DETAILED ACTION
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
Claims 1-19 are currently pending and under exam herein.
Claims 1-19 are rejected.
Priority
Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) is acknowledged. Receipt of the certified copy of the foreign priority application required by 37 CFR 1.55 was completed on October 6, 2022. At this point in the examination, the effective filing date of claims 1-19 is January 24, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 19, 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Drawings
The drawings are objected to because there is a typo in Figure 2B in the apparatus for determining a molecular conformation 250. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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.
Claim 9 is 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.
Claim 9 recites: “wherein the predicting of the energy values comprises comparing the energy values by referring to each energy value of the candidate conformations predicted through the ANN-based energy prediction model.” This appears to be a mistranslation, the way the claim limitation is written can be interpreted that to predict energy values of the candidate conformations one must compare the predicted energy values of the candidate conformations to each other, but a comparison cannot be made until the energy values are already predicted. For the purpose examination, claim 9 will be interpreted similar to claim 19, such that claim 9 will have the step of comparing the predicted energy values from claim 8.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
The first part of the eligibility analysis evaluates whether a claim falls within any statutory category (MPEP 2106.03). Claims 1-10 recites a series of steps performed by a processor and a non-transitory computer readable medium (CRM) storing instructions to perform the series of steps. Claims 11-19 recites a computer apparatus configured to perform the steps in claims 1-9. The claims are directed to a computer-implemented method and an apparatus (i.e., computer system) to perform the method and fall within one of the statutory categories of invention (Step 1: YES).
Step 2A, prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 recites: …determining a molecular conformation…; …comparing energy values between the candidate conformations…; and determining a final conformation based on a result of the comparing.
Claim 2 recites: wherein the determining of the final conformation comprises determining a candidate conformation with a lowest energy value from among the candidate conformations as the final conformation.
Claim 3 recites: …to determine a ranking of a difference between the energy value and a target energy value of each of the candidate conformations.
Claim 4 recites: wherein the comparing of the energy values comprises comparing a predicted value of a loss function of each of the candidate conformations…
Claim 7 recites: wherein the determining of the final conformation comprises: comparing a lowest predicted value of a loss function from among predicted values of loss functions of the candidate conformations with a threshold value; and determining a candidate conformation corresponding to a minimum difference between the lowest predicted value and the threshold value as the final conformation.
Claim 8 recites: predicting the energy values of each of the candidate conformations…
Claim 9 recites: wherein the predicting of the energy values comprises comparing the energy values by referring to each energy value of the candidate conformations predicted through the ANN-based energy prediction model.
Claim 11 recites: …to compare energy values between the candidate conformations…; and to determine a final conformation based on a result of the comparing.
Claim 12 recites: …to determine a candidate conformation with a lowest energy value from among the candidate conformations as the final conformation.
Claim 13 recites: …to determine a ranking of a difference between the energy value and a target energy value of each of the candidate conformations.
Claim 14 recites: …to compare a predicted value of a loss function of each of the candidate conformations…
Claim 17 recites: …to compare a lowest predicted value of a loss function from among predicted values of loss functions of the candidate conformations with a threshold value, and to determine a candidate conformation corresponding to a minimum difference between the lowest predicted value and the threshold value as the final conformation.
Claim 18 recites: …to predict the energy values of each of the candidate conformations…
Claim 19 recites: …to compare the energy values by referring to each energy value of the candidate conformations predicted through the ANN-based energy prediction model.
The limitations of determining a molecular conformation, comparing energy values, determining a final conformation, determining a candidate conformation with a lowest energy value, comparing lowest predicted values of a loss function with the threshold, predicting energy values of candidate conformations, comparing predicted energy values to each other, and determining a ranking of a difference between energy values and a target energy value recited in claims 1-4, 7-9, 11-14, and 17-19, under the broadest reasonable interpretation, encompass mental processes of observing and evaluating data and making judgments. Additionally, the recited limitations encompass mathematical calculations that can be performed in the human mind or with the aid of pen and paper. Therefore, these limitations fall under the “Mental processes” and “Mathematical concepts” groupings of abstract ideas (Step 2A, prong 1: YES).
