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 .
Claim Status
Claim(s) 1-20 are currently pending and under examination herein.
Priority
This instant application claims the benefit of priority to Korean Patent Application 10-2021-0126415, filed on September 24, 2021, and 10-2022-0045226, filed on April 12, 2022. Thus, the effective filling date of the claims is September 24, 2021. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The Information Disclosure Statements filed on 9/26/2022 and 12/06/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
The information disclosure statement filed 4/18/2025 fails to comply with the provisions of 37 CFR 1.98(a)(4) because it lacks the appropriate size fee assertion. It has been placed in the application file, but the information referred to therein has not been considered as to the merits.
Drawings
The drawings filed on 26 September 2022 are accepted.
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) are:
“a data collection and data acquisition unit configured to collect protein data, drug molecular data, and interaction data between a protein and a drug molecule” (claims 1 and 6)
“a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data” (claims 1,2,6,7)
“a model generation unit configured to train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data, and the interaction data as training data to generate a protein-drug interaction prediction model. ” (claim 1)
“An interaction prediction unit configured to, by using a protein-drug interaction prediction model generated by training a Bayesian neural network, predict an interaction between a protein and a drug molecule and the uncertainty of the final predictive value based on the prediction results of the plurality of times.” (claims 6, 8,9,11,12)
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof:
page 14, lines 12-25 describe the structure of the data collection unit as a wired or wireless communication system
page 16, lines 1-6 describe the steps required for the phenotype generation unit to generate protein phenotype data, as using a protein phenotype generation model generated by transfer learning a pre-trained model trained with huge data and may be stored in an internal or external memory of the model generation apparatus; however, as for the drug molecular phenotype data there is no clear algorithm nor explanation on how it is generated by the phenotype generation unit
no sufficient structure or algorithm in the specification on how the model generational unit trains a Bayesian neural network
no sufficient structure nor algorithm in the specification on how on the interaction prediction unit predicts an interaction between a protein and a drug molecule
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 1-12 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 2 recites the limitation "the protein phenotype generation model". There is insufficient antecedent basis for this limitation in the claim.
Claim limitation “a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data.” in claims 1 and 6 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification fails to adequately disclose the structure to perform the second half of the claimed function “generate drug molecular phenotype data In particular, the specification states that the invention may generate drug molecular phenotype data from the drug molecular data (, which is simply a recitation of what was stated in claims, and provides no further detail about the specific structure or algorithm on how it is generates drug molecular phenotype data. See MPEP 2181 (III).
Claim limitation “a model generation unit configured to train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data” in claim 1 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification fails to adequately disclose the structure or acts to “train a Bayesian neural network to generate a protein-drug prediction model.” In particular, the specification states that “using the protein phenotype data, the drug molecular phenotype data, and the interaction data between the protein and the drug molecule as training data for Bayesian neural network,” which is simply a recitation of what was stated in claims, and provides no further detail about the specific structure or algorithm on how it trains the Bayesian neural network. See MPEP 2181 (III).
Finally, claim limitation “interaction prediction unit configured to, by using a protein-drug interaction prediction model generated by training a Bayesian neural network, predict an interaction between a protein and a drug molecule and the uncertainty of the final predictive value based on the prediction results of the plurality of times” in claims 6, 8,9,11, and 12 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The specification fails to adequately disclose the structure to perform the claimed function “predict an interaction between a protein and a drug molecule and the uncertainty of the final predictive value.” In particular, the specification states, “the interaction prediction unit 230 may predict the interaction between the target protein and the target drug molecule based on the protein phenotype data and the drug molecular phenotype data, and determine the uncertainty of the prediction… this case, the uncertainty may include an epistemic uncertainty and an aleatoric uncertainty,” (but provides no detail on the steps or structure used to predict the interaction or the uncertainty of the final predictive value. See MPEP 2181 (III).
Claims 3,4,5,7, and 10 are also rejected under 35 U.S.C. 112(b) for their dependence on the claims above.
Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 2, 6, and 7 recite “the phenotype generation unit configured to…. generate drug molecular phenotype data from the drug molecular data;” however, the specification fails to adequately describe in detail the computer and algorithm used to carry out the function of “generating drug molecular phenotype data”, the specification only discloses that “the phenotype generation unit may generate drug molecular phenotype data of a graph structure from the drug molecular data” (pg. 16 lines 18-21)
Claim 1 recites “a model generation unit configured to train a Bayesian neural network,” the specification fails to disclose how the Bayesian neural network is trained, only a recitation of what is used to train “by using the protein phenotype data, the drug molecular phenotype data, and the interaction data between the protein and the drug molecule as training data” (pg. 17 lines 3-6) there is no sufficient computer or algorithm described herein.
Claims 6, 8,9,11, and 12 recite “an interaction prediction unit configured to, by using a protein-drug interaction prediction model generated by training a Bayesian neural network, predict an interaction between a protein and a drug molecule and determine an uncertainty of the prediction” the specification fails to adequately describe the computer and algorithm used to predict the interaction and determine uncertainty, it merely states “By using the protein-drug interaction prediction model generated by the model generation apparatus 100, the interaction prediction unit 230 may predict the interaction between the target protein and the target drug molecule based on the protein phenotype data and the drug molecular phenotype data, and determine the uncertainty of the prediction. In this case, the uncertainty may include an epistemic uncertainty and an aleatoric uncertainty.” (pg. 20 lines 3-10)
Claims 3-5, 7, and 10 are also rejected under 35 U.S.C. 112(a) for their dependence on the claims above.
If the means- (or step- or generic placeholder) plus-function limitation is computer-implemented and the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention , see MPEP § 2161.01 and MPEP § 2181, subsection IV, then it lacks written description and is therefore rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2 and 7 recite, “wherein the phenotype generation unit is configured to generate drug molecular phenotype data of a graph structure from the drug molecular data, and generate protein phenotype data from the protein data using the protein phenotype generation model generated through transfer learning.” Claims 1 and 6, on which they depend, use a ‘means plus function’ “a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data” we’ve interpreted the structure based on the specification “using a protein phenotype generation model generated by transfer learning a pre-trained model trained with huge data;” the limitations of claim 2 and 7 are merely reiterating what we have already deduced . Therefore, claim 2 and 7 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Applicant may cancel the claims, amend the claims to place the claims in proper dependent form, rewrite the claims in independent form, or present a sufficient showing that the dependent claims complies with the statutory requirements.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention
is directed to an abstract idea without significantly more.
Step 2A, Prong 1
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’ 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 further processes that are practicably performable in the human mind, at least under their broadest reasonable interpretation, including:
Claim 1:
generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data;
Claim 2: generate drug molecular phenotype data of a graph structure from the drug molecular data, and generate protein phenotype data from the protein data using the protein phenotype generation model generated through transfer learning.
Claim 4: wherein the one- dimensional convolutional network is configured to update the protein phenotype data; the graph network is configured to update the drug molecular phenotype data; the combining layer combines the updated protein phenotype data and the updated drug molecular phenotype data to generate combined data
Claim 6:
acquire protein data and drug molecular data
Generate protein phenotype data from the protein data and generate drug molecular phenotype data from the drug molecular data
predict an interaction between a protein and a drug molecule based on the protein phenotype data and the drug molecular phenotype data and determine uncertainty of the prediction
Claim 7: generate drug molecular phenotype data of a graph structure from the drug molecular data, and generate protein phenotype data from the protein data using the protein phenotype generation model generated through transfer learning.”
Claim 8: the interaction prediction unit is configured to predict the interaction between the protein and the drug molecule a plurality of times by applying dropout,
Claim 13:
generating protein phenotype data from the protein data; generating drug molecular phenotype data from the drug molecular data
predicting an interaction between a protein and a drug molecule based on the protein phenotype data and the drug molecular phenotype data,’
Claim 14:
generating protein phenotype data from the protein data using a protein phenotype generation model generated through transfer learning
the generating of the drug molecular phenotype data comprises generating drug molecular phenotype data of a graph structure from the drug molecular data.
