In a rapidly evolving IP landscape, artificial intelligence is not just an enhancement; it’s a game-changer. In a recent CEIPI IP Business Talk, Daniel Holzner, CEO of ABP Patent Network, dived into how AI is transforming patent management and invention processes. As a returning guest, Holzner brought fresh insights into the latest capabilities of AI-based IP management tools, particularly his company’s software solution, uptoIP. Here’s a comprehensive summary of his interview, outlining the key trends, innovations, and implications for the IP world.

From Software to Strategic Solutions

Holzner began by recounting his entry into the IP field over a decade ago when he joined RBP Patent Network. Initially focused on managing uptoIP, the firm’s flagship IP management software, he witnessed firsthand how technology could optimize patent searches and administration. What started as a software development role evolved into a broader strategic effort to integrate AI into every facet of IP operations.

Today, uptoIP represents a complete AI-powered ecosystem for IP professionals, offering tools that simplify tasks such as prior art searches, invention disclosures, claim generation, and patent categorization. The recent advancements have significantly broadened its utility and accessibility.

The Second AI Wave: Quantized Models and Safe Deployment

Holzner emphasized the emergence of a “second wave” of AI adoption in the IP sector. This phase is marked by the widespread integration of quantized models—compact, efficient AI systems like Mistral AI and Gemma. These models can be securely deployed within an organization’s IT infrastructure, making them viable for sensitive environments like IP departments.

Unlike public APIs (e.g., OpenAI or Google), quantized models can be run in private data centres with minimal hardware. This ensures data privacy and compliance, which is crucial when handling unpublished inventions or internal R&D documentation.

Benefits and Use Cases of AI in IP Management

Holzner identified several compelling reasons why AI is rapidly being integrated into IP management tools:

  • Efficiency:
    AI dramatically reduces the time needed for tasks like semantic search, prior art analysis, and claim drafting. What once took hours or days can now be accomplished in minutes with higher accuracy and consistency. This efficiency allows IP professionals to focus on strategy and innovation rather than repetitive administrative tasks.
  • Democratization:
    Engineers and developers, not just patent experts, can now contribute to the patenting process through intuitive interfaces. With user-friendly AI tools, non-specialists can draft preliminary claims, identify relevant prior art, and evaluate novelty without deep legal expertise. This broadens participation and accelerates innovation cycles across organizations.
  • Data Security:
    In-house AI deployments avoid the risks of data leakage associated with cloud-based LLMs. Sensitive intellectual property data remains securely within the organization’s firewall, addressing major concerns around confidentiality and compliance. This makes AI integration not only powerful but also safe for enterprises handling proprietary technologies.

These benefits culminate in a more agile, responsive, and inclusive IP process.

Live Demonstration of uptoIP’s AI Capabilities

Holzner showcased real-time features from uptoIP, illustrating how users can generate patent claims from various inputs—ranging from invention disclosures to hand-drawn sketches. Notably:

  • Text-to-Claim Transformation: The system converts a technical text into independent and dependent claims, including IPC/CPC classifications.
  • Semantic Search: Using the generated claims, it performs full-text semantic searches across global patent databases.
  • Image-to-Claim Capability: Even sketches and technical drawings can be interpreted to suggest patentable claims or detect collisions.
  • Real-Time Feature Analysis: The software can analyse search results to extract key technical features, helping users evaluate novelty and inventive step.

Empowering Non-IP Professionals

A significant highlight of the discussion was the empowerment of non-IP stakeholders. Holzner explained how UptoIP can be used by product developers and engineers during early-stage design to:

  • Assess patentability:
    AI tools can quickly evaluate whether an invention is novel by comparing it to millions of existing patent documents. This allows engineers and innovators to get a fast, initial judgment on whether their idea is likely to meet patentability criteria such as novelty and inventive step. It significantly reduces the time and cost associated with traditional, manual patent assessments.
  • Check for third-party rights:
    Before investing in product development, it’s crucial to verify that your idea does not infringe on existing patents. AI can scan global patent databases to identify potentially conflicting rights and highlight areas of legal risk. This proactive step helps prevent costly litigation and ensures freedom to operate in the intended markets.
  • Explore alternative technical solutions:
    When a potential patent conflict is identified, AI can suggest alternative designs or methods that achieve the same function without infringing on existing patents. These alternatives are generated by analysing patterns in prior art and understanding the core technical problem being solved. This capability empowers teams to innovate around obstacles and keep projects moving forward.

By integrating IP insights early, organizations can reduce costly rework and ensure freedom to operate.

