Ad Text Quality Scoring via Language Model
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Solution Overview
Problem
Online advertising exchanges face challenges in evaluating the quality of textual content within advertisements, leading to poor viewer responses due to grammatical errors and text errors in ads, which affect click-through rates and ad effectiveness.
Innovation Solution
A system utilizing a language model and machine-learning algorithms to generate quality scores for advertisements, filtering or penalizing ads with poor text quality, and ranking advertisers based on their ad quality scores, providing feedback to improve ad content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated ad quality evaluation is implemented, then ad text quality improves, but system complexity increases
Solution Approach 1:
The patent introduces a language model as an intermediary component between the ad evaluation system and the quality assessment process. The language model processes ad text and generates quality scores, acting as a mediator that simplifies the overall system architecture while maintaining high evaluation accuracy. This resolves the contradiction by providing a specialized intermediary that handles the complex language analysis task.
Solution Approach 2:
The patent replaces manual human review of ad text with an automated machine learning system. Instead of relying on human editors to manually evaluate each advertisement, the system uses trained algorithms to automatically assess text quality, grammar, and relevance. This substitution eliminates the need for complex human resource management while maintaining consistent quality standards.
2Manufacturing precision
If manual ad review is performed, then text quality control is thorough, but processing speed decreases
Solution Approach 1:
The patent implements a self-service evaluation system where the ad text automatically undergoes quality assessment without requiring manual intervention. The machine learning model processes ads in real-time as they are submitted, providing immediate quality scores and feedback. This self-service approach maintains thorough quality control while dramatically increasing processing speed compared to manual review.
Solution Approach 2:
The system performs preliminary quality evaluation of ad text before the advertisements are published or displayed. By conducting the assessment in advance, the system identifies and flags problematic ads before they reach viewers, maintaining high quality standards while enabling rapid processing of large volumes of advertisements through automated pre-screening.
3Reliability
If ad quality filtering is applied, then viewer experience improves, but ad inventory decreases
Solution Approach 1:
The patent applies quality filtering selectively based on specific criteria rather than uniformly across all advertisements. The system evaluates different aspects of ad text (grammar, relevance, clarity) independently and applies filtering thresholds locally to each dimension. This approach maintains high viewer experience by blocking only genuinely poor-quality ads while preserving acceptable inventory levels by allowing ads that meet minimum standards in each local quality dimension.
Data Source
AI summary
Methods, systems, and computer-readable media for evaluating the quality of text within online advertisements using output from a language model are provided. The output from the language model may be used by a machine-learning algorithm to generate a quality score for an individual advertisement. The quality score may be used to filter out advertisements with poor text quality or to tax or penalize an advertisement within an online auction. The ad quality scores may also be used to rank or score advertisers that submit the ads. In one embodiment, the advertiser's quality score is combined with an individual ad's quality score to create a final score, which is used to evaluate the advertisement. The advertiser rank/score and ad quality score may be communicated to an advertiser as advertiser feedback.