System

Abstract

A system is provided.SOLUTION: A system comprising: means for recording a search history of a user; means for recording a behavior history of the user based on the search history; means for analyzing the behavior history to generate a behavior model of the user; means for predicting information required next by the user based on the behavior model; and means for providing the predicted information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, information searches using the Internet have become commonplace, and many users use this method to gather information on a daily basis. However, when users search for the same information again, they often cannot remember the site they arrived at the last time, resulting in the hassle of repeating the same search. Another problem is that when searching for new information, it is cumbersome to have to navigate the Internet by entering the same search keywords every time. This makes it difficult for users to quickly access the information they desire. The purpose of this invention is to solve these problems, improve the efficiency of users' search experience, and increase convenience. [Means for solving the problem]

[0005] The present invention provides a means for recording a user's search history and, based on that, recording a behavioral history. It also includes a means for analyzing this behavioral history and generating a behavioral model for each user. This makes it possible to predict the next information the user will need based on the behavioral model and present it to the user. Specifically, a detailed behavioral history is collected by recording the search keywords entered by the user, the URLs of websites accessed, and the access times. This data is then sent to a server, which then generates predictive information based on the received data and displays it on the user's device. This allows the user to quickly access the information they need without having to search again. This mechanism improves search efficiency and convenience.

[0006] "Search history" is a record of the search keywords a user has entered or search queries they have made on the Internet.

[0007] "Behavioral history" is a record of the URL when a user accessed a specific website, the time spent viewing the website, and the actions taken.

[0008] A "behavioral model" is a model that represents a user's search patterns, generated by analyzing the user's past search history and behavioral history.

[0009] A "prediction means" is a means for predicting the next information a user will need based on a user behavior model.

[0010] The "means of provision" is a means of presenting the information estimated by the prediction means to the user.

[0011] A "server" is a central processing unit that receives, stores, and analyzes a user's search history and behavior history to generate a behavior model.

[0012] A "terminal" is a device that a user actually uses to input and obtain information, and is a device that communicates with a server.

[0013] "Information presentation" is the act of providing users with the information they need visually, audibly, or in other ways. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0035] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[0036] System configuration

[0037] server

[0038] The server is the core of this system and has the following main functions:

[0039] 1. Data reception function

[0040] The server receives search history and behavioral history data sent from the user's device.

[0041] 2. Data storage function

[0042] The server records and stores the received data in a database.

[0043] 3. Behavioral model generation function

[0044] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[0045] 4. Prediction function

[0046] The server then uses the generated behavioral model to predict what information the user will need next, and this prediction is made by referring to the user's past behavioral patterns.

[0047] 5. Information provision function

[0048] The server sends the predicted information to the user's terminal.

[0049] Terminal

[0050] A terminal is a device that a user actually uses and has the following main functions:

[0051] 1. Search history recording function

[0052] Record the keywords and queries that users use when searching.

[0053] 2. Activity history recording function

[0054] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[0055] 3. Data transmission function

[0056] The device sends the recorded search history and behavioral history to the server.

[0057] 4. Information display function

[0058] The terminal displays the forecast information received from the server to the user.

[0059] Implementation method

[0060] Server Processing

[0061] The server receives the user's search and behavior history and stores it in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavior model. Based on the behavior model, it predicts the information the user will need next and sends it to the device.

[0062] Terminal handling

[0063] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device also records the user's behavior history, such as the URL and viewing time, and sends this information to the server. Upon receiving prediction information from the server, the device displays this information to the user.

[0064] Specific examples

[0065] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, those behavioral histories are also recorded and sent to the server.

[0066] If the user performs the same search a few days later, the server uses past behavioral models to predict the latest reviews and links to previously visited sites, and sends them to the user's device. The device then displays the predicted information, allowing the user to access the latest and most relevant information more quickly than before.

[0067] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves convenience.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user initiates a search.

[0071] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[0072] Step 2:

[0073] The device records the search keywords.

[0074] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server.

[0075] Step 3:

[0076] The server receives and stores the search keywords.

[0077] The server records and stores the search keywords sent from the terminal in a database.

[0078] Step 4:

[0079] A user visits a specific site from a search result.

[0080] Users can access review sites of interest from the search results and check the content.

[0081] Step 5:

[0082] The device records your activity history.

[0083] The device collects data such as the URLs the user accessed, the viewing time, and the links clicked, and sends it to the server.

[0084] Step 6:

[0085] The server receives and stores the behavioral history.

[0086] The server stores the behavioral history data sent from the terminal in a database.

[0087] Step 7:

[0088] The server analyzes the data and generates a behavioral model.

[0089] The server analyzes the accumulated data using an AI model and generates a user behavior model.

[0090] Step 8:

[0091] The user makes the next request to the avatar.

[0092] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[0093] Step 9:

[0094] The server makes predictions based on the behavioral model.

[0095] The server refers to a model of the user's behavior and predicts what information will be needed next.

[0096] Step 10:

[0097] The server transmits the prediction information to the terminal.

[0098] The server sends the predicted URL and the latest review information to the user's device.

[0099] Step 11:

[0100] The device displays the forecast information.

[0101] The device displays the received forecast information on the screen, allowing users to easily access the information.

[0102] Step 12:

[0103] The user uses the information presented.

[0104] Users can quickly access the desired information by clicking on the latest review information or links provided by the server.

[0105] Example 1

[0106] 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."

[0107] Conventional search systems have the problem that they are unable to streamline users' repeated search behavior, making it difficult for users to quickly reach the information they are looking for. Furthermore, it is difficult to analyze a user's behavioral history and predict the information they will need next, which hinders user convenience. To solve these problems, a system that efficiently and effectively utilizes a user's search history and behavioral history is needed.

[0108] 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.

[0109] In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for transmitting the user's behavioral history to the server, means for receiving the behavioral history and saving it in a database, means for analyzing the saved data and generating a user behavioral model, means for predicting the information the user will need next based on the generated behavioral model, means for transmitting the predicted information to the user's terminal, and means for displaying the transmitted predicted information on the user's terminal. This allows the user to quickly reach the information they are looking for, improving the efficiency and convenience of their search behavior.

[0110] "Means for recording user search history" refers to a function that captures keywords and queries entered by users into any search engine or application and stores them as data.

[0111] "Means for recording user behavior history" refers to a function that collects data on the actions a user takes on the Internet, such as the URLs of online sites accessed, browsing time, and links clicked.

[0112] "Means for sending user behavior history to a server" refers to a function that transfers search history and behavior history recorded locally by a terminal to a server using a specified communication protocol.

[0113] "Means for receiving behavioral history and storing it in a database" is a function by which the server receives behavioral history data sent from the terminal and stores it appropriately in a database.

[0114] "Means for analyzing stored data and generating a user behavior model" refers to a function that analyzes stored data using analytical tools and AI models to generate a model for extracting user behavior patterns and characteristics.

[0115] "A means for predicting the next information a user will need based on the generated behavioral model" refers to a function that refers to the behavioral model and predicts the information a user is likely to search for or the sites they are likely to access in the future.

[0116] The "means for transmitting predicted information to the user's terminal" is a function for transmitting predicted information from the server to the user's terminal in real time or at an appropriate time.

[0117] The "means for displaying the transmitted forecast information on the user's terminal" is a function that visually displays the forecast information received from the server to the user on the user's terminal.

[0118] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[0119] System configuration

[0120] server

[0121] The server is the core of the system and has the following main functions: The server-side software used is MySQL (registered trademark) or PostgreSQL for the database, and Python's TENSORFLOW (registered trademark) or PyTorch for machine learning.

[0122] Data reception function

[0123] The server receives search history and behavioral history data sent from the user's device, including the search keywords entered by the user, the URLs of websites visited, and the duration of browsing.

[0124] Data storage function

[0125] The server records and saves the received data in a database, for example, using MySQL or PostgreSQL.

[0126] Behavioral model generation function

[0127] The server uses AI models (e.g., TensorFlow, PyTorch) to analyze the stored data, generating a behavioral model for each user and analyzing the user's past search patterns and the sites they visited.

[0128] Predictive Function

[0129] The server uses the generated behavioral model to predict what information the user will need next. For example, if a user has frequently searched for "latest smartphone reviews" in the past, it predicts the latest review articles and related news.

[0130] Information provision function

[0131] The server then sends the predicted information to the user's device, allowing the user to receive the information they need at the optimal time.

[0132] Terminal

[0133] The terminal is the device that the user actually uses, and has the following main functions: The terminal software uses browser extensions and dedicated applications.

[0134] Search history recording function

[0135] It records keywords and queries when a user searches and stores them in local storage. For example, if a user searches for "latest smartphone reviews," it captures and stores the query in real time.

[0136] Activity history recording function

[0137] They record the URLs of websites you visit, the time you spend browsing, and the links you click. Behavioral data is saved as a user's activity history. For example, data is collected using browser extensions or in-app tracking features.

[0138] Data transmission function

[0139] The device sends the recorded search history and behavior history to the server. The data is securely transferred using the HTTPS protocol at regular intervals. For example, the data is set to be sent every hour.

[0140] Information display function

[0141] The device receives the forecast information from the server and displays it to the user, allowing the user to instantly check and view the forecast information. For example, the forecast information can be displayed as an auto-complete function below the search bar, or the latest information can be displayed in a dedicated notification area.

[0142] Specific examples

[0143] For example, a user searches for "latest smartphone reviews" and then visits a specific review site. This information is recorded on the device and sent to the server. The server analyzes this data and predicts the next review article the user will need. If the user searches the same query again a few days later, the server will send the latest review articles and related information to the user's device and display them as suggested links at the bottom of the screen. In this way, users can access the latest and most relevant information more quickly than before.

[0144] Prompt Sentence Examples

[0145] "User X has frequently searched for 'latest smartphone reviews' in the past and visited specific review sites. What information do you predict the next time they search?"

[0146] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves the convenience of obtaining information.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1:

[0149] Data collection

[0150] Search history record

[0151] The device records the user's search query, for example, if the user types "latest smartphone reviews," the search keywords are captured in real time to local storage.

[0152] Input: The search keyword entered by the user

[0153] Output: Search keywords saved in local storage

[0154] Activity history record

[0155] The device records behavioral data such as the URLs of websites visited, the time spent there, and the links clicked, using browser extensions and in-app tracking features.

[0156] Input: User's website visit history, time spent, and links clicked

[0157] Output: Activity history saved in local storage

[0158] Data transmission

[0159] The search history and behavioral history collected by the device is sent to the server at regular intervals (e.g., every hour). Data is transferred securely using the HTTPS protocol.

[0160] Input: Search history and behavior history stored in local storage

[0161] Output: Search history and behavior history sent to the server

[0162] Step 2:

[0163] Data storage and management

[0164] Storing data in a database

[0165] The server stores the received data in a database (e.g. MySQL or PostgreSQL). Newly received data is inserted into the appropriate tables, organized by user.

[0166] Input: Search history and behavior history sent to the server

[0167] Output: Search history and behavior history stored in a database

[0168] Data preparation and cleaning

[0169] The server detects duplicates and missing data, and performs maintenance and cleaning using Python's Pandas library.

[0170] Input: Search history and behavior history stored in the database

[0171] Output: Cleaned and organized data

[0172] Step 3:

[0173] Behavioral model generation

[0174] Data analysis

[0175] The server uses AI models (e.g., TensorFlow or PyTorch) to analyze the stored data, detecting patterns and trends based on past search queries and behavioral history.

[0176] Input: Cleaned and sanitized data

[0177] Output: Analyzed data, features

[0178] Behavioral model generation

[0179] The server uses the analyzed data to generate a behavioral model for each user, which includes the user's search behavior and website visit patterns.

[0180] Input: Analyzed data, features

[0181] Output: Generated behavioral model

[0182] Step 4:

[0183] Predicting and providing information

[0184] Generating forecast information

[0185] The server uses a behavioral model to predict what information the user will need next. For example, if a user has previously searched for "latest smartphone reviews," it predicts the latest review articles and related news.

[0186] Input: Generated behavior model

[0187] Output: Forecast information

[0188] Sending information to user devices

[0189] The server sends the prediction information to the user's device, and provides it in real time or at an appropriate time based on the user's behavior.

[0190] Input: Forecast information

[0191] Output: Forecast information sent to the user's device

[0192] Step 5:

[0193] User Interface Display

[0194] Viewing forecast information

[0195] The device receives the forecast information from the server and displays it to the user. When browsing, the forecast information is displayed as an auto-complete feature below the search bar, and the latest information is displayed in a dedicated notification area.

[0196] Input: Forecast information sent from the server

[0197] Output: Forecast information displayed in the user interface

[0198] (Application example 1)

[0199] 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."

[0200] Conventional information provision systems based on users' search history and behavioral history are unable to adequately respond to the specific purchasing patterns and preferences of each user, and are unable to realize personalized product suggestions that utilize purchase history and browsing history. This prevents users from maximizing their purchasing motivation, hindering sales growth.

[0201] 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.

[0202] In this invention, the server includes means for recording a user's search history, means for recording a user's behavior history based on the search history, means for analyzing the behavior history and generating a user behavior model, means for predicting the information the user will need next based on the behavior model, means for providing the user with the predicted information, means for collecting and recording the user's purchase history and browsing history on the online shopping site, and means for predicting the next product that the user is likely to purchase based on the purchase history and browsing history and notifying the user of the predicted product. This enables more accurate personalized product suggestions, increases the user's desire to purchase, and increases sales on the online shopping site.

[0203] "User search history" is a list of keywords or queries a user has entered into a search engine or application, and the websites they visited as a result.

[0204] "Behavioral history" is the sequence of activities a user performs within a website or application, including clicks, time spent on the site, and links followed.

[0205] A "behavioral model" is a model created by analyzing a user's past search history and behavioral history, and is used to predict the behavioral patterns of individual users.

[0206] "Predictive information" is information about the information or products that a user is likely to need next, based on a behavioral model.

[0207] "Purchase history" refers to a list of products that a user has previously purchased on an online shopping site, along with detailed information about those products.

[0208] "Browse history" refers to a list of products that a user has viewed on an online shopping site, along with detailed information about those products.

[0209] "Notification" means information provided to the User by the System, including push notifications and in-app notifications.

[0210] "Server" means a centralized system that receives, stores, and analyzes data from multiple users.

[0211] "Terminal" refers to the device actually used by the user, including smartphones, PCs, tablets, etc.

[0212] MODE FOR CARRYING OUT THE INVENTION

[0213] This invention is a system that records and analyzes a user's search history and behavioral history, and based on that, predicts the information and products that the user will need next. This system is intended for use particularly on online shopping sites, and provides personalized product suggestions to the user.

[0214] System Configuration

[0215] server

[0216] The server of the present invention has the following main functions:

[0217] 1. Data reception function

[0218] The server receives data on search history, behavioral history, purchase history, and browsing history sent from the user's device.

[0219] 2. Data storage function

[0220] The server records and stores the received data in a database, typically using a relational database management system such as MySQL or PostgreSQL.

[0221] 3. Behavioral model generation function

[0222] The server analyzes the stored data and generates a behavioral model for each user, using Python and scikit-learn to build a machine learning model.

[0223] 4. Prediction function

[0224] The server uses the generated behavioral model to predict what information or products the user will need next, again using Python and scikit-learn.

[0225] 5. Information provision function

[0226] The server uses a REST API to send predicted information and products to the user's device, and the API is implemented using a web framework such as Flask or Django.

[0227] Terminal

[0228] The terminal of the present invention is a device such as a smartphone that is actually used by a user and has the following main functions:

[0229] 1. Search history recording function

[0230] Record the keywords and queries that users use when searching.

[0231] 2. Activity history recording function

[0232] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[0233] 3. Data transmission function

[0234] The device sends the recorded search history, behavioral history, purchase history, and browsing history to the server. This communication uses the HTTP protocol.

[0235] 4. Information display function

[0236] The forecast information and products received from the server are displayed to the user. A mobile app using React Native, Swift, or Kotlin is suitable for displaying the information.

[0237] Specific examples

[0238] For example, if User A searches for "smartphone" on an online shopping site and views multiple smartphone pages, User A's search history and browsing history are immediately recorded by the device and sent to the server.

[0239] Based on this data, the server generates a behavioral model of User A and predicts the "smartphone accessories" or "latest smartphone model" that User A is likely to purchase next.

[0240] This prediction information is immediately sent as a push notification to User A's smartphone, allowing User A to immediately check and purchase related products.

[0241] Prompt Sentence Examples

[0242] "Show me the next five items I'm likely to buy based on my past purchase and search history."

[0243] As a result, the system of the present invention can improve the efficiency of users' search and purchasing behavior, and significantly improve convenience and customer satisfaction.

[0244] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0245] Step 1:

[0246] A user searches on an online shopping site. The user enters search keywords and the search results are displayed. The input here is the search keywords entered by the user, and the output is a list of search results. The device records these search keywords.

[0247] Step 2:

[0248] The user views a specific product page. The user clicks to access the product page and checks the product details. The input is the URL of the product the user clicked on, and the output is the detailed information of the product the user viewed. The device records the accessed URL and the viewing time.

[0249] Step 3:

[0250] The user purchases a product. The user adds the product to their cart, pays, and completes the purchase. The input is product information the user is purchasing, and the output is confirmation information upon completion of the purchase. The terminal records the purchase history.

[0251] Step 4:

[0252] The device sends the recorded search history, browsing history, and purchase history to the server. The input is the recorded history data, and the output is the data sent to the server. The device uploads this data to the server using an HTTP request.

[0253] Step 5:

[0254] The server stores the received data in a database. The input is each historical data sent from the terminal, and the output is the data recorded in the database. The server stores the data using a relational database management system (e.g., MySQL or PostgreSQL).

[0255] Step 6:

[0256] The server generates a behavioral model for each user based on the stored data. The input is the stored historical data, and the output is the behavioral model for each user. The server analyzes the data using Python and scikit-learn and builds a machine learning model.

[0257] Step 7:

[0258] The server predicts the next information or product the user will need based on the generated behavioral model. The input is the user's behavioral model, and the output is a list of predicted products. The server makes inferences from the behavioral model and makes predictions based on that data.

[0259] Step 8:

[0260] The server pushes predicted information and products to the user's device. The input is the predicted information, and the output is the notification displayed on the user's device. The server sends the notification using a REST API, and the device displays the notification.

[0261] Step 9:

[0262] The user checks the notification displayed on the device and then browses or purchases the product that is most likely to be purchased next. The input is the notification displayed on the device, and the output is the user's next action (browsing or purchasing). The user checks the details of the displayed product using a smartphone or computer.

[0263] 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.

