A faster, more accurate way to explore innovation data—now available in Cypris.
For innovation teams, speed and accuracy aren’t optional—they’re critical. You need to quickly find all relevant documents, slice and dice datasets however you want, and trust that the results are complete and representative. With this in mind, we’ve upgraded how semantic search works inside Cypris.
Today, we’re launching an upgraded search infrastructure that gives users access to full, exact result sets—unlocking more powerful analysis, faster iteration, and deterministic filtering and charting.
Unlike traditional semantic or vector search engines—which make it difficult to count, filter, or chart large sets of matched documents—our new approach prioritizes transparency and performance while preserving semantic relevance.
Why we moved away from vector search
Our original implementation relied on semantic and vector search to capture the “meaning” behind user queries. But as our platform evolved, it became clear that these systems weren’t well-suited for our core use cases.
Users needed:
- Deterministic filtering (e.g., "how many results match this atom?")
- Transparent, complete result sets to power charts and dashboards
- Fast, repeatable queries that don’t change subtly over time
Modern vector search systems don’t easily support this level of transparency. They return approximate matches and abstract similarity scores, often making it hard to understand why a document was returned—or whether it’s the full picture.
So we made a decision: move away from vector search and lean into what traditional search engines do best.
A return to boolean and lexical search—with a twist
We rebuilt our search infrastructure on top of Elasticsearch’s powerful boolean and lexical search capabilities. This shift brings major advantages:
- Faster query speeds that dramatically improve iteration time
- Deterministic filtering and counts, so every chart is grounded in the full dataset
- Predictable, explainable results that users can trust
But we didn’t stop there.
To preserve the benefits of semantic understanding, we’ve rethought where that intelligence should live—not at query time, but at data ingestion.
Capturing semantic meaning at ingest time
Instead of computing document-query similarity during search, we enrich documents at the time of ingestion. Here’s how:
- Synonym expansion: We find related words and concepts not explicitly mentioned in the document and add them as fields, enabling semantic-style recall via lexical search.
- Stemming: Both queries and documents are reduced to their root forms, allowing consistent matches (e.g., “running” and “run”).
The result? You get the same functionality—semantically relevant results—without the opacity or latency tradeoffs of vector search.
What’s next: Reranking for even better relevance
We’re not done. Coming soon to Cypris is a reranking layer that boosts the most relevant results to the top of the list using lightweight vector techniques.
Here’s how it works:
- A standard lexical search retrieves the full result set.
- We take the top N results and rerank them using vector similarity, powered by Elasticsearch’s new hybrid scoring capabilities.
- You get faster queries with even better relevance—without compromising on counts or transparency.
This layered approach gives us the best of both worlds: precise filtering and fast queries, plus smarter ordering of results where it matters most.
We’re excited to bring this upgrade to our users, and we’re already seeing teams iterate faster and uncover insights more confidently. This is a foundational shift—and just the beginning of what’s to come.
Want a walkthrough of what’s changed? Reach out to our team.
Introducing our upgraded semantic search
A faster, more accurate way to explore innovation data—now available in Cypris.
For innovation teams, speed and accuracy aren’t optional—they’re critical. You need to quickly find all relevant documents, slice and dice datasets however you want, and trust that the results are complete and representative. With this in mind, we’ve upgraded how semantic search works inside Cypris.
Today, we’re launching an upgraded search infrastructure that gives users access to full, exact result sets—unlocking more powerful analysis, faster iteration, and deterministic filtering and charting.
Unlike traditional semantic or vector search engines—which make it difficult to count, filter, or chart large sets of matched documents—our new approach prioritizes transparency and performance while preserving semantic relevance.
Why we moved away from vector search
Our original implementation relied on semantic and vector search to capture the “meaning” behind user queries. But as our platform evolved, it became clear that these systems weren’t well-suited for our core use cases.
Users needed:
- Deterministic filtering (e.g., "how many results match this atom?")
- Transparent, complete result sets to power charts and dashboards
- Fast, repeatable queries that don’t change subtly over time
Modern vector search systems don’t easily support this level of transparency. They return approximate matches and abstract similarity scores, often making it hard to understand why a document was returned—or whether it’s the full picture.
So we made a decision: move away from vector search and lean into what traditional search engines do best.
