DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
As detailed on the Filing Receipt filed 1/2/2024, the instant application is a national stage application of PCT/CN2021/134640, and claims priority to the PCT filing date of 11/30/2021. At this point in prosecution, all claims are accorded the claimed priority date.
Claim Status
Claims 15, 18, 20 and 22-23 are canceled.
Claims 1-14, 16-17, 19, 21 are 24-25 are pending, and under examination.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language, in light of the specification, as it would be understood by one of ordinary skill in the art (MPEP 2111-2111.01). The use of novel or unconventional terminology is permissible, provided the meaning of every term is apparent from the specification at the time of filing (MPEP 2173.05(a)). This section documents the Examiner’s accordant interpretations of certain claim terminology for prosecutorial purposes.
Claims 11-12 and 19 recite the following limitations, containing the term “splicing”:
“obtaining an RNA fusion vector sequence by splicing the relevance vector sequence of the first RNA vector sequence and the first RNA vector sequence” (claim 11, lines 5-6; claim 12, lines 5-6);
“obtaining a protein fusion vector sequence by splicing the relevance vector sequence of the first protein vector sequence and the first protein vector sequence” (claim 11, lines 9-10; claim 12, lines 7-8); and
“obtaining a feature vector to be predicted by splicing the second RNA vector sequence and the second protein vector sequence” (claim 19, lines 5-6).
The term “splicing” is in conventional usage within the field of the invention, to refer to RNA splicing – the biological process in which introns of a primary transcript (pre-mRNA) molecule are removed, and exons are joined together, to render messenger RNA. However, term “splicing” can also refer more generally to a process of joining two elements together. The above cited usage in the claims appears to refer to a process performed upon two data vectors. The specification uses the term in reference to element-wise mathematical combination of two data vectors (e.g., at para. 00215: “A dot product operation may also be performed on the i-th base k-mer vector
h
i
R
and the relevance vector
v
i
P
of the base k-mer vector to splice them, that is
h
i
R
,
v
i
P
”).
For the purpose of prosecution, the term “splicing” as used in the present application is therefore interpreted as a process of joining data vectors which encompasses at least element-wise mathematical combination of two data vectors.
Claim Rejections - 35 USC § 101
35 USC § 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 1-14, 16-17, 19, 21 are 24-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (i.e., non-statutory subject matter).
"Claims directed to nothing more than abstract ideas, natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 § I).
Abstract ideas include mathematical concepts (including formulas, equations and calculations), 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 both individually and in combination, do not amount to significantly more than an abstract idea.
Step 1: The Four Categories of Statutory Subject Matter (MPEP 2106.03)
Claims 1-14, 16-17, 19 and 21 are directed to a method, which falls under the ‘process’ category of statutory subject matter.
Claim 25 is directed to an electronic device, which falls under the ‘machine’ category of statutory subject matter.
Claim 24 is directed to a computer-readable storage medium. The claimed subject matter encompasses transitory embodiments (e.g., propagating signals) which do not fall under any category of statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007); Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294 (Fed. Cir. 2017).
The Examiner suggests amendment to, e.g., “A non-transitory computer-readable storage medium”. Direction of the claim to non-transitory embodiments would cause the claim to fall under a category of statutory subject matter and overcome this portion of the rejection.
However, this amendment alone would likely not overcome rejection for recitation of
judicial exceptions without significantly more. In the interest of compact prosecution, the recited subject matter of claim 24 has been interpreted according to the Examiner’s suggestion for further analysis below regarding recitation of judicial exceptions without significantly more.
Step 2A, Prong One: Whether the Claims Set Forth or Describe a Judicial Exception (MPEP 2106.04 § II.A.1)
‘Mathematical concepts’ are relationships between variables and numbers, numerical formulas or equations, or acts of calculation, which need not be expressed in mathematical symbols (MPEP 2106.04(a)(2) § I). The claims recite elements which encompass mathematical concepts, at least under their broadest reasonable interpretation, including:
obtaining vectors through a selective attention mechanism model (claims 1, 6, 12-13, 16, 21 and 24-25), i.e., evaluating an algorithm for particular input;
determining a probability value according to vectors (claims 1, 10, 19, 21 and 24-25), i.e., calculating a value based on particular input;
inputting vectors into a pre-trained recurrent neural network and outputting vectors (claims 3, 5, 11 and 24-25), i.e., evaluating an algorithm for particular input;
obtaining vectors by performing feature extraction on vectors (claims 6-8, 13-14 and 16-17);
obtaining vectors by performing an operation on vectors, e.g., using a mapping matrix (claims 3, 5-9, 13-14 and 16-17), including:
using a mapping matrix (claims 3 and 5), and
using query, key, and value weight matrices (claims 7-8, 14 and 17);
obtaining attention scores by calculating similarities between vectors (claims 9, 14 and 17);
summing vectors according to attention scores (claims 9, 14 and 17);
obtaining vectors by mathematically combining vectors (claims 11-12 and 19; see ‘Claim Interpretation’ section);
outputting a probability value by inputting a feature vector into a classifier (claim 19);
obtaining a loss value by calculating the interaction prediction value and a label value of each RNA-protein pair in the training data set using a loss function (claim 19); and
adjusting model parameters of the recurrent neural network and the selective attention algorithm model according to the loss value (claim 19).