[0264] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on that history. It also includes a function that recognizes the user's emotions and adjusts the predictive information based on those emotions. This system streamlines the user's repeated search behavior and enables the user to quickly reach the desired information.

[0265] System configuration

[0266] server

[0267] The server is the core of this system and has the following main functions:

[0268] 1. Data reception function

[0269] The server receives the search history, behavior history, and emotion data sent from the user's terminal.

[0270] 2. Data storage function

[0271] The server records and stores the received data in a database.

[0272] 3. Behavioral model generation function

[0273] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[0274] 4. Prediction function

[0275] The server predicts the next information the user will need based on the generated behavioral model and emotional data. This prediction is made by referring to the user's past behavioral patterns and current emotional state.

[0276] 5. Information provision function

[0277] The server sends the predicted information to the user's terminal.

[0278] Terminal

[0279] A terminal is a device that a user actually uses and has the following main functions:

[0280] 1. Search history recording function

[0281] Record the keywords and queries that users use when searching.

[0282] 2. Activity history recording function

[0283] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[0284] 3. Emotion data recording function

[0285] An emotion engine is used to recognize and record the user's emotional state.

[0286] 4. Data transmission function

[0287] The device transmits the recorded search history, behavior history, and emotion data to the server.

[0288] 5. Information display function

[0289] The terminal displays the forecast information received from the server to the user.

[0290] Implementation method

[0291] Server Processing

[0292] The server receives the user's search history, behavioral history, and emotional data and stores them in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavioral model. Based on this behavioral model and emotional data, it predicts the information the user will need next and sends it to the device.

[0293] Terminal handling

[0294] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device records the user's behavioral history, including the URL, viewing time, and links clicked, and the emotion engine recognizes and records the user's emotional state. This data is periodically sent to the server. When the device receives prediction information from the server, it displays that information to the user.

[0295] Specific examples

[0296] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, the emotion engine recognizes and records the user's emotional state along with their behavioral history. This data is then sent to the server, which uses it to generate a behavioral model and provide next-time prediction information.

[0297] A few days later, when the user searches for "smartphone reviews" again, the server will predict the most relevant and up-to-date review information based on past behavioral models and current emotional data, and send it to the user's device. For example, if the user has a strong interest or positive emotion in a particular product, the server will prioritize the latest information and reviews related to that product. In this way, users can access the desired information more quickly and efficiently.

[0298] This system can streamline users' search behavior and significantly improve the convenience of information access. In addition, by utilizing emotion data, it can respond more appropriately to user needs.

[0299] The processing flow will be explained below.

[0300] Step 1:

[0301] The user initiates a search.

[0302] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[0303] Step 2:

[0304] The device records the search keywords.

[0305] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server, thereby recording the user's search history.

[0306] Step 3:

[0307] The server receives and stores the search keywords.

[0308] The server records and saves the search keywords sent from the terminal in a database.

[0309] Step 4:

[0310] A user visits a specific site from a search result.

[0311] Users can access review sites of interest from the search results and check the content.

[0312] Step 5:

[0313] The device records your activity history.

[0314] The device collects data such as the URLs the user visits, the time spent browsing, and the links they click, and also uses an emotion engine to recognize the user's emotional state (e.g., excitement, interest, satisfaction, etc.) and record this information.

[0315] Step 6:

[0316] The device sends behavioral history and emotional data to the server.

[0317] The device transmits the recorded behavioral history and emotional data to the server.

[0318] Step 7:

[0319] The server receives and stores behavioral history and emotional data.

[0320] The server stores the behavioral history and emotional data sent from the device in a database.

[0321] Step 8:

[0322] The server analyzes the data and generates a behavioral model.

[0323] The server analyzes the accumulated data using an AI model to generate a user behavior model, which includes search history, access history, browsing time, and emotional data.

[0324] Step 9:

[0325] The user makes the next request to the avatar.

[0326] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[0327] Step 10:

[0328] The server makes predictions based on behavioral models and emotional data.

[0329] The server refers to the user's behavioral model and emotional data to predict what information the user will need next, especially reviews and related information that are likely to interest the user.

[0330] Step 11:

[0331] The server transmits the prediction information to the terminal.

[0332] The server sends the predicted URL and the latest review information to the user's device.

[0333] Step 12:

[0334] The device displays the forecast information.

[0335] The device displays the received forecast information on the screen, allowing users to easily access the information.

[0336] Step 13:

[0337] The user uses the information presented.

[0338] Users can quickly access the desired information by clicking on the latest review information or links provided by the server. Furthermore, the content displayed reflects the user's emotional state, making the display more user-friendly.

[0339] Example 2

[0340] 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."

[0341] Conventional search systems provide information based on a user's search history and behavioral history, but because they do not take into account the user's emotional state, they have the problem of not being able to fully adapt to user needs. For example, even when a user wants to quickly obtain information about a topic that interests them, there are limitations to relying solely on past history data. As a result, problems arise such as low user satisfaction and poor search efficiency.

[0342] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's search history and behavioral history, a means for recognizing and recording the user's emotional state, a means for analyzing this data to generate a behavioral model for each user, and a means for predicting information based on the behavioral model and emotional data. This enables highly accurate information prediction that reflects the user's emotional state.

[0343] "Search history" is a record of past search queries and keywords a user has performed on the Internet.

[0344] "Behavioral history" is a record of a user's internet activity, such as the web pages they visited, the time they spent there, and the links they clicked.

[0345] "Emotion data" is data that indicates the user's emotional state, and is recorded by recognizing emotions from the user's facial expressions, voice, actions, etc.

[0346] A "behavioral model" is a model for predicting a user's behavioral patterns, generated based on the user's search history and behavior history.

[0347] "Predictive information" is information that is predicted to be needed next by the user based on behavioral models and emotional data.

[0348] The "server" is a device that receives search history, behavioral history, and emotional data sent by users, analyzes and stores this data, and generates predictive information.

[0349] A "terminal" is a device that is actually operated by a user, and is a device that records search history, behavioral history, and emotional data and transmits them to a server.

[0350] A "database" is a system for systematically storing search history, behavioral history, and emotional data received by the server.

[0351] An "emotion recognition API" is a program interface for analyzing a user's emotional state, and is used to recognize emotions from data such as the user's facial expressions and voice.

[0352] "Natural language processing" is a technology that uses machine learning and text mining techniques to analyze text data and understand its meaning and context.

[0353] MODE FOR CARRYING OUT THE INVENTION

[0354] This invention relates to a system that provides highly accurate information by utilizing a user's search history, behavioral history, and emotional data. This system comprehensively analyzes a user's behavior and emotions and predicts the information the user will need next, thereby improving the efficiency of information searches.

[0355] The system mainly consists of a server and terminals, each with the following functions:

[0356] Server Configuration

[0357] Data reception function

[0358] The server receives search history, behavioral history, and emotion data sent from the user's device. Specifically, it processes HTTP requests using a web server such as Nginx or Apache (registered trademark).

[0359] Data storage function

[0360] The server stores the received data in a relational database such as MySQL or PostgreSQL, allowing users' past behavioral data to be systematically accumulated.

[0361] Behavioral model generation function

[0362] The server analyzes the stored data and generates a behavioral model for each user. This process uses machine learning libraries such as Python's Scikit-learn and TensorFlow, making it possible to analyze and predict user behavioral patterns with high accuracy.

[0363] Predictive Function

[0364] The server predicts the next information the user will need based on the generated behavioral model and emotion data. For example, it uses text mining, which uses natural language processing technology, to predict the next keyword the user will search for or the website they are likely to visit based on their past search patterns.

[0365] Information provision function

[0366] The server then sends the predicted information to the user's device. If real-time performance is required, a communication protocol such as WebSocket is used.

[0367] Device configuration

[0368] Search history recording function

[0369] The keywords users type into search engines are recorded using browser extensions and mobile apps, which helps to clarify users' search patterns.

[0370] Activity history recording function

[0371] The device records behavioral data such as the addresses of web pages accessed by the user, the duration of browsing, and the links clicked. JavaScript (registered trademark) is used to capture activity within the browser.

[0372] Emotion data recording function

[0373] The device uses an emotion recognition API (for example, Microsoft's (registered trademark) Emotion API) to record the user's emotional state from facial expressions and voice data.

[0374] Data transmission function

[0375] The recorded search history, behavioral history, and emotion data are sent to the server. Asynchronous communication is performed using Ajax.

[0376] Information display function

[0377] Predictive information received from the server is displayed to the user via pop-ups and notifications, allowing users to access the information they need with increased speed and efficiency.

[0378] Specific examples

[0379] When a user searches for "latest smartphone reviews," the device first records the search keyword and sends it to the server. If the user visits a specific review site and then accesses other sites, the device records the user's behavioral history and the user's emotional state as recognized by the emotion engine, and periodically sends this information to the server.

[0380] The server generates a behavioral model based on this data and provides next-time prediction information. A few days later, when the user tries to search for "smartphone reviews" again, the server predicts the most relevant, up-to-date review information based on the user's past behavioral model and current emotional data, and sends it to the user's device.

[0381] For example, predictive information might include a notification such as, "Here's the latest review article on a new smartphone you were interested in!"

[0382] Prompt Sentence Examples

[0383] You might consider using a prompt like this:

[0384] "I would like to develop a system that predicts and provides the next information a user will need based on their behavioral history and emotional data when they search for specific product reviews several times and visit a specific site. The system will record the time spent on the site, the links clicked, and their emotional state (excitement, interest, etc.), and provide information based on a behavioral model analyzed on the server. Please suggest some algorithms that can predict which site a user will visit next and what information they will want."

[0385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0386] Step 1:

[0387] A user enters a specific keyword (e.g., "latest smartphone reviews") into the browser's search engine. This input is recorded on the device as search history. The input data is the search keyword, and the output data is search history data. Specifically, the browser extension captures keyboard input and saves it in local storage.

[0388] Step 2:

[0389] The device records the user's visit to a specific website, the page address, the viewing time, and the links clicked. The input data is the web page activity, and the output data is the behavioral history data. Specifically, JavaScript code monitors the user's browsing activity in real time, captures the information, and stores it in local storage.

[0390] Step 3:

[0391] The user's device uses an emotion recognition API to acquire and record the user's emotional data. The input data is the user's facial expressions and voice data, and the output data is emotional data. Specifically, a webcam or smartphone front camera captures the user's face, and the emotion recognition software analyzes the data to identify their emotional state.

[0392] Step 4:

[0393] The device sends the recorded search history, behavioral history, and emotion data to the server. The input data is the previously recorded data, and the output data is a notification to the server that transmission has been completed. Specifically, asynchronous communication is performed using Ajax, and this data is sent to the server.

[0394] Step 5:

[0395] The server receives data sent from the device. The input data is the search history, behavioral history, and emotion data sent from the device, and the output data is a notification that the data has been saved to the database. Specifically, the Nginx or Apache web server receives the HTTP request and stores the content in a database (MySQL or PostgreSQL).

[0396] Step 6:

[0397] The server analyzes the data stored in the database and generates a behavioral model. The input data is the saved search history, behavioral history, and emotion data, and the output data is the generated behavioral model. Specifically, it uses Python's Scikit-learn and TensorFlow to build a machine learning model and analyze and predict user behavior patterns.

[0398] Step 7:

[0399] The server predicts the next information the user will need based on the behavioral model and emotional data. The input data is the behavioral model and emotional data, and the output data is the predicted information. Specifically, it uses natural language processing technology to perform text mining and predict the keywords and websites that are likely to be searched next.

[0400] Step 8:

[0401] The server sends the predicted information to the user's device. The input data is the predicted information, and the output data is a notification of completion of transmission to the device. Specifically, real-time communication is performed using WebSocket, and the predicted information is sent to the device.

[0402] Step 9:

[0403] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the output data is the information displayed to the user. Specific operations include displaying a pop-up on a web page or providing information to the user using a notification function.

[0404] (Application example 2)

[0405] 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."

[0406] Conventional information provision systems based on search history and behavioral history have the problem of not being able to provide information that is adequately suited to the user's needs because they do not take into account the user's emotional state. This makes it difficult to provide optimal product information based on the user's interests and emotions, which can lead to a poor user experience. Furthermore, because emotional data is not collected, it is difficult to provide more personalized predictive information.

[0407] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for analyzing the behavioral history to generate a user behavioral model, means for predicting the product information the user will need next based on the behavioral model and the user's emotional data, means for providing the predicted information to the user's terminal, means for recognizing the user's emotional state using the terminal, and means for recording and transmitting the emotional data. This makes it possible to utilize the user's emotional data to quickly provide more personalized and appropriate product information.

[0408] "User" refers to an individual or organization that uses this system.

[0409] "Search history" is a record of the search queries and keywords a user has performed.

[0410] "Behavioral history" refers to records of the URLs of web pages a user accessed, the links they clicked, the duration of their visit, and so on.

[0411] A "behavioral model" is a model of a user's behavioral patterns that is generated based on the user's past search history and behavior history.

[0412] "Emotional data" refers to information that indicates the user's emotional state, and includes data obtained from facial expressions, tone of voice, and the like.

[0413] "Predictive information" refers to information or product information that a user is likely to need next, generated based on a user's behavioral model and emotional data.

[0414] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[0415] "Means for recording" is a function that allows specific data (e.g., search history, behavior history, emotion data) to be recorded.

[0416] "Transmitting means" is a function for transmitting data (for example, search history, behavior history, emotion data) from a terminal to a server.

[0417] The "means for providing" is a function for transmitting the prediction information from the server to the terminal and displaying it to the user.

[0418] "Means of recognition" is a function for analyzing the user's emotional state and recording it as data.

[0419] "Server" refers to a central processing unit that receives, stores, and analyzes data sent by users, and generates and transmits prediction information.

[0420] The present invention is a system that provides predictive information based on a user's search history and behavior history, and also has the function of recognizing the user's emotional data and adjusting the predictive information. This system is mainly composed of a server and a terminal.

[0421] Server Configuration

[0422] Data reception function

[0423] The server receives search history, behavioral history, and emotion data sent from the user's device. The received data is processed in real time, making it possible to make predictions that reflect the user's latest situation.

[0424] Data storage function

[0425] The received data is stored in a database, which is preferably capable of processing large amounts of data at high speed, such as an SQL database (MySQL) or a NoSQL database (MongoDB).

[0426] Behavioral model generation function

[0427] The server analyzes the stored data and generates a behavioral model for each user. The behavioral model is composed of data such as past search queries, web page browsing history, access times, and links clicked. Machine learning algorithms (e.g., random forests and neural networks) are used to generate this model.

[0428] Predictive Function

[0429] Based on behavioral and emotional data, the platform predicts what users will need next using a generative AI model that looks at past data and current emotional state to predict the most relevant product information, reviews, and more.

[0430] Information provision function

[0431] The server sends the predicted information to the user's terminal, providing the user with appropriate information.

[0432] Device configuration

[0433] Search history recording function

[0434] The device records the keywords and queries users use when searching, which are important data points because they reflect user behavior.

[0435] Activity history recording function

[0436] It records behavioral data such as the URL of the web page the user visits, the viewing time, and the links clicked, which allows for a detailed understanding of the user's interests.

[0437] Emotion data recording function

[0438] The device uses an emotion engine to recognize and record the user's emotional state, using a camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data such as "happy" or "excited."

[0439] Data transmission function

[0440] The device transmits the recorded search history, behavioral history, and emotional data to the server periodically, and the data is updated in real time.

[0441] Information display function

[0442] The forecast information received from the server is displayed to the user, allowing the user to quickly access the most relevant product information and reviews.

[0443] Specific examples

[0444] For example, if a user searches for "latest smartphone reviews" and spends a long time on a specific product page, the device sends this search history and behavioral history to the server. At the same time, the device's camera analyzes the user's facial expressions and records the emotion data "happy." The server uses this data to generate a user behavior model and predict relevant product information and positive reviews, which it then provides the next time the user searches.

[0445] Example prompts to input to the generative AI model

[0446] When purchasing a smartphone, if a user frequently searches for "latest smartphone reviews" or "spec comparisons" and spends a long time on a specific product page, prioritize recommendations for reviews of that specific product. Also, if the sentiment data indicates "happy" or "excited," provide positive reviews and discount information related to that product. Predict what information the user will search for next and generate data based on this prediction.

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1:

[0449] The device records the user's search keywords. When a user searches for a specific keyword (e.g., "latest smartphone reviews"), the keyword is saved as a search history. This becomes input data, and the device gets an output that is recorded in local storage.

[0450] Step 2:

[0451] When a user browses a web page, the device records the URL of the page and the browsing time. If a user browses a specific product page for a long time, the URL and browsing time will be saved as a behavioral history. The input data is the URL and browsing time, and the device outputs this data and saves it in local storage.

[0452] Step 3:

[0453] The device uses a built-in camera and microphone to collect user emotional data. It analyzes facial expressions and tone of voice while the user is browsing a web page and recognizes emotions such as "happy" or "excited." The input data is video and audio captured by the camera and microphone, and the device outputs this as emotional data.

[0454] Step 4:

[0455] The device periodically sends the recorded search history, behavior history, and emotion data to the server using HTTP requests. The input data are search history, behavior history, and emotion data, and the device obtains output by sending this to the server.

[0456] Step 5:

[0457] The server stores the data received from the device in a database. The input data is the search history, behavior history, and emotion data sent from the device, and the server obtains an output that records this in the database.

[0458] Step 6:

[0459] The server analyzes the stored data using machine learning algorithms to generate a behavioral model for each user. The input data is search history, behavioral history, and emotional data recorded in the database, and the server analyzes this data to obtain the output as a behavioral model. Algorithms such as random forests and neural networks are used for this analysis.

[0460] Step 7:

[0461] The server predicts the next product information the user will need based on the generated behavioral model and emotional data. The input data are the behavioral model and emotional data, and the server outputs predicted information based on this. A generative AI model is used for the prediction.

[0462] Step 8:

[0463] The server sends the predicted product information to the user's terminal. The input data is the predicted information, and the server obtains an output that is sent to the terminal as an HTTP response.

[0464] Step 9:

[0465] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the terminal displays it on the screen to obtain an output that provides the user with relevant information.

[0466] Step 10:

[0467] As users view the predictions and take their next action, the newly generated search history, behavioral history, and emotional data are returned to step 1, and the process repeats, continuously optimizing the user experience.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] [Second embodiment]

[0472] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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).

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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."

[0484] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[0485] System configuration

[0486] server

[0487] The server is the core of this system and has the following main functions:

[0488] 1. Data reception function

[0489] The server receives search history and behavioral history data sent from the user's device.