Claim 15: “predicting the interaction between the protein and the drug molecule a plurality of times by applying dropout”
‘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:
Claim 8: “determine a final predictive value of the interaction between the protein and the drug molecule and the uncertainty of the final predictive value based on the prediction results of the plurality of times.”
Claim 10 recites “wherein the uncertainty of the final predictive value comprises an epistemic uncertainty and an aleatoric uncertainty.”
Claim 9 and 16 recite “determine the final predictive value by averaging the prediction results of the plurality of times, and determine the uncertainty of the final predictive value from a distribution of the prediction results of the plurality of times.”
Claims 11 and 18 recite “wherein the interaction prediction unit is configured to determine the epistemic uncertainty using an equation: wherein E.U. represents the epistemic uncertainty, T represents the number of predictions, Yt represents the t-th prediction result, and Y represents an average value of the predictions.”
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Claim 15 recite “determining a final predictive value of the interaction between the protein and the drug molecule and the uncertainty of the final predictive value based on the prediction results of the plurality of times.”
Claim 17 recites “wherein the uncertainty of the final predictive value comprises an epistemic uncertainty and an aleatoric uncertainty.”
Claims 12 and 19 recite “wherein the interaction prediction unit is configured to determine the aleatoric uncertainty using an equation: wherein A.U. represents the aleatoric uncertainty, T represents the number of predictions, and Y represents the t-th prediction result.”
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‘Laws of Nature and Natural Phenomena’ are naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature (MPEP 2106.04(b)
Genotype-phenotype relationship described in claims 1-19
The limitations regarding generating phenotype data, and updating phenotype data are generic recitations of data analysis that can practically be performed in the human mind especially when recited at a high level of generality. Likewise, the limitations on predicting interaction between the protein and target drug molecule are considered “Mental processes” because the prediction is simply an observation and analysis of given data that can be done in the human mind. The recited equations and acts of calculation constitute mathematical concepts. The limitations regarding “determining” or “averaging” are verbal equivalents of mathematical formulas used to determine a variable or number, in this case an interaction prediction and uncertainty values. These limitations that can be done in the human mind or by pen and paper. “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation MPEP 2106.04(a)(2). The remaining limitations on what the predicative value comprises in claims 10 and 17, describe the values used in calculation. Therefore, these limitations fall under the abstract idea groupings of “Mathematical concepts” and “Mental processes”.
While claims 6, 8, 13, and 15 recite that some aspect of the present application is done by training a Bayesian neural network and carried out by a “unit” there are no additional limitations that indicate that this neural network requires anything other than carrying out the recited mental process and mathematic concept in a generic way—essentially, carrying out the recited mental process in a generic computer environment. Simply reciting that a mental process is being performed in a generic computer environment does not preclude the steps from being performed practically in the human mind or with pen and paper as claimed. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of training neural networks, then it falls within the “Mental processes” grouping of abstract ideas. As such, claims 1-19 recite an abstract idea (STEP 2A Prong 1: YES).
Step 2A, Prong 2
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). This judicial exception is not integrated into a
practical application because the claims do not recite an additional element that reflects 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 amount to mere
instructions to implement the abstract idea in a generic computing environment or insignificant
extra-solution activity. Specifically, the claims recite the following additional elements:
Claim 1:
collect protein data, drug molecular data, and interaction data
train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data, and the interaction data as training data to generate a protein-drug interaction prediction model.
Claims 1, 2,6,7,8,9, 11,12: data collection/data acquisition unit; phenotype generation unit; model generation unit; interaction prediction unit
Claim 5: wherein the protein data is one-dimensional character string sequence data comprised of an arrangement of amino acid characters, and the drug molecular data is simplified molecular-input line-entry system (SMILES) data in which a structure of molecules is represented as a one-dimensional character string.