Step 2A, prong 2:
Claims found to recite a judicial exception under Step 2A, prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application (Step 2A, prong 2). The claims recite the following additional elements:
Claim 1 recites: A processor-implemented method…; generating candidate conformations based on one or more artificial neural network (ANN)-based conformation generative model; and …inputting the candidate conformations to an ANN-based conformation selecting model.
Claim 3 recites: wherein the ANN-based conformation selecting model is trained...
Claim 4 recites: …inputting the candidate conformations to the ANN-based conformation selecting model.
Claim 5 recites: wherein the generating of the candidate conformations comprises generating the candidate conformations corresponding to molecular information by inputting the molecular information to each of the one or more ANN-based conformation generative models.
Claim 6 recites: wherein the generating of the candidate conformations comprises generating a number of candidate conformations corresponding to molecular information by inputting the molecular information a number of times to the one or more ANN-based conformation generative model.
Claim 8 recites: …inputting the candidate conformations to an ANN-based energy prediction model.
Claim 10 recites: A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the method of claim 1.
Claim 11 recites: a processor configured to generate a candidate conformations based on one or more artificial neural network (ANN)-based conformation generative model; and …inputting the candidate conformations to an ANN-based conformation selecting model…
Claim 12 recites: wherein the processor is further configured…
Claim 13 recites: wherein the ANN-based conformation selecting model is trained…
Claim 14 recites: wherein the processor is further configured…; and …inputting the candidate conformations to the ANN-based conformation selecting model.
Claim 15 recites: wherein the processor is further configured to generate candidate conformations corresponding to molecular information by inputting the molecular information to each of the one or more ANN-based conformation generative models.
Claim 16 recites: wherein the processor is further configured to generate a number of candidate conformations corresponding to molecular information by inputting the molecular information a number of times to the one or more ANN-based conformation generative model.
Claim 17 recites: wherein the processor is further configured…
Claim 18 recites: wherein the processor is further configured…; and …inputting the candidate conformations to an ANN-based energy prediction model.
Claim 19 recites: wherein the processor is further configured…
The additional elements of a processor-implemented method, training an artificial neural network, configuring the processor, a non-transitory computer-readable medium, recited in claims 1, 3, and 10-19 amount to mere instructions to apply the judicial exception on a generic computer environment (MPEP 2106.05f). The additional elements recite the idea of an outcome (i.e., the computer components are used to perform the mental processes and mathematical calculations) without details of how the outcome is achieved. Also, the elements simply invoke a computer to perform an existing process in a highly general way.
The additional elements of generating candidate conformations on an artificial neural network model, inputting candidate conformations into an artificial neural network model, and inputting molecular information into an artificial neural network model recited in claims 1, 4-6, 8, 11, 14-16, and 18 are only tangentially related to the invention and don’t impose significant limits on the recited judicial exception. Additionally, the recited additional elements are necessary data gathering and outputting and all uses of the recited judicial exception requires such data gathering. Therefore, the elements are insignificant extra-solution activity (MPEP 2106.05g).
As such, the judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology or applies/uses the recited judicial exception in some other meaningful way and the claims are directed to the judicial exception (Step 2A, prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims recite additional elements that equate to mere instructions to apply the recited judicial exception in a generic computing environment. Claims that amount to nothing more than
instructions to apply the judicial exception using a generic computer do not render an abstract idea
eligible. Alice Corp., 576 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. The additional elements recited in the claims amount to well-understood, routine, and conventional (WURC) activity because in paragraph 0003 of the specification the description of related art states that “When molecular information is given, an initial conformation is generated … and a final ground state conformation is determined by repeating a process of determining an energy value … and a direction (gradient) in which energy weakens, and gradually modifying the initial conformation until convergence is achieved.” Furthermore, paragraph 0104 of the specification states “Programmers of ordinary skill in the art can readily write the instructions or software based on the block diagrams and the flow charts illustrated in the drawings and the corresponding descriptions in the specification, which disclose algorithms for performing the operations performed by the hardware components and the methods as described above.” The additional elements are also computer functions that the courts have ruled to be WURC such as: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Therefore, the combination of additional elements recited in the claims is well-understood, routine, and conventional. The additional elements do not comprise an inventive concept when considered individually or an ordered combination that transform the claimed judicial exception into a patent-eligible application of the judicial exception, the claims do not amount to significantly more than the judicial exception itself (Step 2B: NO), and claims 1-19 are not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1-6, 8-16, and 18-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kuznetsov et al. (US20220406404A1). The italicized text corresponds to the instant claim limitations.
