Patents Neg - GDI 2024

GDI ’24 – Patents NEG
Adv Ans: Innovation—Defense
AI Innovation Ans
AI Inventions Fake: 1NC
Autonomous inventions are fake.
Lizarralde 22 – Marta Duque Lizarralde LL.M, (Doctoral Candidate and Research Associate at the Technical University of Munich (TUM), Héctor Axel Contreras, IPR Commercialisation Manager at Ericsson) “The real role of AI in patent law debates” Arrow International Journal of Law and Information Technology, Volume 30, Issue 1, Pages 23–46. Spring 2022. Ahttps://doi.org/10.1093/ijlit/eaac008
Market effects and relevance of these topics in the current system The main problem in the inventorship debate is that automation is confused with autonomy, leading to both terms being used as synonyms when, from a technical point of view, they are not.61 While ‘automation’ is a technical term that describes the use of technology to minimize or replace human intervention in a specific process,62 ‘autonomy’ is the capacity of an entity to make independent decisions or to govern itself without an external command.63 When AI is employed in the inventive process, it enables a more automated operation, but the conception of the ‘invention’ still belongs to the human being.64 A different question is that of determining the human inventor of ‘AI-assisted inventions’, since they require the involvement of many people, such as the programmer, those who develop the operational logic model, the machine instructors and the operators. Inasmuch as the contributions of the aforementioned actors differ according to the application of the specific AI technology, the co-inventorship between them should be determined on a case-by-case basis.65 Since AI remains an instrument without real autonomy, AI-assisted inventions should be handled like inventions assisted by any other problem-solving tools for the purpose of identifying the inventor.66 On the other hand, inventions generated autonomously by AI do not exist and will not likely emerge in near the future. Consequently, it may be precocious to review the existing patent law on inventorship at this stage.67 The AI technology that is being used in creative and inventive processes is still in development. Therefore, it is advisable to first allow this technology to evolve, understand how it works and its applications, and only thereafter to adopt regulations that are well adapted to the circumstances.68 The inventorship debate is of significant academic interest, and will certainly need to be addressed from the perspectives of many fields when AGI becomes a reality, but for the moment, it is time to ‘go beyond the exclusive focus on AI-generated outputs’.69
AI Inventions Fake: 2NC
It is structurally incapable of inventing
McCombs et al. ’21 [David McCombs, attorney, Eugene Goryunov, attorney, and Dina Blikshteyn, attorney, “Where We Are on AI Inventorship and Where We Should be Heading,” IP WATCHDOG, 10—12—21, https://www.ipwatchdog.com/2021/10/12/ai-inventorship-heading/id=138648/, accessed 5-7-24]
One might ask: are there immediate ramifications to technological growth if AI cannot be named as an inventor? Unlikely.
AI is Still a Tool
Although AI has become more and more advanced over the years, AI is still far away from inventing meaningful technological improvements and itself driving innovation. AI today is incredibly good at performing tasks, computing and parsing data, finding patterns, determining trends, and identifying solutions. All these tasks, however, are based on training the AI on previous examples, datasets, and patterns to solve for like outcomes. This is true across various AI industries. Cars are capable of driving autonomously because the AI component of these cars has been trained on data recorded over millions of driven miles. By the same token, AI can use natural language processing to summarize text, translate documents, and even chit-chat because it has been trained on millions of documents, question-answering datasets and previous conversations. AI can identify protein sequences and drug formulations, identify financial fraud, or diagnose cybersecurity attacks because AI has been trained to identify those patterns. There is even research in AI writing executable programing code from a proposed business case. Notably, all these tasks require human involvement, oftentimes significant. A human being designs the AI, trains the AI, and verifies when the AI produces a result. Due to advances in computing power and available training data, the AI can perform these tasks faster, more accurately, and better than humans.
AI today excels in performing tasks, yet it is still deficient in creative thinking, abstract and original thought, and identifying problems and undefined corresponding solutions. These deficiencies are, according to some jurisdictions, the hallmarks of inventorship. Until AI can transcend these limitations, it will remain a tool, rather than an inventor.
Prefer industry standards—AI inventions are baked in and can’t operate without humans.
