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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/17/2025 has been entered. Claims 52-62 and 65-72 are pending and are examined.
Withdrawn claim rejections
Claim Rejections - 35 USC § 112
The rejection of claims 52-62 and 65-72 under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement is withdrawn. Applicant argued that Information which is well known in the art need not
be described in detail in the Specification and offered an overly broad interpretation of the claims. Applicant’s view is that “Information which is well known in the art need not
be described in detail in the Specification. See, e.g., Hybritech, Inc. v. Monoclonal Antibodies,Inc., 802 F.2d 1367, 1379-80, 231 USPQ 81, 90 (Fed. Cir. 1986). Further, the Examiner is also reminded, the Federal Court ruled "[w]here software constitutes part of a best mode of carrying out an invention, description of such a best mode is satisfied by a disclosure of the functions of the software ... flow charts or source code listings are not a requirement for adequately disclosing the functions of software." See, Fonar Corp. v. Gen. Elec. Co., 107 F.3d 1543, 1549, 41 USPQ2d 1801, 1805 (Fed. Cir. 1997).”
In view of this extremely broad interpretation, the written description rejection is withdrawn and a careful analysis of the claims in view of this angle is submitted below.
NEW claim rejections
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 55 and 56 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 55 recites the limitation "bioinformatics tool" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 52-62, 65, 66, and 68-72 are rejected under 35 U.S.C. 103 as being unpatentable over Flechtner et al. in view of Lam et al. (both cited previously) and in further view of Hacohen and Wu (U.S. Pub. No. 20200069783) and Hacohen et al. (WO2015095811).
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
The claims are drawn to a method of treating melanoma or breast cancer, comprising:
(a) isolating genetic material from a tumor tissue sample and/or blood sample collected from a patient with melanoma or breast cancer;
(b) sequencing the genetic material to obtain tumor sequencing data;
( c) analyzing the patient blood sample to obtain HLA typing data;
(d) inputting the tumor sequencing data and HLA typing data into bioinformatics
algorithms to receive an output of identified tumor-specific neoantigens, wherein the tumor specific neoantigens have a predicted immunogenicity and solubility:
(e) manufacturing an immunogenic composition based on the output of tumor-specific neoantigens, wherein the immunogenic composition comprises:
(I) a plurality of tumor-specific neoantigen long-peptides, wherein the tumor specific neoantigen long peptides comprise between about 15 to about 30 amino acids;
(II) a plurality of tumor-specific neoantigen short peptides, wherein the tumor specific neoantigen short peptides comprise between about 8 to about 14 amino acids; and
(III) an adjuvant consisting of polyinosinic and polycytidylic acid, stabilized with
poly-I-lysine and carboxymethylcellulose (poly ICLC);
(f) administering the immunogenic composition to a subject in need thereof an effective amount of the immunogenic composition, wherein the effective amount of the immunogenic composition is administered as an intramuscular injection;
(g) administering an immune checkpoint inhibitor; and
(h) administering a single dose adjuvant every week in between each administered dose of the immunogenic composition.
Further limitations refer to specific forms or stages of melanoma or breast cancer. The bioinformatics algorithms use a computational pipeline and machine learning models to predict immunogenicity of tumor specific neoantigens and the prediction of immunogenicity of tumor-specific neoantigens comprises ranking and selecting tumor-specific neoantigens. The prediction of immunogenicity of tumor-specific neoantigens comprises ranking and selecting tumor-specific neoantigens and the immunogenic composition comprises up to about 19 tumor-specific neoantigen long peptides and/or short peptides. The Specification does not indicate the nature of any bioinformatics algorithm used. The whole “description” of the method is an aspirational project to be effected as part of a Phase I clinical trial, with no actual experimental data presented.
The state of the art in very mature in the personalized cancer vaccines.
