1884results about "Advertisements" patented technology

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

A content editor or a plugin thereto automatically generates authorship tokens that identify content authored by a human author or an artificial author. The authorship tokens are applied to the work while the work is being produced. Thus, subsequent review of the work can identify regions produced by a human author and other regions produced by an artificial intelligence.

An Automated AI-Driven Lifestyle Orchestration System proactively manages a user's daily life by intelligently populating calendars with relevant, time-sensitive marketing objects. This is achieved through sophisticated AI-driven analysis of user directives, preferences, and real-time location data obtained via computing devices like smartphones. The system leverages advanced media identification modules employing fingerprinting and watermarking to accurately match user-captured media (via a Percipient Sample Pack (PSP)) to specific content and associated products. It facilitates dynamic commerce through hierarchical linked lists for purchasing authentic or similar items, supported by an affiliate program. This inventive solution significantly improves personal organization and digital commerce by transforming passive interaction into a proactive, intelligent, and monetizable user experience.

The invention belongs to the field of advertisement putting, and provides an advertisement putting method and system based on multi-source data analysis, and the method comprises the steps: collecting multi-source original data related to a user; identifying a plurality of cognitive state nodes based on the click behavior data and the transaction path data; constructing a cognitive behavior causal atlas based on the plurality of cognitive state nodes, wherein nodes of the causal atlas represent user cognitive states; predicting an advertisement response probability and a conversion probability of a target user by adopting a Bayesian inference model in combination with a historical behavior sample and a path structure in the causal atlas, and estimating a state transition probability of the user from a current state node to a target state node based on different advertisement intervention contents; and based on the state transition probability and the causal atlas structure, determining an optimal advertisement intervention path of the user from the current cognitive state to the expected conversion state, and constructing a corresponding advertisement putting sequence based on the path.

The invention discloses an AI-based intelligent marketing advertisement putting method and system, and the method comprises the steps: collecting and fusing heterogeneous user behavior data streams from a plurality of independent platforms in real time, and generating a user behavior feature map with a unified space-time mark; on the basis of the user behavior characteristic spectrum, a deep auto-encoder and a space-time diagram convolutional network are used for joint modeling, and fine-grained scene-decoupled user interest preference vectors are generated; inputting the user interest preference vector into a pre-trained generative adversarial network, and dynamically generating a personalized advertisement material highly matched with the current user interest preference and scene; and taking the personalized advertisement material as a candidate arm, and dynamically generating an advertisement putting strategy including an advertisement display form, a display opportunity and a display channel in combination with a current user state and historical putting feedback data. By utilizing the embodiment of the invention, the accuracy and the real-time performance of advertisement putting can be improved, and the advertisement conversion rate and the user experience are improved.

The invention provides a right content delivery strategy generation method and device based on AI big data, and relates to the technical field of strategy generation, and the right content delivery strategy generation method comprises the steps: obtaining user behavior data corresponding to a content platform type, so as to construct a corresponding target user portrait; dividing the to-be-put right and interest content into corresponding right and interest types according to content platform types; based on the target user portrait, analyzing preference degrees and response rates of user groups of the content platforms to different right and interest types; generating an adaptation relation matrix corresponding to the preference degree and the response rate; and distributing a corresponding right content type to each content platform according to the adaptation relation matrix, and determining a delivery frequency weight corresponding to each content platform. The cross-platform user portrait integration degree is improved, a right and platform dynamic matching mechanism is optimized, and the real-time adjustment capability of the delivery strategy is enhanced by constructing the cross-platform user portrait, dynamically generating the adaptation relation matrix and intelligently distributing the delivery strategy.

The invention relates to the technical field of Internet advertisement putting, in particular to an intelligent huge advertisement putting method, which comprises the following steps of: acquiring and preprocessing multi-dimensional operation data in real time, constructing a user response prediction model based on a deep interest network, and dynamically weighting a user behavior sequence by utilizing an attention mechanism. A dynamic budget allocation optimization model is established; an objective function is set according to the type of an advertiser; a budget weight is adjusted according to a real-time bidding success rate; a real-time decision engine is deployed; according to the method, each link is monitored through an exposure conversion funnel, an abnormal alarm is set, strategy backtracking analysis is started, and continuous optimization is performed in combination with online learning and model gray release, so that the problem of low efficiency of budget allocation in the traditional technology is solved, intelligent and accurate putting of a huge amount of advertisements is realized, the advertisement conversion rate is improved, and the customer obtaining cost is reduced.

