DETAILED ACTION
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
Withdrawal of Objections and Rejections
Applicant's response, filed 06/26/2026, has been fully considered.
In view of the amendment and remarks, the objection to the specification and the rejection of the following claims are withdrawn:
claims 29-30, 35, and 57-60 under 35 USC § 112(b)
claims 18, 29-30, 35, 41 and 48-56 under 35 U.S.C. § 101 – Regarding the 101 analysis as organized in MPEP 2106, the 101 rejections of claims 18, 41 and 48-56 are withdrawn at least in view of the analysis Step 2A, 2nd prong, 2nd consideration related to applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2). The instant claims integrate the judicial elements into a practical application via a particular treatment which is realized by treating the population of subjects who are in need of treatment by the T-cells (claim 18).
claims 29-30 and 35 under 35 U.S.C. § 103.
The following rejections and/or objections are either maintained or newly applied for claims 18, 41, 43 and 47-60. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 12/31/2025.
Status of the Claims
Claims 19-40, 42, 44-46 are canceled.
Claims 18, 41, 43 and 47-60 are pending.
Claims 18, 41, 43 and 47-60 are rejected.
Priority
This US Application 17/414,480 (06/16/2021) is a 371 of PCT/US2019/066104 (12/12/2019) which claims priority from US Application 62/780,832 (12/17/2018) and US Application 62/820,042 (03/18/2019), as reflected in the filing receipt mailed on 10/09/2024. The claims to the benefit of priority are acknowledged; and the effective filing date of claims 18, 41, 43 and 47-60 is 12/17/2018.
Claim interpretation
Claim 57 recites "(i) predicting ORFs at a root level … and predicting ORFs at leaf and branch nodes" which is being interpreted as predicting ORFs considering all reads (root) tissues (branches) and samples (leaves) as per [0055] in the disclosure.
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 43, 47 and 57-60 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed to a method (claims 57-59) and a product (claims 43, 47 and 60), which falls within one of the categories of statutory subject matter.
[Step 1: claims 43, 47 and 57-60: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mathematical concepts (in particular mathematical relationships and formulas) and mental processes (in particular procedures for observing, analyzing and organizing information) as well as a law of nature or a natural phenomenon are as follows.
Mathematical concepts (in particular mathematical relationships and formulas) include:
• "(b) conducting hierarchical ORF prediction on the Ribo-seq data generated in step (a), thereby generating a novel untranslated open reading frame (nuORF) database comprising predicted nuORFs" (claim 57) and
• "(i) predicting ORFs at a root level using pooled sequencing reads from the Ribo- seq data generated in step (a) and predicting ORFs at leaf and branch nodes using sample-specific sequencing reads, and (ii) identifying predicted tumor nuORFs that are predicted only at a leaf or branch node but not at the root level and including the identified predicted tumor nuORFs in the nuORF database” (claim 57).
The claims identified above read on math. The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation and determined each element performed by mathematical operation. The step directed to the “prediction of neoantigens identified from Ribo-seq data” requires mathematical techniques as the only supported embodiments because it describes a mathematical technique (MPEP 2106.04(a)(2) pertains). Further support for the mathematical techniques used in the claims is provided in the specification at [0173 - Support Vector Machine classifiers] describes the claimed prediction analysis; which indicates the use of math., which discloses the use of mathematical operations in the claimed prediction process. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "(c) searching the nuORF database for predicted tumor nuORF matches with a MHC I immunopeptidome dataset, thereby identifying the matched predicted tumor nuORFs as tumor neoantigens" (claim 57); and
• "(d) comparing the identified tumor neoantigens to an annotated proteome database to identify a set of unannotated identified tumor neoantigens from among the identified tumor neoantigens" (claim 58).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind (i.e. concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) or because the method only requires a user to manually determine action based on an added number. Under the BRI, the recited limitations are mental processes because a human mind is also sufficiently capable of evaluating data and searching for matches, selecting data points, comparing data to a database, and identifying a neoantigen based on said comparison.
In addition, the limitations of claims 43, 47 and 60, recite a natural product because the claims are drawn to T cells specific neoantigens, which encompasses naturally occurring products (Lu "Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions." Clinical Cancer Research 20(13):3401-3410 (2014), as cited on the 12/31/2025 Form PTO-892)). While these neoantigens are claimed in combination with experimental techniques to identify subject-specific neoepitopes, it does not change the structure of these neoantigens such that one of skill in the art would recognize it as markedly different from its naturally occurring counterpart already existing. Therefore, these limitations correspond to a natural product. As such, claims 43, 47 and 60 recite an abstract idea and natural products.
[Step 2A Prong One: claims 43, 47 and 57-60: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
MPEP 2106.04.II.B states that if multiple exceptions that are distinct from each other in a claim are recited, then examiners should select one of the exceptions (i.e. a law of nature, a natural phenomenon, or an abstract idea) and conduct an eligibility analysis for that selected exception. If the claim does not recite any additional element or combination of elements that integrate the selected exception into a practical application and also does not recite any additional elements or combination of additional elements that amount to significantly more than the judicial exception, then the claim should be considered ineligible. In view of this and for the purposes of examination, the abstract idea judicial exception is selected.
