Introduction
A leading global technology company was struggling with the complexity of its growing patent portfolio. Conducting prior art searches had become a resource-intensive task, often requiring 40–50 hours of analyst time per invalidity search. The traditional approach depended heavily on manual reading, subjective interpretation, and laborious data collection. As filing volumes increased and timelines tightened, the risk of delays and overlooked references grew.
Recognizing the need for a more scalable and reliable solution, the organization turned to Evalueserve IP and R&D. Together, we built an AI-assisted prior art search framework designed to combine automation with human expertise—accelerating discovery while maintaining accuracy and depth.
Diagnose: Pinpointing the Sources of Inefficiency
Multiple structural bottlenecks burdened the client's prior art workflow:
- Patent comprehension was slow. Analysts had to dissect each document carefully, extracting technical features one by one before meaningful searching could even begin.
- Query formulation lacked consistency. Building effective database queries relied on individual intuition, often missing alternative expressions or technical synonyms.
- Identifying relevant prior art was overwhelming. Sifting through thousands of results consumed most of the search time, increasing the risk of human error or oversight.
- Reporting processes drained capacity. Formatting, applying client-specific templates, and performing quality checks required hours of repetitive work.
This fragmented process created a trade-off: either spend more time to ensure thoroughness or cut corners to meet deadlines. Neither option aligned with the client's need for reliable, repeatable, and timely IP insights.
Design: Creating an AI-Augmented Search Framework
Evalueserve designed a modular, AI-powered workflow that restructured the search process from end to end. Instead of replacing human judgment, the framework amplified analyst capability by automating the most time-consuming and error-prone tasks. This allowed analysts to focus on tasks that require human expertise, such as refining AI-generated queries and interpreting the results, ensuring that the process benefits from the best of both AI and human intelligence.
Patent Understanding
AI engines generated concise summaries and extracted key invention features, providing analysts with an immediate grasp of the patent’s technical scope. This step reduced the need for lengthy manual reading and ensured that searches started with a solid foundation.
Query Builder
Automated query generation expanded search coverage by incorporating synonyms, related terminology, and multiple database formats. Analysts could then refine and validate these AI-generated strings, ensuring both breadth and precision in data collection.
Prior Art Identification
Through embedding-based similarity models, the system compared extracted features with thousands of documents. Instead of manually combing through results, analysts received a ranked list of high-likelihood references with highlighted snippets, making validation faster and more consistent.
Report Preparation
Evalueserve introduced automated templates and a structured QC checklist. This step not only reduced formatting and review time but also ensured that reports met client-specific standards without unnecessary rework.
Design: Creating an AI-Augmented Search Framework
With the new workflow in place, the client saw a measurable transformation:
- Patent understanding time dropped from 6–8 hours to 3–4 hours.
- Query building, previously a 2–4 hours task, was reduced to 1–2 hours.
- Identifying top prior art references, which had taken 25–30 hours, now required 20–25 hours.
- Report preparation time decreased from 6–8 hours to 5–6 hours.
Overall, the company achieved a 20–25% reduction in effort per search, translating to significant cost savings. This freed analyst time for higher-value tasks such as deeper analysis and strategic IP recommendations. With over 1,000 AI-assisted searches conducted across 50+ clients and supported by 70 trained analysts, the framework has proven its ability to scale across industries and geographies, delivering substantial return on investment.
Impact: A Smarter Path to IP Decision-Making
The shift to an AI-assisted framework enabled the client to:
✅ Enhance the quality of prior art searches through broader coverage and intelligent similarity matching.
✅ Accelerate turnaround times while maintaining confidence in results.
✅ Reduce dependence on manual processes and minimize the risk of missed references.
✅ Establish a replicable search model that can evolve alongside new technologies and growing portfolios.
Conclusion
By reimagining the prior art search process, Evalueserve helped the client move beyond the limitations of traditional methods. Instead of being a time-consuming bottleneck, prior art search became a strategic enabler of faster, more informed IP decisions.
This case demonstrates how AI, when combined with domain expertise, does more than save time—it builds a foundation for scalable, future-ready IP operations. With Evalueserve's support, the client can now focus on shaping its innovation pipeline with confidence, knowing that prior art searches are no longer a barrier but a catalyst for progress.
