[ARTICLE]Patents
Samsung received 7,054 U.S. patents in 2025, more than any other company for the fourth year in a row, while IBM moved to more selective patenting and dropped out of the top 10 to 11th place. This stark contrast reveals a fundamental shift in how industry leaders approach intellectual property: from quantity to strategic intelligence.
In 2024, innovators worldwide filed a record 3.7 million patent applications, up 4.9% over the prior year. The United States remained a central innovation hub: the USPTO received 603,194 of those applications, second only to China.
The companies winning the innovation race aren’t just filing more patents. They’re analyzing them smarter. Between 2016 and 2020, Siemens increased the strength of its IoT patent portfolio by 47% by shifting to a quality-focused patenting approach guided by landscape analytics.
Patent analysis represents the structured examination of patents, related documents, and patent literature to extract strategic insights about technologies, market trends, competitors, and innovation opportunities. For tech startups and inventors, this process systematically transforms vast, unstructured legal and technical documents into actionable intelligence that drives informed IP protection decisions.
Modern patent analytics interprets not only the text of patent disclosures but also critical metadata—including filing dates, citation networks, inventor and assignee details, legal status, and more—to build a 360° view of innovation activity. The outcome of robust analysis is a deep understanding of technological landscapes, white space opportunities, patent portfolio strength, blocking positions, and potential litigation risks—information absolutely critical for tech entrepreneurs making strategic IP decisions.
Patent analysis today extends well beyond basic keyword searches. It involves sophisticated data mining and statistical techniques, potent for AI and software innovations. Natural language processing (NLP) algorithms can parse patent text to identify key technical concepts, while machine learning models classify inventions and even predict outcomes. Academic research in 2025 highlights that patent analytics research spans diverse themes—from developing new indicators and methods to applying AI/ML for trend prediction, with emerging focus areas including environmental sustainability and corporate innovation use-cases.
The magnitude and relevance of patent analysis have intensified dramatically, especially in AI and software domains. Patent filings worldwide have risen for five consecutive years, and as of September 2026 the USPTO had about 745,000 unexamined patent applications in its inventory, outpacing traditional information-processing capabilities. For tech innovators, this creates both opportunities and risks that advanced analytics can help navigate.
Major innovators extensively leverage patent research to steer substantial R&D investments and safeguard competitive technological edges worth billions of dollars. The annual “Top 50” patent assignee rankings are dominated by tech companies (Samsung, IBM, TSMC, Qualcomm, etc.), illustrating how patent activity serves as a proxy for innovation focus in the very fields where SaaS and AI startups compete.
Patent analysis for tech innovators employs a sophisticated multi-stage methodology that’s particularly powerful for software, AI, and SaaS innovations. Data cleaning is a crucial initial step in preparing patent data for reliable analysis, especially when handling large datasets.
Effective patent analysis for tech companies begins with AI-enhanced data collection. Modern patent search strategies deploy combinations of Boolean logic (AND, OR, NOT), keyword clusters, and semantic search approaches specifically designed to capture relevant software and AI patents. Searches span critical patent databases including the USPTO (United States), EPO (Europe), JPO (Japan), WIPO’s PATENTSCOPE, and others for global coverage.
For SaaS and AI innovations, natural language processing capabilities have revolutionized patent search, enabling semantic understanding that goes beyond simple keyword matching. For example, an NLP-driven search can recognize that “machine learning algorithm” and “artificial intelligence model” might refer to similar concepts in different patents.
The USPTO itself uses AI in examination. Its examiners have an AI-assisted Similarity Search tool for finding prior art, and in 2024 the agency issued guidance on how practitioners may use AI tools when dealing with the Office (with appropriate human oversight).
Patents are indexed using sophisticated classification systems such as the International Patent Classification (IPC) and Cooperative Patent Classification (CPC). For tech innovators, understanding these systems is crucial. For example, class G06F covers electric digital data processing (most software patents), while G06N covers computer systems based on specific computational models (AI/ML patents). Analysis by classification codes helps cluster related technologies and map technical fields precisely.
