NLP Syntactic Map for Financial Document QA

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Solution Overview

Problem

Conventional natural language processing (NLP) systems struggle to effectively understand and provide context for terms and numerical data in lengthy financial, legal, and corporate policy documents, leading to difficulties in extracting meaningful insights.

Innovation Solution

A system employing novel NLP techniques, including a Question-Answer model and syntactic maps, to parse, summarize, and generate question-answer pairs around key terms and numerical values within documents, thereby improving context understanding and insight extraction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional NLP techniques are used to process documents, then the system can handle basic text processing, but it cannot effectively understand and provide context for terms and numerical data in lengthy financial, legal, and corporate policy documents

Engineering Contradiction:

Improvecontext understanding accuracyVSAvoidability to handle specialized document types

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent applies local quality by implementing specialized processing rules for different document types and data elements. The system identifies specific sections, terms, and numerical data within documents and applies targeted NLP techniques tailored to each context, such as financial terminology processing for 10-K forms or legal term analysis for contracts, rather than using uniform processing across all content.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system changes parameters by adjusting NLP model configurations and processing thresholds based on document type and content characteristics. It dynamically modifies extraction criteria, relation detection sensitivity, and summarization parameters to optimize performance for specific document categories containing terms and numerical data.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If NLP techniques use relations (verbs in sentences) to understand context, then the system can process general text, but it cannot connect terms and numerical values to proper nouns in sentences containing certain terms and numerical data

Engineering Contradiction:

Improvecontext connection accuracyVSAvoidterm-noun association detection

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent introduces intermediary entities and relationship types as mediators between terms/numerical values and proper nouns. It employs intermediate representation layers that capture semantic relationships, such as financial metrics linking to company names, or legal terms connecting to contract parties, enabling indirect but accurate context association through structured relationship graphs.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments sentences into distinct functional components: term identification, numerical value extraction, proper noun detection, and relationship classification. This segmentation allows independent processing of each element type with specialized algorithms, improving the detection of associations between terms/numbers and their contextual proper nouns.

Inventive Principle:

Principle #1Segmentation

3Loss of information

If the system processes documents to extract meaningful insights, then it can provide comprehensive analysis, but it becomes time consuming for individuals to parse through these documents

Engineering Contradiction:

Improveinformation extraction completenessVSAvoiddocument parsing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent extracts only the most relevant information elements from lengthy documents, such as key financial metrics, critical legal terms, or important policy provisions, rather than processing every sentence uniformly. It identifies and extracts high-value data points and relationships, presenting condensed insights that maintain information completeness while dramatically reducing processing time and user effort.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system applies partial action by focusing computational resources on critical document sections and relationships most likely to contain meaningful insights. It uses heuristic filtering to identify high-priority areas for detailed analysis while applying lighter processing to less critical content, optimizing the balance between thoroughness and efficiency.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12333246B1Automated question-answer generation system for documents

Publication Date: 2025.06.17 AMERICAN EXPRESS (INDIA) PTE LTD

AI summary

A system and method for generating question-answer pairs is disclosed. The system and method can receive a document. A sentence and/or a further sentence in the document may be identified. A syntactic map for the sentence and/or the further sentence may be generated. Noun phrases and prepositional phrases may be identified based on the syntactic map. Sentence level questions may be generated based on phrases identified using natural language processing (NLP) techniques. Document level questions can also be generated based on syntactic maps generated and NLP techniques.