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 September 26, 2025, claim(s) 1-16 is/are pending in this application; of these claim(s) 1 and 9 is/are in independent form.
Response to Arguments
Applicant’s arguments, see page 5 lines 22-24, filed September 26, 2025, with respect to claim 12 have been fully considered and are persuasive. The objection of August 14, 2025 has been withdrawn.
Applicant's arguments filed September 26, 2025 have been fully considered but they are not persuasive. Applicant’s Specification includes definitions of “immunogenic” and “non-immunogenic” at page 5 lines 16-18 as filed. The specification recites:
“As used herein, "immunogenic" refers to peptides that invokes responses from immune cells. As used herein, "non-immunogenic" refers to peptides that do not invoke responses from immune cells.”
However, the same epitopes might invoke an immune response or not depending on the individual’s previous exposures, autoimmune disorders, and genetic makeup. Hence, the same epitope may be “immunogenic” and “non-immunogenic” depending on the individual. Furthermore, immunogenicity may vary over time for the same individual.
Applicant’s claims have been recited to include the limitation “the plurality of reference peptides comprises immunogenic peptides and non-immunogenic peptides”. The Examiner interprets that the set of “reference peptides” may include the peptides that are sometimes immunogenic and sometimes not immunogenic. The Examiner believes that this is consistent with the definitions provided in the Specification.
Hence, the reference US 2008/0172215 A1 (“Heckerman”) teaches the precise claim limitations by classifying the peptides according to their immunogenicity in a conditional manner. Therefore, the rejections under 35 U.S.C. § 102 and 35 U.S.C. § 103 are not withdrawn.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-14, and 16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2008/0172215 A1 (“Heckerman”).
As to claim 1, Heckerman teaches a method for predicting immunogenicity of a candidate peptide (Heckerman Para [0057]: predicting epitopes; Heckerman Para [0004] clarifies that the epitopes may be small peptides), the method comprising:
training (Heckerman Para [0057]: a classification model for training) an artificial neural network in an electronic processor (Heckerman Para [0039]: using a neural network as the machine learning algorithm for classification) to generate a plurality of reference measurements (Heckerman Para [0057]: to generate a plurality of measured features such as chemical properties at various positions) by docking a training dataset comprising a plurality of reference peptides into an antigen presenting molecule (Heckerman Para [0010]: the machine learning algorithm be based on binding energy of peptide-HLA pairs, which the examiner interprets as “docking” elements of the peptide dataset into an HLA antigen-presenting molecule), wherein the plurality of reference peptides comprises immunogenic peptides and non-immunogenic peptides, and each reference peptide classified as either an immunogenic peptide or a non-immunogenic peptide (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are an epitope are labeled for the classification model);
docking the candidate peptide into an antigen presenting molecule to obtain a three-dimensional candidate structural representation (Heckerman Para [0010]: the machine learning algorithm for epitope prediction based on the binding energy peptide-HLA pairs, which are based on structural representations of positional information of a peptide and its HLA molecule; note that, definitionally, an HLA molecule is a type of MHC molecule; the Examiner interprets that these structure-based-binding energies imply a virtual docking input to the machine learning algorithm, since no specific details of the docking algorithm is claimed);
generating a plurality of candidate measurements, wherein each candidate measurement is associated with at least one feature of the candidate structural representation (Heckerman Para [0052]: a prediction component operates on special features which are based in inputted protein information to give values for those features); and
using the artificial neural network (Heckerman Para [0039]: using a neural network as the machine learning algorithm for classification) to predict the immunogenicity of the candidate peptide (Heckerman Para [0052]: predicting epitopes), wherein the electronic processor is configured to predict the immunogenicity of the candidate peptide based upon the plurality of candidate measurements (Heckerman Para [0052]: predicting epitopes using the special features derived from the inputted protein information).
As to claim 2, Heckerman teaches the method of claim 1, wherein:
each reference measurement is associated with at least one feature of one or more reference structural representations (Heckerman Para [0052]: a prediction component operates on special features which are based in inputted protein information to give values for those features), and
each reference structural representation is a three-dimensional representation of a reference peptide bound to the antigen presenting molecule (Heckerman Para [0010]: the machine learning algorithm for epitope prediction based on the binding energy peptide-HLA pairs, which are based on structural representations of positional information of a peptide and its HLA molecule; the Examiner interprets that these structure-based-binding energies imply a virtual docking input to the machine learning algorithm).
