For university tech transfer offices handling a steady flow of invention disclosures, the real challenge begins after submission.

Let me elaborate. You see, when disclosures start coming in at scale, each one requisites a timely response. Not every submission points toward a patentable invention. Some may be better suited for other forms of IP too. Others may need refinement before any meaningful decision can be made. 

Yet delaying feedback or letting disclosures sit unanswered creates its own risk. Faculty and students lose confidence in the evaluation process, and inventor momentum drops.

At the same time, running full patentability searches on every disclosure is not realistic. When outsourced to external consultants, costs add up quickly. Relying solely on internal, keyword-based novelty searches can be tedious and add to the review load.

This was the situation one university tech transfer office was dealing with when they reached out to us. They needed a way to screen disclosures earlier, assess novelty, and decide what merited deeper external review, without slowing the process or losing momentum. 

Where the Existing Evaluation Process Started to Strain For The University TTO

This university’s tech transfer office was handling dozens of invention disclosures monthly with a relatively small internal team. The team was expected to review submissions across diverse technical domains, assess early novelty, and decide what merited deeper attention. 

While early searches were already part of the evaluation process, the real bottleneck was volume. As the university encouraged broader participation in innovation programs, the number of invention disclosures increased significantly.

Moreover, manual or keyword-based searches increased review time per disclosure, and the backlog continued to grow. This also affected inventor engagement, as feedback cycles became longer and less predictable.

During this period, the university explored newer AI-based patent search tools and tested several options, including PQAI. While these patent search tools proved useful, the underlying issue remained.

They did not need just another tool. They wanted early screening to happen inside their existing workflow. A way for their officers to run quick, directional prior-art checks, escalate strong disclosures with context to external counsel, and share references when ideas were not novel with inventors. They wanted to do this without switching systems or adding friction.

Why an API-Based Approach Fits the TTO Workflow

At this stage, the university was not looking to overhaul its existing invention disclosure system. What the team wanted was for the existing system to do more work once the disclosure arrived.

They needed early screening to happen as part of the same workflow for the TTOs, without switching tools or re-entering context.

Moreover, building a full prior-art search capability internally was not realistic. Maintaining search infrastructure, keeping patent data current, and tuning relevance models would have shifted focus away from the TTO’s actual role: evaluating inventions and guiding next steps.

An API-based approach offered a cleaner path.

By integrating a prior-art search API directly into their existing platform, early novelty checks could be triggered automatically when disclosures entered review. The disclosure text itself became the input. Moreover, the results would become part of the same record. That was the model they were looking for.

Why the PQAI Patent Search API Made Sense In Their Situation

When evaluating existing API options, the university’s team focused on one core question: Would it support early screening decisions inside the workflow without adding friction?

PQAI’s Patent Search API aligned closely with the TTO’s requirement:

• Natural-language input from disclosures: The API accepts plain-language technical descriptions, allowing invention disclosures to be used directly for early screening without translating them into complex search logic.
• Semantic prior-art discovery:  Instead of relying on surface-level term matching, the API surfaces prior art based on conceptual similarity, helping licensing officers see related work even when different terminology is used.
• Technical context alongside results: Relevant CPC codes and technical concepts are returned with the results, giving reviewers a clearer sense of how an invention maps to existing technology areas.
• Structured, reusable outputs:  Prior-art results are returned in a structured format that can be attached to disclosure records, shared internally, or passed along when escalating a case for external review.
• API-first, workflow-agnostic design: The API does not impose interface or process assumptions. The university could decide when searches run, how results appear, and how they inform internal decisions.

With this setup, early screening became a built-in capability rather than an extra step. 

What Changed After Early Screening Was Embedded

Once early prior-art screening became part of the disclosure workflow, the impact was immediate and practical.

The first visible change was speed. Because disclosures could be screened using their existing technical descriptions, review cycles shortened significantly. TTOs were no longer spending time setting up searches or switching contexts. As a result, the team was able to review 30% more invention disclosures within the same timeframe as before.

The process itself also became tighter. Early screening became a defined part of evaluation, with prior-art context attached directly to each disclosure forwarded to external counsel. This made it easy for outside counsel too who did not need to re-orient from scratch, and fewer clarification cycles were required.

Just as importantly, inventor momentum was preserved. Disclosures no longer sat idle pending review. When ideas showed promise, next steps were triggered without delay.

Overall, early screening shifted from being a bottleneck to becoming a facilitator. The TTO moved faster, handled higher volume, and maintained consistency, without adding friction to the process.

Why PQAI Works Well for University Tech Transfer Offices

For university TTOs, early screening only works if the underlying system is trusted. The decisions that are made at this stage affect which disclosures move forward, which are refined, and which are paused. That makes transparency and control essential.

PQAI’s Patent Search API is built on an open-source foundation, which means there is no black-box logic behind screening outcomes. Teams can understand what is happening, how results are generated, and how searches behave over time. The API is also designed for confidential, pre-publication research, with strict data handling and no reuse of queries.If your office is evaluating how to embed early prior-art screening into its existing infrastructure, we support implementation as well. You can contact our team to get started.