Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant’s amendment
Original claims 1-25 are pending.
Priority
This application filed 4/7/2022 is a continuation of 15/737246 filed 12/15/2017, now abandoned, which is a 371 National stage filing of PCT/CA16/50705 filed 6/16/2016, which claims benefit to US provisional application 62/181115 filed 6/17/2015.
Information Disclosure Statement
No IDS has been filed.
The listing of references in the specification is not a proper information disclosure statement. See throughout, for example [0071]-[0074]. CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. See for example [00180] for site to obtain XGBoost and [00181] for obtaining RDKit toolkit.
Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim analysis
Claim 1 is generally directed to a method of determining cardiotoxicity of a compound and towards a system that implements the method (claim 13). More specifically, in view of the claim limitations and guidance of the specification, the claims are directed to a computer implemented method for predicting where parameters of compounds with known cardiotoxicity and ones without are used to train an algorithm, and then using the algorithm to analyze a compound to predict cardiotoxicity. Dependent claims set forth that the result is a ‘numerical value’ (claims 2, 5) and specific algorithms (claims 10-12), and that variations/redesigned forms of the initial compound are analyzed (claims 3-4), and once analysis is performed selecting by statistical analysis compounds with the least amount of predicted cardiotoxicity (claims 8-9).
For step 1 of the 101 analysis, the claims are found to be directed to a statutory category of a process and a product.
For step 2A of the 101 analysis, the judicial exception of the claims are the steps of analyzing ‘molecular parameters’ of compounds to predict whether a compound may be cardiotoxic. The analysis is provided by initially requiring analysis of compounds/parameters with known cardiotoxicity and comparing them to compound parameters that do not demonstrate cardiotoxicity. Dependent claims set forth specific algorithms to be used which appear to be statistical algorithms (see art of record or specification at [00102] which teaches:
“The machine learning algorithm used in method 100 can be trained using any
suitable technique. In some embodiments, the compounds known to have cardiotoxicity and
the compounds known not to have cardiotoxicity, upon which the machine learning algorithm is trained, can be selected based on a statistical analysis of the molecular parameters of those
compounds. For example, FIG. 1 B illustrates steps in an exemplary method of training a
machine learning algorithm for predicting cardiotoxicity of molecular parameters of a
compound, according to some embodiments of the present invention.”).
The step of comparing ‘parameters’ of compounds with known activity relative to cardiotoxicity are instructional steps. The claims require computing or comparing using algorithms which are statistical in nature, and can result in a ‘numerical value’ representing the predicted cardiotoxicity. The judicial exception is a set of instructions for analysis of data, here ‘parameters’ of compounds. As the claims set forth and require algorithms and machine learning in which these statistical analysis is performed, the judicial exception falls into the category of Mathematical Concepts for the use of mathematical formulas or equations in the statistical analysis and correlation of parameters with cardiotoxicity; and in the broadest reasonable interpretation of claim 1 into the category Mental Processes, that is concepts performed in the human mind (including an observation, evaluation, judgment, opinion), noting that there is no specific definition of ‘parameter’ or any level of validity of the broad analysis for ‘prediction’ the claims encompass comparing two classes of compounds and based on known activities as a parameter evaluating if a test compound fits into one or the other class.
Recent guidance from the office requires that the judicial exception be evaluated under a second prong to determine whether the judicial exception is practically applied. In the instant case, the claims provide that the method is computer implemented and a system to implement the method, but in the most specific embodiments set forth to use known ‘machine learning’ algorithms with the only guidance to use compounds with known activities and do not appear to provide an additional element beyond the use of a computer for the analysis. This judicial exception requires steps recited at high level of generality and are only stored on a non-transitory for the system, and is not found to be a practical application of the judicial exception as broadly set forth. Review of the specification does not provide any evidence that a special computer or processor is necessary, and a review of the art of record for the use of the specific algorithms recited appears to support the need of only a general purpose computer/processor and not a means that affects the functioning of the computer itself.
For step 2B of the 101 analysis, each of the independent claims recites additional elements that the method is computer implemented and for the use of a memory and processor, and are found to be the steps of analysis of ‘parameters’ or data with the aid of a computer. As such, the claims do not provide for any additional element to consider under step 2B as significantly more. It is noted that the courts in explaining the Alice framework wrote that "[i]n cases involving software innovations, [the step one] inquiry often turns on whether the claims focus on the specific asserted improvement in computer capabilities or, instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool." The Court further noted that "[s]ince Alice, we have found software inventions to be patent-eligible where they have made non-abstract improvements to existing technological processes and computer technology." Moreover, these improvements must be specific -- "[a]n improved result, without more stated in the claim, is not enough to confer eligibility to an otherwise abstract idea . . . [t]o be patent-eligible, the claims must recite a specific means or method that solves a problem in an existing technological process."
As indicated in the summary of the judicial exception above and in view of the teachings of the specification, the steps are drawn to analysis of parameters of a compound which appears to be data analysis. While the instruction are stored on a medium and could be implemented on a computer, together the steps do not appear to result in significantly more than a means to compare parameters and make correlations if they exist with properties associated with previously analyzed compounds. A review of the specification does not indicate that new properties are identified anew, only that statistical analysis or machine learning tools are to be used to evaluate compounds with known activities and using what is identified to correlate or predict what an uncharacterized compound activity may be (noting that there is no specific requirement or detail of the analysis such that there is any requirement of the validity of the analysis as broadly claimed). The judicial exception of the method as claimed can be performed by hand and in light of the previous claims to a computer medium and in light of the teaching of the specification on a computer. In review of the instant specification the methods do not appear to require a special type of processor and can be performed on a general purpose computer.
