DETAILED ACTION
This application is being examined under AIA first-to-file provisions.
Status of claims
Canceled:
19-20 and 22-23
Pending:
1-18, 21 and 24
Withdrawn:
4-5, 7-9 and 18
Examined:
1-3, 6, 10-17, 21 and 24
Independent:
1 and 24
Allowable:
none
Rejections applied
Abbreviations
x
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
x
102, 103
JE
Judicial Exception
x
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
x
Double Patenting
MM/DD/YYYY
date format
Priority
As detailed on the 3/21/2024 filing receipt, this application claims priority to no earlier than 9/27/2021. All claims have been interpreted as accorded this priority date.
Restriction/election
Applicant’s 4/28/2026 election without traverse is acknowledged. As listed above, claims are withdrawn as drawn to nonelected inventions pursuant to 37 CFR 1.142(b), and the remaining claims have been examined as listed above. Applicant may request an interview if it becomes clear that examination would be advanced by relaxing the restriction requirement to re-join withdrawn subject matter.
Objection to the specification: title
The title should be amended to more specifically reflect the claims, particularly the independent claims and referencing steps/elements: setting the context of the invention, particular to all claims, and distinguishing the instant application from any related applications, for example a title including terms such as: feature extraction and vectorization. The title should be "descriptive" and "as... specific as possible" (MPEP 606, 1st para. and 37 CFR 1.72; also MPEP 606.01 pertains).
Claim rejections - 112/b
The following is a quotation of 35 USC 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 1-3, 6, 10-17, 21 and 24 are rejected under 112/b, as indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention. Claims depending from rejected claims are rejected similarly, unless otherwise noted, and any amendments in response to the following rejections should be applied throughout the claims, as appropriate. With regard to any suggested amendment below, for claim interpretation during the present examination it is assumed that each amendment suggested here is made. However equivalent amendments also would be acceptable.
The following issues cause the respective claims to be rejected under 112/b as indefinite:
Claim
Recitation
Comment (suggestions in bold)
1, 24
obtaining...
Ambiguous as to whether one or both of physical and virtual embodiments are within the scope of the step. Possibly a different verb, e.g. "identifying..." would fit Applicant's intention, limiting the claim to virtual "obtaining."
10-11
traditional machine learning model
...
deep learning model
The relationships are unclear among the various instantiations of these terms. The terms are instantiated more than once without clear distinction. Later, the recited "the..." does not clearly refer to one particular of the preceding instantiations.
12
marking...
predicated...
Not interpretable and probably should read "predicted"
Claim interpretations
The following claim interpretations apply to all instances of the following terms throughout all claims:
Claim
Recitation
Comment
14
traditional machine learning model
...
deep learning model
The recited "traditional machine learning model" and "deep learning model" are interpreted in light of the specification at [184].
Claim rejections - 102
In the event the determination of the status of the application as subject to AIA 35 USC 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1-3, 6, 10, 13, 21 and 24
Claims 1-3, 6, 10, 13, 21 and 24 are rejected under 35 USC 102(a)(1) as anticipated by Pan 2016 (as cited on the attached form 892).
Regarding claim 1 recites a method for predicting an RNA-protein interaction. Claim 24 recites an electronic device comprising a processor and a memory. As Pan 2016 teaches IPMiner as a computational method (abstract), then Pan 2016 also teaches the claim 24 electronic device.
The method of claim 1 and the actions performed by the processor of claim 24 are taught by Pan 2016 as follows:
obtaining an RNA-protein pair;
Pan 2016 teaches downloading RNA-protein complex data (p. 11, col. 1, par. 2).
feature extraction;
Pan 2016 teaches obtaining raw features by extracting simple sequence component composition features for both RNAs and proteins (p. 11, col. 2, par. 3 through p. 12, col. 1, par. 1), which Pan 2016 more specifically teaches as extracting conjoint triad (3-mer) from protein sequences and 4-mer frequency from RNA sequences (p. 12, col. 1, par. 1).
