A powerful new foundation for custom queries—built on Lucene and designed for R&D precision.
Over the past few years, Cypris has helped innovation teams make faster, more informed decisions by centralizing critical insights across datasets like patents, academic papers, and company activity. But until now, our search experience relied on a legacy query system with limited capabilities, offering little support for advanced search features or dataset-level customization.
Today, we’re excited to introduce an upgraded Advanced Search on Cypris, a complete overhaul of our query engine and search experience, powered by the open-standard Lucene query syntax. This update introduces a more robust and flexible search foundation, unlocking new ways to query data, build complex filters, and extract precisely what you need across patents, research, and more.
Why we rebuilt our search system from the ground up
Cypris’ original query syntax, a proprietary format used internally for years, limited users’ ability to craft advanced queries or tailor searches to specific datasets. It lacked modern capabilities like proximity searches, field-level customization, or true Boolean logic. This made it difficult to build a reliable and intuitive experience for both casual users and advanced researchers.
By moving to Lucene, we’re adopting a powerful, industry-standard query language that makes it easier for developers to build advanced features—and gives users access to a far more capable and flexible search toolset.
What’s new in Advanced Search
1. Custom Queries by Dataset
You can now layer queries to search across datasets or tailor filters to each one. For example, you can run a broad query on drone delivery, and then add separate layers to focus on patents by a specific assignee and papers from a specific country or funding agency.
Navigating the All Datasets tab introduces a new level of complexity—and power—by allowing users to apply dataset-specific logic within a single, unified query workflow. While querying multiple datasets simultaneously might seem straightforward, the underlying differences in schema, metadata, and available fields between our proprietary datasets make this a deeply technical challenge. Patents, for example, include claims, application numbers, and multiple date fields (filed, granted, updated), while academic papers use DOIs, have different structural conventions, and emphasize different metadata. In the past, we sidestepped this complexity by translating general queries like ((drone_allText)) into dataset-specific logic under the hood. Now, instead of obscuring that logic, we allow users to opt in to it. The builder provides progressive layers of customization: start with intuitive keyword searches across all fields, then move into the advanced builder for field-specific targeting, fuzzy logic, and term boosting, and finally, tailor query logic by dataset—such as specifying different countries of interest for papers vs. patents. This approach preserves flexibility while giving users full control, and with tools like our real-time Live Analysis and “Your Query” panel, we make it easy to understand how every decision affects the results.
2. More Fields to Query
We’re exposing deeper fields across datasets—giving you explicit control over the dimensions of your search. For the first time, users can now search academic papers by DOI, a critical identifier previously unsupported on the platform. You can also query by:
- Author or inventor names
- Organizations or assignees
- Countries, journals, funding agencies, and more
3. Full Boolean Support
Advanced Search now leverages powerful Boolean logic—AND, OR, NOT, and grouping—enabling more precise control over search logic and improving performance and accuracy.
4. Lucene Syntax Features
Use built-in Lucene features to create expressive, complex searches:
- Proximity searches to find terms near each other
- Fuzzy searches for flexible matching
- Exact phrase matching
- Boosting to prioritize results (e.g., prioritize results mentioning AI 3x more than others)
- Prefix/Postfix queries to match phrases that start or end a certain way
- Range queries for fields like date, funding amounts, or numerical values
A more powerful user experience
Our new search interface is built to help you tap into these capabilities without needing to know the syntax from the start. You’ll find:
- A Query Builder to guide you through complex searches
- A Help Video to onboard users to Lucene-style searches
- Inline examples and tips for writing queries using grouping, boosting, and more
Built for precision, speed, and customization
With Lucene as our foundation, search results are now not only more flexible but also faster and more accurate. Semantic search continues to offer natural-language ease of use, while Boolean search gives power users the performance and structure they need to uncover insights with greater specificity.
Whether you’re an innovation analyst drilling into AI patents or a business development lead scanning academic papers from Chilean researchers—Advanced Search is built to help you get to the signal, faster.
Available now to all users
Advanced Search is live and available across the Cypris platform today. If you’re already using Cypris, you’ll find the new search interface in your dashboard, complete with updated syntax documentation and walkthroughs.
We’re excited to see what you’ll build, discover, and analyze with this new capability. This is just the beginning—we’ll continue expanding the fields, syntax features, and customization options as we push the boundaries of what intelligent search can do for R&D.

Introducing Advanced Search on Cypris

A powerful new foundation for custom queries—built on Lucene and designed for R&D precision.
Over the past few years, Cypris has helped innovation teams make faster, more informed decisions by centralizing critical insights across datasets like patents, academic papers, and company activity. But until now, our search experience relied on a legacy query system with limited capabilities, offering little support for advanced search features or dataset-level customization.
Today, we’re excited to introduce an upgraded Advanced Search on Cypris, a complete overhaul of our query engine and search experience, powered by the open-standard Lucene query syntax. This update introduces a more robust and flexible search foundation, unlocking new ways to query data, build complex filters, and extract precisely what you need across patents, research, and more.