A return to boolean and lexical search—with a twist
We rebuilt our search infrastructure on top of Elasticsearch’s powerful boolean and lexical search capabilities. This shift brings major advantages:
- Faster query speeds that dramatically improve iteration time
- Deterministic filtering and counts, so every chart is grounded in the full dataset
- Predictable, explainable results that users can trust
But we didn’t stop there.
To preserve the benefits of semantic understanding, we’ve rethought where that intelligence should live—not at query time, but at data ingestion.
Capturing semantic meaning at ingest time
Instead of computing document-query similarity during search, we enrich documents at the time of ingestion. Here’s how:
- Synonym expansion: We find related words and concepts not explicitly mentioned in the document and add them as fields, enabling semantic-style recall via lexical search.
- Stemming: Both queries and documents are reduced to their root forms, allowing consistent matches (e.g., “running” and “run”).
The result? You get the same functionality—semantically relevant results—without the opacity or latency tradeoffs of vector search.
What’s next: Reranking for even better relevance
We’re not done. Coming soon to Cypris is a reranking layer that boosts the most relevant results to the top of the list using lightweight vector techniques.
Here’s how it works:
- A standard lexical search retrieves the full result set.
- We take the top N results and rerank them using vector similarity, powered by Elasticsearch’s new hybrid scoring capabilities.
- You get faster queries with even better relevance—without compromising on counts or transparency.
This layered approach gives us the best of both worlds: precise filtering and fast queries, plus smarter ordering of results where it matters most.
We’re excited to bring this upgrade to our users, and we’re already seeing teams iterate faster and uncover insights more confidently. This is a foundational shift—and just the beginning of what’s to come.
Want a walkthrough of what’s changed? Reach out to our team.
Keep Reading
Perovskite-silicon tandem solar cells are the leading path to higher photovoltaic efficiency, and their patent landscape has become unusually central to competition because the field is commercializing through licensing as much as through manufacturing. A tandem cell places a wide-bandgap perovskite layer on top of a conventional silicon cell, so the two absorb different parts of the solar spectrum and the stack converts more sunlight than either alone, surpassing the single-junction limit that constrains standard silicon.¹,² The theoretical ceiling for a silicon-based tandem is about 43.2 percent, far above the roughly 33 percent limit of a single-junction silicon cell, which is what makes the architecture so attractive.³ The intellectual property divides across several regions, each with different owners and maturity: the perovskite compositions and their stability chemistry; the passivation and interface layers that raise efficiency and lifetime; the tandem device architecture, including the recombination layers that join the sub-cells; the texturing and deposition processes used to build the stack; and the encapsulation and manufacturing that make a durable module. Because a working tandem depends on all of these, freedom-to-operate and white space analysis must span the full stack.
The landscape is being shaped by patents and cross-licensing in real time. Certified efficiencies have climbed steeply: the current certified perovskite/silicon tandem record stands at 34.85 percent, achieved by LONGi and certified by the US National Renewable Energy Laboratory in 2025, and peer-reviewed work now describes certified perovskite/silicon efficiencies approaching 35 percent, with the live record register maintained on the NREL Best Research-Cell Efficiency Chart.⁴,⁵,⁶ These are laboratory cell records rather than commercial-module ratings, and translating them to industry-compatible cells and full modules is a distinct challenge the field is actively working through.¹⁰ Multi-junction routes are advancing in parallel, with triple-junction perovskite/perovskite/silicon devices exceeding 30 percent.⁷ Holders of strong foundational portfolios have begun licensing their technology to large manufacturers, signaling that IP position, not only manufacturing capacity, will determine who benefits from the transition. That structure is visible in the patent record: across the Cypris corpus of more than 500 million patents and scientific papers, the perovskite-tandem space is a comparatively small, fast-moving set of a few thousand de-duplicated families that stepped up sharply in 2025, and its most active assignees mix national laboratories such as CEA and CNRS, the perovskite specialist Oxford PV, and large silicon-module manufacturers, with China, the United States, South Korea, and France the leading jurisdictions; because assignee names are not fully canonicalized, manufacturer totals are best read as indicative. Because applications publish about eighteen months after filing, the most recent composition and process filings are under-represented, so the current frontier is more active than granted-patent counts suggest.