These standardized taxonomies allow consistent comparison across different countries and patent offices, ensuring that a global AI or SaaS patent strategy accounts for all relevant prior art and competitor activity. Recent data show that specific tech fields are seeing rapid patent growth. At the European Patent Office, for instance, digital communication filings grew 11.4% in 2025, the fastest growth among the top ten technical fields.
Tech-Specific Classification Insights:
Evaluating patent citation networks is particularly powerful for AI and software patents, where innovation often builds incrementally on prior work. Forward citations (later patents that cite a given patent) can reveal a patent’s technological influence and market relevance. In contrast, backward citations (prior art references in the patent) show the lineage of ideas and foundational inventions.
For tech startups, patent analytics platforms calculate metrics like patent strength scores, citation counts, and network centrality measures to quantify the importance and influence of individual patents or patent families. This is especially valuable for AI patents, where foundational algorithms often receive heavy citation.
One invention often spawns multiple patent filings worldwide (a patent family). For tech startups planning global expansion, patent family analysis tracks groups of related filings across jurisdictions to quantify global protection strategies and measure an innovation’s true footprint.
A competitor might have 100 filings on a machine learning technology, but if they belong to just five families, the scope of unique inventions is smaller than it appears. Patent families also reveal geographic priorities—e.g., if a company files a family in the U.S., Europe, and China, it signals a global market strategy, whereas a family confined to one region might indicate local focus.
Analysts note that China (5.7 million) and the U.S. (3.5 million) had by far the most patents in force in 2024, out of about 19.7 million worldwide, reflecting that top companies seek protection in these major markets.
Technology landscape mapping for AI and software identifies entire innovation ecosystems within specific fields. Tech entrepreneurs create “patent landscapes” by searching and grouping patents related to specific technologies (e.g., natural language processing, computer vision, SaaS architectures, blockchain). This reveals the major players, the pace of innovation, and niche sub-domains where opportunities might exist.
For example, in AI technologies, patent activity has been exploding: IFI CLAIMS reports that AI patent grants grew at a compound annual rate of 38% over the past decade, and WIPO counts generative AI patent families rising from fewer than 800 in 2014 to more than 14,000 in 2023. For SaaS founders, this data helps identify crowded vs. emerging technology areas.
A good landscape report provides a comprehensive view of technology evolution and key participants in your specific domain. Visual tools like heat maps or bubble charts can highlight technology “hotspots”—where patenting is dense—and also white spaces with little patent coverage. These tools help identify trends in patent activity and technology evolution, enabling you to recognize emerging patterns and shifts. These insights inform long-term R&D and investment decisions crucial for tech startups with limited resources.
Critically, landscape mapping offers an early-warning capability for tech entrepreneurs. By monitoring who is patenting where, you can spot tech trends before they become mainstream threats. As one example, when quantum computing patents began climbing sharply, forward-looking firms in finance and cybersecurity took notice and began exploring quantum-resistant solutions early.
Patent analytics provides a unique window into competitors’ R&D strategies and market focus, particularly valuable for resource-constrained startups that need to make strategic bets. Unlike product announcements (which come late), patents often telegraph a company’s future intentions, years before products hit the market.
SaaS and AI companies routinely monitor competitor patent filings to avoid surprises and identify potential partnership or acquisition targets. Suppose Company A sees Company B filing numerous patents on AI-powered customer analytics. In that case, A might infer B’s strategic roadmap and adjust accordingly (e.g., accelerating its own AI features or exploring partnership opportunities).
The ability to identify market leaders through patent activity also helps in benchmarking and fundraising. VCs often evaluate startup positioning relative to incumbents, and patent analysis provides concrete data. In 2025, U.S.-based companies received 136,131 of the 323,272 U.S. patents granted, about 42%, according to IFI CLAIMS, meaning significant global competition in tech domains.