[0490] 2. Data storage function

[0491] The server records and stores the received data in a database.

[0492] 3. Behavioral model generation function

[0493] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[0494] 4. Prediction function

[0495] The server then uses the generated behavioral model to predict what information the user will need next, and this prediction is made by referring to the user's past behavioral patterns.

[0496] 5. Information provision function

[0497] The server sends the predicted information to the user's terminal.

[0498] Terminal

[0499] A terminal is a device that a user actually uses and has the following main functions:

[0500] 1. Search history recording function

[0501] Record the keywords and queries that users use when searching.

[0502] 2. Activity history recording function

[0503] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[0504] 3. Data transmission function

[0505] The device sends the recorded search history and behavioral history to the server.

[0506] 4. Information display function

[0507] The terminal displays the forecast information received from the server to the user.

[0508] Implementation method

[0509] Server Processing

[0510] The server receives the user's search and behavior history and stores it in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavior model. Based on the behavior model, it predicts the information the user will need next and sends it to the device.

[0511] Terminal handling

[0512] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device also records the user's behavior history, such as the URL and viewing time, and sends this information to the server. Upon receiving prediction information from the server, the device displays this information to the user.

[0513] Specific examples

[0514] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, those behavioral histories are also recorded and sent to the server.

[0515] If the user performs the same search a few days later, the server uses past behavioral models to predict the latest reviews and links to previously visited sites, and sends them to the user's device. The device then displays the predicted information, allowing the user to access the latest and most relevant information more quickly than before.

[0516] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves convenience.

[0517] The processing flow will be explained below.

[0518] Step 1:

[0519] The user initiates a search.

[0520] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[0521] Step 2:

[0522] The device records the search keywords.

[0523] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server.

[0524] Step 3:

[0525] The server receives and stores the search keywords.

[0526] The server records and stores the search keywords sent from the terminal in a database.

[0527] Step 4:

[0528] A user visits a specific site from a search result.

[0529] Users can access review sites of interest from the search results and check the content.

[0530] Step 5:

[0531] The device records your activity history.

[0532] The device collects data such as the URLs the user accessed, the viewing time, and the links clicked, and sends it to the server.

[0533] Step 6:

[0534] The server receives and stores the behavioral history.

[0535] The server stores the behavioral history data sent from the terminal in a database.

[0536] Step 7:

[0537] The server analyzes the data and generates a behavioral model.

[0538] The server analyzes the accumulated data using an AI model and generates a user behavior model.

[0539] Step 8:

[0540] The user makes the next request to the avatar.

[0541] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[0542] Step 9:

[0543] The server makes predictions based on the behavioral model.

[0544] The server refers to a model of the user's behavior and predicts what information will be needed next.

[0545] Step 10:

[0546] The server transmits the prediction information to the terminal.

[0547] The server sends the predicted URL and the latest review information to the user's device.

[0548] Step 11:

[0549] The device displays the forecast information.

[0550] The device displays the received forecast information on the screen, allowing users to easily access the information.

[0551] Step 12:

[0552] The user uses the information presented.

[0553] Users can quickly access the desired information by clicking on the latest review information or links provided by the server.

[0554] Example 1

[0555] 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."

[0556] Conventional search systems have the problem that they are unable to streamline users' repeated search behavior, making it difficult for users to quickly reach the information they are looking for. Furthermore, it is difficult to analyze a user's behavioral history and predict the information they will need next, which hinders user convenience. To solve these problems, a system that efficiently and effectively utilizes a user's search history and behavioral history is needed.

[0557] 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.

[0558] In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for transmitting the user's behavioral history to the server, means for receiving the behavioral history and saving it in a database, means for analyzing the saved data and generating a user behavioral model, means for predicting the information the user will need next based on the generated behavioral model, means for transmitting the predicted information to the user's terminal, and means for displaying the transmitted predicted information on the user's terminal. This allows the user to quickly reach the information they are looking for, improving the efficiency and convenience of their search behavior.

[0559] "Means for recording user search history" refers to a function that captures keywords and queries entered by users into any search engine or application and stores them as data.

[0560] "Means for recording user behavior history" refers to a function that collects data on the actions a user takes on the Internet, such as the URLs of online sites accessed, browsing time, and links clicked.

[0561] "Means for sending user behavior history to a server" refers to a function that transfers search history and behavior history recorded locally by a terminal to a server using a specified communication protocol.

[0562] "Means for receiving behavioral history and storing it in a database" is a function by which the server receives behavioral history data sent from the terminal and stores it appropriately in a database.

[0563] "Means for analyzing stored data and generating a user behavior model" refers to a function that analyzes stored data using analytical tools and AI models to generate a model for extracting user behavior patterns and characteristics.

[0564] "A means for predicting the next information a user will need based on the generated behavioral model" refers to a function that refers to the behavioral model and predicts the information a user is likely to search for or the sites they are likely to access in the future.

[0565] The "means for transmitting predicted information to the user's terminal" is a function for transmitting predicted information from the server to the user's terminal in real time or at an appropriate time.

[0566] The "means for displaying the transmitted forecast information on the user's terminal" is a function that visually displays the forecast information received from the server to the user on the user's terminal.

[0567] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[0568] System configuration

[0569] server

[0570] The server is the core of the system and has the following main functions: The server-side software used is MySQL or PostgreSQL for the database, and Python's TensorFlow or PyTorch for machine learning.

[0571] Data reception function

[0572] The server receives search history and behavioral history data sent from the user's device, including the search keywords entered by the user, the URLs of websites visited, and the duration of browsing.

[0573] Data storage function

[0574] The server records and saves the received data in a database, for example, using MySQL or PostgreSQL.

[0575] Behavioral model generation function

[0576] The server uses AI models (e.g., TensorFlow, PyTorch) to analyze the stored data, generating a behavioral model for each user and analyzing the user's past search patterns and the sites they visited.

[0577] Predictive Function

[0578] The server uses the generated behavioral model to predict what information the user will need next. For example, if a user has frequently searched for "latest smartphone reviews" in the past, it predicts the latest review articles and related news.

[0579] Information provision function

[0580] The server then sends the predicted information to the user's device, allowing the user to receive the information they need at the optimal time.

[0581] Terminal

[0582] The terminal is the device that the user actually uses, and has the following main functions: The terminal software uses browser extensions and dedicated applications.

[0583] Search history recording function

[0584] It records keywords and queries when a user searches and stores them in local storage. For example, if a user searches for "latest smartphone reviews," it captures and stores the query in real time.

[0585] Activity history recording function

[0586] They record the URLs of websites you visit, the time you spend browsing, and the links you click. Behavioral data is saved as a user's activity history. For example, data is collected using browser extensions or in-app tracking features.

[0587] Data transmission function

[0588] The device sends the recorded search history and behavior history to the server. The data is securely transferred using the HTTPS protocol at regular intervals. For example, the data is set to be sent every hour.

[0589] Information display function

[0590] The device receives the forecast information from the server and displays it to the user, allowing the user to instantly check and view the forecast information. For example, the forecast information can be displayed as an auto-complete function below the search bar, or the latest information can be displayed in a dedicated notification area.

[0591] Specific examples

[0592] For example, a user searches for "latest smartphone reviews" and then visits a specific review site. This information is recorded on the device and sent to the server. The server analyzes this data and predicts the next review article the user will need. If the user searches the same query again a few days later, the server will send the latest review articles and related information to the user's device and display them as suggested links at the bottom of the screen. In this way, users can access the latest and most relevant information more quickly than before.

[0593] Prompt Sentence Examples

[0594] "User X has frequently searched for 'latest smartphone reviews' in the past and visited specific review sites. What information do you predict the next time they search?"

[0595] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves the convenience of obtaining information.

[0596] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0597] Step 1:

[0598] Data collection

[0599] Search history record

[0600] The device records the user's search query, for example, if the user types "latest smartphone reviews," the search keywords are captured in real time to local storage.

[0601] Input: The search keyword entered by the user

[0602] Output: Search keywords saved in local storage

[0603] Activity history record

[0604] The device records behavioral data such as the URLs of websites visited, the time spent there, and the links clicked, using browser extensions and in-app tracking features.

[0605] Input: User's website visit history, time spent, and links clicked

[0606] Output: Activity history saved in local storage

[0607] Data transmission

[0608] The search history and behavioral history collected by the device is sent to the server at regular intervals (e.g., every hour). Data is transferred securely using the HTTPS protocol.

[0609] Input: Search history and behavior history stored in local storage

[0610] Output: Search history and behavior history sent to the server

[0611] Step 2:

[0612] Data storage and management

[0613] Storing data in a database

[0614] The server stores the received data in a database (e.g. MySQL or PostgreSQL). Newly received data is inserted into the appropriate tables, organized by user.

[0615] Input: Search history and behavior history sent to the server

[0616] Output: Search history and behavior history stored in a database

[0617] Data preparation and cleaning

[0618] The server detects duplicates and missing data, and performs maintenance and cleaning using Python's Pandas library.

[0619] Input: Search history and behavior history stored in the database

[0620] Output: Cleaned and organized data

[0621] Step 3:

[0622] Behavioral model generation

[0623] Data analysis

[0624] The server uses AI models (e.g., TensorFlow or PyTorch) to analyze the stored data, detecting patterns and trends based on past search queries and behavioral history.

[0625] Input: Cleaned and sanitized data

[0626] Output: Analyzed data, features

[0627] Behavioral model generation

[0628] The server uses the analyzed data to generate a behavioral model for each user, which includes the user's search behavior and website visit patterns.

[0629] Input: Analyzed data, features

[0630] Output: Generated behavioral model

[0631] Step 4:

[0632] Predicting and providing information

[0633] Generating forecast information

[0634] The server uses a behavioral model to predict what information the user will need next. For example, if a user has previously searched for "latest smartphone reviews," it predicts the latest review articles and related news.

[0635] Input: Generated behavior model

[0636] Output: Forecast information

[0637] Sending information to user devices

[0638] The server sends the prediction information to the user's device, and provides it in real time or at an appropriate time based on the user's behavior.

[0639] Input: Forecast information

[0640] Output: Forecast information sent to the user's device

[0641] Step 5:

[0642] User Interface Display

[0643] Viewing forecast information

[0644] The device receives the forecast information from the server and displays it to the user. When browsing, the forecast information is displayed as an auto-complete feature below the search bar, and the latest information is displayed in a dedicated notification area.

[0645] Input: Forecast information sent from the server

[0646] Output: Forecast information displayed in the user interface

[0647] (Application example 1)

[0648] 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."

[0649] Conventional information provision systems based on users' search history and behavioral history are unable to adequately respond to the specific purchasing patterns and preferences of each user, and are unable to realize personalized product suggestions that utilize purchase history and browsing history. This prevents users from maximizing their purchasing motivation, hindering sales growth.

[0650] 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.

[0651] In this invention, the server includes means for recording a user's search history, means for recording a user's behavior history based on the search history, means for analyzing the behavior history and generating a user behavior model, means for predicting the information the user will need next based on the behavior model, means for providing the user with the predicted information, means for collecting and recording the user's purchase history and browsing history on the online shopping site, and means for predicting the next product that the user is likely to purchase based on the purchase history and browsing history and notifying the user of the predicted product. This enables more accurate personalized product suggestions, increases the user's desire to purchase, and increases sales on the online shopping site.

[0652] "User search history" is a list of keywords or queries a user has entered into a search engine or application, and the websites they visited as a result.

[0653] "Behavioral history" is the sequence of activities a user performs within a website or application, including clicks, time spent on the site, and links followed.

[0654] A "behavioral model" is a model created by analyzing a user's past search history and behavioral history, and is used to predict the behavioral patterns of individual users.

[0655] "Predictive information" is information about the information or products that a user is likely to need next, based on a behavioral model.

[0656] "Purchase history" refers to a list of products that a user has previously purchased on an online shopping site, along with detailed information about those products.

[0657] "Browse history" refers to a list of products that a user has viewed on an online shopping site, along with detailed information about those products.

[0658] "Notification" means information provided to the User by the System, including push notifications and in-app notifications.

[0659] "Server" means a centralized system that receives, stores, and analyzes data from multiple users.

[0660] "Terminal" refers to the device actually used by the user, including smartphones, PCs, tablets, etc.

[0661] MODE FOR CARRYING OUT THE INVENTION

[0662] This invention is a system that records and analyzes a user's search history and behavioral history, and based on that, predicts the information and products that the user will need next. This system is intended for use particularly on online shopping sites, and provides personalized product suggestions to the user.

[0663] System Configuration

[0664] server

[0665] The server of the present invention has the following main functions:

[0666] 1. Data reception function

[0667] The server receives data on search history, behavioral history, purchase history, and browsing history sent from the user's device.

[0668] 2. Data storage function

[0669] The server records and stores the received data in a database, typically using a relational database management system such as MySQL or PostgreSQL.

[0670] 3. Behavioral model generation function

[0671] The server analyzes the stored data and generates a behavioral model for each user, using Python and scikit-learn to build a machine learning model.

[0672] 4. Prediction function

[0673] The server uses the generated behavioral model to predict what information or products the user will need next, again using Python and scikit-learn.

[0674] 5. Information provision function

[0675] The server uses a REST API to send predicted information and products to the user's device, and the API is implemented using a web framework such as Flask or Django.

[0676] Terminal

[0677] The terminal of the present invention is a device such as a smartphone that is actually used by a user and has the following main functions:

[0678] 1. Search history recording function

[0679] Record the keywords and queries that users use when searching.

[0680] 2. Activity history recording function

[0681] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[0682] 3. Data transmission function

[0683] The device sends the recorded search history, behavioral history, purchase history, and browsing history to the server. This communication uses the HTTP protocol.

[0684] 4. Information display function

[0685] The forecast information and products received from the server are displayed to the user. A mobile app using React Native, Swift, or Kotlin is suitable for displaying the information.

[0686] Specific examples

[0687] For example, if User A searches for "smartphone" on an online shopping site and views multiple smartphone pages, User A's search history and browsing history are immediately recorded by the device and sent to the server.

[0688] Based on this data, the server generates a behavioral model of User A and predicts the "smartphone accessories" or "latest smartphone model" that User A is likely to purchase next.

[0689] This prediction information is immediately sent as a push notification to User A's smartphone, allowing User A to immediately check and purchase related products.

[0690] Prompt Sentence Examples

[0691] "Show me the next five items I'm likely to buy based on my past purchase and search history."

[0692] As a result, the system of the present invention can improve the efficiency of users' search and purchasing behavior, and significantly improve convenience and customer satisfaction.

[0693] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0694] Step 1:

[0695] A user searches on an online shopping site. The user enters search keywords and the search results are displayed. The input here is the search keywords entered by the user, and the output is a list of search results. The device records these search keywords.

[0696] Step 2:

[0697] The user views a specific product page. The user clicks to access the product page and checks the product details. The input is the URL of the product the user clicked on, and the output is the detailed information of the product the user viewed. The device records the accessed URL and the viewing time.

[0698] Step 3:

[0699] The user purchases a product. The user adds the product to their cart, pays, and completes the purchase. The input is product information the user is purchasing, and the output is confirmation information upon completion of the purchase. The terminal records the purchase history.

[0700] Step 4:

[0701] The device sends the recorded search history, browsing history, and purchase history to the server. The input is the recorded history data, and the output is the data sent to the server. The device uploads this data to the server using an HTTP request.

[0702] Step 5:

[0703] The server stores the received data in a database. The input is each historical data sent from the terminal, and the output is the data recorded in the database. The server stores the data using a relational database management system (e.g., MySQL or PostgreSQL).

[0704] Step 6:

[0705] The server generates a behavioral model for each user based on the stored data. The input is the stored historical data, and the output is the behavioral model for each user. The server analyzes the data using Python and scikit-learn and builds a machine learning model.

[0706] Step 7:

[0707] The server predicts the next information or product the user will need based on the generated behavioral model. The input is the user's behavioral model, and the output is a list of predicted products. The server makes inferences from the behavioral model and makes predictions based on that data.

[0708] Step 8:

[0709] The server pushes predicted information and products to the user's device. The input is the predicted information, and the output is the notification displayed on the user's device. The server sends the notification using a REST API, and the device displays the notification.

[0710] Step 9:

[0711] The user checks the notification displayed on the device and then browses or purchases the product that is most likely to be purchased next. The input is the notification displayed on the device, and the output is the user's next action (browsing or purchasing). The user checks the details of the displayed product using a smartphone or computer.

[0712] 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.

[0713] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on that history. It also includes a function that recognizes the user's emotions and adjusts the predictive information based on those emotions. This system streamlines the user's repeated search behavior and enables the user to quickly reach the desired information.

[0714] System configuration

[0715] server

[0716] The server is the core of this system and has the following main functions:

[0717] 1. Data reception function

[0718] The server receives the search history, behavior history, and emotion data sent from the user's terminal.

[0719] 2. Data storage function

[0720] The server records and stores the received data in a database.

[0721] 3. Behavioral model generation function

[0722] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[0723] 4. Prediction function

[0724] The server predicts the next information the user will need based on the generated behavioral model and emotional data. This prediction is made by referring to the user's past behavioral patterns and current emotional state.

[0725] 5. Information provision function

[0726] The server sends the predicted information to the user's terminal.

[0727] Terminal

[0728] A terminal is a device that a user actually uses and has the following main functions:

[0729] 1. Search history recording function

[0730] Record the keywords and queries that users use when searching.

[0731] 2. Activity history recording function

[0732] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[0733] 3. Emotion data recording function

[0734] An emotion engine is used to recognize and record the user's emotional state.

[0735] 4. Data transmission function

[0736] The device transmits the recorded search history, behavior history, and emotion data to the server.

[0737] 5. Information display function

[0738] The terminal displays the forecast information received from the server to the user.

[0739] Implementation method

[0740] Server Processing

[0741] The server receives the user's search history, behavioral history, and emotional data and stores them in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavioral model. Based on this behavioral model and emotional data, it predicts the information the user will need next and sends it to the device.

[0742] Terminal handling

[0743] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device records the user's behavioral history, including the URL, viewing time, and links clicked, and the emotion engine recognizes and records the user's emotional state. This data is periodically sent to the server. When the device receives prediction information from the server, it displays that information to the user.

[0744] Specific examples

[0745] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, the emotion engine recognizes and records the user's emotional state along with their behavioral history. This data is then sent to the server, which uses it to generate a behavioral model and provide next-time prediction information.

[0746] A few days later, when the user searches for "smartphone reviews" again, the server will predict the most relevant and up-to-date review information based on past behavioral models and current emotional data, and send it to the user's device. For example, if the user has a strong interest or positive emotion in a particular product, the server will prioritize the latest information and reviews related to that product. In this way, users can access the desired information more quickly and efficiently.