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2).
Instant claim 57 recite additional elements that are not abstract ideas:
• "(a) performing Ribosomal profiling (Ribo-seq) on a tumor sample and a non-tumor sample thereby generating Ribo-seg data" (claim 57).
Dependent claim 59 recites further details about the sample analyzed.
Considerations under Step 2A, Prong Two
The steps regarding "performing Ribosomal profiling" (claim 57) reads on data gathering activities; being an insignificant extra-solution activity since this limitation merely serve to gather data. See MPEP 2106.05(g) and MPEP 2106.04(d).
Furthermore, as discussed above, the limitations for the product claims are natural products (claims 43, 47 and 60). The identified T cell specific neoantigens are identified via judicial exceptions and judicial exceptions cannot provide a practical application. MPEP 2106.04.II.A.2 states that another judicial exception is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). Therefore the natural product cannot integrate the recited judicial exception into a practical application.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 43, 47 and 57-60: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Furthermore, as discussed above, the limitations for the composition are a natural product, which is a judicial exception. MPEP 2106.05.I states that an inventive concept under Step 2B cannot be furthered by a judicial exception itself. Therefore the natural product composition cannot provide an inventive concept that provides significantly more than the judicial exception itself.
With respect to the instant claims, the prior art review to (Pastor "An RNA toolbox for cancer immunotherapy." Nature Reviews Drug Discovery 17(10):751-767 (2018); cited on the 12/31/2025 PTO-892 Form) discloses that experimental techniques such as ribosome profiling and T-cell sequencing and transfection to identify neoantigens (pg. 753 Fig. 1 and pg. 754 col. 2) and T cell strategies based on the administration of transduced T cells (pg. 761 col. 2 para. 1) is routine, well-understood and conventional in the art.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 43, 47 and 57-60: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 06/26/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 9 para. 6:
Without acquiescing to the propriety of the rejection, Applicants have amended claim 18 to recite an additional concrete therapeutic step that directly addresses the concerns raised under the subject matter eligibility analysis of 35 U.S.C. § 101. Specifically, as amended, claim 18 expressly requires "administering T cells specific for one or more of the identified neoantigens to a subject in need thereof'. This affirmative treatment step applies the claimed identification process in a particular therapeutic context and, thus, integrates any alleged judicial exception into a practical application. Accordingly, Applicants respectfully submit that claim 18 is not directed to a judicial exception under Step 2A. At a minimum, the claim recites additional elements that amount to significantly more than any alleged exception under Step 2B. Claims 41, 43, and 47-56 depend directly or indirectly from claim 18 and include this therapeutic limitation. Therefore, these claims are likewise patent-eligible. Withdrawal of the§ 101 rejection of claims 18, 41, 43, and 47-56 is respectfully requested.
It is respectfully submitted that this is persuasive. Regarding the 101 analysis as organized in MPEP 2106, the 101 rejections of claims 18, 41 and 48-56 are withdrawn at least in view of the analysis Step 2A, 2nd prong, 2nd consideration related to applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2). The instant claims integrate the judicial elements into a practical application via a particular treatment which is realized by treating the population of subjects who are in need of treatment by the T-cells (claim 18). See Withdrawal of Objections and Rejections above.
Applicant asserts starting in pg. 10 para. 4:
Specifically, as amended, claim 57 includes a structured, multi-level prediction framework in which ORFs are predicted at a root level using pooled sequencing reads and at leaf and branch nodes using sample-specific sequencing reads, followed by selective inclusion of tumor-specific nuORFs that are absent at the root level. These limitations meaningfully limit the claim to a particular technological implementation for identifying tumor neoantigens. The claimed structure imposes a concrete, ordered processing architecture that systematically refines candidate sequences through successive levels of biologically grounded filtering, thereby integrating any underlying natural relationships into a practical, structured application. Moreover, the claimed hierarchical ORF prediction framework recites a multi-stage computational process operating on high-throughput sequencing data that cannot be practically performed in the human mind, similar to the multi-stage neural network training process found eligible in USPTO Example 39. Accordingly, Applicants respectfully submit that claim 57 is not directed to a judicial exception under Step 2A, at least because any alleged abstract idea is integrated into a practical application. At a minimum, the claim recites additional elements that amount to significantly more than any alleged exception under Step 2B, including a specific and non-generic implementation of hierarchical sequence analysis that improves the identification of tumor-specific nuORFs.
It is respectfully submitted that this is not persuasive because the argued “structured, multi-level prediction framework” and “selective inclusion of tumor-specific nuORFs that are absent at the root level” merely informs how the prediction algorithm is being executed, which described the judicial exception executed by a computer; hence not sufficient evidence to provide a practical application. The analysis at Step 2A, Prong 2, considers the claims as a whole, i.e., the additional elements in combination with the judicial exceptions (see MPEP 2106.05(a)), although the integration or improvement provided in the claim must flow from the additional elements and not the judicial exceptions to be considered persuasive. However, Step 2B instructs that an inventive concept cannot be provided the by judicial exception. An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself (Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966)). The arguments pointed by the Applicant are considered to recite a judicial exception as described above and are therefore not considered at Step 2B.