In practice, patent competitive intelligence can reveal whether competitors are doubling down on core technologies, branching into new domains, or potentially abandoning specific lines of research. For instance, an analysis might show that IBM’s patent output has shifted to a more selective, quality-focused approach (its U.S. patent grants fell about 17% in 2023 as it targeted key inventions). A startup could interpret this as IBM focusing on fewer, critical innovations, potentially leaving gaps for nimble competitors to exploit.
Competitive IP analysis is the identification and examination of competitors’ intellectual property (patents, trademarks, and copyrights) to learn what they are building, how far their protection reaches, and where the openings are. Except for trade secrets, most of it is published: U.S. patent applications are generally published about 18 months after their earliest filing date (35 U.S.C. 122(b)), and other countries’ patent offices publish theirs too. Beyond competitor technologies and white spaces, it can show you:
Competitive IP analysis is not a process for stealing trade secrets, performing corporate espionage, or unlawfully interfering with the IP rights of competitors. Instead, competitive IP analysis should be grounded in a legal and systematic process of gathering and organizing publicly available data and information.
Keep in mind that this works both ways. Competitors can study your patents, your website, and your product to find an edge for their own business, so treat your public disclosures as part of the analysis.
One of the most prized outcomes of landscape analysis is identifying “white spaces”—technology gaps where few or no patents exist. For tech entrepreneurs, white spaces often point to millions in untapped market opportunity, as they represent areas open for novel solutions without heavy IP competition.
For example, a SaaS startup might discover a white space in AI-powered legal document analysis with no patents targeting specific, specialized legal processes—a chance to be first in a new market segment while building strong IP protection.
Before investing heavily in developing and launching a new tech product, startups must ensure they have the freedom to operate—i.e., that the product won’t infringe someone else’s patent. For SaaS and AI companies, FTO analysis is a critical risk mitigation step that can save millions in litigation costs and prevent catastrophic product launch delays.
FTO analysis involves systematically reviewing existing patents (granted and pending) in the relevant technology space to identify any that might cover aspects of your software, AI algorithms, or SaaS architecture. This is particularly complex for AI and software patents, where claim language can be broad and technical implementations varied.
The importance of FTO cannot be overstated for tech startups: failures in this area can lead to costly infringement litigation, product launch delays, or forced design changes that could kill a startup. In the U.S., patent litigation is notoriously expensive: AIPLA’s economic surveys put median costs in the millions of dollars for each side when more than $1 million is at stake. For a startup, this can be company-ending.
A robust FTO analysis will parse all active patents in the AI/software domain, often via professional patent searchers and attorneys. Modern patent analysis tools significantly aid this process for tech companies. Automated FTO screening algorithms can flag potential conflicts by matching product feature descriptions against patent claim language, particularly valuable for software products where functionality can be described in multiple ways.
FTO Considerations Specific to Tech:
For tech entrepreneurs, FTO analysis should be integrated into the product development lifecycle, rather than being a one-time check. As products evolve and add features, ongoing FTO monitoring ensures you don’t inadvertently step into patent risks.
[RLG]Free strategy call
Talk it through with a registered patent attorney.
Free 15 to 45 minute IP strategy call, no obligation.
Beyond individual patents, tech companies must manage entire patent portfolios as strategic assets that support fundraising, competitive positioning, and eventual exit opportunities. Portfolio analysis evaluates the size, quality, relevance, and alignment of a company’s patents with its business goals, which is significant for AI and SaaS companies where IP often represents considerable value.
Key metrics for tech patent portfolio assessment include: number of patents by technology area, citation-based influence scores, geographic coverage, portfolio aging (how many are near expiration), and portfolio strength indices compared to competitors.
A crucial part of portfolio optimization for tech companies is identifying underperforming or misaligned patents. Analytics can flag patents that are seldom cited and not aligned to current products—perhaps candidates to drop (to save maintenance fees) or sell. Conversely, it can identify hidden gems—patents with broad claims or significant citations that are underutilized for defensive or licensing purposes.
