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 .
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.
Claim Status
Claims 1-11 are pending.
Claims 1-11 are rejected.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to India App. No. 20241015818, filed 03/22/2022. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 06/01/2022 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The Drawings submitted 06/01/2022 are accepted.
Claim Interpretation
The term “new viral RNA strain” is being interpreted as an RNA strain not in the reference data.
Claim Rejections - 35 USC § 112
35 U.S.C. 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.
Claims 1-11 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 6, and 11 limitation recites ”based on comparison between the strain score of the new viral RNA strain and the one or more reference RNA strains”. It is unclear how it is compared to the one or more reference RNA strains which makes the claim indefinite. Claim(s) 2-5 and 7-10 is/are rejected for the same reason because they depend from claim 1, and does not resolve the indefiniteness issue in those claims.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
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;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to method, system, process, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claims 1, 6, and 11:
determining, …, a similarity between a new viral RNA strain and one or more reference RNA strains; calculating, by the prediction system, a strain score for the new viral RNA strain based on the similarity between the new viral RNA strain and the one or more reference RNA strains;
identifying, xxx, one or more mutation sites for the new viral RNA strain by generating spatial nearness data corresponding to the one or more reference RNA strains based on comparison between the strain score of the new viral RNA strain and the one or more reference RNA strains;
predicting, one or more mutations of the new viral RNA strain by performing a generative modelling of a sequence of the new viral RNA strain with reference to the one or more mutation sites of the new viral RNA strain.
Dependent claims 2 and 7:
constructing,…, a plurality of suffix trees corresponding to the sequence of the new viral RNA strain and sequence of each of the one or more reference RNA strains;
determining, …, a match score for the new viral RNA strain by comparing the plurality of suffix trees
Dependent claims 3 and 8:
calculating, …, an infectivity score by weighing a match score between a viral RNA sequence of the new viral RNA strain and a reference RNA sequence of the one or more reference RNA strains with the infectivity metrics; calculating, by the prediction system, a mortality score by weighing the match score between the viral RNA sequence of the new viral RNA strain and the reference RNA sequence of the one or more reference RNA strains with the mortality metrics;
calculating, …, a normalized infectivity score by dividing the infectivity score with a sum of the infectivity metric data for the one or more reference RNA strains;
calculating, …, a normalized mortality score by dividing the mortality score with the sum of the mortality metric data for the one or more reference RNA strains;
determining, …, the strain score for the one or more reference RNA strains by normalizing the normalized infectivity score and the normalized mortality score using a Euclidean norm, and wherein: the infectivity metrics corresponds to a Basic Reproduction Number (Ro) data of the one or more reference RNA strains from the earlier epidemics; and the mortality metrics corresponds to Case Fatality Ratio (CFR) data of strains from earlier epidemics,
calculating the strain score for the new viral RNA strain further comprises:
determining, …, the one or more reference RNA strains that resemble the new viral RNA strain based on the match score and the strain score;
identifying, …, top contributing structural proteins in the new viral RNA strain responsible for current characteristics of the new viral RNA strain based on one or more match proportions, the match score and the strain score.
Dependent claims 4 and 9:
ordering, …, sequences of each of the one or more reference RNA strains based on the spatial nearness data, wherein the spatial nearness data comprises a temporal similarity and a spatial similarity;
identifying, …, a pattern of the temporal similarity and the spatial similarity using an Artificial Intelligence (AI) based attention transformer model;
identifying, …, the one or more mutation sites by identifying differences in the pattern.
Dependent claims 5 and 10:
predicting, …, a mutated RNA sequence of the new viral RNA strain using a generative model, wherein the generative model takes the viral RNA sequence of the new viral RNA strain, information of possible mutation sites and the experimental data to generate a next generation of the new viral RNA strain with mutations;
calculating, …, a vibrational entropy, wherein an overall structural stability of the mutated RNA sequence is checked to identify one or more stable mutations.