Regarding the argued comparison to Example 39, Example 39 considers a hypothetical method for training a neural network for facial detection. The exemplified claim comprises modifying a set of digital facial images by performing digital transformation steps (e.g., smoothing and contrast reduction) and training a neural network in two stages using unmodified facial images, modified facial images, and non-facial images. However, the Examiner considers the exemplified process as non-analogous to that recited by the instant claims because here, there is no neural network training step or any other additional element that provides a real life practical application.
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)(l) 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.
(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.
Claims 43, 47 and 60 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lu "Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions." Clinical Cancer Research 20(13):3401-3410 (2014).
Claims 43, 47 and 60 are product-by-process claims. MPEP 2113 provides instructions for interpretation of product-by-process claims where the prior art is applied based on the product itself (i.e. T-cell specific neoantigens).
Claim 43 recites:
A T cell specific for a neoantigen identified by the method of claim 18
Claim 47 recites:
A neoantigen library comprising neoantigens identified by the method of claim 18
Claim 60 recites:
A tumor neoantigen library comprising tumor neoantigens identified by the method of claim 57
• Lu teaches a method for the identification of mutated cancer antigens recognized by T Cells associated with durable tumor (i.e. T cell specific for a neoantigen as in claim 43) (pg. 3401 Title); wherein mutated T-cell antigens were identified by screening a tandem minigene library (i.e. neoantigen library comprising neoantigens as in claim 47), which comprised nonsynonymous mutation sequences identified by whole-exome sequencing of autologous tumors (i.e. tumor neoantigen library as in claim 60) (pg. 3401 Abstract); using a technique of library screening (pg. 3401 para. 4 Lu); wherein in-silico analysis identified candidate T-cell epitopes that were predicted to bind to the MHC class I molecules (pg. 3402 col. 1 para. 1 Lu).
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
A. Claims 18, 49-53 and 55-56 are rejected under 35 U.S.C. 103(a) as being unpatentable over Olexiouk (Végvári editor Proteogenomics. Advances in Experimental Medicine and Biology vol 926 Springer, 2016)
in view of Ji "RibORF: Identifying genome‐wide translated open reading frames using ribosome profiling." Current protocols in molecular biology 124(1):e67 (2018) in view of Lu "Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions." Clinical Cancer Research 20(13):3401-3410 (2014)), as cited on the 12/31/2025 Form PTO-892. Claim 18 is additionally evidenced by Lu "Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions." Clinical Cancer Research 20(13):3401-3410 (2014).
Claim 18 recites:
performing Ribosomal profiling (Ribo-seq) on a sample, thereby generating Ribo-seq data
• Olexiouk teaches it as an integrative analyses of various types of omics data using ribosome profiling (Ribo-Seq) to contribute to cancer research and personalized medicine to humans (pg. v para. 3); wherein ribosome profiling is capable of capturing sample specific genomic variations, enabling focused neo-antigen identification (pg. 49 para. 1).
(b) conducting hierarchical ORF prediction on the Ribo-seq data generated in step (a), thereby generating a novel untranslated open reading frame (nuORF) database comprising predicted nuORFs
• Olexiouk teaches "generating a novel untranslated open reading frame (nuORF) database comprising predicted nuORFs" or "predicted tumor nuORFs" as ORFs in 5’ untranslated regions being identified with higher ribosome protected fragments intensity than the main canonical downstream translated ORF (pg. 55 col. 1 para. 1); wherein a database was created containing alterative translation initiation sites and their associated ORF identified by Ribo-Seq (pg. 55 col. 1 para. 1); wherein the identification of ribosome bound RNA's allows the creating of a custom search database (pg. 7 col. 2 para. 2); wherein a neoantigen identification workflow that includes tumor cells being sequenced to identify genomic variations specific in tumor cells, with a database is generated consisting of neo-antigen candidates, and in silico algorithms can be used to predict MHC antigen presentation, resulting in a more confident dataset (pg. 53 Fig. 4.1).
• Olexiouk does not teach "conducting hierarchical ORF prediction on the Ribo-seq data.” However, Ji teaches it as a computational pipeline named RibORF to systematically identify translated open reading frames (pg. 1 para. 1); wherein predicted translated P-values representing the binary classification of candidate ORFs (i.e. hierarchical ORF prediction); wherein values can be smaller than 0 or be great than 1, with P values closer to 1 represents that the ORF is likely to be translated (i.e. the further away from 1 greater is the probability of finding untranslated ORFs); wherein users can select the cutoff P-value to define translated or untranslated ORFs (pg. 9 para. 2).
(c) searching the nuORF database for matches with a MHC I immunopeptidome data set, wherein the matches between the nuORF database and the MHC I immunopeptidome dataset are identified as neoantigens
• Olexiouk teaches it as a neoantigen identification workflow that includes tumor cells being sequenced to identify genomic variations specific in tumor cells, with a database is generated consisting of neo-antigen candidates, and in silico algorithms can be used to predict MHC antigen presentation, resulting in a more confident dataset (pg. 53 Fig. 4.1) by filtering the database of neo-antigen candidates (pg. 52 col. 2 para. 2) using RNA-seq techniques for the detection of peptides candidates (pg. 51 col. 2 para. 2) and using a technique of library screening (pg. 3401 para. 4 Lu) and in-silico analysis identify candidate T-cell epitopes that were predicted to bind to the MHC class I molecules (pg. 3402 col. 1 para. 1 Lu) as evidenced by Lu.