vectorizing;
Pan 2016 teaches applying stacked autoencoders to extract high-level features, called SDA, from the extracted sequence features of RNAs and proteins, respectively, such that two sub-networks for protein and RNA are generated (p. 12, col. 1, par. 1). Pan 2016 teaches adding another softmax layer to merge the two sub-networks of RNA and protein, and then use label information of training data for fine tuning the above stacked autoencoder, update the weights of networks and extracted features from updated stacked autoencoder, the new feature is called SDA-FT (p. 12, col. 1, par. 1). Pan 2016 teaches input data x with d-dimension into the autoencoder to reconstruct z (the high-level features discussed above) of the same shape as x (p. 12, col. 1, par. 3 through col. 2, par. 4), which is considered to read on vectorizing the RNA-protein pair as instantly claimed because a vector may simply be represented by a variable with dimensional data, as is supported by the instant specification as published at least at [0116], which described a 560-dimensional feature value vector of [1, 0, . . . , 0, 1, . . . ].
obtaining a predicted interaction value; and
Pan 2016 teaches feeding the extracted raw features, SDA and SDA-FT features to random forest classifiers, respectively, and the 3 classifiers are named as RPISeq-RF, SDA-RF and SDA-TF-RF, respectively (p. 12, col. 1, par. 1).
determining an interaction.
Pan 2016 teaches using stacked ensembling to integrate the outputs from the above 3 classifiers, which trains a logistic regression model on the outputs from them (p. 12, col. 1, par. 2), where the outputs are probability of RNA-protein interaction (p. 13, col. 1, par. 1; p. 6, col. 1, par. 2; abstract; see also Fig. 1 for a general overview of IPMiner).
Claim 2 adds that obtaining a sequence feature of the RNA-protein pair to be predicted by performing feature extraction on the RNA-protein pair to be predicted comprises obtaining an original sequence feature set; and determining a sequence feature of the RNA-protein pair to be predicted according to the original sequence feature set.
Pan 2016 teaches downloading RNA-protein complex data (p. 11, col. 1, par. 2) and extracting conjoint triad (3-mer) from protein sequences and 4-mer frequency from RNA sequences (p. 12, col. 1, par. 1) for model training (p. 12, col. 2, par. 3; p. 7, col. 2, par. 2) (i.e., an original sequence feature set).
Claim 3 adds converting an RNA sequence and a protein sequence into k-mer subsequences and searching for each.
Pan 2016 teaches extracting conjoint triad (3-mer) from protein sequences and 4-mer frequency from RNA sequences (p. 12, col. 1, par. 1) to obtain raw k-mer features (i.e., converting an RNA sequence and a protein sequence into an RNA and a protein k-mer subsequence, respectively) for model training (p. 11, col. 2, par. 3 through p. 12, col. 2, par. 3; p. 7, col. 2, par. 2). Pan 2016 teaches that the protein and RNA features comprise counts for each of the different k-mers for each protein and RNA (i.e., searching for each of the k-mer subsequences in the original sequence feature set).
Claim 6 adds k-mer subsequences, vectorizing and concatenating.
Pan 2016 teaches obtaining raw features by extracting simple sequence component composition features for both RNAs and proteins, by extracting conjoint triad from protein sequences and 4-mer frequency from RNA sequences to form 343 dimensional features for each protein sequence and 256 dimensional features for each RNA sequence (p. 11, col. 2, par. 3 through p. 12, col. 1, par. 2).
Claim 10 adds inputting into a traditional machine learning model and into a deep learning model.
Pan 2016 teaches feeding the extracted raw features (i.e., sequence feature of the RNA-protein pair), SDA and SDA-FT features (i.e., the RNA sequence representation vector and the protein sequence representation vector) to random forest classifiers, respectively, and the 3 classifiers are named as RPISeq-RF, SDA-RF and SDA-TF-RF (i.e., at least first and second interaction predication models), respectively (p. 12, col. 1, par. 1; Fig. 1). The random forest model acting on the extracted raw features reads on a traditional machine learning model, as is supported by the instant specification as published at [0135], which sets forth that traditional machine learning models refer to processing natural data in an original form and provides random forest models as one example. As Pan 2016 teaches applying stacked autoencoder to automatically extract high-level features, called SDA and SDA-FT, from the extracted sequence features of RNAs and proteins (p. 12, col. 1, par. 1; Fig. 1), which Pan 2016 describes as a deep learning network (p. 2, col. 2, par. 4; p. 9-10, Discussion; p. 12, col. 1, par. 7 through col. 2, par. 6), the combination of the autoencoder and random forest taught by Pan 2016 to predict protein interactions from these features reads on inputting the RNA sequence representation vector and the protein sequence representation vector in the RNA-protein pair to be predicted into at least one deep learning model to obtain at least one second interaction prediction value as instantly claimed.