Why we rebuilt our search system from the ground up
Cypris’ original query syntax, a proprietary format used internally for years, limited users’ ability to craft advanced queries or tailor searches to specific datasets. It lacked modern capabilities like proximity searches, field-level customization, or true Boolean logic. This made it difficult to build a reliable and intuitive experience for both casual users and advanced researchers.
By moving to Lucene, we’re adopting a powerful, industry-standard query language that makes it easier for developers to build advanced features—and gives users access to a far more capable and flexible search toolset.
What’s new in Advanced Search
1. Custom Queries by Dataset
You can now layer queries to search across datasets or tailor filters to each one. For example, you can run a broad query on drone delivery, and then add separate layers to focus on patents by a specific assignee and papers from a specific country or funding agency.
Navigating the All Datasets tab introduces a new level of complexity—and power—by allowing users to apply dataset-specific logic within a single, unified query workflow. While querying multiple datasets simultaneously might seem straightforward, the underlying differences in schema, metadata, and available fields between our proprietary datasets make this a deeply technical challenge. Patents, for example, include claims, application numbers, and multiple date fields (filed, granted, updated), while academic papers use DOIs, have different structural conventions, and emphasize different metadata. In the past, we sidestepped this complexity by translating general queries like ((drone_allText)) into dataset-specific logic under the hood. Now, instead of obscuring that logic, we allow users to opt in to it. The builder provides progressive layers of customization: start with intuitive keyword searches across all fields, then move into the advanced builder for field-specific targeting, fuzzy logic, and term boosting, and finally, tailor query logic by dataset—such as specifying different countries of interest for papers vs. patents. This approach preserves flexibility while giving users full control, and with tools like our real-time Live Analysis and “Your Query” panel, we make it easy to understand how every decision affects the results.
2. More Fields to Query
We’re exposing deeper fields across datasets—giving you explicit control over the dimensions of your search. For the first time, users can now search academic papers by DOI, a critical identifier previously unsupported on the platform. You can also query by:
- Author or inventor names
- Organizations or assignees
- Countries, journals, funding agencies, and more
3. Full Boolean Support
Advanced Search now leverages powerful Boolean logic—AND, OR, NOT, and grouping—enabling more precise control over search logic and improving performance and accuracy.
4. Lucene Syntax Features
Use built-in Lucene features to create expressive, complex searches:
- Proximity searches to find terms near each other
- Fuzzy searches for flexible matching
- Exact phrase matching
- Boosting to prioritize results (e.g., prioritize results mentioning AI 3x more than others)
- Prefix/Postfix queries to match phrases that start or end a certain way
- Range queries for fields like date, funding amounts, or numerical values
A more powerful user experience
Our new search interface is built to help you tap into these capabilities without needing to know the syntax from the start. You’ll find:
- A Query Builder to guide you through complex searches
- A Help Video to onboard users to Lucene-style searches
- Inline examples and tips for writing queries using grouping, boosting, and more
Built for precision, speed, and customization
With Lucene as our foundation, search results are now not only more flexible but also faster and more accurate. Semantic search continues to offer natural-language ease of use, while Boolean search gives power users the performance and structure they need to uncover insights with greater specificity.
Whether you’re an innovation analyst drilling into AI patents or a business development lead scanning academic papers from Chilean researchers—Advanced Search is built to help you get to the signal, faster.
Available now to all users
Advanced Search is live and available across the Cypris platform today. If you’re already using Cypris, you’ll find the new search interface in your dashboard, complete with updated syntax documentation and walkthroughs.
We’re excited to see what you’ll build, discover, and analyze with this new capability. This is just the beginning—we’ll continue expanding the fields, syntax features, and customization options as we push the boundaries of what intelligent search can do for R&D.

Keep Reading

Tightening regulation of per- and polyfluoroalkyl substances is reshaping materials chemistry, and it is opening patent white space for organizations that can develop fluorine-free alternatives. PFAS are used for water, oil, and stain resistance across coatings, textiles, firefighting foams, membranes, semiconductors, and food packaging, and they are now the subject of the broadest chemical restriction ever proposed in Europe. The universal PFAS restriction proposal submitted to the European Chemicals Agency in January 2023 by five national authorities covers on the order of 10,000 substances, and it drew more than 5,600 comments from over 4,400 organizations, an unprecedented response that reflects how many industries are affected.¹ The scope depends on definition: under the 2021 OECD definition, which classifies a substance as PFAS if it contains at least one fully fluorinated carbon, several million catalogued substances qualify, while the number in active commercial use is far smaller.²
The regulatory trajectory is a sequence of tightening actions rather than a single event, which is what makes the resulting innovation demand durable. In the European Union, restrictions moved from PFOS in 2006 to PFOA and related substances in later years, to a PFHxA restriction adopted in 2024, a ban on PFAS in firefighting foams, and a ban on PFAS in food-contact packaging taking effect in 2026, with the universal restriction proposal under scientific evaluation through 2026.¹ In the United States, the Environmental Protection Agency finalized the first national drinking-water limits for several PFAS in 2024, setting maximum contaminant levels of 4.0 parts per trillion for PFOA and PFOS and higher limits for other compounds, and designated PFOA and PFOS as hazardous substances under the federal cleanup statute the same year.³ ECHA has estimated that, absent action, several million tonnes of PFAS would reach the environment over the coming decades.¹
This regulatory pressure is a well-understood driver of innovation. The Porter hypothesis, that well-designed environmental regulation can induce innovation that partly or wholly offsets compliance costs, has been supported across two decades of evidence and a multi-country meta-analysis, and firm-level studies show environmental regulation inducing greener product innovation specifically in chemical industries.⁴,⁵,⁶ For materials developers, the implication is direct: regulation is converting fluorine-free chemistry from a niche into a competitive frontier, and the organizations that build defensible IP positions early will hold advantage as substitution accelerates.
