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 .
Claim status
Original claims 1-20 filed 8/31/2022 are pending.
Priority
This application filed 8/31/2022 is continuation of PCT/CN2021/107509 filed 7/21/2021, and also claim benefit to foreign application CN202010704024.0 filed 7/21/2020.
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 7/21/2020. It is noted, however, that applicant has not filed a certified copy of the PCT/CN2021/107509 application as required by 37 CFR 1.55.
Acknowledgment is made of applicant's claim for priority under 35 U.S.C. 119(a)-(d) or (f), 365(a) or (b), or 386(a) based upon an application filed in China on 7/21/2020. The claim for priority cannot be based on said application because the subsequent nonprovisional or international application designating the United States was filed more than twelve months thereafter and no petition under 37 CFR 1.55 or request under PCT Rule 26bis.3 to restore the right of priority has been granted.
Applicant may wish to file a petition under 37 CFR 1.55(c) to restore the right of priority if the subsequent application was filed within two months from the expiration of the twelve-month period and the delay was unintentional. A petition to restore the right of priority must include: (1) the priority claim under 35 U.S.C. 119(a)-(d) or (f), 365(a) or (b), or 386(a) in an application data sheet, identifying the foreign application to which priority is claimed, by specifying the application number, country (or intellectual property authority), day, month, and year of its filing (unless previously submitted); (2) the petition fee set forth in 37 CFR 1.17(m)(3); and (3) a statement that the delay in filing the subsequent application within the twelve-month period was unintentional. The petition to restore the right of priority must be filed in the subsequent application, or in the earliest nonprovisional application claiming benefit under 35 U.S.C. 120, 121, 365(c), or 386(c) to the subsequent application, if such subsequent application is not a nonprovisional application. The Director may require additional information where there is a question whether the delay was unintentional. The petition should be addressed to: Mail Stop Petition, Commissioner for Patents, P.O. Box 1450, Alexandria, Virginia 22313-1450.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/15/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Several of the cited references are Office actions are from foreign offices, some translated and some providing a summary. It is noted that for review of these, the pending claims that were reviewed in these actions were not provided, but for completeness and clarity of the record the cited references when provided in this prosecution were not considered in light of the instant claims. The foreign statutes and comments of the reviewers were not clearly in context of the pending claims and were not considered for their logic nor factual accuracy of comments in actions from other foreign offices.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Specifically, independent claims 1, 17 and 20 provide for the limitation ‘a first predicted activity value’ and this appears to be an undefined term required of the claims. Review of the specification fails to provide any specific definition of what this term is to represent, nor any specific requirement in obtaining such a value with the various features set forth in the claims. Further, in review of the art of record, this does not appear to a term of art, and thus represents an undefined value and/or means of obtaining an activity value as required of the claims.
More clearly setting forth terms defined in the specification, or steps of providing the value consistent with the steps of the claims would address the basis of the rejection.
Claim 8 recites the limitation "the drug screen model" in step 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 8 recites the limitation "the self attention readout function" in the second step. There is insufficient antecedent basis for this limitation in the claim.
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 1-20 arerejected 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 generally is directed to providing a drug screening mode where structural features of a protein and target model features are provided and prediction of an activity value are provided using a graph neural network. The GNN provides an output for both the protein and target molecule and then a concatenation processing is performed a means of drug screening. The information regarding the protein and target are indicated to be node and edge information since a structure cannot be inputted into a neural network, instead the node and edges represent the structure as a vectorized space (see figure 4 for example or starting at [0065]). The specification does not provide any specific guidance on what the node, edges or vectorization should be or is required to be, and appears to provide that three dimensional structures be represented as data that a neural network can process, so to evaluate the fit or binding ability of a protein and target. Dependent claims appear to provide further indication of edge and node processing using a sub-network, and again without any indication of what is specifically being performed or required for the obtaining and updating steps of the claims. Independent claim 17 is directed to an electronic device that is configured to practice the steps of claim 1, and claim 20 is the instructions of claim 1 stored on a medium. There does not appear to be any evidence in the specification of a special memory or device beyond a processor needed to practice the claimed method.
For step 1 of the 101 analysis, the claims are found to be directed to a statutory category of a process and product.
