The model that generates original outputs is fundamentally an extension of the cognitive efforts and intent of its programmer. The AI system, while capable of creating new content autonomously, has been coded and designed to function in this way by its human developer. Therefore, any outcome produced by the AI can be seen as stemming from the intellectual work of the programmer who created the system. In essence, the AI model is merely a tool—an advanced one, but still an instrument—that reflects the ingenuity and invention of its creator. Consequently, any application or invention generated by the AI should, in theory, be attributed to the inventor of the AI system itself. The enforcement of patents on AI-generated inventions also presents unique challenges. Patent offices may struggle to validate the originality and non-obviousness of an AI-generated invention due to the speed and volume at which AI systems can produce new designs or products. Additionally, enforcing these patents in the marketplace becomes complex, especially when it is unclear who owns the invention or when multiple entities contribute to its development. Additionally, it’s crucial to clarify the object of invention in the context of AI. Traditionally, patent law distinguishes between process and product as two separate categories of patentable subject matter. However, in AI, the focus shifts to the model, which represents a blend of both process and product. The AI model is a procedural system that generates products (outputs), combining two dimensions that traditionally exist separately. Given this dual nature, patentability requires a different perspective.
The inventive differences in AI should be assessed at the model level, where the true ingenuity lies, rather than at the output level. The outputs, such as text or images, may be better suited for protection under other legal frameworks, like copyright, as they can be independently created without utilizing the specific AI model. This distinction is essential to avoid confusion and ensure that the model, as the core invention, is the focus of patent law, while outputs fall under other intellectual property regimes, like copyright. To address these challenges, patent laws may need to evolve significantly. Legislators and policymakers will likely have to consider creating new frameworks that accommodate AI’s role in invention. These could involve hybrid models of ownership that recognize both human and AI contributions, as well as new standards for patent eligibility. Additionally, global collaboration might be necessary to develop harmonized policies that allow for the protection and enforcement of AI-generated inventions across jurisdictions. As generative AI continues to advance, its impact on the patent system will deepen. Current laws governing registration, enforcement, patentability, and ownership are struggling to keep pace with the technology. Adapting these legal frameworks will be crucial to ensure that innovation flourishes while also maintaining a fair and equitable system for recognizing and protecting inventors—both human and AI-driven.
Future of IP: Top 10 changes to expect in the next decade
Generative AI refers to a class of artificial intelligence systems designed to generate new, original content. These systems can create text, images, music, and even video based on the data they have been trained on. Unlike traditional AI, which focuses on tasks like classification or prediction, generative AI models produce novel outputs by learning patterns and structures from vast datasets. Key aspects of generative AI include: Models: Generative models like GPT (for text), DALL·E (for images), and others are based on architectures like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers. Training Data: These models are trained on large datasets, allowing them to learn from diverse examples and mimic creativity in various domains. Applications: (i) Text generation: AI can generate coherent essays, articles, or even code. (ii) Image creation: Tools like DALL·E can generate realistic or artistic images from text descriptions. (iii) Music and video generation: AI can create music compositions or synthesize video content. (iv) Chatbots and conversational agents: Models like ChatGPT can engage in natural language conversations. Generative AI has potential applications in industries like entertainment, content creation, marketing, design, and more, offering tools for automation and creativity.