Claim 8: wherein the Bayesian neural network is a Bayesian neural network to which dropout is applied and the interaction prediction unit is configured to predict the interaction between the protein and the drug molecule a plurality of times by applying dropout
Claim 13:
acquiring protein data and drug molecular data;
generated by training a Bayesian neural network,
Claim 15: wherein the Bayesian neural network is a Bayesian neural network to which dropout is applied;
The limitations on acquiring, collecting, receiving and outputting data merely serve as data gathering and outputting steps. Therefore, they are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application (see MPEP 2106.05(g)).
The additional element on training Bayesian neural networks and remaining additional elements recite various units that serve to implement the steps of the abstract idea. These limitations neither improve the functions of the computing system itself, nor provide specific programming, tailored software, or meaningful guidance for implementing the abstract concept. They state nothing more than that a generic computer system performs the functions that constitute the abstract idea. Hence, these are mere instructions to apply the abstract idea using a computer or machine learning, and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; and MPEP 2106.05(f))
There are no limitations that indicate that the various units and training neural networks requires anything other than mere instructions in a generic system. As such these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-19 are directed to an abstract idea (STEP 2A, Prong 2: NO)
Step 2B
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 mere instructions to apply the recited exception in a generic
computing environment or well-understood, routine and conventional activity.
As discussed above, there are no additional limitations that indicate that using an interaction prediction unit and training neural networks requires anything other than mere instructions in a generic system. Claims that equate to nothing more than applying instructions to implement the abstract idea on a generic computer have been stated in the courts to not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Furthermore, the additional elements recited in the claims amount to well-understood, routine, and conventional activity, as evidenced by Baskin (Expert Opinion on Drug Discovery (2016), 11(8), 785–795). Baskin analyzes recent developments in the application of neural networks to drug discovery: building Quantitative Structure–Activity Relationship (QSAR) models to predict activity profiles and drug–target interactions, binding constants with respect to various targets, drug selectivity, inhibition constants for different enzymes, toxicity profiles...etc. The more traditional approaches, such as ‘shallow’ neural networks, Bayesian, and ensemble/consensus learning, were shown to be very important tools in drug discovery. Baskin discloses that these methods are widely used in the contemporary research and very often generate the most valuable models (pg. 791 col. 2 para 3). Additionally, recited additional elements 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, processing, and outputting 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));
Receiving or transmitting data over a network (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)). As such, the combination of additional elements recited in the claims is well-understood, routine and conventional.
The additional elements 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. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2b: No). As such claims 1-19 are not patent eligible.
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.
Claims 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (arXiv:2009.00805 Sept. 2020) in view of Kim et al (arXiv:2012.08194 Dec. 2020/2024 IDS document).
Regarding claim 1, 6, and 13, Wang teaches a compound-protein interaction prediction formation model. The model is composed of three modules, the C-module, P-module and I-module. The C-module and the P-module take as input protein data, sequences, and molecule (drug) data in SMILES. Wang teaches in the C-module the molecules in SMILES format are converted into graphs, embedded, and used to generate a compound representation (phenotype as defined by the present invention). The protein sequences on the other hand, are split into n-grams, embedded, adding the positional encoding and used to generate a protein representation. Wang discloses that on top of the C-module and P-module, I-module takes the output from both and produces the final interaction prediction (pg. 1 para. 3, pg. 3-4 section 2.2, Fig. 1 and description; a data collection unit configured to collect protein data, drug molecular data, and interaction data between a protein and a drug molecule; apparatus and method for predicting a protein-drug interaction, the apparatus comprising: a data acquisition unit configured to acquire protein data and drug molecular data; a phenotype generation unit configured to generate protein phenotype data from the protein data, and generate drug molecular phenotype data from the drug molecular data, the method comprising; claim 13 acquiring protein data and drug molecular data; generating protein phenotype data from the protein data; generating drug molecular phenotype data from the drug molecular data).
Wang is silent to model generation unit configured to train a Bayesian neural network using the protein phenotype data, the drug molecular phenotype data, and the interaction data as training data to generate a protein-drug interaction prediction model in claim 1,6, and 13.
Wang does not teach a one-dimensional convolutional network to which dropout is applied; a graph network to which dropout is applied; a combining layer; and a fully connected network to which dropout is applied in claim 3.
