Concept-Based Graphical Inference Screening for Copyright Similarity
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Solution Overview
Problem
Generative inference models often generate inferences that are substantially similar to reference works, leading to issues such as copyright infringement and plagiarism, which are difficult to measure due to the unstructured nature of the data, posing compliance risks.
Innovation Solution
Analyze graphical inferences using structured representations based on concepts like human interpretable objects and stylistic elements, comparing them with structured representations of reference works to quantify similarity and manage potential noncompliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative inference models are used to create graphical inferences, then productivity and service capability are improved, but the risk of copyright infringement and plagiarism increases due to unmeasurable similarity with reference works
Solution Approach 1:
The patent introduces structured representations as an intermediary layer between the generative inference model and reference works. These structured representations extract and organize key features, concepts, and attributes from graphical inferences, enabling systematic comparison with reference works to detect potential copyright infringement and plagiarism risks
Solution Approach 2:
The patent replaces subjective human assessment of similarity with an automated computational system. The structured representation system automatically extracts features, compares them against reference works, and identifies compliance issues, substituting manual evaluation with machine-based analysis
2Measurement precision
If unstructured graphical data is analyzed directly, then measurement of similarity is difficult, but structuring the data increases complexity of the analysis system
Solution Approach 1:
The patent segments the complex task of similarity measurement into distinct components: extracting structured representations from graphical inferences, storing reference works in structured format, and comparing specific features between them. This segmentation transforms an intractable problem into manageable steps with improved measurement precision
Solution Approach 2:
The patent changes the parameters of data representation from unstructured graphical formats to structured formats with defined schemas. By transforming the data into organized representations with specific attributes and relationships, the system enables precise similarity measurement while managing complexity through standardized parameter structures
Data Source
AI summary
Methods and systems for managing a generative inference model are disclosed. Using the generative inference model and ingest data, a graphical inference may be obtained. A structured representation for the graphical inference may be populated based on objects and/or stylistic elements displayed by the graphical inference, and may indicate (e.g., when compared to structured representations for the reference works) whether the graphical inference exceeds a predetermined level of similarity with respect to the reference works. If the inference exceeds the predetermined level of similarity, performance of an action set may be initiated to manage an impact of similarities between the graphical inference and the reference works.