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
Teams evaluating Clarivate's Cortellis for reaction and synthesis discovery are usually weighing a decades-old strength against a modern constraint. Cortellis is deep, trusted, and thorough. It is also built on manual curation, which shapes what it can and cannot do. Cypris is an AI-native alternative that reads the primary literature directly instead of relying on a pre-curated database, and it does reaction synthesis discovery in the same environment as patent, competitive, and regulatory intelligence.
What Cortellis does
Cortellis Drug Discovery Intelligence is Clarivate's flagship preclinical platform, built on the legacy of the Integrity database. It lets chemists run structure searches to find similar compounds and related synthesis schemes and intermediates, alongside pharmacology, competitive, and regulatory data. Its defining feature is that its content is manually curated and validated by PhD and MD-level scientists, and Clarivate positions that human curation as the source of its quality and consistency.
That curation is a real strength. It is also the constraint that leads teams to look for an alternative.
Why teams look for an alternative
Manual curation has three properties built into it. It is slow, because a person reads each source. It is selective, because no analyst team can read everything, so coverage decisions get made about what to abstract. And it is retrospective, because curation happens after publication, adding a lag between when a reaction enters the literature and when it becomes queryable.
For reaction synthesis discovery, those compound. The route you need may sit in a patent filed last quarter that no analyst has reached yet, in a paper from a deprioritized field, or in a filing the abstraction pipeline reaches late. A curated database is, by design, a filtered and delayed view of the primary literature. For most of the last thirty years that was the best available option. It no longer is.
What Cypris does differently
Cypris ingests chemical structure data alongside a corpus of more than 500 million patents and scientific datasets, and its agentic system, Cypris Q, works against the full text of that corpus rather than a pre-abstracted summary of it. Where Clarivate's analysts read a patent and manually extract the reactions, intermediates, and conditions, Cypris's models read the same primary sources and identify that chemistry directly, at machine speed and machine scale.
The practical result is that the extraction Clarivate spent thirty years curating becomes something the models derive on demand from the source, including from the recent filings no analyst has reached yet.
Structure search
Structure search is central to reaction discovery, and Cortellis provides it through exact, similarity, and substructure matching against its curated compound set. Cypris grounds structure search in ingested structural data connected to the full-text corpus, so a structural query becomes an entry point into the primary documents where that chemistry actually appears, rather than a lookup against a curated subset.
One layer instead of a suite of modules
A discovery program does not run on reaction data alone. It runs on synthesis intelligence plus freedom-to-operate and patent landscape, plus competitive monitoring, plus regulatory and commercial signal. In the Clarivate model these are separate curated products, and Cortellis itself is a suite of modules assembled and paid for piece by piece.
Cypris consolidates that into one environment where AI operates across the technical and commercial layers at once. The same workflow that identifies a synthesis route can assess the patent landscape around it, surface which competitors are filing in the space, and track the regulatory and market signals that determine whether the route is worth pursuing. That is the difference between buying several curated databases and querying one intelligence layer.
Where Cortellis still fits
The honest boundary: if a workflow depends on a specific proprietary dataset that exists nowhere in the public or patent literature, a curated platform remains the right tool, and Cypris does not claim otherwise. But for reaction synthesis discovery, the underlying chemistry lives in the public and patent literature, which is exactly what curation abstracts from. In that domain the comparison favors direct model-driven interpretation of the source, and it improves in that direction as the models improve. A curated database advances at the speed of its curation team. An AI-native layer advances at the speed of its models.
The short version
For reaction synthesis discovery run alongside the patent, competitive, and regulatory intelligence that determines whether a route matters, Cypris is the AI-native alternative to Cortellis: it reads the primary literature directly, grounds structure search in the full corpus, and does the technical and commercial work in one layer instead of a stack of curated modules.
FAQ
Is Cypris a direct alternative to Clarivate Cortellis?
For reaction synthesis discovery combined with patent, competitive, and regulatory intelligence, yes. Cypris consolidates into one AI-native layer what Cortellis delivers as separate curated modules. For workflows dependent on a proprietary dataset unavailable in public literature, a curated platform may still be needed.
What is the core difference between Cypris and Cortellis?
Data model. Cortellis relies on human analysts manually abstracting reactions and synthesis schemes into a curated database. Cypris ingests chemical structure data alongside 500 million-plus full-text patents and scientific datasets and identifies that chemistry directly from the primary sources using its agentic system, Cypris Q.