The future of intellectual property (IP) is evolving rapidly, especially in response to emerging technologies like AI, machine learning, blockchain, and quantum computing. Over the next decade, IP laws and systems are expected to undergo significant changes to adapt to these technological advancements. Here are the top 10 changes to expect in IP: 1. Recognition of AI-Generated Inventions: Current Situation: Most jurisdictions require human inventors to be listed on patent applications, with AI-generated inventions often facing challenges in being patented. Expected Change: Legal frameworks for AI-generated inventions will evolve, allowing for AI to be recognized as a co-inventor or even the primary inventor in some jurisdictions. This could necessitate new guidelines for determining ownership, authorship, and rights related to AI-generated works. 2. New Classifications for AI and Machine Learning: Current Situation: AI and machine learning patents are assessed under traditional patentability standards, often struggling to meet criteria for technicality and practical application. Expected Change: Specialized categories for AI and machine learning inventions may emerge, with tailored criteria for their patentability. Governments may create distinct IP categories for algorithms, data processing techniques, and AI-driven innovation. 3. Adaptation of Copyright Laws for AI-Generated Content: Current Situation: Copyright laws traditionally protect human-created works, but AI-generated content raises questions about authorship and ownership. Expected Change: Copyright laws may evolve to specifically address works generated by AI, offering limited protection to AI-created content. Hybrid models of shared ownership between human developers and AI systems could emerge. 4. Global Harmonization of AI IP Laws: Current Situation: Countries have disparate views on AI patents and IP protections, with some countries (like Australia) being more open to recognizing AI inventors than others (like the US and EU). Expected Change: International efforts to harmonize IP laws governing AI innovations could lead to more consistent global standards, especially through organizations like the World Intellectual Property Organization (WIPO). This could involve international treaties to streamline AI-related IP protections across borders. 5. Blockchain Integration in IP Management: Current Situation: Blockchain technology is being explored for its ability to verify ownership, timestamp inventions, and enforce IP rights. Expected Change: Blockchain could become an integral tool in IP management, enabling decentralized ownership, real-time tracking of IP rights, and automated enforcement of patents and copyrights using smart contracts. Blockchain-based IP registries could become standard in many jurisdictions. 6. Expanded Patentability for Data-Driven Inventions: Current Situation: Data-related inventions, particularly those involving machine learning models, face challenges in patentability due to their reliance on abstract algorithms and unstructured data. Expected Change: Broader protections for data-driven inventions could emerge, allowing for patents on specific data processing techniques, data models, or novel uses of big data in AI. This shift could expand IP protections to include training data sets and proprietary data used in model development. 7. Rise of Digital and Virtual IP Rights: Current Situation: Virtual worlds, digital assets, and the metaverse are creating new types of content, raising questions about IP protections for digital goods and assets. Expected Change: The IP framework will likely expand to include protections for virtual goods, digital art, and NFT-related works. Jurisdictions may create new laws to safeguard IP rights in virtual and augmented realities, enabling companies to protect digital representations of physical and intangible assets. 8. Increased Focus on Ethical AI and IP: Current Situation: IP laws primarily focus on ownership and protection, with less emphasis on the ethical considerations of AI-generated inventions. Expected Change: The next decade could see ethical guidelines woven into IP laws, with certain AI-generated works (e.g., in healthcare or criminal justice) facing stricter regulatory scrutiny to ensure ethical use. Patents for AI could require an ethical compliance review as part of the approval process. 9. Changes in Patent Eligibility Standards: Current Situation: Abstract algorithms and software, especially those used in AI and blockchain, face challenges in meeting patent eligibility standards, particularly under laws in the US and Europe. Expected Change: Patent eligibility standards may broaden to accommodate new technological paradigms like quantum computing and AI, making it easier to patent software-driven innovations. Alternatively, AI-specific eligibility criteria could emerge to clarify what constitutes an invention in the AI domain. 10. Revised IP Litigation and Dispute Resolution Frameworks: Current Situation: Traditional IP litigation and dispute resolution processes are slow and often not equipped to handle the complexity of emerging technologies like AI and blockchain. Expected Change: We can expect the rise of faster, more tech-savvy dispute resolution mechanisms, including online arbitration platforms that leverage AI for IP disputes. AI tools could assist courts and legal bodies in assessing the validity and scope of patents or copyrights in complex technical fields.