Wang is silent to wherein the one- dimensional convolutional network is configured to update the protein phenotype data; the graph network is configured to update the drug molecular phenotype data, the combining layer combines the updated protein phenotype data and the updated drug molecular phenotype data to generate combined data, and the fully connected network is configured to receive the combined data and outputs a predictive value of the interaction between the protein and the drug molecule in claim 4.
Wang does not explicitly teach the apparatus and method wherein the Bayesian neural network is a Bayesian neural network to which dropout is applied and the interaction prediction unit is configured to predict the interaction between the protein and the drug molecule a plurality of times by applying dropout, and determine a final predictive value of the interaction between the protein and the drug molecule and the uncertainty of the final predictive value based on the prediction results of the plurality of times as stated in claim 8 and 15.
Wang is also silent to determining the final predictive value by averaging the prediction results of the plurality of times, and determining the uncertainty of the final predictive value from a distribution of the prediction results of the plurality of times in claims 9 and 16.
Wang does not disclose wherein the uncertainty of the final predictive value comprises an epistemic uncertainty and an aleatoric uncertainty in claims 10 and 17; and determining the epistemic uncertainty using the equation wherein E.U. represents the epistemic uncertainty, T represents the number of predictions, Yt represents the t-th prediction result, and Y represents an average value of the predictions in claims 11 and 18; and determine the aleatoric uncertainty using the provided equation wherein A.U. represents the aleatoric uncertainty, T represents the number of predictions, and Y represents the t-th prediction result in claims 12 and 19.
However, these limitations were known in the art at the time of effective filing date of the invention as taught by Kim et al.
Regarding claims 1,6, and 13, Kim provides for an end-to-end deep learning framework for highly accurate DPI prediction with transfer learning and BNN. Using a pretrained model as a stacked transformer architecture, which is trained with 250 million unlabeled protein sequences in an unsupervised manner. The drug is represented by the molecular graph and encoded through the graph interaction network layers.
Regarding claim 3, Kim teaches that with the protein-level embedding x (0) p, using three 1-dimensional convolutional neural networks work (1D-CNN) to smooth patterns in protein features (page 5; para. 4) Kim also presents an overview of the proposed neural network architecture, in which the figure below depicts dropout applied at the various layers (Figure 1; a one-dimensional convolutional network to which dropout is applied; a graph network to which dropout is applied; a combining layer; and a fully connected network to which dropout is applied).
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As to claim 4, Kim discloses that the GraphNet exchanges information between graph edges and nodes and recursively updates them; after the update of node states is finalized, a graph feature (molecular feature) is obtained by gathering all the node and edge states. Kim further discloses a connection in the drug - protein feature vector in equation number 4 (pg. 6) and a combination layer wherein the graph network updates the edge between the j number node and the ith node, and the feature vector in which the classification machine block is connected passes the complete linkage layer using the ReLU activation which leads to outputting the final predictive value (pg. 6 para 2, pg. 7 para. 2; wherein the one- dimensional convolutional network is configured to update the protein phenotype data; the graph network is configured to update the drug molecular phenotype data, the combining layer combines the updated protein phenotype data and the updated drug molecular phenotype data to generate combined data, and the fully connected network is configured to receive the combined data and outputs a predictive value of the interaction between the protein and the drug molecule).
Concerning claim 5, Kim teaches the input data is a pair of strings consisting of a protein sequence, the protein is represented as a 1-dimensional long sequence of amino acid characters and drug SMILES strings. (pg. 4-5 para 1-2, pg. 2 para 2;wherein the protein data is one-dimensional character string sequence data comprised of an arrangement of amino acid characters, and the drug molecular data is simplified molecular-input line-entry system (SMILES) data in which a structure of molecules is represented as a one-dimensional character string).
As to claim 14 Kim discloses an end-to-end deep learning framework for highly accurate DPI prediction with transfer learning and Bayesian neural network and the pretrained model as a stacked transformer architecture, which is trained with 250 million unlabeled protein sequences in an unsupervised manner. Kim further discloses that the drug is represented by the molecular graph and encoded through the graph interaction network layers (pg. 3 para 3; wherein the phenotype generation unit is configured to generate drug molecular phenotype data of a graph structure from the drug molecular data, and generate protein phenotype data from the protein data using the protein phenotype generation model generated through transfer learning).
