Real-Time Ad Generation via User Intent Matching
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Solution Overview
Problem
Advertisers face challenges in managing and optimizing bid processes for thousands or millions of keywords, generating relevant keywords, and creating personalized ad copy in real-time, leading to limited keyword sets and generic ad content.
Innovation Solution
A method that receives user information in real-time, derives user features including intents, matches them with advertiser intents, and generates advertisements and bids dynamically, allowing for real-time ad generation and bidding based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advertisers manually manage and optimize bids for thousands or millions of keywords, then bid management precision may improve, but the complexity and time required for management increases significantly
Solution Approach 1:
The system enables self-service automated bid management where the advertising system automatically optimizes bids for thousands or millions of keywords without requiring manual advertiser intervention. The system learns from performance data and autonomously adjusts bidding strategies, resolving the contradiction by eliminating manual complexity while maintaining optimization precision.
Solution Approach 2:
The patent replaces manual mechanical bid management processes with automated computational systems. Machine learning algorithms and automated bidding systems substitute human advertisers directly managing individual keyword bids, thereby reducing management complexity while preserving or improving bid optimization through data-driven decision-making.
2Loss of time
If advertisers pre-select keywords based on predictions, then advertising setup time is reduced, but the relevance and personalization of advertisements to individual users decreases
Solution Approach 1:
The system transitions from static pre-selected keywords to dynamic real-time keyword generation and ad personalization. Instead of fixed predetermined keywords, the system dynamically adapts keyword selection and ad content based on real-time user context, behavior data, and query analysis, thereby maintaining quick setup while achieving high personalization.
Solution Approach 2:
The system performs preliminary setup actions by pre-configuring flexible templates and frameworks for keyword generation and ad personalization, rather than pre-selecting specific keywords. This allows rapid initial setup while enabling real-time adaptation to individual user needs through automated personalization engines.
3Productivity
If generic ad copy is used across multiple keywords, then ad creation time and effort are reduced, but advertisement effectiveness and user engagement decreases
Solution Approach 1:
The system applies local quality by generating customized ad copy tailored to specific keywords, user contexts, and target audiences rather than using uniform generic copy. Each advertisement is locally optimized for its specific context while the system maintains high productivity through automated template-based generation and real-time personalization.
Solution Approach 2:
The system changes ad copy parameters dynamically based on user data, query context, and performance metrics. Instead of static generic copy, ad parameters such as headline, description, and call-to-action are automatically adjusted in real-time to match user intent and maximize engagement, thereby improving effectiveness without sacrificing generation efficiency.
4Measurement precision
If real-time bid adjustments are made for each user query, then advertisement relevance improves, but the computational complexity and processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing user profiles, intent models, and bid strategies before real-time queries arrive. This pre-processing enables fast real-time bid adjustments without excessive computational complexity, as the heavy lifting is done in advance and real-time operations only require matching and retrieval.
Solution Approach 2:
The system introduces intermediary components such as pre-processed user profiles, intent classification models, and bid strategy engines that mediate between raw user queries and final bid decisions. These intermediaries simplify real-time processing by structuring and pre-analyzing data, thereby reducing computational complexity while maintaining high matching accuracy.
Data Source
AI summary
In one embodiment, a method includes receiving, in real-time, user information associated with a user of a client computing device. One or more user features are derived from the user information associated with the user. The one or more user features include one or more user intents. The one or more user intents are matched with one or more of a plurality of advertiser intents. The user information is determined to be accepted. In response to accepting the user information, an advertisement is generated based on the matching of the one or more user intents and the one or more of the plurality of advertiser intents.