For instance Malone et al. (WO2020104539) teaches a method of ranking epitopes derived from neoantigens as targets for personalized immunotherapy by collecting
candidate epitopes based on patient data of a cancer patient. A set of scores are calculated for each of the candidate epitopes, each of the scores in a respective one of the sets for a respective one of the candidate epitopes representing an independent measure of a likelihood of the respective one of candidate epitopes to elicit an immune response in the cancer patient. The scores in each of the sets of scores are combined into a single score for each of the candidate epitopes. The single scores for the candidate epitopes in each case reflect an overall likelihood of eliciting the immune response in the patient. The candidate epitopes are ranked using the single scores for the immunotherapy (abstract). The method for prioritizing epitopes derived from neoantigens based on their likelihood to elicit an immune response, comprises the steps of:
1) Extracting experimentally-verified epitope properties;
2) Extracting domain knowledge about epitopes;
3) Embedding all epitopes in a vector space based on the experimentally-verified
properties;
4) Collecting a set of candidate epitopes;
5) Calculating a set of scores for each epitope which each give an independent measure of the epitope's likelihood to elicit an immune response;
6) Combining the set of scores for each epitope into a single score reflecting the overall
likelihood that the epitope elicits an immune response; and
7) Ranking the epitopes based both on their immune response likelihoods, embeddings,
and sequence diversity.
Steps 1)-3) can be performed offline and steps 4) -7) can be performed online.
The modular scoring approach according to embodiments of the present invention
advantageously allows to naturally incorporate epitope immunogenicity. To date, all
described neoantigen selection pipelines have only considered HLA binding as the "end
point" for selecting neoantigens ([0070]-[0072]).
To determine the patient HLA genotype, whole exome sequencing reads are trimmed and aligned against the IMGT/HLA database using RazerS3. HLA class I alleles are identified using OptiType. The tumor RNA-seq reads are trimmed after filtering for low quality reads using Flexbar, and ribosomal RNA reads are filtered out using bowtie2. After quality control, they are aligned against the IMGT/HLA database using bowtie. HLA class II alleles are identified using seq2HLA ([0082]). Further in step S3, immunogenicity scoring and ranking of candidate epitopes takes place. Relevance of candidate epitopes for the design of a vaccine are scored and ranked using a series of biological and biochemical factors driving their relevance as tumor specific immune targets. These factors include binding affinity to the patient HLA, similarity to epitopes known to be immunogenic, level of expression at transcriptional level, frequency of
the mutation, homology to normal human sequences, homology to viral protein, and
likelihood that a given sequence will be processed by the intracellular machinery for
presentation. These factors are taken in account in the scoring by the calculation of several evidence components, as in the above embodiments, which define an index reflecting each of these factors. Evidence components are used to derive an overall score and rank for each candidate epitope. Evidence components can be generally the same for both class-I and class-II epitopes, although specific differences are pointed out when relevant below. The evidence components are computer processing components specially configured to receive their respective inputs, preferably from memory or databases, and output a respective score ([0084]). To determine similarity to epitopes of known immunogenicity, an evidence component uses deep convolutional neural networks (CNN) to score a candidate epitope likelihood to elicit a T-cell response in in vitro immunogenicity assays. Rather than learning an arbitrary embedding of each amino acid, instead known biochemical properties ( e.g., polarity and hydrophobicity) are used, as well as evolutionary features (BLOSUM62 mutation values). These models are trained using public CD4 or CD8 immune response data from the immune epitope database (IEDB) ([0087]).
The art, at the time that the invention was filed , was aware of similar methods of
personal vaccination for various types of cancers. For instance, methods of identifying
neoantigens were known to be performed by Ribosomal profiling (Ribo-seq) on a sample or set of samples; generating a novel untranslated open reading frame (nuORF) database comprising predicted nuORFs by conducting hierarchical ORF prediction on the Ribo-seq data generated; generating a final set of neoantigens by searching the nuORF database for predicted nuORFs in the nuORF database matching data in a MHC I immunopeptidome data set, the identified presented nuORFs comprising the final neoantigen set. The method may further comprise searching an annotated proteome database for ORFs in the annotated proteome database matching data in the MHC I immunopeptidome dataset. The method may also further comprise selecting presented nuORFs identified in the nuORF database but not the annotated proteome database to generate the final set of neoantigens. In an aspect, the MHC I immunopeptidome data is obtained on biological sampled from a subject to be treated. (Ouspenskaia et al., WO2020131586, [0011 ]- cited previously).