The recited acts of calculation constitute mathematical concepts.
‘Mental processes’ are processes that can be performed in the human mind at least with use of a physical aid, e.g., a slide rule or pen and paper (MPEP 2106.04(a)(2) § III). The claims recite elements that encompass processes that are practicably performable in the human mind, at least under their broadest reasonable interpretation, including:
encoding the RNA sequence and the protein sequence (claim 1 and 24-25);
converting the RNA and protein sequences into k-mer subsequences (claims 2 and 4), i.e., segmenting a character string;
vectorizing each k-mer subsequence (claims 2 and 4); and
encoding each k-mer subsequence (claims 3 and 5).
The recited string manipulation steps, which are practicably performable in the human mind, constitute mental processes.
Hence, the claims recite elements that, individually and in combination, constitute an abstract idea. The claims must therefore be examined further to determine whether they integrate this abstract idea into a practical application (MPEP 2106.04(d)).
Step 2A, Prong Two: Whether the Claims Contain Additional Elements that Integrate the Judicial Exception(s) into a Practical Application (MPEP 2106.04 § II.A.2)
The claims recite additional elements that gather data necessary for performance of claimed method steps, including:
obtaining an RNA sequence to be predicted and a protein sequence to be predicted (claims 1 and 24-25); and
obtaining a training data set comprising a positive-example RNA-protein pair and a negative-example RNA-protein pair (claim 21).
Necessary data gathering is considered to be insignificant pre-solution activity, and as such insufficient to integrate an abstract idea into a practical application (MPEP 2106.05(g)).
The claims further recite additional elements that require performance of claimed functions on a computer, including:
a non-transitory computer readable storage medium, storing with a computer program thereon, wherein, when the computer program is executed by a processor, a method is implemented comprising claimed functions (claim 24; see ‘’ section); and
an electronic device, comprising a processor, and a memory configured to store an executable instruction of the processor, wherein the processor is configured to execute a method by executing the instruction, the method comprising claimed functions (claim 25).
The claims do not describe any specific computational steps by which a computer performs or carries out functions drawn to the abstract idea, nor do they provide any details of how specific structures of a computer are used to implement these functions. The claims state nothing more than that a generic computer performs functions drawn to the abstract idea, and are therefore mere instructions to apply the abstract idea using a computer. As such, the claims do not integrate the abstract idea into a practical application (see MPEP 2106.04(d) § I and 2106.05(f)).
No further additional elements are recited.
When the claims are considered as a whole: they do not improve the functioning of a computer, other technology, or technical field (MPEP 2106.04(d)(1) and 2106.05(a)); they do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (MPEP 2106.04(d)(2)); they do not implement the abstract idea with, or in conjunction with, a particular machine (MPEP 2106.05(b)); they do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)); and they do not apply or use the abstract idea in some other meaningful way beyond linking the use of the abstract idea to a particular technological environment and/or field of use (e.g., RNA-protein interaction prediction; MPEP 2106.05(e) and 2106.05(h)).
Hence, the recited abstract idea is not integrated into a practical application. See MPEP 2106.04(d) § I.
Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are directed to the abstract idea. Claims that are directed to an abstract idea must be examined further to determine whether the additional elements besides the abstract idea render the claims significantly more than the abstract idea. Additional elements besides the abstract idea may constitute inventive concepts that are sufficient to render the claims significantly more (MPEP 2106.05).
Step 2B: Whether the Claims Contain Additional Elements that Amount to an Inventive Concept (MPEP 2106.05)
As noted above, several recited additional elements amount to insignificant extra-solution activity. Mere addition of insignificant extra-solution activity does not amount to an inventive concept that would render the claims significantly more than the recited abstract idea, particularly when the activities are well-understood or conventional (MPEP 2106.05(g)). The conventionality of recited additional elements that amount to insignificant extra-solution activity must be further considered.
Recited additional elements amounting to insignificant extra-solution activity encompass the following processes, which are indicated as activity that may be performed with publicly-available resources by the instant specification (see MPEP 2106.07(a) § III):
obtaining RNA and protein sequences, obtaining training data (para. 00282: “For example, each model may be trained based on the RPI1807 data set. There are 3242 RNA-protein pairs in the data set, specifically including 1807 pairs of positive examples and 1436 pairs of negative examples”).
Additionally, recited additional elements amounting to insignificant extra-solution activity encompass the following computer-implemented functions, which the courts have held as coextensive with a general-purpose computer and/or well-understood, routine and conventional:
Receiving, storing, and processing data (In re Katz Interactive Call Processing Patent Litigation, 639 F.3d 1303, 1316 (Fed. Cir. 2011); EON Corp. IP Holdings LLC v. AT&T Mobility LLC, 785 F.3d 616, 622 (Fed. Cir. 2015)).
Hence, the encompassed extra-solution activity is considered well-understood, routine and conventional. Well-understood, routine and conventional activity is insufficient to constitute an inventive concept that would render the claims significantly more than the abstract idea (MPEP 2106.05(d)).
Mere instructions to implement an abstract using a computer are, when considered individually, similarly insufficient to constitute an inventive concept that would render the claims significantly more than said abstract idea (see MPEP 2106.05(f)).
