Automated Semantic Information Processing for Content Items

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Solution Overview

Problem

Content provider services face the impracticality of manually identifying and tagging vast numbers of content items with semantic information due to the sheer volume of ever-increasing content, making it impossible to scale out manual semantic labeling effectively.

Innovation Solution

Automated processes are implemented to generate and utilize semantic information from multiple data sources, using authority scores and machine learning models to identify and process semantic information for content items, enabling real-time tagging and recommendation of relevant content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual semantic labeling is used for content items, then precision and accuracy of semantic information can be maintained, but productivity and scalability deteriorate due to the sheer volume of ever-increasing content

Engineering Contradiction:

Improveprecision of semantic informationVSAvoidproductivity of semantic labeling

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical semantic labeling with automated machine learning models and natural language processing systems. These systems use algorithms to extract semantic information from content items, metadata, and user interactions, substituting human cognitive work with computational processes that can scale to millions of content items while maintaining consistent precision through trained models.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables content items to effectively label themselves by automatically extracting semantic information from their own metadata, descriptions, and associated data. The machine learning models process content attributes and generate semantic tags without human intervention, allowing the content ecosystem to self-organize and self-describe at scale.

Inventive Principle:

Principle #25Self-service

2Productivity

If automated processes are implemented for semantic information generation, then productivity and scalability are improved, but measurement precision and manufacturing precision of semantic information may deteriorate

Engineering Contradiction:

Improveproductivity of semantic processingVSAvoidprecision of semantic tagging

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system incorporates feedback loops where user interactions, engagement metrics, and correction data are continuously fed back into the machine learning models. This allows the automated semantic processing system to learn from real-world usage patterns and improve precision over time, with user behavior serving as ground truth for validating and refining semantic annotations.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent combines multiple data sources and processing methods to create composite semantic information. Instead of relying on a single automated process, the system integrates metadata analysis, natural language processing, user behavior data, and engagement metrics to generate robust semantic tags that are more precise than any single source could provide alone.

Inventive Principle:

Principle #40Composite materials

3Measurement precision

If multiple data sources are processed to generate semantic information, then comprehensiveness and accuracy are improved, but device complexity and processing time increase

Engineering Contradiction:

Improveaccuracy of semantic informationVSAvoidcomplexity of processing system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the complex task of semantic information generation into distinct processing modules, each handling specific data sources or extraction tasks. The system divides semantic analysis into separate functions such as metadata processing, text analysis, user behavior interpretation, and tag generation, allowing each component to be optimized independently and processed in parallel.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary processing and filtering of data sources before comprehensive semantic analysis. Metadata and content attributes are pre-processed and validated in advance, with obvious semantic signals extracted early to guide subsequent more complex analysis, reducing the computational burden of processing all data sources in full detail.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250005382A1Automated identification and utilization of semantic information for content items

Publication Date: 2025.01.02 YAHOO ASSETS LLC

AI summary

One or more systems and/or methods for identifying and utilizing semantic information for content items are provided. Data is collected from data sources that provide information about content items. Authority scores are assigned to the data sources based upon authoritativeness of the data sources. The data from the data sources is processed to create candidate collections of semantic information. The authority scores are utilized to select semantic information for the content item from the candidate collections. A semantic-based action is performed using the semantic information.