DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Status of claim
Examined:
1-8
Independent:
1, 6
Priority
As detailed on the 12/20/2023 filing receipt, this application does not claim domestic or foreign priority. The effective filing date of this application is 09/08/2023.
Drawings
The drawings filed 9/08/2023 are accepted.
Information Disclosure Statement
No Information Disclosure Statements are provided.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“…a sample set processing module configured for acquiring a training sample set…;” “…an unsupervised pre-training module configured for performing unsupervised pre-training…;” “…an input setting module configured for coupling two matrices for the protein sequence and the ligand sequence into a multi-dimensional symmetric matrix…” and “…a model training module configured for fitting the experimental measurement classification and regression value of protein and small molecule interaction…” in claim 6.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 (line 14) recites “…fitting the experimental measurement classification and regression value…” which requires but lacks antecedent basis in the claims because there is no previous recitation of " experimental measurement classification " in the claims.
Claim 3 recites “generating a one-dimensional…” (line 3) “…the feature representation being a one-dimensional or multidimensional symmetric matrix.” (line 14). It is not clear whether a one-dimensional in line 14 is referring to the same one-dimensional as in line 3.
Claim 4 recites “a multi-dimensional symmetric matrix” (line 2) and “to obtain a multi-dimensional symmetric matrix” (Line 5). It is not clear whether a multi-dimensional in line 5 is referring to the same multi-dimensional as in line 2.
See as discussed above (claim interpretation under 35 U.S.C. 112(f)), regarding claim 6, there is no disclosure in the originally filed specification of structure or material that is a “sample set processing module" that is "configured for acquiring a training sample set,” “an unsupervised pre-training module” that is “configured for performing unsupervised pre-training,” “an input setting module” that is “configured for coupling two matrices for the protein sequence and the ligand sequence into a multi-dimensional symmetric matrix” and “a model training module” that is “configured for fitting the experimental measurement classification and regression value of protein and small molecule interaction” as presently claimed. Because there is no clear structure associated with the claimed terminology “sample set processing module,” “unsupervised pre-training module,” “an input setting module,” and “a model training module” for performing the recited function, the claim is indefinite.
Dependent claims are rejected for being dependent on rejected claim 1.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5 and 7-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis of claims in Step 1.
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Independent claim 1 is directed to a 101 process, here a "protein virtual screening method," with process steps such as " acquiring…, performing…"
[Step 1: claims 1-5 and 7-8: YES]
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Mental processes recited include:
Claim 1 recites: "… predicting different protein prediction tasks by the screening model to output prediction results…” The limitation is an act of evaluating, analyzing, observing and judging data that could be practically performed in the human mind and/or with pen and paper (See MPEP 2106.04(a)(2) subsection III).
Claim 2 recites: “…executing a masking strategy based on a BERT-type masking language model, randomly masking binding sites of protein targets…”
Mathematical concepts recited include:
Claim 1 recites “…performing unsupervised pre-training on a Transformer model with the source data as input and the sample data as verification, and respectively generating a one- dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence; coupling two matrices for the protein sequence and the ligand sequence into a multi-dimensional symmetric matrix, and using the multi-dimensional symmetric matrix as an input of an hidden layer of a BiLSTM network model; fitting the experimental measurement classification and regression value of protein and small molecule interaction by the Bilstm network model to obtain to a trained screening model; and predicting different protein prediction tasks by the screening model to output prediction results.”
Claim 2 recites “…executing a masking strategy based on a BERT-type masking language model, randomly masking binding sites of protein targets…”
Claim 3 recites “…performing unsupervised pre-training on a Transformer model with the source data as input and the sample data as verification; and respectively generating a one-dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence comprises: inputting the training sample sets of the protein sequence and the ligand sequence into an input embedding layer of the Transformer model, respectively; embedding the training sample sets by the input embedding layer of the Transformer model; inputting the embedded training sample sets into an intermediate hidden layer of the Transformer model; learning feature representation of the training sample sets by the intermediate hidden layer of the Transformer model; and outputting the learned feature representation by an output prediction layer of the Transformer model, the feature representation being a one-dimensional or multi-dimensional symmetric matrix.”
