A treatment that works once and keeps working for years is a very different commercial asset from a pill taken every day. The value is created up front. It then has to be defended for decades. That turns the patent vs trade secret question into a horizon question, and it is one every IP strategy should answer before the first filing.
The prompt is a Phase 1 result reported by Cleveland Clinic last month. CTX310, a single-infusion CRISPR therapy developed by CRISPR Therapeutics, switches off the ANGPTL3 gene in the liver. At the highest dose, LDL cholesterol fell 52.5 per cent and triglycerides 47.8 per cent, and the effect held for the full year with no serious treatment-related adverse events. Fifteen patients, early days. But the shape of the asset is already clear.
Durability of effect needs durability of protection
A one-time therapy has no repeat prescription to price. The return has to come from exclusivity over the years in which the market adopts it. The same logic applies outside medicine: a process change, a formulation or an algorithm that permanently lowers a cost base earns over a long horizon, and the protection has to last as long as the earnings do.
The two main forms of protection run on different clocks:
– A patent runs for a fixed term, 20 years from filing, in return for full public disclosure. It does not depend on secrecy surviving, it can be licensed or sold, and it can be enforced against a competitor who develops the same thing independently. But it ends on a known date, and everyone can read it.
– A trade secret has no expiry, but only while the secret holds. It needs constant defending through access controls, contracts and exit procedures, and it gives no remedy against reverse engineering or independent development.
For a therapy that will pass through regulators, clinical publication and contract manufacturers, secrecy for the core invention was never realistic. The gene target, the editing construct and the delivery formulation are patent territory. The manufacturing know-how may be better kept confidential. The decision is rarely one or the other. It is which parts get which form of protection, and how long each will actually hold.
Three questions follow for any board looking at an innovation with a long tail:
– Over what horizon does this earn, and does the protection last that long? The Five-Year IP Roadmap Is Over: How to Build an IP Strategy for a Future You Can’t Predict makes the point that patents outlive most product plans, so the filing has to be written for year 15, not only for today.
– What will the product, the regulator or the customer reveal? Anything they will reveal cannot be a trade secret. What remains genuinely hidden needs the discipline described in When AI is embedded in your Workforce, Trade Secrets Become the Strategy.
– How will exclusivity be extended once the first patent runs down? In pharmaceuticals the answer is layered filings over a public timetable, as The Pharmaceutical Patent Cliff Has a Timetable – And It’s Public shows.
Decide which clock each innovation is running on, then make sure someone is watching it. A result that lasts for decades is only worth what the protection around it lasts.
Read the article: Cleveland Clinic First-In-Human Trial of CRISPR Gene-Editing Therapy Shown to Safely and Continuously Lower Cholesterol and Triglycerides After One Year, Cleveland Clinic Newsroom.
Patent law only pays for outliers. Novelty and inventive step exist to screen out the expected, so a patent portfolio is, by design, a collection of exceptions.
Hold that thought against new research from Harvard, Wharton, Northwestern and Columbia, published in Harvard Business Review on 14 August 2026, on what generative AI actually does inside the innovation pipeline. The finding is uncomfortable: used naively, AI deepens the human bottlenecks it is meant to remove. In ideation, a model gravitates to the statistically typical answer – and once a person reads that answer, they fixate on it and produce fewer unusual ideas than they would have unprompted.
Because most models draw on similar training data, independent teams at different companies converge on the same handful of design directions: individual productivity rises while collective diversity falls. Screening compounds the problem, because committees mistake AI fluency for quality and fund the polished pitch over the original one. For anyone responsible for intellectual property, the translation is blunt. The statistically typical idea is the unpatentable idea.
An innovation pipeline that quietly optimises for the expected is manufacturing prior art, not property. And convergence carries a second cost: the same “invention” is being generated at the same time inside your competitors’ businesses, and in a first-to-file world, whoever turns it into a patent application first owns it.
What AI convergence means for invention harvesting and patent strategy
The durable lessons are about process design, not tool selection.
First, audit the output. Map the past twelve months of invention disclosures and patent filings against the landscape: if your applications cluster where everyone else’s do, AI has homogenised your R&D without anyone deciding it should – and using the tools was never the moat, as I argued in AI Isn’t Your Advantage—Your IP Strategy Is.
