RFP Response Generation Using NLP and Logical Sub-trees

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Solution Overview

Problem

Conventional systems for generating responses to Request For Proposal (RFP) documents are inefficient due to manual intervention, which leads to errors and increased time consumption, and existing automated systems fail to effectively handle domain-specific aspects and customer parameters, resulting in suboptimal response generation.

Innovation Solution

A processor-implemented method and system that uses Natural Language Processing (NLP) to extract requirements from RFP documents, generate logical sub-trees, identify Words of Interest, and apply a matching algorithm to create search queries that satisfy domain-specific parameters, thereby searching a reference solution database to compose accurate and efficient response documents.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual intervention is used for RFP response preparation, then flexibility and adaptability are maintained, but time consumption and error rates increase

Engineering Contradiction:

Improveresponse accuracyVSAvoidresponse preparation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system segments the RFP response generation process into distinct modules: requirement extraction from RFP documents, logical sub-tree generation, Words of Interest extraction, matching algorithm application, and response composition. Each module handles a specific aspect of the process, enabling automated processing while maintaining accuracy through structured analysis of domain-specific parameters.

Inventive Principle:

Principle #1Segmentation

2Extent of automation

If existing automated systems use similarity-based approach, then automation extent is increased, but domain-specific precision deteriorates

Engineering Contradiction:

Improveresponse generation automationVSAvoiddomain-specific matching accuracy

Core Design Contradiction:

Extent of automationVSMeasurement precision

Solution Approach 1:

The system applies local quality by focusing the matching algorithm specifically on domain-specific parameters and Words of Interest extracted from logical sub-trees. Rather than general similarity matching, the algorithm prioritizes precise matching of technical requirements, concerns, and K-types that are critical to the domain, thereby maintaining high precision while achieving full automation.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system transforms the matching approach by changing from general text similarity to parameter-based matching. It extracts specific parameters (concerns, K-types, Words of Interest) from the RFP document and uses these as matching criteria, thereby improving domain-specific precision while maintaining automated processing capability.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If user intervention is required at different processing stages, then response customization is improved, but overall efficiency deteriorates

Engineering Contradiction:

Improveresponse customizationVSAvoidresponse generation efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system implements self-service by automatically performing all processing stages without user intervention. It autonomously extracts requirements, generates logical sub-trees, identifies Words of Interest, applies matching algorithms, and composes responses. The system adapts to different RFP types, business units, geographies, and proposal offerings automatically, maintaining both customization and high efficiency.

Inventive Principle:

Principle #25Self-service

4Device complexity

If existing systems only suggest matching responses, then processing simplicity is maintained, but response completeness deteriorates

Engineering Contradiction:

Improvesystem processing complexityVSAvoidresponse completeness

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The system performs preliminary action by proactively analyzing the entire RFP document structure, extracting all requirements, and generating comprehensive logical sub-trees before the matching process. This preliminary analysis ensures that all relevant information is captured and considered, enabling the system to compose complete responses that address all RFP requirements rather than just suggesting partial matches.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10810643B2Method and system for request for proposal (RFP) response generation

Publication Date: 2020.10.20 TATA CONSULTANCY SERVICES LTD

AI summary

This disclosure relates generally to document analysis, and more particularly to a method and system for Request For Proposal (RFP) response generation. In one embodiment, the system automatically generates at least one search query in response to a RFP received as input, searches in at least one reference solution database using the generated search query, finds matching data, filters the matching data based on RFP parameter specific data, and prepares a response document. The response document is then provided as an output of the system, in response to the RFP collected as input.