As to claim 3, Heckerman teaches the method of claim 2, wherein the artificial neural network is configured to predict immunogenicity of the candidate peptide using the plurality of reference measurements (Heckerman Para [0039]: using a neural network as the machine learning algorithm for classification of immunogenicity by virtue of the peptide being an epitope).
As to claim 4, Heckerman in view of Tong teaches the method of claim 2, wherein the artificial neural network is further configured to predict the immunogenicity of the candidate peptide based upon whether each reference peptide is an immunogenic peptide or a non-immunogenic peptide (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are an epitope are labeled for the classification model; the labels indicate whether or not they are immunogenic epitopes or not).
As to claim 5, Heckerman teaches the method of claim 1, wherein the antigen presenting molecule is a class I MHC molecule or a class II MHC molecule (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are used to make and use the model are MHC molecules, by definition).
As to claim 6, Heckerman teaches the method of claim 5, wherein the antigen presenting molecule is HLA-A2 (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are used to make and use the model are comprise HLA molecules; according to Heckerman Para [0032], these HLA may be part of the A2 supertype).
As to claim 8, Heckerman teaches the method of claim 1, wherein the candidate peptide is a neoantigen , a viral peptide , a non-mutated self peptide , or a post- translationally modified peptide (these elements are claimed in the alternative so they do not all need to be mapped; Heckerman Para [0004] establishes that antigens often are viral proteins).
As to claim 9, Heckerman teaches a method for producing a vaccine (Heckerman Para [0057]: predicting epitopes; Heckerman Para [0004] clarifies that the epitopes may be small peptides; Heckerman Para [0007] suggests that the method is for vaccine design using the predicted epitopes), the method comprising:
training (Heckerman Para [0057]: a classification model for training) an artificial neural network in an electronic processor (Heckerman Para [0039]: using a neural network as the machine learning algorithm for classification) to generate a plurality of reference measurements (Heckerman Para [0057]: to generate a plurality of measured features such as chemical properties at various positions) by docking a training dataset comprising a plurality of reference peptides into an antigen presenting molecule (Heckerman Para [0010]: the machine learning algorithm be based on binding energy of peptide-HLA pairs, which the examiner interprets as “docking” elements of the peptide dataset into an HLA antigen-presenting molecule), wherein the plurality of reference peptides comprises immunogenic peptides and non-immunogenic peptides, and each reference peptide classified as either an immunogenic peptide or a non-immunogenic peptide (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are an epitope are labeled for the classification model);
docking the candidate peptide into an antigen presenting molecule to obtain a three-dimensional candidate structural representation (Heckerman Para [0010]: the machine learning algorithm for epitope prediction based on the binding energy peptide-HLA pairs, which are based on structural representations of positional information of a peptide and its HLA molecule; note that, definitionally, an HLA molecule is a type of MHC molecule; the Examiner interprets that these structure-based-binding energies imply a virtual docking input to the machine learning algorithm, since no specific details of the docking algorithm is claimed);
generating a plurality of candidate measurements for each candidate structural representation, wherein each candidate measurement is associated with at least one feature of each candidate structural representation (Heckerman Para [0052]: a prediction component operates on special features which are based in inputted protein information to give values for those features);
using the artificial neural network (Heckerman Para [0039]: using a neural network as the machine learning algorithm for classification) to predict the immunogenicity of each candidate peptide (Heckerman Para [0052]: predicting epitopes) based upon the plurality of candidate measurements for each candidate structural representation (Heckerman Para [0052]: predicting epitopes using the special features derived from the inputted protein information);
producing a vaccine comprising one or more candidate peptides predicted to be immunogenic by the artificial neural network (Heckerman Para [0007] suggests that the purpose of the method is for vaccine design using the predicted epitopes).
As to claim 10, Heckerman teaches the method of claim 9, wherein:
each reference measurement is associated with at least one feature of one or more reference structural representations (Heckerman Para [0052]: a prediction component operates on special features which are based in inputted protein information to give values for those features), and
each reference structural representation is a three-dimensional representation of a reference peptide bound to the antigen presenting molecule (Heckerman Para [0010]: the machine learning algorithm for epitope prediction based on the binding energy peptide-HLA pairs, which are based on structural representations of positional information of a peptide and its HLA molecule; the Examiner interprets that these structure-based-binding energies imply a virtual docking input to the machine learning algorithm).