Where the white space is
Firefighting foams. Fluorine-free foams are the most advanced substitution area, driven by bans on PFAS-containing aqueous film-forming foams, though performance and toxicity gaps relative to legacy foams remain an active research and patenting frontier.⁷
Textile and coating treatments. Water- and oil-repellent finishes are a major PFAS use, and fluorine-free hydrophobic and oleophobic coatings, including bio-based and hierarchical-structured approaches, are an active area of development with room for defensible positions.⁸,⁹
Membranes and packaging. Food-contact packaging faces near-term bans, and membrane and barrier applications require substitutes that match performance, which keeps white space open where a fluorine-free chemistry can meet the functional requirement.
Semiconductors and specialty uses. Certain high-performance uses have few current substitutes, so these areas are simultaneously the hardest to displace and the most valuable to solve, and the patent landscape around viable alternatives is comparatively sparse.
How to find PFAS-alternative white space
Scope the application area and functional requirement precisely, since PFAS substitution is application-specific and a fluorine-free chemistry that works for textiles may not work for firefighting foam.
Map patents and scientific literature across the fluorine-free chemistries relevant to that application, because materials research precedes patenting and gives the earliest signal of a viable alternative.
Cluster activity by concept and attribute it to organizations, using an ontology to group related chemistry and normalize assignees, so dense and sparse regions are visible.
Identify the sparse, defensible regions, distinguishing genuine white space from areas that are sparse only because a chemistry does not yet meet the functional requirement.
Monitor continuously, tracking both the chemistry and the regulatory timeline, so filings and restrictions are surfaced as they publish and a white space position is secured before substitution accelerates.
Where Cypris fits
Cypris runs patent landscape and white space analysis for regulation-driven fields such as PFAS alternatives across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. Semantic search across patents and scientific literature surfaces fluorine-free chemistry regardless of nomenclature and connects filings to the underlying materials research, which is where the earliest signals of viable alternatives appear. The ontology clusters activity by application and chemistry and normalizes organizations to canonical entities, so a team can resolve which fluorine-free approaches are crowded and which remain open as white space. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the search, attribution, and gap analysis, and Agentic Monitoring tracks a defined chemistry over time and flags new filings as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.
FAQ
Why is PFAS regulation creating patent white space?
PFAS regulation is creating patent white space by driving demand for fluorine-free alternatives across many applications, faster than defensible IP positions have been established. The EU REACH universal restriction covers roughly 10,000 substances and US EPA rules now limit several PFAS, so substitution is accelerating. Organizations that build fluorine-free IP early can hold advantage as demand rises.
What is the EU REACH universal PFAS restriction?
The EU REACH universal PFAS restriction is a proposal submitted to the European Chemicals Agency in January 2023 by five national authorities to restrict the manufacture and use of PFAS as a class, covering on the order of 10,000 substances. It drew more than 5,600 comments from over 4,400 organizations. It is under scientific evaluation, with the outcome expected to shape substitution across many industries.
What US rules apply to PFAS?
In the United States, the Environmental Protection Agency finalized the first national drinking-water limits for several PFAS in 2024, setting maximum contaminant levels of 4.0 parts per trillion for PFOA and PFOS and higher limits for other compounds, and designated PFOA and PFOS as hazardous substances under the federal cleanup statute the same year. These actions increase the pressure to substitute PFAS. They apply alongside state-level restrictions.
Which application areas have the most PFAS-alternative white space?
Firefighting foams, textile and coating treatments, membranes and packaging, and certain semiconductor and specialty uses all have PFAS-alternative white space, though the amount varies. Firefighting-foam alternatives are the most advanced, while high-performance specialty uses have few substitutes and are the most valuable to solve. White space is largest where a fluorine-free chemistry can meet the functional requirement but few patents yet exist.
How does regulation drive innovation in materials?
