 1. Hardware: Cloud servers, smartphones

[1173] 2. Software: Natural language processing libraries (e.g., TextBlob), image recognition libraries (e.g., pytesseract), database systems, web scraping tools (e.g., BeautifulSoup)

[1174] Specific examples of processing

[1175] The user inputs text or voice via the application. If the user inputs something like "I feel really tired today," the device analyzes this as text data and uses an emotion engine to determine the user's emotional state. If the current emotional state is determined to be "stressed," the server recommends a menu of foods that have a relaxation effect to the user based on the collected usage data and emotion data. This recommendation information is sent to the user via a push notification from the device. For example, in response to a prompt such as "What menu items do you recommend for relieving stress?" the server might provide information such as "Our special herbal tea and relaxing salad are currently on special offer."

[1176] In this way, the present invention improves user satisfaction in food delivery services by analyzing the user's emotional state and providing personalized recommendations. Furthermore, it is possible to improve the algorithm based on user feedback and provide more accurate information.

[1177] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1178] System program processing flow

[1179] Step 1:

[1180] The device collects user usage data, including application usage history, purchase history, and search history. The input data is the user activity log, and the output data is saved in a database.

[1181] Step 2:

[1182] The device collects information from the Internet, using web scraping or APIs to obtain content such as text, video, and flyer images. The input data is the URL of the web page or the API endpoint, and the output data is the information obtained from the scraping or API.

[1183] Step 3:

[1184] The server analyzes the collected data. Specifically, it uses statistical analysis and machine learning algorithms to identify the best services and deals for users. The input data is usage data and information collected from the Internet, and the output data is recommendation information as a result of the analysis.

[1185] Step 4:

[1186] The device collects the user's emotional input (e.g., text, voice, image). The input data is text or voice data entered by the user, which is converted into text data and sent to the emotion engine for analysis. The output data is the emotional state resulting from the analysis.

[1187] Step 5:

[1188] The server uses a generative AI model to make optimal recommendations based on the emotional state analyzed by the emotion engine. The input data is the emotional state, usage data, and information from the internet, and the generative AI model operates based on this to generate a recommendation menu as output data.

[1189] Step 6:

[1190] The device provides the user with the recommendation information sent from the server. Specifically, the information is presented through push notifications or in-app displays. The input data is the recommendation information sent from the server, and the output data is the notification or push message displayed to the user.

[1191] Step 7:

[1192] Users input feedback on the provided recommendation information within the app. The input data is feedback information, which is collected by the application and sent to the server to become output data.

[1193] Step 8:

[1194] The server analyzes the collected feedback and uses it to improve the algorithm: the input data is the user feedback, and the output data is the improved algorithm and the new recommendation model generated based on it.

[1195] At each step, we manage in detail how servers and devices collect, analyze, and provide data, aiming to improve user satisfaction and optimize services.

[1196] 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.

[1197] 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.

[1198] 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.

[1199] 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.

[1200] 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.

[1201] 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.

[1202] 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).

[1203] 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.

[1204] 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."

[1205] 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.

[1206] 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).

[1207] 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.

[1208] 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.

[1209] 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.

[1210] 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.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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.

[1217] The following is further disclosed regarding the above embodiment.

[1218] (Claim 1)

[1219] a means of collecting usage data;

[1220] How to collect information from the internet;

[1221] A means of analyzing the collected data to identify optimal service selections and deals;

[1222] A means for providing optimal information to a user;

[1223] A system that includes a means of collecting user feedback and improving the algorithm.

[1224] (Claim 2)

[1225] 2. The system according to claim 1, further comprising means for profiling preferences based on a user's usage history and consumption patterns in analyzing the collected data.

[1226] (Claim 3)

[1227] 2. The system according to claim 1, further comprising means for analyzing text information, video information, and flyer images in said information collection from the Internet.

[1228] (Claim 4)

[1229] 2. The system of claim 1, wherein the means for providing optimal information includes means for transmitting information through push notification.

[1230] (Claim 5)

[1231] 10. The system of claim 1, further comprising means for users to assess the success of using the provided information in collecting said feedback.

