Form Structure Hierarchy Anaphora Resolution via NLP
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Solution Overview
Problem
Existing systems face challenges in identifying unchecked criteria within forms, particularly in unstructured and semi-structured data, due to difficulties in discerning completeness, hierarchical structure, and checkbox orientation, which can lead to incomplete or inaccurate data processing.
Innovation Solution
The method employs Natural Language Processing (NLP) to convert unstructured text into hierarchical form structure elements, applying analytic analysis to identify implicit selections and using hierarchy metadata to validate input data and disambiguate checkmarks, thereby reassembling the form's hierarchy for accurate anaphora resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Natural Language Processing is applied to unstructured form data to identify form elements, then the ability to process complex form structures is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the form processing task into distinct stages: NLP processing to identify form elements, analytic analysis to determine hierarchical structure, and rules-based processing to identify implicit selections. This segmentation allows each component to specialize in specific aspects of form processing, improving overall capability while managing system complexity through modular design.
Solution Approach 2:
The patent introduces hierarchy metadata as an intermediary structure that bridges unstructured form data and structured processing requirements. This metadata layer captures hierarchical relationships, element placement, and orientation information, serving as a mediator that enables complex form processing without requiring the entire system to handle all complexity simultaneously.
2Measurement precision
If hierarchical structure analysis is applied to identify form elements, then the accuracy of form data interpretation is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary NLP processing and hierarchical structure analysis before final form data interpretation. By establishing the hierarchical structure and element relationships in advance, the system creates a structured framework that accelerates subsequent processing and validation steps, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent applies rules-based processing to identify implicit selections based on partial information from the hierarchical structure. Rather than analyzing every possible relationship in detail, the system uses targeted rules to identify the most critical implicit selections, achieving sufficient accuracy without exhaustive analysis that would increase processing time.
3Reliability
If rules-based processing is applied to identify implicit selections, then the completeness of form data is improved, but the complexity of processing logic increases
Solution Approach 1:
The patent applies different processing rules to different regions and types of form elements based on their specific characteristics. Rather than using a single complex rule set for all elements, the system tailors processing logic to local requirements, such as applying specific rules for checkbox orientations, hierarchical relationships, and element types, thereby improving completeness while managing overall logic complexity.
Solution Approach 2:
The patent changes processing parameters dynamically based on the form structure and element characteristics. The system adjusts which rules are applied, the threshold for identifying implicit selections, and the depth of analysis based on local form properties, enabling comprehensive processing without requiring a static complex rule set for all scenarios.
4Measurement precision
If hierarchy metadata is used to disambiguate checkmarks, then the accuracy of unchecked criteria identification is improved, but the data processing complexity increases
Solution Approach 1:
The patent uses hierarchy metadata as an intermediary structure that disambiguates checkmark meanings without requiring complex processing logic. The metadata captures orientation, hierarchical position, and element type information, allowing the system to interpret checkmarks accurately by querying this pre-processed metadata rather than analyzing raw form data directly, thereby improving accuracy while managing complexity.
Data Source
AI summary
A method, system and computer-usable medium are disclosed for identifying unchecked criteria within a form. Natural Language Processing (NLP) is applied to unstructured data within a target form to identify elements of a form structure. Analytic analysis is then applied to the resulting form structure elements to identify a hierarchical structure and associated element placement. Implicit selections within the form are then identified by applying rules based upon other selections and their orientation to anchor terms to determine the completeness of the form, based upon aggregation of form elements. The form structure elements and the hierarchy metadata are then processed logically reassemble the form's hierarchy in flattened forms for multi-layer, sub-element anaphora resolution.