Regulation drives innovation in materials by creating demand for compliant substitutes, a pattern described by the Porter hypothesis and supported by two decades of evidence and firm-level studies in chemical industries. Well-designed regulation induces innovation that can partly offset compliance costs. For PFAS, this is converting fluorine-free chemistry from a niche into a competitive frontier.
How do you find white space in PFAS alternatives?
Finding white space in PFAS alternatives means scoping a specific application and functional requirement, mapping patents and scientific literature across the relevant fluorine-free chemistries, clustering activity by concept, and identifying the sparse, defensible regions. Because materials research precedes patenting, literature coverage gives early signal. The analysis must distinguish genuine white space from areas that are sparse because no chemistry yet meets the requirement.
Why does PFAS-alternative analysis need scientific literature?
PFAS-alternative analysis needs scientific literature because fluorine-free chemistries appear in research before they are patented, so the literature gives the earliest signal of a viable alternative. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.
Which teams work on PFAS alternatives?
PFAS alternatives are developed by R&D, innovation, and IP teams in chemicals, advanced materials, coatings, textiles, consumer products, and their suppliers, alongside regulatory affairs. The work is driven by tightening regulation and customer demand for fluorine-free products. Cypris serves hundreds of enterprise customers across chemicals, advanced materials, and other regulated industries.
How do you keep a PFAS-alternatives landscape current? Keeping a PFAS-alternatives landscape current requires continuous monitoring of both the chemistry and the regulatory timeline, because filings and restrictions evolve constantly. A one-time landscape ages quickly as new rules and patents publish. Cypris uses Agentic Monitoring to track a defined chemistry over time and flag new filings as they publish.
Endnotes
- European Chemicals Agency. Registry of restriction intentions: per- and polyfluoroalkyl substances (PFAS) universal restriction proposal (2023) and related consultation and evaluation materials. https://echa.europa.eu/
- OECD (2021). Reconciling Terminology of the Universe of Per- and Polyfluoroalkyl Substances: Recommendations and Practical Guidance; and US Environmental Protection Agency PFAS inventory materials.
- US Environmental Protection Agency (2024). PFAS National Primary Drinking Water Regulation; and CERCLA designation of PFOA and PFOS as hazardous substances. https://www.epa.gov/pfas
- Ambec, S., Cohen, M. A., Elgie, S. & Lanoie, P. (2013). The Porter Hypothesis at 20. Review of Environmental Economics and Policy. https://doi.org/10.1093/reep/res016
- Yan, Z., Li, Y., Zhang, X. & Zhu, J. (2024). Revisiting the Porter hypothesis: a multi-country meta-analysis. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-024-02671-9
- Choi, J., Kang, J. & Chung, S. (2025). Environmental regulation, induced innovation, and greener transition: firm-level evidence. Journal of Development Economics. https://doi.org/10.1016/j.jdeveco.2025.103678
- Hossain, T., Ormond, R. B. et al. (2024). Exploring the Prospects and Challenges of Fluorine-Free Firefighting Foams (F3) as Alternatives to AFFF: A Review. ACS Omega. https://doi.org/10.1021/acsomega.4c03673
- Likozar, B. et al. (2024). Unveiling PFAS-free Solutions for Hydrophobic and Oleophobic Textile Coatings. https://doi.org/10.55295/psl.2024.i19
- Nicolas, M. et al. (2024). PFAS-free hierarchical superhydrophobic textiles. Advanced Engineering Materials. https://doi.org/10.1002/adem.202401736

Freedom-to-operate for GLP-1 receptor agonists and peptide therapeutics is among the most demanding FTO problems in pharmaceuticals, because protection in this class is built as a dense, layered thicket that extends far beyond the active ingredient. Freedom-to-operate determines whether making, using, or selling a product would infringe another party's active patent claims. In the GLP-1 and peptide space, answering that question requires reading many claim types across many patents, because a single product is protected by a stack of filings covering the molecule, its formulation, its dosing, its delivery device, and its manufacture. A peer-reviewed analysis of GLP-1 receptor agonists approved between 2005 and 2021 found that manufacturers listed a median of 19.5 patents per product, that 54 percent of those patents were on delivery devices rather than the active ingredient, that the median expected protection was 18.3 years after approval, and that no generic manufacturer had yet successfully challenged a GLP-1 receptor agonist patent.¹
The commercial stakes are large. Industry analyst forecasts vary widely with scope, placing the GLP-1 market anywhere from the low tens of billions of dollars to well over one hundred billion by 2030 and projecting double-digit annual growth; these are analyst estimates rather than authoritative figures, and they differ mainly in what they count.² The scale of the opportunity is what drives the density of the patent thicket, because each additional protected feature can delay competition on a high-revenue product. For any organization developing a follow-on peptide, a biosimilar, or a differentiated GLP-1 product, FTO is therefore a gating analysis rather than a formality.