When the claims are considered as a whole, they do not integrate the judicial exception into a practical application; they do not confine the use of the judicial exception to a particular technology; they do not solve a problem rooted in or arising from the use of a
particular technology; they do not improve a technology by allowing the technology to
perform a function that it previously was not capable of performing; and they do not
provide any limitations beyond generally linking the use of the judicial exception to a particular technological environment and/or field of use (e.g., RNA-protein interaction prediction; MPEP 2106.05(e) and 2106.05(h)).
Hence, the claims do not include additional elements that are sufficient to amount to significantly more than the recited abstract idea. See MPEP 2106.05.
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 a judicial exception 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.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 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, 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 USC § 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.
Claims 1, 6-10, 19, 21 and 24-25 are rejected under 35 USC § 103 as being unpatentable over Gao (IEEE Access 8: 189869-189877; published 10/28/2020), in view of Kim (arXiv:2109.08360v1 [cs.LG], 16 pages; published 9/17/2021).
Claim 1 is directed to a method for RNA-protein interaction prediction, comprising: obtaining an RNA sequence to be predicted and a protein sequence to be predicted; obtaining a first RNA vector sequence by encoding the RNA sequence to be predicted; obtaining a first protein vector sequence by encoding the protein sequence to be predicted; obtaining a relevance vector sequence of the first RNA vector sequence and a relevance vector sequence of the first protein vector sequence through a selective attention mechanism model; and determining a probability value of interaction between the RNA sequence to be predicted and the protein sequence to be predicted according to the relevance vector sequence of the first RNA vector sequence and the relevance vector sequence of the first protein vector sequence.
With respect to claim 1, Gao discloses a method of predicting RNA-protein interactions via a hybrid deep learning model, RPI-MCNNBLSTM, implementing steps of: pre-processing input data by encoding RNA sequences and protein sequences as binary matrices (i.e., vector sequences), obtaining a weighted representation (i.e., relevance vector) of the encoded RNA sequence matrix and a weighted representation (i.e., relevance vector) of the encoded protein sequence matrix through a bidirectional long short-term memory (BLSTM) model (pg. 189873, r. column – pg. 189874, l. column and Fig. 5), and determining the probability of interaction between the RNA sequence and the protein sequence (pg. 189874, l. column – pg. 189875, l. column).
Gao does not disclose obtaining relevance vectors of the input sequences through a selective attention mechanism model.
Kim presents a deep learning-based cross-attention framework, for prediction of drug-target interactions (pg. 1, Abstract), based on extraction of features and calculation of attention weight sequences from an input molecular (drug) sequence and an input protein (target) sequence (pp. 2-3, section 2.1). Kim teaches a multi-head (i.e., selective) attention mechanism (pg. 4, Fig. 1; pp. 5-6, section 3.2.1).
Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2). Kim also demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2).
With respect to claim 6, Gao discloses application of convolutional neural networks and a BLSTM to extract hidden features and feature dependencies, and mathematically depicts the “running principle of LSTM” as a series of evaluated equations wherein the final evaluated variable is denoted as ht (pg. 189870, r. column; pg. 189873, r. column – pg. 189874, l. column). As one of ordinary skill in the art would be aware, the output of a LSTM is a hidden state vector, which is conventionally represented with the notation ht.
Kim discloses calculation of attention weight sequences via a multi-head (i.e., selective) attention mechanism based on extracted sequence features (pp. 2-3, section 2.1; pg. 4, Fig. 1; pp. 5-6, section 3.2.1).
With respect to claim 7-8, Kim discusses calculation of self-attention, comprising transformation of input drug and protein sequence features by query, key and value weight matrices to obtain query, key, and value features that share the same dimensionality as the inputs, i.e., first, second, and third vectors (pp. 5-6, section 3.2.1).
With respect to claim 9, Kim illustrates decoder cross-attention scoring for drug features via an equation that mathematically implements: calculating QdKp, i.e., similarity between the drug query (Qd, first vector) feature and the protein key (Kp, second vector) feature, and summing the protein value (Vp, third vector) feature according to the normalized QdKp (pg. 6, Eq. 7). Kim states that attention scoring for protein features is performed in a manner symmetrical to that illustrated with respect to drug features (pg. 5, section 3.2.1).
With respect to claim 10, Gao discloses determining the probability of interaction between the RNA sequence and the protein sequence based on weighted representations of the RNA and protein sequences (pg. 189873, r. column – pg. 189875, l. column).
With respect to claim 19, Kim teaches a method of RNA-protein interaction prediction that computes attended drug features (d’) and attended protein features (p’), pools each over the length of the inputs (nd and np), concatenates drug and protein features, and assigns probabilities therefrom (pg. 7, section 3.2.2).
With respect to claim 21, Gao discloses implementation of their method by training a model, using a training set selected from datasets including positive RNA-protein pairs and negative RNA-protein pairs, to classify whether input protein and RNA pairs will interact or not (pg. 189870, r. column ; pg. 189872, r. column). Gao further describes training model architecture to minimize a loss function (pg. 189874, l. column).
Kim teaches a method of RNA-protein interaction prediction that computes attended drug features (d’) and attended protein features (p’) via a multi-head (i.e., selective) attention mechanism, pools each over the length of the inputs (nd and np), and assigns probabilities therefrom (pg. 7, section 3.2.2). Kim teaches that predicted drug-protein interaction can be measured as scores or categories, according to which the model task is either regression or classification (pg. 4, section 3.1).
