!20170621 Accelerated discovery/inventing - resulting inventions unpatentably obvious results of PHOSITA using (AI) tools?
In a non-patent case (blood-thinner lawsuit), the Supreme Court just ruled again to limit venue shopping. Article at: https://www.wsj.com/articles/supreme-court-further-curbs-plaintiffs-venue-shopping-with-bristol-myers-ruling-1497913350.
Is Amazon/WholeFoods this cycle's AOL/TimeWarner - a sign that the party's over? Nice sarcastic title for everyone in the world's biggest guessing game. Article at: www.zerohedge.com/news/2017-06-20/amazonwhole-foods-cycles-aoltime-warner-sign-partys-over
Below are abstracts to three more papers on the ever-relentless development of powerful computer-aided discovery/inventing tools. Which once again raises a very serious question of patentability (pretty much ignored by the patent bar to the detriment of their clients) - are discoveries and inventions output by such tools the KSR-predictable obvious results of PHOSITAs using standard tools - automated tools? And are inventions and discoveries just as obvious if they COULD HAVE been done by PHOSITAs using such tools? If a drug WAS invented with a drug discovery tool, is that KSR-obvious? If a drug COULD HAVE BEEN invented with a drug discovery tool, is that KSR-obvious? If so, IPISC will issue a new patent insurance policy:Same questions for mechanical structure design, electronic circuit design automation, etc. When do the design tools become so powerful that the complete unconstitutional idiocy of Cuno-to-KSR makes all inventions unpatentably obvious?the IPISC Patent Defense Against CAD Inventions Insurance Policy.
I have to ask: is anyone in the patent bar, or at the USPTO, paying attention? Any of you all reading any science/engineering journal/conference papers? There is a huge world beyond cut-and-paste law review articles. This issue ain't going away, and will get worse for your clients. One recent article does discuss this issue:Computers as Inventors - Legal and Policy Implications of AI on Patent Law
Erica Fraser, SCRIPTED, December 2016
Abstract: Recent applications of machine learning and statistical inference provide case studies demonstrating how such approaches can accelerate the discovery process in physical chemistry and related fields. Examples discussed in this review include the introduction of automated approaches to systematically improve experimental design, increase the efficiency of computationally expensive molecular simulations, facilitate construction of predictive models for complex biological processes, and discover interparticle potentials that lead to materials which meet specified design goals. A common theme is the synergy between experiment and computation enabled by such approaches.
Available at: Univ. Texas Austion - arxiv.org/pdf/1706.05405.pdf.
Abstract: The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people’s likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.
Available at: Carnegie Mellon & Hebrew University - arxiv.org/pdf/1706.05585.pdf.
Abstract: Drug-target interaction (DTI) prediction plays a very important role in drug development. Biochemical experiments or in vitro methods to identify such interactions are very expensive, laborious and time-consuming. Therefore, in silico approaches including docking simulation and machine learning have been proposed to solve this problem. In particular, machine learning approaches have attracted increasing attentions recently. However, in addition to the known drug-target interactions, most of the machine learning methods require extra information such as chemical structures, genome sequences, binding types and so on. Whenever such information is not available, they may perform poor. Very recently, the similarity-based link prediction methods were extended to bipartite networks, which can be applied to solve the DTI prediction problem by using topological information only. In this work, we propose a sparse learning method to solve the DTI prediction problem, which does not require extra information and performs much better than similarity-based methods. We compare the proposed method with similarity-based methods including common neighbor index, Katz index and Jaccard index on the DTI prediction problem over the four renowned and benchmark datasets. The proposed method performs remarkably better. The results suggest that although the proposed method utilizes only the known drug-target interactions, it performs very satisfactorily. The method is very suitable to predict the potential uses of the existing drugs, especially, when extra information about the drugs and targets is not available.
Available at: Univ. Elect. Sci and Tech. of China - arxiv.org/pdf/1706.01876.pdf.
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