Straus 21 – Joseph Straus (Emeritus Professor of Law at University of Munich and University of Ljubljana and Director at the Max-Planck Institute for Innovation and Competition) "Will Artificial Intelligence Change Some Patent Law Paradigms?." Zbornik Znanstvenih Razprav 81 (2021): 11.
The answer to the question raised in the title of this contribution, whether Al will change some patent law paradigms, seemingly depends entirely on whose position one follows. On the one hand, the main patent offices seem confident they can adequately handle Al core inventions, as well as AI-applied inventions, 215 based on the existing legal instruments. Moreover, the patent offices, without exception, accept only a natural person as an inventor, and a natural or legal person as a patent owner, without explicitly addressing the issue of inventions autonomously generated by Al. Patent authorities see no necessity to alter current standards determining either the prior art, the person skilled in the art (of ordinary skill), or those of the enabling disclosure requirement. Although they acknowledge that AI-generated data may/will increase the amount of data forming prior art to search and consider when assessing novelty and inventive step, they seemingly assume that the use of pertinent Al tools will become, in appropriate cases, an inherent characteristic of the person skilled in the art. 216 The results of the USPTO 2019 Request for Comments show that the majority of public commenters, among them many industry-leading players in Al, favourably assess the practice of the USPTO in the Al area. According to them, the current state of the art is limited to 'narrow' Al, and AGI has not yet arrived. They argue that the Al currently could not invent without human intervention and "that human beings remain integral to the operation of Al". Finally, that the existing U.S. IP laws are calibrated correctly to address the evolution of Al, 2 17 in other words, the patent granting authorities and the majority of the main users of the patent system in the field of Al, at least for the time being and for the foreseeable future do not see Al radically changing the patent law paradigms. They view the system as flexible enough to adapt to changes the Al developments generate in an evolutionary way. Available patent statistics seem to confirm these findings.218 Very similar results have been produced by an extensive Report commissioned by the EU Commission, accomplished in September 2020,219 aimed at examining the state of copyright and patent protection in Europe for AI-assisted outputs in genera2 2 0 and in three priority domains: Science (in particular meteorology), media (journalism) and pharmaceutical research, as regards the EPC and the EPO practice. The Report reached, inter alia, the following conclusions: The EPC is currently suitable to address the challenges posed by Al technologies in the context of Al-assisted inventions or outputs. - While the increasing use of Al systems for inventive purposes does not require material changes to the core concepts of patent law, the emergence of Al may have practical consequences for national Intellectual Property Offices (IPOs) and the EPO. Also, certain rules may in specific cases be difficult to apply to AI-assisted outputs and, where that is the case, it may be justified to make minor adjustments."2
AI Inventions Fake: A2 “Abbott Ev”
Abbott’s examples are just hype
Summerfield ’18 [Mark Summerfield, patent attorney and former Special Counsel, Watermark Intellectual Asset Management, “The Impact of Machine Learning on Patent Law, Part 1: Can a Computer ‘Invent’?” PATENTOLOGY, 1—13—18, https://blog.patentology.com.au/2018/01/the-impact-of-machine-learning-on.html, accessed 4-30-24]
Returning to the question of ‘computational invention’, a key assertion made by Abbott is that computers already independently or autonomously create potentially patentable inventions. He offers three examples in support of this claim:
the ‘Creativity Machine’, which was developed in the early 1990’s by computer scientist Stephen Thaler;
the ‘Invention Machine’, a term for systems based on ‘genetic programming’ (GP), coined by Dr John Koza; and
IBM’s Watson.
In all three cases, however, I fear that the claims of ‘creativity’ and ‘inventiveness’ – which invariably originate with the developers and their associates – are more marketing hype than a fair and accurate assessment of technical capability.
The ‘Creativity Machine’
Thaler’s ‘Creativity Machine’ is based on a neural network – the same general type of ML structure that lies at the heart of AlphaGo. As I have already explained, while such systems can ‘learn’, by updating their internal state in response to training data and/or past performance, for a given (trained) state they are entirely deterministic, in the sense that the same inputs will always generate the same output.