With respect to claim 24, the claim is directed to a computer-readable storage medium with a computer program stored thereon, wherein, when the computer program is executed by a processor, a method for RNA-protein interaction prediction is implemented, the method comprising functional limitations of substantive similarity to the process limitations of claim 1.
Gao discusses software implementation of a data pre-processing step (pg. 189872, r. column), and characterizes their disclosure as a computational method (pg. 189875, r. column). Before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to implement a computerized method on a computer-readable storage medium as claimed. The teachings of Gao, in view of Kim, are considered to read on the functional limitations of the claim in the same way as outlined above with respect to the process limitations of claim 1.
With respect to claim 25, the claim is directed to an electronic device, comprising:
a processor; and a memory, configured to store an executable instruction of the processor; wherein the processor is configured to execute a method for RNA-protein interaction prediction by executing the executable instruction, the method comprising functional limitations of substantive similarity to the process limitations of claim 1.
Gao discusses software implementation of a data pre-processing step (pg. 189872, r. column), and characterizes their disclosure as a computational method (pg. 189875, r. column). Before the effective filing date of the claimed invention, one of ordinary skill in the art would have found it obvious to implement a computerized method on an electronic device, comprising a processor and memory, as claimed. The teachings of Gao, in view of Kim, are considered to read on the functional limitations of the claim in the same way as outlined above with respect to the process limitations of claim 1.
An invention would have been obvious to one of ordinary skill in the art if it simply applies a known technique to a known method to yield predictable results. Before the effective filing date of the claimed invention, said practitioner would have combined calculation of cross-attention between an input molecular sequence and an input protein sequence, as taught by Kim, with the RNA-protein interaction prediction method of Gao, because Gao teaches that considering the sequence of both the RNA and the protein as model input is critical to predicting interaction between a given RNA-protein pair (pg. 189875, l. column), while Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2), demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2). Said practitioner would have had a reasonable expectation of success because Gao and Kim are directed to similar fields of endeavor, both concerning methods of predicting molecular-protein interaction, via a model implementing neural network architecture, based on joint processing of an input molecular sequence and protein sequence.
In this way the disclosure of Gao, in view of Kim, makes obvious the limitations of claims 1, 6-10, 19, 21 and 24-25. Thus, the claimed invention is prima facie obvious.
Claims 2-5, 11-14 and 16-17 are rejected under 35 USC § 103 as being unpatentable over Gao, in view of Kim, as applied to claim 1 above, and further in view of Tongji (CN 113053462 A; published 6/29/2021).
With respect to claim 2, Gao discloses encoding input RNA sequences as binary matrices (pg. 189873, r. column – pg. 189874, l. column and Fig. 5), Gao does not disclose converting the RNA sequence into k-mer subsequences; or vectorizing each k-mer subsequence.
Kim discusses related work wherein drug and target were represented as substructure words (pg. 3, section 2.1). Kim does not teach converting the RNA sequence into k-mer subsequences; or vectorizing each k-mer subsequence.
Tongji discusses an RNA-protein binding preference prediction method based on a bidirectional attention mechanism (para. 0001), comprising steps of: converting an input RNA sequence into a plurality of k-mer sentences, and encoding each k-mer sentence via word2vec (paras. 0006-7; Fig. 1); obtaining RNA feature vectors, representing k-mer importance to RNA-protein binding, via attention mechanism models (paras. 0009-12); and determining binding preference based on probability values predicted according to the feature vectors (para. 12).
Tongji teaches that different sites within an RNA sequence have different importance to RNA-protein binding, which can be further affected by short sequences upstream and downstream of a given site, and existing algorithms fail to account for this variable importance while their disclosed method accounts for it through k-mer based attention scoring (paras. 0003-4).
With respect to claim 3, Tongji teaches encoding each k-mer sentence via word2vec, combining the obtained vectors to construct an embedding matrix, and passing the obtained embeddings through a trained bidirectional LSTM (i.e., recurrent neural network) to output k-mer vectors (paras. 0006-7, 0013 and 0117; Fig. 1).
With respect to claim 4, the unique limitations of the claim substantively differ from those of claim 2 only in their application of the recited processing techniques to an input protein sequence, rather than to an input RNA sequence. Extending the k-mer segmentation and vectorization techniques of Tongji, as outlined above with respect to claim 2, to processing of an input protein sequence is considered an obvious variant in light of the combined teachings of Gao, Kim and Tongji.
With respect to claim 5, the unique limitations of the claim substantively differ from those of claim 3 only in their application of the recited processing techniques to an input protein sequence, rather than to an input RNA sequence. Extending the k-mer segmentation and vectorization techniques of Tongji, as outlined above with respect to claim 3, to processing of an input protein sequence is considered an obvious variant in light of the combined teachings of Gao, Kim and Tongji.
With respect to claim 11, Gao discloses a method of RNA-protein interaction prediction that extracts RNA and protein features from input RNA and protein sequences via convolutional neural networks, learns between-feature dependencies (i.e., fusion vectors) via a BLSTM network (i.e., RNN), and determines probabilities therefrom (pg. 189874, r. column).