The strategic question is which layer to own, and the white space sits where durability is hardest to achieve. Perovskite stability under heat, humidity, and light remains the central unsolved problem, and the degradation mechanisms and stabilization techniques that address it are an area of intense, well-mapped research,⁸ as are the barrier and encapsulation designs that protect the cell over its service life.⁹ Compositions, passivation chemistries, and encapsulation that extend lifetime therefore carry high, defensible value, while tandem architecture and light-management texturing are contested and improving quickly, and scalable deposition and manufacturing are where laboratory records must survive the move to gigawatt production. Reading the landscape by composition, layer, and process, and tracking both the patents and the underlying materials research, is what separates a crowded region from an open one.
Where the perovskite tandem white space is
Stability and encapsulation. Compositions, passivation, and encapsulation that keep efficiency under heat, humidity, and light are the central unsolved problem and the highest-value, still-open target.⁸,⁹
Wide-bandgap perovskite compositions. Formulations tuned for the top cell that resist phase segregation are a contested, fast-moving composition layer.
Tandem architecture. Recombination layers, interconnection, and two-terminal versus four-terminal designs are a distinct device-engineering layer.⁷
Texturing and light management. Surface texturing and optical designs that maximize capture across the stack are an active process-IP area.
Scalable deposition and manufacturing. Moving high-efficiency processes from small cells to gigawatt-scale modules is where cost is decided and where durable process IP concentrates.¹⁰
How AI-powered landscape and white space analysis helps
Resolving a device landscape that spans compositions, interface and architecture layers, and manufacturing processes, across institutions and regions moving at different speeds, requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by layer and process across varied terminology, attribution that normalizes academic and commercial filers to canonical entities and tracks the licensing structure, and continuous monitoring that keeps pace with a fast-commercializing field. Because perovskite advances appear in scientific literature before they are patented, reading both patents and literature gives the earliest signal of where the frontier is moving.
Where Cypris fits
Cypris runs patent landscape and white space analysis for fast-commercializing energy fields such as perovskite tandem solar across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by layer, perovskite composition, passivation and interfaces, tandem architecture, texturing and deposition, and encapsulation and manufacturing, and normalizes filers to canonical entities, so a team can resolve which layers are crowded and which remain open as white space, and can see the licensing structure clearly rather than as a flat list. Semantic search across patents and scientific literature connects filings to the underlying materials and device research, which is where perovskite advances appear first. 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 perovskite tandem solar patent landscape? The perovskite tandem solar patent landscape is the set of patents covering perovskite-silicon and all-perovskite tandem cells that exceed the single-junction efficiency limit. It divides across perovskite compositions and stability, passivation and interfaces, tandem architecture, texturing and deposition, and encapsulation and manufacturing. Each is a distinct region with different owners and maturity.
Why is IP so central to perovskite tandem solar? IP is central because the field is commercializing through licensing as much as through manufacturing, with holders of strong foundational portfolios licensing their technology to large manufacturers. Foundational process and architecture patents are concentrated among a few institutions and companies. That makes licensing and freedom-to-operate, not only production capacity, decisive.
What layers does the perovskite tandem landscape cover? The landscape covers perovskite composition and stability chemistry, passivation and interface layers, tandem device architecture including recombination layers, texturing and deposition processes, and encapsulation and manufacturing. A working tandem depends on all of them. Freedom-to-operate and white space analysis must span the full stack.
Where is the white space in perovskite tandem solar? The white space sits where durability is hardest: stability and encapsulation, wide-bandgap compositions that resist phase segregation, tandem architecture, light-management texturing, and scalable deposition. Stability under heat, humidity, and light is the central unsolved problem. The highest-value, most defensible positions are in lifetime and manufacturability.
Why is stability the key problem in the patent record? Stability is the key problem because perovskites can degrade under heat, humidity, and light, so the compositions, passivation, and encapsulation that extend lifetime are where the most valuable and defensible IP concentrates. Efficiency records matter, but durable modules require solving stability. The patent record reflects intense activity in these layers.
Why does perovskite analysis need scientific literature? Perovskite analysis needs scientific literature because new compositions, passivation chemistries, and device architectures appear in materials research before they are patented, so the literature gives the earliest signal. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.
What software helps analyze the perovskite tandem solar patent landscape? Software for the perovskite tandem landscape should cluster activity by device layer and process, resolve academic and commercial filers and the licensing structure to canonical owners, search patents and scientific literature semantically, and monitor a fast-commercializing 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.
