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
Claims 1-10 are currently pending and examined on the merits.
Claims 1-10 are rejected.
Claim 10 is objected to.
Priority
The instant application claims foreign priority to Application CN 2022105370156 filed on 17 May 2022, in China. At this point in examination, the effective filing date of claims 1-10 is 17 May 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7 July 2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Objections
Claim 10 is objected to because of the following informalities: Claim 9, line 7 recites "in a part of the full variable range". However, claim 10, line 2 recites "in a part of the full". There is a typographical error. Appropriate correction is required.
Claim Interpretation – 35 USC § 112(f)
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 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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) 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) 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) because the claim limitations use 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 limitations are:
A: "an interaction module, the interaction module is set to: input template sequence information of antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information" in claims 1 and 4-5.
B: “an affinity modification module, the affinity modification module is set to: according to the interaction antibody/macromolecular drug sequence information, perform corresponding partial or exhaustive numeration of possible sequence in a part of the full variable range to obtain a mutation library, and perform sequence-based affinity prediction on the mutation library based on a deep learning model, so as to obtain the sequence information of the modified antibody/macromolecular drug” in claim 1.
C: “an output module, the output module is designed to: according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug” in claims 1 and 6.
D: “the affinity design module” in claims 2 and 3.
E: “the visual analysis display module provides the complete sequence information of the sequence information of the candidate antibody/macromolecular drug” in claims 6-8.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
In cases involving a special purpose computer-implemented means-plus-function limitation, the Federal Circuit has consistently required that the structure be more than simply a general purpose computer or microprocessor and that the specification must disclose an algorithm for performing the claimed function. See e.g., Noah Systems Inc. v. Intuit Inc., 675 F.3d 1302, 1312, 102 USPQ2d 1410, 1417 (Fed. Cir. 2012); Aristocrat, 521 F.3d at 1333, 86 USPQ2d at 1239. The corresponding structure in the instant specification is as follows:
A: “an interaction module” – para. [102] of the instant specification states that the system provided by the present invention and its various devices, modules, and units can be regarded as hardware components, and the devices, modules, and units included in the system for implementing various functions can also be regarded as structures within the hardware components. This suggests that the interaction module is associated with a computer or processor to carry out its functions. However, the instant specification does not disclose the specific steps of an algorithm that inputs template sequence information of antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information. Thus, the description in the specification for the claimed interaction module does not satisfy the requirements under 35 U.S.C. 112(a) and (b); see below.
B: “an affinity modification module” - para. [0102] of the instant specification states that the system provided by the present invention and its various devices, modules, and units can be regarded as hardware components, and the devices, modules, and units included in the system for implementing various functions can also be regarded as structures within the hardware components. This suggests that the affinity modification module is associated with a computer or processor to carry out its functions. para. [0092] of the instant specification states design/operation steps of the affinity modification module that includes an interaction module, a calculation module, and a visual analysis display module. In particular, the calculation module is described to evaluate the mutation space of the antibody and narrow the mutation range for screening if the mutation space exceeds an upper limit. The calculation module is also described to calculate candidate mutation amino acid sequence affinities based on a deep learning model and score them, where either the highest or lowest affinity sequence becomes the final modification sequence. Therefore, the instant specification discloses the specific steps of an algorithm that performs partial or exhaustive numeration of possible sequence and sequence-based affinity prediction on a mutation library. Thus, the description in the specification for the claimed affinity modification module has adequate corresponding structure.
C: “an output module” - para. [0102] of the instant specification states that the system provided by the present invention and its various devices, modules, and units can be regarded as hardware components, and the devices, modules, and units included in the system for implementing various functions can also be regarded as structures within the hardware components. This suggests that the output module is associated with a computer or processor to carry out its functions. However, the instant specification does not disclose the specific steps of an algorithm that outputs the sequence information of the candidate antibody/macromolecular drug. Thus, the description in the specification for the claimed output module does not satisfy the requirements under 35 U.S.C. 112(a) and (b); see below.