Peptide therapeutics compound the difficulty. Peptides can be claimed as sequences and modifications, formulated for stability and half-life extension, delivered by injection or increasingly by oral routes, and manufactured through distinct synthesis and purification processes, so the claim surface is broad. Recent filing activity has shifted toward oral delivery, dual and triple receptor agonists, and combination therapies, which is where both the newest FTO risk and the remaining white space now sit.³ An FTO analysis in this class has to cover all of these dimensions, and it has to stay current as the frontier moves.
What creates FTO risk in GLP-1 and peptide products
Composition-of-matter claims. These cover the peptide itself, including sequences, analogues, and modifications, and are the primary protection, though in a mature class many core molecules approach expiry.
Formulation claims. These cover stabilized, extended-release, and oral formulations, which are heavily patented, as formulation is where much peptide innovation and differentiation occurs.
Dosing-regimen and method-of-use claims. These cover titration schedules and specific therapeutic uses, and can block a product for a particular indication or regimen even when the molecule is otherwise available.
Delivery-device claims. These cover injection pens and other devices and are a large share of the thicket; peer-reviewed analysis found delivery devices accounted for the majority of listed GLP-1 patents and function as a distinct barrier to entry.¹,⁴
Process and manufacturing claims. These cover synthesis and purification routes, so a developer can be free to use a molecule yet blocked from a particular manufacturing method.
A single molecule illustrates the layering. A published patent landscape of one dual GLP-1/glucagon receptor agonist identified twelve patent families spanning composition-of-matter, process chemistry, formulation, dosing regimen, and method-of-use, a clean worked example of how all five claim types stack on one product.⁵
The thicket dynamic and the expiry landscape
The density of GLP-1 protection reflects a broader pharmaceutical pattern. Empirical analysis shows the number of patents filed per active ingredient rose from 1.86 in 2001 to nearly six by 2019, driven substantially by continuation applications, which account for roughly a third of small-molecule pharmaceutical patents.⁶ These secondary filings extend the effective protection period, and the economics of that extension, including how patent challenges and settlements shape effective market life, are well documented.⁷ Pharmaceutical thickets also differ structurally from thickets in complex-technology industries, which is why FTO methods developed for electronics do not transfer cleanly to peptides.⁸
The expiry landscape is the other half of the picture. As core molecules approach the end of composition-of-matter protection, the surrounding formulation, device, and process claims determine when and where competition can actually enter. Analysis of one leading GLP-1 molecule found that the timing of primary-patent expiry varies substantially by market, so freedom-to-operate for a follow-on product is jurisdiction-specific, and the practical entry date is governed by the secondary thicket rather than the headline molecule expiry.⁹ For a developer, this means FTO must be assessed claim-by-claim and market-by-market, not at the level of the molecule.
How AI-powered FTO helps
Navigating a thicket of this density by manual search is slow and prone to coverage gaps, which are the main source of FTO risk. AI-powered FTO addresses this with semantic search that retrieves relevant claims regardless of terminology, claim-level analysis that focuses on the independent claims defining infringement scope across all five claim types, and continuous monitoring that keeps a cleared position current as new formulation, device, and combination filings publish. Because peptide innovation appears in scientific literature before it is patented, reading both patents and literature gives earlier warning of where the thicket is extending.
Where Cypris fits
Cypris runs claim-level, semantic, AI-powered freedom-to-operate across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. Semantic search across patents and scientific literature surfaces relevant claims regardless of terminology, across composition, formulation, dosing-regimen, delivery-device, and process claims, which is what a dense peptide thicket demands. The ontology clusters the thicket by concept and normalizes assignees, so a team sees the structure of protection around a molecule rather than a flat list. Cypris Q, the platform's agentic layer, lets teams run and chain FTO analysis conversationally, and Agentic Monitoring tracks a molecule and its surrounding thicket over time, flagging new formulation, device, and combination filings as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.
FAQ
Why is freedom-to-operate hard for GLP-1 and peptide therapeutics?
Freedom-to-operate is hard for GLP-1 and peptide therapeutics because protection is built as a dense, layered thicket extending well beyond the active ingredient. A peer-reviewed analysis found GLP-1 products carry a median of 19.5 listed patents each, most of them on delivery devices. Assessing FTO requires reading composition, formulation, dosing, device, and process claims across many patents and markets.
What claim types create FTO risk for GLP-1 products?
Five claim types create FTO risk for GLP-1 products: composition-of-matter claims on the peptide, formulation claims on stabilized and oral forms, dosing-regimen and method-of-use claims, delivery-device claims, and process or manufacturing claims. Each can independently block a product. Delivery-device claims are a particularly large share of the GLP-1 thicket.
How many patents protect a typical GLP-1 product?
A peer-reviewed analysis of GLP-1 receptor agonists approved between 2005 and 2021 found a median of 19.5 listed patents per product, with 54 percent on delivery devices rather than the active ingredient, and a median of 18.3 years of expected protection after approval. No generic manufacturer had successfully challenged a GLP-1 receptor agonist patent as of that analysis. These figures illustrate the density of the thicket.
What is a pharmaceutical patent thicket?
