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 .
Status of the Claims
Claims 1-21 are pending and examined herein. No claims are canceled.
Priority
As detailed on the date filing receipt, the application claims priority as early as 17 December 2020. At this point in examination, all claims have been interpreted as being accorded this priority date as the effective filing date.
Claim Interpretation
The claims recite “small molecules.” In light of the specification, the term “small” will be interpreted as less than 5 kDA (pg. 9, paragraph [45]).
Claim Rejections - 35 USC § 112(b)
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 1-21 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.
Claims 1 and 12 recite accessing a machine learning model trained with molecule-spectrum pairs and later recite the molecule-spectrum pairs being generated by a number of steps. The past tense of accessing a model that was trained and the active tense of pairs being generated makes it unclear whether the molecule-spectrum pairs are being actively generated or whether they constitute product-by-process limitations. MPEP 2113 pertains. Claims 2-11 and 13-21 are dependent on claims 1 and 12 and rejected on similar grounds. Due to the active step being “accessing” and the model was “trained,” the generating steps are interpreted as outside the metes and bounds of the claim.
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 1-21 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I). Abstract ideas include mathematical concepts, and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). The claims as a whole, considering all claim elements individually and in combination, are directed to a judicial exception at Step 2A, Prong 2, and the additional elements of the claims, considered individually and in combination, do not provide significantly more at Step 2B than the abstract idea of identifying molecular compounds from mass spectrometry data.
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of
nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The claims are directed to a method (claims 1-11) and a computer system (claim 12-21), each of which falls within one of the categories of statutory subject matter. [Step 1: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Claims 1 and 12 recite a machine learning model. Embodiments of the machine learning model are disclosed as a probabilistic model (pg. 10, paragraph [47]) or a neural network (pg. 14, paragraph [65]). Under a broadest reasonable interpretation, this claim limitations reads on a mathematical concept. Furthermore, claim 1 recites generating a score representing a probability, where generating a score and probability are mathematical concepts. A mathematical relationship may be expressed in words and there is no particular word or set of words that indicates a claim recites a mathematical calculation (MPEP 2106.04(a)(2)), and a machine learning model is interpreted as a verbal description of a mathematical concept.
Mental processes, defined as concepts practically performed in the human mind such as steps of observing, evaluating, or judging information, recited in claims 1 and 12 include “selecting, based on each of the scores, a small molecule.” The human mind is practically equipped to make a selection based on the output of a model.
Claims 2 and 13 recite further information about the parameters of the model.
Claims 3 and 14 recite generating a fragmentation graph representing subsets of the small molecule and assigning data to like bond type and log rank to said fragmentation graphs. Generating fragment graphs is interpreted as virtually dividing molecules’ bonds and assigning information to the fragments, which is interpreted a mental step of data evaluation and organization.
Claims 4-5 and 15-16 recites additional information about the data associated with the fragmentation graphs.
Claims 6 and 17 recite additional steps in cutting bonds in the graph, which is interpreted as virtually dividing the molecular structures and thus practically performed in the human mind.
Claims 7 and 18 recite determining intensity and assigning a value for the intensity, which the human mind is practically equipped to do based on the data.
Claim 8 recites identifying a drug, where identification is a step performable by the human mind.
Claims 9 and 19 recite evaluating accuracy of the probability by applying a constrained graph variational auto-encoder. The specification discloses this is a model for generating random molecules (pg. 29, paragraph [131]) and is interpreted as a mathematical concept.
Claims 10 and 20 recite generating a predicted mass spectrum and training the model based on that data. These steps are generically recited. Predicting a mass spectrum, under a broadest reasonable interpretation, may be a step practically performed by the human mind, and training a model based on that data culminates in a probability and thus a mathematical step.
Claims 11 and 21 recite determining a p-value and Markov Chain Monte Carlo, which are mathematical concepts.
Hence, the claims explicitly recite numerous elements that, individually and in combination,
constitute abstract ideas. The claims must therefore be examined further to determine whether they
integrate that abstract idea into a practical application (MPEP 2106.04(d)). [Step 2A: Yes]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Claims 1 and 12 recite additional elements that are not abstract ideas: "receiving a query,” "accessing a machine learning model,” “inputting the query,” and “outputting, on a user interface, a representation of the small molecule,” as well as a computer/system, processor, and memory.