(d) administering T cells specific for one or more of the identified neoantigens to a subject in need thereof.
• Neither Olexiouk or Ji teach the recitation above. However, Lu teaches it as the administration of tumor-infiltrating lymphocytes (TILs) to patient in which TILs were grown from individual tumor fragments to allow the selection of cultures with antitumor reactivity (pg. 3409 col. 1para. 2).
Claim 49 recites:
wherein the MHC I immunopeptidome dataset of step (c) is obtained using mass spectrometry.
• Olexiouk teaches it as MS -based proteomic studies comparing the obtained spectra against protein databases of known/predicted proteins, resulting in a high number of unidentified spectra used to map novel peptides absent from the used protein database (pg. 51 col. 2 para. 2).
Claim 50 recites:
wherein the sample is a subject-specific sample, a tissue- specific sample, a disorder-specific sample, or a disease-specific sample
• Olexiouk teaches it as identifying patient-specific protein forms (i.e. comprising T-cell receptor pairs) and getting a better picture of molecular mechanisms of disease for precision and personalized medicine (pg. 21 para. 1).
Claim 51 recites:
wherein the tissue-specific sample is a cancer tissue sample
• Olexiouk teaches it as variations obtained in a single cell in cancer research may result in tumorous behavior and where these variations are frequently distinct between different tumor types, capturing sample specific variations (pg. 60 col. 1 para. 2).
Claim 52 recites:
wherein conducting hierarchical ORF prediction comprises implementing a computational algorithm
Claim 53 recites:
wherein the computational algorithm is RibORF
• Olexiouk does not teach the recitations above. However, Ji teaches it as a computational pipeline named RibORF to systematically identify translated open reading frames (i.e. computer algorithm as in claims 52 and RibORF as in claim 53) (pg. 1 para. 1).
Claim 55 recites:
wherein ORFs with a RibORF score greater than 0.7 are selected, ORFs having two reads and a RibORF score of 0.9 are selected, and/or ORFs having 250 reads and a RibORF score of 0.7 are selected
• Olexiouk does not teach the recitation above. However, Ji teaches it as a computational pipeline named RibORF to systematically identify translated open reading frames (pg. 1 para. 1); wherein predicted translated P-values representing the binary classification of candidate ORFs (i.e. hierarchical ORF prediction); wherein values can be smaller than 0 or be great than 1, with P values closer to 1 represents that the ORF is likely to be translated (i.e. the further away from 1 greater is the probability of finding untranslated ORFs).
• Regarding the selection of score cutoffs in claim 55, MPEP 2144.05 “Obviousness of Similar and Overlapping Ranges, Amounts, and Proportions (E9_R-01.2024)” affirms that one of ordinary skill in the art would be motivated to optimize a parameter when there is evidence in the record that the prior art recognized that. Thus the teachings by Ji would motivate one of ordinary skill in the art to modify the RibORF score cutoff to improve the quality of and optimize the data being identified by the method because Ji teaches that users can select the cutoff P-value to define translated or untranslated ORFs (pg. 9 para. 2).
Claim 56 recites:
wherein conducting hierarchical ORF prediction comprises: (i) determining trinucleotide codon periodicity; (ii) correcting offsets based on sequencing read length; (iii) combining sequencing reads; (iv) selecting for reads with NTG start codons; (v) selecting for reads with TAA/TGA/TAG stop codons; and/or (vi) annotating reads.
• Olexiouk does not teach the recitation above. However, Ji teaches it as output files generated by the algorithm containing fractions of reads in each nucleotide of codons (1st, 2nd, and 3rd) with 3-nt periodicity, with a high percentage of reads in 1st nucleotides of codons (pg. 6 para. 4).
Rationale for combining (MPEP §2142-2143)
Regarding claims 18, 49-53 and 55-56, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Olexiouk in view of Ji and Lu because all references disclose methods for combining sequencing techniques and prediction algorithms for the identification of neoantigens. The motivation would have been to:
• facilitate the antigen repertoire analysis of tumor reactive T cells, and lead to the development of new adoptive cell therapies with purified T cells that recognize candidate-mutated antigens derived from genes essential for the carcinogenesis (pg. 3401 Abstract Lu);
• use ribosome profiling and computational tools for pervasive translation in putative ‘noncoding’ regions, such as 5 UTRs (untranslated regions) (pg. 1 para. 1 Ji).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the combined sequencing techniques and prediction algorithms for the identification of neoantigens of Olexiouk to the methods by Ji and Lu because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for combining sequencing techniques and prediction algorithms for the identification of neoantigens.
B. Claim 41 is rejected under 35 U.S.C. 103(a) as being unpatentable over Olexiouk, Ji and Lu as applied to claim 18 above further in view of Hu "A cloning and expression system to probe T-cell receptor specificity and assess functional avidity to neoantigens." Blood, The Journal of the American Society of Hematology 132(18):1911-1921 (2018),, as cited on the 12/31/2025 Form PTO-892.