As to claim 11, Heckerman teaches the method of claim 9, wherein the artificial neural network is configured to predict immunogenicity of each candidate peptide using the plurality of reference measurements (Heckerman Para [0039]: using a neural network as the machine learning algorithm for classification of immunogenicity by virtue of the peptide being an epitope).
As to claim 12, Heckerman teaches the method of claim 10, wherein the artificial neural network is further configured to predict the immunogenicity of the candidate peptide based upon whether each reference peptide is an immunogenic peptide or a non-immunogenic peptide (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are an epitope are labeled for the classification model; the labels indicate whether or not they are immunogenic epitopes or not).
As to claim 13, Heckerman teaches the method of claim 10, wherein the antigen presenting molecule is a class I MHC molecule or a class II MHC molecule (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are used to make and use the model are MHC molecules, by definition).
As to claim 14, Heckerman teaches the method of claim 13, wherein the antigen presenting molecule is HLA-A2 (Heckerman Para [0040]: in exemplary embodiments, peptide-HLA pairs that are used to make and use the model are comprise HLA molecules; according to Heckerman Para [0032], these HLA may be part of the A2 supertype).
As to claim 16, Heckerman teaches the method of claim 10, wherein each candidate peptide is a neoantigen or a viral peptide (Heckerman Para [0004] establishes that antigens often are viral proteins).
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
Claim(s) 7 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2008/0172215 A1 (“Heckerman”) in view of US 2015/0205911 A1 (“Aswad”).
As to claim 7, Heckerman teaches the method of claim 1, but does not teach wherein the plurality of candidate measurements and/or the plurality of reference measurements are selected from the group consisting of solvent accessible surface areas , solvation energies , hydrophobicity, electrostatic interactions , and van der Waals interactions.
Nevertheless, Aswad teaches:
wherein the plurality of candidate measurements and/or the plurality of reference measurements (the broadest reasonable interpretation of “and/or” is “or”) are selected from the group consisting of solvent accessible surface areas (this element is claimed in the alternative and does not need to be mapped), solvation energies (Aswad Para [0080]: desolvation), hydrophobicity (this element is claimed in the alternative and does not need to be mapped), electrostatic interactions (Aswad Para [0080]: electrostatics), and van der Waals interactions (Aswad Para [0080]: van der Waals interactions).
Heckerman and Aswad are in the same field of bioinformatics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Heckerman to include the teachings of Aswad because using structural features of the protein improves model by predicting the strength of intermolecular interactions of a complex containing the peptide and an MHC/HLA (See Aswad, abstract).
As to claim 15, Heckerman teaches the method of claim 10, but does not teach wherein the plurality of candidate measurements and/or the plurality of reference measurements are selected from the group consisting of solvent accessible surface areas , solvation energies , hydrophobicity , electrostatic interactions , and van der Waals interactions.
Nevertheless, Aswad teaches:
wherein the plurality of candidate measurements and/or the plurality of reference measurements (the broadest reasonable interpretation of “and/or” is “or”) are selected from the group consisting of solvent accessible surface areas (this element is claimed in the alternative and does not need to be mapped), solvation energies (Aswad Para [0080]: desolvation), hydrophobicity (this element is claimed in the alternative and does not need to be mapped), electrostatic interactions (Aswad Para [0080]: electrostatics), and van der Waals interactions (Aswad Para [0080]: van der Waals interactions).
Heckerman and Aswad are in the same field of bioinformatics. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Heckerman to include the teachings of Aswad because using structural features of the protein improves model by predicting the strength of intermolecular interactions of a complex containing the peptide and an MHC/HLA (See Aswad, abstract).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US-20130330335-A1: Neural networks for predicting binding of peptides to MHC ; less mention of immunogenicity; for immunogenicity, see Para [0034] and elsewhere
US-20230207068-A1: Neural network, classification of features, to determine MHC binding; uses mass spectrometry
US 20210202043 A1: neural network MHC binding prediction
US 12394502 B2
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 - Saturday, 10-5 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESSE P FRUMKIN/Primary Examiner, Art Unit 1685 December 22, 2025