Summary of Potential Future Developments: (i) AI-generated inventions might receive formal legal recognition as inventors. (ii) New classifications and guidelines specific to AI and machine learning inventions. (iii) Copyright protections may be extended to AI-generated content. (iv) International IP law harmonization efforts for AI inventions. (v) Blockchain integration in IP management for decentralized tracking and enforcement. (vi) Expanded patentability of data-driven inventions and machine learning models. (viii) New IP protections for virtual assets, NFTs, and digital goods. (viii) Ethical guidelines embedded into IP law to regulate AI inventions. (ix) Patent eligibility standards adapted for AI, quantum computing, and software. (x) Modernized IP litigation frameworks using AI-based dispute resolution.
Countries and jurisdictions will likely continue to monitor the fast-paced technological advancements in AI, which will lead to further evolution of IP systems. The integration of AI into IP management systems, the expansion of IP protection for data-driven and virtual inventions, and the potential recognition of AI as inventors are expected to be some of the major changes over the next decade.
Future of IP Registrations
The future of IP registrations is set to be transformed by advances in technology, particularly AI, blockchain, and automation. Here are some key trends and changes we can expect: 1. Automation of IP Filing and Management: Current State: Manual processes dominate IP filing, requiring significant legal expertise and time. Future Expectation: AI-driven automation tools will streamline patent searches, IP filings, and documentation. Automated systems will help applicants identify prior art, suggest claim language, and even generate filing drafts. Companies like PatSnap and LexisNexis are already integrating AI in this space. 2. Blockchain-Based IP Registries: Current State: IP rights are recorded in centralized databases, leading to delays in verifying ownership and resolving disputes. Future Expectation: Blockchain will be integrated into IP registries, creating a decentralized ledger for IP assets. This will provide a secure, tamper-proof record of ownership, making it easier to track IP transfers, licensing, and infringement cases. Countries like China and Singapore are already exploring blockchain for IP management. 3. Real-Time IP Monitoring and Enforcement: Current State: IP owners often face delays in identifying and enforcing violations. Future Expectation: AI systems will be able to monitor markets, the web, and supply chains in real-time for infringements, automatically notifying IP holders and initiating enforcement actions. AI-driven IP enforcement will reduce litigation costs and speed up dispute resolution. 4. Simplified Global IP Filing: Current State: Filing for IP protection across multiple countries is complex and expensive, with each country having its own laws and procedures. Future Expectation: The emergence of global IP systems that allow for unified, single-point filings. International organizations like the World Intellectual Property Organization (WIPO) may play a leading role in creating frameworks for global filings. This will lower the cost and complexity of obtaining IP protection across multiple jurisdictions. 5. Patentability of AI-Generated Works: Current State: IP systems struggle to address AI-generated inventions, as many jurisdictions require a human inventor. Future Expectation: IP laws will likely recognize AI-generated works and develop frameworks for assigning ownership. This will lead to new categories in patent filings, such as co-inventorship between AI and humans or even AI as the sole inventor in some jurisdictions. 6. IP Protection for Data and Algorithms: Current State: Patent and copyright laws don’t fully cover datasets and algorithms, leading to gaps in protection. Future Expectation: New forms of IP protection will be created for data-driven innovations and algorithms, addressing the unique challenges posed by AI, machine learning models, and proprietary datasets. This could include IP protection for training data, algorithm architecture, or data processing techniques. 7. AI-Assisted Patent Examination: Current State: Patent examination is a time-consuming process that involves manual searches for prior art. Future Expectation: Patent offices will integrate AI-based tools to assist in the examination process, allowing for faster and more accurate searches for prior art. These systems will improve examination timelines and reduce human error. 8. Instant Trademark Searches and Registrations: Current State: Trademark registration can take months due to manual reviews of existing marks and potential conflicts. Future Expectation: AI-driven instant trademark searches will allow applicants to check the availability of trademarks in real-time, while IP offices will speed up the examination and approval process through automated conflict detection and classification. 9. Unified Digital IP Portals: Current State: Most IP registration processes are fragmented, with different platforms for patents, trademarks, copyrights, etc. Future Expectation: Unified digital IP platforms will offer a one-stop solution for all types of IP registration (patents, trademarks, designs, copyrights). This will include end-to-end services, from filing and payment to examination and enforcement. 10. Evolution of IP Valuation and Licensing : Current State: Valuation of IP assets is subjective and varies across industries, making licensing and IP monetization complex. Future Expectation: AI-powered tools will help standardize IP valuation, allowing for more accurate and data-driven evaluations of patents, trademarks, and copyrights. Blockchain and smart contracts could also facilitate automated and transparent IP licensing deals.