For step 2A of the 101 analysis, the judicial exception of the claims are the steps of determining and evaluating structural features of a protein and target molecule and providing an activity value. The claims are generic to concatenated node features and what they correspond to, but in light of the specification it appears as a whole that protein and target molecule structural information is to be converted into information that can be represented in a graph neural network and the activity assessed and valued is based on how the two interact. For example, claim 3 sets forth that the spacing of amino acids is assessed and providing a ‘diagram’ and providing an assessment of chemical bonds of a target molecule. The process overall appears to assess the fitting of the two structures and providing a value, where the claim provides generic elements like nodes and edges and processing steps that would be part of a neural network processing without any specificity to what they represent or how they are specifically processed. The steps provided by the generating, obtaining, concatenating and updating appear to be generic instructional steps describing how a neural network would assess the interaction and value the interaction of two molecules. The judicial exception is a set of instructions for analysis of the potential interaction of two molecules and appears to fall into the category of a Mental Processes, that is concepts performed in the human mind (including an observation, evaluation, judgment, opinion) to the extent that generic analysis of two molecules interacting and a generic valuation of what is observed can be provided, and in the category of Mathematical Concepts, that is mathematical formulas or equations that convert a ‘structural feature’ into a data form that can be processed using a neural network. The breadth of “determining”, “obtaining”, and “predicting” (in claim 1) encompasses non-transformative steps of providing a representation of one data type as another, in this case for generic modelling using a GNN. This breadth does not impose a meaningful limit on the claim scope, such that all others are not precluded from using the natural principle of evaluating how two molecules interact, here using GNN instead of visual assessment of the structures. Although the claims recite the use of nodes, edges and use of GNN, the courts have also identified limitations that did not integrate a judicial exception into a practical application; for example, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The recited steps appear to provide very broadly any interactions (i.e. concatenated node features possibly) and any ‘activity value’. As the steps of “determining”, “obtaining”, and “predicting” are very generally recited, the combination of of “determining”, “obtaining”, and “predicting” is together reasonably interpreted as mere data gathering and assessment using a computer neural network. Computing, constructing datasets and using statistical models was well understood, conventional, and routinely performed in the art at the time the application was filed. See MPEP § 2106.05(g) for a discussion on adding insignificant extra-solution (both pre-solution and post-solution) activity to the judicial exception. See also MPEP § 2106.05(h) for a discussion on generally linking the use of a judicial exception to a particular technological environment or field of use, this case using a computer to analyze structural data using a neural network model. The claims appear to fall into the category of Mathematical Concepts, as it applies the use of statistics and mathematical relationships in analyzing probabilities and values of the interactions, and also into the category of mental processes, as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because there is no apparent required complexity to or amount of data that is collected and analyzed as presently claimed.
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 do not have an additional element to which the screening is applied. This judicial exception requires steps recited at high level of generality and are only stored on a non-transitory, and is not found to be a practical application of the judicial exception as broadly set forth.
For step 2B of the 101 analysis, each of the independent claims can be interpreted to encompass and recite additional elements to the extent an ‘electronic device’ is used, and are found to be the steps of analyzing data using a computer. As such, the claims do not provide for any additional element to consider under step 2B. With respect to using a neural network and more specifically GNN, it is noted that in explaining the Alice framework, the Court 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 molecular data, and while very broad implies a ‘screening method’ which would provide analysis of possible interactions of a protein and a target molecule. For the products of the device and medium with the instructions, 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 data forms. The judicial exception of the method as claimed can be performed by observing the original protein and molecule structure, and conversion to a data form for GNN processing does not material change the analysis for broadly assessing the interaction of two molecules. Further, 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. 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. Step 2B of the two step analysis is to determine whether any element or combination of elements, in the claim is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception. 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 (aligning structure elements) to manipulate existing information to generate additional information (a value score) 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 (designing probes to a target sequence) 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 (i.e., changing from AIA to pre-AIA ) 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al. (2018) and Torng et al. (2019).
At the time, the use of computers to assess protein-molecule interactions was known. For claim 1 it broadly provides a drug screening mode where structural features of a protein and target model features are provided and prediction of an activity value are provided using a graph neural network (GNN). While there is no specific requirements of the claim steps, in view of the specification and art of record, the GNN provides an output for both the protein and target molecule and then a concatenation processing is performed a means of evaluating possible interactions in the drug screening process, also broadly provided as an ‘activity value’. The information regarding the protein and target are indicated to be node and edge information since a structure cannot be inputted into a neural network, instead the node and edges represent the structure as a vectorized space. The specification does not provide any specific definition, requirements or guidance on what the node, edges or vectorization should be or is required to be, and appears to provide that three dimensional structures be represented as data that a neural network can process, so to evaluate the fit or binding ability of a protein and target. Dependent claims appear to provide further indication of edge and node processing using a sub-network, and again without any indication of what is specifically being performed or required for the obtaining and updating steps of the claims. For the products, independent claim 17 is directed to an electronic device that is configured to practice the steps of claim 1, and claim 20 is the instructions of claim 1 stored on a medium.
The use of computers and neural networks for drug screening were known, for example Torng et al. teach Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. More specifically, Torng et al. provide for an accurate determination of target-ligand interactions is crucial in the drug discovery process. They state: ‘In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-autoencoder to learn fixed-size representations of protein pockets from a set of representative druggable protein binding sites. Second, we trained two Graph-CNNs to automatically extract features from pocket graphs and 2D ligand graphs, respectively, driven by binding classification labels. We demonstrate that graph-autoencoders can learn fixed-size representations for protein pockets of varying sizes and the Graph-CNN framework can effectively capture protein-ligand binding interactions without relying on target-ligand complexes.’ Torng et al. do not provide the exact language of the claims, but clearly provide steps encompassed by the instant claims for providing a protein and target structure and an ‘interaction layer’ (concatemer) and a classifier (activity score), compare Figure 1 with figures 4 and 5 of the instant application. Ahmed et al. is provided to demonstrate that target molecules of proteins can also be other proteins, and to provide additional evidence for the ability to analyze protein–protein interactions using multi-layer neural network as encompassed by the instant claims.
Given the successful use of neural networks to process protein-target molecule interactions, it would have been prima facie obvious to one having ordinary skill in the art at the time the invention was made to use such a method to screen potential drugs as set forth in the preamble of the claims. One having ordinary skill in the art would have been motivated to use these successful and detailed methods for determining interactions. Given the results of both Ahmed et al. and Torng et al. there would have been a reasonable expectation of success given that neural networks which use nodes and edges, and which Torng et al. teach can be assessed separately then compared, that one could screen both small molecules and proteins as potential drug targets.
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