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 .
Remarks
In response to communications sent May 1, 2026, claim(s) 29-37, 39-45, and 47-51 is/are pending in this application; of these claim(s) 29, 30, 49, and 50 is/are in independent form. Claim(s) 1-28, 38, and 46 is/are cancelled.
Response to Arguments
Applicant’s arguments, see page 10 lines 13-16 and page 11 lines 19-23, filed May 1, 2026, with respect to the abstract of the specification have been fully considered and are persuasive. The objection of to the abstract mailed February 2, 2026 has been withdrawn.
Applicant’s arguments, see page 10 lines 17-21 and page 11 lines 12-18, filed May 1, 2026, with respect to the drawings have been fully considered and are persuasive. The objection to the drawings mailed February 2, 2026 has been withdrawn.
Applicant’s arguments, see page 12 lines 1-7, filed May 1, 2026, with respect to claims 29-46 have been fully considered and are persuasive. The rejection of claims 29-46 mailed February 2, 20026 has been withdrawn.
Applicant’s arguments, see page 12 lines 8-26, filed May 1, 2026, with respect to claims 29-37 and 39-45 have been fully considered and are persuasive. The rejection of claims 29-37 and 39-45 mailed February 2, 20026 has been withdrawn.
Applicant’s arguments, see page 13 line 1 to page 16 line 9, filed May 1, 2026, with respect to the rejection(s) of claim(s) 29-37, 39-45 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of US 2024/0412810 A1 (“Ingraham”) and 35 U.S.C. § 102. Claim 38 has been cancelled so the issue is moot for that claim.
Applicant’s arguments, see page 16 lines 10-19, filed May 1, 2026, with respect to claims 29 and 30 have been fully considered and are persuasive. The rejection of claims 29 and 30 has been withdrawn.
Applicant’s arguments, see page 16 lines 10-19, filed May 1, 2026, with respect to claims 29 and 30 have been fully considered and are persuasive. The provisional rejection of claims 29 and 30 has been withdrawn.
Terminal Disclaimer
The terminal disclaimer filed on May 1, 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of Patent Number 11,742,057. has been reviewed and is accepted. The terminal disclaimer has been recorded.
The terminal disclaimer filed on May 1, 2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of any patent granted on Application Number 18/216172. has been reviewed and is accepted. The terminal disclaimer has been recorded.
Drawings
The drawings were received on May 1, 2026. These drawings are acceptable.
Claim Interpretation
The claims recite the term “graph,” which the Examiner interprets as a network with nodes and edges, as in the mathematical field of graph theory. This interpretation is different from a more common use of the term graph to refer to charts and other visualizations.
Priority
The disclosure of the prior-filed application, Application No. 17384104, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. The Application does not mention the graph (i.e. network) data structure (i.e. a graph that involves nodes and edges). Note that the provisional patent application 63/224,801 also lacks support or enablement. Therefore, claims 29-46 are given the filing date of another provisional patent application 63/353,481, which is June 17, 2022.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 29-37, 39-45, and 47-51 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2024/0412810 A1 (“Ingraham”). Note that the filing date of the Ingraham reference is based on the provisional patent application 63/261,646 filed September 24, 2021.
As to claim 29, Ingraham teaches the method for the in-silico design of an amino acid interface of a biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target protein (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the method comprising:
(a) receiving, by a processor of a computing device, an initial scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprising a graph representation of at least a portion of a biologic complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target protein (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the in-progress custom biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”);
(b) generating, by the processor, based on the initial scaffold-target complex graph and using a machine learning model comprising a graph neural network (GNN) (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) providing the predicted interface for use in designing the amino acid interface of the biologic (Ingraham Para [0064]: generating new interfaces) and/or using the predicted interface to design the amino acid interface of the biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 30, Ingraham teaches a system for the in-silico design of an amino acid interface of a biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target protein (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the system comprising:
a processor of a computing device (Ingraham Para [0096]); and
a memory having instructions stored thereon, wherein the instructions, when executed by the processor (Ingraham Para [0096]), cause the processor to:
(a) receive an initial scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprising a graph representation of at least a portion of a biologic complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target protein (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”);
(b) generate, based on the initial scaffold-target complex graph and using a machine learning model comprising a graph neural network (GNN) (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) provide the predicted interface for use in designing the amino acid interface of the biologic (Ingraham Para [0064]: generating new interfaces) and/or use the predicted interface to design the amino acid interface of the biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 31, Ingraham teaches the method of claim 29, wherein the initial scaffold-target complex graph comprises a plurality of nodes and edges (Ingraham Figure 2A and Para [0067]: the backbone complex structure is represented by nodes and edges).