[0747] This system can streamline users' search behavior and significantly improve the convenience of information access. In addition, by utilizing emotion data, it can respond more appropriately to user needs.

[0748] The processing flow will be explained below.

[0749] Step 1:

[0750] The user initiates a search.

[0751] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[0752] Step 2:

[0753] The device records the search keywords.

[0754] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server, thereby recording the user's search history.

[0755] Step 3:

[0756] The server receives and stores the search keywords.

[0757] The server records and saves the search keywords sent from the terminal in a database.

[0758] Step 4:

[0759] A user visits a specific site from a search result.

[0760] Users can access review sites of interest from the search results and check the content.

[0761] Step 5:

[0762] The device records your activity history.

[0763] The device collects data such as the URLs the user visits, the time spent browsing, and the links they click, and also uses an emotion engine to recognize the user's emotional state (e.g., excitement, interest, satisfaction, etc.) and record this information.

[0764] Step 6:

[0765] The device sends behavioral history and emotional data to the server.

[0766] The device transmits the recorded behavioral history and emotional data to the server.

[0767] Step 7:

[0768] The server receives and stores behavioral history and emotional data.

[0769] The server stores the behavioral history and emotional data sent from the device in a database.

[0770] Step 8:

[0771] The server analyzes the data and generates a behavioral model.

[0772] The server analyzes the accumulated data using an AI model to generate a user behavior model, which includes search history, access history, browsing time, and emotional data.

[0773] Step 9:

[0774] The user makes the next request to the avatar.

[0775] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[0776] Step 10:

[0777] The server makes predictions based on behavioral models and emotional data.

[0778] The server refers to the user's behavioral model and emotional data to predict what information the user will need next, especially reviews and related information that are likely to interest the user.

[0779] Step 11:

[0780] The server transmits the prediction information to the terminal.

[0781] The server sends the predicted URL and the latest review information to the user's device.

[0782] Step 12:

[0783] The device displays the forecast information.

[0784] The device displays the received forecast information on the screen, allowing users to easily access the information.

[0785] Step 13:

[0786] The user uses the information presented.

[0787] Users can quickly access the desired information by clicking on the latest review information or links provided by the server. Furthermore, the content displayed reflects the user's emotional state, making the display more user-friendly.

[0788] Example 2

[0789] 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."

[0790] Conventional search systems provide information based on a user's search history and behavioral history, but because they do not take into account the user's emotional state, they have the problem of not being able to fully adapt to user needs. For example, even when a user wants to quickly obtain information about a topic that interests them, there are limitations to relying solely on past history data. As a result, problems arise such as low user satisfaction and poor search efficiency.

[0791] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's search history and behavioral history, a means for recognizing and recording the user's emotional state, a means for analyzing this data to generate a behavioral model for each user, and a means for predicting information based on the behavioral model and emotional data. This enables highly accurate information prediction that reflects the user's emotional state.

[0792] "Search history" is a record of past search queries and keywords a user has performed on the Internet.

[0793] "Behavioral history" is a record of a user's internet activity, such as the web pages they visited, the time they spent there, and the links they clicked.

[0794] "Emotion data" is data that indicates the user's emotional state, and is recorded by recognizing emotions from the user's facial expressions, voice, actions, etc.

[0795] A "behavioral model" is a model for predicting a user's behavioral patterns, generated based on the user's search history and behavior history.

[0796] "Predictive information" is information that is predicted to be needed next by the user based on behavioral models and emotional data.

[0797] The "server" is a device that receives search history, behavioral history, and emotional data sent by users, analyzes and stores this data, and generates predictive information.

[0798] A "terminal" is a device that is actually operated by a user, and is a device that records search history, behavioral history, and emotional data and transmits them to a server.

[0799] A "database" is a system for systematically storing search history, behavioral history, and emotional data received by the server.

[0800] An "emotion recognition API" is a program interface for analyzing a user's emotional state, and is used to recognize emotions from data such as the user's facial expressions and voice.

[0801] "Natural language processing" is a technology that uses machine learning and text mining techniques to analyze text data and understand its meaning and context.

[0802] MODE FOR CARRYING OUT THE INVENTION

[0803] This invention relates to a system that provides highly accurate information by utilizing a user's search history, behavioral history, and emotional data. This system comprehensively analyzes a user's behavior and emotions and predicts the information the user will need next, thereby improving the efficiency of information searches.

[0804] The system mainly consists of a server and terminals, each with the following functions:

[0805] Server Configuration

[0806] Data reception function

[0807] The server receives search history, behavioral history, and emotion data sent from the user's device, and processes HTTP requests using a web server such as Nginx or Apache.

[0808] Data storage function

[0809] The server stores the received data in a relational database such as MySQL or PostgreSQL, allowing users' past behavioral data to be systematically accumulated.

[0810] Behavioral model generation function

[0811] The server analyzes the stored data and generates a behavioral model for each user. This process uses machine learning libraries such as Python's Scikit-learn and TensorFlow, making it possible to analyze and predict user behavioral patterns with high accuracy.

[0812] Predictive Function

[0813] The server predicts the next information the user will need based on the generated behavioral model and emotion data. For example, it uses text mining, which uses natural language processing technology, to predict the next keyword the user will search for or the website they are likely to visit based on their past search patterns.

[0814] Information provision function

[0815] The server then sends the predicted information to the user's device. If real-time performance is required, a communication protocol such as WebSocket is used.

[0816] Device configuration

[0817] Search history recording function

[0818] The keywords users type into search engines are recorded using browser extensions and mobile apps, which helps to clarify users' search patterns.

[0819] Activity history recording function

[0820] The device records behavioral data such as the addresses of web pages accessed by the user, the duration of browsing, the links clicked, etc. It uses JavaScript to capture activity within the browser.

[0821] Emotion data recording function

[0822] The device uses an emotion recognition API (e.g., Microsoft's Emotion API) to record the user's emotional state from facial expressions and voice data.

[0823] Data transmission function

[0824] The recorded search history, behavioral history, and emotion data are sent to the server. Asynchronous communication is performed using Ajax.

[0825] Information display function

[0826] Predictive information received from the server is displayed to the user via pop-ups and notifications, allowing users to access the information they need with increased speed and efficiency.

[0827] Specific examples

[0828] When a user searches for "latest smartphone reviews," the device first records the search keyword and sends it to the server. If the user visits a specific review site and then accesses other sites, the device records the user's behavioral history and the user's emotional state as recognized by the emotion engine, and periodically sends this information to the server.

[0829] The server generates a behavioral model based on this data and provides next-time prediction information. A few days later, when the user tries to search for "smartphone reviews" again, the server predicts the most relevant, up-to-date review information based on the user's past behavioral model and current emotional data, and sends it to the user's device.

[0830] For example, predictive information might include a notification such as, "Here's the latest review article on a new smartphone you were interested in!"

[0831] Prompt Sentence Examples

[0832] You might consider using a prompt like this:

[0833] "I would like to develop a system that predicts and provides the next information a user will need based on their behavioral history and emotional data when they search for specific product reviews several times and visit a specific site. The system will record the time spent on the site, the links clicked, and their emotional state (excitement, interest, etc.), and provide information based on a behavioral model analyzed on the server. Please suggest some algorithms that can predict which site a user will visit next and what information they will want."

[0834] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0835] Step 1:

[0836] A user enters a specific keyword (e.g., "latest smartphone reviews") into the browser's search engine. This input is recorded on the device as search history. The input data is the search keyword, and the output data is search history data. Specifically, the browser extension captures keyboard input and saves it in local storage.

[0837] Step 2:

[0838] The device records the user's visit to a specific website, the page address, the viewing time, and the links clicked. The input data is the web page activity, and the output data is the behavioral history data. Specifically, JavaScript code monitors the user's browsing activity in real time, captures the information, and stores it in local storage.

[0839] Step 3:

[0840] The user's device uses an emotion recognition API to acquire and record the user's emotional data. The input data is the user's facial expressions and voice data, and the output data is emotional data. Specifically, a webcam or smartphone front camera captures the user's face, and the emotion recognition software analyzes the data to identify their emotional state.

[0841] Step 4:

[0842] The device sends the recorded search history, behavioral history, and emotion data to the server. The input data is the previously recorded data, and the output data is a notification to the server that transmission has been completed. Specifically, asynchronous communication is performed using Ajax, and this data is sent to the server.

[0843] Step 5:

[0844] The server receives data sent from the device. The input data is the search history, behavioral history, and emotion data sent from the device, and the output data is a notification that the data has been saved to the database. Specifically, the Nginx or Apache web server receives the HTTP request and stores the content in a database (MySQL or PostgreSQL).

[0845] Step 6:

[0846] The server analyzes the data stored in the database and generates a behavioral model. The input data is the saved search history, behavioral history, and emotion data, and the output data is the generated behavioral model. Specifically, it uses Python's Scikit-learn and TensorFlow to build a machine learning model and analyze and predict user behavior patterns.

[0847] Step 7:

[0848] The server predicts the next information the user will need based on the behavioral model and emotional data. The input data is the behavioral model and emotional data, and the output data is the predicted information. Specifically, it uses natural language processing technology to perform text mining and predict the keywords and websites that are likely to be searched next.

[0849] Step 8:

[0850] The server sends the predicted information to the user's device. The input data is the predicted information, and the output data is a notification of completion of transmission to the device. Specifically, real-time communication is performed using WebSocket, and the predicted information is sent to the device.

[0851] Step 9:

[0852] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the output data is the information displayed to the user. Specific operations include displaying a pop-up on a web page or providing information to the user using a notification function.

[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 smart glasses 214 will be referred to as a "terminal."

[0855] Conventional information provision systems based on search history and behavioral history have the problem of not being able to provide information that is adequately suited to the user's needs because they do not take into account the user's emotional state. This makes it difficult to provide optimal product information based on the user's interests and emotions, which can lead to a poor user experience. Furthermore, because emotional data is not collected, it is difficult to provide more personalized predictive information.

[0856] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for analyzing the behavioral history to generate a user behavioral model, means for predicting the product information the user will need next based on the behavioral model and the user's emotional data, means for providing the predicted information to the user's terminal, means for recognizing the user's emotional state using the terminal, and means for recording and transmitting the emotional data. This makes it possible to utilize the user's emotional data to quickly provide more personalized and appropriate product information.

[0857] "User" refers to an individual or organization that uses this system.

[0858] "Search history" is a record of the search queries and keywords a user has performed.

[0859] "Behavioral history" refers to records of the URLs of web pages a user accessed, the links they clicked, the duration of their visit, and so on.

[0860] A "behavioral model" is a model of a user's behavioral patterns that is generated based on the user's past search history and behavior history.

[0861] "Emotional data" refers to information that indicates the user's emotional state, and includes data obtained from facial expressions, tone of voice, and the like.

[0862] "Predictive information" refers to information or product information that a user is likely to need next, generated based on a user's behavioral model and emotional data.

[0863] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[0864] "Means for recording" is a function that allows specific data (e.g., search history, behavior history, emotion data) to be recorded.

[0865] "Transmitting means" is a function for transmitting data (for example, search history, behavior history, emotion data) from a terminal to a server.

[0866] The "means for providing" is a function for transmitting the prediction information from the server to the terminal and displaying it to the user.

[0867] "Means of recognition" is a function for analyzing the user's emotional state and recording it as data.

[0868] "Server" refers to a central processing unit that receives, stores, and analyzes data sent by users, and generates and transmits prediction information.

[0869] The present invention is a system that provides predictive information based on a user's search history and behavior history, and also has the function of recognizing the user's emotional data and adjusting the predictive information. This system is mainly composed of a server and a terminal.

[0870] Server Configuration

[0871] Data reception function

[0872] The server receives search history, behavioral history, and emotion data sent from the user's device. The received data is processed in real time, making it possible to make predictions that reflect the user's latest situation.

[0873] Data storage function

[0874] The received data is stored in a database, which is preferably capable of processing large amounts of data at high speed, such as an SQL database (MySQL) or a NoSQL database (MongoDB).

[0875] Behavioral model generation function

[0876] The server analyzes the stored data and generates a behavioral model for each user. The behavioral model is composed of data such as past search queries, web page browsing history, access times, and links clicked. Machine learning algorithms (e.g., random forests and neural networks) are used to generate this model.

[0877] Predictive Function

[0878] Based on behavioral and emotional data, the platform predicts what users will need next using a generative AI model that looks at past data and current emotional state to predict the most relevant product information, reviews, and more.

[0879] Information provision function

[0880] The server sends the predicted information to the user's terminal, providing the user with appropriate information.

[0881] Device configuration

[0882] Search history recording function

[0883] The device records the keywords and queries users use when searching, which are important data points because they reflect user behavior.

[0884] Activity history recording function

[0885] It records behavioral data such as the URL of the web page the user visits, the viewing time, and the links clicked, which allows for a detailed understanding of the user's interests.

[0886] Emotion data recording function

[0887] The device uses an emotion engine to recognize and record the user's emotional state, using a camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data such as "happy" or "excited."

[0888] Data transmission function

[0889] The device transmits the recorded search history, behavioral history, and emotional data to the server periodically, and the data is updated in real time.

[0890] Information display function

[0891] The forecast information received from the server is displayed to the user, allowing the user to quickly access the most relevant product information and reviews.

[0892] Specific examples

[0893] For example, if a user searches for "latest smartphone reviews" and spends a long time on a specific product page, the device sends this search history and behavioral history to the server. At the same time, the device's camera analyzes the user's facial expressions and records the emotion data "happy." The server uses this data to generate a user behavior model and predict relevant product information and positive reviews, which it then provides the next time the user searches.

[0894] Example prompts to input to the generative AI model

[0895] When purchasing a smartphone, if a user frequently searches for "latest smartphone reviews" or "spec comparisons" and spends a long time on a specific product page, prioritize recommendations for reviews of that specific product. Also, if the sentiment data indicates "happy" or "excited," provide positive reviews and discount information related to that product. Predict what information the user will search for next and generate data based on this prediction.

[0896] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0897] Step 1:

[0898] The device records the user's search keywords. When a user searches for a specific keyword (e.g., "latest smartphone reviews"), the keyword is saved as a search history. This becomes input data, and the device gets an output that is recorded in local storage.

[0899] Step 2:

[0900] When a user browses a web page, the device records the URL of the page and the browsing time. If a user browses a specific product page for a long time, the URL and browsing time will be saved as a behavioral history. The input data is the URL and browsing time, and the device outputs this data and saves it in local storage.

[0901] Step 3:

[0902] The device uses a built-in camera and microphone to collect user emotional data. It analyzes facial expressions and tone of voice while the user is browsing a web page and recognizes emotions such as "happy" or "excited." The input data is video and audio captured by the camera and microphone, and the device outputs this as emotional data.

[0903] Step 4:

[0904] The device periodically sends the recorded search history, behavior history, and emotion data to the server using HTTP requests. The input data are search history, behavior history, and emotion data, and the device obtains output by sending this to the server.

[0905] Step 5:

[0906] The server stores the data received from the device in a database. The input data is the search history, behavior history, and emotion data sent from the device, and the server obtains an output that records this in the database.

[0907] Step 6:

[0908] The server analyzes the stored data using machine learning algorithms to generate a behavioral model for each user. The input data is search history, behavioral history, and emotional data recorded in the database, and the server analyzes this data to obtain the output as a behavioral model. Algorithms such as random forests and neural networks are used for this analysis.

[0909] Step 7:

[0910] The server predicts the next product information the user will need based on the generated behavioral model and emotional data. The input data are the behavioral model and emotional data, and the server outputs predicted information based on this. A generative AI model is used for the prediction.

[0911] Step 8:

[0912] The server sends the predicted product information to the user's terminal. The input data is the predicted information, and the server obtains an output that is sent to the terminal as an HTTP response.

[0913] Step 9:

[0914] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the terminal displays it on the screen to obtain an output that provides the user with relevant information.

[0915] Step 10:

[0916] As users view the predictions and take their next action, the newly generated search history, behavioral history, and emotional data are returned to step 1, and the process repeats, continuously optimizing the user experience.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] [Third embodiment]

[0921] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0922] 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.

[0923] 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).

[0924] 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.

[0925] 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.

[0926] 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).

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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."

[0933] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[0934] System configuration

[0935] server

[0936] The server is the core of this system and has the following main functions:

[0937] 1. Data reception function

[0938] The server receives search history and behavioral history data sent from the user's device.

[0939] 2. Data storage function

[0940] The server records and stores the received data in a database.

[0941] 3. Behavioral model generation function

[0942] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[0943] 4. Prediction function

[0944] The server then uses the generated behavioral model to predict what information the user will need next, and this prediction is made by referring to the user's past behavioral patterns.

[0945] 5. Information provision function

[0946] The server sends the predicted information to the user's terminal.

[0947] Terminal

[0948] A terminal is a device that a user actually uses and has the following main functions:

[0949] 1. Search history recording function

[0950] Record the keywords and queries that users use when searching.

[0951] 2. Activity history recording function

[0952] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[0953] 3. Data transmission function

[0954] The device sends the recorded search history and behavioral history to the server.

[0955] 4. Information display function

[0956] The terminal displays the forecast information received from the server to the user.

[0957] Implementation method

[0958] Server Processing

[0959] The server receives the user's search and behavior history and stores it in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavior model. Based on the behavior model, it predicts the information the user will need next and sends it to the device.

[0960] Terminal handling

[0961] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device also records the user's behavior history, such as the URL and viewing time, and sends this information to the server. Upon receiving prediction information from the server, the device displays this information to the user.

[0962] Specific examples

[0963] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, those behavioral histories are also recorded and sent to the server.

[0964] If the user performs the same search a few days later, the server uses past behavioral models to predict the latest reviews and links to previously visited sites, and sends them to the user's device. The device then displays the predicted information, allowing the user to access the latest and most relevant information more quickly than before.

[0965] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves convenience.

[0966] The processing flow will be explained below.

[0967] Step 1:

[0968] The user initiates a search.

[0969] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[0970] Step 2:

[0971] The device records the search keywords.

[0972] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server.

[0973] Step 3:

[0974] The server receives and stores the search keywords.

[0975] The server records and stores the search keywords sent from the terminal in a database.

[0976] Step 4:

[0977] A user visits a specific site from a search result.

[0978] Users can access review sites of interest from the search results and check the content.

[0979] Step 5:

[0980] The device records your activity history.

[0981] The device collects data such as the URLs the user accessed, the viewing time, and the links clicked, and sends it to the server.