Claim 4 recites: “…coupling two matrices for the protein sequence and the ligand sequence by an QSAR model to obtain a multi-dimensional symmetric matrix.”
Claim 5 recites: “…characterized in that the fitting the experimental measurement classification and regression value of protein and small molecule interaction by the Bilstm network model to obtain to a trained screening model comprises: acquiring a protein sequence X= … wherein gt is the output of first several layers, the first several layers are in a forward input, and LSTM t is a last layer of the forward LSTM; g, and gt are defined similarly and in opposite directions; LSTM r and LSTM r are defined similarly and in opposite directions; and fitting the experimental measurement classification and regression value of protein and small molecule interaction by embedding vector z, by means of the learning transformation and softmax function the to obtain to a trained screening model; the softmax function is ) = softmax t Wg Ib) where W and b are learning parameters,” which is a mathematical model and formula.
Claim 1, as indicated above, recite mental processes, because the claim elements are involved with predicting and claim 2 is involved with masking sites of a protein, which are all acts of evaluating, analyzing, observing and judging data. Acts of evaluating and analyzing data could be practically performed in the human mind and/or with pen and paper because they merely require making observations, evaluations, judgments, and opinions (See MPEP 2106.04(a)(2), subsection III). Overall, under the broadest reasonable interpretation, the indicated claims above can be practically carried out in the human mind or with pen and paper as claimed, which falls under the "mental processes" grouping of abstract ideas.
Claims 1-6 as indicated above, are mathematical concepts and/or mathematical formulas that requires performing a series of calculations/operations. Therefore, under the broadest reasonable interpretation, the indicated claims above falls under the “mathematical concepts” grouping of abstract ideas.
As such, claims 1-5 and 7-8 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The above indicated judicial exceptions are not integrated into a practical application because the claims do not recite an additional elements that apply, rely on or use the judicial exception in such a manner to amount to integration into a practical application. For example, there are no limitations that reflect an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that equate to mere instructions to implement an abstract idea or insignificant extra solution activity. Specifically, the instant claims recite the following additional elements:
Claim 1 recites "acquiring a training sample set, wherein the training sample set comprises source data and sample data corresponding to the source data; the source data comprises protein sequences and ligand sequences of a small molecule compound which can bind to the protein; the sample data comprises subsequences of binding sites of the protein and the small molecule compound… with the source data as input and the sample data as verification, and respectively generating a one- dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence… using the multi-dimensional symmetric matrix as an input of an hidden layer of a BiLSTM network model…"
Claim 2 recites “…acquiring a training sample set... obtaining a discontinuous protein sequence characterization and using the discontinuous protein sequence characterization as sample data; and correspondingly storing the source data and the sample data to obtain training sample sets.”
Claim 3 recites “…source data as input and the sample data as verification; and respectively generating a one-dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence comprises: inputting the training sample sets of the protein sequence and the ligand sequence into an input embedding layer of the Transformer model, respectively; embedding the training sample sets by the input embedding layer of the Transformer model; inputting the embedded training sample sets into an intermediate hidden layer of the Transformer model; learning feature representation of the training sample sets by the intermediate hidden layer of the Transformer model; and outputting the learned feature representation by an output prediction layer of the Transformer model, the feature representation being a one-dimensional or multi-dimensional symmetric matrix.”