Claims 7 and 18 recite accessing mass spectrum information.
Claims 10 and 20 recite receiving data.
The claims comprising computer components do not describe any specific computational steps by which the computer performs or carries out the abstract idea, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than that a generic computer performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer, and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The claims recite accessing and transmitting data. These steps are interpreted as related to gathering data to perform the abstract steps and thus insignificant extra solution activity (MPEP 2106.05(g)).
The claim elements comprising generating mass spectra associated with biosynthetic gene clusters for training the model is interpreted as a data collecting step prior to using the model to generate a score for a structure corresponding to a spectrum. Generating a predicted mass spectrum is interpreted above as a mental process, but if interpreted as an additional element, it is further interpreted as insignificant extra-solution activity related to data gathering (MPEP 2106.05(g)).
Similarly, outputting a representation is interpreted as a step following the score-generating step that does not materially change the abstract ideas and is also insignificant extra-solution activity. Finally, accessing, receiving, and inputting data are all related to data collection to perform the abstract steps and are interpreted as insignificant extra-solution activity.
Therefore, any additional non-abstract elements are not interpreted as integrating the abstract ideas into a practical application. [Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
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 of 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05).
Claims 1 and 12 recite additional elements that are not abstract ideas: a “database,” "receiving a query,” “accessing a machine learning model,” “inputting the query,” and “outputting, on a user interface, a representation of the small molecule,” as well as a computer/system, processor, and memory.
Claims 7 and 18 recite accessing mass spectrum information.
Claims 10 and 20 recite receiving data.
The claims recite a computer, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions, which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
The claims recite sending or receiving information over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), which is a conventional computer activity (MPEP 2106.05(d)).
The claims recite storing information in a computer database, which is a conventional computer function (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).
Display steps are interpreted as insignificant extra-solution activity (MPEP 2106.05(g)) which do not impose meaningful limits on the claim, here displaying an output of the analysis (Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55; MPEP 2106.05(g)).
Generating a predicted mass spectrum is taught in the review of Hufsky 2017 (Mass Spectrometry Reviews 36: 624-633, 2017; previously cited on the 15 July 2025 PTO-892 form), where it is disclosed that fragmentation mass spectrometry data is analyzed using computational methods to identify compounds (abstract) and predicting a mass spectrum using a Markov process (pg. 627, col. 2, second paragraph). Therefore, predicting a mass spectrum is interpreted as conventional.
Therefore, the recited additional elements, alone or in combination with the judicial exceptions, do not appear to provide an inventive concept. [Step 2B: No]
Conclusion: Claims are Directed to Non-statutory Subject Matter
For these reasons, the claims, when the limitations are considered individually and as a whole,
are directed to an abstract idea and lack an inventive concept. Hence, the claimed invention does not
constitute significantly more than the abstract idea, so the claims are rejected under 35 USC § 101 as
being directed to non-statutory subject matter.
Response to the 14 January 2026 Applicant Remarks
Applicant remarks state the instant claims do not recite abstract ideas at Step 2A Prong One of the 101 analysis. The arguments are unpersuasive.
Applicant remarks state that claim 1 is not directed to a mathematical concept because it does not recite a mathematical formula, expression, or correlation (pg. 11, third paragraph) and is instead directed to the functioning of a computer and/or database (pg. 12, first and second paragraphs). It is true that the claims recite computer components, but the presence of mathematical concepts require further analysis in the 101 framework (MPEP 2106.04(d)), particularly generating a score and selecting a small molecule.
Applicant remarks state the subject matter does not fall into the category of methods of organizing human activity (pg. 12, third paragraph). This is agreed to and is not asserted otherwise in the office action.
Applicant remarks state claim 1 does not recite mental processes because the steps are only performable using a computer device (pg. 13, second paragraph). The claims generically recite a machine learning model and computer elements. Therefore, under a broadest reasonable interpretation, these step are considered to be using mathematical concepts and a generic computer to perform abstract steps.