D: “the affinity design module” - para. [0102] of the instant specification states that the system provided by the present invention and its various devices, modules, and units can be regarded as hardware components, and the devices, modules, and units included in the system for implementing various functions can also be regarded as structures within the hardware components. This suggests that the affinity design module is associated with a computer or processor to carry out its functions. However, the instant specification does not disclose the specific steps of an algorithm that produces affinity design. Thus, the description in the specification for the claimed visual analysis display module does not satisfy the requirements under 35 U.S.C. 112(a) and (b); see below.
E: “the visual analysis display module” - para. [0102] of the instant specification states that the system provided by the present invention and its various devices, modules, and units can be regarded as hardware components, and the devices, modules, and units included in the system for implementing various functions can also be regarded as structures within the hardware components. This suggests that the visual analysis display module is associated with a computer or processor to carry out its functions. However, the instant specification does not disclose the specific steps of an algorithm that provides the complete sequence information of the sequence information of the candidate antibody/macromolecular drug. Thus, the description in the specification for the claimed visual analysis display module does not satisfy the requirements under 35 U.S.C. 112(a) and (b); see below.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 112(a)
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.
Claims 1-8 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains 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, at the time the application was filed, had possession of the claimed invention.
Claims 1 and 4-5 recite "an interaction module, the interaction module is set to: input template sequence information for antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information" which has been interpreted to invoke 35 U.S.C. 112(f).
Claims 1 and 6 recite “an output module, the output module is designed to: according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug”, which has been interpreted to invoke 35 U.S.C. 112(f).
Claims 2 and 3 recite “the affinity design module”, which has been interpreted to invoke 35 U.S.C. 112(f).
Claims 6-8 recite “the visual analysis display module provides the complete sequence information of the sequence information of the candidate antibody/macromolecular drug”, which have been interpreted to invoke 35 U.S.C. 112(f).
For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b). When a claim containing a computer-implemented 35 U.S.C. 112(f) claim limitation is found to be indefinite under 35 U.S.C. 112(b) for failure to disclose sufficient corresponding structure (e.g., the computer and the algorithm) in the specification that performs the entire claimed function, it will also lack written description under 35 U.S.C. 112(a). See MPEP § 2163.03, subsection VI.
In this case, para [0102] in the instant specification discloses that the system provided by the present invention and its various devices, modules, and units can be regarded as hardware components, and the devices, modules, and units included in the system for implementing various functions can also be regarded as structures within the hardware components. This suggests that modules such as the interaction module (claims 1 and 4-5), output module (claims 1 and 6), affinity design module (claims 2 and 3), and visual analysis display module (claims 6-8) are associated with a computer or processor to carry out its functions. However, the specific steps of the algorithm to perform the functions are not recited in the specification.
While one of ordinary skill in the art could be capable of writing software for an algorithm which inputs template sequence information, modification requirements, and screening requirements (claims 1 and 4-5), outputs sequence information of the candidate antibody/macromolecular drug (claims 1 and 6), produces affinity design (claims 2 and 3), and provides complete sequence information of the sequence information of the candidate antibody/macromolecular drug (claims 6-8), the understanding of one skilled in the art does not relieve the patentee of the duty to disclose sufficient structure to support means-plus-function claim terms.
For the reasons discussed above, the specification does not provide a sufficient disclosure of the limitations of “input template sequence information of antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information” recited in claims 1 and 4-5, “output the sequence information of the candidate antibody/macromolecular drug” recited in claims 1 and 6, “affinity design” recited in claims 2 and 3, and “provides the complete sequence information of the sequence information of the candidate antibody/macromolecular drug” recited in claims 6-8 to demonstrate to one of ordinary skill in the art that the inventor possessed the invention at the time the application was filed. For more information regarding the written description requirement, see MPEP §2161.01 - §2163.07(b).
Because dependent claims 2-8 incorporate the unsupported limitations of independent claim 1 and do not include further limitations that correct the issue, they are likewise rejected under 35 U.S.C. 112(a).
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
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.
The following claim limitations invoke 35 U.S.C. 112(f):
Claims 1 and 4-5 recite "an interaction module, the interaction module is set to: input template sequence information for antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information".