Similarly, Samuels et al. (WO2020/234875- cited previously) discloses a method of selecting a recurrent HLA-presented neoantigen which can be targeted in a cancer-immunotherapy treatment, the method comprising:
(a) analyzing the frequency of occurrence of a cancer-associated mutated protein
in the context of an individual HLA allele in a plurality of cancer patients; and
(b) determining the binding affinity of peptides of 8-14 amino acids in length derived
from the cancer-associated mutated protein to the individual HLA allele, wherein the
peptides comprise a mutation compared to the wild-type protein, wherein a candidate
peptide which binds with an affinity above a first predetermined level to an HLA allele
having a frequency of occurrence above a second predetermined level, is selected as an HLA-presented neoantigen that can be targeted in a cancer-immunotherapy treatment (p. 3-4).
Thus this area of therapy research was very active at the time that the invention
was filed.
Flechtner et al. teaches methods and compositions for identifying tumor antigens of human lymphocytes, and for identifying subjects for cancer therapy. The method comprises administering to the subject an immunogenic composition comprising one or more selected stimulatory antigens (e.g., one or more stimulatory antigens described herein) or immunogenic fragments thereof, wherein the immunogenic composition is administered according to a dosing regimen comprising an initial dose of the immunogenic composition and additional doses of the immunogenic composition, wherein after an initial dose is administered, an additional dose is administered 3 weeks following the initial dose, an additional dose is administered 6 weeks following the initial dose, an additional dose is administered 12 weeks following the initial dose, and an additional dose is administered 24 weeks following the initial dose (abstract).
Regarding neoantigens, the reference indicates that they are mutated protein
fragments that are presented by the peptide human leukocyte antigen (pHLA) complexes on the cell surface and recognizable by the immune system. Neoantigens may induce reactive T cells that can mediate the killing of cancer cells by the host immune system. Neoantigens represent dominant targets in tumor-infiltrating lymphocyte populations in patients benefiting from adoptive T cell therapy, and a neoantigen specific T cell population is sufficient to induce tumor regression in mouse and man. That is, by administering a population of peptides/neoantigens one can induce a T cell response conducive to treatment of the tumor. The widespread detection of spontaneously occurring neoantigen-specific T cells demonstrates that processing and presentation of multiple neoantigens on tumors occurs despite the current insensitivity of biochemical detection. Checkpoint blockade therapy has revealed new and amplified neoantigen specific T cell responses which are central to disease control. Memory cytotoxic T lymphocyte responses to mutated antigens are generated in patients with unexpected long-term survival or those who have undergone effective immunotherapy. Further, a neoantigen-specific CD4+ T cell product caused regression of a metastatic cholangiocarcinoma (p.23-24).
Tumor specific antigens (neoantigens) are known in the art and gene sequences
encoding polypeptides that are potential or putative neoantigens are determined by
sequencing the genome and/or exome of tumor tissue and healthy tissue from a subject
having cancer using next generation sequencing technologies. In some embodiments,
genes that are selected based on their frequency of mutation and ability to encode a
potential or putative neoantigen are sequenced using next-generation sequencing
technology. Another method for identifying potential or putative neoantigens is direct
protein sequencing. Protein sequencing of enzymatic digests using multidimensional MS techniques (MSn) including tandem mass spectrometry (MS/MS)) can also be used to identify neoantigens. Such proteomic approaches can be used for rapid, highly automated analysis. High-throughput methods for de novo sequencing of unknown proteins can also be used to analyze the proteome of a subject's tumor to identify expressed potential or putative neoantigens. For example, meta shotgun protein sequencing may be used to identify expressed potential or putative neoantigens. Potential or putative neoantigens may also be identified using MHC multimers to
identify neoantigen-specific T cell responses. For example, high-throughput analysis of
neoantigen-specific T cell responses in patient samples may be performed using MHC
tetramer based screening techniques ([0016]-[0017]). The immunogenic compositions
described may include an adjuvant. Adjuvants can be used as vaccine delivery systems
and/or for their immunostimulatory properties. Vaccine delivery systems are often
particulate formulations, e.g., emulsions, microparticles, immune stimulating complexes
(ISCOMs), which may be, for example, particles and/or matrices, and liposomes.
