67 results about "Online advertising" patented technology

A logo detection system for detecting logos in digital images, such as online advertisements, is disclosed. In response to receiving an image containing a logo, the logo detection system generates synthetic advertisements by randomly transforming and placing the image of the logo into advertisement templates. The logo detection system uses these synthetic advertisements, along with advertisements that do not include the image, to generate a training set by applying an image encoder to the advertisements to extract feature vectors for each advertisement. The logo detection system constructs and trains a number of classification models by randomly assigning values to parameter values for the classification models. Each of these models is trained using the extracted feature vectors and the models are scored to find, from among the trained models, the model with the highest score. This model is retained for purposes of detecting the logo in images, such as online advertisements.

The present application describes systems, methods and devices for creative asset management, some of which can be utilized in closed / batch supply and / or open real-time bidding (RTB) supply opportunities. The present application further describes systems, methods and devices for omni-channel creative asset management that may have bi-directional features. In particular, in some embodiments, the creative asset management systems, methods, and devices described herein can allow advertisers to consolidate their online advertising efforts into a central platform.

The invention discloses an online advertisement intelligent pushing system based on big data, and relates to the technical field of advertisement pushing, the online advertisement intelligent pushing system comprises a data acquisition module, a data processing module, a data storage module, an intelligent pushing module and a user feedback module, the intelligent pushing module comprises a user interest analysis unit, a pushing opportunity unit and an effect evaluation unit, the user interest analysis unit analyzes the interest degrees of different advertisement categories, the pushing opportunity unit divides a day into a plurality of time periods, and determines the advertisement pushing strength and the advertisement pushing time periods, so that the preference of the user for the advertisement categories is analyzed through an interest evaluation algorithm, the most popular advertisement category is selected for pushing, and the advertisement pushing efficiency is improved. The optimal advertisement pushing time period and pushing strength are determined through the segmentation evaluation algorithm, the pushing effect is evaluated through the advertisement effect evaluation algorithm, the threshold value is set, the competitive advertisement influence is adjusted in real time, the advertisement strategy is dynamically adjusted in the high competitive time period, and the advertisement pushing accuracy is improved.

The invention belongs to the technical field of pushing algorithms, and discloses an e-commerce platform online advertisement competitive product recommendation method, which comprises the steps of capturing commodity information data, performing data cleaning on the commodity information data, performing model construction by using the cleaned data, analyzing consumer preferences by using a clustering and classification method, predicting a market trend, and performing online advertisement competitive product recommendation on the basis of the constructed model. And competitive products are recommended by using machine learning and an artificial intelligence algorithm. According to the scheme, the commodity search result and the detail page information are automatically captured in real time, the difficulty of manually selecting competitive products is reduced, the competitive products are recommended through an intelligent algorithm, the commodity orientation accuracy of advertisements is improved, and the purposes of optimizing the advertisement putting effect and promoting sales are achieved.

The invention relates to the technical field of online advertisement analysis based on artificial intelligence, and discloses an online advertisement analysis method and system based on artificial intelligence. By setting an advertisement set and a standard record thereof, each element of advertisement data acquisition is determined, and the problems of data source inconsistency and data confusion are solved. The advertisement set comprises text description, image data, video data and display timestamps, and it is ensured that data formats are unified. A text value is calculated through a character counting function, so that text information can be quantified; the unique color type number is obtained through pixel-by-pixel scanning of image values, and the problem of image data quantification is solved; the video playing duration is analyzed from the file, and time information standardization is ensured. Finally, the values of the text, the image and the video data are summed to obtain an advertisement comprehensive description value, unified processing of multi-modal data is realized, and data integration and cross-modal analysis effects are improved.