Which teams use perovskite tandem patent landscape analysis? Perovskite tandem patent landscape analysis is used by R&D, innovation, IP, and strategy teams at solar manufacturers, materials developers, and equipment makers, as well as investors and research institutions. It informs which layer to back, where to file, where to license, and where competitors are concentrated. Cypris serves hundreds of enterprise customers across energy, advanced materials, chemicals, and other regulated industries.
Endnotes
- Zhang, F., Shi, Y., & Berry, J. J. (2024). Perovskite/silicon tandem solar cells: insights and outlooks. ACS Energy Letters, 9(4). https://doi.org/10.1021/acsenergylett.4c00172
- Zheng, X., Tan, H., et al. (2023). Efficient perovskite/silicon tandem solar cells on industrially compatible textured silicon. Advanced Materials, 35(10). https://doi.org/10.1002/adma.202207883
- Schubert, M. C., Glunz, S. W., et al. (2025). Elucidating the efficiency limit of silicon-based monolithic tandem cells through the combination of Auger and Shockley-Queisser limits. EES Solar. https://doi.org/10.1039/d5el00085h
- Chen, W., Mularso, K. T., Jung, H. S., & Jo, B., et al. (2026). Strategies toward maximizing power conversion efficiency in all-perovskite tandem solar cells. Solar RRL. https://doi.org/10.1002/solr.202500911
- LONGi (2025, April 16). LONGi breaks world record for crystalline silicon-perovskite tandem solar cell efficiency (34.85%). https://www.longi.com/en/news/silicon-perovskite-tandem-solar-cells-new-world-efficiency
- National Renewable Energy Laboratory. Best research-cell efficiency chart. https://www.nrel.gov/pv/cell-efficiency
- Aydin, E., Xu, L., De Wolf, S., et al. (2024). Four-terminal perovskite/perovskite/silicon triple-junction tandem solar cells with over 30% power conversion efficiency. ACS Energy Letters, 9(8). https://doi.org/10.1021/acsenergylett.4c01292
- Ahn, N., & Choi, M. (2023). Towards long-term stable perovskite solar cells: degradation mechanisms and stabilization techniques. Advanced Science, 10(35). https://doi.org/10.1002/advs.202306110
- Yang, Z., Liu, Z., Chen, W., et al. (2020). Barrier designs in perovskite solar cells for long-term stability. Advanced Energy Materials, 10(26). https://doi.org/10.1002/aenm.202001610
- Jost, M., et al. (2021). 27.9% efficient monolithic perovskite/silicon tandem solar cells on industry-compatible bottom cells. Solar RRL, 5(6). https://doi.org/10.1002/solr.202100244
Metal-organic frameworks have moved from a laboratory curiosity to a commercial materials platform, and their patent landscape is being staked out just as the field reaches scale. A MOF is a porous crystalline material built by linking metal nodes with organic linkers into an ordered framework, producing extraordinarily high surface areas and pores that can be tuned for a target molecule; more than 20,000 distinct MOFs had already been reported by the early 2010s, and the reticular chemistry behind them has continued to mature.¹,² Recognition by the 2025 Nobel Prize in Chemistry, awarded to Susumu Kitagawa, Richard Robson, and Omar Yaghi for the development of metal-organic frameworks, underscored the field's arrival and named applications from carbon-dioxide capture and toxic-gas storage to water harvesting, catalysis, and the separation of per- and polyfluoroalkyl substances from water.³ The intellectual property now divides across three broad regions: the specific framework compositions and structures themselves; the synthesis, shaping, and manufacturing processes that turn a powder into a usable, scalable product; and the application-level systems that integrate a MOF into a working device. Because MOFs serve many functions, freedom-to-operate and white space analysis must span all of them.