Claims 1 and 6 recite “an output module, the output module is designed to: according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug”.
Claims 2 and 3 recite “the affinity design module”.
Claims 6-8 recite “the visual analysis display module provides the complete sequence information of the sequence information of the candidate antibody/macromolecular drug”.
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.
As described above, para [0102] in the instant specification discloses that the system provided by the present invention and its various devices, modules, and units can be regarded as hardware components, and the devices, modules, and units included in the system for implementing various functions can also be regarded as structures within the hardware components. This suggests that modules such as the interaction module (claims 1 and 4-5), output module (claims 1 and 6), affinity design module (claims 2 and 3), and visual analysis display module (claims 6-8) are associated with a computer or processor to carry out its functions. However, the specific steps of the algorithm to perform the functions are not recited in the specification.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b).
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 term “affinity modification module” in claim 1, line 7 is a relative term which renders the claim indefinite. The term “affinity modification module” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is unclear what function the "affinity modification module" has in the affinity modification system. The specification defines the module as design/operation steps including an interaction module, a calculation module, and a visual analysis display module (Page 11-12, paragraph 92, lines 1-35). However, the overall affinity modification system in claim 1 also recites a.
Claims 2 and 3 recite the limitation "the affinity design module" in line 3 of both claims. There is insufficient antecedent basis for this limitation in the claim. The rejection might be overcome by amending the claim to introduce clear antecedent basis for the “affinity design module”. For compact prosecution, it is assumed that the preceding suggested will be implemented.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-8 are directed to a system (machine). Claims 9-10 are directed to a method (process) for affinity modification of antibody/macromolecular drug. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1, 5, and 8-10 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, as noted below.
Independent claim 1 further recites:
an affinity modification module, the affinity modification module is set to: according to the interaction antibody/macromolecular drug sequence information, perform corresponding partial or exhaustive numeration of possible sequence in a part of the full variable range to obtain a mutation library (i.e., mental processes);
an affinity modification module, the affinity modification module is set to: perform sequence-based affinity prediction on the mutation library (i.e., mental processes).
Dependent claim 5 further recites:
at least one element of a set comprising marking the variable range and specifying the variable range (i.e., mental processes);
defining a modification direction (i.e., mental processes).
Dependent claim 8 further recites:
wherein, the visual analysis display module further comprises a comparative analysis of the template sequence information of the antibody/macromolecular drug and the sequence information of the candidate antibody/macromolecular drug in a variable range (i.e., mental processes).
Dependent claim 9 further recites:
according to the interaction antibody/macromolecular drug sequence information, perform partial or exhaustive numeration of possible sequence in a part of the full variable range to obtain a mutation library (i.e., mental processes);
perform a sequence-based affinity prediction on the mutation library (i.e., mental processes).
Dependent claim 10 further recites:
when performing the partial or exhaustive numeration of possible sequence in a part of the full (i.e., mental processes).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1, 5, and 8-10 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A, Prong One are not integrated into a practical application because of the reasons noted below.
Claims 5 and 10 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
Claim 1 recites an affinity modification module, the affinity modification module is set to: perform sequence-based affinity prediction on the mutation library based on a deep learning model. The limitation recites “based on a deep learning model”, which provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Therefore, the claimed additional element does not integrate the abstract ideas into a practical application.
Claim 9 recites perform a sequence-based affinity prediction on the mutation library based on a deep learning model. The limitation recites “based on a deep learning model”, which provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). Therefore, the claimed additional element does not integrate the abstract ideas into a practical application.
Claims 1, 7, and 9 recite the additional non-abstract elements of data gathering:
an interaction module, the interaction module is set to: input template sequence information of antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information (claim 1);
to obtain the sequence information of the modified antibody/macromolecular drug (claims 1 and 9);
an output module, the output module is designed to: according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug (claim 1);
the visual analysis display module provides the complete sequence information of the sequence information of the candidate antibody/macromolecular drug (claim 7);
according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug (claim 9).