Applicant remarks state the abstract ideas are integrated into a practical application at Step 2A Prong Two of the 101 analysis, specifically stating the claims improve the functioning of a computer or another technology, specifically citing Ex Parte Desjardins (pg. 14, second paragraph). The arguments are unpersuasive. The claims are directed to an alleged improvement in identifying structures of molecular compounds from mass spectrometry data, where identifying structures is a biological problem and not a computational problem. The computer functionality is not being improved, and so the analogy to the decision found in Ex Parte Desjardins is lacking. Applicant remarks state additional information is used to search for molecular structures in a database (pg. 15, first paragraph), but this is not interpreted as an improvement to a computer. Applicant remarks further assert steps of generating molecule-spectrum pairs are improvements (pg. 15, second paragraph), but these steps are not clearly recited as active steps. Additionally, even if they were, these steps are interpreted as performable by the human mind and thus abstract steps, and abstract steps cannot integrate a judicial exception into a practical application.
Conventionality of additional elements is not specifically discussed in the remarks, but as stated above, the elements in addition to the abstract ideas are interpreted as conventional computer elements and insignificant extra-solution activity, which do not provide significantly more than the abstract idea.
Therefore, the rejection under 35 USC 101 is maintained.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1, 3-5, 10, 12, and 14-16
Claims 1, 3-5, 10, 12, and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Hufsky 2014 (Trends in Analytical Chemistry 53: 41-48, 2014; previously cited on the 15 July 2025 PTO-892 form) in view of Duhrkop (Nature Methods 16: 299-308, 2019; previously cited on the 17 March 2022 IDS form).
Claim 1 recites a computer-implemented method including “receiving a query for a target molecular structure in the database, the query representing a query spectrum.” Hufsky 2014 teaches inputting a query spectrum (pg. 42, col. 1, last paragraph), which is predicated on receiving it.
Claim 1 recites “accessing a machine learning model trained with molecule-spectrum pairs, wherein a molecule-spectrum pair of the molecule-spectrum pairs comprises structure data representing two-dimensional molecular structures of small molecules, the structure data being associated with spectrum data representing a mass spectrum that is generated from the small molecules represented in the structure data.” Hufsky teaches “pairs of mass spectra and corresponding molecular structures” used as a training for classifiers (pg. 45, Fig. 3 caption).
Claim 1 recites “inputting the query spectrum into the machine learning model.” Hufsky 2014 teaches inputting a query spectrum (pg. 42, col. 1, last paragraph).
Claim 1 recites “generating, from the machine learning model, a score for each of one or more
molecular structures, each score representing a probability that a molecular structure corresponds to the query spectrum.” Hufsky 2014 teaches whether a particular substructure is present by a “likelihood” based on the trained set of spectra and associated structures (pg. 44, col. 2, last paragraph).
Claim 1 recites “selecting, based on each of the scores, a small molecule.” Hufsky 2014 doesn’t teach an example of determining a small molecule based on the scores or “outputting, on a user interface, a representation of the small molecule.”
Duhrkop recites a method including determining a structure based off the highest score (pg. 301, Fig. 2 caption) and a user interface which “displays graphical examples of the predicted substructures” (pg. 305, col. 2, second paragraph), where displaying the prediction is considered outputting.
It is noted that the steps of generating the molecule-spectrum pairs are outside the metes and bounds of the claims as explained in the rejection under 35 USC 112(b) above.
Claim 12 recites the limitations of claim 1 being implemented using a system comprising at least one processor and a memory storing instructions. Hufsky 2014 teaches automated processing and search libraries which are interpreted as electronic (pg. 41, col. 2, paragraphs 1-2), and thus use of a computer with processors and memory would be inherent. Similarly, Duhrkop teaches a computational method which requires processors (pg. 303, col. 1, third paragraph).
Claims 3 and 14 recite training the machine learning model by performing operations comprising: generating, for a set of the small molecules of the structure data, a fragmentation graph, a respective fragmentation graph and assigning each a bond type and log rank.
Hufsky 2014 teaches “combinatorial fragmenters [which] use bond disconnection to find these fragments” (pg. 44, col. 1, sixth paragraph) and “intensities in the fragmentation spectrum” (pg. 42, col. 2, sixth paragraph), where log rank is interpreted as peak intensity.
Claims 4 and 15 recite “the bond type represents chemical bonds that are disconnected in a parent fragment to produce a fragment.” Hufsky 2014 teaches “combinatorial fragmenters [which] use bond disconnection to find these fragments” (pg. 44, col. 1, sixth paragraph).