Regarding claims 1-2, 5, 10-12, and 15, Kuznetsov et al. teach a computer system with processors and non-transitory computer-readable media storing instructions for operations that include: obtaining molecule graph data, inputting the molecule graph data into a machine learning platform, generating a plurality of conformations for the molecule, and selecting at least one conformation based on at least one parameter related to molecular conformations (p. 3, paragraphs 0033-0034). In addition, Kuznetsov et al. teach that their molecular conformation generating method can include considering potential energies of the conformations, where lower potential energy conformations are selected (p. 3, paragraph 0023), and a conformation discriminator (as part of the machine learning platform architecture) that assesses generated conformations based on potential energy estimations (p. 3, paragraphs 0023, 0030, 0034). Together these teachings read on all the limitations of claims 1-2, 5, 10-12, and 15. The molecule graph data reads on the claimed molecular information in claims 5 and 15. An embodiment is taught where a conformation with the lowest energy is selected reading on determining a candidate conformation with a lowest energy value from among the candidate conformations as the final conformation as recited in claims 2 and 12.
Regarding claims 3, 8-9, 13, and 18-19, Kuznetsov et al. teach in an embodiment of their machine learning platform a potential energy prediction module estimates the energy of a molecular conformation and the energies for each conformation can be used for various purposes including ranking the conformations (p. 6, paragraph 0080). This teaching reads on the claims 8-9 and 18-19 limitations of: predicting the energy values of each of the candidate conformations by inputting the candidate conformations to an ANN-based energy prediction model, wherein the predicting of the energy values comprises comparing the energy values by referring to each energy value of the candidate conformations predicted through the ANN-based energy prediction model, wherein the processor is further configured to predict the energy values of each of the candidate conformations by inputting the candidate conformations to an ANN-based energy prediction model, and wherein the processor is further configured to compare the energy values by referring to each energy value of the candidate conformations predicted through the ANN-based energy prediction model. By ranking conformations based on their energy values a comparison between each energy value must be made to determine a ranking.
Furthermore, Kuznetsov et al. teach determining the relative energy difference (RED) metric between median potential energies in generated conformations and ground truth conformations to assess plausibility of generated conformations (p. 14, paragraph 0174). These teaching read on the claims 3 and 13 limitations of: wherein the ANN-based conformation selecting model is trained to determine a ranking of a difference between the energy value and a target energy value of each of the candidate conformations with the median potential energy being the claimed energy value and the median ground truth potential energy being the claimed target energy value.
Regarding claimed 4 and 14, Kuznetsov et al. teach their computing system can have a reconstruction loss module which calculates the reconstruction loss between an original conformation of a molecule compared to a reconstructed conformation (p. 5, paragraph 0065) and a report of molecular conformations can be provided by the system which can rank conformations based on reconstruction loss (p. 6, paragraph 0083). This teaching reads on the claims 4 and 14 limitations of: wherein the comparing of the energy values comprises comparing a predicted value of a loss function of each of the candidate conformations by inputting the candidate conformations to the ANN-based conformation selecting model and wherein the processor is further configured to compare a predicted value of a loss function of each of the candidate conformations by inputting the candidate conformations to the ANN-based conformation selecting model. The reconstruction loss computed by the reconstruction loss module is the specific name for the claimed predicted value of a loss function for a molecular conformation and the module solves a loss function to determine the value and ranking conformations according to reconstruction loss requires comparing those values to each other.