A user directed video generation method and system obtains a natural language-based communication from a user requesting that a computer-implemented system generate a virtual environment that is based on a description that is provided by the user. The description is interpreted by a trained neural network. Representations of pixel patterns are generated by a trained neural network in accordance with the interpretation. The representations of the pixel patterns are evaluated for consistency with context and then selected based on the evaluation. The selected pixel patterns are embodied in a video stream that is provided to the user. Natural language that may be in audio form may be generated to accompany the video stream.

The invention relates to an intelligent marketing content generation and optimization system, and belongs to the technical field of artificial intelligence and digital marketing. According to the system, through cooperative operation of the data acquisition module, the intelligent text generation module, the multi-modal synthesis module, the risk management and control module and the dynamic optimization module, whole-process closed-loop management of marketing content is realized. The data acquisition module captures unstructured data from a multi-source platform based on a user authorization protocol, semantic cleaning and structured processing are performed through a large model interface, and the problems of data dispersion and fragmentation are solved. The intelligent text generation module receives user parameters through an interactive interface, matches a preset prompt word bank, calls a large model interface to generate multi-version copywriting in batches, and supports concurrent processing of single-choice or multi-choice product lists. The multi-modal synthesis module extracts keywords according to copywriting semantics, matches pre-annotated visual materials and generates multilingual speech paraphrases, so as to ensure the consistency of graphic and text information. The risk management and control module intercepts sensitive content in real time based on an industry rule base, and guarantees output compliance. And the dynamic optimization module integrates the user behavior data to generate a delivery strategy, updates a prompt word bank and a generation model through a closed-loop feedback mechanism, and realizes dynamic adaptation of contents and market demands. Compared with a traditional method, the system has the advantages that the content generation efficiency, the cross-modal matching precision and the compliance control level are remarkably improved, and the system is suitable for the global digital marketing requirements of multiple industry scenes such as e-commerce, industrial equipment and cross-border trade.

The invention discloses a multi-channel advertisement effect prediction and optimization method and system based on artificial intelligence. The method comprises the following steps: obtaining and preprocessing advertisement putting data, user behavior data and external environment data; performing feature extraction on the preprocessed data, mapping different types of content feature vectors to a unified semantic space through a cross-channel feature projection matrix, and generating an advertisement feature set; fusing a prediction model of Transform and a graph neural network, and synchronously predicting a conversion rate, a delivery return rate and a user interaction index of the target advertisement in each channel based on the advertisement feature set; constructing a strategy optimization network based on reinforcement learning, taking a prediction result output by the prediction model as state input, and dynamically generating a budget allocation matrix and a channel selection weight through a strategy gradient algorithm; and pushing the adjusted advertisement putting strategy to an advertisement management platform according to the optimization strategy, and executing advertisement putting. The accuracy and stability of advertisement effect prediction can be improved.

Generative media content (e.g., generative audio) can be dynamically generated based on various inputs, which can include blockchain data. A playback device accesses blockchain data stored via a distributed ledger and generates media content based at least in part on the blockchain data. The playback device can access a library of pre-existing media segments and arrange a selection of pre-existing media segments from the library for playback according to a generative media content model and based at least in part on the blockchain data. The generated media content can then be played back via the playback device.

The invention discloses an AI-based digital advertisement material multi-modal adaptive generation method and system, and relates to the technical field of artificial intelligence and digital content, and the method comprises the steps: S1, obtaining historical operation records of a user in a material creation process, carrying out the classified storage of selection actions in each interaction, and obtaining a preliminary data set of the selection tendency of the user; s2, extracting style preference characteristics of the user in different creation scenes by adopting a behavior pattern analysis method according to the preliminary data set, and determining a dynamic change rule of the style preference of the user; s3, aiming at the dynamic change rule, constructing a dynamic capture mechanism, obtaining the latest selection tendency data, and judging the instant adjustment demand of the style preference of the user; according to the AI-based digital advertisement material multi-mode adaptive generation method and system, intelligence and individuation in the material creation process are realized, and a new technical scheme is provided for the field of creative design.

The invention discloses a content recommendation method and system based on an industry knowledge graph and reinforcement learning, and relates to the technical field of advertisement recommendation, and the method comprises the steps: collecting original advertisement data and user behavior data, carrying out the entity recognition, relation extraction and attribute extraction, and constructing the industry knowledge graph; a graph attention network is adopted to analyze association strength among different modal entity nodes in the industry knowledge graph, advertisement elements are dynamically recombined according to the association strength, and a personalized advertisement material package is generated; mapping the personalized advertisement material package into a reinforcement learning action space, extracting a user historical behavior path from the industry knowledge graph to construct a state space, and obtaining a reinforcement learning environment; in a reinforcement learning environment, mapping the user behavior nodes into a relation chain of an industry knowledge graph, allocating reward values to intermediate nodes, and constructing an RL strategy model; deep modeling and strategy optimization of user behavior intentions are realized, and relevance of recommended content and intelligence of decision making are enhanced.