Based upon an analysis with respect to the claim as a whole, claims 1-25 do not recite something significantly different than a judicial exception. Dependent claims set forth additional steps which are more specifically define the considerations and steps of calculating, and comparing, and do not add additional elements which result in significantly more to the claimed method for the analysis. No additional steps are recited in the instantly claimed invention that would amount to significantly more than the judicial exception. Without additional limitations, a process that employs mathematical algorithms to manipulate existing information (‘parameters of known compounds) to generate additional information is not patent eligible. Furthermore, if a claim is directed essentially to a method of calculating, using a mathematical formula, even if the solution is for a specific purpose, the claimed method is non-statutory. In other words, patenting abstract idea (using statistical analysis or algorithms to correlated activities to uncharacterized compounds) cannot be circumvented by attempting to limit the use to a particular technological environment or purpose and desired result.
One way to overcome a rejection for non-patent-eligible subject matter is to persuasively argue that the claimed subject matter is not directed to a judicial exception. Another way for the applicants to overcome the rejection is to persuasively argue that the claims contain elements in addition to the judicial exception that either individually or as an ordered combination are not well understood, routine, or conventional. Another way for the applicants to overcome the rejection is to persuasively argue that the claims as a whole result in an improvement to a technology. Persuasive evidence for an improvement to a technology could be a comparison of results of the claimed subject matter with results of the prior art, or arguments based on scientific reasoning that the claimed subject matter inherently results an improvement over the prior art. The applicants should show why the claims require the improvement in all embodiments.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (QSAR 2008-of record), Kratz et al. (2014-of record), Klon (2010-of record), Greene (2002), Doddareddy et al (2010-of record) and Wacker et al (2012-of record).
Wang et al. QSAR & Combinatorial Science 27(8) 1028-1035 2008
Kratz et al. Journal of Chemicla Information and Modeling 54:2887-2901 2014
Klon et al. Exper Opinion Drug Metab Toxicol 6(7):821-833 2010
Greene et al. Adv Drug Delivery Review 54:417-431 2002
Doddareddy et al. ChemMed Chem 5:716-729 2010
Wacker et al. Mol Membrane Biology 30(3):246-260 2013
Claim 1 is directed to a method of determining cardiotoxicity of a compound for HERG1, and towards a system that implements the method set forth in claim 13, wherein the claims provide limitations to a computer implemented method for predicting where parameters of compounds with known cardiotoxicity and ones without are used to train an algorithm, and then using the algorithm to analyze a compound to predict cardiotoxicity. Dependent claims set forth that the result is a ‘numerical value’ (claims 2, 5) and specific algorithms (claims 10-12), and that variations/redesigned forms of the initial compound are analyzed (claims 3-4), and once analysis is performed selecting by statistical analysis compounds with the least amount of predicted cardiotoxicity (claims 8-9).
Methods of using machine learning for in silico analysis of compounds, for example potassium channel inhibitors of HERG set forth as a species in claim 7, by using machine learning tools were used to make predictions for initial analysis in the drug discovery process as evidenced by the teachings of Wang et al. Wang et al. provide an analysis of multiple methods and use of various compounds and associated parameters in the analysis of HERG. Wang et al. provide a variety of parameters that are assessed for compounds and note that for some resulting in blockade of the HERG channel can cause QT prolongation of torsade de pointes to the heart of a patient. Similarly, Katz et al. examine and validate HERG pharmacophore models as cardiotoxic prediction tools by obtaining information about compounds that are known HERG blockers and provide theoretical validation using programs such as LigandScout in assessing models and perform additional validation in additional experimentation on the evaluated compounds. Klon is provided as an expert opinion and review of the use of machine learning for the use of drug development and the use of machine learning algorithms for predicting the properties of compounds in the drug discovery pipeline. Greene is provided for evidence that computer based analysis can be extended to a variety of parameters including the prediction of toxicity of compounds which may be identified in HTS in drug development. Similar to Klon, Doddareddy et al. provides a review of a comprehensive in silico analysis and modelling of HERG and the use of the analysis for analysis of commercial compounds in databases. Wacker et al. is provided as additional evidence and guidance for HTS in a virtual environment and additional analysis for validation of compound libraries in drug development.
Several of the cited references teach to provide or predict the parameter of binding of a compound to HERG using machine learning, and provide experimental evidence for the use of machine learning as a tool in HTS of predicting the activity of a compound based on the properties of other known compounds, and it would have been prima facie obvious to one having ordinary skill in the art at the time the invention was made to screen as a parameter associated with cardiotoxicity since it has important consequences in drug development as a possible side effect. It was known that channel blocking compounds resulted in long QT or TdP (see Kratz for example) and that analysis of models could be used for drug development. Given the guidance of the art of record, one having ordinary skill in the art would have been motivated to identify compounds that could be developed as HERG inhibitors, but also use in silico screening to identify compounds that may have negative or cardiotoxic side effects. There would have been a reasonable expectation of success given the results and guidance of each Wang et al., Kratz et al., Klon, Greene (2002), Doddareddy et al and Wacker et al. to provide for the use of machine learning to screen compounds that may be cytotoxic to cardiac function.
Thus, the claimed invention as a whole was clearly prima facie obvious.
Conclusion
No claim is allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joseph T Woitach whose telephone number is (571)272-0739. The examiner can normally be reached Mon-Fri; 8:00-4:00.
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/Joseph Woitach/Primary Examiner, Art Unit 1687
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