which are each a data gathering step, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claim 1 recites the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
an affinity modification system of antibody/macromolecular drug (claim 1).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-10 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1, 7, and 9 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claims 1, 7, and 9: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to the recited affinity modification system of antibody/macromolecular drug in claim 1: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
The additional element of an affinity modification module, the affinity modification module is set to: perform sequence-based affinity prediction on the mutation library based on a deep learning model (claim 1) is conventional. Evidence for conventionality is shown by Kang et al. (arXiv preprint, 2021, 1-9). Kang et al. reviews “We performed antibody-antigen complex affinity prediction based on sequence data with Hag-Net network structure.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, line 1). This shows that the Hag-Net network structure is a deep learning model used to perform affinity prediction on mutation libraries or sequence data. Therefore, the deep learning model is a conventional element in the art.
The additional element of perform a sequence-based affinity prediction on the mutation library based on a deep learning model (claim 9) is conventional. Evidence for conventionality is shown by Kang et al. (arXiv preprint, 2021, 1-9). Kang et al. reviews “We performed antibody-antigen complex affinity prediction based on sequence data with Hag-Net network structure.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, line 1). This shows that the Hag-Net network structure is a deep learning model used to perform affinity prediction on mutation libraries or sequence data. Therefore, the deep learning model is a conventional element in the art.
[Step 2B: NO]
Therefore, claims 1-10 are patent ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
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.
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (arXiv preprint, 2021, 1-9).
With respect to claim 1:
With respect to the recited an affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
With respect to the recited an interaction module, the interaction module is set to: input template sequence information of antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information, Kang et al. discloses “Antibody-Bind (AB-Bind) is a manually curated and organized database that includes 1101 mutants across 32 complexes” (Page 2, Section “2.1 Data Collection”, line 1). Also, further discloses “we explore the graph representation of antibody-antigen complex with three different representation strategies. 1) Full-seq model: the full-seq model simply takes antibody and antigen sequences as two separated graph sequences (Figure 2(A)). The intuition is to incorporate both interact contacts (ICs) and non-interacting surface (NIS) into modeling as the binding strength between antibody and antigen relies on the full conformation of the formed complex [9, 12]. 2) Contacts-only model: the contacts-only model produces a compact representation of the complex by utilizing residues on the interfacial surface (distance <5 Angstrom) only (Figure 2(B)); Given limited high-quality training data, this approach is presumably more adequate since it models the most relevant information for antibody-antigen interactions. The identification of interface contacts was obtained using Prodigy-based prediction service [9] based on complex’s 3D structure. 3) Antibody-only model: the antibody-only model aims to address the promiscuous binding capability of antibodies cross diverse antigens [15, 16, 17, 18]. We investigated on antibody-only modeling for binding affinity prediction, and evaluate if Hag-Net based network structure captures the enabling features for antibodies’ natural binding capability.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 2, lines 1-12). Kang et al. discloses “Antibody maturation aims to optimize the binding affinity based on known antibody leads targeting specific antigens. To this purpose, we construct the pairwise problem and study the binary relations between mutated variants of each therapeutic lead. Specifically, for each wildtype antibody, its associated mutations are grouped into unique ordered pairs. Each pair consists of two complexes with same target antigen but different mutated variants. If the first complex ranks higher than the second complex with respect to their binding affinities (i.e. the first complex possesses higher affinity), the pair is labeled as 1, otherwise as 0. Therefore, our goal is to obtain classifier f:
f
a
,
b
=
1
i
f
∆
∆
G
a
<
∆
∆
G
b
0
e
l
s
e
” (Page 3, Section “2.2.3 Pairwise-study: binding affinity pairwise rank prediction”, paragraph 1, lines 1-7). Antibody-Bind (AB-Bind) is a database of mutants across complexes, which suggests an input template sequence information of antibody drugs. The three graph representation strategies for antibody-antigen complexes indicate modification requirements for single or multi-targets of antibody drugs because they model bindings and interactions between antibodies and antigens, which can be single or multi-targeted. The pairwise problem suggests a user-defined screening requirement to generate a classifier f, which represents interaction antibody drug sequence information.