Kim teaches a method of RNA-protein interaction prediction that computes attended RNA features (d’) and attended protein features (p’) via a cross-attention mechanism, pools each over the length of the inputs (nd and np), and assigns probabilities therefrom (pg. 7, section 3.2.2). Kim further discloses a gated cross-attention mechanism wherein attended features are calculated by performing an element-wise multiplication operation (i.e., splicing) between calculated attention weights and values features, for each respective input sequence (pp. 6-7, section 3.2.2, see Eq. 8-9). In this way, Kim teaches splicing of input-derived and attention-derived features.
Tongji discloses an RNA-protein binding preference prediction method that learns RNA and protein feature dependencies from input RNA and protein sequences via a LSTM network (i.e., RNN), computes attention vectors via an attention mechanism, learns features dependencies via a second LSTM (i.e., RNN), computes attention vectors via a second attention mechanism, and determines probabilities therefrom (Fig. 1). In this way, Tongji teaches feeding attention outputs to an RNN for further processing and determining probabilities therefrom.
With respect to claim 12, the limitations of the claim are substantively similar to those of claim 11. Claim 12 further requires the obtaining of a self-relevance vector, via a selective attention mechanism model, and obtaining a second protein vector sequence therefrom.
The teachings of Gao, in view of Kim and Tongji, are considered to read on the limitations of substantive similarity in the same way as outlined above with respect to claim 12. Kim teaches a self-attention mechanism, and the attention weight sequences and scores thereby generated are considered equivalent to self-relevance vectors.
With respect to claim 13, the limitations of the claim are substantively similar to those of claims 6 and 11. Claim 13 further requires the obtaining of a self-relevance vector, via a selective attention mechanism model, and obtaining a second RNA vector sequence therefrom.
The teachings of Gao, in view of Kim and Tongji, are considered to read on the limitations of substantive similarity in the same way as outlined above with respect to claims 6 and 11. Kim teaches a self-attention mechanism, and the attention weight sequences and scores thereby generated are considered equivalent to self-relevance vectors.
With respect to claim 14, the limitations of the claim are substantively similar to those of claims 6-9, 11 and 13. The teachings of Gao, in view of Kim and Tongji, are considered to read on the limitations of substantive similarity in the same way as outlined above with respect to claims 6-9, 11 and 13.
With respect to claim 16, the limitations of the claim are substantively similar to those of claims 11-12. The teachings of Gao, in view of Kim and Tongji, are considered to read on the limitations of substantive similarity in the same way as outlined above with respect to claims 11-12.
With respect to claim 17, the limitations of the claim are substantively similar to those of claims 6-9, 11 and 13. The teachings of Gao, in view of Kim and Tongji, are considered to read on the limitations of substantive similarity in the same way as outlined above with respect to claims 6-9, 11 and 13.
An invention would have been obvious to one of ordinary skill in the art if it simply applies a known technique to a known method to yield predictable results. Before the effective filing date of the claimed invention, said practitioner would have implemented k-mer based preprocessing of input sequences and feeding feature/attention vectors to an RNN for probability prediction, as taught by Tongji, in combination with the method of Gao, in view of Kim, because Tongji teaches that k-mer based attention accounts for the fact that different sites and neighborhoods within an RNA sequence have different importance to RNA-protein binding, a fact that existing prediction algorithms fail to account for (paras. 0003-4). Said practitioner would have had a reasonable expectation of success because Tongji and Gao are directed to similar fields of endeavor, both concerning methods of predicting RNA-protein interaction, via a model implementing bidirectional long short-term memory architecture, based on joint processing of an input RNA sequence and protein sequence.
In this way the disclosure of Gao, in view of Kim and Tongji, makes obvious the limitations of claims 2-5, 11-14 and 16-17. Thus, the claimed invention is prima facie obvious.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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 reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Instant claims 1-5 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5 of U.S. Patent No. 12,555,647 (hereafter “‘647”), in view of Kim (previously cited). ‘647 shares an inventor (Zhenzhong Zhang) and assignee (BOE Technology Group Co Ltd) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other for the following reasons:
Instant claim 1 is directed to a method for RNA-protein interaction prediction, comprising: obtaining an RNA sequence to be predicted and a protein sequence to be predicted; obtaining a first RNA vector sequence by encoding the RNA sequence to be predicted; obtaining a first protein vector sequence by encoding the protein sequence to be predicted; obtaining a relevance vector sequence of the first RNA vector sequence and a relevance vector sequence of the first protein vector sequence through a selective attention mechanism model; and determining a probability value of interaction between the RNA sequence to be predicted and the protein sequence to be predicted according to the relevance vector sequence of the first RNA vector sequence and the relevance vector sequence of the first protein vector sequence.
With respect to instant claim 1, ‘647 claims a method for training a vector model, comprising: obtaining at least one Ribonucleic acid (RNA) sequence and at least one protein sequence; obtaining at least one first RNA vector by vectorizing the at least one RNA sequence; obtaining at least one first protein vector by vectorizing the at least one protein sequence; and determining an interaction between the RNA sequence and the protein sequence according to the first RNA vector and the first protein vector, wherein determining the interaction comprises calculating a probability value of presence of the interaction (claim 1).
‘647 does not claim utilization of a selective attention mechanism model.
Kim presents a deep learning-based cross-attention framework, for prediction of drug-target interactions (pg. 1, Abstract), based on extraction of features and calculation of attention weight sequences from an input molecular (drug) sequence and an input protein (target) sequence (pp. 2-3, section 2.1). Kim teaches a multi-head (i.e., selective) attention mechanism (pg. 4, Fig. 1; pp. 5-6, section 3.2.1).
Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2). Kim also demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2).
With respect to instant claim 2, ‘647 claims the method according to claim 1, wherein the obtaining the first RNA vector by vectorizing the at least one RNA sequence comprises: converting each RNA sequence into N base k-mer subsequences; and obtaining the first RNA vector by vectorizing each of the N base k-mer subsequences (claim 2).
With respect to instant claim 3, ‘647 claims the method according to claim 2, wherein the obtaining the first RNA vector by vectorizing each of the N base k-mer subsequences comprises obtaining first vectors of the N base k-mer subsequences by encoding each of the N base k-mer subsequences; inputting the first vectors of the N base k-mer subsequences into a recurrent neural network to output N base k-mer vectors; and obtaining the first RNA vector according to the N base k-mer vectors (claim 3).
With respect to instant claim 4, ‘647 claims the method according to claim 1, wherein the obtaining the first protein vector by vectorizing the at least one protein sequence comprises: converting each protein sequence into M amino acid k-mer subsequences; and obtaining the first protein vector by vectorizing each of the M amino acid k-mer subsequences (claim 4).
With respect to instant claim 5, ‘647 claims the method according to claim 4, wherein the obtaining the first protein vector by vectorizing each of the M amino acid k-mer subsequences comprises obtaining first vectors of the M amino acid k-mer subsequences by encoding each of the M amino acid k-mer subsequences; inputting the first vectors of the M amino acid k-mer subsequences into a recurrent neural network to output M amino acid k-mer vectors; and obtaining the first protein vector according to the M amino acid k-mer vectors (claim 5).
An invention would have been obvious to one of ordinary skill in the art if it simply applies a known technique to a known method to yield predictable results. Before the effective filing date of the claimed invention, said practitioner would have combined calculation of cross-attention between an input molecular sequence and an input protein sequence, as taught by Kim, with the RNA-protein interaction prediction method of ‘647, because Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2), demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2). Said practitioner would have had a reasonable expectation of success because ‘647 and Kim are directed to similar fields of endeavor, both concerning methods of predicting molecular-protein interaction, via a model implementing neural network architecture, based on joint processing of an input molecular sequence and protein sequence.
In this way, instant claims 1-5 are not patentably distinct from claims of ‘647, in view of Kim.
Instant claims 1-2 and 4 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2 and 4 of co-pending Application No. 17/915,391 (hereafter “‘391”), in view of Kim (previously cited). ‘391 shares an inventor (Zhenzhong Zhang) and assignee (BOE Technology Group Co Ltd) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other for the following reasons:
Instant claim 1 is directed to a method for RNA-protein interaction prediction, comprising: obtaining an RNA sequence to be predicted and a protein sequence to be predicted; obtaining a first RNA vector sequence by encoding the RNA sequence to be predicted; obtaining a first protein vector sequence by encoding the protein sequence to be predicted; obtaining a relevance vector sequence of the first RNA vector sequence and a relevance vector sequence of the first protein vector sequence through a selective attention mechanism model; and determining a probability value of interaction between the RNA sequence to be predicted and the protein sequence to be predicted according to the relevance vector sequence of the first RNA vector sequence and the relevance vector sequence of the first protein vector sequence.
With respect to instant claim 1, ‘391 claims a method for explaining a molecular- mechanism of a cellular process involving noncoding RNAs, comprising: acquiring an RNA protein pair to be predicted; obtaining a sequence feature of the RNA-protein pair to be predicted by performing feature extraction on the RNA-protein pair to be predicted; obtaining an RNA sequence representation vector and a protein sequence representation vector in the RNA-protein pair to be predicted by vectorizing the RNA-protein pair to be predicted; obtaining respectively, by using multiple interaction prediction models, multiple interaction prediction vaIues of the RNA-protein pair to be predicted, based on the sequence feature of the RNA-protein pair to be predicted, the RNA sequence representation vector and the protein sequence representation vector in the RNA-protein pair to be predicted; and determining an interaction between the RNA and the protein according to the multiple interaction prediction values (claim 1).
‘391 does not claim utilization of a selective attention mechanism model.
Kim presents a deep learning-based cross-attention framework, for prediction of drug-target interactions (pg. 1, Abstract), based on extraction of features and calculation of attention weight sequences from an input molecular (drug) sequence and an input protein (target) sequence (pp. 2-3, section 2.1). Kim teaches a multi-head (i.e., selective) attention mechanism (pg. 4, Fig. 1; pp. 5-6, section 3.2.1).
Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2). Kim also demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2).
With respect to claims 2 and 4, ‘391 claims wherein performing the feature extraction on each RNA-protein pair in the original data set to obtain the original sequence feature set further comprises: converting an RNA sequence and a protein sequence in each RNA-protein pair into k-mer subsequences, respectively, and forming a first candidate itemset by using the k-mer subsequences, wherein the k-mer subsequences comprise an RNA k-mer subsequence and a protein k-mer subsequence (claim 1).