Claim 41 recites:
further comprising expanding a population of T cells obtained from the sample that are specific for one or more of the identified neoantigens
• Neither Olexiouk or Ji or Lu teach the recitation above. However, Hu teaches it as a system to identify viral antigen-specific TCRs (pg. 1911 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claim 41, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Olexiouk, Ji and Lu in view of Hu because all references disclose methods for investigating neoantigens. The motivation would have been to:
• select the identified subject-specific TCR pairs that bind specific neoepitopes to guide the design of effective T-cell-based immunotherapies (pg. 1911 para. 1 Hu).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the investigation of neoantigens of Olexiouk, Ji and Lu to the methods by Hu because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for investigating neoantigens.
C. Claims 48 is rejected under 35 U.S.C. 103(a) as being unpatentable over Olexiouk, Ji and Lu as applied to claim 18 above further in view of Tang ("neoantigenR: an annotation based pipeline for tumor neoantigen identification from sequencing data." bioRxiv 171843 (2017)), as cited on the 12/31/2025 Form PTO-892. amendment.
Claim 48 recites:
wherein step (c) searching the nuORF database further comprises comparing the identified neoantigens to an annotated proteome database to identify a set of unannotated identified neoantigens from among the identified neoantigens
• Neither Olexiouk or Ji teach the recitation above. However, Tang teaches it as a method that relies on canonical protein sequence database for neoepitope discovery; wherein after performing the NCBI protein search to remove annotated neoepitopes, unannotated and novel neoepitopes were used as candidates for subsequent neoantigen identification (pg. 7 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 48, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Olexiouk, Ji and Lu in view of Tang because all references disclose methods for neoantigen identification from sequencing data. The motivation would have been to:
• directly screen for neoantigens in the future allowing the evaluation of tumor-specific epitopes in a wide range of sequencing technologies (pg. 8 para. 2 Tang).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the neoantigen identification from sequencing data of Olexiouk, Ji and Lu to the methods by Tang because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for neoantigen identification from sequencing data.
D. Claim 54 is rejected under 35 U.S.C. 103(a) as being unpatentable over Olexiouk, Ji and Lu as applied to claim 18 above further in view of Erhard "Improved Ribo-seq enables identification of cryptic translation events." Nature methods 15(5):363-366 (2018), as cited on the 12/31/2025 Form PTO-892.
Claim 54 recites:
wherein the computational algorithm is PRICE
• Neither Olexiouk or Ji teach the recitation above. However, Erhard teaches as PRICE - a computational method that models experimental noise to enable researchers to accurately resolve overlapping short open reading frames and noncanonical translation initiation and predict short open reading frames-derived peptides efficiently enter the MHC I presentation pathway (pg. 363 col. 1 para. 1).
Rationale for combining (MPEP §2142-2143)
Regarding claim 54, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Olexiouk, Ji and Lu in view of Erhard because all references disclose methods for analyzing Ribo-seq data. The motivation would have been to:
• incorporate probabilistic inference of codon activities by an EM algorithm (PRICE) to model the stochastic processes involved in Ribo-seq (pg. 363 col. 2 para. 3 Erhard).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the method for analyzing Ribo-seq data of Olexiouk, Ji and Lu to the methods by Erhard because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for analyzing Ribo-seq data.
E. Claims 57-59 are rejected under 35 U.S.C. 103(a) as being unpatentable over Olexiouk "Identification of small novel coding sequences, a proteogenomics endeavor." Proteogenomics 49-64 (2016) as cited on the 12/31/2025 Form PTO-892) in view of Ji "RibORF: Identifying genome‐wide translated open reading frames using ribosome profiling." Current protocols in molecular biology 124(1):e67 (2018) as cited on the 12/31/2025 Form PTO-892) in view of Farkas (Transcriptome analyses of the human retina identify unprecedented transcript diversity and 3.5 Mb of novel transcribed sequence via significant alternative splicing and novel genes. BMC genomics 14.1:486 (2013) – newly cited). Claim 57 is additionally evidenced by Lu "Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions." Clinical Cancer Research 20(13):3401-3410 (2014).
Claim 57 recites:
A method for identifying tumor neoantigens, comprising:
(a) performing Ribosomal profiling (Ribo-seq) on a tumor sample and a non-tumor sample thereby generating Ribo-seg data
• Olexiouk teaches it as an integrative analyses of various types of omics data using ribosome profiling (Ribo-Seq) to contribute to cancer research and personalized medicine to humans (pg. v para. 3); wherein ribosome profiling is capable of capturing sample specific genomic variations, enabling focused neo-antigen identification (pg. 49 para. 1).