Claim 13 adds that obtaining an original sequence feature set comprises obtaining an original data set; and performing feature extraction on each RNA-protein pair in the original data set to obtain the original sequence feature set.
Pan 2016 teaches downloading RNA-protein complex data (i.e., an original data set) (p. 11, col. 1, par. 2) and extracting conjoint triad (3-mer) from protein sequences and 4-mer frequency from RNA sequences (p. 12, col. 1, par. 1) for model training (p. 12, col. 2, par. 3; p. 7, col. 2, par. 2) (i.e., an original sequence feature set).
Claim 21 adds outputting a prediction result of the interaction between the RNA and the protein.
Pan 2016 at least teaches constructing RNA-protein networks using the predicted scores from IPMiner (p. 9, col. 1, par. 2 through col. 2, par. 1; Fig. 4), which teaches outputting a prediction result as instantly claimed.
Claims 14-17
Claims 14-17 are rejected under 35 USC 102(a)(1) as anticipated by Pan 2016, as applied to claims 1-2 and 13 above, as further evidenced by Muppirala (as cited on the attached form 892) and Shen (as cited on the attached form 892).
Claim 14 adds permutation, combination and average calculations.
Claims 15-16 add original data set and variance analysis.
Pan 2016 teaches extracting conjoint k-mers of RNA and protein sequences, obtaining the frequency of the k-mers, and normalizing the frequency of the k-mers to obtain the feature values 4-mer nucleotides in RNA sequences, which is AAAA, AAAC. . .TTTT (p. 11, col. 2, par. 3 through p. 12, col. 1, par. 1). While Pan 2016 does not explicitly teach calculating variance of the k-mers, Pan 2016 teaches following the method of Muppirala to determine these features (p. 12, col. 1, par. 1). Muppirala discloses the framework for RPISeq (abstract), which is employed in the method of Pan 2016 as described above. Muppirala teaches representations of protein and RNA sequences as vectors composed of conjoint k-mers (p. 8, col. 2, par. 4 through p. 9, col. 1, par. 1), according to the method of Shen. Shen discloses a method for protein-protein interaction prediction using only the information of protein sequences (abstract), where they developed a conjoint triad method for describing protein-protein sequences (p. 4338, col. 1, par. 5 through col. 2, par. 2). Shen discloses an equation to normalize the frequencies of the k-mers according to the frequency of a given k-mer in the protein (i.e., marking as in claim 8) in accordance with the frequency of all the k-mers found (i.e., by traversing the original data set as in claim 8), which reads on calculating variance of each of the k-mer subsequences according to the frequency of occurrence as instantly claimed. As Pan 2016 teaches using the method of Muppirala, who teaches using the method of Shen, it is considered that Pan 2016 as evidenced by Muppirala and Shen teaches the instant limitation.
Claim 17 adds a preset condition.
Pan 2016 as evidenced by Muppirala and Shen teaches determining the variance of the k-mers as described above. Shen teaches that the frequencies of the k-mers are normalized to produce numerical values of each protein in a certain range of 0 to 1, which are then used to make the feature vectors (i.e., determining a k-mer subsequence that meets a preset condition according to the variance of each of the k-mer subsequences) (p. 4338, col. 2, par. 2).
Claim rejections - 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 102 and 103 is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 USC 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 USC 102(b)(2)(C) for any potential 35 USC 102(a)(2) prior art against the later invention.
Claim 11
Claim 11 is rejected under 35 USC 103 as unpatentable over Pan 2016 as applied to claims 1 and 10 above, as further evidenced by Hu (as cited on the attached form 892) and in further view of Pan 2018 (as cited on the attached form 892).
Claim 11 adds that the traditional machine learning model comprises at least one of a logistic regression model, a support vector machine model and a decision tree model, and the deep learning model comprises at least one of a convolutional neural network model and a recurrent neural network model.
Pan 2016 teaches using a random forest model to analyze the raw features extracted from the RNA and protein sequences as described above (p. 3, col. 2, par. 3; Fig. 1). Hu teaches that random forest is a classifier that uses multiple decision trees to train and predict samples (p. 804, col. 1, par. 2). Therefore, Pan 2016 as evidenced by Hu teaches a traditional machine learning model which is a decision tree model as instantly claimed. Pan 2016 teaches that in future work, better network architectures to learn high-level features will be achieved by introducing convolutional neural networks (p. 10, col. 2, par. 3), but does not explicitly teach using a convolutional neural network.