The future of IP registrations will be increasingly shaped by the convergence of AI, blockchain, and automation. These technologies will streamline IP filing, enforce rights in real-time, and introduce new IP categories for digital and AI-driven innovations. Global harmonization and digital platforms will further reduce complexity, making the IP registration process faster, more secure, and more accessible.
Future of IP Enforcement Practice
The future of IP enforcement practices will be significantly shaped by advances in technologies like AI, blockchain, big data, and automation, as well as evolving global legal frameworks. These developments will enhance the ability to protect intellectual property (IP) rights, enforce them more efficiently, and provide a more streamlined dispute resolution process. Here are some of the key trends and changes expected in IP enforcement practices over the next decade: 1. AI-Driven IP Monitoring and Enforcement Current Situation: Traditional IP enforcement relies heavily on manual monitoring of markets, websites, and competitors, which can be slow and inefficient. Future Change: AI-powered tools will enable real-time monitoring of IP violations across digital platforms, supply chains, and global markets. These systems will automate the identification of infringements, such as copyright, patent, and trademark violations, allowing rights holders to take action more quickly. Example: AI can scan vast amounts of data, including e-commerce platforms, to detect counterfeit products, trademark misuse, or unauthorized use of copyrighted materials. 2. Blockchain for IP Enforcement: Current Situation: Tracking IP ownership and enforcing rights, especially across multiple jurisdictions, is complex and prone to delays. Future Change: Blockchain technology will enable the creation of immutable digital records of IP ownership, licensing, and transactions. This will allow for faster enforcement of IP rights by providing a clear, transparent, and decentralized record of IP ownership and transfers. Example: Blockchain could be used for smart contracts that automatically enforce licensing agreements, triggering payments or actions when certain conditions are met, reducing the need for legal intervention. 3. Smart Contracts for Licensing and Royalty Enforcement: Current Situation: Licensing agreements and royalty payments are often subject to disputes and require manual enforcement and monitoring. Future Change: Smart contracts—self-executing contracts with terms of the agreement directly written into code—will automate the enforcement of IP licenses and royalties. Once terms are met, such as usage or sales milestones, the contract will automatically trigger royalty payments or enforcement actions. Example: In music and entertainment, smart contracts could automatically distribute royalties based on song streams or downloads. 4. Real-Time IP Enforcement in Digital Spaces: Current Situation: IP enforcement in digital spaces (e.g., social media, streaming platforms, and e-commerce sites) is reactive, often initiated after an infringement has occurred. Future Change: IP enforcement will become proactive and real-time, with AI tools monitoring the web for potential infringements and automatically issuing takedown notices or warnings to violators. Example: Social media platforms could integrate AI systems to detect copyright violations (e.g., the unauthorized use of images or videos) and remove infringing content immediately without the need for manual reporting. 5. Cross-Border IP Enforcement Harmonization: Current Situation: IP enforcement across borders is fragmented, with countries having different laws and processes, making it difficult to pursue international infringements. Future Change: International harmonization of IP laws and collaboration between countries will simplify cross-border enforcement. Regional and global organizations, such as the World Intellectual Property Organization (WIPO), may develop frameworks for coordinated IP enforcement. Example: Unified IP enforcement agreements could allow for a streamlined process to take legal action against infringers in multiple jurisdictions simultaneously.
