Novelty Score
AI-powered cancer detection in chest X-rays and mammography. FDA-cleared, CE-marked, deployed in 4000+ hospitals across 50+ countries.
31
Overall Novelty
Weighted score: how differentiated is this product's research?
48
Uniqueness
3.5 other products use the same papers on avg
39
Research Recency
Are the underlying papers recent (cutting-edge) or old (commoditized)?
0
Founder Authorship
Built on external research — execution-dependent
How to read this report
Novelty Score (0–100)
Measures how differentiated this product's technical approach is. Combines three signals: Uniqueness (40%) — fewer products on the same papers means a more unique approach. Research Recency (30%) — building on recent papers (2020+) suggests cutting-edge work; older papers (pre-2015) are more commoditized. Founder Authorship (30%) — if the founder authored the underlying papers, they have deep domain expertise and a technical moat.
Research Lineage
The academic papers this product builds on. Each link has a source type (who declared it: the maintainer, automated extraction from READMEs, community contribution, or AI detection) and a confidence score (0–100%). Higher confidence = stronger evidence.
Competitive Map
Other products that build on the same research papers. The overlap % shows what fraction of this product's papers are shared. 100% overlap = building on identical research. 10% = mostly different foundations.
Domain Trends
Are the domains this product operates in accelerating (more products being built recently), steady, or slowing? Based on the rate of new paper-to-product links over the last 30 and 90 days.
Paper Adoption Timeline
Shows when each product adopted each paper. If many products adopted the same paper recently, it's a trending technique. If only this product uses it, it's a differentiated bet.
Research Lineage (4 papers)
The academic papers this product builds on, with provenance
Competitive Map (13 products on same research)
Other products building on the same papers — higher overlap = more similar technical approach
MIT 6.S191 — Introduction to Deep Learning
by Alexander Amini · Cambridge
50%
2 shared papers
timm (PyTorch Image Models)
by Ross Wightman · Vancouver
25%
1 shared papers
Covariant Brain
by Peter Chen · Berkeley
25%
1 shared papers
Weights & Biases
by Lukas Biewald · Boston
25%
1 shared papers
Cleanlab
by Jeremy Nixon · Cambridge
25%
1 shared papers
Neurala Brain Builder
by Marc Pare · Boston
25%
1 shared papers
DataRobot
by Jeremy Fiance · Boston
25%
1 shared papers
Xnor.ai (acquired by Apple)
by Rory Finnegan · Seattle
25%
1 shared papers
Mighty AI (acquired by Uber)
by Max Friedman · Seattle
25%
1 shared papers
Machine Learning Mastery
by Jason Brownlee · Sydney
25%
1 shared papers
Domain Trends
Is this product's domain accelerating or cooling down? Based on new paper→product links over time
Paper Adoption Timeline
When did each product adopt each paper? Clustering = trending technique. Solo adoption = differentiated bet
Deep Residual Learning for Image Recognition
7 products built on this paper
Towards Deep Learning Models Resistant to Adversarial Attacks
3 products built on this paper
A Generative Model for Molecular Distance Geometry
1 product built on this paper
ImageNet: A Large-Scale Hierarchical Image Database
7 products built on this paper
About this report
Research lineage is based on builder-declared paper links with provenance tracking. Novelty scores are computed from paper uniqueness (fewer products = more novel), research recency, and founder authorship. Competitive maps show other products building on the same research papers. This is not investment advice.