Claim 5 recites: “…acquiring a protein sequence…”
Claim 7 recites: “…computer equipment comprising a memory and a processor, with the memory having stored there on a computer program…”
Claim 8 recites: “…computer-readable storage medium having stored there on a computer program…processor…”
As indicated above, claims 7-8 recite generic computer components, which are tools used to execute the abstract idea. The use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. (see MPEP 2106.05(f)). The elements of claims 1-6, as indicated above, equate to insignificant extra solutional activities of data gathering and outputting. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent (See MPEP 2106.05(g)). Limitations that add insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) have been identified by the courts to not integrate a judicial exception into a practical application (MPEP 2106.04(d)(I)). Additionally, the listed additional elements are mere instructions to apply an exception because they recite no more than an idea of a solution or outcome and does not recite a technological solution to a technological problem. (See MPEP 2106.05(f)(1)). As such, as currently recited, the claims do not appear to recite an improvement to technology or apply or use the recited judicial exception in some other meaningful way. Therefore, claims 1-5 and 7-8 are directed to an abstract idea (Step 2A, Prong 2: NO).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to well-understood, routine and conventional activities, insignificant extra-solution activity or mere instructions to implement the abstract idea on a generic computer. The instant claims recite the following additional elements:
Claim 1 recites "acquiring a training sample set, wherein the training sample set comprises source data and sample data corresponding to the source data; the source data comprises protein sequences and ligand sequences of a small molecule compound which can bind to the protein; the sample data comprises subsequences of binding sites of the protein and the small molecule compound… with the source data as input and the sample data as verification, and respectively generating a one- dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence… using the multi-dimensional symmetric matrix as an input of an hidden layer of a BiLSTM network model…"
Claim 2 recites “…acquiring a training sample set... obtaining a discontinuous protein sequence characterization and using the discontinuous protein sequence characterization as sample data; and correspondingly storing the source data and the sample data to obtain training sample sets.”
Claim 3 recites “…source data as input and the sample data as verification; and respectively generating a one-dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence comprises: inputting the training sample sets of the protein sequence and the ligand sequence into an input embedding layer of the Transformer model, respectively; embedding the training sample sets by the input embedding layer of the Transformer model; inputting the embedded training sample sets into an intermediate hidden layer of the Transformer model; learning feature representation of the training sample sets by the intermediate hidden layer of the Transformer model; and outputting the learned feature representation by an output prediction layer of the Transformer model, the feature representation being a one-dimensional or multi- dimensional symmetric matrix.”
Claim 5 recites: “…acquiring a protein sequence…”
Claim 7 recites: “…computer equipment comprising a memory and a processor, with the memory having stored there on a computer program…”
Claim 8 recites: “computer-readable storage medium having stored there on a computer program…processor…”
The additional elements in claims 1-5 and 7-8 do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. The limitations equate to insignificant extra solutional activities. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. (see MPEP 2106.05(g)). Additionally, inputting protein sequences and protein structure information into machine learning and language models are known methods as disclosed by Chandra ("Transformer-based deep learning for predicting protein properties in the life sciences." Elife 12 (2023): e82819.; published Jan. 18, 2023; as cited on the attached 892 form). Limitations that add insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) have been identified by the courts to not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception.
Furthermore, limitations that equate to mere data gathering and outputting via generic computer components, such as receiving data at a computer or outputting data, amount to insignificant extra-solution activity as set forth by the courts in Mayo, 566 U.S. at 79, 101 USPQ2d at 1968 and OIP Techs., Inc, v, Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). Storing and retrieving information in memory were identified by the courts as well-understood, routine and conventional in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93. Also, the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more as identified by the courts in Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-5 and 7-8 are not patent eligible.
Matter belonging to no statutory category – claim 6
Claim 6 is rejected under 35 USC 101 because the claimed inventions are directed to non-statutory subject matter.
Claim 6 is to "device," which is not, in all embodiments within a BRI, interpreted as belonging to any category listed in 101. In a BRI, the claim reads on data and/or software comprising no structure other than data and/or software. The claim is not recited as a process, and the claim is not limited to any particular structure as a 101 machine or manufacture. The claim reads on transitory propagating signals which are not proper patentable subject matter because it does not fit within any of the four statutory categories of invention (In re Nuijten, Federal. Circuit, 2006).
Claim Rejections - 35 USC § 102
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 and 5-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhu ("ULDNA: Integrating Unsupervised Multi-Source Language Models with LSTM-Attention Network for Protein-DNA Binding Site Prediction." bioRxiv (2023): 2023-05. Posted May 30, 2023.; cited on the attached 892 form).
