Technology Title
Recommendation Engine
Recommendation Engine
Project Title
TechBridge: Explainable AI for Matching Lab Technologies to Industry Needs
TechBridge: Explainable AI for Matching Lab Technologies to Industry Needs
Category
Geoscience
Geoscience
Authors
alishertalking@gmail.com
alishertalking@gmail.com
Short Description
A web platform that uses explainable AI to match university technologies with industry needs and scores each one’s commercial readiness, speeding up tech transfer.
A web platform that uses explainable AI to match university technologies with industry needs and scores each one’s commercial readiness, speeding up tech transfer.
Long Description
Most university inventions never reach the market. Tech-transfer offices have few staff, and industry teams can’t search research output efficiently because papers, patents and technology listings use different vocabularies. The result is missed licensing deals and valuable research left unused. TechBridge is a web platform with two core capabilities: Semantic matching. Technology descriptions and industry challenges are converted into embeddings using open-source language models and ranked with a hybrid of semantic and keyword search. Every match comes with an explanation: the shared concepts, the supporting papers and patents, and a confidence score. This explainability is what makes the results trustworthy for non-technical users. Commercial readiness score. A transparent scoring model estimates Technology Readiness Level (TRL), patent landscape crowdedness, and market signals from public data. It outputs a one-page “commercial snapshot” for each technology. The system is built on an Angular front end, a Python API, and a vector-enabled PostgreSQL database. It ingests open data from OpenAlex (papers) and PatentsView (patents), plus technology listings from the platform itself. During the five-month build we will (a) build and index the dataset, (b) evaluate matching quality against a labeled test set, (c) run a pilot with real users (researchers and industry contacts), and (d) deliver a full business plan with a 10-year NPV. Success metrics: precision@10 of matches against expert-labeled pairs, share of matches users rate “relevant”, time saved versus manual search, and number of pilot users who contact a matched technology owner.
Most university inventions never reach the market. Tech-transfer offices have few staff, and industry teams can’t search research output efficiently because papers, patents and technology listings use different vocabularies. The result is missed licensing deals and valuable research left unused. TechBridge is a web platform with two core capabilities: Semantic matching. Technology descriptions and industry challenges are converted into embeddings using open-source language models and ranked with a hybrid of semantic and keyword search. Every match comes with an explanation: the shared concepts, the supporting papers and patents, and a confidence score. This explainability is what makes the results trustworthy for non-technical users. Commercial readiness score. A transparent scoring model estimates Technology Readiness Level (TRL), patent landscape crowdedness, and market signals from public data. It outputs a one-page “commercial snapshot” for each technology. The system is built on an Angular front end, a Python API, and a vector-enabled PostgreSQL database. It ingests open data from OpenAlex (papers) and PatentsView (patents), plus technology listings from the platform itself. During the five-month build we will (a) build and index the dataset, (b) evaluate matching quality against a labeled test set, (c) run a pilot with real users (researchers and industry contacts), and (d) deliver a full business plan with a 10-year NPV. Success metrics: precision@10 of matches against expert-labeled pairs, share of matches users rate “relevant”, time saved versus manual search, and number of pilot users who contact a matched technology owner.
Potential Applications
University and institute tech-transfer offices; corporate R&D scouting and open innovation teams; startups looking for licensable technology; investors and accelerators screening deep-tech; research funders tracking commercialization potential; and open science platforms such as this one to connect posted projects with partners and sponsors.
University and institute tech-transfer offices; corporate R&D scouting and open innovation teams; startups looking for licensable technology; investors and accelerators screening deep-tech; research funders tracking commercialization potential; and open science platforms such as this one to connect posted projects with partners and sponsors.
Open Questions
How accurately can embeddings match a research description to an industry problem stated in business language?
Can TRL and commercial readiness be estimated reliably from public text alone?
What explanation format builds the most trust with non-technical users?
How do we avoid bias toward well-funded institutions or English-language papers?
What do tech-transfer offices pay, and is it a subscription, per-match fee, or success fee?
How accurately can embeddings match a research description to an industry problem stated in business language?
Can TRL and commercial readiness be estimated reliably from public text alone?
What explanation format builds the most trust with non-technical users?
How do we avoid bias toward well-funded institutions or English-language papers?
What do tech-transfer offices pay, and is it a subscription, per-match fee, or success fee?
AI Assistant
TechBridge uses explainable AI to align lab technologies with industry needs. It identifies industry requirements, catalogs lab innovations, develops transparent AI models, and tests them in real-world scenarios. The initiative fosters collaboration between research institutions and industry, promoting innovation and economic growth.
TechBridge uses explainable AI to align lab technologies with industry needs. It identifies industry requirements, catalogs lab innovations, develops transparent AI models, and tests them in real-world scenarios. The initiative fosters collaboration between research institutions and industry, promoting innovation and economic growth.
Keywords
technology transfer, recommendation engine, semantic search, explainable AI, commercialization
technology transfer, recommendation engine, semantic search, explainable AI, commercialization
Email
alishertalking@gmail.com
alishertalking@gmail.com