Systems and methods are for identifying online advertisements to display to a user based on historical user data collected across the user's use of a plurality of Internet devices. One method includes receiving, over a network, a request for an advertisement to display at a first device of the user, the request including a unique identifier stored on the user's first device; accessing, in a database, a demographic or browsing history information generated from the user's use of a second device, the demographic or browsing history information being stored in the database in relation to the unique identifier; and identifying, based on the demographic or browsing history information, an advertisement to display at the user's first device. The demographic or browsing history information is synchronized based on the unique identifier being stored on the user's first device and the user's second device.

The present invention discloses a remarketing advertising effect prediction method and system based on multi-task learning, which belongs to the field of online advertising marketing information. The present invention learns and predicts multiple user behaviors (clicks / browsing / add-to-cart / purchase) after the advertisement is displayed, considers multiple influencing factors at the same time in the display link, automatically learns the correlation between features and targets, and solves the remarketing advertising effect prediction problem with one model. Specifically, knowledge is learned from massive data through offline feature design, target determination, sample construction and model training, and feature extraction and model scoring are completed for requests online in real time. The knowledge trained from the offline model is applied to the production environment to realize real-time remarketing advertising effect prediction. The present invention uses one model to realize the prediction of multiple indicators, which reduces the complexity of traditional multi-model solutions and overcomes the problem of poor training effect caused by sparse data of purchase / add-to-cart and other behaviors.

An online advertisement content platform providing system includes a server and an electronic device, wherein the server is configured to obtain at least one content of a specific product from the electronic device, wherein the at least one content includes an image of the specific product, acquire product attribute information of the specific product based on the at least one content, wherein the product attribute information corresponds to a plurality of product features, and provide, based on the product attribute information, recommendation information of a famous image identified based on a plurality of weight values related to the plurality of product features to the electronic device, wherein the recommendation information of the famous image includes at least one famous image.

This application relates to the field of online advertising API optimization, and discloses an online advertising API optimization method and device based on traffic statistics. The method includes: obtaining the traffic of the first online advertising API in the first time period and in the second time period respectively, and obtaining the traffic of the second online advertising API in the first time period and in the second time period; determining a first traffic difference between the traffic of the first online advertising API and the second online advertising API in the first time period, determining a second traffic difference between the traffic of the first online advertising API and the second online advertising API in the second time period, and determining the difference between the first traffic difference and the second traffic difference as the traffic difference fluctuation value; when both the first traffic difference and the second traffic difference are greater than or equal to a preset traffic difference, and the traffic difference fluctuation value is less than a preset traffic difference fluctuation value, optimizing the first online advertising API. This application can accurately locate the optimization direction of the online advertising API and improve the optimization effect of the online advertising API.

Currently, online advertising systems are primarily used to attract human attention and monetize it by selling products and services to humans. A superior way to monetize human attention specifically, and the attention of any intelligent entity more generally, is to focus it on solving valuable problems. This invention shows how to capture specific human (or non-human) expertise via online ads and then use that expertise to train advanced AI systems which then solve valuable problems using that expertise. Using the invention, Alphabet / Google, Meta, Amazon, Alibaba, ByteDance / TikTok, Microsoft, Apple, TenCent, Baidu, Twitter / X, Spotify, PubMatic, Pinterest, Snap and other online advertisers can significantly increase their online advertising revenue. The system and methods also enable existing online advertising technology to power AI, Artificial General Intelligence (AGI), and SuperIntelligent systems. The technology includes systems and methods that increase AI safety and maximize the chances of human survival and prosperity in the age of AI.

The present disclosure relates to a context online advertisement delivery method and device, a server and a storage medium, and relates to the technical field of computers. The method comprises: obtaining a text to be processed; in response to a user performing a context operation on the text to be processed, obtaining a target text unit and a target text, obtaining a word document corresponding table and a candidate sentence library; obtaining first target advertisement information and second target advertisement information according to the target text unit, the word document corresponding table, the target text and the candidate sentence library, providing the target text and / or the target text unit, the first target advertisement information and / or the second target advertisement information to the user. Thus, the user can establish a global view of the language structure and train the language sense to quickly master the language. At the same time, the user is provided with advertisement information related to the learning text, which improves the click rate of the advertisement while realizing the implantation of the advertisement scene.