Data Privacy and the Role of Private Infrastructure

Addressing concerns about AI-induced data leakage, Holzner detailed how RBP Patent Network implements AI models within its own servers. This local deployment ensures:

  • Full control over proprietary data.
  • No external data sharing with public models.
  • Compliance with cybersecurity and privacy regulations.

Such architecture is especially important for high-value, confidential data such as pending patent applications or internal product documentation.

Scalability and Practicality of Private AI Models

Interestingly, Holzner demystified the infrastructure requirements for deploying local AI. Even modest GPU-equipped servers can support effective model use for up to 50 users. Larger installations can be scaled incrementally, making this a feasible solution for companies of various sizes.

Beyond Search: AI for Creativity and Conflict Resolution

UptoIP’s AI tools go beyond search and drafting. Holzner demonstrated how the software:

  • Identifies potential IP collisions between internal ideas and third-party assets:
    AI-powered systems can automatically compare new invention disclosures with existing patents to detect overlaps in claims or technical features. This early identification of potential IP conflicts allows teams to address legal risks before filing or launching a product. By flagging these collisions in real time, the software ensures a more secure and compliant innovation process.
  • Suggests technical alternatives or design-arounds:
    When a conflict with existing IP is detected, AI tools can propose alternative configurations or methods that achieve the same result without infringing. These suggestions are generated by analysing thousands of related patents and known engineering solutions. This helps developers stay within legal boundaries while continuing to innovate effectively.
  • Supports invention sessions by analysing images and generating improvement ideas:
    AI can interpret hand-drawn sketches, technical diagrams, or product photos and extract meaningful components or functions. Based on this analysis, it can propose ways to enhance the invention or identify features that might be patentable. This enables more productive brainstorming sessions and supports early-stage ideation with concrete, data-driven suggestions.

These functions enable more strategic R&D and reduce litigation risks.

Reimagining Patent Workflows: From Reactive Support to Proactive Innovation Leadership

Daniel Holzner drew a compelling analogy between the current transformation in IP workflows and the evolution of word processing software. Decades ago, the introduction of spell check marked a major improvement in productivity and accuracy for document creation. Over time, these tools evolved into grammar checkers, predictive text systems, and ultimately full-text generators—changing not just how people write, but who writes and how quickly meaningful content can be produced. Similarly, AI is now ushering in a comparable revolution in the world of patenting.

Traditionally, patent professionals have functioned in a largely reactive capacity—stepping in after an invention is disclosed to assess its patentability, draft applications, and manage filings. This linear model often placed IP teams downstream in the innovation process, limiting their influence to compliance and protection. With AI-driven tools like uptoIP, however, the IP function is shifting upstream. AI enables professionals to generate claims directly from sketches, draft detailed invention disclosures, and evaluate prior art on the fly—all from within a collaborative environment accessible to R&D teams, engineers, and product managers.

This transformation does more than streamline tasks; it redefines the role of IP teams within organizations. Instead of waiting for ideas to arrive, IP professionals can now initiate innovation dialogues, flag promising developments in real time, and strategically guide R&D toward white space opportunities. They become co-creators of value, rather than just custodians of legal rights.

But for this shift to take hold, more than just software is needed—it requires a cultural and procedural change. Organizations must foster a mindset where IP is seen not merely as a legal necessity, but as a strategic asset that influences early-stage decision-making. Cross-functional teams need to collaborate closely, with shared access to AI tools that make patent information more intuitive and actionable. Processes must be updated to allow idea capture from non-traditional sources—like images, discussions, or raw product sketches—and to incorporate AI analysis seamlessly into ideation and development phases.

In this reimagined workflow, IP becomes a dynamic and visible part of innovation strategy. It enables businesses to move faster, avoid risk, and generate competitive advantage—not only by protecting inventions but by actively steering how and where innovation happens. As Holzner made clear, this is not just a technological shift—it’s a structural rethinking of how innovation is organized, executed, and secured in the AI era.

Choosing the Right AI: Matching Models to Domains and Inputs

Daniel Holzner highlighted a critical but often overlooked factor in successful AI integration: model selection. Not all AI models are created equal, and their effectiveness can vary significantly depending on the context in which they are used. To extract the most value from AI in intellectual property and innovation processes, it’s essential to choose or configure models that align precisely with the nature of the task, the technical domain, and the type of input data.