Regarding independent claim 1, Zhu teaches acquiring a training sample set, wherein the training sample set comprises source data and sample data corresponding to the source data; the source data comprises protein sequences and ligand sequences of a small molecule compound which can bind to the protein; the sample data comprises subsequences of binding sites of the protein and the small molecule compound with “Figure 1 illustrates the performance of seven feature embeddings on three training datasets (i.e., PDNA-543, PDNA-335, and PDNA-316) under cross-validation and two test datasets (i.e., PDNA-41 and PDNA 52) under independent validation.” (page 10 of 35, para. 1) and Table 1 (page 4 of 35) and “…protein-DNA binding site datasets, where the results demonstrated significant advantage on accurate DNA-binding site prediction over the current state-of-the-art of the field.” (page 4 of 35, para. 1).
Zhu teaches performing unsupervised pre-training on a Transformer model with “We proposed a novel deep learning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases.” (page 1 of 35, Abstract) and “The algorithm was built on transformer embedding and LSTM-attention decoding.” (page 14 of 35, para. 1, Conclusions).
Zhu teaches the source data as input and the sample data as verification, and respectively generating a one- dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence; coupling two matrices for the protein sequence and the ligand sequence into a multi-dimensional symmetric matrix, and using the multi-dimensional symmetric matrix as an input of an hidden layer of a BiLSTM network model with “The input sequence is fed to ESM2 and ProtTrans transformers to output two feature embedding matrices with the scales of 𝐿 × 2560 and 𝐿 × 1024, respectively.” (page 5 of 35, para. 1) and “The concentrated feature embedding is fed to a designed LSTM-attention network to generate a score vector with 𝐿 dimensions, indicating the confidence scores of belonging DNA-binding sites for all residues in query sequence. In LSTM-attention network, a BiLSTM layer and a self-attention layer are combined to enhance the correlation between evolution diversity and DNA-binding in residue-level to improve DNA-binding prediction.” (page 5 of 35, para. 2)
Zhu teaches fitting the experimental measurement classification and regression value of protein and small molecule interaction by the Bilstm network model to obtain to a trained screening model with “M0: Model is trained on BiLSTM consisting of 256 cells with a one-hot coding matrix [60] extracted from the input sequence, followed by a fully connected network, in which an output layer with SoftMax function [61] is used to generate the confidence scores of belonging DNA-binding sites for all residues in the input sequence. In the training stage, the cross-entropy loss [62] is used as the loss function, as described in Eq 9.” (page 11 of 35, para. 2)
Zhu teaches predicting different protein prediction tasks by the screening model to output prediction results with “We proposed a novel deep learning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases.” (page 1 of 35, Abstract)
Regarding claim 2, Zhu teaches acquiring a training sample set comprises: executing a masking strategy based on a BERT-type masking language model, randomly masking binding sites of protein targets, obtaining a discontinuous protein sequence characterization, and using the discontinuous protein sequence characterization as sample data; and correspondingly storing the source data and the sample data to obtain training sample sets with “For an input sequence, the masking strategy is performed on the corresponding tokens (i.e., amino acids). Specifically, we randomly sample 15% tokens, each of which is changed as a special “masking” token with 80% probability, a randomly chosen alternate amino acid with 10% probability, and the original input token (i.e., no change) with 10% probability.” (page 1 of 35, para. 1).