With respect to the recited an affinity modification module, the affinity modification module is set to: according to the interaction antibody/macromolecular drug sequence information, perform corresponding partial or exhaustive numeration of possible sequence in a part of the full variable range to obtain a mutation library, Kang et al. discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen. The resulting graph was then converted to an input matrix by one-hot encoding, where each row represents a specific residue, as well as an adjacent matrix, where each 1 represents connection between amino acids in two positions. Thus, a 200 amino acid sequence will result in a 200x22 matrix as the node input and a 200x200 adjacent matrix as edge input.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-7). One-hot encoding suggests an exhaustive numeration method of amino acid sequences in a range of residues and connections to obtain a mutation library represented as matrices.
With respect to the recited an affinity modification module, the affinity modification module is set to: perform sequence-based affinity prediction on the mutation library based on a deep learning model, so as to obtain the sequence information of the modified antibody/macromolecular drug, Kang et al. discloses “We performed antibody-antigen complex affinity prediction based on sequence data with Hag-Net network structure.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, line 1). Also, further discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen. The resulting graph was then converted to an input matrix by one-hot encoding, where each row represents a specific residue, as well as an adjacent matrix, where each 1 represents connection between amino acids in two positions. Thus, a 200 amino acid sequence will result in a 200x22 matrix as the node input and a 200x200 adjacent matrix as edge input.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-7). Kang et al. discloses “In the presented table, each wildtype (e.g. 1AK4) are listed with their associated mutated and binding affinity measurement. To construct an ordered pair, we take two mutations (mutation A and mutation B) and generate corresponding graph representations respectively. The outputs of Hag-Net are used as affinity scores for the comparison between mutations. The difference between affinity scores then goes through sigmoid function to predict the binary relation label, specifically in this example, label is set to 0 since mutate A has lower binding affinity than mutate B.” (Page 4, Figure 3, lines 1-6). This suggests that the Hag-Net network structure is the deep learning model used to perform sequence-based affinity prediction on the mutation library represented in matrices. The affinity scores generated from Hag-Net are then used to obtain the binary relation label, which is the sequence information of the modified antibody drug.
With respect to the recited an output module, the output module is designed to: according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug, Kang et al. discloses “Overall, 97711 pairs were generated from the original 1101 mutants of 32 complexes.” (Page 6, Section “3.2 Pairwise Study”, paragraph 1, line 1). This indicates an output of sequence information of the candidate antibody drug from the original input database.
With respect to claim 2:
With respect to the recited affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
With respect to the recited in the affinity design module, a single quantity level of the mutation library is not less than
10
10
, Kang et al. discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen. The resulting graph was then converted to an input matrix by one-hot encoding, where each row represents a specific residue, as well as an adjacent matrix, where each 1 represents connection between amino acids in two positions. Thus, a 200 amino acid sequence will result in a 200x22 matrix as the node input and a 200x200 adjacent matrix as edge input.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-7). This suggests that if the input was a
10
10
amino acid sequence, the mutation library represented as matrices will result in a
10
10
x22 matrix as the node input and a
10
10
x
10
10
adjacent matrix as edge input, which together indicates a quantity level that is not less than
10
10
.
With respect to claim 3:
With respect to the recited affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
With respect to the affinity design module, the variable range includes one or more variable regions, variable spaces, variable number of sites, or combinations thereof, Kang et al. discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen. The resulting graph was then converted to an input matrix by one-hot encoding, where each row represents a specific residue, as well as an adjacent matrix, where each 1 represents connection between amino acids in two positions. Thus, a 200 amino acid sequence will result in a 200x22 matrix as the node input and a 200x200 adjacent matrix as edge input.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-7). This suggests a variable range of rows, which includes specific residues and amino acid connections between two positions as the variable regions with variable number of sites depending on the encodings in the matrices.
With respect to claim 4:
With respect to the recited affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
With respect to the recited in the interaction module, the template sequence information of the antibody/macromolecular drug includes at least one element of a set comprising an antigen/antibody template sequence, a protein/protein template sequence, and a protein/polypeptide template sequence of the antibody/macromolecular drug, Kang et al. discloses “Antibody-Bind (AB-Bind) is a manually curated and organized database that includes 1101 mutants across 32 complexes” (Page 2, Section “2.1 Data Collection”, line 1). Also, further discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-4). This suggests that the input template sequence information of the antibody drug includes elements of residues and connections with interacting residues of a set of complexes from the Antibody-Bind database, which can contain amino acid sequences representing antigen/antibody sequences, protein/protein sequences, and protein/polypeptide sequences of antibody drugs.