[0982] Step 6:

[0983] The server receives and stores the behavioral history.

[0984] The server stores the behavioral history data sent from the terminal in a database.

[0985] Step 7:

[0986] The server analyzes the data and generates a behavioral model.

[0987] The server analyzes the accumulated data using an AI model and generates a user behavior model.

[0988] Step 8:

[0989] The user makes the next request to the avatar.

[0990] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[0991] Step 9:

[0992] The server makes predictions based on the behavioral model.

[0993] The server refers to a model of the user's behavior and predicts what information will be needed next.

[0994] Step 10:

[0995] The server transmits the prediction information to the terminal.

[0996] The server sends the predicted URL and the latest review information to the user's device.

[0997] Step 11:

[0998] The device displays the forecast information.

[0999] The device displays the received forecast information on the screen, allowing users to easily access the information.

[1000] Step 12:

[1001] The user uses the information presented.

[1002] Users can quickly access the desired information by clicking on the latest review information or links provided by the server.

[1003] Example 1

[1004] 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."

[1005] Conventional search systems have the problem that they are unable to streamline users' repeated search behavior, making it difficult for users to quickly reach the information they are looking for. Furthermore, it is difficult to analyze a user's behavioral history and predict the information they will need next, which hinders user convenience. To solve these problems, a system that efficiently and effectively utilizes a user's search history and behavioral history is needed.

[1006] 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.

[1007] In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for transmitting the user's behavioral history to the server, means for receiving the behavioral history and saving it in a database, means for analyzing the saved data and generating a user behavioral model, means for predicting the information the user will need next based on the generated behavioral model, means for transmitting the predicted information to the user's terminal, and means for displaying the transmitted predicted information on the user's terminal. This allows the user to quickly reach the information they are looking for, improving the efficiency and convenience of their search behavior.

[1008] "Means for recording user search history" refers to a function that captures keywords and queries entered by users into any search engine or application and stores them as data.

[1009] "Means for recording user behavior history" refers to a function that collects data on the actions a user takes on the Internet, such as the URLs of online sites accessed, browsing time, and links clicked.

[1010] "Means for sending user behavior history to a server" refers to a function that transfers search history and behavior history recorded locally by a terminal to a server using a specified communication protocol.

[1011] "Means for receiving behavioral history and storing it in a database" is a function by which the server receives behavioral history data sent from the terminal and stores it appropriately in a database.

[1012] "Means for analyzing stored data and generating a user behavior model" refers to a function that analyzes stored data using analytical tools and AI models to generate a model for extracting user behavior patterns and characteristics.

[1013] "A means for predicting the next information a user will need based on the generated behavioral model" refers to a function that refers to the behavioral model and predicts the information a user is likely to search for or the sites they are likely to access in the future.

[1014] The "means for transmitting predicted information to the user's terminal" is a function for transmitting predicted information from the server to the user's terminal in real time or at an appropriate time.

[1015] The "means for displaying the transmitted forecast information on the user's terminal" is a function that visually displays the forecast information received from the server to the user on the user's terminal.

[1016] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[1017] System configuration

[1018] server

[1019] The server is the core of the system and has the following main functions: The server-side software used is MySQL or PostgreSQL for the database, and Python's TensorFlow or PyTorch for machine learning.

[1020] Data reception function

[1021] The server receives search history and behavioral history data sent from the user's device, including the search keywords entered by the user, the URLs of websites visited, and the duration of browsing.

[1022] Data storage function

[1023] The server records and saves the received data in a database, for example, using MySQL or PostgreSQL.

[1024] Behavioral model generation function

[1025] The server uses AI models (e.g., TensorFlow, PyTorch) to analyze the stored data, generating a behavioral model for each user and analyzing the user's past search patterns and the sites they visited.

[1026] Predictive Function

[1027] The server uses the generated behavioral model to predict what information the user will need next. For example, if a user has frequently searched for "latest smartphone reviews" in the past, it predicts the latest review articles and related news.

[1028] Information provision function

[1029] The server then sends the predicted information to the user's device, allowing the user to receive the information they need at the optimal time.

[1030] Terminal

[1031] The terminal is the device that the user actually uses, and has the following main functions: The terminal software uses browser extensions and dedicated applications.

[1032] Search history recording function

[1033] It records keywords and queries when a user searches and stores them in local storage. For example, if a user searches for "latest smartphone reviews," it captures and stores the query in real time.

[1034] Activity history recording function

[1035] They record the URLs of websites you visit, the time you spend browsing, and the links you click. Behavioral data is saved as a user's activity history. For example, data is collected using browser extensions or in-app tracking features.

[1036] Data transmission function

[1037] The device sends the recorded search history and behavior history to the server. The data is securely transferred using the HTTPS protocol at regular intervals. For example, the data is set to be sent every hour.

[1038] Information display function

[1039] The device receives the forecast information from the server and displays it to the user, allowing the user to instantly check and view the forecast information. For example, the forecast information can be displayed as an auto-complete function below the search bar, or the latest information can be displayed in a dedicated notification area.

[1040] Specific examples

[1041] For example, a user searches for "latest smartphone reviews" and then visits a specific review site. This information is recorded on the device and sent to the server. The server analyzes this data and predicts the next review article the user will need. If the user searches the same query again a few days later, the server will send the latest review articles and related information to the user's device and display them as suggested links at the bottom of the screen. In this way, users can access the latest and most relevant information more quickly than before.

[1042] Prompt Sentence Examples

[1043] "User X has frequently searched for 'latest smartphone reviews' in the past and visited specific review sites. What information do you predict the next time they search?"

[1044] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves the convenience of obtaining information.

[1045] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1046] Step 1:

[1047] Data collection

[1048] Search history record

[1049] The device records the user's search query, for example, if the user types "latest smartphone reviews," the search keywords are captured in real time to local storage.

[1050] Input: The search keyword entered by the user

[1051] Output: Search keywords saved in local storage

[1052] Activity history record

[1053] The device records behavioral data such as the URLs of websites visited, the time spent there, and the links clicked, using browser extensions and in-app tracking features.

[1054] Input: User's website visit history, time spent, and links clicked

[1055] Output: Activity history saved in local storage

[1056] Data transmission

[1057] The search history and behavioral history collected by the device is sent to the server at regular intervals (e.g., every hour). Data is transferred securely using the HTTPS protocol.

[1058] Input: Search history and behavior history stored in local storage

[1059] Output: Search history and behavior history sent to the server

[1060] Step 2:

[1061] Data storage and management

[1062] Storing data in a database

[1063] The server stores the received data in a database (e.g. MySQL or PostgreSQL). Newly received data is inserted into the appropriate tables, organized by user.

[1064] Input: Search history and behavior history sent to the server

[1065] Output: Search history and behavior history stored in a database

[1066] Data preparation and cleaning

[1067] The server detects duplicates and missing data, and performs maintenance and cleaning using Python's Pandas library.

[1068] Input: Search history and behavior history stored in the database

[1069] Output: Cleaned and organized data

[1070] Step 3:

[1071] Behavioral model generation

[1072] Data analysis

[1073] The server uses AI models (e.g., TensorFlow or PyTorch) to analyze the stored data, detecting patterns and trends based on past search queries and behavioral history.

[1074] Input: Cleaned and sanitized data

[1075] Output: Analyzed data, features

[1076] Behavioral model generation

[1077] The server uses the analyzed data to generate a behavioral model for each user, which includes the user's search behavior and website visit patterns.

[1078] Input: Analyzed data, features

[1079] Output: Generated behavioral model

[1080] Step 4:

[1081] Predicting and providing information

[1082] Generating forecast information

[1083] The server uses a behavioral model to predict what information the user will need next. For example, if a user has previously searched for "latest smartphone reviews," it predicts the latest review articles and related news.

[1084] Input: Generated behavior model

[1085] Output: Forecast information

[1086] Sending information to user devices

[1087] The server sends the prediction information to the user's device, and provides it in real time or at an appropriate time based on the user's behavior.

[1088] Input: Forecast information

[1089] Output: Forecast information sent to the user's device

[1090] Step 5:

[1091] User Interface Display

[1092] Viewing forecast information

[1093] The device receives the forecast information from the server and displays it to the user. When browsing, the forecast information is displayed as an auto-complete feature below the search bar, and the latest information is displayed in a dedicated notification area.

[1094] Input: Forecast information sent from the server

[1095] Output: Forecast information displayed in the user interface

[1096] (Application example 1)

[1097] 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."

[1098] Conventional information provision systems based on users' search history and behavioral history are unable to adequately respond to the specific purchasing patterns and preferences of each user, and are unable to realize personalized product suggestions that utilize purchase history and browsing history. This prevents users from maximizing their purchasing motivation, hindering sales growth.

[1099] 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.

[1100] In this invention, the server includes means for recording a user's search history, means for recording a user's behavior history based on the search history, means for analyzing the behavior history and generating a user behavior model, means for predicting the information the user will need next based on the behavior model, means for providing the user with the predicted information, means for collecting and recording the user's purchase history and browsing history on the online shopping site, and means for predicting the next product that the user is likely to purchase based on the purchase history and browsing history and notifying the user of the predicted product. This enables more accurate personalized product suggestions, increases the user's desire to purchase, and increases sales on the online shopping site.

[1101] "User search history" is a list of keywords or queries a user has entered into a search engine or application, and the websites they visited as a result.

[1102] "Behavioral history" is the sequence of activities a user performs within a website or application, including clicks, time spent on the site, and links followed.

[1103] A "behavioral model" is a model created by analyzing a user's past search history and behavioral history, and is used to predict the behavioral patterns of individual users.

[1104] "Predictive information" is information about the information or products that a user is likely to need next, based on a behavioral model.

[1105] "Purchase history" refers to a list of products that a user has previously purchased on an online shopping site, along with detailed information about those products.

[1106] "Browse history" refers to a list of products that a user has viewed on an online shopping site, along with detailed information about those products.

[1107] "Notification" means information provided to the User by the System, including push notifications and in-app notifications.

[1108] "Server" means a centralized system that receives, stores, and analyzes data from multiple users.

[1109] "Terminal" refers to the device actually used by the user, including smartphones, PCs, tablets, etc.

[1110] MODE FOR CARRYING OUT THE INVENTION

[1111] This invention is a system that records and analyzes a user's search history and behavioral history, and based on that, predicts the information and products that the user will need next. This system is intended for use particularly on online shopping sites, and provides personalized product suggestions to the user.

[1112] System Configuration

[1113] server

[1114] The server of the present invention has the following main functions:

[1115] 1. Data reception function

[1116] The server receives data on search history, behavioral history, purchase history, and browsing history sent from the user's device.

[1117] 2. Data storage function

[1118] The server records and stores the received data in a database, typically using a relational database management system such as MySQL or PostgreSQL.

[1119] 3. Behavioral model generation function

[1120] The server analyzes the stored data and generates a behavioral model for each user, using Python and scikit-learn to build a machine learning model.

[1121] 4. Prediction function

[1122] The server uses the generated behavioral model to predict what information or products the user will need next, again using Python and scikit-learn.

[1123] 5. Information provision function

[1124] The server uses a REST API to send predicted information and products to the user's device, and the API is implemented using a web framework such as Flask or Django.

[1125] Terminal

[1126] The terminal of the present invention is a device such as a smartphone that is actually used by a user and has the following main functions:

[1127] 1. Search history recording function

[1128] Record the keywords and queries that users use when searching.

[1129] 2. Activity history recording function

[1130] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[1131] 3. Data transmission function

[1132] The device sends the recorded search history, behavioral history, purchase history, and browsing history to the server. This communication uses the HTTP protocol.

[1133] 4. Information display function

[1134] The forecast information and products received from the server are displayed to the user. A mobile app using React Native, Swift, or Kotlin is suitable for displaying the information.

[1135] Specific examples

[1136] For example, if User A searches for "smartphone" on an online shopping site and views multiple smartphone pages, User A's search history and browsing history are immediately recorded by the device and sent to the server.

[1137] Based on this data, the server generates a behavioral model of User A and predicts the "smartphone accessories" or "latest smartphone model" that User A is likely to purchase next.

[1138] This prediction information is immediately sent as a push notification to User A's smartphone, allowing User A to immediately check and purchase related products.

[1139] Prompt Sentence Examples

[1140] "Show me the next five items I'm likely to buy based on my past purchase and search history."

[1141] As a result, the system of the present invention can improve the efficiency of users' search and purchasing behavior, and significantly improve convenience and customer satisfaction.

[1142] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1143] Step 1:

[1144] A user searches on an online shopping site. The user enters search keywords and the search results are displayed. The input here is the search keywords entered by the user, and the output is a list of search results. The device records these search keywords.

[1145] Step 2:

[1146] The user views a specific product page. The user clicks to access the product page and checks the product details. The input is the URL of the product the user clicked on, and the output is the detailed information of the product the user viewed. The device records the accessed URL and the viewing time.

[1147] Step 3:

[1148] The user purchases a product. The user adds the product to their cart, pays, and completes the purchase. The input is product information the user is purchasing, and the output is confirmation information upon completion of the purchase. The terminal records the purchase history.

[1149] Step 4:

[1150] The device sends the recorded search history, browsing history, and purchase history to the server. The input is the recorded history data, and the output is the data sent to the server. The device uploads this data to the server using an HTTP request.

[1151] Step 5:

[1152] The server stores the received data in a database. The input is each historical data sent from the terminal, and the output is the data recorded in the database. The server stores the data using a relational database management system (e.g., MySQL or PostgreSQL).

[1153] Step 6:

[1154] The server generates a behavioral model for each user based on the stored data. The input is the stored historical data, and the output is the behavioral model for each user. The server analyzes the data using Python and scikit-learn and builds a machine learning model.

[1155] Step 7:

[1156] The server predicts the next information or product the user will need based on the generated behavioral model. The input is the user's behavioral model, and the output is a list of predicted products. The server makes inferences from the behavioral model and makes predictions based on that data.

[1157] Step 8:

[1158] The server pushes predicted information and products to the user's device. The input is the predicted information, and the output is the notification displayed on the user's device. The server sends the notification using a REST API, and the device displays the notification.

[1159] Step 9:

[1160] The user checks the notification displayed on the device and then browses or purchases the product that is most likely to be purchased next. The input is the notification displayed on the device, and the output is the user's next action (browsing or purchasing). The user checks the details of the displayed product using a smartphone or computer.

[1161] 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.

[1162] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on that history. It also includes a function that recognizes the user's emotions and adjusts the predictive information based on those emotions. This system streamlines the user's repeated search behavior and enables the user to quickly reach the desired information.

[1163] System configuration

[1164] server

[1165] The server is the core of this system and has the following main functions:

[1166] 1. Data reception function

[1167] The server receives the search history, behavior history, and emotion data sent from the user's terminal.

[1168] 2. Data storage function

[1169] The server records and stores the received data in a database.

[1170] 3. Behavioral model generation function

[1171] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[1172] 4. Prediction function

[1173] The server predicts the next information the user will need based on the generated behavioral model and emotional data. This prediction is made by referring to the user's past behavioral patterns and current emotional state.

[1174] 5. Information provision function

[1175] The server sends the predicted information to the user's terminal.

[1176] Terminal

[1177] A terminal is a device that a user actually uses and has the following main functions:

[1178] 1. Search history recording function

[1179] Record the keywords and queries that users use when searching.

[1180] 2. Activity history recording function

[1181] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[1182] 3. Emotion data recording function

[1183] An emotion engine is used to recognize and record the user's emotional state.

[1184] 4. Data transmission function

[1185] The device transmits the recorded search history, behavior history, and emotion data to the server.

[1186] 5. Information display function

[1187] The terminal displays the forecast information received from the server to the user.

[1188] Implementation method

[1189] Server Processing

[1190] The server receives the user's search history, behavioral history, and emotional data and stores them in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavioral model. Based on this behavioral model and emotional data, it predicts the information the user will need next and sends it to the device.

[1191] Terminal handling

[1192] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device records the user's behavioral history, including the URL, viewing time, and links clicked, and the emotion engine recognizes and records the user's emotional state. This data is periodically sent to the server. When the device receives prediction information from the server, it displays that information to the user.

[1193] Specific examples

[1194] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, the emotion engine recognizes and records the user's emotional state along with their behavioral history. This data is then sent to the server, which uses it to generate a behavioral model and provide next-time prediction information.

[1195] A few days later, when the user searches for "smartphone reviews" again, the server will predict the most relevant and up-to-date review information based on past behavioral models and current emotional data, and send it to the user's device. For example, if the user has a strong interest or positive emotion in a particular product, the server will prioritize the latest information and reviews related to that product. In this way, users can access the desired information more quickly and efficiently.

[1196] This system can streamline users' search behavior and significantly improve the convenience of information access. In addition, by utilizing emotion data, it can respond more appropriately to user needs.

[1197] The processing flow will be explained below.

[1198] Step 1:

[1199] The user initiates a search.

[1200] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[1201] Step 2:

[1202] The device records the search keywords.

[1203] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server, thereby recording the user's search history.

[1204] Step 3:

[1205] The server receives and stores the search keywords.

[1206] The server records and saves the search keywords sent from the terminal in a database.

[1207] Step 4:

[1208] A user visits a specific site from a search result.

[1209] Users can access review sites of interest from the search results and check the content.

[1210] Step 5:

[1211] The device records your activity history.

[1212] The device collects data such as the URLs the user visits, the time spent browsing, and the links they click, and also uses an emotion engine to recognize the user's emotional state (e.g., excitement, interest, satisfaction, etc.) and record this information.

[1213] Step 6:

[1214] The device sends behavioral history and emotional data to the server.

[1215] The device transmits the recorded behavioral history and emotional data to the server.

[1216] Step 7:

[1217] The server receives and stores behavioral history and emotional data.

[1218] The server stores the behavioral history and emotional data sent from the device in a database.

[1219] Step 8:

[1220] The server analyzes the data and generates a behavioral model.

[1221] The server analyzes the accumulated data using an AI model to generate a user behavior model, which includes search history, access history, browsing time, and emotional data.

[1222] Step 9:

[1223] The user makes the next request to the avatar.

[1224] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[1225] Step 10:

[1226] The server makes predictions based on behavioral models and emotional data.

[1227] The server refers to the user's behavioral model and emotional data to predict what information the user will need next, especially reviews and related information that are likely to interest the user.

[1228] Step 11:

[1229] The server transmits the prediction information to the terminal.

[1230] The server sends the predicted URL and the latest review information to the user's device.

[1231] Step 12:

[1232] The device displays the forecast information.

[1233] The device displays the received forecast information on the screen, allowing users to easily access the information.

