Automated Gap Clustering for Product Roadmap Alignment
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Solution Overview
Problem
PreSales teams face challenges in identifying and prioritizing Opportunity Gaps and Account Gaps due to a lack of efficient computer-based methods for making actionable inferences and forming unbiased recommendations, leading to misalignment between PreSales and Product teams on product roadmaps.
Innovation Solution
A computer-implemented system using natural language processing (NLP) to programmatically associate Opportunity Gaps or Account Gaps with existing Product Gaps, employing techniques like vectorization, similarity metric analysis, and clustering to generate new digital data relationships and create actionable insights for product prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes are used to capture and analyze customer feedback, then human judgment and flexibility are maintained, but the process becomes manually intensive and noisy, resulting in missed opportunities and lack of alignment
Solution Approach 1:
The patent replaces manual mechanical processes with an automated computer-based system that uses natural language processing, vectorization, and machine learning algorithms to analyze customer feedback, opportunity gaps, and account gaps, thereby eliminating manual intensity while maintaining or improving analysis quality
Solution Approach 2:
The system enables self-service by automatically processing and analyzing feedback data without requiring manual intervention, using automated clustering and association algorithms to generate insights that would otherwise require extensive human effort to compile and analyze
2Productivity
If computer-based methods are implemented to automate feedback analysis, then productivity and alignment are improved, but the complexity of the system increases
Solution Approach 1:
The patent creates a universal platform that handles multiple functions including feedback collection, natural language processing, vectorization, clustering, and association analysis within a single integrated system, reducing the need for multiple separate tools and processes
Solution Approach 2:
The system introduces intermediate processing layers including vectorization of text data and similarity metric calculations that bridge the gap between raw unstructured feedback and structured insights, managing complexity through modular intermediate steps
3Measurement precision
If human analysts manually determine similarity of gaps and generate recommendations, then nuanced judgment is applied, but the process is slow and prone to bias
Solution Approach 1:
The patent transforms unstructured text data into structured vector representations, changing the parameter space from textual similarity (hard to measure) to vector space distance (easily quantifiable), enabling rapid and objective similarity assessments through mathematical operations
Solution Approach 2:
The system replaces human judgment with automated natural language processing and machine learning models that objectively assess similarity based on vector representations, eliminating bias and acceleration the analysis process while maintaining or improving precision
4Loss of information
If extensive data from multiple systems is collected to improve decision-making, then more comprehensive insights are gained, but the noise and difficulty of sorting through duplicate requests increases
Solution Approach 1:
The patent merges data from multiple disparate systems into a unified analysis platform, consolidating opportunity gaps, account gaps, and customer feedback into a single coherent dataset that can be processed together, eliminating the need to separately manage multiple data sources
Solution Approach 2:
The system extracts meaningful signals from noisy data by using natural language processing to identify and isolate key information from unstructured feedback, separating relevant insights from duplicate requests and irrelevant noise through automated filtering and clustering
Data Source
AI summary
An example computer-implemented method embodying the disclosed technology comprises digitally storing a plurality of digital objects comprising first type digital objects and second type digital objects, each digital object comprising an electronic digital representation of natural language text, and certain first type digital objects each being associated with exactly one second type digital object; programmatically generating a corpus of digital documents from the plurality of digital objects; programmatically embedding the electronic digital representation of natural language text of each first type digital object not associated with one of the second type digital objects, thereby generating a second set of vectors; programmatically determining, for each of the second set of vectors, a corresponding set of nearest neighbor vectors from the first set of vectors; programmatically generating recommendation data indicating potential new associations between first type digital objects and second type digital objects; and displaying a representation of the recommendation data.