Claims 5 and 16 recite “the log rank represents an intensity of a mass peak corresponding to the fragment.” Hufsky 2014 teaches “presence and the intensity of peaks across spectra are highly correlated, as these depend on the nonrandom distribution of molecular (sub-)structures” (pg. 42, col. 1, last paragraph).
Combining Hufsky 2017 and Duhrkop
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Duhrkop, which teaches scoring structure and a visual interface, with the teaching regarding computational mass spectrometry and small molecule fragmentation of Hufsky 2014, because Hufsky 2014 teaches scoring candidates from a database and a short list of options (Fig. 3) but not selecting a single molecule with a high score. Duhrkop teaches scoring the output and selection of a best option based on posterior probability and display of the structure (Fig. 1), where determining and display high scoring structures are desirable properties when attempting to pick a best option. Hufsky 2017 and Duhrkop are both directed to determining molecular structures based on mass spectrum information and their combination would be expected to success, and thus the invention is prima facie obvious.
Claims 2, 7-8, 10, 13, 18, and 20
Claims 2, 7-8, 10, 13, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hufsky 2014 in view of Duhrkop as applied to claims 1, 3-5, 10, 12, and 14-16 above and further in view of Hufsky 2017 (Mass Spectrometry Reviews 36: 624-633, 2017; previously cited on the 15 July 2025 PTO-892 form).
Claims 2 and 13 recite “the machine learning model enables a reduction in computing memory and a decrease in latency of returning the representation of the small molecule in response to receiving the query spectrum, the reduction being relative to a rule-based model using at least one of bond type
data, hydrogen rearrangement data, and dissociation energy data for searching the database.” Hufsky 2014 teaches “rule-based fragmentation spectrum prediction” (pg. 42, col. 2, Section 3) including a cost for cleaving assigned to bonds. Hufsky 2014 does not specifically teach a reduction in latency or memory.
Hufsky 2017 teaches a method achieving an increase in speed (pg. 301, col. 2, first paragraph), interpreted as reading on latency, and also reduces memory requirements (pg. 304, col. 1, fourth paragraph).
Claims 7 and 18 recite “assigning the fragmentation graph a log rank comprises: accessing mass spectra data including a plurality of mass spectra; for each mass spectra of the plurality: determining an intensity of each mass peak of the mass spectrum; and assigning a value for the log rank, wherein the value represents a higher rank as a function of a number of instances of the fragment in the fragmentation
graph. Hufsky 2017 teaches running a Markov chain multiple times as a means of predicting a complete mass spectrum, which allows intensities of peaks to be predicted (pg. 627, col. 2, second paragraph) and likelihood of peaks to be observed (pg. 627, col. 1, second paragraph). It is interpreted that multiple runs of the same query would result in instances of the same peak occurring, which would then contribute to the intensity as taught by Hufsky 2017.
Claim 8 recites “identifying a drug based on the identified small molecule.” Hufsky 2017 teaches identification of metabolites by mass spectrum predictions for the purposes of drug discovery (pg. 624, col. 1, first paragraph).
Claims 10 and 20 recite “receiving data representing at least one biosynthetic gene cluster including a nonribosomal peptide biosynthetic gene cluster, a ribosomally synthesized and posttranslationally modified biosynthetic gene cluster, a polyketide biosynthetic gene cluster, a carbohydrate gene cluster, a polysaccharide gene cluster, or an aminoglycoside gene cluster; generating a predicted mass spectrum associated with the at least one biosynthetic gene cluster; and training the machine learning model using the predicted mass spectrum and the at least one biosynthetic gene cluster. Hufsky 2014 teaches “rule-based fragmentation spectrum prediction” (pg. 42, col. 2, Section 3) and classifiers trained on mass spectra of known references (pg. 44, col. 2, last paragraph) but does not mention biosynthetic gene clusters. Hufsky 2017 teaches “small molecules” which are “intermediates and products of all chemical reactions” which may be used in drug design (pg. 624, col. 1, first paragraph). These small, intermediate, medically useful products are interpreted as the result of biosynthetic gene clusters and thus are metabolites associated with the biosynthetic gene clusters.