[1234] Step 13:

[1235] The user uses the information presented.

[1236] Users can quickly access the desired information by clicking on the latest review information or links provided by the server. Furthermore, the content displayed reflects the user's emotional state, making the display more user-friendly.

[1237] Example 2

[1238] 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."

[1239] Conventional search systems provide information based on a user's search history and behavioral history, but because they do not take into account the user's emotional state, they have the problem of not being able to fully adapt to user needs. For example, even when a user wants to quickly obtain information about a topic that interests them, there are limitations to relying solely on past history data. As a result, problems arise such as low user satisfaction and poor search efficiency.

[1240] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's search history and behavioral history, a means for recognizing and recording the user's emotional state, a means for analyzing this data to generate a behavioral model for each user, and a means for predicting information based on the behavioral model and emotional data. This enables highly accurate information prediction that reflects the user's emotional state.

[1241] "Search history" is a record of past search queries and keywords a user has performed on the Internet.

[1242] "Behavioral history" is a record of a user's internet activity, such as the web pages they visited, the time they spent there, and the links they clicked.

[1243] "Emotion data" is data that indicates the user's emotional state, and is recorded by recognizing emotions from the user's facial expressions, voice, actions, etc.

[1244] A "behavioral model" is a model for predicting a user's behavioral patterns, generated based on the user's search history and behavior history.

[1245] "Predictive information" is information that is predicted to be needed next by the user based on behavioral models and emotional data.

[1246] The "server" is a device that receives search history, behavioral history, and emotional data sent by users, analyzes and stores this data, and generates predictive information.

[1247] A "terminal" is a device that is actually operated by a user, and is a device that records search history, behavioral history, and emotional data and transmits them to a server.

[1248] A "database" is a system for systematically storing search history, behavioral history, and emotional data received by the server.

[1249] An "emotion recognition API" is a program interface for analyzing a user's emotional state, and is used to recognize emotions from data such as the user's facial expressions and voice.

[1250] "Natural language processing" is a technology that uses machine learning and text mining techniques to analyze text data and understand its meaning and context.

[1251] MODE FOR CARRYING OUT THE INVENTION

[1252] This invention relates to a system that provides highly accurate information by utilizing a user's search history, behavioral history, and emotional data. This system comprehensively analyzes a user's behavior and emotions and predicts the information the user will need next, thereby improving the efficiency of information searches.

[1253] The system mainly consists of a server and terminals, each with the following functions:

[1254] Server Configuration

[1255] Data reception function

[1256] The server receives search history, behavioral history, and emotion data sent from the user's device, and processes HTTP requests using a web server such as Nginx or Apache.

[1257] Data storage function

[1258] The server stores the received data in a relational database such as MySQL or PostgreSQL, allowing users' past behavioral data to be systematically accumulated.

[1259] Behavioral model generation function

[1260] The server analyzes the stored data and generates a behavioral model for each user. This process uses machine learning libraries such as Python's Scikit-learn and TensorFlow, making it possible to analyze and predict user behavioral patterns with high accuracy.

[1261] Predictive Function

[1262] The server predicts the next information the user will need based on the generated behavioral model and emotion data. For example, it uses text mining, which uses natural language processing technology, to predict the next keyword the user will search for or the website they are likely to visit based on their past search patterns.

[1263] Information provision function

[1264] The server then sends the predicted information to the user's device. If real-time performance is required, a communication protocol such as WebSocket is used.

[1265] Device configuration

[1266] Search history recording function

[1267] The keywords users type into search engines are recorded using browser extensions and mobile apps, which helps to clarify users' search patterns.

[1268] Activity history recording function

[1269] The device records behavioral data such as the addresses of web pages accessed by the user, the duration of browsing, the links clicked, etc. It uses JavaScript to capture activity within the browser.

[1270] Emotion data recording function

[1271] The device uses an emotion recognition API (e.g., Microsoft's Emotion API) to record the user's emotional state from facial expressions and voice data.

[1272] Data transmission function

[1273] The recorded search history, behavioral history, and emotion data are sent to the server. Asynchronous communication is performed using Ajax.

[1274] Information display function

[1275] Predictive information received from the server is displayed to the user via pop-ups and notifications, allowing users to access the information they need with increased speed and efficiency.

[1276] Specific examples

[1277] When a user searches for "latest smartphone reviews," the device first records the search keyword and sends it to the server. If the user visits a specific review site and then accesses other sites, the device records the user's behavioral history and the user's emotional state as recognized by the emotion engine, and periodically sends this information to the server.

[1278] The server generates a behavioral model based on this data and provides next-time prediction information. A few days later, when the user tries to search for "smartphone reviews" again, the server predicts the most relevant, up-to-date review information based on the user's past behavioral model and current emotional data, and sends it to the user's device.

[1279] For example, predictive information might include a notification such as, "Here's the latest review article on a new smartphone you were interested in!"

[1280] Prompt Sentence Examples

[1281] You might consider using a prompt like this:

[1282] "I would like to develop a system that predicts and provides the next information a user will need based on their behavioral history and emotional data when they search for specific product reviews several times and visit a specific site. The system will record the time spent on the site, the links clicked, and their emotional state (excitement, interest, etc.), and provide information based on a behavioral model analyzed on the server. Please suggest some algorithms that can predict which site a user will visit next and what information they will want."

[1283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1284] Step 1:

[1285] A user enters a specific keyword (e.g., "latest smartphone reviews") into the browser's search engine. This input is recorded on the device as search history. The input data is the search keyword, and the output data is search history data. Specifically, the browser extension captures keyboard input and saves it in local storage.

[1286] Step 2:

[1287] The device records the user's visit to a specific website, the page address, the viewing time, and the links clicked. The input data is the web page activity, and the output data is the behavioral history data. Specifically, JavaScript code monitors the user's browsing activity in real time, captures the information, and stores it in local storage.

[1288] Step 3:

[1289] The user's device uses an emotion recognition API to acquire and record the user's emotional data. The input data is the user's facial expressions and voice data, and the output data is emotional data. Specifically, a webcam or smartphone front camera captures the user's face, and the emotion recognition software analyzes the data to identify their emotional state.

[1290] Step 4:

[1291] The device sends the recorded search history, behavioral history, and emotion data to the server. The input data is the previously recorded data, and the output data is a notification to the server that transmission has been completed. Specifically, asynchronous communication is performed using Ajax, and this data is sent to the server.

[1292] Step 5:

[1293] The server receives data sent from the device. The input data is the search history, behavioral history, and emotion data sent from the device, and the output data is a notification that the data has been saved to the database. Specifically, the Nginx or Apache web server receives the HTTP request and stores the content in a database (MySQL or PostgreSQL).

[1294] Step 6:

[1295] The server analyzes the data stored in the database and generates a behavioral model. The input data is the saved search history, behavioral history, and emotion data, and the output data is the generated behavioral model. Specifically, it uses Python's Scikit-learn and TensorFlow to build a machine learning model and analyze and predict user behavior patterns.

[1296] Step 7:

[1297] The server predicts the next information the user will need based on the behavioral model and emotional data. The input data is the behavioral model and emotional data, and the output data is the predicted information. Specifically, it uses natural language processing technology to perform text mining and predict the keywords and websites that are likely to be searched next.

[1298] Step 8:

[1299] The server sends the predicted information to the user's device. The input data is the predicted information, and the output data is a notification of completion of transmission to the device. Specifically, real-time communication is performed using WebSocket, and the predicted information is sent to the device.

[1300] Step 9:

[1301] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the output data is the information displayed to the user. Specific operations include displaying a pop-up on a web page or providing information to the user using a notification function.

[1302] (Application example 2)

[1303] 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."

[1304] Conventional information provision systems based on search history and behavioral history have the problem of not being able to provide information that is adequately suited to the user's needs because they do not take into account the user's emotional state. This makes it difficult to provide optimal product information based on the user's interests and emotions, which can lead to a poor user experience. Furthermore, because emotional data is not collected, it is difficult to provide more personalized predictive information.

[1305] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for analyzing the behavioral history to generate a user behavioral model, means for predicting the product information the user will need next based on the behavioral model and the user's emotional data, means for providing the predicted information to the user's terminal, means for recognizing the user's emotional state using the terminal, and means for recording and transmitting the emotional data. This makes it possible to utilize the user's emotional data to quickly provide more personalized and appropriate product information.

[1306] "User" refers to an individual or organization that uses this system.

[1307] "Search history" is a record of the search queries and keywords a user has performed.

[1308] "Behavioral history" refers to records of the URLs of web pages a user accessed, the links they clicked, the duration of their visit, and so on.

[1309] A "behavioral model" is a model of a user's behavioral patterns that is generated based on the user's past search history and behavior history.

[1310] "Emotional data" refers to information that indicates the user's emotional state, and includes data obtained from facial expressions, tone of voice, and the like.

[1311] "Predictive information" refers to information or product information that a user is likely to need next, generated based on a user's behavioral model and emotional data.

[1312] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[1313] "Means for recording" is a function that allows specific data (e.g., search history, behavior history, emotion data) to be recorded.

[1314] "Transmitting means" is a function for transmitting data (for example, search history, behavior history, emotion data) from a terminal to a server.

[1315] The "means for providing" is a function for transmitting the prediction information from the server to the terminal and displaying it to the user.

[1316] "Means of recognition" is a function for analyzing the user's emotional state and recording it as data.

[1317] "Server" refers to a central processing unit that receives, stores, and analyzes data sent by users, and generates and transmits prediction information.

[1318] The present invention is a system that provides predictive information based on a user's search history and behavior history, and also has the function of recognizing the user's emotional data and adjusting the predictive information. This system is mainly composed of a server and a terminal.

[1319] Server Configuration

[1320] Data reception function

[1321] The server receives search history, behavioral history, and emotion data sent from the user's device. The received data is processed in real time, making it possible to make predictions that reflect the user's latest situation.

[1322] Data storage function

[1323] The received data is stored in a database, which is preferably capable of processing large amounts of data at high speed, such as an SQL database (MySQL) or a NoSQL database (MongoDB).

[1324] Behavioral model generation function

[1325] The server analyzes the stored data and generates a behavioral model for each user. The behavioral model is composed of data such as past search queries, web page browsing history, access times, and links clicked. Machine learning algorithms (e.g., random forests and neural networks) are used to generate this model.

[1326] Predictive Function

[1327] Based on behavioral and emotional data, the platform predicts what users will need next using a generative AI model that looks at past data and current emotional state to predict the most relevant product information, reviews, and more.

[1328] Information provision function

[1329] The server sends the predicted information to the user's terminal, providing the user with appropriate information.

[1330] Device configuration

[1331] Search history recording function

[1332] The device records the keywords and queries users use when searching, which are important data points because they reflect user behavior.

[1333] Activity history recording function

[1334] It records behavioral data such as the URL of the web page the user visits, the viewing time, and the links clicked, which allows for a detailed understanding of the user's interests.

[1335] Emotion data recording function

[1336] The device uses an emotion engine to recognize and record the user's emotional state, using a camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data such as "happy" or "excited."

[1337] Data transmission function

[1338] The device transmits the recorded search history, behavioral history, and emotional data to the server periodically, and the data is updated in real time.

[1339] Information display function

[1340] The forecast information received from the server is displayed to the user, allowing the user to quickly access the most relevant product information and reviews.

[1341] Specific examples

[1342] For example, if a user searches for "latest smartphone reviews" and spends a long time on a specific product page, the device sends this search history and behavioral history to the server. At the same time, the device's camera analyzes the user's facial expressions and records the emotion data "happy." The server uses this data to generate a user behavior model and predict relevant product information and positive reviews, which it then provides the next time the user searches.

[1343] Example prompts to input to the generative AI model

[1344] When purchasing a smartphone, if a user frequently searches for "latest smartphone reviews" or "spec comparisons" and spends a long time on a specific product page, prioritize recommendations for reviews of that specific product. Also, if the sentiment data indicates "happy" or "excited," provide positive reviews and discount information related to that product. Predict what information the user will search for next and generate data based on this prediction.

[1345] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1346] Step 1:

[1347] The device records the user's search keywords. When a user searches for a specific keyword (e.g., "latest smartphone reviews"), the keyword is saved as a search history. This becomes input data, and the device gets an output that is recorded in local storage.

[1348] Step 2:

[1349] When a user browses a web page, the device records the URL of the page and the browsing time. If a user browses a specific product page for a long time, the URL and browsing time will be saved as a behavioral history. The input data is the URL and browsing time, and the device outputs this data and saves it in local storage.

[1350] Step 3:

[1351] The device uses a built-in camera and microphone to collect user emotional data. It analyzes facial expressions and tone of voice while the user is browsing a web page and recognizes emotions such as "happy" or "excited." The input data is video and audio captured by the camera and microphone, and the device outputs this as emotional data.

[1352] Step 4:

[1353] The device periodically sends the recorded search history, behavior history, and emotion data to the server using HTTP requests. The input data are search history, behavior history, and emotion data, and the device obtains output by sending this to the server.

[1354] Step 5:

[1355] The server stores the data received from the device in a database. The input data is the search history, behavior history, and emotion data sent from the device, and the server obtains an output that records this in the database.

[1356] Step 6:

[1357] The server analyzes the stored data using machine learning algorithms to generate a behavioral model for each user. The input data is search history, behavioral history, and emotional data recorded in the database, and the server analyzes this data to obtain the output as a behavioral model. Algorithms such as random forests and neural networks are used for this analysis.

[1358] Step 7:

[1359] The server predicts the next product information the user will need based on the generated behavioral model and emotional data. The input data are the behavioral model and emotional data, and the server outputs predicted information based on this. A generative AI model is used for the prediction.

[1360] Step 8:

[1361] The server sends the predicted product information to the user's terminal. The input data is the predicted information, and the server obtains an output that is sent to the terminal as an HTTP response.

[1362] Step 9:

[1363] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the terminal displays it on the screen to obtain an output that provides the user with relevant information.

[1364] Step 10:

[1365] As users view the predictions and take their next action, the newly generated search history, behavioral history, and emotional data are returned to step 1, and the process repeats, continuously optimizing the user experience.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] [Fourth embodiment]

[1370] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1371] 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.

[1372] 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).

[1373] 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.

[1374] 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.

[1375] 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).

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] 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."

[1383] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[1384] System configuration

[1385] server

[1386] The server is the core of this system and has the following main functions:

[1387] 1. Data reception function

[1388] The server receives search history and behavioral history data sent from the user's device.

[1389] 2. Data storage function

[1390] The server records and stores the received data in a database.

[1391] 3. Behavioral model generation function

[1392] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[1393] 4. Prediction function

[1394] The server then uses the generated behavioral model to predict what information the user will need next, and this prediction is made by referring to the user's past behavioral patterns.

[1395] 5. Information provision function

[1396] The server sends the predicted information to the user's terminal.

[1397] Terminal

[1398] A terminal is a device that a user actually uses and has the following main functions:

[1399] 1. Search history recording function

[1400] Record the keywords and queries that users use when searching.

[1401] 2. Activity history recording function

[1402] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[1403] 3. Data transmission function

[1404] The device sends the recorded search history and behavioral history to the server.

[1405] 4. Information display function

[1406] The terminal displays the forecast information received from the server to the user.

[1407] Implementation method

[1408] Server Processing

[1409] The server receives the user's search and behavior history and stores it in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavior model. Based on the behavior model, it predicts the information the user will need next and sends it to the device.

[1410] Terminal handling

[1411] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device also records the user's behavior history, such as the URL and viewing time, and sends this information to the server. Upon receiving prediction information from the server, the device displays this information to the user.

[1412] Specific examples

[1413] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, those behavioral histories are also recorded and sent to the server.

[1414] If the user performs the same search a few days later, the server uses past behavioral models to predict the latest reviews and links to previously visited sites, and sends them to the user's device. The device then displays the predicted information, allowing the user to access the latest and most relevant information more quickly than before.

[1415] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves convenience.

[1416] The processing flow will be explained below.

[1417] Step 1:

[1418] The user initiates a search.

[1419] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[1420] Step 2:

[1421] The device records the search keywords.

[1422] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server.

[1423] Step 3:

[1424] The server receives and stores the search keywords.

[1425] The server records and stores the search keywords sent from the terminal in a database.

[1426] Step 4:

[1427] A user visits a specific site from a search result.

[1428] Users can access review sites of interest from the search results and check the content.

[1429] Step 5:

[1430] The device records your activity history.

[1431] The device collects data such as the URLs the user accessed, the viewing time, and the links clicked, and sends it to the server.

[1432] Step 6:

[1433] The server receives and stores the behavioral history.

[1434] The server stores the behavioral history data sent from the terminal in a database.

[1435] Step 7:

[1436] The server analyzes the data and generates a behavioral model.

[1437] The server analyzes the accumulated data using an AI model and generates a user behavior model.

[1438] Step 8:

[1439] The user makes the next request to the avatar.

[1440] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[1441] Step 9:

[1442] The server makes predictions based on the behavioral model.

[1443] The server refers to a model of the user's behavior and predicts what information will be needed next.

[1444] Step 10:

[1445] The server transmits the prediction information to the terminal.

[1446] The server sends the predicted URL and the latest review information to the user's device.

[1447] Step 11:

[1448] The device displays the forecast information.

[1449] The device displays the received forecast information on the screen, allowing users to easily access the information.

[1450] Step 12:

[1451] The user uses the information presented.

[1452] Users can quickly access the desired information by clicking on the latest review information or links provided by the server.

[1453] Example 1

[1454] 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."

[1455] Conventional search systems have the problem that they are unable to streamline users' repeated search behavior, making it difficult for users to quickly reach the information they are looking for. Furthermore, it is difficult to analyze a user's behavioral history and predict the information they will need next, which hinders user convenience. To solve these problems, a system that efficiently and effectively utilizes a user's search history and behavioral history is needed.

[1456] 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.

[1457] In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for transmitting the user's behavioral history to the server, means for receiving the behavioral history and saving it in a database, means for analyzing the saved data and generating a user behavioral model, means for predicting the information the user will need next based on the generated behavioral model, means for transmitting the predicted information to the user's terminal, and means for displaying the transmitted predicted information on the user's terminal. This allows the user to quickly reach the information they are looking for, improving the efficiency and convenience of their search behavior.

[1458] "Means for recording user search history" refers to a function that captures keywords and queries entered by users into any search engine or application and stores them as data.

[1459] "Means for recording user behavior history" refers to a function that collects data on the actions a user takes on the Internet, such as the URLs of online sites accessed, browsing time, and links clicked.

[1460] "Means for sending user behavior history to a server" refers to a function that transfers search history and behavior history recorded locally by a terminal to a server using a specified communication protocol.