Second, redesign the funnel for outliers. The research shows that prompting a model deliberately toward bolder, more distinct territory works, while telling a fixated human to “think more broadly” does not – so build divergence into the machine step, score invention disclosures blind to their polish, and keep a human genuinely directing the inventive work. That last discipline is also what inventorship law demands of a patent that has to survive challenge.
Third, guard the loop itself. The researchers describe an automation trap in which ideation, screening and testing are all mediated by models trained on each other’s output, and the whole system drifts from reality – the same failure of human ownership I examined in AI Transformation Is Not a Strategy Problem — It’s an Ownership Problem. And when a capability is available to everyone, value moves to what cannot be downloaded – the pattern from When Anyone Can Build It in an Afternoon, What’s Left to Own?.
The closing test is simple. If your innovation pipeline would generate the same ideas as your competitor’s, you no longer have an R&D advantage – you have a subscription.
Read the article: Research: The Innovation Problems AI Can’t Solve, Julian De Freitas, Ayelet Israeli, Gideon Nave, Artem Timoshenko and Olivier Toubia, Harvard Business Review, 14 August 2026.
Some assets cannot be built on any timeline a budget can shorten. When that is true, the choice is no longer build, buy or licence. It is licence, or stay out. Bayer’s move into hybrid wheat this year is a clean example, and a useful test of any IP licensing strategy. The world’s largest seed company already runs its own hybrid wheat breeding programme and an established North American wheat franchise. It has still taken an exclusive licence to RAGT’s elite European wheat germplasm rather than breeding an equivalent or buying the French company outright. The stated ambition is a launch in the early 2030s and sales of up to a billion euros a year by the mid-2040s.
Why licence? Germplasm is the genetic base material a breeder starts from. Elite germplasm adapted to European growing conditions is the product of decades of selection, and no amount of capital compresses those decades. RAGT’s head of R&D said the deal reflects the value of genetics its breeders built up over that time. Bayer’s own wheat breeder put it plainly: they chose to license from RAGT because RAGT leads in Europe. For a company that wants to be selling seed by the early 2030s, the licence was the only route that arrived in time.
What a build, buy or licence decision looks like when time is the constraint
The durable lesson is that the licence is not the fallback option here. It is the strategy. Three disciplines follow, and none of them is specific to agriculture.
– Separate the inputs that are money-bound from the inputs that are time-bound. A plant, a sales force or a data centre can be bought. Decades of adapted breeding, a validated clinical dataset or a certified process cannot. Time-bound inputs are licensing decisions, and they need to be priced before the launch date is announced, because once the market knows your timetable the leverage sits with the licensor. As I argued in Capacity Is Not a Moat: What India’s Electrification Build-Out Means for Your IP Strategy, the in-licence is priced from the weakest seat once the capital is committed.
– If you are the one holding the time-bound asset, licensing beats selling. RAGT keeps its genetics, converts decades of breeding into a revenue line, and retains the option to license elsewhere by territory and crop class. That is the position I described in The Cash Behind the Compute: the asset that cannot be acquired on demand is the one everyone else must come to you for.
– Follow the value downstream. A hybrid bred several generations from licensed material blends RAGT genetics, Bayer breeding technology and new data. Much of that sits in know-how and breeding records protected by confidentiality and contract rather than by registered rights. So the agreement has to say who owns the new varieties, the improvements and the data, and who may use them outside the collaboration. Those clauses decide who is paid in the 2040s, and they are being written now.
Which inputs to your next product can only be licensed, and was that licence priced before the timetable went public?
Read the article: Bayer-RAGT IP Licensing Agreement Expands Hybrid Wheat Strategy, Pejman Javaheri, Juris Law Group.
A patent can outlive four corporate strategies, three CEOs and every market assumption it was filed under. Yet most intellectual property portfolios are still planned the way careers used to be – pick a destination, build a roadmap, work steadily towards it.
Writing in Harvard Business Review on 10 August 2026, London Business School professor Lynda Gratton argues that this style of planning has quietly stopped working for executive careers, because two long-term shifts are compounding: working lives are stretching, and AI is making the future of work unpredictable. Her answer is not a better plan. It is a set of decision rules that hold up when the destination keeps moving: run small, low-risk experiments before change forces your hand; invest in the capabilities and relationships that compound over time; and protect time for reflection so that judgement, not momentum, makes the important calls.