System, apparatus, article of manufacture, method and / or computer program embodiments are provided for generating synthetic video advertisements. An example method can include obtaining input data that includes one or more business attributes associated with a business; choosing, based on the one or more business attributes, a video advertisement template that includes a plurality of modular elements; producing, based on the one or more business attributes, textual content and image content for promoting the business; generating, based on the textual content, at least one audio track that is associated with one or more of the plurality of modular elements in the video advertisement template; and assembling a synthetic video advertisement by populating the plurality of modular elements in the video advertisement template with the at least one audio track and the image content.

A computer-vision system or engine that (a) generates from a pixel stream a digital representation of a person and (b) determines attributes or characteristics of the person from that digital representation and (c) based on those attributes or characteristics, outputs data to a cloud-based analytics system that enables that analytics system to identify and also to authenticate the person. The attributes or characteristics of the person include their pose, and the system or engine analyses that pose to extract a facial image from a video stream that is the best facial image for use by the cloud-based analytics system to identify and authenticate the person. The computer-vision system or engine outputs the facial image to the cloud-based analytics system, but does not output the full-frame real-time video to the cloud-based analytics system.

Certain aspects of the disclosure provide artificial intelligence (AI) methods and systems for generating personalized social media content with trend integration. A method generally includes retrieving data from data sources that includes customer interactions with a business, and inventory data of the business, determining trending-product pairs that increase engagement of the customers with products recorded in the inventory data of the business based on the retrieved data. A generative artificial intelligence (AI) model is used to generate one or more of a caption, a hashtag, and a promotional image that are personalized to each of the customers in response to receiving prompts that contain information about the customers, information about trending-product pairs, and social media platforms of the customers. The method sends one or more of the captions, the hashtags, and the promotional images that are personalized to the customers to social media platforms of the customers.

The invention discloses a large language model advertisement generation method based on retrieval enhancement generation model and conversion feedback optimization. The method comprises the following steps: S1, collecting advertisement input data; s2, generating a user behavior vector; s3, constructing a retrieval query vector and inputting the retrieval query vector into a retrieval module; s4, obtaining document content matched with the query vector from an advertisement knowledge base; s5, fusing the document content and the multi-source context to generate an advertisement context embedding vector; s6, inputting the context embedding vector into an advertisement generation model to generate a candidate advertisement text; s7, performing conversion rate prediction on the candidate advertisement texts; and S8, constructing a preference sample pair based on user feedback, and optimizing the advertisement generation model by adopting a direct preference optimization algorithm. According to the method, context modeling, semantic information enhancement and behavior-driven generation strategy optimization in the advertisement content generation process are realized, the context correlation and the putting conversion effect of the advertisement generation result are improved, and the method is suitable for a multi-platform intelligent advertisement generation scene.

The invention relates to the technical field of cross-border advertisement putting, and provides a cross-border e-commerce advertisement accurate putting method and system based on a user portrait, and the method comprises the steps: building a unified data flow through multi-source data collection and standardization processing; calculating and fusing the real-time statistical features, the historical interest vectors and the context features in real time by using a stream processing engine to generate multi-dimensional feature vectors; inputting the feature vector into a multi-modal time sequence neural network model which captures a user behavior rule through time sequence coding and an attention mechanism and outputs a user behavior weight and an interest attenuation coefficient; dynamically updating the real-time interest score of the user by adopting an exponential decay model; and when the interest score reaches a threshold value, triggering a reinforcement learning agent to make a decision according to the comprehensive state information, and dynamically outputting an action strategy including release triggering, a bidding coefficient and a creative type. The problems that in the prior art, user interest modeling lags behind, and the self-adaptive capacity of the putting strategy is poor are effectively solved.

The invention provides an advertisement putting dynamic game decision-making method and system based on multi-objective optimization, and the method comprises the steps: obtaining user behavior key data which comprises user visual focus position information; transmitting the user visual focus position information to a multi-target optimization model in a preset decision server, and generating multi-target optimization model input data; analyzing the input data of the multi-target optimization model and a preset game constraint condition by using the multi-target optimization model to generate a multi-target game decision parameter; and carrying out dynamic game deduction calculation based on the multi-target game decision parameters, and generating a dynamic advertisement position adjustment scheme so as to carry out advertisement putting dynamic game decision. According to the method, millisecond data is transmitted to the multi-target optimization model, the problem of strategy oscillation caused by data delay and target conflict in traditional advertisement decision making in a real-time attention scene is solved, and accurate dynamic matching of user focus drift and advertisement display is realized.