Regarding claims 6 and 16, Kuznetsov et al. teach in an embodiment of their invention, a conformation generation protocol transforms input data and then inputs it into a Euclidean Distance Geometry (EDG) optimizer (Figure 1E, number 192) iteratively to obtain a conformation (p. 6, paragraph 0081-0083). Optimizing EDG is one of the approaches generative neural networks use to model conformations (p. 1, paragraph 0005; p. 2, paragraph 0011). These teachings read on the claims 6 and 16 limitations of: wherein the generating of the candidate conformations comprises generating a number of candidate conformations corresponding to molecular information by inputting the molecular information a number of times to the one or more ANN-based conformation generative model and wherein the processor is further configured to generate a number of candidate conformations corresponding to molecular information by inputting the molecular information a number of times to the one or more ANN-based conformation generative model since the Cartesian Coordinates are derived from the input data (which is the claimed molecular information) and are iterated several times before a conformation is generated.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsov et al. (US20220406404A1) as applied to claims 1-6, 8-16, and 18-19 above, and further in view of Hawkins (Journal of Chemical Information and Modeling, vol. 57, no. 8, pp. 1747-56). The italicized text corresponds to the instant claim limitations.
The limitations of claims 1-6, 8-16, and 18-19 have been taught by Kuznetsov et al.
Kuznetsov et al. teach evaluating their machine learning platform against other conformation generators where “The goal of this task is to evaluate the ability of the proposed model to generate diverse and physically plausible conformations whose distribution matches the ground truth” (p. 13, paragraph 0170-0171). Kuznetsov et al. used the Root Mean Square Deviation (RMSD) loss function to measure dissimilarity between two conformations, as in previous works, in addition to the COV and MAT metrics which indicate the percentage of ground truth conformations covered with generated conformations under a δ RMSD threshold and how close the ground-truth conformations are to the generated conformations in terms of RMSD, respectively (p. 13, paragraph 0172; p. 14, paragraph 0175).
These teachings show that the invention of Kuznetsov et al. can compare values of a loss function and it is desirable to have a low RMSD value which will result in favorable COV and MAT metrics. These teachings read on the claims 7 and 17 limitations of: wherein the determining of the final conformation comprises: comparing a lowest predicted value of a loss function from among predicted values of loss functions of the candidate conformations with a threshold value… and wherein the processor is further configured to compare a lowest predicted value of a loss function from among predicted values of loss functions of the candidate conformations with a threshold value… Lastly, Kuznetsov et al. teach after molecular conformations are generated, “…selecting at least one conformation for the molecule based on at least one parameter related to molecular conformation…” (p. 3, paragraph 0033) which suggests using the minimum difference between a RMSD threshold value as the parameter to select a final conformation.
Kuznetsov et al. are silent on the claims 7 and 17 limitations of: …and determining a candidate conformation corresponding to a minimum difference between the lowest predicted value and the threshold value as the final conformation and …to determine a candidate conformation corresponding to a minimum difference between the lowest predicted value and the threshold value as the final conformation. However, these limitations were known in the art at the effective filing date of the invention, as taught by Hawkins.
Regarding claims 7 and 17, Hawkins teaches, “Success in reproducing a solid-state conformation is usually calculated based on the closest matching conformer generated, where the match between the calculated conformer and the one derived from experiment is almost always measured by RMSD of the heavy atoms of the molecule. It is common to apply RMSD cutoffs for success in reproducing a target conformation” (p. 1751, Accuracy of Reproduction, paragraph 1). This reads on the claims 7 and 17 limitations of: …and determining a candidate conformation corresponding to a minimum difference between the lowest predicted value and the threshold value as the final conformation and …to determine a candidate conformation corresponding to a minimum difference between the lowest predicted value and the threshold value as the final conformation.
The prior art contains a teaching of using a threshold value to determine successful conformations as taught by Hawkins and a suggestion of choosing at least one conformation based on a parameter related to molecular conformations as taught by Kuznetsov et al. One of ordinary skill in the art would be motivated to use the RMSD cutoff to select a final conformation generated from the methods of Kuznetsov et al. because they would want a generated conformation that can exist in the real world and be further studied. The ordinary artisan would have a reasonable expectation of success because RSMD are commonly applied for such purposes in the art. The invention is therefore prima facie obvious.
Conclusion
No claims are allowed.
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/T.Y.O./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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