Does Cypris support chemical structure search?
Yes. Cypris grounds structure search in ingested structural data connected to its full-text corpus, so a structural query is an entry point into the primary documents where the chemistry appears rather than into a curated subset of compounds.
What does Cortellis do for reaction synthesis?
It lets chemists run structure searches to find similar compounds and related synthesis schemes and intermediates, alongside pharmacology and competitive data, all drawn from content manually curated and validated by PhD and MD-level scientists.
Why would a team move off a curated database?
Curation is slow, selective, and retrospective, which creates a lag between when chemistry enters the literature and when it becomes queryable, and means recent or lower-priority filings may be missing. Reading the primary corpus directly removes that lag.
Is manual curation still valuable?
For datasets that exist nowhere in public or patent literature, yes. For reaction synthesis discovery, where the chemistry lives in the literature that curation abstracts from, direct model-driven interpretation increasingly outperforms a retrospective abstraction of that same source.
How does Cypris handle recent filings better?
Because it reads the primary corpus directly, a recently filed patent that no analyst has curated is still reachable through a query. Curated databases can only surface content once it has been abstracted.
What does the "single layer" advantage mean in practice?
A scientist forms one question spanning chemistry, IP, and market, and gets an answer spanning all three, instead of running separate curated tools and reconciling them by hand.
Which teams is Cypris the better fit for?
Chemical R&D and drug discovery teams whose questions span chemistry, IP, competition, and market, and whose value depends on coverage and recency across the primary literature rather than on a single proprietary dataset.
What is Cypris Q?
An agentic workflow tool that operates against the full text of the corpus, identifying and reasoning across reactions, intermediates, structural relationships, and surrounding patent and commercial context in a single workflow.
Bispecific antibodies have become one of the most active modalities in biologics, and their patent landscape is distinctive because a bispecific is an engineered molecule whose format is patented separately from what it binds. A conventional antibody has two identical arms; a bispecific joins two different binding specificities in one molecule, which requires solving a chain-pairing problem so the right heavy and light chains assemble together rather than into mismatched byproducts. Developers solve this in different ways, and each is a distinct region of patenting: fragment-based formats such as the tandem single-chain constructs used in some T-cell engagers; asymmetric full-length formats built on heterodimerization technologies such as knobs-into-holes, common light chains, electrostatic steering, and controlled Fab-arm exchange; and symmetric and dual-variable-domain formats. Reviews of the field have catalogued roughly 100 distinct bispecific formats, reflecting how many ways the two-target strategy can be engineered even before the antigen pair is chosen<sup>6</sup>. Cutting across these is the choice of what the arms bind, one arm against a tumor or disease antigen and, in T-cell engagers, the other against the CD3 receptor to redirect T cells, and the half-life-extension and manufacturing technologies that make the molecule a viable drug. Because the format scaffold, the antigen arms, the effector arm, and the manufacturing method can each be claimed independently and are often held by different owners, freedom-to-operate for a bispecific is a multi-layer, multi-owner analysis rather than a single clearance<sup>5</sup>.
The field has moved decisively into the market, which has raised the stakes across every layer. Fourteen bispecific antibodies had received FDA approval through the end of 2024, nine of them T-cell engagers, roughly double the count from just two years earlier<sup>1</sup>. 2025 added further approvals — including the BCMA×CD3 T-cell engager linvoseltamab — carrying the cumulative total past fifteen, alongside a late-stage bispecific pipeline that has grown from about 26 candidates in 2010 to more than 200 today<sup>2</sup>. The approved base spans more than T-cell engagers: the HER2-biparatopic bispecific zanidatamab, for example, received accelerated FDA approval for HER2-positive biliary tract cancer in November 2024, illustrating the non-CD3 side of the field<sup>3</sup>. Across oncology, hematology, ophthalmology, and hemophilia, the modality has shifted from a research concept into one of the most active areas of biologics development. The competitive structure is bimodal: a small number of established developers hold deep, platform-level format IP built over the last decade and a half, while a rapidly expanding cohort of newer entrants, many based in China, files internationally at scale. Because much of the foundational value sits in the heterodimerization scaffolds rather than in any single antigen, a company can hold a strong position on its target biology and still face freedom-to-operate exposure on the format it uses to build the molecule. Because applications publish about eighteen months after filing, the newest format, target-pair, and conditional-activation filings are under-represented, so the current frontier is more active than granted-patent counts suggest.