Leading commercial platforms provide comprehensive patent data coverage and advanced analytical features specifically valuable for SaaS and AI patent analysis. These commercial platforms are examples of patent analytics software designed for patent professionals, offering specialized tools to build and enhance patent portfolios:
PatSnap covers more than 215 million patents across 174 jurisdictions, offering AI-driven analytics particularly powerful for software and AI patent landscapes. It provides semantic search and landscape visualization that can quickly identify related technologies even when described using different terminology, crucial for software patents.
Clarivate Derwent (Innovation) builds on the well-known Derwent World Patents Index, integrating powerful patent search with curated metadata, handy for complex AI and software patent analysis. It includes patent valuation indicators and machine learning tools that can help tech startups assess patent strength and competitive positioning. Derwent is renowned for data quality—their analysts often clean and normalize records, which is crucial for reliable analytics in the complex software patent landscape.
LexisNexis TotalPatent One / PatentSight provides deep search capabilities and legal-status tracking, useful for tech entrepreneurs, ensuring comprehensive prior art coverage and monitoring competitor patent status. PatentSight focuses on portfolio benchmarking and valuation analytics, with its Patent Asset Index metric widely used to compare patent portfolios quantitatively, which is valuable for tech startups preparing for funding rounds or M&A discussions.
For Tech Startups on Limited Budgets: Commercial tools can be pricey, and enterprise solutions often range from tens to hundreds of thousands of dollars annually. However, many offer startup-friendly pricing tiers or trial periods. Fortune Business Insights values the global patent analytics market at about $1.26 billion in 2025 and projects $3.7 billion by 2034, reflecting how businesses are investing in these tools for strategic IP management.
Artificial intelligence has revolutionized patent analytics, enabling more sophisticated analysis, particularly powerful for AI and software patent landscapes. These AI-powered solutions can also generate comprehensive patent landscape reports, providing in-depth overviews of technology trends, patent portfolios, and market insights to support strategic IP decisions:
AI-based Prior Art Search for Software Patents: Tools like IdeaScout or platforms incorporating GPT models can read a software invention disclosure and automatically search patents and scientific literature for relevant prior art. From October 2025 to mid-2026, the USPTO ran an Automated Search Pilot that used an internal AI tool to send applicants a list of likely prior art before examination began. For tech startups needing comprehensive analysis on limited budgets, AI search can surface candidate references faster than manual keyword searching.
Semantic and Image Search for AI Patents: Traditional search might miss software patents due to terminology differences—a critical issue in AI where the same concept might be described as “machine learning,” “artificial intelligence,” or “neural networks.” AI models overcome this by understanding context. For instance, IPRally employs graph neural networks to model patent text, delivering highly accurate semantic search that finds similar inventions even if keywords differ, particularly valuable for software patents where technical descriptions can vary significantly.
Automated Categorization and Clustering for Software Patents: AI can classify patents into technologies or group similar patents without manual tagging—invaluable for software and AI patent landscape analysis. For example, clustering 5,000 AI patents into subgroups by algorithm to see themes like “natural language processing,” “computer vision,” or “recommendation systems.” One provider, SolveIP (Solve Intelligence), offers automated patent clustering and competitive mapping that can cut analysis time dramatically.
Predictive Analytics for Tech Patents: Machine learning models are used to predict outcomes such as the likelihood of a software patent application being granted, time to grant, or chances of a patent being litigated. The USPTO’s economists have released an Artificial Intelligence Patent Dataset that flags AI-related U.S. patents and publications from 1976 through 2023, specifically to foster research in this area.
AI integration is rapidly expanding analytical capabilities for tech patent analysis. AI tools now handle much of the routine work, such as sorting patents into categories and running first-pass prior art searches, which frees human analysts for higher-value strategic work and saves time and cost for resource-constrained startups.
For tech startups with strong internal data science capabilities or those on limited budgets, several open data resources can be leveraged for sophisticated patent analysis:
Google Patents Public Datasets: Google provides a BigQuery dataset of worldwide patent publications (with full text, claims, citations, etc.). This dataset, with tens of millions of patent publications from around the world, allows tech-savvy startups to run custom queries or train models on patent text, particularly powerful for AI companies that can leverage machine learning for patent analysis.