Regarding claim 3, Zhu teaches performing unsupervised pre-training on a Transformer model with the source data as input and the sample data as verification; and respectively generating a one-dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence comprises: inputting the training sample sets of the protein sequence and the ligand sequence into an input embedding layer of the Transformer model, respectively; embedding the training sample sets by the input embedding layer of the Transformer model with “The input sequence is fed to ESM2 and ProtTrans transformers to output two feature embedding matrices with the scales of 𝐿 × 2560 and 𝐿 × 1024, respectively.” (page 5 of 35, para. 1) and “The concentrated feature embedding is fed to a designed LSTM-attention network to generate a score vector with 𝐿 dimensions, indicating the confidence scores of belonging DNA-binding sites for all residues in query sequence. In LSTM-attention network, a BiLSTM layer and a self-attention layer are combined to enhance the correlation between evolution diversity and DNA-binding in residue-level to improve DNA-binding prediction.” (page 5 of 35, para. 2)
Zhu teaches inputting the embedded training sample sets into an intermediate hidden layer of the Transformer model; learning feature representation of the training sample sets by the intermediate hidden layer of the Transformer model; and outputting the learned feature representation by an output prediction layer of the Transformer model, the feature representation being a one-dimensional or multi-dimensional symmetric matrix with Figure S1, The workflow of ESM2 transformer (page 30 of 35).
Regarding claim 5, Zhu teaches characterized in that the fitting the experimental measurement classification and regression value of protein and small molecule interaction by the Bilstm network model to obtain to a trained screening model comprises: acquiring a protein sequence X=,..,xL), i =1,..., L, wherein X is all amino acids; represents a point mutation at a position I, a mutation sequence x ) = (x1, .., x;,..,xL), and sequence context x[=(x1, ....X Xi+1,...,xL encoding the sequence context by a vector where in fe is an embedding function mapping a discrete sequence to a D-dimensional continuous space; the embedding function is instantiated by a bidiectional LSTM neural network and is connected to the output of a final LSTM layer to for man embedding vector so as to obtain wherein gt is the output of first several layers, the first several layers are in a forward input, and LSTM is a last layer of the forward LSTM; g, and gt are defined similarly and in opposite directions; LSTM r and LSTM r are defined similarly and in opposite directions; and fitting the experimental measurement classification and regression value of protein and small molecule interaction by embedding vector z, by means of the learning transformation and softmax function the to obtain to a trained screening model with Figure 1(page 5 of 35) and “The output of the last attention layer is fed to a fully connected layer with SoftMax function to generate a 𝐿 × 28 probability matrix” (equation S14, page 21 of 35). Figure 1 teaches query sequence and feature embedding that is feed to BiLSTM
Regarding independent claim 6, Zhu teaches a sample set processing module configured for acquiring a training sample set, wherein the training sample set comprises source data and sample data corresponding to the source data; the source data comprises protein sequences and ligand sequences of a small molecule compound which can bind to the protein; the sample data comprises subsequences of binding sites of the protein and the small molecule compound with “Figure 1 illustrates the performance of seven feature embeddings on three training datasets (i.e., PDNA-543, PDNA-335, and PDNA-316) under cross-validation and two test datasets (i.e., PDNA-41 and PDNA 52) under independent validation.” (page 10 of 35, para. 1) and Table 1 (page 4 of 35); “…protein-DNA binding site datasets, where the results demonstrated significant advantage on accurate DNA-binding site prediction over the current state-of-the-art of the field.” (page 4 of 35, para. 1) and Figure 1 (page 5 of 35).
Zhu teaches an unsupervised pre-training module configured for performing unsupervised pre-training on a Transformer model with the source data as input and the sample data as verification with “We proposed a novel deep learning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases.” (page 1 of 35, Abstract); “The algorithm was built on transformer embedding and LSTM-attention decoding.” (page 14 of 35, para. 1, Conclusions) and Figure 1 (page 5 of 35).
Zhu teaches the source data as input and the sample data as verification, and respectively generating a one- dimensional or multi-dimensional symmetric matrix for the protein sequence and the ligand sequence; an input setting module configured for coupling two matrices for coupling two matrices for the protein sequence and the ligand sequence into a multi-dimensional symmetric matrix, and using the multi-dimensional symmetric matrix as an input of an hidden layer of a BiLSTM network model with “The input sequence is fed to ESM2 and ProtTrans transformers to output two feature embedding matrices with the scales of 𝐿 × 2560 and 𝐿 × 1024, respectively.” (page 5 of 35, para. 1); “The concentrated feature embedding is fed to a designed LSTM-attention network to generate a score vector with 𝐿 dimensions, indicating the confidence scores of belonging DNA-binding sites for all residues in query sequence. In LSTM-attention network, a BiLSTM layer and a self-attention layer are combined to enhance the correlation between evolution diversity and DNA-binding in residue-level to improve DNA-binding prediction.” (page 5 of 35, para. 2) and Figure 1 (page 5 of 35).