The commercial tipping point is reshaping the patent picture. After years in which scale-up and cost were the barriers, industrial-scale production of MOFs for carbon capture has begun, and a wave of startups is pursuing modular capture systems that are easier to scale than incumbent solvent processes, alongside chemical majors moving into manufacturing. Direct air capture of carbon dioxide has been demonstrated in purpose-designed frameworks from the laboratory through pilot scale, distinct from higher-concentration point-source capture.⁶ The scale of activity is large: across the Cypris corpus of more than 500 million patents and scientific papers, the MOF and reticular-chemistry space holds well over 100,000 de-duplicated families and has sustained high-volume filing since around 2018, with the assignee base led by academic institutions and chemical majors such as Sinopec, BASF, and ExxonMobil also prominent, while China accounts for the large majority of families, ahead of the United States, Japan, Germany, and South Korea. This shift moves value from the bare framework composition, where foundational academic estates are concentrated, toward the synthesis, shaping, and system-integration layers. Because applications publish about eighteen months after filing, the most recent synthesis and application filings are under-represented, so the current frontier is more active than granted-patent counts suggest.
The landscape divides by application, and the white space sits where a MOF must survive real conditions at low cost. Water stability, cycling durability, and inexpensive, scalable synthesis are the recurring bottlenecks; the criteria for a high-performance water-adsorbing framework, pore size and shape, hydrophilicity, and stability, are well defined but hard to meet at once.⁴ Carbon capture, both point-source and direct air capture, is the most active application and the one drawing the most new entrants; gas separation, increasingly through mixed-matrix membranes,⁷ and gas adsorption and storage⁸ are established; atmospheric water harvesting has advanced from concept toward passive devices,⁵ and newer uses such as direct lithium extraction and contaminant removal are earlier and less crowded. Underlying all of them is the reticular-design principle that lets chemists build frameworks to order for a target function.⁹ Reading the landscape by composition, synthesis route, and application is what separates a crowded region from an open one.
Where the MOF white space is
Water-stable, low-cost frameworks. MOFs that keep performance under humidity and real operating conditions, made by inexpensive routes, are the central bottleneck and a high-value, still-open target.⁴
Scalable synthesis and shaping. Converting powders into pellets, monoliths, and coatings by manufacturable processes is where deployment is decided and where hard-to-design-around process IP concentrates.
Direct air capture sorbents. MOFs tuned for capturing dilute atmospheric carbon dioxide are an active, high-value frontier distinct from point-source capture.⁶
Non-carbon separations. Direct lithium extraction, contaminant and per- and polyfluoroalkyl-substance removal, and other selective separations are earlier and less crowded application layers.
System integration. Contactors, modules, and regeneration systems that turn a MOF into a working unit are a distinct engineering layer separate from the framework chemistry.
How AI-powered landscape and white space analysis helps
Resolving a materials platform that spans many framework chemistries, synthesis routes, and application areas requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by composition, synthesis route, and application across varied terminology, attribution that normalizes academic and commercial filers to canonical entities and tracks new entrants, and continuous monitoring that keeps pace with a field reaching commercial scale. Because MOF advances appear in scientific literature before they are patented, reading both patents and literature gives the earliest signal of where deployable materials are emerging.
Where Cypris fits
Cypris runs patent landscape and white space analysis for materials platforms such as metal-organic frameworks across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by framework composition, by synthesis and shaping route, and by application, carbon capture, gas separation and storage, water harvesting, catalysis, and mineral recovery, and normalizes filers to canonical entities, so a team can resolve which compositions, routes, and applications are crowded and which remain open as white space, and can track new entrants as the field scales. Semantic search across patents and scientific literature connects filings to the underlying materials chemistry research, which is where MOF advances appear first. 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 application 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 metal-organic framework patent landscape? The metal-organic framework patent landscape is the set of patents covering MOFs, porous crystalline materials built from metal nodes and organic linkers, and their uses. It divides across framework compositions, synthesis and shaping processes, and application-level systems. Each application, from carbon capture to gas storage, is a distinct region of the landscape.
Why are MOFs a patenting hotspot now? MOFs are a patenting hotspot now because the field has reached a commercial tipping point, with industrial-scale production beginning for carbon capture and a wave of startups pursuing modular systems. Recognition by the 2025 Nobel Prize in Chemistry has further raised the field's profile. That shift is concentrating new filings in synthesis and application layers.
What application areas does the MOF landscape cover? The MOF landscape covers carbon capture from flue gas and directly from air, gas separation and storage, water harvesting, catalysis, sensing, drug delivery, and recovery of critical minerals such as lithium. Each demands different framework and system properties. Freedom-to-operate and white space analysis must span all of them.