Swap “career” for “patent portfolio” and the diagnosis lands even harder. Patents run for twenty years – far longer than most product strategies now survive – and AI is redrawing the technology landscape those filings were meant to cover. A five-year filing roadmap aimed at a fixed destination is a plan for a future that has already declined to cooperate.
What decision rules look like in patent portfolio management
Gratton’s three rules translate directly into IP strategy:
– Experiment before events force commitment. Provisional patent applications, staged filing programs and pilot licences are the IP system’s built-in instruments for buying options cheaply – a provisional is a twelve-month option on a direction you are not yet sure of, and a PCT application defers the expensive jurisdiction calls while the market shows its hand. That is the discipline between betting the whole budget and doing nothing, and doing nothing is getting dearer, as I argued in Why “Wait and See” Is Becoming the Most Expensive IP Decision You Can Make.
– Fund what compounds. Invention harvesting, inventor relationships, trade secret capture and clean chain of title grow more valuable every year they are maintained – and they are exactly the work that gets squeezed when the renewals deadline shouts loudest, the pattern I examined in When Everything Is Urgent, Your IP Portfolio Decides Itself.
– Protect the judgement. The decisions that actually shape portfolio value – what to stop renewing, which jurisdictions no longer earn their place, patent versus trade secret – deserve scheduled reflection, and ideally rehearsal against futures you did not plan for, the exercise I described in Could Your IP Strategy Survive a Wargame?.
A portfolio built on a prediction is only as good as the prediction. A portfolio built on decision rules – optionality bought early, compounding assets funded first, judgement protected from urgency – gets stronger the less predictable the future becomes. If your IP strategy still assumes you know where the business will be in ten years, that is the assumption to review first.
Read the article: The Predictable Executive Career Arc Is Over, Lynda Gratton, Harvard Business Review, 10 August 2026.
Most boards plan around a single date. The patent expires, competitors arrive, the revenue line falls off a ledge. That model is tidy, it sits in the forecast, and for high-value products it is usually wrong.
A recent analysis on DrugPatentWatch works through the loss-of-exclusivity histories of Revlimid, Humira, Eliquis and Keytruda and finds no cliff anywhere in them. Exclusivity ends in steps. A patent term extension here. A secondary or formulation patent there. A settlement date that matches no patent’s printed expiry. A regulatory exclusivity running on its own clock. Humira’s compound patent expired in December 2016. The first US biosimilar launched in January 2023. A forecast keyed to the compound patent was out by more than six years.
Why the exclusivity timeline beats the patent expiry date
The steps are the interesting part, because they are built rather than granted. Revlimid’s generics entered under volume caps fixed by settlement, not by any patent. Eliquis produced two different entry years for the same drug, years apart, depending only on whether a challenger settled or kept litigating. Merck is building a subcutaneous formulation ahead of Keytruda’s core expiry, and that defensive move has already drawn a fresh infringement fight of its own. Every step traces back to a decision someone took years earlier about what to file, what to extend, what to settle and what to reformulate.
Three things follow, and none of them are specific to pharmaceuticals:
– Model the timeline, not the date. What matters commercially is when competition arrives at full strength, and that number is nowhere on the front page of the patent. The Australian extension record makes the same point from the other side, as I set out in The Pharmaceutical Patent Cliff Has a Timetable — And It’s Public: many molecules are defended by three or more separately extended patents, and those dates are a published calendar of when each competitor becomes vulnerable.
– Treat the settlement date as the operative date, and structure it with that in mind. It usually controls, it can fall before the last patent expires, and it carries a long tail of risk – the lesson of The Reverse Payment You Didn’t Know You Made, where the economics of a deal signed in 2014 were still being unwound more than a decade later.
– Build the second layer while the first still has years to run. Formulation, method-of-use, device and delivery rights are what turn one date into several. The discipline is claiming every commercially viable variant before a competitor carves around you, as When Your Strongest Asset Is What You Leave Off the Label shows.
A step you negotiated is also a step someone can take from you. A listed patent is not a tested one, a settlement can be reopened, and a reformulation can invite a new opponent with a portfolio of its own.
So the useful question for your next board paper is not when the patent expires. It is what your exclusivity timeline actually looks like, which steps you built deliberately, and which ones you are relying on without ever having decided to.