lmmunostimulatory adjuvants include ISCOMS or may be derived from pathogens and
can represent pathogen associated molecular patterns (PAMP), e.g., lipopolysaccharides (LPS), monophosphoryl lipid (MPL), or CpG-containing DNA, which
activate cells of the innate immune system. An exemplary adjuvant is Poly-lCLC (0159]). The immunogenic compositions can be prepared as formulations suitable for route of administration. Formulations suitable for parenteral administration, such as, for
example, by intraarticular (in the joints), intravenous, intramuscular, intradermal,
intraperitoneal, intranasal, and subcutaneous routes, include aqueous and non-aqueous, isotonic sterile injection solutions, which can contain antioxidants, buffers, bacteriostats, and solutes that render the formulation isotonic with the blood of the intended recipient, and aqueous and non-aqueous sterile suspensions that can include suspending agents, solubilizers, thickening agents, stabilizers, and preservatives. The formulations can be presented in unit-dose or multi-dose sealed containers, such as ampoules and vials ([0158]). The immunogenic composition is administered to a subject according to a dosing regimen or dosing schedule. The amount of antigen in each immunogenic composition dose (e.g., a vaccine, vaccine formulation and/or pharmaceutical composition) is selected to be a therapeutically effective amount, which induces a prophylactic or therapeutic response, in either a single dose or over multiple doses. A single dose will comprise about 100 to about 1500 μg total peptide. The total volume of a single dose is 0.5 ml to 1.0 ml. A single dose may comprise more than one antigen, for example, 2, 3, 4, 5 or more. A dosing regimen comprises an initial dose of an immunogenic composition and at least one additional dose of the immunogenic
composition. After an initial dose is administered, an additional dose is administered
about 3, 6, 12 or 24 weeks following the initial dose. The dosing regimen may comprise
administration of different immunogenic compositions, e.g., 2, 3, 4, 5, 6, 7, 8, or more
different immunogenic compositions comprising antigens. A dosing regimen can include
an initial dose of 2, 3, 4, 5, 6, 7, 8, or more different immunogenic compositions, and at
least one additional dose of the 2, 3, 4, 5, 6, 7, 8, or more different immunogenic
compositions. An immunogenic composition may comprise one antigen or 2, 3, 4, 5, 6, 7, 8, 9, 10, or more antigens ([0177]-[0180]).
Specifically, the reference discloses GEN-009, a personalized neoantigen vaccine for solid tumors. A system called ATLAS (Antigen Lead Acquisition System), is used to identify neoantigens in each patient's tumor that are recognized by their CD4+ and/or CD8+ T cells. ATLAS-identified neoantigens that are recognized by their CD4+ and/or CD8+ cells, and are shown to be stimulatory antigens, are incorporated into a patient's personalized vaccine in the form of synthetic long peptides (SLPs). A personalized vaccine, consisting of 4 to 20 SLPs, is generated for each patient. The SLPs are divided into 4 pools, with each pool containing 1 to 5 SLPs. The 4 pools are administered subcutaneously (SC) in each of the patient's limbs. Collectively, these pools of SLPs are the GEN 009 drug product. Each pool of GEN 009 drug product consists of 100 to 1500 μg total peptide administered with 0.45 mg poly ICLC adjuvant per injection ([0209]). GEN 009 dosing is initiated in combination with the PD-1 inhibitor which, for cutaneous melanoma is nivolumab ([0217]).
