Dynamic Context-Based Advertisement Generation
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Solution Overview
Problem
Conventional online advertising is limited to statically generated advertisements that do not adapt to changes in web page content, product offerings, or prices, and fails to facilitate comparative shopping, requiring manual updates and limiting user experience.
Innovation Solution
A system and method for dynamically generating advertisements based on web session information, using a dynamic advertisement generator that retrieves and assembles relevant data from commerce networks, allowing for real-time updates and comparative shopping features within the web page context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static advertisements are used, then the advertisement structure is simple and easy to implement, but the advertisement cannot adapt to changes in web page content, product offerings, or prices
Solution Approach 1:
The patent implements dynamic advertisement generation by extracting keywords from web page content and automatically assembling relevant product information from commerce networks. The advertisement content changes dynamically based on the current web page context, product availability, and pricing, transforming the static advertisement model into a dynamic system that adapts to real-time changes without requiring manual updates.
Solution Approach 2:
The system performs self-service by automatically extracting keywords from web pages, querying commerce networks for relevant product data, and assembling advertisements without human intervention. The dynamic advertisement generator operates autonomously to create context-relevant ads based on extracted keywords and available product information, eliminating the need for manual advertisement creation and updates.
2Ease of operation
If conventional static advertisements are used, then the implementation is straightforward, but they do not facilitate comparative shopping and require multiple page visits
Solution Approach 1:
The patent merges multiple shopping functions into a single advertisement unit by incorporating product information, pricing, and comparative shopping capabilities directly within the advertisement structure. Users can view multiple product options and compare prices within the same web page context without needing to visit separate product pages, combining information retrieval and comparison in one location.
Solution Approach 2:
The advertisement system achieves multi-functionality by serving multiple purposes: it provides product information display, enables comparative shopping, and facilitates direct purchasing decisions. The dynamic advertisement generator creates universal advertisement structures that can adapt to different product types and shopping scenarios, allowing users to complete shopping tasks without leaving the current web page.
3Productivity
If manual advertisement updates are used, then the system complexity is low, but the advertisement cannot reflect real-time changes in product offerings or prices
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring web page content, product availability, and pricing changes. The dynamic advertisement generator extracts keywords from current web pages and automatically queries commerce networks for updated product information, ensuring advertisements reflect real-time changes. This feedback loop enables automatic adaptation to changing conditions without manual intervention.
Solution Approach 2:
The system performs preliminary actions by pre-configuring the dynamic advertisement generator with commerce network connections and product data structures. When web page content changes, the system is already prepared to extract keywords, query relevant products, and assemble updated advertisements immediately, eliminating delays associated with manual update processes.
Data Source
AI summary
A method and a system dynamically generates an advertisement based on one or more tokens distilled from information about a web session that requested a web page. For example, a token is distilled from information about a web session. Data is retrieved from a commerce database based on the token. The retrieved data is assembled into an advertisement, which is then supplied for rendering with the web page.