With respect to claim 5:
With respect to the recited affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
With respect to the recited in the interaction module, in the modification requirements of single/multiple targets of the antibody/macromolecular drug, Kang et al. discloses “we explore the graph representation of antibody-antigen complex with three different representation strategies. 1) Full-seq model: the full-seq model simply takes antibody and antigen sequences as two separated graph sequences (Figure 2(A)). The intuition is to incorporate both interact contacts (ICs) and non-interacting surface (NIS) into modeling as the binding strength between antibody and antigen relies on the full conformation of the formed complex [9, 12]. 2) Contacts-only model: the contacts-only model produces a compact representation of the complex by utilizing residues on the interfacial surface (distance <5 Angstrom) only (Figure 2(B)); Given limited high-quality training data, this approach is presumably more adequate since it models the most relevant information for antibody-antigen interactions. The identification of interface contacts was obtained using Prodigy-based prediction service [9] based on complex’s 3D structure. 3) Antibody-only model: the antibody-only model aims to address the promiscuous binding capability of antibodies cross diverse antigens [15, 16, 17, 18]. We investigated on antibody-only modeling for binding affinity prediction, and evaluate if Hag-Net based network structure captures the enabling features for antibodies’ natural binding capability.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 2, lines 1-12). The three graph representation strategies for antibody-antigen complexes indicate modification requirements for single or multi-targets of antibody drugs because they model bindings and interactions between antibodies and antigens, which can be single or multi-targeted.
With respect to the recited at least one element of a set comprising marking the variable range and specifying the variable range, Kang et al. discloses “2) Contacts-only model: the contacts-only model produces a compact representation of the complex by utilizing residues on the interfacial surface (distance <5 Angstrom) only” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 2, lines 5-6). This suggests marking and specifying the variable range of residues with distance <5 Angstrom.
With respect to the recited defining a modification direction, Kang et al. discloses “1) Full-seq model: the full-seq model simply takes antibody and antigen sequences as two separated graph sequences (Figure 2(A)). The intuition is to incorporate both interact contacts (ICs) and non-interacting surface (NIS) into modeling as the binding strength between antibody and antigen relies on the full conformation of the formed complex” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 2, lines 2-5). This indicates defining a modification direction as towards both interact contacts and non-interacting surfaces for modeling based on binding strength.
With respect to claim 6:
With respect to the recited affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
Claim 6 recites the output module further comprises a visual analysis display module. It would be obvious to one of ordinary skill in the art to incorporate a visual output device or monitor in the affinity modification system because a display is a well-known component used to provide information to the user.
With respect to claim 7:
With respect to the recited affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
With respect to the recited the visual analysis display module provides the complete sequence information of the sequence information of the candidate antibody/macromolecular drug, Kang et al. discloses “Overall, 97711 pairs were generated from the original 1101 mutants of 32 complexes.” (Page 6, Section “3.2 Pairwise Study”, paragraph 1, line 1). This suggests an output of the complete sequence information of the candidate antibody drug from the original input database. Figure 3 shows the prediction of binary relation labels for the pairs of sequences, which indicates that the information is being displayed.
With respect to claim 8:
With respect to the recited affinity modification system of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). Also, further discloses “in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.” (Abstract, lines 6-7). This suggests an affinity modification system with prediction models used to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs. This also indicates that the models are run on a computer as computational processing power was incorporated in the design of this approach.