However, Pan 2018 discloses a computational method iDeepE to predict RNA–protein binding sites from RNA sequences by combining global and local convolutional neural networks to learn high-level features (abstract; entire document is relevant).
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, in the course of routine experimentation and with a reasonable expectation of success, Pan 2016 as evidenced by Hu and Pan 2018 because each reference discloses machine learning methods for predicting RNA-protein interaction. Pan 2016 specifically motivates incorporating a convolutional neural network into IPMiner to improve high-level feature learning (Pan 2016, p. 10, col. 2, par. 3), and Pan 2018 specifically motivates incorporating IPMiner to improve iDeepE’s performance (p. 3434, col. 2, par. 2). Therefore, it would have been obvious to one of ordinary skill in the art to combine Pan 2016 and Pan 2018 to include a convolutional neural network in the architecture of IPMiner.
Claim 12
Claim 12 is rejected under 35 USC 103 as unpatentable over Pan 2016 as applied to claim 1 above and further in view Shen.
Claim 12 adds marking and summing, not taught by Pan 2016.
Shen teaches Eq. 1 and determining a vector space (p. 4338, col. 2, par. 2), which teaches summing as instantly recited.
In the absence of a secondary consideration to the contrary, it would have been prima facie obvious for PHOSITA to modify the teaching of Pan 2016 using the related teaching of Shen. As motivation to combine, an advantage taught by Shen of modifying methods such as those of Pan 2016 would have been the teaching of Shen that "The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks." (Shen: abstract). Thus, PHOSITA would have been motivated to modify Pan 2016 using the above techniques of Shen in order to achieve the above advantage. One would have had a reasonable expectation of success in doing so because Pan 2016 and Shen are generally drawn to related teaching, and PHOSITA would have understood how to and would have been motivated to apply the teaching of Shen to the related teaching of Pan 2016.
Claim rejections - 101
35 USC 101 reads:
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.
For each rejection below, dependent claims are rejected similarly as not remedying the rejection, unless otherwise noted.
Judicial exceptions (JE) to 101 patentability
Claims 1-3, 6, 10-17, 21 and 24 are rejected under 35 USC 101 because the claimed inventions are not directed to patent eligible subject matter. After consideration of relevant factors with respect to each claim as a whole, each claim is directed to one or more JEs (i.e. an abstract idea, a natural phenomenon, a law of nature and/or a product of nature), as identified below. Any elements or combination of elements beyond the JE(s) (i.e. "additional elements") are conventional and do not constitute significantly more than the JE(s). Thus, no claim includes additional elements amounting to significantly more than the JE(s), as explained below.
In Alice, citing Mayo and Bilski, two Mayo/Alice questions determine eligibility under 101: First, is a claim directed to a JE? And second, if so, does the claim recite significantly more than the JE?
MPEP 2106 organizes JE analysis into Steps 1, 2A (1st & 2nd prongs) and 2B as follows below.
MPEP 2106 and the following USPTO website provide further explanation and case law citations: www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter? -- MPEP 2106.I and 2106.03
[Step 1: claims 1-3, 6, 10-17, 21 and 24: YES]
Step 2A, 1st prong: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea? -- abstract idea -- MPEP 2106.I and 2106.04
Preliminarily, in a 1st prong of Step 2A, elements of independent claim 1 are interpreted as directed to the abstract idea of RNA-protein interaction prediction including the JE elements of "performing...," each of which, including all recitation within each listed element, in at least some embodiments within a BRI, involves only manipulation of data. While manipulation of data is not per se directed to an abstract idea, in this instance the above-identified elements are directed to the abstract ideas identified below.
Claim 18 is analyzed similarly to claim 1.
BRIs of the claims are analogous to an abstract idea in the form of at least a mental process, at least equivalent to a computer-implemented process, including obtaining and comparing intangible data (e.g. Cybersource, Synopsys and Electric Power Group). In a BRI, it is not clear that the claim embodiments are limited so as to require complexity precluding analogy to a mental process.