6. Big Data and Predictive Analytics for Enforcement Strategy: Current Situation: IP owners often react to infringements without a comprehensive strategy for where and how violations are likely to occur. Future Change: Big data and predictive analytics will help rights holders identify trends in IP infringement and predict where future violations are likely to occur. This will allow for more strategic enforcement actions, focusing resources on high-risk regions or industries. Example: Data analytics could help brands anticipate where counterfeit goods are likely to appear, based on market data, sales trends, and past infringement patterns. 7. AI-Assisted Litigation and Legal Research: Current Situation: IP litigation is often lengthy and complex, requiring extensive research and analysis of case law, prior art, and technical documentation. Future Change: AI-assisted legal tools will streamline litigation by conducting legal research, analyzing previous cases, and helping to craft arguments. These tools will improve the efficiency and accuracy of IP lawsuits, enabling faster resolutions and reducing costs. Example: AI tools could assist IP lawyers by suggesting strategies based on past cases, automating the analysis of prior art in patent cases, or predicting the outcome of litigation based on court records. 8. Specialized Courts for AI and Digital IP Disputes: Current Situation: Traditional courts often lack the specialized knowledge needed to handle complex disputes involving emerging technologies like AI, blockchain, and digital IP. Future Change: Specialized IP courts or tribunals focused on digital and AI-related IP disputes may be established. These courts would have the expertise and technological knowledge to handle highly technical cases, ensuring faster and more informed rulings. Example: A specialized court could handle disputes involving AI-generated inventions, algorithmic copyrights, or digital tokens, with a focus on the unique technical aspects of these cases. 9. Faster, Tech-Enhanced Dispute Resolution: Current Situation: Traditional litigation processes for IP disputes can be slow and expensive, particularly when handled through courts. Future Change: Tech-enabled dispute resolution mechanisms, such as online arbitration platforms powered by AI, will become more common. These systems can automate parts of the legal process, reducing the time and cost of resolving disputes. Example: Platforms like Modria (an AI-driven online arbitration tool) could be adapted for IP disputes, providing a fast and efficient alternative to traditional litigation. 10. Ethical Considerations and Regulatory Scrutiny: Current Situation: IP enforcement largely focuses on protecting ownership and rights, with little consideration of the broader ethical implications of emerging technologies like AI. Future Change: Governments and regulatory bodies will increasingly scrutinize IP enforcement practices involving ethical AI use and privacy concerns. In particular, IP enforcement in fields like healthcare and surveillance technology will need to comply with ethical standards, potentially requiring new legal frameworks to ensure responsible enforcement. Example: IP rights related to AI models used in healthcare may require additional review to ensure they comply with ethical standards before enforcement actions can be taken.
The future of IP enforcement practices will be increasingly shaped by AI, blockchain, and automation, providing faster, more efficient, and strategic ways to protect IP rights. Companies will have access to real-time monitoring tools, automated enforcement mechanisms, and tech-enabled dispute resolution processes, leading to a more proactive and globally harmonized approach to IP protection. As new technologies emerge, IP enforcement will also need to balance technological innovation with ethical and legal considerations, particularly in fields like AI and digital assets.
Evolving role and upskilling of in-house IP Attorneys
In-house IP (Intellectual Property) counsel are playing an increasingly pivotal role in navigating the complexities of GenAI (Generative AI) technologies, especially with regard to patents, copyrights, trade secrets, and other forms of intellectual property protection. As Generative AI systems evolve rapidly, so too must the strategies and skillsets of in-house IP counsel. Below are the key ways in which in-house IP counsel are improving or could improve their work in relation to GenAI, how they are being skilled or trained, and their overall impact:
1. Specialized Training in AI and Emerging Technologies: Ongoing Education: In-house IP counsel are receiving specialized training in AI technologies, algorithms, and machine learning models to better understand the intricacies of how GenAI operates. Understanding these technologies helps them make more informed decisions regarding IP protection strategies, patent filings, and copyright issues. Upskilling: Legal departments are investing in continuous education programs, attending AI-focused conferences, enrolling in technology certification courses (e.g., machine learning and AI), and participating in collaborative workshops with technical teams. Collaboration with R&D: IP counsel are collaborating more closely with their company’s research and development (R&D) teams to stay updated on the latest innovations, particularly with respect to Generative AI models like LLMs (Large Language Models) and their commercial applications.