As to claim 32, Ingraham teaches the method of claim 29, wherein the initial scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprises a scaffold graph representing at least a portion of the peptide backbone of the biologic (Ingraham Para [0067]: a backbone polymer structure represented by the graph), the scaffold graph comprising a plurality of scaffold nodes, each representing a particular amino acid site of the peptide backbone (Ingraham Para [0004]-[0005]: monomers of amino acids may be nodes in the graph).
As to claim 33, Ingraham teaches the method of claim 32, wherein a subset of the scaffold nodes are unknown interface nodes, each representing a particular amino acid interface site located in proximity to the target and having an unknown, to-be-determined amino acid side chain (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains).
As to claim 34, Ingraham teaches the method of claim 32, wherein a subset of the scaffold nodes are known scaffold nodes, each representing a particular amino acid site having a known side chain type (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 35, Ingraham teaches the method of claim 32, wherein the scaffold graph comprises a plurality of scaffold edges, each associated with two particular scaffold nodes and representing a relative position and/or orientation of two amino acid sites represented by the two particular scaffold nodes (Ingraham para [0071]: edge features include distance and orientation between monomers).
As to claim 36, Ingraham teaches the method of claim 29, wherein the target is or comprises a protein and/or a peptide (Ingraham Para [0005]: the target complex is a protein biopolymer) and the initial scaffold-target complex graph comprises a target graph comprising a plurality of target nodes, each representing a particular amino acid site of the target (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 37, Ingraham teaches the method of claim 36, wherein the target graph comprises a plurality of target edges, each associated with two particular target nodes and representing a relative position and/or orientation of two amino acid sites represented by the two particular target nodes (Ingraham para [0071]: edge features include distance and orientation between monomers).
As to claim 39, Ingraham teaches the system of claim 30, wherein the initial scaffold-target complex graph comprises a plurality of nodes and edges (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges).
As to claim 40, Ingraham teaches the system of claim 30, wherein the initial scaffold-target complex graph comprises a scaffold graph representing at least a portion of the peptide backbone of the biologic (Ingraham Para [0067]: a backbone polymer structure represented by the graph), the scaffold graph comprising a plurality of scaffold nodes (Ingraham Para [0067]: a backbone polymer structure represented by the graph), each representing a particular amino acid site of the peptide backbone (Ingraham Para [0004]-[0005]: monomers of amino acids may be nodes in the graph).
As to claim 41, Ingraham teaches the system of claim 40, wherein a subset of the scaffold nodes are unknown interface nodes, each representing a particular amino acid interface site located in proximity to the target and having an unknown, to-be-determined amino acid side chain (Ingraham Para [0064]: some of the nodes of the interface of the graph are generated; the nodes to be generated are interpreted as “to-be-determined” amino acid chains).
As to claim 42, Ingraham teaches the system of claim 40, wherein a subset of the scaffold nodes are known scaffold nodes, each representing a particular amino acid site having a known side chain type (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 43, Ingraham teaches the system of claim 40, wherein the scaffold graph comprises a plurality of scaffold edges, each associated with two particular scaffold nodes and representing a relative position and/or orientation of two amino acid sites represented by the two particular scaffold nodes (Ingraham para [0071]: edge features include distance and orientation between monomers).
As to claim 44, Ingraham teaches the system of claim 30, wherein the target is or comprises a protein and/or a peptide (Ingraham Para [0005]: the target complex is a protein biopolymer) and the initial scaffold-target complex graph comprises a target graph comprising a plurality of target nodes, each representing a particular amino acid site of the target (Ingraham Para [0086]: some monomers/residues of the backbone of the complex are constrained, such as with a known sequence of residues).