With respect to the recited the visual analysis display module further comprises a comparative analysis of the template sequence information of the antibody/macromolecular drug and the sequence information of the candidate antibody/macromolecular drug in a variable range, Kang et al. discloses “In the presented table, each wildtype (e.g. 1AK4) are listed with their associated mutated and binding affinity measurement. To construct an ordered pair, we take two mutations (mutation A and mutation B) and generate corresponding graph representations respectively. The outputs of Hag-Net are used as affinity scores for the comparison between mutations. The difference between affinity scores then goes through sigmoid function to predict the binary relation label, specifically in this example, label is set to 0 since mutate A has lower binding affinity than mutate B.” (Page 4, Figure 3, lines 1-6). This suggests a comparative analysis of the sequence information between mutation A and mutation B, which represent the template antibody drug and candidate antibody drug in a variable range, respectively.
With respect to claim 9:
With respect to the recited affinity modification method of antibody/macromolecular drug, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). This suggests a method that incorporates prediction models using deep learning techniques to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs.
With respect to the recited an input template sequence information of antibody/macromolecular drugs, modification requirements of single/multi-targets of antibody/macromolecular drugs, and optional user-defined screening requirements to generate interaction antibody/macromolecular drug sequence information, Kang et al. discloses “Antibody-Bind (AB-Bind) is a manually curated and organized database that includes 1101 mutants across 32 complexes” (Page 2, Section “2.1 Data Collection”, line 1). Also, further discloses “we explore the graph representation of antibody-antigen complex with three different representation strategies. 1) Full-seq model: the full-seq model simply takes antibody and antigen sequences as two separated graph sequences (Figure 2(A)). The intuition is to incorporate both interact contacts (ICs) and non-interacting surface (NIS) into modeling as the binding strength between antibody and antigen relies on the full conformation of the formed complex [9, 12]. 2) Contacts-only model: the contacts-only model produces a compact representation of the complex by utilizing residues on the interfacial surface (distance <5 Angstrom) only (Figure 2(B)); Given limited high-quality training data, this approach is presumably more adequate since it models the most relevant information for antibody-antigen interactions. The identification of interface contacts was obtained using Prodigy-based prediction service [9] based on complex’s 3D structure. 3) Antibody-only model: the antibody-only model aims to address the promiscuous binding capability of antibodies cross diverse antigens [15, 16, 17, 18]. We investigated on antibody-only modeling for binding affinity prediction, and evaluate if Hag-Net based network structure captures the enabling features for antibodies’ natural binding capability.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 2, lines 1-12). Kang et al. discloses “Antibody maturation aims to optimize the binding affinity based on known antibody leads targeting specific antigens. To this purpose, we construct the pairwise problem and study the binary relations between mutated variants of each therapeutic lead. Specifically, for each wildtype antibody, its associated mutations are grouped into unique ordered pairs. Each pair consists of two complexes with same target antigen but different mutated variants. If the first complex ranks higher than the second complex with respect to their binding affinities (i.e. the first complex possesses higher affinity), the pair is labeled as 1, otherwise as 0. Therefore, our goal is to obtain classifier f:
f
a
,
b
=
1
i
f
∆
∆
G
a
<
∆
∆
G
b
0
e
l
s
e
” (Page 3, Section “2.2.3 Pairwise-study: binding affinity pairwise rank prediction”, paragraph 1, lines 1-7). Antibody-Bind (AB-Bind) is a database of mutants across complexes, which suggests an input template sequence information of antibody drugs. The three graph representation strategies for antibody-antigen complexes indicate modification requirements for single or multi-targets of antibody drugs because they model bindings and interactions between antibodies and antigens, which can be single or multi-targeted. The pairwise problem suggests a user-defined screening requirement to generate a classifier f, which represents interaction antibody drug sequence information.
With respect to the recited according to the interaction antibody/macromolecular drug sequence information, perform partial or exhaustive numeration of possible sequence in a part of the full variable range to obtain a mutation library, Kang et al. discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen. The resulting graph was then converted to an input matrix by one-hot encoding, where each row represents a specific residue, as well as an adjacent matrix, where each 1 represents connection between amino acids in two positions. Thus, a 200 amino acid sequence will result in a 200x22 matrix as the node input and a 200x200 adjacent matrix as edge input.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-7). One-hot encoding suggests an exhaustive numeration method of amino acid sequences in a range of residues and connections to obtain a mutation library represented as matrices.