An invention would have been obvious to one of ordinary skill in the art if it simply applies a known technique to a known method to yield predictable results. Before the effective filing date of the claimed invention, said practitioner would have combined calculation of cross-attention between an input molecular sequence and an input protein sequence, as taught by Kim, with the RNA-protein interaction prediction method of ‘391, because Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2), demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2). Said practitioner would have had a reasonable expectation of success because ‘391 and Kim are directed to similar fields of endeavor, both concerning methods of predicting molecular-protein interaction, via a model implementing neural network architecture, based on joint processing of an input molecular sequence and protein sequence.
In this way, instant claims 1-2 and 4 are not patentably distinct from claims of ‘391, in view of Kim. This is a provisional nonstatutory double patenting rejection, as the claims at issue have not in fact been patented.
Instant claims 1-5 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of co-pending Application No. 17/916,540 (hereafter “‘540”), in view of Kim (previously cited). ‘540 shares an inventor (Zhenzhong Zhang) and assignee (BOE Technology Group Co Ltd) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other for the following reasons:
Instant claim 1 is directed to a method for RNA-protein interaction prediction, comprising: obtaining an RNA sequence to be predicted and a protein sequence to be predicted; obtaining a first RNA vector sequence by encoding the RNA sequence to be predicted; obtaining a first protein vector sequence by encoding the protein sequence to be predicted; obtaining a relevance vector sequence of the first RNA vector sequence and a relevance vector sequence of the first protein vector sequence through a selective attention mechanism model; and determining a probability value of interaction between the RNA sequence to be predicted and the protein sequence to be predicted according to the relevance vector sequence of the first RNA vector sequence and the relevance vector sequence of the first protein vector sequence.
With respect to instant claim 1, ‘540 claims an RNA-protein interaction prediction method, comprising: identifying an RNA-protein pair to be predicted; performing feature extraction on the RNA-protein pair to be predicted to obtain sequence features of the RNA-protein pair to be predicted; vectorizing the RNA-protein pair to be predicted to obtain an RNA sequence representation vector and a protein sequence representation vector in the RNA-protein pair to be predicted; based on the sequence features of the RNA-protein pair to be predicted, the RNA sequence representation vector and the protein sequence representation vector in the RNA protein pair to be predicted, obtaining at least one predicted interaction value of the RNA-protein pair to be predicted using at least one interaction prediction model; and determining interaction between the RNA and the protein according to the at least one predicted interaction value (claim 1).
‘540 does not claim utilization of a selective attention mechanism model.
Kim presents a deep learning-based cross-attention framework, for prediction of drug-target interactions (pg. 1, Abstract), based on extraction of features and calculation of attention weight sequences from an input molecular (drug) sequence and an input protein (target) sequence (pp. 2-3, section 2.1). Kim teaches a multi-head (i.e., selective) attention mechanism (pg. 4, Fig. 1; pp. 5-6, section 3.2.1).
Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2). Kim also demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2).
With respect to instant claims 2-5, ‘540 claims wherein vectorizing the RNA-protein pair to be predicted to obtain the RNA sequence representation vector and the protein sequence representation vector in the RNA-protein pair to be predicted, comprises: converting an RNA sequence and a protein sequence m the RNA-protein pair to be predicted into k-mer subsequences, respectively, wherein the k-mer subsequences comprise M RNA k-mer subsequences and N protein k-mer subsequences; vectorizing each of the RNA k-mer subsequences to obtain M RNA k-mer One-Hot vectors; concatenating the M RNA k-mer One-Hot vectors in turn in a row direction to obtain a two-dimensional matrix RNA representation vector; vectorizing each of the protein k-mer subsequences to obtain N RNA k-mer One-Hot vectors; and concatenating the N RNA k-mer One-Hot vectors in turn in the row direction to obtain a two-dimensional matrix protein sequence representation vector (claim 1).
An invention would have been obvious to one of ordinary skill in the art if it simply applies a known technique to a known method to yield predictable results. Before the effective filing date of the claimed invention, said practitioner would have combined calculation of cross-attention between an input molecular sequence and an input protein sequence, as taught by Kim, with the RNA-protein interaction prediction method of ‘540, because Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2), demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2). Said practitioner would have had a reasonable expectation of success because ‘540 and Kim are directed to similar fields of endeavor, both concerning methods of predicting molecular-protein interaction, via a model implementing neural network architecture, based on joint processing of an input molecular sequence and protein sequence.
In this way, instant claims 1-5 are not patentably distinct from claims of ‘540, in view of Kim. This is a provisional nonstatutory double patenting rejection, as the claims at issue have not in fact been patented.
Instant claims 1-5 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-2, 6-7, 11 and 17-18 of co-pending Application No. 18/025,394 (hereafter “‘394”), in view of Kim (previously cited). ‘394 shares an inventor (Zhenzhong Zhang) and assignee (BOE Technology Group Co Ltd) with the instant application. Although the claims at issue are not identical, they are not patentably distinct from each other for the following reasons:
Instant claim 1 is directed to a method for RNA-protein interaction prediction, comprising: obtaining an RNA sequence to be predicted and a protein sequence to be predicted; obtaining a first RNA vector sequence by encoding the RNA sequence to be predicted; obtaining a first protein vector sequence by encoding the protein sequence to be predicted; obtaining a relevance vector sequence of the first RNA vector sequence and a relevance vector sequence of the first protein vector sequence through a selective attention mechanism model; and determining a probability value of interaction between the RNA sequence to be predicted and the protein sequence to be predicted according to the relevance vector sequence of the first RNA vector sequence and the relevance vector sequence of the first protein vector sequence.