The Lens (lens.org): A free platform integrating patent data with scholarly literature, particularly valuable for AI and tech research where academic papers often precede patents. It allows robust search and analysis and even has APIs for programmatic access—perfect for SaaS companies wanting to integrate patent monitoring into their business intelligence systems.
USPTO Open Data Portal / PatentsView: The USPTO’s Open Data Portal offers bulk downloads and APIs for U.S. patent and application data, including the PatentsView tables with inventor locations, CPC codes, and citations. This is particularly valuable for AI companies that can build custom analysis tools.
WIPO IP Statistics: WIPO’s annual reports and databases provide high-level stats (filings by country/sector, etc.) that can be used to benchmark and contextualize tech innovation trends.
Free official search tools. Most competitive IP research can start with free public databases:
Open resources often require more technical effort to use effectively, but they democratize access to sophisticated patent intelligence. A tech startup with data science capabilities could use Google’s dataset to ensure they aren’t infringing any major player’s patents by writing clever queries, all without spending on expensive commercial database licenses.
First and foremost, clarify what you want to achieve with patent analysis based on your specific business model and growth stage. Different goals call for different approaches:
Skilled people are at the heart of effective patent intelligence, but startups need cost-effective approaches. Consider building a lean, multidisciplinary approach:
To scale patent analysis with limited resources, define streamlined processes that integrate with existing product development workflows:
Regular Reporting Cadence for Startups:
Failing to understand the invention or the patent disclosures. Whether you are performing a search or analyzing search results, make sure that you understand the relevant technology and recited claims. A flawed analysis of invention features can lead to the use of improper search terms during a search or the wrong interpretation of patent disclosures and references.
Performing random searches. If you do not understand the purpose of your search, you are likely to fail to properly target your search and thereby search for the wrong features or the wrong proprietary aspects of your competitors’ technologies.
If you are performing a patentability search, you are looking for prior art that might be cited by a patent examiner in their Office Action and prevent your patent application from being granted. If you are doing a freedom-to-operate search, you are looking for active and enforceable patents that your SaaS might infringe. Because U.S. utility patents generally last 20 years from their earliest non-provisional filing date (35 U.S.C. 154(a)(2)), you can date-limit the scope of your search, leaving a margin for any patent term adjustment or extension.
Focusing only on patent claims. Novelty searches should not be focused solely on patent claims. Any part of a patent may be considered and cited as prior art and used to reject your patent application by the USPTO’s patent examiner. Even if you are performing a freedom-to-operate search, claims might not include particular terms due to a means-plus-function construction, where the claim references back to the description in the patent application to further define and limit the claim.
Missing the obvious. If you are performing a prior art search, consider reviewing your competitors’ websites and other easily accessible public sources. Remember: prior art is not required to be in the form of a patent or a patent application. Published information that is publicly available on any website could be cited as prior art.
Similarly, consider simple searches for products or services through internet search engines, e-commerce marketplaces, and other publicly accessible websites and printed publications. You may not only save time, but you may also identify new competitors in other geographical areas or new markets that you had not previously considered.
Relevant KPIs help measure the success and value of patent analysis programs, which are significant for resource-conscious startups:
Defensive Value Metrics:
No startup operates in isolation, and strategic partnerships can amplify patent analysis capabilities while managing costs:
Law Firm Partnerships: Many tech startups benefit from relationships with IP-focused law firms that understand startup constraints:
AI’s role in patent analysis will continue to expand, particularly benefiting tech companies that understand these technologies. As models get more sophisticated and are trained on larger patent datasets, expect AI to take on a growing share of routine patent analysis.
We’re already seeing early examples that tech entrepreneurs should watch: tools where you input a software invention summary and they output a list of closest prior art with relevance scores.
The speed of software innovation means periodic patent analysis (even quarterly) may become too slow to maintain a competitive edge. Tech companies are moving towards real-time patent intelligence, particularly valuable for SaaS businesses where feature development cycles are rapid.