Read the article: The Patent Cliff Is a Myth. What Actually Happens Is a Patent Staircase, and Most Models Miss the Steps, DrugPatentWatch.
Intellectual property rights don’t dissolve the moment you stop paying attention to them. That’s the thread running through several developments last week, and it’s worth more attention than most companies give it, because the moment you decide to stop using a mark, retire a product line, or settle a patent fight is precisely the moment your IP position is being tested – whether you notice or not.
Start with Twitter. A Virginia startup called Operation Bluebird has launched Twitter.now, reviving the old blue bird branding under the argument that X Corp abandoned the Twitter and Tweet trademarks when it rebranded in 2023. X disagrees, and is suing in Delaware federal court. What makes this more than a curiosity is a Delaware judge’s comment from the bench earlier this year that X “appears to have abandoned” its rights in Tweet and the bird logo, and possibly the word Twitter itself. No written order has issued, and the case is unresolved. But the lesson doesn’t wait for a verdict: trademark rights are use-based, not sentiment-based. If your business retires a name, a logo, or an old product line, someone else can start building a case that you gave it up – and “we didn’t mean to” is not a defence courts take seriously. Decide deliberately whether you’re keeping a mark alive, and keep evidence of continued use if you are.
Second, Huawei and HP have signed a multiyear global patent cross-licensing deal covering Wi-Fi technology, including the new Wi-Fi 7 standard. This resolves a dispute that started in 2025 when Huawei sued HP over Wi-Fi 6 patents at the Unified Patent Court. Nobody walked away from anything here – HP is paying for continued access, and both sides now hold reciprocal rights to each other’s portfolios. It’s a clean example of patent litigation doing exactly the job it’s designed for: turning an infringement standoff into a priced, ongoing commercial relationship. If your products touch standardised technology, you are already inside someone else’s licensing model, whether you’ve mapped it or not. The interesting move is asking, before a dispute forces the question, whose patents you’re already relying on and what a licence would cost if they came knocking first.
Third, the EUIPO’s Board of Appeal has confirmed that a well-known brand can defeat a copycat purely on reputation, without the earlier mark even needing to cover the same goods. Rovio successfully cancelled a gambling-industry trade mark that echoed Angry Birds’ colour scheme, character design and “crashing birds” concept, on the basis that the applicant’s dishonest intent could be inferred from the strength of Rovio’s reputation alone. This is a materially easier enforcement path than most brand owners realise exists: you don’t need a matching registration if your reputation does the work instead. It also means the return on building genuine brand recognition compounds in ways beyond marketing – it becomes a legal asset in its own right.
The pattern across all three: IP rights are shaped by what you actually do, documented and provable, not by what you intended. X Corp’s intentions about Twitter don’t matter if its conduct reads as abandonment. Huawei’s and HP’s dispute didn’t need to end in court once both sides priced the alternative. Rovio’s decades of visible brand-building is now doing enforcement work that no registration alone could achieve.
None of this is really about trademarks or patents specifically – it’s about whether your IP strategy is a record of decisions you actually made, or a set of assumptions nobody has tested. The three questions worth asking this week:
(1) What have we stopped using that we haven’t formally decided to abandon?
(2) Whose technology are we relying on without a licence? and
(3) Is our own reputation strong enough to do enforcement work if a registration alone won’t cut it?
Related reading: Why IP Strategies Fail: The Gap Between the Decision and the Portfolio, The $60,000 Business Name: Why an ASIC Search Is Not a Trade Mark Search and Who Gets Paid for the Platform? Lessons From a Big Week in AI and Intellectual Property.
Sources for this post
- Twitter.now / X Corp v Operation Bluebird – TechCrunch, 27 August 2026; primary docket at CourtListener, X Corp. v. Operation Bluebird, Inc., 1:25-cv-01510.
- Huawei/HP Wi-Fi patent cross-licence – Huawei press release, 26 August 2026; South China Morning Post commentary.
- EUIPO Angry Birds bad faith decision – EUIPO Board of Appeal decision R1791/2025-4; The IPKat, 27 August 2026.
Most IP portfolios are not shaped by strategy. They are shaped by whatever was urgent that week. Renewals get paid because the deadline arrived. Examination responses get filed because the clock was running. Meanwhile, the decisions that actually determine the value of an intellectual property portfolio – what to stop protecting, which jurisdictions no longer earn their place, which inventions justify the next filing – wait for a quiet month that never comes.