The invention discloses a data processing method and system based on a business management platform, particularly relates to the technical field of advertisement putting business management, and is used for solving the problems of difficulty in abnormal attribution and low decision-making efficiency caused by dimension data isolation of an existing system. The method comprises the following steps: synchronously acquiring a channel dimension click rate and a contact dimension blacklist interception rate; when the channel click rate is abnormally reduced, extracting a blacklist interception log associated with the corresponding channel and a product dimension conversion rate; user behavior path topology distortion intensity and decision manifold compactness caused by a blacklist strategy are verified based on an interception log; when the distortion intensity exceeds a threshold value or the compactness is lower than a threshold value, backtracking an advertisement exposure user path of a corresponding channel product; analyzing time sequence association of blacklist interception nodes and product exposure nodes in the path, and generating a candidate root cause set; and detecting the product conversion rate fluctuation direction in the abnormal time period, thereby determining the root cause from the candidate set, and remarkably improving the operation decision-making efficiency and accuracy.

The invention discloses a multi-platform-based advertisement creativity and putting strategy intelligent generation system, equipment and medium, multi-source data are integrated through a cross-platform creativity intelligent generation module to generate creativity materials adaptive to different platforms, and a cross-platform collaborative strategy optimization module is used for quantifying a platform collaborative effect and dynamically allocating budget. An advertisement creative life cycle is managed by means of a real-time dynamic optimization module, a strategy is adjusted based on multi-dimensional feedback, and finally, output of each module is fused through a unified decision engine to form a unified decision. The system realizes full-process intelligentization of advertisement originality and putting strategies, quantifies a cross-platform synergistic effect to improve budget distribution efficiency, constructs an advertisement originality life cycle dynamic management mechanism, realizes strategy real-time evolution through multi-dimensional feedback, and visualizes full-link data in combination with a unified decision engine, so that the advertisement putting return on investment is remarkably improved, and the advertisement putting efficiency is improved. The creative manufacturing cost is reduced, the strategy adjustment efficiency is improved, and an efficient and accurate multi-platform putting solution is provided for advertisers.

The invention discloses a scenarized vehicle-mounted advertisement recommendation system for intelligent traffic, and relates to the technical field of intelligent traffic, and the system comprises a data collection module which collects a direction deviation angle, a speed matching degree and a path following rate in real time through a vehicle sensor and a navigation system, and obtains original behavior data; the feature extraction module is used for processing the original behavior data by adopting a time sequence analysis method, extracting dynamic features of steering frequency, pause time difference and signal acquisition frequency, and determining a behavior signal sequence; the correlation analysis module is used for calculating a correlation coefficient according to the matching degree of the behavior signal sequence and the navigation setting information on the aspects of time synchronism and environmental interference factors to obtain a correlation feature vector; according to the intelligent traffic-oriented scenarized vehicle-mounted advertisement recommendation system, through combination of multi-level behavior analysis and a recommendation algorithm, the pertinence of advertisement pushing and the user acceptability are remarkably improved, and an efficient advertisement putting effect is realized.

The invention belongs to the technical field of advertisement putting, and discloses a multi-modal data driven intelligent advertisement putting system and method. Comprising the following steps: acquiring and analyzing historical behavior data of a user, extracting a periodic rule of user activity, and identifying candidate delivery windows; when it is detected that the current time is matched with the candidate delivery window, collecting real-time behavior data; performing feature extraction on the real-time behavior data to obtain situation features, and identifying key situation factors to form a user situation portrait; according to the situation portrait of the user, dynamically evaluating interruption cost and response probability, and judging whether the current time is a suitable delivery opportunity, and if so, performing advertisement delivery; according to the invention, comprehensive perception and analysis of user behaviors and situations are realized, and forced pushing under the situation that the user is not suitable for receiving the advertisement is avoided, so that the user experience is improved, and the accuracy and effect of advertisement putting are effectively improved.

The invention relates to a scene advertisement triggering method, terminal and equipment linked with Internet of Things equipment, and the method comprises the steps: obtaining terminal perception data associated with a user in real time; the terminal sensing data are input into a preset scene recognition model for feature correlation analysis, the scene recognition model comprises a scene mapping rule base, and a three-dimensional digital label of a physical scene is output; matching an advertisement content database according to the three-dimensional digital label, and obtaining an associated advertisement material set; and according to the associated advertisement material set, generating and pushing adaptive scene advertisement content based on user terminal equipment attributes so as to achieve the purposes of deep binding of the advertisement content and a real-time scene and cross-terminal equipment adaptive accurate pushing.