Systems and methods are for identifying online advertisements to display to a user based on historical user data collected across the user's use of a plurality of Internet devices. One method includes receiving, over a network, a request for an advertisement to display at a first device of the user, the request including a unique identifier stored on the user's first device; accessing, in a database, a demographic or browsing history information generated from the user's use of a second device, the demographic or browsing history information being stored in the database in relation to the unique identifier; and identifying, based on the demographic or browsing history information, an advertisement to display at the user's first device. The demographic or browsing history information is synchronized based on the unique identifier being stored on the user's first device and the user's second device.

The invention relates to the technical field of cloud service systems, in particular to an application-based online advertisement management method and platform in a hybrid cloud mode, the method comprises a putting step and an account checking step, and the putting step comprises the following steps executed by a public cloud server: adding an advertisement service provider; sending a test instruction to an advertisement service provider, and notifying an application owner or a traffic owner to start an advertisement service after the test is passed; and the advertisement server obtains the delivered advertisement from an application main interface or a flow main interface, and supplies the advertisement to a plurality of cloud subscription ends for subscription depending on the operated public service application, and the plurality of cloud subscription ends comprise a subscription end of a mixed cloud composed of public clouds and / or private clouds. According to the technical scheme of the invention, the problems of tedious configuration of each advertisement, different settlement periods of various advertisements and controllable advertisement putting granularity can be solved.

A measurement platform configured to measure performance of an advertisement, the measurement platform comprising a demand-side platform (DSP), the measurement platform configured to send to an end user a survey configured to measure the advertisement's performance on behalf of a client DSP, wherein the measurement platform is further configured to update an exposed group of exposed users, wherein the measurement platform is further configured to update a control group of end users with similar statistical properties to the exposed group except that the control users have not seen the winning advertisement, wherein the measurement platform is further configured to send the survey to the end user, wherein the measurement platform is further configured to receive a survey response from the end user, wherein the measurement platform determines a metric measuring performance of the winning advertisement, wherein the measurement platform is further configured to send the metric to the client DSP.

The invention relates to a collaborative optimization method for feature construction and over-fitting tendency in a programmed advertisement, which comprises the following steps: acquiring original data of the programmed advertisement, and dividing the obtained original data of the programmed advertisement into a first-order feature combination, a second-order feature combination and a third-order feature combination according to feature attributes of the original data of the programmed advertisement; calculating an OPI value of each feature data in the first-order feature combination, the second-order feature combination and the third-order feature combination; performing risk assessment by using the OPI value of each feature data; performing risk processing on the high-risk and medium-risk feature data; using the processed feature data as a training set to train a CTR prediction model to obtain a trained CTR prediction model; using the trained CTR prediction model to predict the CTR value of the daily online advertising; the problems of high over-fitting risk, large resource waste, poor alleviation effect and high labor cost in the prior art are solved, and the method has significant practical value in real-time bidding and accurate delivery scenes of programmed advertisements.

The application discloses a kind of multi-channel advertisement intelligent distribution control system and method based on deep learning, comprising the following steps: collecting multi-source advertisement delivery data, structured input dataset including user behavior sequence, advertisement content sequence and channel attribute sequence are constructed;Structured input dataset is encoded to feature vector, and three kinds of feature vectors are generated;Three kinds of feature vectors are input into ternary gate fusion structure, and fusion feature representation is generated;Fusion feature representation is input into improved PLE model, and click rate prediction value, conversion rate prediction value and budget cost prediction value are output;Strategy scoring function is constructed based on the above three prediction results, and the distribution score of candidate advertisement and channel combination is generated;According to distribution score ranking, the highest score combination is obtained, distribution instruction is generated and output to multi-channel advertisement system.The application improves the precision and cost control ability of advertisement distribution strategy under multi-target delivery, and is suitable for large-scale online advertisement delivery scene.