Where is the white space in MOFs? The white space sits where a MOF must survive real conditions cheaply: water-stable, low-cost frameworks and scalable synthesis and shaping are the central bottlenecks, and direct air capture sorbents, non-carbon separations, and system integration are less crowded. The bare framework composition is where foundational estates concentrate. The higher-value opportunities are in deployability.
How is MOF value shifting from composition to manufacturing? MOF value is shifting because, as the field scales, the barrier moves from discovering a framework to making it durable and affordable at volume. Foundational composition estates are concentrated among a few academic groups, while synthesis, shaping, and system-integration IP is where deployment is now decided. That is where much of the defensible, hard-to-design-around value sits.
Why does MOF analysis need scientific literature? MOF analysis needs scientific literature because new frameworks, synthesis routes, and application concepts appear in materials research before they are patented, so the literature gives the earliest signal. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.
What software helps analyze the MOF patent landscape? Software for the MOF landscape should cluster activity by framework composition, synthesis route, and application, resolve academic and commercial filers to canonical owners, search patents and scientific literature semantically, and monitor a scaling 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.
Which teams use MOF patent landscape analysis? MOF patent landscape analysis is used by R&D, innovation, IP, and strategy teams at chemicals, materials, carbon-capture, and energy companies, as well as investors and research institutions. It informs where to invest, where to file, and where competitors are concentrated. Cypris serves hundreds of enterprise customers across chemicals, advanced materials, energy, and other regulated industries.
Endnotes
- O'Keeffe, M., Cordova, K. E., Furukawa, H., & Yaghi, O. M. (2013). The chemistry and applications of metal-organic frameworks. Science, 341(6149). https://doi.org/10.1126/science.1230444
- Li, H., Rampal, N., & Yaghi, O. M. (2025). Reticular chemistry: past, present, and future. Molecular Frontiers Journal. https://doi.org/10.1142/s2529732525300034
- Royal Swedish Academy of Sciences (2025, October 8). The Nobel Prize in Chemistry 2025 [press release]. https://www.nobelprize.org/prizes/chemistry/2025/press-release/
- Furukawa, H., Queen, W. L., Yaghi, O. M., et al. (2014). Water adsorption in porous metal-organic frameworks and related materials. Journal of the American Chemical Society, 136(11). https://doi.org/10.1021/ja500330a
- Diercks, C. S., Kalmutzki, M. J., & Yaghi, O. M. (2018). Metal-organic frameworks for water harvesting from air. Advanced Materials, 30(37). https://doi.org/10.1002/adma.201704304
- Yao, M.-S., et al. (2023). Direct air capture of CO2 in designed metal-organic frameworks at lab and pilot scale. Carbon Capture Science & Technology, 8. https://doi.org/10.1016/j.ccst.2023.100145
- Chai, M., Hou, J., & Chen, R. (2023). Metal-organic framework-based mixed matrix membranes for gas separation: recent advances and opportunities. Carbon Capture Science & Technology, 8. https://doi.org/10.1016/j.ccst.2023.100130
- Sculley, J., Yu, J., Zhou, H.-C., et al. (2011). Carbon dioxide capture-related gas adsorption and separation in metal-organic frameworks. Coordination Chemistry Reviews, 255(15-16). https://doi.org/10.1016/j.ccr.2011.02.012
- Chen, Z., Kirlikovali, K. O., Li, P., & Farha, O. K. (2022). Reticular chemistry for highly porous metal-organic frameworks: the chemistry and applications. Accounts of Chemical Research, 55(4). https://doi.org/10.1021/acs.accounts.1c00707
Lipid nanoparticles are the delivery system that made mRNA medicines practical, and their patent landscape is distinctive because the delivery layer, rather than the therapeutic payload, is frequently the binding freedom-to-operate constraint. An LNP is built from four carefully selected lipid components, an ionizable lipid that carries the nucleic acid and enables its release inside the cell, a helper phospholipid, cholesterol, and a PEG-lipid that stabilizes the particle, combined in specific molar ratios and manufactured by a defined process.¹ The ionizable lipid is the primary determinant of potency, protonating in the acidic endosome to release the cargo, which is why it is the most heavily engineered and contested element,² and the lipid molar ratio is a first-order formulation variable that developers optimize through statistical design-of-experiments screens.³ Each of these elements can be claimed independently, and the ionizable lipid and the molar-ratio composition are the most heavily contested, so freedom-to-operate for an mRNA vaccine, an RNA therapeutic, or a gene-editing product delivered by LNP is a layered analysis across many owners rather than a single clearance of the drug substance.