Writing in Harvard Business Review on 27 July 2026, executive coach Karen Walker gives this condition a name: ambient urgency. It is the chronic, low-grade state in which everything feels critical, nothing can wait, and decision quality quietly degrades. Her prescription is not another prioritisation framework. It is three tests to run before committing your attention: the replacement test (am I making this decision because only I can make it, or because I am the default?), the compounding test (will this create value that grows over time, or does it just relieve today’s pressure?) and the fear test (am I deferring this call because it is wrong, or because I don’t want to face its consequences?).
What the three tests look like in IP portfolio management
Run those tests against your portfolio and the results are usually uncomfortable. Renewal decisions made by habit fail the replacement test – as I argued in Why IP Strategies Fail: The Gap Between the Decision and the Portfolio, most companies’ IP spending correlates more than 90% with last year’s, which means nobody is really deciding at all. Invention harvesting, inventor relationships and trade secret capture pass the compounding test, and they are exactly the work ambient urgency squeezes out first – the slow erosion I described in The Quiet Decay: Why IP Value Slips When No One Is Watching. And portfolio pruning fails the fear test almost everywhere. Abandoning a granted patent or dropping a jurisdiction reads as an admission that an earlier decision no longer holds, so a company keeps paying renewals in fourteen countries for a product line it exited three years ago. Three questions are worth putting to your next patent portfolio review or IP audit:
– Which IP decisions genuinely need you – the patent-versus-trade-secret calls, the enforcement calls, the direction-setting – and which are you making by default?
– What in this year’s IP budget compounds, and what merely keeps the machine turning?
– What are we still protecting only because ending it would feel like failure?
The test of clear allocation is the same one that applies to any capital decision – the discipline I explored in The Bet You Make Before You Make Any Bet. An IP strategy is a record of trade-offs, made consciously. If your portfolio only ever grows, urgency is making your decisions for you.
Read the article: 3 Questions to Pressure-Test Your Priorities, Karen Walker, Harvard Business Review, 27 July 2026.
If a capability you spent years building can be downloaded on a Sunday afternoon, the question is not how to stop that. It is what you own that survives it. Ben Thompson’s recent Stratechery piece works through the economics of the new Chinese open-weight models and lands somewhere useful for anyone thinking about IP. Open weights are not free. They shift spending out of R&D, which is fixed, and into cost of goods sold, which is not. Serving a model costs real money every time, so the winner in a commoditised market is not the one who charges more. It is the one with the better cost structure. Thompson also makes the point that tokens are not fungible but intelligence is, which is a precise way of saying the capability commoditises while the efficiency of delivering it does not. That is the same problem in a new setting: when anyone can build it in an afternoon, what is left to own is a question about defensibility, not about speed. The model itself was never the asset. As I have said before about the AI stack, the returns accrue to whoever owns the foundations, and the foundations here are everything that makes your version cheaper, stickier and better informed than the one anyone can download.
That reframing changes what is worth protecting and how. Note that the mechanisms Thompson identifies as durable – inference and memory efficiency, batching and caching, token efficiency, the harness the customer actually works in, and the data flywheel that comes from running inference at scale – are precisely the things that patents, trade secrets and well-drafted contracts can hold, in a way that published weights never could. Note too that the fight over distillation is a licensing fight, not a patent fight. It is being run through terms of service, which means it depends on contract drafting, on jurisdiction and on whether you can realistically enforce against the counterparty. This is the decision I have described elsewhere as choosing, in advance, which right to assert, where, and to what end.
Four questions follow, and none of them are about AI specifically:
– Ask what remains proprietary if a competitor obtains your core capability tomorrow. If the answer is nothing, the moat was rented.
– Check whether your operational efficiencies – the serving, caching and pipeline work nobody outside the team can see – have been captured as filings or as controlled trade secrets, or whether they are simply undocumented practice.
– Read the licence, not the label. Open-weight is not open source. Field-of-use limits, acceptable-use terms and downstream obligations travel with the weights into your product, and they are a freedom-to-operate question for your business, not a procurement footnote.
– If a supply contract is doing your protective work, confirm it is enforceable where the counterparty actually sits. A term of service that cannot be enforced is a preference, not a right.