The invention provides a DMP-based crowd selection method and apparatus, and an electronic device, a data management platform pre-processes multi-source user data, generates user features corresponding to the pre-processed data based on a preset rule, converts the obtained user features into user tags based on a pre-constructed tag system, and sends the user tags to the user management platform; the method comprises the following steps: acquiring a user tag, storing the acquired user tag in a distributed storage mode, displaying the stored user tag through an operation interface, converting a circling operation for a target user tag displayed on the operation interface into a rule tree, and circling a target user corresponding to the target user tag based on the rule tree. And associating the circling result with the identification information of the target user corresponding to the circling result to generate a crowd packet, and expanding the obtained crowd packet by adopting a similar crowd expansion algorithm. According to the invention, the problems of complex rule configuration, insufficient real-time performance, limited expansibility and the like of a DMP-based crowd selection system in the prior art can be solved.

The invention relates to a financial customer precision marketing system and method based on big data and artificial intelligence in the field of financial science and technology and intelligent marketing cross technology, and provides a financial customer precision marketing system and method based on big data and artificial intelligence by integrating multi-dimensional customer data, constructing a dynamic customer portrait, predicting customer demands by using a deep learning model and combining the real-time reaching capability of enterprise WeChat ecology. According to the method, automatic execution and closed-loop optimization of a personalized marketing strategy are realized, and the customer conversion rate and loyalty of a financial institution are improved; the method is innovatively embodied in deep combination of an emotion recognition technology and a financial scene, reinforcement learning optimization of a dynamic rule engine and a privacy compliance data desensitization mechanism, and solves the problems of lagging customer demand recognition and low strategy matching efficiency in traditional marketing.

The invention discloses a multi-agent cooperative driven AI marketing content generation method, and belongs to the technical field of artificial intelligence and digital marketing, the whole process of marketing content from planning to generation and auditing is completed through cooperative work of a plurality of AI agents with different functions, and the method comprises the steps that S1, a strategy planning agent plans the marketing content direction and key points, generating a content creative outline or scheme; s2, creating a specific content material by the creative generation agent according to the content creative outline or scheme; s3, the content auditing agent continuously monitors the generated content and audits the content; s4, the creative generation agent reads the modification suggestions and modifies the modification suggestions; s5, after the content auditing agent passes auditing, final marketing content is output; according to the multi-agent collaborative driven AI marketing content generation method provided by the invention, the AI agents with different functions cooperate with each other to jointly complete the whole process from content planning to generation and auditing, so that the generation quality and efficiency of the marketing content are remarkably improved.

The invention discloses a cross-border advertisement accurate delivery method, system and device based on a user portrait, relates to the technical field of cross-border digital marketing, and discloses the cross-border advertisement accurate delivery method, system and device based on the user portrait, which generates an accurate user portrait by integrating cross-regional behavior data. An advertisement putting strategy is dynamically calculated in combination with exchange rate, tax and logistics economic parameters, and compliance adaptation is performed based on legal and cultural rules, so that the problems of single data dimension, inaccurate user portraits and lack of economic variables in the prior art are solved; the method has the advantages that the cross-border advertisement putting accuracy and conversion effect are improved, and the compliance risk is reduced.

Various embodiments of the present disclosure relate to providing AI-based generation of responsive content. In an example embodiment, generative response content is an AI-powered interactive interface embedded within presented content, allowing users to ask questions and receive relevant, real-time answers directly within the content space, without requiring a click or further interaction to access a landing page.

The invention relates to the field of advertisement putting, and provides an advertisement putting management platform and a management terminal thereof, and the platform comprises an account management module which provides account registration and login for a user; the advertisement putting management module is used for providing advertisement putting setting for an advertiser; the advertisement creativity management module provides advertisement creativity for an advertiser and supports the user to upload or create the advertisement creativity; the audience orientation and marketing module collects and arranges user data, constructs a user portrait, and helps an advertiser to carry out audience orientation and accurately put an advertisement to a target user group according to the user portrait; and the data monitoring and effect evaluation module is used for collecting advertisement putting data for the put advertisement, creating a data report according to the advertisement putting data, and analyzing and evaluating the advertisement data. According to the technical scheme, the problem that the advertisement effect is difficult to improve due to the fact that an advertisement putting management platform cannot accurately position audiences in the prior art is solved.