The landscape is dense, multi-owner, and among the most litigated in biotechnology. The foundational LNP work traces to a small set of academic and company lineages, and rights have been licensed to many developers, so a single product can implicate several estates at once. The stakes are large: in March 2026, Genevant Sciences and Arbutus Biopharma reached a global settlement with Moderna resolving their lipid-nanoparticle patent dispute for up to $2.25 billion, comprising a $950 million upfront payment and a further $1.3 billion contingent on a pending appellate ruling over a government-use defense.⁴,⁵,⁶ Multiple parallel lipid-nanoparticle suits remain pending across US, European, and Canadian forums, and outcomes have turned on the specific patents asserted rather than on any single view of the technology. The concentration of rights is visible in the patent record: across the Cypris corpus of more than 500 million patents and scientific papers, the LNP and ionizable-lipid space holds on the order of 29,400 de-duplicated families, with filings inflecting sharply during the COVID-19 period, roughly tripling between 2020 and 2023, and the most active assignees, led by mRNA and RNA-therapeutics developers, mapping onto the same entities visible in the litigation; the United States leads on geography, followed by China, with a notable Canadian share reflecting the field's foundational lipid lineage. Because applications publish about eighteen months after filing, the newest lipid, targeting, and process filings are under-represented, so the current frontier is more active than granted-patent counts suggest.
The practical consequence is that delivery IP now shapes the economics of the entire RNA field. A developer typically needs freedom to operate on the ionizable lipid and the composition, plus the formulation and manufacturing process, and that can mean licensing from or designing around several holders. The durable value is concentrating in novel ionizable lipids, where iterative and structure-activity design continues to yield new, patentable chemistries,⁷ down to fine distinctions such as lipid isomerism that measurably change performance,⁸ in compositions that fall outside the contested molar-ratio claims, in targeting chemistries that reach tissues beyond the liver, and in manufacturing processes. Reading the landscape by lipid, layer, and owner, and tracking the live proceedings, is what separates a workable position from a blocked one.
What creates FTO risk in LNP delivery
Ionizable lipid claims. These cover the structures that carry and release the nucleic acid, the most heavily contested layer and the frequent center of litigation.²
Molar-ratio and composition claims. These cover the specific percentage ranges of the four lipid components, a layer that can block a formulation independently of the individual lipids.³
PEG-lipid and helper-lipid claims. These cover the stabilizing and structural lipids, a distinct and separately owned layer.
Formulation and manufacturing claims. These cover the process by which LNPs are assembled at scale, where practical, hard-to-design-around barriers concentrate.
Targeting and application claims. These cover tissue-targeting chemistries and specific cargo applications, so a delivery system can be free for one use and blocked for another.
How AI-powered landscape and FTO analysis helps
A dense, multi-owner, heavily litigated delivery landscape is beyond manual clearance. AI-powered analysis addresses this with semantic search that retrieves relevant ionizable-lipid, composition, PEG-lipid, formulation, and targeting claims regardless of terminology, attribution that resolves the many company and academic owners to canonical entities and captures the license chains, claim-level analysis that separates the layers, and continuous monitoring that tracks new filings and the live disputes. Because delivery advances appear in scientific literature before they are patented, reading both patents and literature gives earlier warning of where the field is extending.
Where Cypris fits
Cypris runs patent landscape and freedom-to-operate analysis for dense, contested fields such as LNP delivery across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters the landscape by layer, ionizable lipid, composition, PEG-lipid, formulation, and targeting, and normalizes company and academic owners to canonical entities, so a team sees how rights are distributed across the web of holders rather than a flat list. Semantic search across patents and scientific literature surfaces relevant claims regardless of terminology and connects filings to the underlying chemistry research, which is where novel lipids and targeting approaches emerge first. Cypris Q, the platform's agentic layer, lets teams run landscape and FTO analysis conversationally and chain the attribution, clustering, and claim-level analysis across layers, and Agentic Monitoring tracks the landscape over time and flags new filings and developments 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 lipid nanoparticles? Freedom-to-operate is hard for lipid nanoparticles because an LNP is built from four lipid components combined in specific ratios by a specific process, each independently patentable and held across many owners. The ionizable lipid and molar-ratio composition are especially contested. FTO must be assessed layer by layer across multiple estates, often for a delivery system rather than the drug itself.