  • Firstly, the technical field plays a major role in determining model suitability. For instance, an AI model trained predominantly on mechanical or electrical engineering literature may perform well in parsing circuit diagrams, classifying hardware claims, or generating suggestions for mechanical innovations. However, the same model might struggle when applied to domains like pharmaceutical patents, biotechnology, or materials science, where the terminology, claim structures, and innovation logic differ vastly. Holzner emphasized that identifying the right model for each technical vertical—whether chemical, software, mechanical, or hybrid systems—is key to producing relevant, high-quality outputs.
  • Secondly, input formats can significantly influence performance. Some AI models are optimized for textual data, excelling at processing large volumes of patent documents, generating claims, or summarizing invention disclosures. Others are tuned to handle visual inputs, such as hand-drawn sketches, technical diagrams, or even CAD files—capable of extracting key components and generating descriptive or claim-like text from them. Still others are trained on multilingual datasets, making them suitable for organizations operating in global markets or dealing with non-English patent literature. The ability to handle diverse input types ensures that AI tools are not limited to narrow tasks, but instead can support real-world innovation processes in their full complexity.

What sets ABP Patent Network’s approach apart, according to Holzner, is its flexible infrastructure. Unlike off-the-shelf solutions tied to a single model or provider, ABP’s AI framework is modular and adaptable. Companies can switch between models, fine-tune them for specific tasks, or even combine different models in a hybrid setup—for example, using one model for image recognition and another for legal language processing. This adaptability ensures that each use case is matched with the best-performing AI capabilities, rather than forcing all processes through a one-size-fits-all tool.

In essence, Holzner’s insight is clear: effective AI use in IP is not just about having powerful tools—it’s about using the right tool for the job. Thoughtful model selection and deployment create a tailored, high-performance environment where AI can deliver measurable impact across a wide range of innovation scenarios.

The Road Ahead: AI as an Innovation Catalyst

Looking ahead, Daniel Holzner painted a compelling vision of AI not just as a tool for streamlining existing IP tasks, but as a co-pilot for innovation itself. In this future, artificial intelligence becomes deeply integrated into the broader fabric of business operations—especially in product development, engineering, and strategic planning. Rather than functioning as a separate support function, AI-enabled IP tools will participate in real-time decision-making, helping organizations invent smarter, faster, and with greater strategic foresight.

One of the most promising applications Holzner discussed is the ability for AI to suggest optimal design parameters during the early stages of R&D. For example, by analysing vast datasets of past patents, technical literature, and even internal company documents, AI can recommend materials, configurations, or methods that have historically performed well—or point to novel combinations that haven’t yet been explored. This allows engineers and designers to make better-informed decisions before any physical prototype is built, increasing efficiency and reducing trial-and-error cycles.

Beyond design assistance, AI can predict development bottlenecks by recognizing patterns in workflows, team inputs, and historical project outcomes. It might flag potential delays in regulatory compliance, identify risks associated with specific design choices, or forecast where patent conflicts are likely to arise based on similar filings in the past. This predictive intelligence enables project managers to allocate resources more effectively and mitigate issues long before they materialize.

Another transformative application Holzner highlighted is the use of AI to assist in cross-functional innovation planning. Traditionally, IP strategy, product design, marketing, and compliance have operated in somewhat isolated silos. With AI acting as a centralized knowledge hub, these functions can now align more closely. AI can facilitate scenario planning, suggest innovation roadmaps based on market trends and IP gaps, and even propose filing strategies that align with anticipated competitor movements. The result is not just smoother coordination, but a unified innovation strategy supported by real-time data.

These advancements culminate in what Holzner described as the embedding of IP thinking into the DNA of product development. Rather than being considered late in the process—as an afterthought once a product is ready for launch—intellectual property becomes a core part of ideation, experimentation, and design. AI enables this integration by making patent data accessible and actionable across disciplines, guiding innovators to create with foresight rather than just hindsight.

In short, the road ahead is one where AI doesn’t just support innovation—it helps direct it. Companies that embrace this shift will gain a powerful edge, not only by protecting their ideas more effectively but by continuously identifying and seizing new innovation opportunities. As Holzner emphasized, this is the next frontier in digital transformation: one where creativity, strategy, and machine intelligence converge to build the future.

Conclusion: A Game-Changer for the IP Ecosystem

Daniel Holzner’s insights confirm that AI is not a passing trend but a foundational shift in how intellectual property is managed and leveraged. By making IP processes faster, safer, and more inclusive, tools like uptoIP enable organizations to stay competitive and strategically focused.

For companies looking to future-proof their innovation pipelines, the message is clear: embrace AI not just as a tool, but as a transformative force.

Here is the recording of this live interview at the 🖥️𝗜𝗣 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗧𝗮𝗹𝗸𝘀:

Picture by ChatGPT