A pharmaceutical patent thicket is a dense set of overlapping patents around a single product that extends protection beyond the core molecule. Empirical analysis shows patents per active ingredient rose from 1.86 in 2001 to nearly six by 2019, driven substantially by continuation applications. Thickets shape when and where competition can enter.
How does the expiry of GLP-1 patents affect freedom-to-operate?
The expiry of GLP-1 patents affects freedom-to-operate market-by-market, because primary-patent expiry timing varies by jurisdiction and the practical entry date is governed by the surrounding formulation, device, and process claims rather than the molecule alone. FTO must therefore be assessed claim-by-claim and market-by-market. A molecule can be off-patent in one country and still protected in another.
Where is the white space in GLP-1 and peptide development?
Recent filing activity has shifted toward oral delivery, dual and triple receptor agonists, and combination therapies, which is where both new FTO risk and remaining white space now sit. Mapping this frontier requires reading patents and scientific literature together, since peptide innovation appears in research first. White space analysis identifies the areas that are still open.
How does AI-powered FTO help with peptide therapeutics?
AI-powered FTO helps with peptide therapeutics by using semantic search to retrieve relevant claims regardless of terminology, claim-level analysis to focus on the independent claims that define infringement across all claim types, and continuous monitoring to keep a cleared position current. This is what a dense, fast-moving thicket requires. Cypris runs this across more than 500 million patents and scientific papers.
Which teams need GLP-1 and peptide FTO analysis?
GLP-1 and peptide FTO analysis is needed by pharmaceutical and biotech R&D, IP, and business-development teams developing follow-on peptides, biosimilars, differentiated formulations, or combination products. It is also relevant to generics manufacturers assessing entry. Cypris serves hundreds of enterprise customers across pharmaceuticals and other regulated industries.
How current does GLP-1 FTO need to be?
GLP-1 FTO needs to be continuously current, because new formulation, device, dosing, and combination filings publish constantly and can change a cleared position. A one-time assessment reflects only the moment it was run. Cypris uses Agentic Monitoring to track a molecule and its surrounding thicket over time and flag new filings as they publish.
Endnotes
- Tu, S. S., Feldman, W. B., Alhiary, R., Gabriele, S., Kesselheim, A. S. & Beall, R. F. (2023). Patents and Regulatory Exclusivities on GLP-1 Receptor Agonists. JAMA. https://doi.org/10.1001/jama.2023.13872
- Industry analyst estimates (e.g., Research and Markets; BCC Research). GLP-1 market forecasts vary widely by scope and are presented here as order-of-magnitude estimates, not authoritative figures.
- Han, J., Zhou, Z., Jiang, N. & Lu, W. (2023). An updated patent review of GLP-1 receptor agonists (2020–present). Expert Opinion on Therapeutic Patents. https://doi.org/10.1080/13543776.2023.2274905
- Tu, S. S., Feldman, W. B. et al. (2024). Delivery Device Patents on GLP-1 Receptor Agonists. JAMA. https://doi.org/10.1001/jama.2024.0919
- Fasi, M. A. (2026). Patent landscape and therapeutic evolution of mazdutide. Expert Opinion on Therapeutic Patents. https://doi.org/10.1080/13543776.2026.2645812
- Tu, S. S. (2024). The Long CON: An Empirical Analysis of Pharmaceutical Patent Thickets. University of Pittsburgh Law Review. https://doi.org/10.5195/lawreview.2024.1049
- Hemphill, C. S. & Sampat, B. N. (2012). Evergreening, patent challenges, and effective market life in pharmaceuticals. Journal of Health Economics. https://doi.org/10.1016/j.jhealeco.2012.01.004
- Tu, S. S. & Carrier, M. A. (2023). Why Pharmaceutical Patent Thickets Are Unique. SSRN. https://doi.org/10.2139/ssrn.4571486
- Ramesh, S., Cross, S., Levi, J., Hill, A. & Venter, F. (2026). How Low Could Semaglutide Prices Fall? Implications for Global Access Ahead of Patent Expiry. Obesity. https://doi.org/10.1002/oby.70241

Commercial fusion energy has moved from a distant public-research goal to a well-funded private race, and its patent landscape is being staked out as companies compress decades of physics into engineering programs. Fusion fuses light nuclei to release energy, and its progress is measured by the fusion gain, or Q, and the triple product of density, temperature, and confinement time, the parameters that determine whether a device produces more energy than it consumes.³ It is pursued through several competing confinement approaches, each a distinct region of patenting: magnetic confinement, including tokamaks, spherical tokamaks such as Globus-M2, stellarators, mirrors, and field-reversed configurations; inertial confinement using lasers; and magneto-inertial hybrids, running on fuels such as deuterium-tritium, deuterium-deuterium, and proton-boron.⁵ The intellectual property divides across the enabling technologies these approaches share: the magnets that confine the plasma, especially high-temperature superconducting magnets; the systems that heat and control the plasma; the tritium breeding blankets that must produce fuel and capture energy; the first-wall and divertor materials that survive intense neutron flux; and, for inertial approaches, the targets and drivers, whose implosion physics is an active research area.¹,⁶ Because a viable plant depends on several of these layers, freedom-to-operate and white space analysis must span the confinement approaches and the enabling layers together.