BRIs of the claims also are analogous to an abstract idea in the form of a mathematical concept, including mathematical relationships and calculations, as found in the following case law, as cited and discussed above: collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) and/or obtaining and comparing intangible data (e.g. Cybersource, Ambry and Myriad CAFC) and/or execution of an algorithm to implement mathematical relationships and/or formulas, including image processing (e.g. TLI, Digitech, Benson, Flook, Diehr, FuzzySharp, In re Grams and In re Abele all as cited in MPEP 2106).
Instant examples of math concepts include at least vectorizing and obtaining a predicted interaction value, as well as relationships inherent in recitations as the only supported embodiments.
The preceding case law examples are cited for the basic form of their identified abstract ideas, and analogy to these example abstract ideas need not be within the same technology field, 101 analysis generally being assumed to be neutral with respect to technology field.
Regarding inherency of abstract ideas, MPEP 2106.04.II.A.1 includes: "the claims in Alice Corp. v. CLS Bank, 'described' the concept of intermediated settlement without ever explicitly using the words 'intermediated' or 'settlement'" (emphasis added, p. 1). Similarly, inherency can effectively be recitation, as in, for example, "By claiming simply 'crystalline paroxetine hydrochloride hemihydrate' with no reference to how it was produced, SKB effectively claimed 'crystalline paroxetine hydrochloride hemihydrate whether non-naturally occurring or arising through natural conversion.' Claim 1, as issued, therefore combines patentable and unpatentable subject matter, and is invalid under Section 101." (capitalization added, SmithKline Beecham Corp. v. Apotex Corp., 365 F.3d 1306, 1321-33, Fed. Cir. 2004).
In the instant type of data processing claims, the specification is not merely adding background explanation as to how a claimed process works, e.g. a physical process based on, involving or further explained by abstract ideas and natural laws. Rather, the specification is detailing the only disclosed way that a programmer may proceed from the recited inputs to the recited outputs, e.g. through actual performance of the disclosed judicial exceptions (JEs).
Regarding the "Meaning of 'Recites,'" MPEP 2106.04.II.A.1 states:
In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
While the "set forth" language approximates explicit recitation, it also is fundamental that all recitation must be interpreted and that to be patent eligible a claim must satisfy 101 according to its properly interpreted scope, e.g. for all embodiments on which the claim reads, e.g. according to any inherency pertinent to a given claim and disclosure accompanying that claim, i.e. consistent with the "described" meaning of "recites" as in the MPEP. Thus, within a BRI, the identified abstract idea elements read on one or more embodiments which only involve manipulation of data. It is not clear than any improvement argument clearly on the record causes a claim not to be directed to a JE for all embodiments within the scope of the claim.
As in Alice (at 306, as cited in the MPEP above) and Bilski (as cited in Alice, id), an abstract idea may comprise multiple abstract elements or steps (i.e. from Alice: "a series of steps" at 306) and need not be a single equation, relationship or principle.
It is not clear that the identified elements must represent other than an abstract idea according to any relevant analysis or case law.
[Step 2A, 1st prong, abstract idea: claims 1 and 18: YES]
Step 2A, 2nd prong: If the claims recite a judicial exception under the 1st prong, then is the judicial exception integrated into a practical application? -- MPEP 2106.I and 2106.04(d)
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
In Step 2A, 1st prong above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In Step 2B below, any remaining steps and/or elements are therefore in addition to the identified JE(s). Any such additional steps and additional elements are further discussed in Step 2B.
Here in Step 2A, 2nd prong, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
At this point in examination it is not yet the case that any of the Step 2A, 2nd prong considerations enumerated above clearly demonstrates integration of the identified JE(s) into a practical application. Referring to the considerations above, none of 1. an improvement, 2. treatment, 3. a particular machine or 4. a transformation is clear in the record.
For example, regarding the first consideration at MPEP 2106.04(d)(1), the record, including for example the specification, does not yet clearly disclose an explanation of improvement over the previous state of the technology field. The claims do not yet clearly result in such an improvement (e.g. specification: [113, 138, 191, 198, 219]).