[1232] "Example 1"

[1233] (Claim 1)

[1234] a means of collecting usage data;

[1235] How to collect information from the internet;

[1236] A means to analyze collected data using generative AI models to identify optimal service selections and deals,

[1237] A means for providing optimal information to users in real time;

[1238] A means of collecting user feedback and improving the algorithm;

[1239] Video analysis means;

[1240] A system including image OCR technology means.

[1241] (Claim 2)

[1242] The system of claim 1, further comprising means for profiling preferences based on a user's usage history and consumption patterns using a generative AI model in analyzing the collected data.

[1243] (Claim 3)

[1244] The system of claim 1, further comprising a means for analyzing text information, video information, and flyer images in the information collection from the Internet, and a means for preferentially analyzing product information of a specific brand or category using a generative AI model.

[1245] "Application Example 1"

[1246] (Claim 1)

[1247] a means of collecting usage data;

[1248] How to collect information from the internet;

[1249] A means of analyzing the collected data to identify optimal service selections and deals;

[1250] A means for providing optimal information to a user;

[1251] A means of collecting user feedback and improving the algorithm;

[1252] A means to notify users in real time of special sales information on products and brands that interest them when shopping in physical stores,

[1253] A means for extracting text information from an image using image recognition technology;

[1254] A means for displaying the collected sale information in list form;

[1255] A means to customize information based on user preferences

[1256] A system including:

[1257] (Claim 2)

[1258] 10. The system of claim 1, further comprising means for profiling preferences based on user usage history and consumption patterns in analyzing the collected data.

[1259] (Claim 3)

[1260] 2. The system according to claim 1, further comprising means for analyzing text information, video information, and flyer images when collecting information from the Internet.

[1261] "Example 2: Combining Emotion Engines"

[1262] (Claim 1)

[1263] a means of collecting usage data;

[1264] How to collect information from the internet;

[1265] A means of analyzing the collected data to identify optimal service selections and deals;

[1266] means for recognizing the emotional state of a user;

[1267] A means for providing optimal information to a user;

[1268] A system that includes a means of collecting user feedback and improving the algorithm.

[1269] (Claim 2)

[1270] The system of claim 1, further comprising means for profiling preferences based on a user's usage history and consumption patterns and means for optimizing service provision based on an emotional state in analyzing the collected data.

[1271] (Claim 3)

[1272] 2. The system according to claim 1, further comprising means for analyzing text information, video information, and image information in said information collection from the Internet.

[1273] "Application example 2 when combining emotion engines"

[1274] (Claim 1)

[1275] a means of collecting usage data;

[1276] How to collect information from the internet;

[1277] A means of analyzing the collected data to identify optimal service selections and deals;

[1278] A means for providing optimal information to a user;

[1279] A means of collecting user feedback and improving the algorithm;

[1280] A means for analyzing user emotion data and making optimal recommendations;

[1281] A method for suggesting recommended menu items based on a user's emotional state in a food delivery service;

[1282] A system including:

[1283] (Claim 2)

[1284] 2. The system according to claim 1, further comprising means for profiling preferences based on a user's usage history and consumption patterns in analyzing the collected data.

[1285] (Claim 3)

[1286] 2. The system according to claim 1, further comprising means for analyzing text information, video information, and flyer images in said information collection from the Internet. [Explanation of symbols]

[1287] 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. a means of collecting usage data; How to collect information from the internet; A means of analyzing the collected data to identify optimal service selections and deals; A means for providing optimal information to a user; A system that includes a means of collecting user feedback and improving the algorithm.

2. The system according to claim 1, further comprising means for profiling preferences based on a user's usage history and consumption patterns in analyzing the collected data.

3. 2. The system according to claim 1, further comprising means for analyzing text information, video information, and flyer images in said information collection from the Internet.

4. The system of claim 1 , wherein the means for providing optimal information includes means for transmitting information through push notifications.

5. 2. The system of claim 1, further comprising means for users to assess the success of using the provided information in collecting said feedback.

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