In order to complete the click-through rate estimation according to the object features of the object to be tested, it can be used as a data sorting link in the fields of enterprise-level recommendation systems, search systems and online advertising systems. The present invention provides a click-through rate estimation model based on feature representation under an attention mechanism, the model includes: a feature embedding layer, which is used to vectorize continuous features and discrete features to form stacked features, an explicit feature cross network, an explicit feature combination of the stacked features through an attention cross network, an implicit feature cross network, an implicit feature combination of the stacked features through a multi-layer perceptron, and an estimated probability output layer, and the click-through rate is estimated according to the received combined features. Among them, the attention cross network eliminates the dependence of the estimation model on artificial feature engineering, and the introduction of the attention mechanism distinguishes the importance of each combined feature to the model estimation, and eliminates the influence of useless and redundant features on the model.

The subject technology receives frames of a source media content, the frames of the source media content including representations of a head and a face of a source actor. The subject technology generates sets of source pose parameters. The subject technology receives at least one target image, the at least one target image including representations of a target head and a target face of a target entity. The subject technology generates, based at least in part on the sets of source pose parameters, an output media content, each frame of the output media content includes an image of the target face in at least one frame of the output media content. The subject technology provides an online advertisement based at least in part on the output media content for display on a computing device.

Online advertisers may demand compliance with certain standards for the content of emails and other digital content with which its advertisements may be associated. Emails may contain control objects directing users to a web page showing content related to oil change coupons. However, in some systems, methods, and processes, a small variable proportion of users may instead be directed to a different sequence of pages to conduct a compliance check on the contents of the email received by the user. The compliance check sequence of pages may offer the user an incentive to forward the email to a specified email address for subsequent compliance review. Delivery of an incentive may be conditioned on successful receipt of the forwarded email.

This application discloses a user profiling analysis method and server based on online advertising. The method includes: first, acquiring the target user's long-tail search keyword sequence and community interaction record sequence; merging the two sequences into a user behavior action unit chain and generating a set of behavior evolution relationship edges to form a user behavior semantic script; then, performing dependency parsing to generate an intent discourse structure containing a set of subordinate relationship identifiers and a set of causal modification relationship identifiers; performing hierarchical attention propagation operations to generate a hierarchical attention distribution map; and finally, extracting key plot nodes and semantic transition relationship nodes, mapping them to a set of advertising placement semantic anchor points, and generating a user profile feature cluster. By constructing a user behavior semantic script and parsing its intent discourse structure, this application can generate an accurate profile feature cluster that reflects the hierarchical organization and evolution of user interests.

The invention relates to a system for sharing and monetizing data through web3 technology enabled router, wherein the system comprises a plurality of router devices for connecting one or more user devices, and functions as a gateway for communications between the user device and one or more servers. Further, the router device comprises a remote management server module for managing the one or more servers, a data acquisition module for gathering all the data from the user device, an advertisement blocking module for blocking online advertisement in a web browser on the user device, a voice response module for responding to the user' command and a federated learning module for training, constructing and updating a model in a central learning system.

Advertising methods, advertising systems, and advertising management methods. Technology is provided to enable online advertising operators to more easily manage outdoor advertising. The advertising method includes the following steps: a second management device obtains first advertising information representing an advertisement for a first store from a first management device, the first management device managing advertising information for multiple stores that actually exist, including the first store; a display device obtains the first advertising information from the second management device; the display device displays the advertisement for the first store in a public place at a size viewable by multiple people based on the first advertising information; the second management device obtains visitor access information for the first store; and the second management device sends information related to advertising costs to the first management device based on the access information, the visitor being a person who visited the first store based on the advertisement displayed in the public place.