(b) conducting hierarchical ORF prediction on the Ribo-seq data generated in (a), thereby generating a novel untranslated open reading frame (nuORF) database comprising predicted tumor nuORFs
• Olexiouk teaches "generating a novel untranslated open reading frame (nuORF) database comprising predicted nuORFs" or "predicted tumor nuORFs" as ORFs in 5’ untranslated regions being identified with higher ribosome protected fragments intensity than the main canonical downstream translated ORF (pg. 55 col. 1 para. 1); wherein a database was created containing alterative translation initiation sites and their associated ORF identified by Ribo-Seq (pg. 55 col. 1 para. 1); wherein the identification of ribosome bound RNA's allows the creating of a custom search database (pg. 7 col. 2 para. 2); wherein a neoantigen identification workflow that includes tumor cells being sequenced to identify genomic variations specific in tumor cells, with a database is generated consisting of neo-antigen candidates, and in silico algorithms can be used to predict MHC antigen presentation, resulting in a more confident dataset (pg. 53 Fig. 4.1).
• Olexiouk does not teach "conducting hierarchical ORF prediction on the Ribo-seq data.” However, Ji teaches it as a computational pipeline named RibORF to systematically identify translated open reading frames (pg. 1 para. 1); wherein predicted translated P-values representing the binary classification of candidate ORFs (i.e. hierarchical ORF prediction); wherein values can be smaller than 0 or be great than 1, with P values closer to 1 represents that the ORF is likely to be translated (i.e. the further away from 1 greater is the probability of finding untranslated ORFs); wherein users can select the cutoff P-value to define translated or untranslated ORFs (pg. 9 para. 2).
(i) predicting ORFs at a root level using pooled sequencing reads from the Ribo- seq data generated in step (a) and predicting ORFs at leaf and branch nodes using sample-specific sequencing reads, and
(ii) identifying predicted tumor nuORFs that are predicted only at a leaf or branch node but not at the root level, and including the identified predicted tumor nuORFs in the nuORF database
• Olexiouk does not teach the recitation above. However, Farkas teaches it as a RNA capture method to perform large-scale validation of novel transcriptome features that maintain an open reading frame (pg. 1 Abstract); wherein custom scripts were developed to determine the open reading frames of novel features within annotated transcripts (pg. 11 col. 2 para. 2); wherein the predicted novel exons are located in UTRs and maintain an ORF (pg. 4 col. 2 para. 3); wherein RNA-seq library preparation and sequencing screened all samples (i.e. root level) (pg. 10 col. 2 para. 3) and by tissues categories (i.e. branches level) (pg. 10 col. 1 para. 3) and individual capture analyses based on the specific set of novel events from each sample (i.e. sample specific – leaves level – here it is implicit that the analysis is not done at root level since said analysis is sample specific – therefore at the leaves level) (pg. 5 col. 2 para. 2); wherein a list of the novel captured events and their corresponding rate of enrichment was created (i.e. including the identified prediction to a database) (pg. 6 col. 1 para. 1). The described root/branch/leave interpretation is supported by this instant specification as per [0055] in the disclosure.
(c) searching the nuORF database for predicted tumor nuORF matches with a MHC I immunopeptidome dataset, thereby identifying the matched predicted tumor nuORFs as tumor neoantigens.
• Olexiouk teaches it as a neoantigen identification workflow that includes tumor cells being sequenced to identify genomic variations specific in tumor cells, with a database is generated consisting of neo-antigen candidates, and in silico algorithms can be used to predict MHC antigen presentation, resulting in a more confident dataset (pg. 53 Fig. 4.1) by filtering the database of neo-antigen candidates (pg. 52 col. 2 para. 2) using a technique of library screening (pg. 3401 para. 4 Lu) and in-silico analysis identify candidate T-cell epitopes that were predicted to bind to the MHC class I molecules (pg. 3402 col. 1 para. 1 Lu) as evidenced by Lu.
Claim 59 recites:
wherein the tumor sample and the non-tumor sample are obtained from the same patient, thereby making the identified tumor neoantigens patient-specific tumor neoantigens
• Olexiouk teaches as a neoantigen identification workflow that includes tumor cells being sequenced to identify genomic variations specific in tumor cells (i.e. reading on identified tumor neoantigens patient-specific tumor neoantigens as in claim 59), with a database is generated consisting of neo-antigen candidates, and in silico algorithms can be used to predict MHC antigen presentation, resulting in a more confident dataset (pg. 53 Fig. 4.1) by filtering the database of neo-antigen candidates (pg. 52 col. 2 para. 2) using a technique of library screening (pg. 3401 para. 4 Lu) and in-silico analysis identify candidate T-cell epitopes that were predicted to bind to the MHC class I molecules (pg. 3402 col. 1 para. 1 Lu) as evidenced by Lu.
Rationale for combining (MPEP §2142-2143)
Regarding claims 57-59, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Olexiouk in view of Lu and Ji because all references disclose methods for combining sequencing techniques and prediction algorithms for the identification of neoantigens. The motivation would have been to:
• facilitate the antigen repertoire analysis of tumor reactive T cells, and lead to the development of new adoptive cell therapies with purified T cells that recognize candidate-mutated antigens derived from genes essential for the carcinogenesis (pg. 3401 Abstract Lu);
• use ribosome profiling and computational tools for pervasive translation in putative ‘noncoding’ regions, such as 5 UTRs (untranslated regions) (pg. 1 para. 1 Ji).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the combined sequencing techniques and prediction algorithms for the identification of neoantigens of Olexiouk to the methods by Lu and Ji because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for combining sequencing techniques and prediction algorithms for the identification of neoantigens.