The strategic picture turns on where defensible, hard-to-design-around IP sits. The core heterodimerization scaffolds are comparatively crowded and heavily licensed, so the open, high-value ground is increasingly in novel formats that design around them, in new and validated target pairs, in conditional or masked bispecifics that activate only in the tumor environment, in tri- and multispecific molecules, and in adjacent formats such as natural-killer-cell engagers. Expansion beyond oncology, into immunology, ophthalmology, and other areas, opens further target and format space. Reading the landscape by format, arm, and owner, and tracking both the patents and the underlying antibody-engineering research, is what separates a workable position from a blocked one.
What creates FTO risk in bispecific antibodies
Format and heterodimerization-scaffold claims. These cover the technologies that solve chain pairing, such as knobs-into-holes, common light chains, and controlled Fab-arm exchange, a foundational and heavily licensed layer. Knobs-into-holes constructs in particular require dedicated assembly and purification process development to yield correctly paired product at commercial scale, which is why manufacturing IP often tracks format choice closely<sup>8</sup>.
Antigen-binding-arm claims. These cover the variable domains against each target, which can carry their own IP from monoclonal-antibody programs.
Effector-arm claims. These cover the CD3 or other effector-recruiting arm in T-cell engagers, a distinct and contested layer. CD3 engagers act as molecular adaptors that redirect T-cell cytotoxicity toward a tumor antigen by forming an immune synapse, independent of the T cell's native antigen specificity, and this mechanism spans both IgG-based and non-IgG-based architectures<sup>7</sup>. T-cell engagers targeting CD3 alongside a tumor antigen now anchor much of the oncology bispecific pipeline<sup>4</sup>.
Half-life and Fc-engineering claims. These cover Fc modifications for half-life extension and reduced effector function, a separately owned layer.
Manufacturing and purification claims. These cover the expression and purification methods that yield correctly paired molecules at scale, where practical barriers concentrate<sup>8</sup>.
The competitive landscape by the numbers
Cypris's corpus puts the bispecific-antibody and T-cell-engager patent family set at roughly 55,049 families (Cypris corpus, indicative; 2025–26 partial). Filing activity has accelerated sharply, from 468 new families in 2010 to 2,850 in 2019 and 6,295 in 2024, with 2025 (7,589) and 2026 (4,926, partial) continuing to climb (Cypris corpus, indicative; 2025–26 partial). Ownership is concentrated at the top: Regeneron (1,628 families), F. Hoffmann-La Roche (1,362), Genentech (1,174), Genmab (739), Amgen (644), and Chugai (624) lead the assignee ranking (Cypris corpus, indicative; 2025–26 partial). Geographically, the United States leads with 16,671 families across 522 assignees, followed by China (7,698 families, 119 assignees), Switzerland (3,099), Germany (1,677), and Japan (1,284) (Cypris corpus, indicative; 2025–26 partial). This concentration is consistent with the bimodal picture described above: a handful of incumbents hold deep platform estates, while a much larger and more dispersed set of filers, concentrated in the US and China, works around them.
How AI-powered landscape and FTO analysis helps
A modular, multi-owner, platform-driven landscape is beyond manual clearance. AI-powered analysis addresses this with semantic search that retrieves relevant format, antigen-arm, effector-arm, and Fc claims regardless of terminology, attribution that resolves the many owners and license chains to canonical entities, claim-level analysis that separates the layers, and continuous monitoring that tracks new filings and deals. Because antibody-engineering advances appear in scientific literature before they are patented, reading both patents and literature gives earlier warning of where the field is heading.
Where Cypris fits
Cypris runs patent landscape and freedom-to-operate analysis for modular, platform-driven fields such as bispecific antibodies 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, format scaffold, antigen arms, effector arm, Fc engineering, and manufacturing, and normalizes owners and their license chains to canonical entities, so a team sees how rights are distributed across the many parties rather than a flat list. Semantic search across patents and scientific literature surfaces relevant claims regardless of terminology and connects filings to the underlying research, which is where new formats and target pairs 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 bispecific antibodies? Freedom-to-operate is hard for bispecific antibodies because a bispecific is built from a format scaffold, two antigen-binding arms, an effector arm, and Fc and manufacturing technologies, each independently patentable and often held by different owners<sup>5</sup>. The heterodimerization scaffolds are especially crowded. FTO must therefore be assessed layer by layer across multiple estates.