As to claim 45, Ingraham teaches the system of claim 44, wherein the target graph comprises a plurality of target edges, each associated with two particular target nodes and representing a relative position and/or orientation of two amino acid sites represented by the two particular target nodes (Ingraham para [0071]: edge features include distance and orientation between monomers).
As to claim 47, Ingraham teaches the method of claim 29, wherein the machine learning model receives at least a portion of the initial scaffold-target complex graph as input (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network).
As to claim 48, Ingraham teaches the system of claim 30, wherein the machine learning model receives at least a portion of the initial scaffold-target complex graph as input (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network).
As to claim 49, Ingraham teaches a method for the in-silico design of an amino acid interface of a biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target protein (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the method comprising:
(a) receiving, by a processor of a computing device, an initial scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprising a graph representation of at least a portion of a biologic complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target protein (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the in-progress custom biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”)
(b) generating, by the processor, based on the initial scaffold-target complex graph and using a machine learning model (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network) , a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure), wherein the machine learning model receives at least a portion of the initial scaffold-target complex graph as input (Para [0068] provides evidence that the graph representation may be processed by a graph neural network); and
(c) providing the predicted interface for use in designing the amino acid interface of the biologic (Ingraham Para [0064]: generating new interfaces) and/or using the predicted interface to design the amino acid interface of the biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 50, Ingraham teaches a method for the in-silico design of an amino acid interface of a biologic (Ingraham Figure 2A Para [0067]: outputting a biological sequence) for binding to a target protein (Ingraham Para [0087] provides evidence that the designed sequence may be an interface design for a biopolymer complex), the method comprising:
(a) receiving, by a processor of a computing device, an initial scaffold-target complex graph (Ingraham Figure 2A and Para [0067]: “3D structure of backbones in complex”) comprising a graph representation of at least a portion of a biologic complex (Ingraham Figure 2A and Para [0067]: the structure is represented by nodes and edges) comprising the target protein (Ingraham Para [0005]: the reference structure includes a target complex) and a peptide backbone of the in-progress custom biologic (Ingraham Para [0066]: provides evidence that the method is a generative method given the 3D structure of the backbones in a biopolymer complex; the presence of a backbone prior to the generative steps suggest that the backbone is “in-progress”);
(b) generating, by the processor, based on the initial scaffold-target complex graph and using a machine learning model comprising a plurality of transformer layers (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network; according to Ingraham Para [0020], the neural network may involve a transformer in the form of GPT3), a predicted interface comprising, for each of a plurality of interface sites, an identification of a particular amino acid side chain type (Ingraham Para [0087]: filling in amino acids to form a complete biopolymer sequence given the structure); and
(c) providing the predicted interface for use in designing the amino acid interface of the biologic (Ingraham Para [0064]: generating new interfaces) and/or using the predicted interface to design the amino acid interface of the biologic (this element is claimed in the alternative and does not need to be mapped).
As to claim 51, Ingraham teaches the method of claim 50, wherein the machine learning model receives at least a portion of the initial scaffold-target complex graph as input (Ingraham Figure 2A and Para [0087]: using a decoder top generate a network interface; Para [0068] provides evidence that the graph representation may be processed by a graph neural network).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 7751987 B1: predicting amino acid from specified 3D structure
US 20130303387 A1: See Figure 10A regarding ligands
US 20230083810 A1: See element S310a
US 20230083810 A1: graph neural networks and masked regions
Designing real novel proteins using deep graph neural networks. Alexey Strokach, David Becerra, Carles Corbi, Albert Perez-Riba, Philip M. Kim. bioRxiv 868935; doi: https://doi.org/10.1101/868935
Strokach, Alexey, et al. "Fast and flexible protein design using deep graph neural networks." Cell systems 11.4 (2020): 402-411.
US-20240038337-A1: this reference is a pre-grant publication of an application in the same patent family
US-20230034425-A1: this reference is a pre-grant publication of an application in the same patent family
US-20240412810-A1: this reference is a pre-grant publication of an application in the same patent family
US 12027235 B1: Pertinent because it is similar and has a similar Applicant
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse P Frumkin whose telephone number is (571)270-1849. The examiner can normally be reached Monday - Friday, 10-5 ET.
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 July 2, 2026
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