The reference does not specific the length of the "long peptides" or specifically, the solubility aspect.
Hacohen and Wu (U.S. Pub. No. 20200069783) teach a method of identifying tumor specific neoantigens that alone or in combination with other tumor-associated peptides serve as active pharmaceutical ingredients of vaccine compositions which stimulate anti-tumor responses (abstract). The reference claims a method comprising:
(a) identifying a plurality of nucleic acid sequences from nucleic acid sequences from cancer cells of a subject that are unique to the cancer cells and that do not include nucleic acid sequences from non-cancer cells of the subject, wherein the identified plurality of nucleic acid sequences encode two or more different peptide sequences, wherein each of the two or more different peptide sequences are expressed by the cancer cells and comprise a cancer specific mutation; (b) predicting which epitopes of the two or more different peptide sequences form a complex with an expressed protein encoded by an HLA allele of the subject by an HLA peptide binding analysis; and (c) selecting at least two epitopes predicted in (b) based on the HLA peptide binding analysis. The method further comprises (a) producing at least two cancer neoantigen peptides comprising the at least two epitopes selected in (c), wherein producing the at least two cancer neoantigen peptides comprises expressing or synthesizing the at least two cancer neoantigen peptides; or (b) producing one or more polynucleotides encoding the at least two cancer neoantigen peptides. A pharmaceutical composition comprising the at least two cancer neoantigen peptides is formulated in conjunction with an adjuvant. Each of the at least two cancer neoantigen peptides is present in the pharmaceutical composition at an amount of from 50 μg to 1.5 mg and the composition is administered to the subject the pharmaceutical composition to the subject. Each of the epitopes predicted to form a complex with an expressed protein encoded by a class I HLA allele of the subject in (b) has a length of from 8 to 12 amino acids, and (ii) each of the epitopes predicted to form a complex with an expressed protein encoded by a class II HLA allele of the subject in (b) has a length of from 15-24 amino acids. Each epitope of the at least two epitopes \ comprises a point mutation and is predicted to bind to the protein encoded by an HLA allele of the subject with an IC.sub.50 less than 500 nM. The HLA peptide binding analysis comprises using a program implemented on computer system. The at least two epitopes selected comprises at most 20 epitopes (claims 1-20).
More specifically the vaccine used in the method is used for treatment of breast cancer melanoma (inter alia) ([0016]).
Hacohen et al. (WO2015095811) address one of the limitations of the instant claim, namely the solubility of the peptides used in cancer vaccines. Modifications of the neoantigenic peptides can affect the solubility, bioavailability and rate of metabolism of the peptides, thus providing control over the delivery of the active species. Solubility can be assessed by preparing the neoantigenic peptide and testing according to known methods well within the routine practitioner's skill in the art ([0174], [0404]). The reference also shows that whole exome DNA sequence of tumor and normal tissue samples from the patient is used to identify the specific coding-sequence mutations that have occurred in the tumor of that participant. These mutations include both single-amino acid missense mutations (the
predominant type of mutation) and novel open reading frames (neoORFs) varying in length from one up to hundreds of amino acids. A well-established algorithm (netMHCpan) is used to identify mutation-containing epitopes that are predicted to bind to the MHC class I molecules of each participant. From this list of candidate mutations, 20-40 mutations are selected and prioritized for peptide preparation based on a pre-defined set of criteria including:
Type of mutation (missense vs neoORF);
Predicted binding potential of peptides encoded by the mutated region to the MHC class I alleles of the particular individual;
Predicted binding potential of the corresponding native peptide;
The likelihood that the mutation is directly or indirectly related to the
tumorigenic phenotype (i.e. an ''oncogenic driver" mutation or a mutation in a
related biochemical pathway);
RNA expression;
Biochemical properties of the full peptide ( e.g. predicted poor solubility
secondary to hydrophobic amino acid number or distribution and/or cysteine
content) ([00497]).