The landscape is being reshaped by a technology shift and a funding boom. High-temperature superconducting magnets, which reach much stronger fields than conventional superconductors, allow far more compact and potentially cheaper machines: the SPARC device, for example, is designed around a high-field, compact tokamak concept, and the physics basis for such burning-plasma machines is now well documented.² The 2025 edition of the IAEA's World Fusion Outlook gave these magnets a special focus, reflecting their role across tokamaks, stellarators, and mirror concepts.⁹ Public milestones anchor the field: in December 2022 the US National Ignition Facility achieved fusion ignition, producing about 3.15 megajoules of fusion energy from about 2.05 megajoules of laser energy delivered to the target, a scientific, target-level energy gain rather than a net-grid gain, and later experiments repeated ignition.⁷ On the magnetic side, ITER's 2024 re-baseline set the start of research operations in 2034 and the start of deuterium-tritium operations in 2039, a four-year delay from the earlier reference, and changed the first-wall material from beryllium to tungsten.⁸ Public programs are also advancing the physics, as with China's HL-3 tokamak.⁴ The intellectual-property picture is therefore a mix, because much of the underlying plasma physics is in the public domain from decades of open research, while the proprietary value concentrates in the specific engineering that turns physics into a machine. That split is visible in the record: across the Cypris corpus of more than 500 million patents and scientific papers, the fusion set holds on the order of 11,694 families and grew from about 376 in 2020 to roughly 711 in 2024, with the most active assignees being public institutes and diversified industrials, led by the Hefei Institutes of Physical Science of the Chinese Academy of Sciences alongside Toshiba, Hitachi, and the Japan Atomic Energy Agency, and China ahead of the United States and the United Kingdom on geography; 2025 and 2026 counts are partial because of the publication lag.
The strategic question is which enabling layer to own, and the white space sits where engineering, not physics, is the barrier. High-temperature superconducting magnet design and the manufacturing of the superconducting tape and cable are a high-value layer where a compact-machine advantage is won.² Tritium breeding, producing more tritium than the plant consumes, has not been demonstrated at commercial scale and is a critical, comparatively open area, as are the first-wall and divertor materials that must withstand neutron damage over a plant's life. Plasma heating and control, increasingly aided by machine learning, and, for inertial approaches, target fabrication and drivers, are further contested layers.⁶ Private-venture pilot-plant dates and net-gain targets should be read as company projections rather than demonstrated results. Reading the landscape by approach, enabling layer, and owner, and tracking both the patents and the underlying fusion-science research, is what separates a crowded region from an open one.
Where the fusion white space is
High-temperature superconducting magnets. Magnet design and the manufacturing of superconducting tape and cable for compact, high-field machines are a high-value, capital-intensive layer.²
Tritium breeding blankets. Breeding more tritium than the plant consumes, and capturing the fusion energy, is unproven at commercial scale and a critical, comparatively open area.
First-wall and divertor materials. Materials such as tungsten that survive intense neutron flux over a plant's life, and the strategies to replace them, are a distinct, high-stakes engineering layer.⁸
Plasma heating and control. Systems that heat, shape, and stabilize the plasma, increasingly using machine learning, are an active and contested layer.
Inertial targets and drivers. For inertial-confinement approaches, target fabrication and driver technologies are a separate region of patenting.¹,⁶
How AI-powered landscape and white space analysis helps
Resolving a landscape that spans several confinement approaches and enabling layers, built on public physics but proprietary engineering, requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by approach and enabling layer across varied terminology, attribution that normalizes private, public, and academic filers to canonical entities, and continuous monitoring that keeps pace with a fast-funding field. Because fusion advances appear in scientific literature before they are patented, and because so much of the science is public while the engineering is proprietary, reading both patents and literature is essential to separate open physics from claimable engineering.
Where Cypris fits
Cypris runs patent landscape and white space analysis for engineering-intensive deep-tech fields such as commercial fusion energy across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by confinement approach, tokamak, stellarator, inertial, and others, and by enabling layer, magnets, heating and control, tritium breeding, materials, and targets, and normalizes private, public, and academic filers to canonical entities, so a team can resolve which approaches and layers are crowded and which remain open as white space. Semantic search across patents and scientific literature connects filings to the underlying fusion-science research, which is where advances appear first and where public physics must be separated from proprietary engineering. Cypris Q, the platform's agentic layer, lets teams run landscape and white space analysis conversationally and chain the clustering, attribution, and gap analysis, and Agentic Monitoring tracks a defined layer over time and flags new patents and papers as they publish. Cypris provides enterprise API partnerships with OpenAI, Anthropic, and Google, and is built with enterprise-grade security. Cypris serves hundreds of enterprise customers across pharmaceuticals, chemicals, advanced materials, energy, and other regulated industries.