Continuous monitoring systems can track new filings, issuances, legal status changes, and litigation events as they happen. For SaaS founders, this means you could know about a competitor’s latest patent application the day it publishes, giving you early insight into their product roadmap.
For example, if a key competitor’s patent application on AI-powered analytics publishes today, by tomorrow, an automated summary could be in your inbox highlighting its scope and potential impact on your product features. APIs and webhooks from patent databases will facilitate integrating these triggers into existing business intelligence systems that SaaS companies already use.
Benefits for Tech Entrepreneurs:
The future likely lies in combining patent data with other innovation indicators into unified intelligence platforms, particularly powerful for tech businesses. Patents are one piece of the puzzle; others include scientific publications, technical standards, regulatory filings, product launches, GitHub repositories, and even talent flows between companies.
Combining patent and publication data can identify when an AI breakthrough moves from academia (papers) to industry (patents). Adding venture funding data might show which patented technologies are getting commercial backing, crucial intelligence for startup competitive positioning.
While core patent analysis principles apply broadly, tech sectors have unique dynamics that innovative entrepreneurs leverage for competitive advantage.
The AI patent landscape is exploding, and generative AI patent grants are rising even faster than AI overall, at about 58% a year over the past decade, according to IFI CLAIMS.
AI Patent Landscape Considerations:
For AI startups, patent analysis helps identify crowded areas (like basic neural network architectures) versus emerging opportunities (like specialized AI for vertical industries). The key is finding white spaces where you can build defensible IP positions while avoiding the patent thickets created by tech giants.
Strategic Considerations for AI Entrepreneurs:
Understanding the AI patent landscape is crucial for fundraising, as VCs increasingly evaluate AI startups’ IP strategies and freedom to operate in their investment decisions.
SaaS companies face unique patent challenges due to the broad scope of software patents and the complex interactions between user interfaces, data processing methods, and integration technologies. Patent analysis for SaaS businesses must account for multiple patent categories that could affect different aspects of the business.
SaaS Patent Categories to Monitor:
For SaaS entrepreneurs, the challenge is that patent coverage can span multiple technology areas. A customer relationship management (CRM) SaaS might need to consider patents covering database architectures, user interface designs, integration methods, analytics algorithms, and business process workflows.
SaaS-Specific Patent Strategy:
A SaaS business usually relies on four kinds of intellectual property, and a competitive IP analysis should look at all four, for your company and for your competitors:
If you are designing a SaaS or developing a SaaS MVP (Minimum Viable Product), a competitive IP analysis report could inform you whether your SaaS design and/or development includes features covered by a competitor’s patents, giving you the opportunity to design around and improve upon your competitor’s patent(s) and technology. Similarly, a competitive IP analysis could reveal areas where your competitors have not developed technologies or filed for patent protection, giving you the opportunity to further develop your SaaS in those discovered technology areas.
A prior art search can help you refine your SaaS design or MVP, improve your patent application, and find out about technologies that competitors are using. There are different types of prior art searches, including novelty and landscape searches. A novelty search looks for prior art that might be a barrier to obtaining a patent. A landscape search looks for innovations that competitors are using, technology areas they are exploring, and opportunity areas where technology has yet to be further developed. Novelty searches, which are common before the drafting of a patent application, can still reveal what competitors are developing or trying to protect with their own patent filings.
If you are in the initial phase of your research, follow this three-step process for novelty searches:
Consider starting with the USPTO Patent Public Search. Using Google Patents might be easier and faster, and Google has stated that “Google does not inspect Google Patents logs to inform its own patenting strategies.” However, keep in mind that there is no guarantee that Google or other third‑parties are not monitoring and storing your searches and results.
For a landscape search, identify and search for general elements found in any competing SaaS product or service. To search for your competitors’ IP, determine the legal names of your competitors and their businesses. Generally, you can search patents by inventor, applicant (which may be a person or a business entity), or assignee (person or business to whom patent rights were assigned). Note that some businesses may use holding companies that hold (own) their intellectual property, so a search for the business name might not yield the same results as a search of the holding company’s name.