F. Claims 58 is rejected under 35 U.S.C. 103(a) as being unpatentable over Olexiouk, Ji and Farkas as applied to claim 57 above further in view of Tang ("neoantigenR: an annotation based pipeline for tumor neoantigen identification from sequencing data." bioRxiv 171843 (2017)), as cited on the 12/31/2025 Form PTO-892. Claim 57 is additionally evidenced by Lu "Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions." Clinical Cancer Research 20(13):3401-3410 (2014).
Claim 58 recites:
wherein step (c) searching the nuORF database further comprises comparing the identified tumor neoantigens to an annotated proteome database to identify a set of unannotated identified tumor neoantigens from among the identified tumor neoantigens
• Neither Olexiouk or Ji teach the recitation above. However, Tang teaches it as a method that relies on canonical protein sequence database for neoepitope discovery; wherein after performing the NCBI protein search to remove annotated neoepitopes, unannotated and novel neoepitopes were used as candidates for subsequent neoantigen identification (pg. 7 para. 2).
Rationale for combining (MPEP §2142-2143)
Regarding claim 58, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Olexiouk, Ji and Farkas in view of Tang because all references disclose methods for neoantigen identification from sequencing data. The motivation would have been to:
• directly screen for neoantigens in the future allowing the evaluation of tumor-specific epitopes in a wide range of sequencing technologies (pg. 8 para. 2 Tang).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the neoantigen identification method from sequencing data of Olexiouk, Ji and Farkas to the methods by Tang because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to methods for neoantigen identification from sequencing data.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 06/26/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts starting in pg. 12 para. 3:
Olexiouk treats neoantigen identification and micropeptide discovery as distinct analytical pathways. It does not teach using Ribo-seq derived ORFs as input for immunopeptidome matching or integrating hierarchical ORF prediction with MHC I datasets. A person of ordinary skill in the art would not have been motivated to combine these elements in the claimed manner … Ji's focus is limited to detecting translation events. It does not address neoantigen discovery, immunopeptidomics, or immune recognition. While Ji discloses RibORF, it does so in isolation and provides no suggestion to incorporate this tool into a neoantigen identification workflow, much less the claimed integration with an MHC I immunopeptidome dataset. Ji therefore provides, at most, an isolated analytical tool … However, Ji's teaching "to systematically identify translated open reading frames" (Office Action at p. 20) provides no specific direction to as to how Ji is to be combined with Olexiouk and does not provide the proper motivation to modify Olexiouk' s alleged teaching to arrive at Applicants' claimed invention. The Office Action does not explain whether or how Olexiouk's alleged teaching would require modification to incorporate Ji. Instead, the rejection relies on an unsupported assumption that the cited combination discloses or renders obvious the claimed invention.
It is respectfully submitted that this is not persuasive because "One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references" (MPEP 2145 § IV). This argument is unpersuasive, because it analyzes the teachings of the references separately and independently, whereas the rejection is based on the combined teachings of the references. While none of the references teach all claim limitations, and the examiner does not dispute Applicant’s identification of material missing from each one, all the claim limitations are taught by the combination of references, as explained in the previous office action and examined in detail in this instant office action.
Regarding the argued “using Ribo-seq derived ORFs as input for immunopeptidome matching or integrating hierarchical ORF prediction with MHC I dataset,” Olexiouk teaches it as a neoantigen identification workflow that includes tumor cells being sequenced to identify genomic variations specific in tumor cells, with a database is generated consisting of neo-antigen candidates, and in silico algorithms can be used to predict MHC antigen presentation, resulting in a more confident dataset (pg. 53 Fig. 4.1) by filtering the database of neo-antigen candidates (pg. 52 col. 2 para. 2) using RNA-seq techniques for the detection of peptides candidates (pg. 51 col. 2 para. 2) and using a technique of library screening (pg. 3401 para. 4 Lu) and in-silico analysis identify candidate T-cell epitopes that were predicted to bind to the MHC class I molecules (pg. 3402 col. 1 para. 1 Lu) as evidenced by Lu.
Regarding the argued incorporation of Ji’s teachings to Olexiouk's, it would have been obvious to one of ordinary skill in the art to substitute the combined sequencing techniques and prediction algorithms for the identification of neoantigens of Olexiouk to the methods by Ji and Lu because such a substitution is no more than the simple substitution of one known element for another because Ji uses ribosome profiling and computational tools for pervasive translation in putative ‘noncoding’ regions, such as 5 UTRs (untranslated regions) (pg. 1 para. 1 Ji). Ji does not “provide, at most, an isolated analytical tool” because Ji teaches a computational tool used in field to analyze ribosome profiling dataset to generate candidate ORF files and reveal pervasive translation in putative untranslated regions (pg. 2 para. 2)
Regarding the arguments that there is no specific direction on how to combine the teachings, obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The motivation that would lead one of ordinary skill to combine the teachings from each reference is described in detail in the Claim Rejection above. The detailed explanation of subject matter suggested by a prior art reference is entirely acceptable when establishing a rationale for obviousness: "to support the conclusion that the claimed invention is directed to obvious subject matter, either the references must expressly or impliedly suggest the claimed invention or the examiner must present a convincing line of reasoning as to why the artisan would have found the claimed invention to have been obvious in light of the teachings of the references" (Ex parte Clapp, 227 USPQ 972 at 973 (BPAI 1985)); "Office personnel may also take into account 'the inferences and creative steps that a person of ordinary skill in the art would employ' (KSR v. Teleflex Inc., 82 USPQ2d 1385 at 1396 (SC 2007))" (MPEP 2141 § 11.C); "the rationale to support a rejection under 35 U.S.C. 103 may rely on logic and sound scientific principle" (MPEP 2144.02). The examiner's explanation of how the combination of teachings read over the claimed invention presents a convincing line of reasoning, takes into account and explains the inferences and creative steps by a person of ordinary skill in the art, and relies on logic and sound scientific principle.