Zhu teaches a model training module configured for fitting the experimental measurement classification and regression value of protein and small molecule interaction by the Bilstm network model to obtain to a trained screening model with “M0: Model is trained on BiLSTM consisting of 256 cells with a one-hot coding matrix extracted from the input sequence, followed by a fully connected network, in which an output layer with SoftMax function is used to generate the confidence scores of belonging DNA-binding sites for all residues in the input sequence. In the training stage, the cross-entropy loss is used as the loss function, as described in Eq 9.” (page 11 of 35, para. 2) and and Figure 1 (page 5 of 35).
Zhu teaches a prediction module configured for predicting different protein prediction tasks by the screening model to output prediction results with “We proposed a novel deep learning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases.” (page 1 of 35, Abstract) and Figure 1 (page 5 of 35).
Regarding claim 7, Zhu teaches a computer equipment comprising a memory and a processor, with the memory having stored there on a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of claim 1 with “The standalone package and an online server of ULDNA are made freely available through URL http://csbio.njust.edu.cn/bioinf/uldna/,” “We proposed a novel deep learning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases” and Figure 1. Zhu teaches a software package and storing and retrieving data from databases. Therefore, the recited computer equipment comprising a memory and processor corresponds to the online server as taught by Zhu and the recited computer program corresponds to the standalone package as taught by Zhu.
Regarding claim 8, Zhu teaches a computer-readable storage medium having stored there on a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of claim 1 with “The standalone package and an online server of ULDNA are made freely available through URL http://csbio.njust.edu.cn/bioinf/uldna/,” “We proposed a novel deep learning method, ULDNA, to predict DNA-binding sites from protein sequences through a LSTM-attention architecture embedded with three unsupervised language models pretrained in multiple large-scale sequence databases” and Figure 1. Zhu teaches a software package and storing and retrieving data from databases.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhu ("ULDNA: Integrating Unsupervised Multi-Source Language Models with LSTM-Attention Network for Protein-DNA Binding Site Prediction." bioRxiv (2023): 2023-05. Posted May 30, 2023.; cited on the attached 892 form) as applied to claims 1-3 and 5-8 above, and further in view of Dunn III (3D QSAR of flexible molecules using tensor representation. Perspectives in Drug Discovery and Design 12, 167–182 (1998).; cited on the attached 892 form).
Zhu is applied to claims 1-3 and 5-8 as discussed above in the 35 USC 102 section above,
Regarding claim 4, Zhu teaches coupling two matrices for the protein sequence and the ligand sequence into a multi-dimensional symmetric matrix with “…we concentrate the above mentioned three feature embedding matrices from different transformer models as a combination embedding matrix with the scale of 𝐿 × 4352.” (page 5 of 35, para. 1).
Zhu does not teach coupling two matrices for the protein sequence and the ligand sequence by an QSAR model to obtain a multi-dimensional symmetric matrix in claim 4, However, this limitation is taught by Dunn III.
Dunn III teaches characterized in that the coupling two matrices for the protein sequence and the ligand sequence into a multi-dimensional symmetric matrix comprises: coupling two matrices for the protein sequence and the ligand sequence by an QSAR model to obtain a multi-dimensional symmetric matrix with section 3.2 titled 3-way arrays.
It would have also been prima facia obvious to combine the teachings of Zhu and Dunn III to arrive at the claimed invention. A person of ordinary skill in the art would have also been motivated to modify the method of Zhu to include the QSAR model as taught by Dunn III to obtain information indirectly about the receptor geometry. There would have been a reasonable expectation of success, since Zhu and Dunn III teach methods that pertain to the analysis of protein-ligand interactions.
Conclusion
No claims are allowed.
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/K.K./Examiner, Art Unit 1686
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