Combining Hufsky 2014, Duhrkop, and Hufsky 2017
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Hufsky 2017, which teaches a review of small molecule identification based on mass spectra, with the previously combined work of Hufsky 2014 and Duhrkop, which teach a similar topic, because Hufsky 2017 teaches using a machine learning model (also taught by Hufsky 2014) for the purposes of decreasing memory requirements and latency, which are desirable properties for automated computational methods. Furthermore, Hufsky 2017 teaches application of the method to drug discovery using small molecule intermediates in biological systems, which is consistent with Duhrkop’s teaching regarding exploitation of metabolic pathways (pg. 299, col. 2, second paragraph). Hufsky 2017, Hufsky 2014, and Duhrkop are both directed to determining molecular structures based on mass spectrum information and their combination would be expected to success, and thus the invention is prima facie obvious.
Claims 9 and 19
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hufsky 2014 in view of Duhrkop as applied to claims 1, 3-5, 10, 12, and 14-16 above and further in view of Samanta (Molecules 25(2446): 1-16, 2020; previously cited on the 15 July 2025 PTO-892 form).
Claims 9 and 19 recite “evaluating an accuracy of the probability that a molecular structure corresponds to the query spectrum by applying a constrained graph variational auto-encoder.” Samanta teaches using a variational autoencoder as a metric of similarity (abstract), which is interpreted as reading on accuracy.
Combining Hufsky 2014, Duhrkop, and Samanta
An invention would have been obvious to one of ordinary skill in the art if some motivation in the prior art would have led that person to modify prior art reference teachings to arrive at the claimed invention prior to the effective filing date of the invention. One would have been motivated to combine the work of Samanta, which teaches a variational autoencoder for structure determination, with the previously combined work of Hufsky 2014 and Duhrkop, which together teach using mass spectra and a machine learning algorithm for structure determination, because Samanta teaches determining similarity between structures using different fingerprint encodings (abstract), and mass spectra are taught as a fingerprint (pg. 45, col. 1, second paragraph). Therefore, a variational autoencoder would be valuable for validating structures generated by the algorithm determining structures from the spectra. The use of a variational autoencoder for molecular similarity based on fingerprints would be expected to succeed as the art is directed to determining structure from spectra, and the invention would be prima facie obvious.
Claims 11 and 21
Claims 11 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Hufsky 2014 in view of Duhrkop as applied to claims 1, 3-5, 10, 12, and 14-16 above and further in view of Kou (Journal of Proteome Research 18: 878-889, 2019; previously cited on the 15 July 2025 PTO-892 form).
Claims 11 and 21 recite “a p-value associated with the molecular structure and the query spectrum is determined based on a Markov Chain Monte Carlo model.” Hufsky 2017 teaches “a stochastic, homogenous Markov process” to predict the mass spectrum but not a p-value, while Duhrkop teaches scoring using a posterior probability.
Kou teaches determining a statistical significance score based on the structure and spectrum (abstract), including “p-values of identifications” (pg. 2, col. 2, first paragraph).
Combining Hufsky 2014, Duhrkop, and Kou
Kou which teaches scoring significance using p-values for molecular identification while the previously combined work of Hufsky 2014 and Duhrkop together teach using mass spectra and a machine learning algorithm for structure determination. Both teach Markov methods for determining likelihood of structure; Kou teaches p-value significance and Duhrkop teaches a posterior probability. Substitution of a p-value for a posterior probability using similar methods is interpreted as a plausible substitution because both are metrics for determining statistical likelihood of an outcome (MPEP 2143). Therefore, the invention is prima facie obvious.
Response to the 14 January 2026 Applicant Remarks
The applicant remarks state the applied prior art does not teach the steps of generating the molecule-spectrum pairs (pgs. 16-17). However, as found in the rejection under 35 USC 112(b) above, the claim does not clearly recite the training involving the generating of these pairs as an active step. Therefore, these elements are outside the metes and bounds of the claims. Amending to recite the steps actively would likely overcome at least the rejections under 35 USC 112(b) and 103.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Robert J Kallal whose telephone number is (571)272-6252. The examiner can normally be reached Monday through Friday 8 AM - 4 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia M. Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.J.K./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685