Automated Disputed Claim Identification in Electronic Content
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Solution Overview
Problem
The vast amount of electronic content items on the internet makes it difficult for users to identify and analyze disputed claims, as they may not be aware of the disputed nature of claims made in other content items, and determining the prevalence of disputed claims across numerous sources is challenging.
Innovation Solution
A method that extracts claims from electronic content items using Natural Language Processing (NLP) algorithms to identify disputed claims, determines the supporting and disputing entities, and stores this information in a repository, allowing users to receive filtered results based on analysis parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting and analysis of electronic content items is performed, then users can identify disputed claims, but the time and effort required becomes prohibitively large when dealing with millions of content items
Solution Approach 1:
The patent replaces manual mechanical analysis of content items with an automated computer-based system that uses natural language processing algorithms to extract and analyze claims. This substitution of automated computational methods for human manual work resolves the contradiction by maintaining accurate identification of disputed claims while eliminating the prohibitively large time investment required for manual sorting of millions of content items.
Solution Approach 2:
The patent introduces an intermediary automated processing system that acts as a mediator between the vast corpus of electronic content items and the user. This intermediary system automatically extracts claims, identifies disputes, and presents results to users, thereby resolving the contradiction by handling the time-consuming analysis task automatically while preserving the accuracy of disputed claim identification.
2Productivity
If automated NLP algorithms are used to extract claims from electronic content items, then the time required for analysis is reduced, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex task of disputed claim identification into distinct modular components: (1) retrieving electronic content items, (2) extracting claims using NLP algorithms, (3) identifying disputed claims, (4) presenting results to users. This segmentation resolves the contradiction by organizing system complexity into manageable, independent modules that can be developed and maintained separately, thereby enabling high productivity through automation while controlling overall system complexity.
Solution Approach 2:
The patent creates a multi-functional automated system that performs multiple tasks: retrieving content items from various sources, extracting claims using NLP, identifying disputed claims through comparison, and presenting results in user-friendly formats. This universal system resolves the contradiction by consolidating multiple functions into a single integrated platform, achieving high productivity across diverse content types while managing complexity through unified architecture.
3Loss of information
If all claims across millions of content items are analyzed, then comprehensive identification of disputed claims is achieved, but the computational resources and processing time required become unsustainable
Solution Approach 1:
The patent applies preliminary action by implementing efficient claim extraction and comparison algorithms that identify potentially disputed claims early in the processing pipeline. The system uses NLP to extract claims and immediately compares them against previously extracted claims to identify disputes, rather than processing all content items sequentially. This resolves the contradiction by achieving comprehensive disputed claim coverage while reducing computational burden through early identification and efficient processing strategies.
Solution Approach 2:
The patent implements partial action by focusing computational resources specifically on extracting and comparing claims rather than analyzing all content uniformly. The system applies excessive action by using sophisticated NLP algorithms and multiple comparison passes to ensure no disputed claims are missed. This resolves the contradiction by concentrating processing power on the critical task of claim identification and comparison, achieving comprehensive coverage of disputed claims while optimizing resource utilization.
Data Source
AI summary
The technology disclosed herein identifies claims made in electronic content items that are disputed. In a particular implementation, a method provides extracting first claims from language in a set of electronic content items and determining that a disputed claim of the first claims is disputed by one or more disputing entities. The method further includes storing claim information about the disputed claim in a repository. The claim information indicates the disputed claim, one or more supporting entities that support the disputed claim, and the disputing entities. The method also includes receiving analysis parameters from a user, wherein the claim information satisfies the analysis parameters. In response to receiving the analysis parameters, the method includes presenting at least a portion of the claim information to the user.