Determine patentable features for your SaaS. Prior art might reveal that a feature of your SaaS is likely unpatentable. However, finding such prior art early in the process (1) avoids wasting time and money in a patent application that is weak or will be rejected, and (2) gives you the opportunity to further develop your technology to obtain a competitive advantage in the market.
Map the IP space. To further develop your technology, consider creating a patent map. You can create tables, charts, and graphs (for example, Venn diagrams) showing different technology spaces related to your SaaS. A graphical view of where the technology spaces intersect can reveal holes or spots where the technology spaces do not meet. A patent map might also reveal that your technologies are entering a “crowded” patented space, where patent infringement is possible or likely and can help you avoid future legal issues.
Design around your competitors’ IP. Create a SaaS that does not infringe any patents. You can find which elements or process steps your competitor’s patents claim and avoid implementing at least one of those elements or steps in your SaaS product and patent application. When practical, consider seeking and negotiating a patent license.
Find infringement of your patents. Patent applications require an enabling disclosure, meaning that patent applications must disclose how the invention is made or performed. If a competitor describes or otherwise incorporates a description of one of your patented inventions in their disclosure of a different invention in a patent application or issued patent, it is possible that the competitor’s invention requires your patented technology to operate.
Search for prior art against competitors’ patents. Finding prior art to a competitor’s patent can allow that prior art to be used as leverage in a negotiation or as a defense in a patent infringement case.
Organize and collate your findings. You might want to quantify the number of patents and patent applications that each of your competitors own. This will provide insight into your competitors’ intellectual property budgets and allow you to better understand your competitor’s IP portfolio strategy, scope, and strength.
For tech entrepreneurs working on cutting-edge technologies, patent analysis helps identify emerging opportunities before they become crowded.
Quantum Computing Patents: Patent landscapes show rapid growth in quantum tech filings, with quantum computing patent growth averaging 49% annually from 2019 to 2023. For entrepreneurs in cryptography, optimization, or financial modeling, understanding quantum patent landscapes helps prepare for both opportunities and threats.
Green Tech and Sustainability Patents: Climate tech is seeing accelerated patent activity. At the European Patent Office, battery filings grew 14.6% in 2025, creating opportunities for entrepreneurs developing novel approaches to energy storage, renewable energy optimization, or sustainable manufacturing.
The competitive urgency is real. In our first-to-file patent system, timing isn’t just important—it’s everything. Companies that delay patent analysis often find themselves designing around competitor patents instead of pioneering new solutions.
Your immediate action steps:
About the Author: Andrew Rapacke, Managing Partner, Registered Patent Attorney at The Rapacke Law Group, specializes in AI patents, software patent strategy, and IP protection for tech startups. With extensive experience helping SaaS founders and AI entrepreneurs build strong patent portfolios, Andrew combines deep technical understanding with practical business strategy to deliver IP solutions that support growth and fundraising objectives.
To Your Success,
Andrew Rapacke
Managing Partner & Registered Patent Attorney
The Rapacke Law Group
Follow Andrew on LinkedIn and @rapackelaw for the latest insights on AI patents and tech IP strategy.
[RLG]Free strategy call
Talk it through with a registered patent attorney.
[01]Keep reading
It is estimated that up to 30% of all R&D spending is wasted reinventing existing technology that a proper patent search would have revealed. This staggering inefficiency…
USPTO examiners must now run an AI similarity search on every utility application. See the leading AI patent search tools in 2026, what each one does, and where an attorney still matters.
Bank of America’s AI patent portfolio exploded 94% between 2022 and 2024, reaching nearly 1,100 patents: more than any other financial services company. This isn’t…
The #1 top reviewed IP law firm.* Talk to a registered patent attorney, free.
*#1 top reviewed means the most public client reviews on Google, Trustpilot and Avvo of any US patent or trademark firm.