[1461] "Means for receiving behavioral history and storing it in a database" is a function by which the server receives behavioral history data sent from the terminal and stores it appropriately in a database.

[1462] "Means for analyzing stored data and generating a user behavior model" refers to a function that analyzes stored data using analytical tools and AI models to generate a model for extracting user behavior patterns and characteristics.

[1463] "A means for predicting the next information a user will need based on the generated behavioral model" refers to a function that refers to the behavioral model and predicts the information a user is likely to search for or the sites they are likely to access in the future.

[1464] The "means for transmitting predicted information to the user's terminal" is a function for transmitting predicted information from the server to the user's terminal in real time or at an appropriate time.

[1465] The "means for displaying the transmitted forecast information on the user's terminal" is a function that visually displays the forecast information received from the server to the user on the user's terminal.

[1466] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on the history. This system improves the efficiency of repeated user searches, enabling users to quickly reach desired information.

[1467] System configuration

[1468] server

[1469] The server is the core of the system and has the following main functions: The server-side software used is MySQL or PostgreSQL for the database, and Python's TensorFlow or PyTorch for machine learning.

[1470] Data reception function

[1471] The server receives search history and behavioral history data sent from the user's device, including the search keywords entered by the user, the URLs of websites visited, and the duration of browsing.

[1472] Data storage function

[1473] The server records and saves the received data in a database, for example, using MySQL or PostgreSQL.

[1474] Behavioral model generation function

[1475] The server uses AI models (e.g., TensorFlow, PyTorch) to analyze the stored data, generating a behavioral model for each user and analyzing the user's past search patterns and the sites they visited.

[1476] Predictive Function

[1477] The server uses the generated behavioral model to predict what information the user will need next. For example, if a user has frequently searched for "latest smartphone reviews" in the past, it predicts the latest review articles and related news.

[1478] Information provision function

[1479] The server then sends the predicted information to the user's device, allowing the user to receive the information they need at the optimal time.

[1480] Terminal

[1481] The terminal is the device that the user actually uses, and has the following main functions: The terminal software uses browser extensions and dedicated applications.

[1482] Search history recording function

[1483] It records keywords and queries when a user searches and stores them in local storage. For example, if a user searches for "latest smartphone reviews," it captures and stores the query in real time.

[1484] Activity history recording function

[1485] They record the URLs of websites you visit, the time you spend browsing, and the links you click. Behavioral data is saved as a user's activity history. For example, data is collected using browser extensions or in-app tracking features.

[1486] Data transmission function

[1487] The device sends the recorded search history and behavior history to the server. The data is securely transferred using the HTTPS protocol at regular intervals. For example, the data is set to be sent every hour.

[1488] Information display function

[1489] The device receives the forecast information from the server and displays it to the user, allowing the user to instantly check and view the forecast information. For example, the forecast information can be displayed as an auto-complete function below the search bar, or the latest information can be displayed in a dedicated notification area.

[1490] Specific examples

[1491] For example, a user searches for "latest smartphone reviews" and then visits a specific review site. This information is recorded on the device and sent to the server. The server analyzes this data and predicts the next review article the user will need. If the user searches the same query again a few days later, the server will send the latest review articles and related information to the user's device and display them as suggested links at the bottom of the screen. In this way, users can access the latest and most relevant information more quickly than before.

[1492] Prompt Sentence Examples

[1493] "User X has frequently searched for 'latest smartphone reviews' in the past and visited specific review sites. What information do you predict the next time they search?"

[1494] In this way, the system of the present invention makes users' search behavior more efficient and significantly improves the convenience of obtaining information.

[1495] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1496] Step 1:

[1497] Data collection

[1498] Search history record

[1499] The device records the user's search query, for example, if the user types "latest smartphone reviews," the search keywords are captured in real time to local storage.

[1500] Input: The search keyword entered by the user

[1501] Output: Search keywords saved in local storage

[1502] Activity history record

[1503] The device records behavioral data such as the URLs of websites visited, the time spent there, and the links clicked, using browser extensions and in-app tracking features.

[1504] Input: User's website visit history, time spent, and links clicked

[1505] Output: Activity history saved in local storage

[1506] Data transmission

[1507] The search history and behavioral history collected by the device is sent to the server at regular intervals (e.g., every hour). Data is transferred securely using the HTTPS protocol.

[1508] Input: Search history and behavior history stored in local storage

[1509] Output: Search history and behavior history sent to the server

[1510] Step 2:

[1511] Data storage and management

[1512] Storing data in a database

[1513] The server stores the received data in a database (e.g. MySQL or PostgreSQL). Newly received data is inserted into the appropriate tables, organized by user.

[1514] Input: Search history and behavior history sent to the server

[1515] Output: Search history and behavior history stored in a database

[1516] Data preparation and cleaning

[1517] The server detects duplicates and missing data, and performs maintenance and cleaning using Python's Pandas library.

[1518] Input: Search history and behavior history stored in the database

[1519] Output: Cleaned and organized data

[1520] Step 3:

[1521] Behavioral model generation

[1522] Data analysis

[1523] The server uses AI models (e.g., TensorFlow or PyTorch) to analyze the stored data, detecting patterns and trends based on past search queries and behavioral history.

[1524] Input: Cleaned and sanitized data

[1525] Output: Analyzed data, features

[1526] Behavioral model generation

[1527] The server uses the analyzed data to generate a behavioral model for each user, which includes the user's search behavior and website visit patterns.

[1528] Input: Analyzed data, features

[1529] Output: Generated behavioral model

[1530] Step 4:

[1531] Predicting and providing information

[1532] Generating forecast information

[1533] The server uses a behavioral model to predict what information the user will need next. For example, if a user has previously searched for "latest smartphone reviews," it predicts the latest review articles and related news.

[1534] Input: Generated behavior model

[1535] Output: Forecast information

[1536] Sending information to user devices

[1537] The server sends the prediction information to the user's device, and provides it in real time or at an appropriate time based on the user's behavior.

[1538] Input: Forecast information

[1539] Output: Forecast information sent to the user's device

[1540] Step 5:

[1541] User Interface Display

[1542] Viewing forecast information

[1543] The device receives the forecast information from the server and displays it to the user. When browsing, the forecast information is displayed as an auto-complete feature below the search bar, and the latest information is displayed in a dedicated notification area.

[1544] Input: Forecast information sent from the server

[1545] Output: Forecast information displayed in the user interface

[1546] (Application example 1)

[1547] 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."

[1548] Conventional information provision systems based on users' search history and behavioral history are unable to adequately respond to the specific purchasing patterns and preferences of each user, and are unable to realize personalized product suggestions that utilize purchase history and browsing history. This prevents users from maximizing their purchasing motivation, hindering sales growth.

[1549] 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.

[1550] In this invention, the server includes means for recording a user's search history, means for recording a user's behavior history based on the search history, means for analyzing the behavior history and generating a user behavior model, means for predicting the information the user will need next based on the behavior model, means for providing the user with the predicted information, means for collecting and recording the user's purchase history and browsing history on the online shopping site, and means for predicting the next product that the user is likely to purchase based on the purchase history and browsing history and notifying the user of the predicted product. This enables more accurate personalized product suggestions, increases the user's desire to purchase, and increases sales on the online shopping site.

[1551] "User search history" is a list of keywords or queries a user has entered into a search engine or application, and the websites they visited as a result.

[1552] "Behavioral history" is the sequence of activities a user performs within a website or application, including clicks, time spent on the site, and links followed.

[1553] A "behavioral model" is a model created by analyzing a user's past search history and behavioral history, and is used to predict the behavioral patterns of individual users.

[1554] "Predictive information" is information about the information or products that a user is likely to need next, based on a behavioral model.

[1555] "Purchase history" refers to a list of products that a user has previously purchased on an online shopping site, along with detailed information about those products.

[1556] "Browse history" refers to a list of products that a user has viewed on an online shopping site, along with detailed information about those products.

[1557] "Notification" means information provided to the User by the System, including push notifications and in-app notifications.

[1558] "Server" means a centralized system that receives, stores, and analyzes data from multiple users.

[1559] "Terminal" refers to the device actually used by the user, including smartphones, PCs, tablets, etc.

[1560] MODE FOR CARRYING OUT THE INVENTION

[1561] This invention is a system that records and analyzes a user's search history and behavioral history, and based on that, predicts the information and products that the user will need next. This system is intended for use particularly on online shopping sites, and provides personalized product suggestions to the user.

[1562] System Configuration

[1563] server

[1564] The server of the present invention has the following main functions:

[1565] 1. Data reception function

[1566] The server receives data on search history, behavioral history, purchase history, and browsing history sent from the user's device.

[1567] 2. Data storage function

[1568] The server records and stores the received data in a database, typically using a relational database management system such as MySQL or PostgreSQL.

[1569] 3. Behavioral model generation function

[1570] The server analyzes the stored data and generates a behavioral model for each user, using Python and scikit-learn to build a machine learning model.

[1571] 4. Prediction function

[1572] The server uses the generated behavioral model to predict what information or products the user will need next, again using Python and scikit-learn.

[1573] 5. Information provision function

[1574] The server uses a REST API to send predicted information and products to the user's device, and the API is implemented using a web framework such as Flask or Django.

[1575] Terminal

[1576] The terminal of the present invention is a device such as a smartphone that is actually used by a user and has the following main functions:

[1577] 1. Search history recording function

[1578] Record the keywords and queries that users use when searching.

[1579] 2. Activity history recording function

[1580] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[1581] 3. Data transmission function

[1582] The device sends the recorded search history, behavioral history, purchase history, and browsing history to the server. This communication uses the HTTP protocol.

[1583] 4. Information display function

[1584] The forecast information and products received from the server are displayed to the user. A mobile app using React Native, Swift, or Kotlin is suitable for displaying the information.

[1585] Specific examples

[1586] For example, if User A searches for "smartphone" on an online shopping site and views multiple smartphone pages, User A's search history and browsing history are immediately recorded by the device and sent to the server.

[1587] Based on this data, the server generates a behavioral model of User A and predicts the "smartphone accessories" or "latest smartphone model" that User A is likely to purchase next.

[1588] This prediction information is immediately sent as a push notification to User A's smartphone, allowing User A to immediately check and purchase related products.

[1589] Prompt Sentence Examples

[1590] "Show me the next five items I'm likely to buy based on my past purchase and search history."

[1591] As a result, the system of the present invention can improve the efficiency of users' search and purchasing behavior, and significantly improve convenience and customer satisfaction.

[1592] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1593] Step 1:

[1594] A user searches on an online shopping site. The user enters search keywords and the search results are displayed. The input here is the search keywords entered by the user, and the output is a list of search results. The device records these search keywords.

[1595] Step 2:

[1596] The user views a specific product page. The user clicks to access the product page and checks the product details. The input is the URL of the product the user clicked on, and the output is the detailed information of the product the user viewed. The device records the accessed URL and the viewing time.

[1597] Step 3:

[1598] The user purchases a product. The user adds the product to their cart, pays, and completes the purchase. The input is product information the user is purchasing, and the output is confirmation information upon completion of the purchase. The terminal records the purchase history.

[1599] Step 4:

[1600] The device sends the recorded search history, browsing history, and purchase history to the server. The input is the recorded history data, and the output is the data sent to the server. The device uploads this data to the server using an HTTP request.

[1601] Step 5:

[1602] The server stores the received data in a database. The input is each historical data sent from the terminal, and the output is the data recorded in the database. The server stores the data using a relational database management system (e.g., MySQL or PostgreSQL).

[1603] Step 6:

[1604] The server generates a behavioral model for each user based on the stored data. The input is the stored historical data, and the output is the behavioral model for each user. The server analyzes the data using Python and scikit-learn and builds a machine learning model.

[1605] Step 7:

[1606] The server predicts the next information or product the user will need based on the generated behavioral model. The input is the user's behavioral model, and the output is a list of predicted products. The server makes inferences from the behavioral model and makes predictions based on that data.

[1607] Step 8:

[1608] The server pushes predicted information and products to the user's device. The input is the predicted information, and the output is the notification displayed on the user's device. The server sends the notification using a REST API, and the device displays the notification.

[1609] Step 9:

[1610] The user checks the notification displayed on the device and then browses or purchases the product that is most likely to be purchased next. The input is the notification displayed on the device, and the output is the user's next action (browsing or purchasing). The user checks the details of the displayed product using a smartphone or computer.

[1611] 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.

[1612] The present invention relates to a system that records a user's search history and behavior history and provides predictive information based on that history. It also includes a function that recognizes the user's emotions and adjusts the predictive information based on those emotions. This system streamlines the user's repeated search behavior and enables the user to quickly reach the desired information.

[1613] System configuration

[1614] server

[1615] The server is the core of this system and has the following main functions:

[1616] 1. Data reception function

[1617] The server receives the search history, behavior history, and emotion data sent from the user's terminal.

[1618] 2. Data storage function

[1619] The server records and stores the received data in a database.

[1620] 3. Behavioral model generation function

[1621] The server analyzes the stored data and creates a behavioral model for each user, which shows the user's past search patterns and the sites they visited.

[1622] 4. Prediction function

[1623] The server predicts the next information the user will need based on the generated behavioral model and emotional data. This prediction is made by referring to the user's past behavioral patterns and current emotional state.

[1624] 5. Information provision function

[1625] The server sends the predicted information to the user's terminal.

[1626] Terminal

[1627] A terminal is a device that a user actually uses and has the following main functions:

[1628] 1. Search history recording function

[1629] Record the keywords and queries that users use when searching.

[1630] 2. Activity history recording function

[1631] It records behavioral data such as the URLs of websites accessed by users, the duration of their visits, and the links clicked.

[1632] 3. Emotion data recording function

[1633] An emotion engine is used to recognize and record the user's emotional state.

[1634] 4. Data transmission function

[1635] The device transmits the recorded search history, behavior history, and emotion data to the server.

[1636] 5. Information display function

[1637] The terminal displays the forecast information received from the server to the user.

[1638] Implementation method

[1639] Server Processing

[1640] The server receives the user's search history, behavioral history, and emotional data and stores them in a database. It then analyzes the past data using AI models and data analysis tools to generate a behavioral model. Based on this behavioral model and emotional data, it predicts the information the user will need next and sends it to the device.

[1641] Terminal handling

[1642] When a user performs a search, the device records the search keywords and sends them to the server. When the user accesses a specific site, the device records the user's behavioral history, including the URL, viewing time, and links clicked, and the emotion engine recognizes and records the user's emotional state. This data is periodically sent to the server. When the device receives prediction information from the server, it displays that information to the user.

[1643] Specific examples

[1644] For example, if a user searches for "latest smartphone reviews," the user's device records the search keywords and sends them to the server. If the user visits a specific review site and then accesses other sites, the emotion engine recognizes and records the user's emotional state along with their behavioral history. This data is then sent to the server, which uses it to generate a behavioral model and provide next-time prediction information.

[1645] A few days later, when the user searches for "smartphone reviews" again, the server will predict the most relevant and up-to-date review information based on past behavioral models and current emotional data, and send it to the user's device. For example, if the user has a strong interest or positive emotion in a particular product, the server will prioritize the latest information and reviews related to that product. In this way, users can access the desired information more quickly and efficiently.

[1646] This system can streamline users' search behavior and significantly improve the convenience of information access. In addition, by utilizing emotion data, it can respond more appropriately to user needs.

[1647] The processing flow will be explained below.

[1648] Step 1:

[1649] The user initiates a search.

[1650] A user enters a search keyword such as "latest smartphone reviews" into their device's browser or app.

[1651] Step 2:

[1652] The device records the search keywords.

[1653] The device stores the search keywords entered by the user in a local database and simultaneously transmits them to the server, thereby recording the user's search history.

[1654] Step 3:

[1655] The server receives and stores the search keywords.

[1656] The server records and saves the search keywords sent from the terminal in a database.

[1657] Step 4:

[1658] A user visits a specific site from a search result.

[1659] Users can access review sites of interest from the search results and check the content.

[1660] Step 5:

[1661] The device records your activity history.

[1662] The device collects data such as the URLs the user visits, the time spent browsing, and the links they click, and also uses an emotion engine to recognize the user's emotional state (e.g., excitement, interest, satisfaction, etc.) and record this information.

[1663] Step 6:

[1664] The device sends behavioral history and emotional data to the server.

[1665] The device transmits the recorded behavioral history and emotional data to the server.

[1666] Step 7:

[1667] The server receives and stores behavioral history and emotional data.

[1668] The server stores the behavioral history and emotional data sent from the device in a database.

[1669] Step 8:

[1670] The server analyzes the data and generates a behavioral model.

[1671] The server analyzes the accumulated data using an AI model to generate a user behavior model, which includes search history, access history, browsing time, and emotional data.

[1672] Step 9:

[1673] The user makes the next request to the avatar.

[1674] A few days later, the user says to the avatar, "I'd like to see more smartphone reviews."

[1675] Step 10:

[1676] The server makes predictions based on behavioral models and emotional data.

[1677] The server refers to the user's behavioral model and emotional data to predict what information the user will need next, especially reviews and related information that are likely to interest the user.

[1678] Step 11:

[1679] The server transmits the prediction information to the terminal.

[1680] The server sends the predicted URL and the latest review information to the user's device.

[1681] Step 12:

[1682] The device displays the forecast information.

[1683] The device displays the received forecast information on the screen, allowing users to easily access the information.

[1684] Step 13:

[1685] The user uses the information presented.

[1686] Users can quickly access the desired information by clicking on the latest review information or links provided by the server. Furthermore, the content displayed reflects the user's emotional state, making the display more user-friendly.

[1687] Example 2

[1688] 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."

[1689] Conventional search systems provide information based on a user's search history and behavioral history, but because they do not take into account the user's emotional state, they have the problem of not being able to fully adapt to user needs. For example, even when a user wants to quickly obtain information about a topic that interests them, there are limitations to relying solely on past history data. As a result, problems arise such as low user satisfaction and poor search efficiency.

[1690] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's search history and behavioral history, a means for recognizing and recording the user's emotional state, a means for analyzing this data to generate a behavioral model for each user, and a means for predicting information based on the behavioral model and emotional data. This enables highly accurate information prediction that reflects the user's emotional state.

[1691] "Search history" is a record of past search queries and keywords a user has performed on the Internet.

[1692] "Behavioral history" is a record of a user's internet activity, such as the web pages they visited, the time they spent there, and the links they clicked.

[1693] "Emotion data" is data that indicates the user's emotional state, and is recorded by recognizing emotions from the user's facial expressions, voice, actions, etc.

