Abstract

Over 90 million scientific papers form the foundation of modern innovation, yet much of this potential remains untapped. Tech2Transfer bridges the gap between cutting-edge scientific research and real-world market applications. By leveraging advanced Large Language Models (LLMs) to systematically assess this massive corpus of literature, Tech2Transfer enables researchers, investors, and industry partners to quickly identify and commercialize breakthrough innovations. We are committed to accelerating global science and technology.

1. Introduction

The transition from research to market remains one of the greatest challenges in innovation. Universities and research institutions generate groundbreaking discoveries, yet many of these innovations never reach commercial viability due to inefficiencies in evaluation, communication, and funding. Tech2Transfer provides an AI-powered solution to bridge this gap, making it easier to assess research potential, connect stakeholders, and accelerate commercialization.

The Problem

Despite the vast number of research projects being developed globally, only a fraction successfully transition into viable products or services. The primary challenges include:

• Lack of visibility: Researchers struggle to showcase their work.
• Complex evaluation processes: Assessing commercial potential requires expertise.
• Funding barriers: Many projects fail to secure necessary funding.
• Inefficient matchmaking: Slow, outdated connections between researchers and investors.

Tech2Transfer Solution

AI-Powered Research Analysis

Our platform integrates machine learning models that evaluate research papers, patents, and technical reports to determine commercial viability, intellectual property (IP) strength, market potential, and scalability.

• Automated technology assessment: AI-driven analysis of key innovation factors.
• Patent landscape analysis: Identification of existing IP challenges.
• Market potential evaluation: Data-driven insights into scalability.

2. AI Agent Design

2.1: Input & Parsing

Document Ingestion

• Manuscripts are uploaded in PDF via a web interface or API.
• Each document is assigned a paper_id for traceability.

Text Extraction

• Library pdf-parse converts the manuscript into raw text.
• Each document is assigned a paper_id for traceability.

2.2: Chunking & Embedding (RAG Approach)

1. Chunking

• Text is split into segments (often ~1,000 tokens each) based on paragraphs, sections, or page boundaries.
• Each chunk is stored with metadata, including page range, section header, and paper_id.

Vector Embedding & Indexing

• Each chunk is embedded using the Sentence-BERT model.
• The resulting embeddings are stored in a vector database for fast similarity searches.

Retrieval

• When the AI needs to perform a specific check, it creates an embedded query.
• Relevant chunks are retrieved via similarity search.
• The top N chunks form a context prompt, which is then fed into the LLM for focused analysis.

This approach maximizes accuracy (by including only relevant text) and efficiency (by avoiding the cost of processing an entire document at once).

3. Roadmap

Phase 1: Platform Development

• AI model training for research assessment.
• Beta launch with initial university and investor partnerships.

Phase 2: Expansion

• Investors & Venture Capitalists: Seeking promising startups.
• Industry Partners: Looking for cutting-edge innovations.
• Licensing Opportunities: Connecting R&D teams worldwide.

Expansion into European and North American research and Tech Transfer institutions.

4. Conclusion

Tech2Transfer is supporting the technology transfer process by leveraging AI and automation. By enhancing research visibility and accelerating commercialization, Tech2Transfer aims to drive global innovation forward.

Join us in transforming the future of technology transfer.