The wider pattern is one that shows up well outside AI. Whenever a capability becomes broadly available, value moves to cost position, to proprietary data and to whoever owns the customer relationship. Those are IP decisions, taken early, or they are not taken at all.
Read the article: Who’s Afraid of Chinese Models?, Ben Thompson, Stratechery, 20 July 2026.
When a market is about to triple in a decade, the patent filing map you drew for yesterday’s demand is the one that will let you down. A McKinsey analysis, written with the Indian Electrical and Electronics Manufacturers Association, puts numbers on the shift: India’s electrical-equipment production is projected to rise from roughly US$50 billion to more than US$195 billion by 2035, with the fastest growth in power electronics, batteries and grid management.
The IP signal sits in the remedy. Sector R&D runs at less than half a per cent of revenue, against 1.5 to 2.0 per cent in China, so the report advises companies to “forge global technology alliances” in order to “access frontier intellectual property”, and to pursue “targeted cross-border M&A and strategic investments in niche technologies”. That is a technology licensing market forming in public. If you hold patents in silicon carbide devices, cell chemistry, thin-film photovoltaics or high-voltage cable processing, a new class of licensees and acquirers is being told to come and find you. If you are the one scaling, you are being told to buy a position you do not own.
The durable lesson is in the title. Capacity is not a competitive position. Five times the plant, built on technology someone else owns, buys volume at someone else’s margin, and it fixes a royalty negotiated from the weakest possible seat, because leverage moves to the licensor the moment your capital is committed. Three disciplines follow, and none of them are specific to India:
– Price the in-licence before you commit the plant, not after.
– Match your patent filing strategy to the ten-year demand map rather than the historical sales map. A patent only works where it is in force, and import substitution is a plan to manufacture your technology somewhere you may never have filed.
– Treat the acquisition of niche technology as an IP due diligence question first and a manufacturing question second. What you are buying is a set of rights, encumbrances and freedom-to-operate assumptions, not a production line.
Export ambition raises the same question again. Certification, standards and testing are the gate to European and North American tenders, and that gate is where standard-essential patents and licensing obligations sit waiting. So the useful question for your next board paper is not how much capacity you are adding. It is whether the build, licence or acquire decision was taken before the capex was approved, or after.
Read the article: Wired for growth: India’s electrical-equipment opportunity, McKinsey and IEEMA, 21 May 2026.
If AI can do the work, what exactly does your company still own? That is the question sitting underneath McKinsey’s new report, The symbiotic enterprise, which argues that AI agents and intelligent robots are becoming a workforce — close to 60 per cent of work hours are now theoretically automatable — and that the traditional moats of expertise, scale and coordination erode as a result. Read it with an IP strategy lens and the most important finding is buried in the competitive analysis: as access to frontier models commoditises, durable competitive advantage shifts to proprietary intelligence — unique data assets, “agentic skills” that encode how your organisation actually works, and learning loops fed by your own operations. Every one of those is an intangible asset, and most of them are protectable, if at all, as trade secrets and contractual rights. Consider the report’s own example: a claims-handling skill encoding your escalation rules, pricing thresholds and compliance logic. That is decades of operational know-how, extracted from people’s heads and written down in deployable form — enormously valuable, trivially copyable, and only defensible if you have deliberately made it so.
The strategic work, then, is to treat the intelligence layer as an IP portfolio from day one. Three questions to consider now. First, ownership: when know-how is codified into agent skills — often with a vendor’s platform and people involved — do your contracts actually vest those skills, and the improvements from learning loops, in you? That engagement-and-ownership problem is the subject of AI Transformation Is Not a Strategy Problem — It’s an Ownership Problem. Second, secrecy: trade secret protection survives only with reasonable steps — access controls, provenance logging, and limits on what agents (and their providers) can see and disclose — which is precisely where autonomous systems create new leak paths, as explored in Your AI Agent Won’t Keep a Secret. Third, stewardship: compounding assets decay without active management, the pattern examined in The Quiet Decay: Why IP Value Slips When No One Is Watching. McKinsey warns of a “cognitive tax” — value flowing to the AI providers everyone depends on. The companies that avoid paying it twice will be those whose data, skills and know-how are identified, owned and protected as rigorously as any patent family. In the symbiotic enterprise, IP strategy isn’t just a legal workstream within the AI programme. It is the part that decides who keeps the value.
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