The present teaching relates to method, system, medium, and implementations for online advertising. Bids are solicited from multiple bidders for an online ad display opportunity. A current value of a budget factor is retrieved and used for computing, for each advertisement corresponding to a respective bid, a wrapper function value based on the current value of the budget factor and a flow type of the advertisement. Based on the wrapper function value for each advertisement, a ranking score is determined and used to rank the advertisements associated with the bids. A winning bid is accordingly selected based on the ranking scores.

Identifying potentially unfair practices in content and serving relevant advertisements is described. A total risk score associated with an online advertisement is determined by a computer system based on a first risk score and an issue risk score. The first risk score is based on online content associated with the online advertisement and the issue risk score is based on one or more risk attributes associated with the online advertisement. The total risk score is transmitted to a client computer, where the total risk score is displayed through a user interface in conjunction with the display of the online advertisement on the client computer.

The present disclosure provides a system for detection of online advertisement fraud and commerce fraud. The system includes a first step of collecting a first set of data from a plurality of components associated with each device of a plurality of devices and receiving a second set of data associated with each device of a plurality of third party devices. The system includes yet another step of calculating a probabilistic score for detection of the online advertisement and the commerce fraud in real-time. The system includes yet another step of analyzing the first set of data and the second set of data after a periodic interval of time. The system includes another step of detecting the online advertisement fraud and commerce fraud based on the analysis of the first set of data and the second set of data.

A system and method is provided for dynamically managing advertiser affiliate links for online marketing. A global redirect server accepts a visitor's web browser request for a product or non-product webpage associated with an affiliate network advertiser URL, and returns a corresponding product or non-product webpage to the visitor that is corrected for the visitor's geographic region if such a webpage exists, or returns the originally requested webpage if it does not. The server determines the visitor's geographic region based upon request metadata, and can generate a corrected webpage that affords analytic and commission tracking even if no corresponding affiliate network exists in the determined geographic region. The server includes a database for locating products within the determined geographic region that are comparable to the product of the product webpage request, in the event that the exact product is not available in the geographic region.

The present invention relates to methods, devices, and systems for interactive online advertising that maximize user engagement by incorporating interactive quizzes. The method dynamically generates quizzes based on the user's current content engagement, presenting questions relevant to the content to enhance user interest and engagement. Additionally, sponsored questions are strategically inserted into the quiz, seamlessly blending with the user experience to foster deeper interaction with the advertisement. This approach optimizes user engagement with the advertisement, promoting effective and targeted content delivery while creating an engaging and interactive advertising experience.

The present invention discloses an advertisement flow control method, system and medium. The advertisement flow control method comprises the following steps: obtaining a delivery threshold, configuring a statistical function, and setting a first statistical time period; obtaining flow data within the first statistical time period, and obtaining a flow distribution curve based on the statistical function and the flow data; setting a second statistical time period based on the flow distribution curve, and calculating a first flow value within the second statistical time period based on the flow distribution curve; obtaining a second flow value, and calculating a flow control threshold based on the first flow value, the second flow value and the delivery threshold; controlling the online flow value of the advertisement based on the second statistical time period and the flow control threshold; the present invention can control the advertisement flow in real time, and evenly distribute the advertisement flow in each time period of each day, so as to maximize the revenue of online advertisements, reduce the cost of advertisement flow control, and make up for the shortcomings of the prior art.

In a computer-implemented method, a selected online advertisement is automatically directed to, or online activity is automatically tracked and recorded from, an online user interface device which corresponds, at the time the advertisement is directed or the activity is tracked, to a first online access identifier. The advertisement is directed or the activity is tracked based on information from a user profile that references or includes a first set-top box identifier. That information is derived at least in part from automatically collected data related to user behavior with respect to at least one television advertisement delivered to a set-top box, which set-top box corresponds, at the time of that user behavior, to the first set-top box identifier. The first online access identifier is associated with the first set-top box identifier, by being referenced or included in the user profile, by being associated in a database, or otherwise.