Search Engine Quality Scoring with AI Source and Industry Factors
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Solution Overview
Problem
Current search engines do not provide quality assessments or political lean evaluations for search results, leading to users encountering low-quality or biased content without clear indicators.
Innovation Solution
A search engine system that utilizes machine learning techniques to generate quality scores and political lean assessments for each piece of content, incorporating journalistic principles and domain expertise to evaluate factors such as ad hominem attacks, clickbait, subjectivity, and source attribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If search engines return results based on query terms without quality checks, then search speed and simplicity are maintained, but users encounter low-quality or biased content without clear indicators
Solution Approach 1:
The patent segments the search result evaluation into multiple independent quality factors (e.g., source credibility, content accuracy, bias detection, clickbait identification) that are assessed separately and then aggregated into an overall quality score. This segmentation allows the system to maintain reliability while managing complexity through modular assessment components.
Solution Approach 2:
The patent applies preliminary quality assessment actions by pre-evaluating search results against multiple quality criteria before presenting them to users. Quality scores and political lean assessments are generated in advance for each search result, allowing users to make informed decisions without experiencing the complexity of the assessment process during their search.
2Measurement precision
If search engines provide detailed quality assessments for each result, then users can identify high-quality content, but the user interface becomes more complex and time-consuming
Solution Approach 1:
The patent applies local quality by providing differentiated quality information at different levels of user interaction. The system generates comprehensive quality assessments including multiple factors (source credibility, accuracy, bias, clickbait) but presents them in a simplified format (overall quality score, political lean indicator) that users can quickly perceive. Detailed factor breakdowns are available only when users need deeper inspection.
Solution Approach 2:
The patent uses visual indicators (such as color-coded quality scores and political lean markers) to communicate complex quality assessment information in an intuitive manner. Different quality levels and political orientations are represented through distinct visual cues that allow users to rapidly assess search results without reading detailed analyses.
3Reliability
If search engines filter out low-quality content, then result quality improves, but the quantity of available information decreases
Solution Approach 1:
The patent implements dynamic quality filtering where the threshold for displaying search results is not fixed but adjusts based on user preferences, query context, and quality distribution across results. Users can dynamically adjust their quality expectations, allowing the system to maintain reliability while preserving access to a broader range of information when appropriate.
Solution Approach 2:
The patent creates multiple versions or representations of search results with varying levels of quality annotation. High-quality results receive comprehensive quality assessments and prominent display, while lower-quality results are still accessible through alternative pathways or with reduced prominence, ensuring both quality improvement and information availability.
Data Source
AI summary
A quality score system and method for a piece of content. The system and method may use artificial intelligence/machine learning to determine the one or more scores for each piece of content. In one embodiment, the quality scoring system and method may incorporate source and industry score factors, use a transformer model to generate the quality score and adjust the quality score based on scenarios.