[1694] A "behavioral model" is a model for predicting a user's behavioral patterns, generated based on the user's search history and behavior history.

[1695] "Predictive information" is information that is predicted to be needed next by the user based on behavioral models and emotional data.

[1696] The "server" is a device that receives search history, behavioral history, and emotional data sent by users, analyzes and stores this data, and generates predictive information.

[1697] A "terminal" is a device that is actually operated by a user, and is a device that records search history, behavioral history, and emotional data and transmits them to a server.

[1698] A "database" is a system for systematically storing search history, behavioral history, and emotional data received by the server.

[1699] An "emotion recognition API" is a program interface for analyzing a user's emotional state, and is used to recognize emotions from data such as the user's facial expressions and voice.

[1700] "Natural language processing" is a technology that uses machine learning and text mining techniques to analyze text data and understand its meaning and context.

[1701] MODE FOR CARRYING OUT THE INVENTION

[1702] This invention relates to a system that provides highly accurate information by utilizing a user's search history, behavioral history, and emotional data. This system comprehensively analyzes a user's behavior and emotions and predicts the information the user will need next, thereby improving the efficiency of information searches.

[1703] The system mainly consists of a server and terminals, each with the following functions:

[1704] Server Configuration

[1705] Data reception function

[1706] The server receives search history, behavioral history, and emotion data sent from the user's device, and processes HTTP requests using a web server such as Nginx or Apache.

[1707] Data storage function

[1708] The server stores the received data in a relational database such as MySQL or PostgreSQL, allowing users' past behavioral data to be systematically accumulated.

[1709] Behavioral model generation function

[1710] The server analyzes the stored data and generates a behavioral model for each user. This process uses machine learning libraries such as Python's Scikit-learn and TensorFlow, making it possible to analyze and predict user behavioral patterns with high accuracy.

[1711] Predictive Function

[1712] The server predicts the next information the user will need based on the generated behavioral model and emotion data. For example, it uses text mining, which uses natural language processing technology, to predict the next keyword the user will search for or the website they are likely to visit based on their past search patterns.

[1713] Information provision function

[1714] The server then sends the predicted information to the user's device. If real-time performance is required, a communication protocol such as WebSocket is used.

[1715] Device configuration

[1716] Search history recording function

[1717] The keywords users type into search engines are recorded using browser extensions and mobile apps, which helps to clarify users' search patterns.

[1718] Activity history recording function

[1719] The device records behavioral data such as the addresses of web pages accessed by the user, the duration of browsing, the links clicked, etc. It uses JavaScript to capture activity within the browser.

[1720] Emotion data recording function

[1721] The device uses an emotion recognition API (e.g., Microsoft's Emotion API) to record the user's emotional state from facial expressions and voice data.

[1722] Data transmission function

[1723] The recorded search history, behavioral history, and emotion data are sent to the server. Asynchronous communication is performed using Ajax.

[1724] Information display function

[1725] Predictive information received from the server is displayed to the user via pop-ups and notifications, allowing users to access the information they need with increased speed and efficiency.

[1726] Specific examples

[1727] When a user searches for "latest smartphone reviews," the device first records the search keyword and sends it to the server. If the user visits a specific review site and then accesses other sites, the device records the user's behavioral history and the user's emotional state as recognized by the emotion engine, and periodically sends this information to the server.

[1728] The server generates a behavioral model based on this data and provides next-time prediction information. A few days later, when the user tries to search for "smartphone reviews" again, the server predicts the most relevant, up-to-date review information based on the user's past behavioral model and current emotional data, and sends it to the user's device.

[1729] For example, predictive information might include a notification such as, "Here's the latest review article on a new smartphone you were interested in!"

[1730] Prompt Sentence Examples

[1731] You might consider using a prompt like this:

[1732] "I would like to develop a system that predicts and provides the next information a user will need based on their behavioral history and emotional data when they search for specific product reviews several times and visit a specific site. The system will record the time spent on the site, the links clicked, and their emotional state (excitement, interest, etc.), and provide information based on a behavioral model analyzed on the server. Please suggest some algorithms that can predict which site a user will visit next and what information they will want."

[1733] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1734] Step 1:

[1735] A user enters a specific keyword (e.g., "latest smartphone reviews") into the browser's search engine. This input is recorded on the device as search history. The input data is the search keyword, and the output data is search history data. Specifically, the browser extension captures keyboard input and saves it in local storage.

[1736] Step 2:

[1737] The device records the user's visit to a specific website, the page address, the viewing time, and the links clicked. The input data is the web page activity, and the output data is the behavioral history data. Specifically, JavaScript code monitors the user's browsing activity in real time, captures the information, and stores it in local storage.

[1738] Step 3:

[1739] The user's device uses an emotion recognition API to acquire and record the user's emotional data. The input data is the user's facial expressions and voice data, and the output data is emotional data. Specifically, a webcam or smartphone front camera captures the user's face, and the emotion recognition software analyzes the data to identify their emotional state.

[1740] Step 4:

[1741] The device sends the recorded search history, behavioral history, and emotion data to the server. The input data is the previously recorded data, and the output data is a notification to the server that transmission has been completed. Specifically, asynchronous communication is performed using Ajax, and this data is sent to the server.

[1742] Step 5:

[1743] The server receives data sent from the device. The input data is the search history, behavioral history, and emotion data sent from the device, and the output data is a notification that the data has been saved to the database. Specifically, the Nginx or Apache web server receives the HTTP request and stores the content in a database (MySQL or PostgreSQL).

[1744] Step 6:

[1745] The server analyzes the data stored in the database and generates a behavioral model. The input data is the saved search history, behavioral history, and emotion data, and the output data is the generated behavioral model. Specifically, it uses Python's Scikit-learn and TensorFlow to build a machine learning model and analyze and predict user behavior patterns.

[1746] Step 7:

[1747] The server predicts the next information the user will need based on the behavioral model and emotional data. The input data is the behavioral model and emotional data, and the output data is the predicted information. Specifically, it uses natural language processing technology to perform text mining and predict the keywords and websites that are likely to be searched next.

[1748] Step 8:

[1749] The server sends the predicted information to the user's device. The input data is the predicted information, and the output data is a notification of completion of transmission to the device. Specifically, real-time communication is performed using WebSocket, and the predicted information is sent to the device.

[1750] Step 9:

[1751] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the output data is the information displayed to the user. Specific operations include displaying a pop-up on a web page or providing information to the user using a notification function.

[1752] (Application example 2)

[1753] 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."

[1754] Conventional information provision systems based on search history and behavioral history have the problem of not being able to provide information that is adequately suited to the user's needs because they do not take into account the user's emotional state. This makes it difficult to provide optimal product information based on the user's interests and emotions, which can lead to a poor user experience. Furthermore, because emotional data is not collected, it is difficult to provide more personalized predictive information.

[1755] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for recording a user's search history, means for recording a user's behavioral history, means for analyzing the behavioral history to generate a user behavioral model, means for predicting the product information the user will need next based on the behavioral model and the user's emotional data, means for providing the predicted information to the user's terminal, means for recognizing the user's emotional state using the terminal, and means for recording and transmitting the emotional data. This makes it possible to utilize the user's emotional data to quickly provide more personalized and appropriate product information.

[1756] "User" refers to an individual or organization that uses this system.

[1757] "Search history" is a record of the search queries and keywords a user has performed.

[1758] "Behavioral history" refers to records of the URLs of web pages a user accessed, the links they clicked, the duration of their visit, and so on.

[1759] A "behavioral model" is a model of a user's behavioral patterns that is generated based on the user's past search history and behavior history.

[1760] "Emotional data" refers to information that indicates the user's emotional state, and includes data obtained from facial expressions, tone of voice, and the like.

[1761] "Predictive information" refers to information or product information that a user is likely to need next, generated based on a user's behavioral model and emotional data.

[1762] "Terminal" refers to a device used by a user, including a smartphone, tablet, or PC.

[1763] "Means for recording" is a function that allows specific data (e.g., search history, behavior history, emotion data) to be recorded.

[1764] "Transmitting means" is a function for transmitting data (for example, search history, behavior history, emotion data) from a terminal to a server.

[1765] The "means for providing" is a function for transmitting the prediction information from the server to the terminal and displaying it to the user.

[1766] "Means of recognition" is a function for analyzing the user's emotional state and recording it as data.

[1767] "Server" refers to a central processing unit that receives, stores, and analyzes data sent by users, and generates and transmits prediction information.

[1768] The present invention is a system that provides predictive information based on a user's search history and behavior history, and also has the function of recognizing the user's emotional data and adjusting the predictive information. This system is mainly composed of a server and a terminal.

[1769] Server Configuration

[1770] Data reception function

[1771] The server receives search history, behavioral history, and emotion data sent from the user's device. The received data is processed in real time, making it possible to make predictions that reflect the user's latest situation.

[1772] Data storage function

[1773] The received data is stored in a database, which is preferably capable of processing large amounts of data at high speed, such as an SQL database (MySQL) or a NoSQL database (MongoDB).

[1774] Behavioral model generation function

[1775] The server analyzes the stored data and generates a behavioral model for each user. The behavioral model is composed of data such as past search queries, web page browsing history, access times, and links clicked. Machine learning algorithms (e.g., random forests and neural networks) are used to generate this model.

[1776] Predictive Function

[1777] Based on behavioral and emotional data, the platform predicts what users will need next using a generative AI model that looks at past data and current emotional state to predict the most relevant product information, reviews, and more.

[1778] Information provision function

[1779] The server sends the predicted information to the user's terminal, providing the user with appropriate information.

[1780] Device configuration

[1781] Search history recording function

[1782] The device records the keywords and queries users use when searching, which are important data points because they reflect user behavior.

[1783] Activity history recording function

[1784] It records behavioral data such as the URL of the web page the user visits, the viewing time, and the links clicked, which allows for a detailed understanding of the user's interests.

[1785] Emotion data recording function

[1786] The device uses an emotion engine to recognize and record the user's emotional state, using a camera and microphone to analyze the user's facial expressions and tone of voice to collect emotional data such as "happy" or "excited."

[1787] Data transmission function

[1788] The device transmits the recorded search history, behavioral history, and emotional data to the server periodically, and the data is updated in real time.

[1789] Information display function

[1790] The forecast information received from the server is displayed to the user, allowing the user to quickly access the most relevant product information and reviews.

[1791] Specific examples

[1792] For example, if a user searches for "latest smartphone reviews" and spends a long time on a specific product page, the device sends this search history and behavioral history to the server. At the same time, the device's camera analyzes the user's facial expressions and records the emotion data "happy." The server uses this data to generate a user behavior model and predict relevant product information and positive reviews, which it then provides the next time the user searches.

[1793] Example prompts to input to the generative AI model

[1794] When purchasing a smartphone, if a user frequently searches for "latest smartphone reviews" or "spec comparisons" and spends a long time on a specific product page, prioritize recommendations for reviews of that specific product. Also, if the sentiment data indicates "happy" or "excited," provide positive reviews and discount information related to that product. Predict what information the user will search for next and generate data based on this prediction.

[1795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1796] Step 1:

[1797] The device records the user's search keywords. When a user searches for a specific keyword (e.g., "latest smartphone reviews"), the keyword is saved as a search history. This becomes input data, and the device gets an output that is recorded in local storage.

[1798] Step 2:

[1799] When a user browses a web page, the device records the URL of the page and the browsing time. If a user browses a specific product page for a long time, the URL and browsing time will be saved as a behavioral history. The input data is the URL and browsing time, and the device outputs this data and saves it in local storage.

[1800] Step 3:

[1801] The device uses a built-in camera and microphone to collect user emotional data. It analyzes facial expressions and tone of voice while the user is browsing a web page and recognizes emotions such as "happy" or "excited." The input data is video and audio captured by the camera and microphone, and the device outputs this as emotional data.

[1802] Step 4:

[1803] The device periodically sends the recorded search history, behavior history, and emotion data to the server using HTTP requests. The input data are search history, behavior history, and emotion data, and the device obtains output by sending this to the server.

[1804] Step 5:

[1805] The server stores the data received from the device in a database. The input data is the search history, behavior history, and emotion data sent from the device, and the server obtains an output that records this in the database.

[1806] Step 6:

[1807] The server analyzes the stored data using machine learning algorithms to generate a behavioral model for each user. The input data is search history, behavioral history, and emotional data recorded in the database, and the server analyzes this data to obtain the output as a behavioral model. Algorithms such as random forests and neural networks are used for this analysis.

[1808] Step 7:

[1809] The server predicts the next product information the user will need based on the generated behavioral model and emotional data. The input data are the behavioral model and emotional data, and the server outputs predicted information based on this. A generative AI model is used for the prediction.

[1810] Step 8:

[1811] The server sends the predicted product information to the user's terminal. The input data is the predicted information, and the server obtains an output that is sent to the terminal as an HTTP response.

[1812] Step 9:

[1813] The terminal displays the prediction information received from the server to the user. The input data is the prediction information sent from the server, and the terminal displays it on the screen to obtain an output that provides the user with relevant information.

[1814] Step 10:

[1815] As users view the predictions and take their next action, the newly generated search history, behavioral history, and emotional data are returned to step 1, and the process repeats, continuously optimizing the user experience.

[1816] 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.

[1817] 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.

[1818] 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.

[1819] 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.

[1820] 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.

[1821] 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.

[1822] 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).

[1823] 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.

[1824] 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."

[1825] 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.

[1826] 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).

[1827] 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.

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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.

[1834] 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.

[1835] 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.

[1836] 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.

[1837] The following is further disclosed regarding the above embodiment.

[1838] (Claim 1)

[1839] a means of recording a user's search history;

[1840] A means for recording a user's behavior history based on the search history;

[1841] means for analyzing the behavioral history to generate a user behavior model;

[1842] A means for predicting the next information required by the user based on the behavior model;

[1843] A system including a means for providing said predicted information to a user.

[1844] (Claim 2)

[1845] a means of recording search keywords entered by users;

[1846] A means of recording the URLs of websites visited by users;

[1847] 10. The system of claim 1, further comprising means for recording access times of the URLs.

[1848] (Claim 3)

[1849] A means for transmitting the user's search history and behavior history to a server;

[1850] 2. The system of claim 1, further comprising means for displaying the forecast information received from the server on the user's terminal.

[1851] "Example 1"

[1852] (Claim 1)

[1853] a means of recording a user's search history;

[1854] A means for recording a user's behavior history based on the search history;

[1855] means for transmitting the behavior history to a server;

[1856] means for receiving the behavioral history and storing it in a database;

[1857] means for analyzing the stored data and generating a user behavior model;

[1858] A means for predicting the next information required by the user based on the generated behavioral model;

[1859] means for transmitting the predicted information to a user terminal;

[1860] The system includes means for displaying the transmitted forecast information on a user terminal.

[1861] (Claim 2)

[1862] a means of recording search keywords entered by users;

[1863] A means of recording the URLs of online sites visited by users;

[1864] 10. The system of claim 1, further comprising means for recording access times of the URLs.

[1865] (Claim 3)

[1866] A means for transmitting the user's search history and behavior history to a server;

[1867] 2. The system of claim 1, further comprising means for displaying the forecast information received from the server on the user's terminal.

[1868] "Application Example 1"

[1869] (Claim 1)

[1870] a means of recording a user's search history;

[1871] A means for recording a user's behavior history based on the search history;

[1872] means for analyzing the behavioral history to generate a user behavior model;

[1873] A means for predicting the next information required by the user based on the behavior model;

[1874] means for providing the predicted information to a user;

[1875] A means for collecting and recording users' purchase history and browsing history on the online shopping site;

[1876] The system includes a means for predicting the next product that may be purchased based on the purchase history and browsing history, and notifying the user of the predicted product.

[1877] (Claim 2)

[1878] a means of recording search keywords entered by users;

[1879] A means of recording the URLs of websites visited by users;

[1880] means for recording the access time of said URL;

[1881] 2. The system according to claim 1, further comprising means for recording product viewing data and purchase data on the online shopping site.

[1882] (Claim 3)

[1883] A means for transmitting the user's search history and behavior history to a server;

[1884] A means for displaying the forecast information received from the server on the user's terminal;

[1885] The system of claim 1, further comprising means for sending a push notification of a product based on a purchase prediction.

[1886] "Example 2: Combining Emotion Engines"

[1887] (Claim 1)

[1888] a means of recording a user's search history;

[1889] A means for recording a user's behavior history based on the search history;

[1890] means for recording the user's emotional state in addition to the behavioral history;

[1891] means for analyzing the behavioral history and emotion data to generate a user behavior model;

[1892] a means for predicting the next information required by the user based on the behavioral model and emotion data;

[1893] A system including a means for providing said predicted information to a user.

[1894] (Claim 2)

[1895] a means for recording search keywords entered by users;

[1896] means for recording the addresses of web pages visited by users;

[1897] 10. The system of claim 1, further comprising: means for recording an access time of the address.

[1898] (Claim 3)

[1899] means for transmitting the user's search history, behavior history, and emotional state to a server;

[1900] 2. The system of claim 1, further comprising means for displaying the forecast information received from the server on the user's terminal.

[1901] "Application example 2 when combining emotion engines"

[1902] (Claim 1)

[1903] a means of recording a user's search history;

[1904] A means for recording a user's behavior history based on the search history;

[1905] means for analyzing the behavioral history to generate a user behavior model;

[1906] a means for predicting the next product information required by the user based on the behavioral model and the user's emotional data;

[1907] means for providing the predicted information to a user terminal;

[1908] means for recognizing an emotional state of a user using said terminal;

[1909] means for recording and transmitting said emotion data;

[1910] A system including:

[1911] (Claim 2)

[1912] a means of recording search keywords entered by users;

[1913] A means of recording the URLs of the web pages you visit;

[1914] means for recording the duration of stay at said URL;

[1915] 10. The system of claim 1, further comprising means for analyzing and recording user emotions.

[1916] (Claim 3)

[1917] A means for transmitting the user's search history, behavior history, and emotion data to a server;

[1918] 2. The system of claim 1, further comprising means for displaying the predicted product information received from the server on a user terminal. [Explanation of symbols]

[1919] 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 recording a user's search history; A means for recording a user's behavior history based on the search history; means for analyzing the behavioral history to generate a user behavior model; A means for predicting the next information required by the user based on the behavior model; A system including a means for providing said predicted information to a user.

2. a means of recording search keywords entered by users; A means of recording the URLs of websites visited by users; 2. The system of claim 1, further comprising means for recording access times of the URLs.

3. A means for transmitting the user's search history and behavior history to a server; 2. The system of claim 1, further comprising means for displaying the forecast information received from the server on the user's terminal.

Images