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation because the method only requires a user to manually calculate, predict, determine, order, and identify. Without further detail as to the methodology involved in “constructing,…, a plurality of suffix trees”, “determining, …, the one or more reference RNA strains that resemble the new viral RNA “, “identifying, …, top contributing structural proteins “, “ordering, …, sequences “, “identifying, …, a pattern of the temporal similarity“, and “identifying, …, the one or more mutation sites” under the BRI, one may simply, for example, use pen and paper to predicting mutations in ribonucleic acid strains. Some of these steps and those recited in the dependent claims require mathematical techniques such as “determining, …, a match score “, “calculating, …, a normalized infectivity score”, “calculating, …, an infectivity score “, “calculating, …, a normalized mortality score”, “determining, …, the strain score”, and “calculating the strain score”.
Therefore, claims 1, 6, and 11 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 1:
prediction system
Dependent claims 2 and 7:
collecting, by the prediction system, the sequence of the new viral RNA strain and a sequence of the one or more reference RNA strains
Dependent claims 3 and 8:
collecting, by the prediction system, an infectivity metrics and a mortality metrics of the one or more reference RNA strains from earlier epidemics
Independent claim 6:
Processor
Memory
Independent claim 11:
a non-transitory computer readable medium
The claims also include non-abstract computing elements. For example, independent claims 1, 6, and 11 include a prediction system, processor, memory, and non -transitory media.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “collecting” perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “prediction system, processor, memory, and non -transitory media “do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), 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), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1, 6, and 11 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0006, 0022, 0034-0036, 0060, 0063-0068]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
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.
A. Claim(s) 1, 6, and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (Yin R, Luusua E, Dabrowski J, Zhang Y, Kwoh CK. Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks. Bioinformatics. 2020 May 1;36(9):2697-2704, cited on IDS dated 06/01/2022), in view of Xia et al. (Xia, Zhen, et al. "Using a mutual information-based site transition network to map the genetic evolution of influenza A/H3N2 virus." Bioinformatics 25.18 (2009): 2309-2317, newly cited).
Claim 1 is directed to a method for predicting mutations in Ribonucleic acid (RNA) strains, the method comprising: determining, by a prediction system, a similarity between a new viral RNA strain and one or more reference RNA strains;
Yin discloses Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks [title]. Yin further discloses predicting whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data [abstract]. Yin also discloses Tempel remembers all the residue information of previous sequences and assigns the attention weights measuring the importance of the k-th residue (driving series) at time t to perform time-series prediction [p. 2698, col. 2, par. 3] which reads on a similarity between a new viral RNA strain and one or more reference RNA strains.
calculating, by the prediction system, a strain score for the new viral RNA strain based on the similarity between the new viral RNA strain and the one or more reference RNA strains;
Yin discloses the label is created by comparing the residues between the last year and the penultimate year for each site, indicating whether the mutation of the amino acids occurs in the last year. We classify ‘1’ as mutation and ‘0’ for non-mutation [p. 2699, col. 1, par. 2] which reads on the similarity between the new viral RNA strain and the one or more reference RNA strains.
identifying, by the prediction system, one or more mutation sites for the new viral RNA strain by generating spatial nearness data corresponding to the one or more reference RNA strains based on comparison between the strain score of the new viral RNA strain and the one or more reference RNA strains; and
Yin discloses temporal attention mechanisms that consider the long-time dependencies of the residues for mutation prediction, but is silent on generating spatial nearness data corresponding to the one or more reference RNA strains based on comparison between the strain score of the new viral RNA strain and the one or more reference RNA strains.