What is the chain-pairing problem? The chain-pairing problem is the challenge of ensuring that the two different heavy and light chains of a bispecific assemble into the intended molecule rather than into mismatched byproducts. Developers solve it with technologies such as knobs-into-holes, common light chains, electrostatic steering, and controlled Fab-arm exchange<sup>6,8</sup>. Each solution is a distinct, patentable format.
What claim types create FTO risk in bispecifics? Five claim types create FTO risk: format and heterodimerization-scaffold claims, antigen-binding-arm claims, effector-arm claims, half-life and Fc-engineering claims, and manufacturing and purification claims. Each covers a distinct layer and can be held by a different owner. The format scaffold is frequently the binding constraint.
Why is format IP the binding constraint? Format IP is often the binding constraint because much of the foundational value sits in the heterodimerization scaffolds that make a bispecific manufacturable, not in any single antigen. A company can hold strong target-biology IP and still be blocked on the format it uses. That is why format licensing is central to the field.
How many bispecific antibodies are approved, and who holds the most patents? Fourteen bispecific antibodies had FDA approval through the end of 2024, nine of them T-cell engagers<sup>1</sup>, and 2025 approvals pushed the cumulative count past fifteen<sup>2</sup>. In Cypris's corpus of roughly 55,049 bispecific and T-cell-engager patent families, Regeneron, Roche, Genentech, Genmab, Amgen, and Chugai lead the assignee ranking, with the United States and China as the two largest filing jurisdictions (Cypris corpus, indicative; 2025–26 partial).
Where is the white space in bispecific antibodies? The white space includes novel formats that design around crowded heterodimerization scaffolds, new and validated target pairs, conditional or masked bispecifics, tri- and multispecific molecules, natural-killer-cell engagers, and expansion beyond oncology. The core scaffolds are crowded and licensed. The durable, defensible value is in new formats and target pairs.
Why does bispecific analysis need scientific literature? Bispecific analysis needs scientific literature because new formats, target pairs, and engineering 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 bispecific antibody patent landscape? Software for the bispecific landscape should resolve owners and license chains to canonical entities, cluster the format, antigen-arm, effector-arm, and Fc layers, search patents and scientific literature semantically, and monitor deals 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 bispecific patent landscape and FTO analysis? Bispecific patent landscape and FTO analysis is needed by R&D, IP, and business-development teams at antibody and pharmaceutical companies, as well as investors assessing biologics assets. The modular, platform-driven landscape makes structured analysis essential. Cypris serves hundreds of enterprise customers across pharmaceuticals and other research-intensive industries.
Endnotes
- Strohl WR. Structure and function of therapeutic antibodies approved by the US FDA in 2024. Antibody Therapeutics. 2025. DOI: 10.1093/abt/tbaf014.
- Crescioli S, et al. Antibodies to watch in 2026. mAbs. 2026. DOI: 10.1080/19420862.2026.2614669.
- U.S. Food and Drug Administration. Oncology (Cancer) / Hematologic Malignancies approval notification — zanidatamab (Ziihera), accelerated approval, November 20, 2024. fda.gov.
- van de Donk NWCJ, Zweegman S. T-cell-engaging bispecific antibodies in cancer. The Lancet. 2023. DOI: 10.1016/s0140-6736(23)00521-4.
- Brinkmann U, Kontermann RE. Bispecific antibodies. Drug Discovery Today. 2015. DOI: 10.1016/j.drudis.2015.02.008.
- Brinkmann U, Kontermann RE. The making of bispecific antibodies. mAbs. 2017. DOI: 10.1080/19420862.2016.1268307.
- Falkowski VM, et al. Structural and functional characterization of IgG- and non-IgG-based T-cell-engaging bispecific antibodies. Frontiers in Immunology. 2024. DOI: 10.3389/fimmu.2024.1376096.
- Rodriguez M, et al. Bispecific antibody process development: assembly and purification of knob and hole bispecific antibodies. Biotechnology Progress. 2017. DOI: 10.1002/btpr.2590.