Thaler’s idea was to randomly perturb the state of a trained neural network with ‘noise’, to see if this would then result in it doing something unexpected, new and useful. Here is an excerpt from his own description of the resulting invention, for which US patent no. 5,659,666 was granted in 1997:
Whereas the discovery of just how to adjust the noise level within a trained neural network to produce new ideas is a significant scientific finding, a viable patent was not achieved until a critic algorithm was added, whether heuristic, Bayesian, or neural network based, to monitor for the very best notions emerging from the perturbed network. This is the preferred embodiment of the invention called a Creativity Machine, a “dreaming” network, “imagination engine,” or “imagitron” that is monitored by another constantly vigilant algorithm that we appropriately call an ‘alert associative center’.
Numerous exceptional claims for the Creativity Machine are reproduced in Abbott’s paper. These include: that ‘the two artificial neural networks mimic the human brain’s major cognitive circuit: the thalamo-cortical loop’; that ‘like the human brain, the Creativity Machine is capable of generating novel patterns of information rather than simply associating patterns, and it is capable of adapting to new scenarios without additional human input’; and that the Creativity Machine was actually responsible for inventing the subject matter of Thaler’s second patent.
If all of this sounds more like marketing hype than sound academic evaluation, there may be a good reason for that: Thaler, his associates, and/or documents on the web site of Thaler’s company, Imagination Engines Inc., are the direct or ultimate source of every one of these extraordinary claims.
Genetic Programming – the ‘Invention Machine’
Abbott’s second example of existing ‘computational invention’ is a technology known as ‘Genetic Programming’ (GP). As the name suggests, the basic idea of GP is to develop computer programs structured in such a way that they are able to ‘evolve’. As with other ML systems, such programs generally have inputs and outputs corresponding with some specific pre-defined task. A ‘genetic program’ may be represented, for example, as a set of mathematical or other operations organised into a data structure, and which act on the inputs in order to generate the outputs. Modifying the data structure thus changes the program, and its outputs. Various rules (i.e. ‘genetic algorithms’) can be applied to produce ‘generations’ of such programs, where each new generation consists of variations on the previous generation, and to ‘select’ better-performing variations to carry their code forward into further generations.
According to Abbott, the USPTO granted US patent no. 6,847,851 on an invention that was ‘created by the “Invention Machine”— the moniker for a GP-based AI developed by John Koza.’ Abbott goes on to describe Dr Koza as ‘a computer scientist and pioneer in the field of GP’ who ‘claims the Invention Machine has created multiple “patentable new invention[s].”’ I have a number of issues with this description.
Firstly, as far as I have been able to ascertain, there is no single ‘GP-based AI’ that Koza, or anyone else, has developed and called ‘the Invention Machine’. Rather, ‘Invention Machine’ appears to be a generic term used by Koza to describe GP as applied to generating new solutions to technical problems (see Genetic Programming is an Automated Invention Machine on Koza’s web site genetic-programming.com). Koza contends that he is ‘considered the inventor’ of GP, although the Wikipedia article on the topic identifies significant prior work on the subject dating back to the 1950s.
Since 2004, Koza has been running an annual competition called the ‘Human-Competitive Awards’ (or ‘Humies’), in which cash prizes are given for applications of GP which result in solutions to real-world problems that are competitive with the work done by creative and inventive humans. There are a number of alternative criteria for assessing human-competitiveness, including that ‘the result was patented as an invention in the past, is an improvement over a patented invention, or would qualify today as a patentable new invention.’ In 2010, Koza published an article, ‘Human-Competitive Results Produced by Genetic Programming,’ Genetic Programming & Evolvable Machines, Vol. 11, March 2010 (PDF copy available, 400kB), in which he summarised the various successes of entrants in the Humies. This one paper is Abbott’s sole source for his assertion that ‘by 2010, there were at least thirty-one instances in which GP generated a result that duplicated a previously patented invention, infringed a previously issued patent, or created a patentable new invention.’
It is therefore apparent that Koza – a person with a clear interest in promoting the virtues of GP and his own contributions to the field – is the ultimate source of every one of the claims documented by Abbott that GP is an ‘Invention Machine’.
Which is More Creative – Watson or IBM’s Marketing Team?