[Step 2A, 2nd prong: claims 1 and 18: NO]
Step 2B: Do the claims recite a non-conventional arrangement of additional elements in addition to the identified JEs? -- MPEP 2106.I and 2106.05
Addressing the second Mayo/Alice question, all elements of claims 1 and 18 are part of one or more identified JEs (as described above), except for elements identified here as conventional elements in addition to the above judicial exceptions:
The recited "obtaining...," "processor" and "memory" are conventional elements of a laboratory and/or computing environment and/or conventional data gathering elements, as exemplified in MPEP 2106.05(d).II and 2106.05(f-g), and as exemplified by Pan 2016 (as cited on the attached form 892), and generally it is understood that the examples in the reference are well-known and routine.
Data gathering does not impose any meaningful limitation on the judicial exceptions or on how the judicial exceptions are performed. Data gathering is insufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
[Step 2B: claims 1 and 18: NO]
Summary and conclusion regarding claims 1 and 18
Summing up the above analysis of claims 1 and 18, each viewed as a whole and considering all elements individually and in combination, no claim recites limitations that transform the claim, finally interpreted as directed to the identified JE(s), into patent eligible subject matter, and it is not clear that any claim is sufficiently analogous to controlling case law identifying an example of an eligible claim.
Remaining claims
Claims 2-3, 6 and 10-17 add elements which also are part of the identified JEs for the same reasons described above regarding the independent claims and therefore do not provide the something significantly more necessary to satisfy 101.
Elements of the following claims are additional elements but nonetheless are conventional elements of a laboratory or computing environment, conventional data gathering elements or conventional post-processing elements, as in the following specific examples which also are understood to be well-known and routine:
claim 21: "outputting..." is a conventional post-processing element, as exemplified in MPEP 2106.05(d).II and 2106.05(f-g), and as exemplified by Pan 2016, and generally it is understood that the examples in the reference are well-known and routine.
None of the dependent claim elements provides the something significantly more than the identified JE(s) necessary to satisfy 101.
Nonstatutory double patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine to prevent the improper timewise extension of the "right to exclude" granted by a patent and to prevent multiple suits against an accused infringer by different assignees of the same invention (MPEP 804.II.B, 1st para.). A nonstatutory double patenting rejection is appropriate where the conflicting claims (instant v. reference) are not identical, but an examined-application claim (instant claim) is not patentably distinct from a reference claim because the instant claim is either anticipated by, or would have been obvious over, the reference claim (MPEP 804.II.B, 2nd para.).
In cases of double patenting rejections versus reference claims of pending applications, as opposed to claims of an issued patent, the rejections are provisional because the reference claims have not been patented. Presently, no rejections are provisional.
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the application or patent of the reference claim either is shown to be commonly owned with the instant application or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must comply fully with 37 CFR 3.73(b).
Applicant may wish to consider electronically filing a terminal disclaimer (MPEP 1490.V pertains, along with https://www.uspto.gov/patents-application-process/applying-online/eterminal-disclaimer). Electronic filing may lead to faster approval of the disclaimer. Also, if filing electronically, Applicant is encouraged to notify the examiner by telephone so that examination may resume more quickly.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used.
A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Double patenting rejections of instant claims 1-3, 6, 10-17, 21 and 24
Instant claims 1-3, 6, 10-17, 21 and 24 are rejected on the grounds of nonstatutory double patenting as unpatentable over one or more claims in reference applications 17/915,391, 18/025,394 and 18/264,254 in view of Pan 2016, Muppirala, Shen, Hu and Pan 2018 (each as cited on the attached form 892).
Each reference application as well as the instant application recite claims which involve predicting RNA-protein interaction, beginning with RNA and protein sequence information, feature extraction, vectorization and ending with predicting interaction.
Although the reference claims are not identical to the instant claims, in a BRI they also are not patentably distinct from the instant claims: either (i) because the instant claims recite obviously equivalent or broader limitations in comparison to the reference claims or (ii) because the instant claims recite limitations which are obvious over the cited art. It is not clear that the instant claims recite limitations which are narrower than limitations in the reference claims.
It would have been obvious in view of the cited art to modify reference claims to arrive at the rejected instant claims. Either the instant limitations are interpreted as reading on a reference limitation, or the instant limitations would have been obvious in view of the cited art. That is, to the extent that any instant claims are narrower than reference claims, then any such narrowing would have been obvious over the cited art.
Citations to art
In the above citations to documents in the art, rejections refer to the portions of each document cited as example portions as well as to the entirety of each document, unless otherwise noted in the situation of lengthy, multi-subject documents. Other passages not specifically cited within a document may apply as well.
Conclusion
No claim is allowed.
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