GMP peptides are synthesized by standard solid phase synthetic peptide chemistry and purified by RP-HPLC. Each individual peptide is analyzed by a variety of qualified assays ([00499]). Synthesis are initiated with up to 25 peptides if possible so that additional peptides are immediately available for replacement of insoluble peptides if needed ([00498]-[00499]). The patients are to be immunized with as many peptides as possible, up to a maximum of 20. Peptides are mixed together in 4 pools of up to 5 peptides each. The selection criteria for each pool is based on the particular MHC allele to which the peptide is predicted to bind. Peptides predicted to bind to the same MHC allele is placed into separate pools whenever possible in order to limit antigenic competition. Some of the neoORF peptides may not be predicted to bind to any MHC allele of the patient These peptides can still be utilized however, primarily because they are completely novel and therefore not subject to the immune-dampening effects of central tolerance, thus having a high probability of being immunogenic. NeoORF peptides also cany a dramatically reduced potential for autoimmunity as there is no equivalent molecule in any normal cell. In addition, there can be false negatives arising from the prediction algorithm and it is possible that the peptide can contain an HLA class II epitope (HLA class II epitopes are not reliably predicted based on current algorithms) ([00500]). The amounts of each peptide are predicated on a final dose of 300 μg of each peptide per injection. The peptide pools are prepared by dissolving appropriate quantities of each peptide individually at high concentration (approximately 50 mg/ml) in dimethyl sulfoxide (HMSO) and dilution with 5% dextrose in water/ 5mM succinate to a final concentration of 2 mg/mL. Equal quantities of each of 5 peptides can then he admixed, effectively diluting each peptide to a concentration of 400 μg/ml.
The treatment is combined with a checkpoint blockade (ipimilumab or nivolumab) (([00386], [00431]).
Lam et al. (nota bene, the senior author of this reference is the same as the
Applicant for the W02020231408- cited supra) indicates that the length of the SLPs is 27 amino acids (p. 710). The GEN-009 does not feature a mix of neoantigen peptides of different lengths. However the present applications fails to provide experimental support and disclosure for any technical effect which may result from the use of neoantigens of different lengths.
Therefore, it would have been obvious for a person of ordinary skill in the art at the time that the invention was filed to have used the teachings of Flechtner et al. combined with Lam et al., Hacohen, and Hacohen and Wu to reach a method of treatment of melanoma or breast cancer with a reasonable expectation of success. This is because the skill in the art is very high and the claims are very broad in scope. Thus, a person of ordinary skill in the art is always motivated to pursue the known options within her or his technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Claim 67 is rejected under 35 U.S.C. 103 as being unpatentable over Flechtner et al. in view of Lam et al. (both cited previously), Hacohen and Wu (U.S. Pub. No. 20200069783) and Hacohen et al. (WO2015095811) and in further view of Franke et al. (cited in the previous Office actions).
The teachings of the over Flechtner et al., Lam et al., Hacohen and Wu and Hacohen et al. were presented above and they were silent about using the PADRE epitope as a CD4 helper peptide.
However, this was a long known and used technique to boost antibody responses in vivo. Franke et al. disclosed that the Pan-DR epitope (PADRE) peptides have demonstrated the capacity to deliver significant helper T-cell activity in vivo (abstract).
it would have been obvious for a person of ordinary skill in the art at the time that the invention was filed to have used the teachings of Flechtner et al. combined with Lam et al., Hacohen, and Hacohen and Wu together with Franke et al.to reach a method of treatment of melanoma or breast cancer with a reasonable expectation of success. This is because, as indicated supra, the skill in the art is very high and the claims are very broad in scope. Thus, a person of ordinary skill in the art is always motivated to pursue the known options within her or his technical grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELLY GERALD STOICA whose telephone number is (571)272-9941. The examiner can normally be reached M-F 8-5 EST.
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, Joanne Hama can be reached at 571-272-2911. 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.
ELLY-GERALD STOICA
Primary Examiner
Art Unit 1647
/Elly-Gerald Stoica/ Primary Examiner, Art Unit 1647