As Abbott notes, ‘IBM describes Watson as one of a new generation of machines capable of “computational creativity”’ (emphasis added). Abbott also quotes IBM documents which contend that such machines generate ‘ideas the world has never imagined before’ and that Watson ‘generates millions of ideas out of the quintillions of possibilities, and then predicts which ones are [best], applying big data in new ways.’
To misquote Mandy Rice-Davies, ‘well, they would say that, wouldn’t they?’ IBM markets a range of Watson-based products and services to a variety of potential customers – including Australian government agencies such as IP Australia – to provide ‘actionable insights from large amounts of unstructured data via natural language processing and machine learning.’
This is not to say that Watson is not remarkable, however a more sober analysis concludes that Watson is not some ‘super-smart Siri’, but IBM’s branding for ‘a wide range of AI techniques and related applications’ that it is offering to a growing market of organisations looking for ways ‘to apply these techniques, and to tap into the expertise required to do so.’
AI Inventions Fake: A2 “Autonomy”
No autonomy—it’s an expression of human intelligence
Kim ’20 [Dari aKim, LLM, M.A. Max Planck Institute for Innovation and Competition, “‘AI-Generated Inventions’: Time to Get the Record Straight?” GRUR INTERNATIONAL v. 69 n. 5, 2020, p. 445-447]
II. ‘AI-generated’, or aided by AI?
1. The lack of technical definitions in policy Inquiries
Notably, while raising the questions of how patent law and policy should respond to ‘autonomously generated’ inventions, none of the reviewed policy documents provides a technical definition of such inventions. For instance, the WIPO draft issues paper states that ‘it would now seem clear that inventions can be autonomously generated by AI’.24 While no explicit reference is provided in support,25 it is worth noting that, only recently, this scenario was considered by WIPO to be ‘a science fiction’.26 The World Economic Forum white paper assumes that ‘AI is no longer “just crunching numbers” but is generating works of a sort that have historically been protected as “creative” or as requiring human ingenuity’.27 However, no technical literature but only legal sources are referenced.28
Somewhat puzzlingly, the request for comments initiated by the USPTO uses the term ‘AI inventions’ to refer to both inventions that utilise AI and inventions developed by AI.29 It is assumed that both types can comprise elements such as ‘the application of AI, the structure of the database on which the AI will be trained and will act; the training of the algorithm on the data; the algorithm itself; the results of the AI invention through an automated process’.30 (This view, however, requires further precision.31 For instance, if machine learning is applied in the process of drug discovery and development, the AI technique involved in that process would not be part of the resulting drug claimed as an invention.) Further, the document deliberates that, in both cases, a natural person can contribute to the conception of an invention, including by ‘designing the algorithm and/or weighting adaptations, structuring the data on which the algorithm runs, running the AI algorithm on the data and obtaining the results’.32 This suggests that, in the USPTO’s view, the development of an invention ‘by AI’ can still involve human input, which raises a critical question as to where to draw the line between situations where AI ‘develops’ an invention and where it is used as a tool (i.e. as a problem-solving technique).
2. Examples of ‘artificial inventions’
Legal narratives of AI-generated inventions often refer to almost the same set of examples: the Oral-B toothbrush and other accomplishments of the ‘Creativity Machine’ designed by Stephen L. Thaler,33 the NASA antenna,34 achievements in the field of genetic programming reported by John Koza,35 and AI applications in drugs discovery and development.36 More recently, the project ‘Artificial Inventor’37 presented several inventions attributed to the connectionist system DABUS:38 a method for constructing and simulating artificial neural networks,39 a food container,40 and devices and methods for attracting enhanced attention.41
None of the reviewed legal sources, however, provide a technical explanation of how the computational process was set up.42 Without such understanding, it does not appear straightforward in what way the allegedly ‘AI-generated’ inventions differ from AI-aided inventions. In this regard, it is worth mentioning that AI techniques such as EAs and ANNs have been applied in various domains of science and engineering for decades,43 including in life sciences,44 molecular modelling and drug design,45 aerospace engineering,46 mechanical engineering,47 civil engineering,48 etc.49 Yet, one does not find in these accounts the language that would refer to inventions generated by ‘autonomous en

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