FAQ
What is the commercial fusion energy patent landscape? The commercial fusion energy patent landscape is the set of patents covering the technologies needed to build a fusion power plant. It spans confinement approaches, tokamaks, stellarators, inertial, and others, and enabling layers such as magnets, plasma heating and control, tritium breeding, first-wall materials, and inertial targets. Each is a distinct region of patenting.
Why are high-temperature superconducting magnets so important? High-temperature superconducting magnets are important because they reach much stronger magnetic fields than conventional superconductors, which allows far more compact and potentially cheaper fusion machines. They have become a central engineering and patenting focus across several confinement approaches, and the 2025 IAEA World Fusion Outlook gave them a special focus. Magnet design and superconducting-tape manufacturing are high-value layers.
Why is tritium breeding a key white space? Tritium breeding is a key white space because a deuterium-tritium plant must produce more tritium than it consumes to be self-sufficient, and this has not been demonstrated at commercial scale. The breeding blanket must also capture the fusion energy and survive neutron flux. That combination makes it a critical, comparatively open engineering layer.
What did the NIF ignition result actually show? The National Ignition Facility achieved fusion ignition in December 2022, producing about 3.15 megajoules of fusion energy from about 2.05 megajoules of laser energy delivered to the target. This is a scientific, target-level energy gain, not a net-grid gain, because the laser system draws far more energy from the grid than reaches the target. Later experiments repeated ignition.
How does public physics affect fusion IP? Public physics affects fusion IP because decades of open, publicly funded research placed much of the underlying plasma physics in the public domain, so the proprietary, patentable value concentrates in the specific engineering, magnets, blankets, materials, targets, and control systems, that turns physics into a working machine. Distinguishing public science from claimable engineering is central to fusion freedom-to-operate.
Where is the white space in fusion energy? The white space includes high-temperature superconducting magnets and their manufacturing, tritium breeding blankets, first-wall and divertor materials, plasma heating and control including machine-learning approaches, and inertial targets and drivers. The physics is largely public. The most open, high-value opportunities are in the engineering layers that remain unproven at commercial scale.
Why does fusion analysis need scientific literature? Fusion analysis needs scientific literature because so much of the field's knowledge is in public research, and new engineering advances appear in the literature before they are patented, so reading both is essential to separate open physics from claimable engineering. Analyzing patents alone gives a partial view. Cypris analyzes both across more than 500 million patents and scientific papers.
What software helps analyze the fusion energy patent landscape? Software for the fusion landscape should cluster activity by confinement approach and enabling layer, resolve private, public, and academic filers to canonical owners, search patents and scientific literature semantically, and monitor a fast-funding field continuously. Cypris does this across more than 500 million patents and scientific papers using a proprietary R&D ontology, semantic search, Cypris Q, and Agentic Monitoring.
Endnotes
- Chapman, T. D., Ralph, J. E., Woodworth, B., et al. (2024). Present understanding of ignition and gain using indirect-drive inertial confinement fusion on the U.S. National Ignition Facility. Plasma Physics and Controlled Fusion, 67(1). https://doi.org/10.1088/1361-6587/ad994f
- Creely, A. J., Rice, J. E., Sorbom, B. N., Hartwig, Z. S., et al. (2022). Overview of the SPARC physics basis toward burning-plasma regimes in high-field, compact tokamaks. Nuclear Fusion, 62(4). https://doi.org/10.1088/1741-4326/ac1654
- Costley, A. E. (2016). On the fusion triple product and fusion power gain of tokamak pilot plants and reactors. Nuclear Fusion, 56(6). https://doi.org/10.1088/0029-5515/56/6/066003
- Chen, W., & Zhong, W. (2025). Breakthrough in China's fusion energy: HL-3 tokamak achieves high ion temperature and fusion triple product. The Innovation, 6. https://doi.org/10.1016/j.xinn.2025.101167
- Sakharov, N. V., et al. (2021). Tenfold increase in the fusion triple product in the spherical tokamak Globus-M2. Nuclear Fusion, 61(6). https://doi.org/10.1088/1741-4326/abe08c
- Zhou, Y., Sadler, J. D., & Hurricane, O. A. (2024). Instabilities and mixing in inertial confinement fusion. Annual Review of Fluid Mechanics, 57. https://doi.org/10.1146/annurev-fluid-022824-110008
- U.S. Department of Energy (2022, December 13). DOE National Laboratory makes history by achieving fusion ignition. https://www.energy.gov/articles/doe-national-laboratory-makes-history-achieving-fusion-ignition
- ITER Organization (2024). New baseline to prioritize a robust start to exploitation. https://www.iter.org/node/20687/new-baseline-prioritize-robust-start-exploitation
- International Atomic Energy Agency (2025). Fusion energy in 2025: six global trends to watch (World Fusion Outlook 2025). https://www.iaea.org/newscenter/news/fusion-energy-in-2025-six-global-trends-to-watch