Concerning claims 8 and 15, Kim shares that the expectation and variance of output can be easily obtained with the collection of outputs sampled by repeated inference of new input x∗ while the dropout layers are turned on. In BNN, the integration in Eq. (6) is replaced with a predictive mean of T times of MC sampling, which is estimated by the equation below (pg. 8 para. 2; interaction prediction unit is configured to predict the interaction between the protein and the drug molecule a plurality of times by applying dropout, and determine a final predictive value of the interaction between the protein and the drug molecule and the uncertainty of the final predictive value based on the prediction results of the plurality of times.)
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Equation 6
With regards to claims 9 and 16, Kim teaches a Bayesian neural network in which the integration in the equation 6 is replaced with a predictive mean of T times of MC sampling.
As to claims 10 and 17, in estimating its predictive variance Kim et al. decomposes the source of uncertainty into aleatoric and epistemic and optimized for classification tasks (pg. 8 para 3; wherein the uncertainty of the final predictive value comprises an epistemic uncertainty and an aleatoric uncertainty)
Regarding claims 11 and 18 Kim teaches that in estimating its predictive variance, they decompose the source of uncertainty into aleatoric and epistemic and optimized for classification tasks by Kwon et al. The epistemic uncertainty arises due to model prediction variability. The predictive variance equation below, comprises the epistemic uncertainty equation for the final predictive value (wherein the interaction prediction unit is configured to determine the epistemic uncertainty using an equation: wherein E.U. represents the epistemic uncertainty, T represents the number of predictions, Yt represents the t-th prediction result, and Y represents an average value of the predictions):
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Finally, in regards to claim 12 and 19, the aforementioned predictive variance disclosed by Kim, also illustrates the aleatoric uncertainty. The aleatoric uncertainty originates from the inherent noise of data points. The predictive variance equation below, comprises the epistemic uncertainty equation for the final predictive value (wherein the determining of the uncertainty of the final predictive value comprising determining the aleatoric uncertainty using an equation: wherein A.U. represents the aleatoric uncertainty, T represents the number of predictions, and Yt represents the t-th prediction result.):
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Regarding claims 2 and 7, the claims fail to further limit the subject matter of claims 1 and 6 above as discussed under 35 U.S.C. 112(d) section, and therefore these claims are rejected for the same reasons discussed above for claims 1 and 6.
An invention would have been prima facie obvious to one of ordinary skill in the art at the effective filing date of the present application if some motivation in the prior art would have led that person to combine or modify the prior art teachings to arrive at the claimed invention. Wang explains that using deep neural have achieved significantly better performance than traditional machine learning algorithms. Kim teaches “another proposed method to obtain a more robust and reliable model with a small dataset is Bayesian neural network (BNN). Compared to a conventional DNN, which gives definite point prediction for each given input, a BNN returns a distribution of predictions” (pg.3 para 2). While Wang highlights the high performance of standard deep neural networks, Kim explains that Bayesian neural networks improve reliability on small datasets by returning a distribution of predictions instead of a single point. One skilled in the art would have been motivated to modify Wang with Kim to incorporate Bayesian neural networks and had reasonable expectations of success: “our model performs better than the previous baselines for predicting drug protein interactions” (Kim et al. pg. 1 para 1). Therefore, the claims are prima facie obvious.
Conclusion
It is noted that claim 3 and 4 were found to be patent eligible. The limitations were found to be unconventional.
No claims are allowed.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Ryu, S., Kwon, Y., & Kim, W. Y. (2019, March 20). Uncertainty quantification of molecular property prediction with Bayesian Neural Networks. arXiv.org. https://arxiv.org/abs/1903.08375 .
Claims 1-19 are rejected
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/A.A.A./Examiner, Art Unit 1685 /OLIVIA M. WISE/ Supervisory Patent Examiner, Art Unit 1685
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