2. Adaptation of Patent Filing Strategies: Drafting AI-Related Patents: IP counsel are improving their skills in drafting patents that protect AI-related inventions, including innovations in neural networks, model architectures, and training processes. Drafting such patents requires a deep understanding of the technical nuances of how AI systems function. Training on AI-Specific Patentability: Counsel are learning how to frame patent claims to meet the standards for patentability in jurisdictions where AI-related inventions, especially process patents, might face resistance or legal challenges. Global Patent Strategies: Given the variations in how different countries handle AI-related patents (e.g., method patents being accepted in some countries but not others), in-house IP counsel are refining strategies for filing patents in multiple jurisdictions to ensure comprehensive protection.
3. Handling Copyright and Licensing Issues : Understanding AI-Generated Content: In-house IP counsel are increasingly dealing with the question of whether AI-generated content (e.g., text, images, music, code) can be copyrighted. They are studying relevant case law and legislative developments to advise their companies on the ownership and protection of AI-generated works. Training in Copyright Law for AI: Counsel are gaining expertise in areas such as fair use, copyright licensing, and derivative works, particularly when AI systems are trained on large datasets that may contain copyrighted material. Drafting Licensing Agreements: They are improving their skills in drafting AI-related licensing agreements, especially when dealing with the use of data and AI models that may incorporate third-party intellectual property.
4. Navigating Ethical and Legal Implications: Ethics and AI Accountability: IP counsel are increasingly involved in navigating ethical and legal implications, such as the potential for bias in AI models or the misuse of generative technologies. They are advising on how to ensure that AI systems are used responsibly and in compliance with regulations (e.g., data privacy laws). Ethical IP Frameworks: Many are becoming proficient in developing ethical frameworks around the use of GenAI in a way that complies with legal and social expectations, while still maximizing innovation and protection for their companies. Collaboration on AI Governance: They are working closely with compliance and ethics officers to ensure that AI governance frameworks include policies for IP protection, ethical use of data, and clear guidelines for mitigating legal risks.
5. Influence on Policy and Regulatory Discussions: Shaping AI Legislation: In-house IP counsel are increasingly participating in legislative discussions and industry coalitions to shape policies around AI and intellectual property. By staying informed about upcoming regulations and contributing to the dialogue, they ensure that their companies’ interests are represented. Training in Policy Advocacy: Some counsel are expanding their roles to include advocacy for or against certain legislative changes, which may impact how AI-related IP is regulated (e.g., lobbying for changes in patent or copyright law to accommodate AI-generated inventions).
6. Managing Risks Related to Data Ownership and Privacy: Data Licensing and Ownership: As GenAI models often require vast datasets for training, in-house IP counsel are refining their approaches to data ownership and licensing. They are advising companies on how to properly license data for AI training and ensuring that the data used complies with privacy regulations, such as GDPR. Developing Data Use Agreements: IP counsel are improving their skills in drafting agreements that govern the use of third-party data for AI training and use. They ensure that these agreements provide adequate protections for intellectual property and privacy rights. Risk Mitigation for Data Usage: Counsel are assessing potential legal risks associated with using large-scale datasets and mitigating exposure to liability, especially when it comes to personal or copyrighted data.
7. Leveraging AI Tools for Legal Work: AI-Powered IP Management: In-house IP counsel are using AI tools to assist in tasks such as patent search, trademark monitoring, and drafting legal documents. AI can help in speeding up the analysis of prior art, identifying potential patent infringement, and managing IP portfolios more efficiently. Automation of Legal Processes: AI-powered tools are enabling legal teams to streamline their workflows, improve the accuracy of IP assessments, and reduce the time spent on manual tasks like patent prosecution or trademark analysis. Training in Legal AI Tools: Legal professionals are being trained to use AI tools that help with tasks such as reviewing legal contracts, conducting due diligence for patent filings, and predicting litigation outcomes.