Why is LNP the binding constraint for RNA products? LNP is frequently the binding constraint because delivery, not the nucleic acid payload, is the hardest part of an RNA medicine, and the delivery IP is densely held. A product can clear its therapeutic sequence and still be blocked on the lipid or the composition. That is why delivery litigation has been so consequential.
What claim types create FTO risk in LNP delivery? Five claim types create FTO risk: ionizable-lipid claims, molar-ratio and composition claims, PEG-lipid and helper-lipid claims, formulation and manufacturing claims, and targeting and application claims. Each covers a distinct layer and can independently block a product. Ionizable lipids and molar ratios are the most litigated.
Why has LNP patent litigation been so significant? LNP patent litigation has been significant because the technology enabled a very large market, and rights are held across several estates traceable to a few foundational lineages. Disputes over ionizable lipids, molar ratios, and formulation have produced high-value cases and settlements across jurisdictions, including a multi-billion-dollar 2026 settlement between Genevant and Arbutus and Moderna. Outcomes turn on the specific patents asserted rather than a single view of the technology.
Where is the white space in LNP delivery? The white space sits in novel ionizable lipids, compositions outside the contested molar-ratio claims, targeting chemistries that reach tissues beyond the liver, non-PEG stabilization, and manufacturing processes. The core lipid and composition ground is crowded and litigated. The durable, defensible value is in these newer chemistry and process layers.
Why does LNP analysis need scientific literature? LNP analysis needs scientific literature because new lipids, targeting chemistries, and formulation advances appear in research before they are patented, so the literature gives the earliest signal. Analyzing patents alone gives a lagging view. Cypris analyzes both across more than 500 million patents and scientific papers.
What software helps analyze the LNP delivery patent landscape? Software for the LNP delivery landscape should resolve the many company and academic owners and license chains to canonical entities, cluster the ionizable-lipid, composition, formulation, and targeting layers, search patents and scientific literature semantically, and monitor active litigation and new filings 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.
Which teams need LNP patent landscape and FTO analysis? LNP patent landscape and FTO analysis is needed by R&D, IP, and business-development teams at mRNA, RNA-therapeutic, vaccine, and gene-editing companies, as well as investors assessing RNA assets. Because delivery is often the binding constraint, structured analysis is essential. Cypris serves hundreds of enterprise customers across pharmaceuticals and other research-intensive industries.
Endnotes
- Liu, S., Zhang, X., Zhang, Y., & Gao, Y. (2024). Principles of lipid nanoparticle design for mRNA delivery. BMEMat. https://doi.org/10.1002/bmm2.12116
- Han, X., Tang, X., & Zhang, Y. (2023). Ionizable lipid nanoparticles for mRNA delivery. Advanced NanoBiomed Research, 3. https://doi.org/10.1002/anbr.202300006
- Fenton, O. S., Anderson, D. G., et al. (2015). Optimization of lipid nanoparticle formulations for mRNA delivery in vivo with fractional factorial and definitive screening designs. Nano Letters, 15(11). https://doi.org/10.1021/acs.nanolett.5b02497
- Genevant Sciences & Arbutus Biopharma (2026, March 3). Genevant Sciences and Arbutus Biopharma announce $2.25 billion global settlement with Moderna. https://www.genevant.com/genevant-sciences-and-arbutus-biopharma-announce-2-25-billion-global-settlement-with-moderna
- Roivant Sciences (2026). Settlement disclosure (Exhibit 99.1), U.S. Securities and Exchange Commission. https://www.sec.gov/Archives/edgar/data/1635088/000114036126007548/ef20067067_ex99-1.htm
- Arbutus Biopharma (2026, March 3). Form 8-K. https://investor.arbutusbio.com/static-files/f6868345-37b9-4bd3-9ba3-799e754e6ce1
- Manning, A. M., Khan, O. F., et al. (2023). Iterative design of ionizable lipids for intramuscular mRNA delivery. Journal of the American Chemical Society, 145(4). https://doi.org/10.1021/jacs.2c10670
- Zuo, T., He, Z., Li, Z., et al. (2026). Unraveling the role of ionizable lipid isomerism in modulating lipid nanoparticles for mRNA delivery. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.5c20438