Contextual Linguistic AI for DuPont Similarity Analysis
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Solution Overview
Problem
Existing linguistic tools struggle with inefficiencies in analyzing strings of characters due to varying terminologies and contextual ambiguities, leading to inaccurate and irrelevant product categorization and similarity assessments.
Innovation Solution
A method and system utilizing a neural network and deep learning algorithms to analyze contextual relevance of keywords on products and web pages, coupled with linguistic AI, to determine semantic similarity and generate arguments on likelihood of confusion based on DuPont factors, supported by a trusted authority database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional textual string and character-matching algorithms are used to analyze product identifiers, then the analysis process is simple and fast, but the accuracy of product categorization and similarity identification deteriorates due to varying terminologies and contextual ambiguities
Solution Approach 1:
The patent introduces an intermediary linguistic analysis system that includes a script identifier, language model, and contextual analysis module. This intermediary layer processes the alphanumeric string through multiple stages: identifying the script type, selecting appropriate language models, analyzing contextual relevancy from photographs and descriptions, and determining semantic meaning. This complex intermediary system resolves the contradiction by enabling accurate categorization despite varying terminologies and contextual ambiguities, while managing complexity through modular architecture.
Solution Approach 2:
The linguistic analysis system is segmented into distinct functional modules: script identification module, language model selection module, contextual analysis module (processing photographs, descriptions, and screenshots separately), and semantic meaning determination module. Each module handles a specific aspect of the analysis, allowing the system to process complex linguistic data through manageable segments rather than a monolithic algorithm.
2Measurement precision
If multiple contextual sources (photographs, descriptions, screenshots) are analyzed to determine semantic meaning, then the accuracy of keyword association improves, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by first identifying the script type and selecting appropriate language models before conducting full contextual analysis. It also pre-processes multiple contextual sources (photographs, descriptions, screenshots) in parallel, extracting relevant features beforehand. This preliminary processing reduces the computational burden during the final semantic determination stage, mitigating the time loss despite comprehensive analysis.
Solution Approach 2:
The patent applies partial action by analyzing only the most relevant contextual sources based on the specific case. The system can selectively process photographs, descriptions, and screenshots depending on their availability and relevance, rather than always processing all possible sources. This approach maintains high accuracy while reducing unnecessary computational overhead and time consumption.
3Reliability
If neural networks and deep learning algorithms are used to verify photograph authenticity and analyze contextual relevancy, then the reliability of product identification improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent introduces intermediary verification modules that act as mediators between the raw photograph data and the final product identification. These modules include neural network-based authenticity verification and contextual relevancy analysis components that process photograph data through multiple intermediate stages, extracting meaningful features while filtering out noise and manipulation artifacts. This intermediary processing enhances reliability while managing complexity through specialized verification layers.
Data Source
AI summary
Disclosed are a method and/or a system of linguistic analysis to automatically generate a hypothetical likelihood of confusion office action using Dupont factors. The method associates a first keyword formed with an alphanumeric string of characters in a first written script with a semantic meaning based on an analysis of the contextual relevancy of the first keyword affixed on an article of manufacture, a description of the article of manufacture, and a web page screenshot bearing the first keyword. The method compares the first keyword with a second keyword with a known semantic meaning as documented in a trusted authority and applying a linguistic artificial intelligence algorithm to determine which of a set of comparative rules form a basis of an arguable similarity. Further, the method automatically drafts a proposed argument in issue, rule, application, and conclusion format to support a position on the similarity between the semantic inference.