With respect to the recited perform a sequence-based affinity prediction on the mutation library based on a deep learning model, so as to obtain the sequence information of the modified antibody/macromolecular drug, Kang et al. discloses “We performed antibody-antigen complex affinity prediction based on sequence data with Hag-Net network structure.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, line 1). Also, further discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen. The resulting graph was then converted to an input matrix by one-hot encoding, where each row represents a specific residue, as well as an adjacent matrix, where each 1 represents connection between amino acids in two positions. Thus, a 200 amino acid sequence will result in a 200x22 matrix as the node input and a 200x200 adjacent matrix as edge input.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-7). Kang et al. discloses “In the presented table, each wildtype (e.g. 1AK4) are listed with their associated mutated and binding affinity measurement. To construct an ordered pair, we take two mutations (mutation A and mutation B) and generate corresponding graph representations respectively. The outputs of Hag-Net are used as affinity scores for the comparison between mutations. The difference between affinity scores then goes through sigmoid function to predict the binary relation label, specifically in this example, label is set to 0 since mutate A has lower binding affinity than mutate B.” (Page 4, Figure 3, lines 1-6). This suggests that the Hag-Net network structure is the deep learning model used to perform sequence-based affinity prediction on the mutation library represented in matrices. The affinity scores generated from Hag-Net are then used to obtain the binary relation label, which is the sequence information of the modified antibody drug.
With respect to the recited according to the sequence information of the modified antibody/macromolecular drug, output the sequence information of the candidate antibody/macromolecular drug, Kang et al. discloses “Overall, 97711 pairs were generated from the original 1101 mutants of 32 complexes.” (Page 6, Section “3.2 Pairwise Study”, paragraph 1, line 1). This indicates an output of sequence information of the candidate antibody drug from the original input database.
With respect to claim 10:
With respect to the recited affinity modification method, Kang et al. discloses “To examine the potential of sequence data- guided in silico antibody maturation, we proposed affinity prediction models that utilize deep learning techniques based on the complex sequence information only. The intuition of such modeling strategy is to capture the enabling features encrypted at amino acids level that contributes to the interactions and the resulting binding strength between antibody and antigen.” (Page 8, Section “4 Discussions”, paragraph 1, lines 3-6). This suggests a method that incorporates prediction models using deep learning techniques to examine sequence data-guided in silico antibody maturation, or affinity modification, in antibody drugs.
With respect to the recited when performing the partial or exhaustive numeration of possible sequence in a part of the full, a single quantity level of the mutation library is not less than
10
10
, Kang et al. discloses “In the proposed modeling approach, each node represents a single residue, and its edges represent connections with associated/interacting residues. This modeling approach represents each antibody-antigen complex as a single graph structure that corresponds to the amino acid sequences of antibody and antigen. The resulting graph was then converted to an input matrix by one-hot encoding, where each row represents a specific residue, as well as an adjacent matrix, where each 1 represents connection between amino acids in two positions. Thus, a 200 amino acid sequence will result in a 200x22 matrix as the node input and a 200x200 adjacent matrix as edge input.” (Page 3, Section “2.2.2 Baseline study: binding affinity change (
∆
∆
G
) prediction”, paragraph 1, lines 2-7). This suggests that when performing one-hot encoding or exhaustive enumeration on a
10
10
amino acid sequence, the mutation library represented as matrices will result in a
10
10
x22 matrix as the node input and a
10
10
x
10
10
adjacent matrix as edge input, which together indicates a quantity level that is not less than
10
10
.
Therefore, the differences in the prior art were encompassed in known variations or in principle known in the prior art. The rationale would have been the predictable use of prior art elements according to their established functions. KSR 550 U.S. at 417.
For these reasons, the instant claims do not recite any new element or new function or unpredictable result, and the examiner invites the applicant to provide evidence demonstrating the novel or unobvious difference between the claimed limitations and those used in the prior art, as mere argument cannot take the place of evidence lacking in the record. Estee Lauder Inc. v. L’Oreal, S.A., 129 F .3d 588, 595 (Fed. Cir. 1997).
Conclusion
No claims are allowed.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686