Applicant asserts starting in pg. 13 para. 3:
The MPEP states that a "reference is analogous art to the claimed invention if: (1) the reference is from the same field of endeavor as the claimed invention ( even if it addresses a different problem); or (2) the reference is reasonably pertinent to the problem faced by the inventor (even if it is not in the same field of endeavor as the claimed invention)". (MPEP 2141.0l(a)). The Office's articulated motivation states that the references "pertain to" which is synonymous with "the reference is from" and then simply recites an allegedly shared field of endeavor between Olexiouk and Ji, namely, "methods for studying non-canonical translation events in biological functional roles". (Office Action at p. 21). Even accepting, arguendo, the Office's characterization of the references as pertaining to similar methods, this assertion does not, by itself, provide the required articulated reasoning with rational underpinning to support a motivation to combine or a reasonable expectation of success. Rather, the Office has not explained why a person of ordinary skill would have modified or supplemented Olexiouk with Ji in the particular manner necessary to arrive at the claimed invention.
It is respectfully submitted that this is not persuasive because the correct focus of the analogous art test is not whether the prior art references are analogous to each other, but whether the references are analogous to the claimed subject matter. In re Kahn, 441 F.3d 977, 986-87 (Fed. Cir. 2006). Olexiouk, Ji and the additional references provided in this examination all apply methods for the investigation of novel sequencing data with the aim to impact various biological processes; hence an analogous field of study or methods applied by the claimed invention.
Applicant asserts starting in pg. 14 para. 3:
The cited references do not teach or suggest (i) generating such a nuORF database via hierarchical ORF prediction from Ribo-seq data, nor (ii) using such an enriched database in a matching step with an immunopeptidome dataset to identify neoantigens. Nor do they recognize that inclusion of overlapping ORFs would improve peptide identification in this context. Therefore, the claimed combination provides a specific, technical improvement in neoantigen identification workflows and is not rendered obvious by the cited art.
It is respectfully submitted that this is not persuasive because the applicant offers no counter-evidence or explanation against the examiner's interpretation of the art. Instead, simply contradict the examiner's findings for the argued features of the claimed invention. Either way, in this instant application, the art has been correctly applied to address each claim, in which the recited limitations are all addressed, see Claim Rejections above. Regarding the arguments above, an immunopeptidome dataset reads on a set of peptides presented on a cell’s surface by Major Histocompatibility Complex. Olexiouk teaches a neoantigen identification workflow that includes tumor cells being sequenced to identify genomic variations specific in tumor cells, with a database of neo-antigen candidates, and in silico algorithms can be used to predict MHC antigen presentation, resulting in a more confident dataset (pg. 53 Fig. 4.1) by filtering the database of neo-antigen candidates (pg. 52 col. 2 para. 2) using RNA-seq techniques for the detection of peptides candidates (pg. 51 col. 2 para. 2) and using a technique of library screening (pg. 3401 para. 4 Lu) and in-silico analysis identify candidate T-cell epitopes that were predicted to bind to the MHC class I molecules (pg. 3402 col. 1 para. 1 Lu) as evidenced by Lu. Additionally, Ji teaches it as a computational pipeline named RibORF to systematically identify translated open reading frames (pg. 1 para. 1); wherein predicted translated P-values representing the binary classification of candidate ORFs (i.e. hierarchical ORF prediction); wherein values can be smaller than 0 or be great than 1, with P values closer to 1 represents that the ORF is likely to be translated (i.e. the further away from 1 greater is the probability of finding untranslated ORFs); wherein users can select the cutoff P-value to define translated or untranslated ORFs (pg. 9 para. 2). "One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references" (MPEP 2145 § IV).
The argued “the claimed combination provides a specific, technical improvement in neoantigen identification workflows and is not rendered obvious by the cited art” is not a consideration done under art rejections. The fact that there is an improvement or not, does not negate the fact that every recitation is being taught by the combination of references. Due to the described reasons above, it is interpreted that the claims do not patentably distinguish the claimed invention from the teachings found in the prior art. Furthermore, in this instant application, the amendments support existing claim rejections, in which the recited limitations are all addressed, see Claim Rejections above.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANCINI A FONSECA LOPEZ whose telephone number is (571)270-0899. The examiner can normally be reached Monday - Friday 8AM - 5PM 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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