With respect to instant claim 1, ‘394 claims a method for predicting an RNA-protein interaction, comprising: acquiring a candidate RNA sequence and a candidate protein sequence; encoding the candidate RNA sequence to obtain an RNA vector sequence; encoding the candidate protein sequence to obtain a protein vector sequence; constructing a matching feature matrix according to the RNA vector sequence and the protein vector sequence; and performing feature extraction on the matching feature matrix, and determining, according to an extracted matching feature, an interaction between the candidate RNA sequence and the candidate protein sequence (claim 1), wherein the determining, according to an extracted matching feature, an interaction between the candidate RNA sequence and the candidate protein sequence, comprising: obtaining an interaction predicted value between the candidate RNA sequence and the candidate protein sequence according to the extracted matching feature; and determining the interaction between the candidate RNA sequence and the candidate protein sequence according to the interaction predicted value (claim 17), wherein the obtaining an interaction predicted value between the candidate RNA sequence and the candidate protein sequence according to the extracted matching feature, comprising: inputting the extracted matching feature into a classifier, and outputting a probability of the presence of the interaction between the candidate RNA sequence and the candidate (claim 18).
‘394 does not claim utilization of a selective attention mechanism model.
Kim presents a deep learning-based cross-attention framework, for prediction of drug-target interactions (pg. 1, Abstract), based on extraction of features and calculation of attention weight sequences from an input molecular (drug) sequence and an input protein (target) sequence (pp. 2-3, section 2.1). Kim teaches a multi-head (i.e., selective) attention mechanism (pg. 4, Fig. 1; pp. 5-6, section 3.2.1).
Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2). Kim also demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2).
With respect to instant claim 2, ‘394 claims the method of claim 1, wherein the encoding the candidate RNA sequence to obtain an RNA vector sequence, comprising: converting the candidate RNA sequence into N base k-mer subsequences; and vectorizing each base k-mer subsequence of the N base k-mer subsequences to obtain the RNA vector sequence (claim 2).
With respect to instant claim 3, ‘394 claims the method of claim 2, wherein the vectorizing each base k-mer subsequence of the N base k-mer subsequences to obtain the RNA vector sequence, comprising: encoding each base k-mer subsequence of the N base k-mer subsequences to obtain first vectors of the N base k-mer subsequences; performing operation on the first vectors of the N base k-mer subsequences by using a first mapping matrix to obtain second vectors of the N base k-mer subsequences; and inputting the second vectors of the N base k-mer subsequences sequentially into a pre-trained recurrent neural network, outputting N base k-mer vectors, and constituting the RNA vector sequence by the N base k-mer vectors (claim 6).
With respect to instant claim 4, ‘394 claims the method of claim 1 wherein the encoding the candidate protein sequence to obtain a protein vector sequence, comprising: converting the candidate protein sequence into M amino acid k-mer subsequences; and vectorizing each amino acid k-mer subsequence of the M amino acid k-mer subsequences to obtain the protein vector sequence (claim 7).
With respect to instant claim 5, ‘394 claims the method of claim 7, wherein the vectorizing each amino acid k-mer subsequence of the M amino acid k-mer subsequences to obtain the protein vector sequence, comprising: encoding each amino acid k-mer subsequence of the M amino acid k-mer subsequences to obtain first vectors of the M amino acid k-mer subsequences; performing operation on the first vectors of the M amino acid k-mer subsequences by using a second mapping matrix to obtain second vectors of the M amino acid k-mer subsequences; and inputting the second vectors of the M amino acid k-mer subsequences sequentially into a pre-trained recurrent neural network, outputting M amino acid k-mer vectors, and constituting the protein vector sequence by the M amino acid k-mer vectors (claim 11).
An invention would have been obvious to one of ordinary skill in the art if it simply applies a known technique to a known method to yield predictable results. Before the effective filing date of the claimed invention, said practitioner would have combined calculation of cross-attention between an input molecular sequence and an input protein sequence, as taught by Kim, with the RNA-protein interaction prediction method of ‘394, because Kim discusses the known general strengths of attention mechanisms at generating better representations for predicting drug-target interactions (pg. 3, section 2.2), demonstrates provision of improvement in performance to existing models by explicit consideration of cross-feature interaction (pg. 2, section 1; pp. 9-10, section 4.2), and suggests application of their framework to parallel lines of research (pg. 3, section 2.2). Said practitioner would have had a reasonable expectation of success because ‘394 and Kim are directed to similar fields of endeavor, both concerning methods of predicting molecular-protein interaction, via a model implementing neural network architecture, based on joint processing of an input molecular sequence and protein sequence.
In this way, instant claims 1-5 are not patentably distinct from claims of ‘394, in view of Kim. This is a provisional nonstatutory double patenting rejection, as the claims at issue have not in fact been patented.
Conclusion
At this point in prosecution, no claims are allowed.
The following prior art, made of record and not relied upon, is considered pertinent to applicant's disclosure:
Muppirala (BMC Bioinformatics 12: 489, 11 pages; published 2011) presents RPISeq, a family of classifiers for predicting RNA-protein interactions based on input RNA and protein sequence information (pg. 1, Abstract);
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/T.C.S./Examiner, Art Unit 1685
/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 September 4, 2026
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