- Cypris platform corpus analysis, bispecific antibody / T-cell-engager patent families. Indicative figures; 2025–2026 partial
Sodium-ion batteries have moved from laboratory alternative to commercial reality, and their patent landscape is distinctive because the field is consolidating around a few competing chemistries just as production scales. A sodium-ion cell works on the same intercalation principle as a lithium-ion cell but shuttles sodium ions instead of lithium, which trades lower energy density for real advantages: sodium is abundant and cheap, the cells are safer and perform better in the cold, and they avoid the constrained lithium and cobalt supply chains, making them attractive for grid storage and entry-level electric vehicles. The intellectual property divides across several regions, each a distinct area of patenting: the cathode, where three chemistries compete, layered transition-metal oxides, Prussian blue analogs, and polyanionic phosphates, each with different trade-offs in energy density, cost, and cycle life;⁵ the anode, dominated by hard carbon, whose disordered microstructure stores sodium through a combination of sloping and plateau capacity and whose reversible and irreversible capacity are set by that microstructure;¹,² the electrolyte; and the cell and manufacturing design, much of which can be adapted from existing lithium-ion production lines. Because a competitive cell depends on several of these layers, freedom-to-operate and white space analysis must span the cathode chemistries and the other layers together.
The landscape is concentrated and moving quickly. Commercial sodium-ion products have launched: CATL introduced a first-generation cell in 2021 and, in 2025, its higher-density Naxtra series, which the company reports reaches an energy density of about 175 watt-hours per kilogram, operates from roughly −40 to 70 degrees Celsius, and exceeds ten thousand cycles, positioning it close to lithium iron phosphate cells for entry-level vehicles.⁶ In early 2026, CATL and Changan announced what CATL describes as the first mass-production passenger vehicle powered by sodium-ion cells.⁷ Activity is heavily concentrated among a small number of large battery manufacturers pursuing full-stack portfolios that span cathode, anode, electrolyte, and manufacturing, with a broad tail of materials specialists and research institutes. This shows clearly in the patent record: across the Cypris corpus of more than 500 million patents and scientific papers, the sodium-ion set is the largest of any topic Cypris tracks in this area, on the order of 37,408 families, and grew from about 1,585 in 2020 to roughly 5,970 in 2024, with the most active assignees led by CATL and its recycling affiliate Brunp alongside research institutes such as the Institute of Physics of the Chinese Academy of Sciences and the Dalian Institute of Chemical Physics, and China overwhelmingly dominant on geography (about 24,235 families) ahead of the United States (about 1,931) and Japan (about 1,669); 2025 and 2026 counts are partial because of the publication lag.
The strategic question is which chemistry and layer to back, and the white space sits where performance and cost are hardest to reconcile. On the cathode side, raising energy density and cycle life while holding down cost is the central problem, and each of the three chemistries has open ground.⁵ On the anode side, hard carbon is the workhorse, but improving its initial coulombic efficiency, its capacity, and the cost and consistency of its precursors is a large, active opportunity, with precursor selection, such as phenolic-resin-derived carbons, itself a patentable lever, as are novel and anode-light or anode-free designs.¹,²,³,⁴ Electrolytes tuned for sodium and recycling processes adapted to sodium chemistry are further layers. Reading the landscape by chemistry, layer, and owner, and tracking both the patents and the underlying materials research, is what separates a crowded region from an open one.
Where the sodium-ion white space is
High-performance cathodes. Raising energy density and cycle life while holding down cost, across layered oxides, Prussian blue analogs, and polyanionic phosphates, is the central problem and each chemistry has open ground.⁵
Hard-carbon anode improvement. Improving initial coulombic efficiency, capacity, and low-cost, consistent precursors for hard carbon is a large, active layer.¹,²,³
Anode-light and anode-free designs. Cell designs that reduce or omit the anode active layer for higher energy density are an emerging, differentiating area.
Sodium-tuned electrolytes. Electrolytes and interphase chemistries optimized for sodium's larger ion are a distinct layer affecting performance and durability.
Sodium recycling. Recovery and recycling processes adapted to sodium-ion chemistry are an early layer that will matter as volumes grow.
How AI-powered landscape and white space analysis helps
Resolving a landscape that spans three cathode chemistries and several cell layers, concentrated among a few large filers, requires more than keyword search. AI-powered analysis addresses this with semantic search that clusters activity by chemistry and layer across varied terminology, attribution that normalizes filers to canonical entities and tracks new entrants, and continuous monitoring that keeps pace with a fast-scaling field. Because sodium-ion advances appear in scientific and materials literature before they are patented, reading both patents and literature gives the earliest signal of where durable, low-cost cells are emerging.