However, Xia discloses using a mutual information-based site transition network to map the genetic evolution of influenza A/H3N2 virus [title]. Xia further discloses mapping the antigenic and genetic evolution pathways of influenza A is of critical importance in the vaccine development and drug design of influenza virus [abstract]. Xia also discloses analyzing more than 4000 A/H3N2 hemagglutinin (HA) sequences from 1968 to 2008 to model the evolutionary path of the influenza virus, which allows us to predict its future potential drifts with specific mutations [abstract]. Xia further discloses strong selection pressures on certain sites with a strong preference in mutation sites can be found by calculating the level of conservation (the mutation frequency) for each site of HA1 [p. 2314, col. 1, par. 2]. Xia also discloses using program Consurf to map the residue conservation levels on HA 3D X-ray crystal structure [p. 2314, col. 1, par. 2]. Xia further discloses the conservation scores calculated from the 4064 HA1 sequences are mapped onto the HA1 structure and these results indicate that the HA1 mutations have a strong preference for the positive selection sites [p. 2314, col. 1, par. 2]. Xia also discloses the varieties and time sequences of mutations (site substitutions) in HA were analyzed, especially for the sites of HA1 [p. 2310, col. 1, par. 1]. Xia further discloses the mutual information (MI) method was used to calculate the MI score, or correlation, between any two residue sites, thus generating a MI matrix for all pairs of sites [p. 2310, col. 1, par. 1].
predicting, by the prediction system, one or more mutations of the new viral RNA strain by performing a generative modelling of a sequence of the new viral RNA strain with reference to the one or more mutation sites of the new viral RNA strain.
Yin discloses the label is created by comparing the residues between the last year and the penultimate year for each site, indicating whether the mutation of the amino acids occurs in the last year. Yin also discloses classifying ‘1’ as mutation and ‘0’ for non-mutation [p. 2699, col. 1, par. 2]. Yin further discloses a framework designed to predict the mutations at specific individual sites, given the time-series training samples X1 to Xk that each sample consists of sequential data Xk1 to Xkt -1, extract the data of 3-3-grams to predict mutations [p. 2701, col. 2, par. 1]. Yin also discloses after the site selection and data generation, the input data are feed into M proposed models, which correspond to the M different sites for the mutation prediction [p. 2701, col. 2, par. 1].
In regards to claim(s) 1, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yin with Xia as they both discloses algorithmic solutions for the alignment of evolutionarily related sequences while taking into account evolutionary events such as mutations, insertions, deletions, and rearrangements under certain conditions. The motivation would have been to combine the Temporal attention mechanism of Yin with the structural data of Xia for a better understanding of the evolutionary direction of the influenza virus is critical for subsequent development of effective vaccines against future strains [p. 2310, col. 1, par. 1]. One could have therefore combined the elements as claimed by the known methods of Yin and Xia, and that in combination, each element merely would have performed the same function as it did separately for the predictable result that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
B. Claim(s) 2 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yin et al. (Yin R, Luusua E, Dabrowski J, Zhang Y, Kwoh CK. Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks. Bioinformatics. 2020 May 1;36(9):2697-2704, cited on IDS dated 06/01/2022), and Xia as applied to claims 1 and 6 above, and further in view of Su et al. (Su W, Liao X, Lu Y, Zou Q, Peng S. Multiple Sequence Alignment Based on a Suffix Tree and Center-Star Strategy: A Linear Method for Multiple Nucleotide Sequence Alignment on Spark Parallel Framework. J Comput Biol. 2017 Dec, newly cited).
Claims 2 and 7 are directed to the method as claimed in claim 1, wherein determining the similarity between the new viral RNA strain and the one or more reference RNA strains comprises: collecting, by the prediction system, the sequence of the new viral RNA strain and a sequence of the one or more reference RNA strains; constructing, by the prediction system, a plurality of suffix trees corresponding to the sequence of the new viral RNA strain and sequence of each of the one or more reference RNA strains; and determining, by the prediction system, a match score for the new viral RNA strain by comparing the plurality of suffix trees.