8. Impact and Role of In-House IP Counsel: Strategic Advisors: In-house IP counsel are becoming key strategic advisors to companies working with GenAI technologies, ensuring that IP strategies align with the broader business objectives. They provide guidance on how to protect and leverage AI innovations to maintain competitive advantages.
By staying at the forefront of AI-related IP developments, in-house counsel play a critical role in protecting their companies from competitors and ensuring that innovations in AI are legally safeguarded. In-house IP counsel are increasingly taking on cross-disciplinary roles, working closely with engineering, data science, and product development teams to ensure that IP issues are addressed early in the innovation process. The role of in-house IP counsel is evolving significantly with the rise of GenAI technologies. They are improving their work by gaining deep technical knowledge, adapting legal strategies to emerging trends, and enhancing their expertise in patent and copyright law as it relates to AI. Their ability to navigate complex legal landscapes and influence policy makes them indispensable in protecting and commercializing AI innovations. As the GenAI landscape continues to expand, the importance of skilled in-house IP counsel will grow, ensuring that companies can protect their intellectual assets while navigating the legal and ethical challenges of AI.
References
Emerging Tech and IP Workshop: The event concludes with valuable insights and actionable takeaways to navigate this evolving legal landscape effectively.
WIPO Technology Trends 2019: Artificial Intelligence – This report from the World Intellectual Property Organization (WIPO) provides an in-depth analysis of patent filings and trends related to AI inventions, including discussions on IP challenges specific to AI technologies.
European Patent Office (EPO) – Patenting Artificial Intelligence – This document discusses the approach taken by the EPO toward AI patenting and addresses the challenges of AI-related inventions.
Artificial Intelligence and Intellectual Property: An Introduction: Ryan Abbott, Artificial Intelligence and Intellectual Property: An Introduction, In RESEARCH HANDBOOK ON INTELLECTUAL PROPERTY AND ARTIFICIAL INTELLIGENCE, Edward Elgar (Ryan Abbott, ed., Forthcoming)
About Author
Kanwal Rai brings over 25 years of expertise in Intellectual Property and Information Technology. As Vice President at Wells Fargo, he spearheaded critical digital transformation initiatives, including a corporate start-up accelerator aligned with the company’s open innovation strategy. His prior roles at Infosys, Capgemini, and Wipro provided him with deep, hands-on experience in both IP and technology. As a former registered patent agent, Kanwal offered a range of services, including patent prosecution, analytics, valuation, and portfolio management, always focused on using technology to drive innovation.
Kanwal holds an MBA from IIM Calcutta, along with engineering and master’s degrees from DCE and BITS Pilani. He co-authored a book on AHP and decision engineering, which has garnered over 1,450 citations. In addition to his professional career, he has mentored Data Science students at IIT Madras and taught AI/ML at Ascencia Business School. He frequently speaks at industry events and conducts corporate consulting and coaching programs on Enterprise Architecture, IP strategy, TRIZ, product development, digital transformation, and entrepreneurship. Currently, Kanwal serves as an advisor to Softqubes, working on the development of a data analytics platform leveraging Generative AI.
Agenda (Event)
Emerging Tech and IP Workshop (28th September 2024)
A 15-minute talk on Generative AI and its impact on Intellectual Property (IP) would start by explaining what Generative AI is, emphasizing its ability to autonomously create content, and its rapid adoption across industries. It would then discuss the challenges AI presents to patenting such inventions.. The talk would explore model-driven innovation, the potential strain on patent systems, and legal dilemmas fueling potential reforms in patent laws essential to address AI’s role in invention. Finally, it would briefly highlight newer opportunities for collaboration, cross-disciplinary innovation, and evolving IP models.