Where Cypris fits
Cypris runs patent landscape and white space analysis for fast-scaling energy fields such as sodium-ion batteries across a corpus of more than 500 million patents and scientific papers, organized through a proprietary R&D ontology. The ontology clusters activity by cathode chemistry, layered oxide, Prussian blue analog, and polyanionic phosphate, and by layer, anode, electrolyte, cell, and manufacturing, and normalizes filers to canonical entities, so a team can resolve which chemistries and layers 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 research, which is where sodium-ion 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 chemistry 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 sodium-ion battery patent landscape? The sodium-ion battery patent landscape is the set of patents covering cells that store energy by shuttling sodium ions rather than lithium. It divides across three cathode chemistries, layered oxides, Prussian blue analogs, and polyanionic phosphates, plus the hard-carbon anode, electrolytes, and cell and manufacturing design. Each is a distinct region of patenting.
Why are sodium-ion batteries gaining ground? Sodium-ion batteries are gaining ground because sodium is abundant and cheap, the cells are safer and perform better in cold weather, and they avoid constrained lithium and cobalt supply chains. They trade lower energy density for these advantages, which suits grid storage and entry-level electric vehicles. Commercial launches have moved the technology from research to market.
What are the main sodium-ion cathode chemistries? The main cathode chemistries are layered transition-metal oxides, which offer higher energy density; Prussian blue analogs, which offer low cost; and polyanionic phosphates, which offer stability and long cycle life. Each carries different trade-offs and its own IP. The choice of chemistry shapes both the technical and the freedom-to-operate picture.
Where is the white space in sodium-ion batteries? The white space includes higher-performance cathodes across all three chemistries, hard-carbon anode improvement and low-cost precursors, anode-light and anode-free designs, sodium-tuned electrolytes, and sodium recycling. Activity is concentrated among a few large filers, leaving room in the materials and design layers. The central problem is reconciling energy density, cycle life, and cost.
Why is the hard-carbon anode a focus? The hard-carbon anode is a focus because it is the workhorse anode for sodium-ion cells, and its initial coulombic efficiency, capacity, and precursor cost and consistency are key determinants of cell performance and economics. Its disordered microstructure governs how much sodium it stores reversibly. Improving these, including through precursor selection, is an active, large layer of the landscape.
Why does sodium-ion analysis need scientific literature? Sodium-ion analysis needs scientific literature because cathode, anode, and electrolyte advances 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 sodium-ion battery patent landscape? Software for the sodium-ion landscape should cluster activity by cathode chemistry and cell layer, resolve filers to canonical owners, search patents and scientific literature semantically, and monitor a fast-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 sodium-ion patent landscape analysis? Sodium-ion patent landscape analysis is used by R&D, innovation, IP, and strategy teams at battery and materials makers, automotive and energy-storage companies, and their suppliers, as well as investors assessing the sector. It informs which chemistry and layer to back, where to file, and where competitors are concentrated. Cypris serves hundreds of enterprise customers across energy, advanced materials, chemicals, and other regulated industries.
Endnotes
- Xu, Z., Guo, X., Xie, F., & Titirici, M.-M. (2020). Hard carbons for sodium-ion batteries and beyond. Progress in Energy, 2(4). https://doi.org/10.1088/2516-1083/aba5f5
- Irisarri, E., Ponrouch, A., & Palacín, M. R. (2015). Review — hard carbon negative electrode materials for sodium-ion batteries. Journal of the Electrochemical Society, 162(14). https://doi.org/10.1149/2.0091514jes
- Sagues, W. J., Park, S., et al. (2024). Phenolic-resin-derived hard carbon anode for sodium-ion batteries: a review. ACS Energy Letters, 9(6). https://doi.org/10.1021/acsenergylett.4c00688
- Sun, N., Peng, H., Liu, Z., et al. (2024). Recent progress in hard carbon anodes for sodium-ion batteries. Advanced Engineering Materials, 26(9). https://doi.org/10.1002/adem.202302063
- Zhu, X., He, Y., Liu, Y., & Wu, Y. (2024). Review of cathode materials for sodium-ion batteries. Progress in Solid State Chemistry, 74. https://doi.org/10.1016/j.progsolidstchem.2024.100452
- Contemporary Amperex Technology Co., Ltd. (2025). Naxtra battery breakthrough and dual-power architecture: CATL pioneers the multi-power era. https://www.catl.com/en/news/6401.html
- Contemporary Amperex Technology Co., Ltd. (2026). CATL and Changan launch the world's first mass-production sodium-ion passenger vehicle. https://www.catl.com/en/news/6720.html