Yin discloses Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks [title]. Yin further discloses predicting whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data [abstract]. Yin also discloses Tempel remembers all the residue information of previous sequences and assigns the attention weights measuring the importance of the k-th residue (driving series) at time t to perform time-series prediction [p. 2698, col. 2, par. 3] which reads on a similarity between a new viral RNA strain and one or more reference RNA strains. Yin is silent on plurality of suffix trees.
However, Su discloses multiple sequence alignment based on a suffix tree and center-star strategy: a linear method for multiple nucleotide sequence alignment on spark parallel framework [title]. Su further discloses Multiple sequence alignment (MSA) is an essential prerequisite and dominant method to deduce the biological facts from a set of molecular biological sequences [abstract]. Su also discloses it refers to a series of algorithmic solutions for the alignment of evolutionarily related sequences while taking into account evolutionary events such as mutations, insertions, deletions, and rearrangements under certain conditions [abstract]. Su further discloses these methods can be applied to DNA, RNA, or protein sequences and this work, we take advantage of a center-star strategy to reduce the MSA problem to pairwise alignments, and we use a suffix tree to match identical substrings between two pairwise sequences[ abstract].
In regards to claim(s) 2 and 7, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Yin with Su as they both discloses algorithmic solutions for the alignment of evolutionarily related sequences while taking into account evolutionary events such as mutations, insertions, deletions, and rearrangements under certain conditions. The motivation would have been to combine the multiple sequence alignment based on a suffix tree of Su with the multiple sequence alignment of Yin to accelerate the MSA process and to improve the capacity to handle massive data. as disclosed by Su [p. 1232, par. 5 and p. 1237, par. 2]. One could have therefore combined the elements as claimed by the known methods of Yin and Su, and that in combination, each element merely would have performed the same function as it did separately for the predictable result that one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
No claims are allowed.
It is noted that claims 3-5 and 8-10 are free from the prior because the prior art does not teach nor fairly suggest (wherein calculating the strain score for the new viral RNA strain comprises: collecting, by the prediction system, an infectivity metrics and a mortality metrics of the one or more reference RNA strains from earlier epidemics; calculating, by the prediction system, an infectivity score by weighing a match score between a viral RNA sequence of the new viral RNA strain and a reference RNA sequence of the one or more reference RNA strains with the infectivity metrics; calculating, by the prediction system, a mortality score by weighing the match score between the viral RNA sequence of the new viral RNA strain and the reference RNA sequence of the one or more reference RNA strains with the mortality metrics; calculating, by the prediction system, a normalized infectivity score by dividing the infectivity score with a sum of the infectivity metric data for the one or more reference RNA strains; calculating, by the prediction system, a normalized mortality score by dividing the mortality score with the sum of the mortality metric data for the one or more reference RNA strains; determining, by the prediction system, the strain score for the one or more reference RNA strains by normalizing the normalized infectivity score and the normalized mortality score using a Euclidean norm, and wherein: the infectivity metrics corresponds to a Basic Reproduction Number (Ro) data of the one or more reference RNA strains from the earlier epidemics; and the mortality metrics corresponds to Case Fatality Ratio (CFR) data of strains from earlier epidemics, and wherein, calculating the strain score for the new viral RNA strain further comprises: determining, by the prediction system, the one or more reference RNA strains that resemble the new viral RNA strain based on the match score and the strain score; and identifying, by the prediction system, top contributing structural proteins in the new viral RNA strain responsible for current characteristics of the new viral RNA strain based on one or more match proportions, the match score and the strain score). The closest prior art is Yin. Yin teaches (employing recurrent neural networks with attention mechanisms, Tempel is capable of considering the historical residue information. Attention mechanisms are being increasingly used to improve the performance of mutation prediction by selectively focusing on the parts of the residues. A framework is established based on Tempel that enables us to predict the mutations at any specific residue site).
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dawn M. Bickham whose telephone number is (703)756-1817. The examiner can normally be reached M-Th 7:30 - 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia 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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/D.M.B./Examiner, Art Unit 1685
/Soren Harward/Primary Examiner, TC 1600