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 .
Status of the Claims
Claims 1-20 are examined.
Priority
This US Application 17/988,043 (11/16/2022) is a CON of PCT/US2021/036439 (06/08/2021) which claims priority from US Provisional Application 63/036,637 (06/09/2020); as reflected in the filing receipt mailed on 12/01/2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-20 is 06/09/2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/05/2023 considered by the examiner.
Specification
The disclosure is objected to because pg. 31 line 3 contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Objections
Claim 15 is objected to because of the following informalities: the recited “The claim of claim 14” should read “The method of claim 14”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC § 101 because the claimed inventions are directed to an abstract idea without significantly more. "Claims directed to nothing more than abstract ideas (such as a mathematical formula or equation), 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)).
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below.
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)?
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 1-20), which falls within one of the categories of statutory subject matter. [Step 1: 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))?
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)).
Mathematical concepts recited in instant claims 1, 14 and 20, include the terms “providing a predictive model of phenotypic performance”, “metabolite fingerprint variable” and “utilizing the predictive model to predict the expected phenotypic performance”, which are mathematical concepts. The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one having ordinary skill in the art. Thus, the recited terms corresponds 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).
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 " selecting a test host cell for culture based" (claim 20). Under the BRI, the recited limitations are mental processes because a human mind is sufficiently capable of selecting a cell to be tested.
Dependent claims 2-3, 7, 15 and 19 recite further details about the “predictive model”; dependent claims 4-6, 8 and 16-18 recite further details about the “metabolite fingerprint variable”; not reciting any additional non-abstract elements; all reciting further aspects of the information being analyzed, the manner in which that analysis is performed. Hence, the claims explicitly recite numerous elements that, individually and in combination, constitute abstract ideas. The instant claims must therefore be examined further to determine whether they integrate that abstract idea into a practical application (MPEP 2106.04(d)). [Step 2A Prong One: Yes]
The instant claims recite a natural correlation by correlating the measurement of an amount of a protein naturally found in the body with the effect of an active ingredient of a medication. (see MPEP 2106.04(b).I).
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))?
Instant claims 1, 9-14 and 20 recite additional elements that are not abstract ideas: “computer-implemented method” (claims 1-19); “providing a first chemical spectra for a first host cell” (claims 1 and 14); “providing a plurality of test chemical spectra produced from mass spectroscopy analysis” (claim 20); “growing the first host cell in an industrial culture in growth media” (claim 14).
The recited “computer-implemented method” (claims 1-19); “providing a first chemical spectra for a first host cell” (claims 1 and 14); and “providing a plurality of test chemical spectra produced from mass spectroscopy analysis” (claim 20) are interpreted to require the use of a computer. The use of a computer is broadly interpreted and not actually described in the claims or specification. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. The recited steps read on receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321.
The recited “growing the first host cell in an industrial culture in growth media” (claim 14) is interpreted as mere apply it steps since the limitation only recites “to grow the host cell” but there is no indication that the predictive model is utilized in any way to affect that growth. Dependent claims 9-13 are merely further limiting where the chemical spectra data was obtained from; reading on mere data gathering activity because these are used to gather information that is used as input for the subsequent mathematical calculations (i.e. the predictive model).
The recited claims state nothing more than that a generic computer performs the functions that constitute the abstract idea (MPEP 2106.05(f)). Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claim does not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)). None of the dependent claims recite any additional non-abstract elements; they are all directed to further aspects of the information being analyzed, the manner in which that analysis is performed, or the mathematical operations performed on the information. [Step 2A Prong Two: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself. Step 2B of the 35 USC § 101 analysis determines whether the claims contain additional elements that amount to an inventive concept, and an inventive concept cannot be furnished by an abstract idea itself (MPEP 2106.05).
Claims 1, 9-14 and 20 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Claims directed to “providing” data (claims 1, 14 and 20) read on performing a standard computer task, which the courts have identified as a conventional computer function in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Dependent claims 9-13 merely limit the type of data being provided but do not change the nature of the step from a conventional computer data receiving step.
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). [Step 2B: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For these reasons, 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. Hence, the claimed invention does not constitute significantly more than the abstract idea, so instant claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 7-9, 14-16 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by over Povey et. al. "Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling." Journal of biotechnology 184:84-93 (2014) – referred to in the action as Povey
Independent claim 1 recites “a computer-implemented method for predicting phenotypic performance of a host cell, said method comprising: a) providing a first chemical spectra for a first host cell, said first chemical spectra having been produced from an analysis of mass spectroscopy of a first spent media from a culture of the first host cell; b) providing a predictive model of phenotypic performance, said model comprising a metabolite fingerprint variable, and a phenotypic performance variable: i) wherein the metabolite fingerprint variable is based on chemical spectra of a plurality of spent media, each spent media having been derived from a plurality of different host cells ; and ii) wherein the phenotypic performance variable is based on known phenotypic performance measurements associated with each of the plurality of different host cells of part (i); and c) utilizing the predictive model to predict the expected phenotypic performance of the first host cell by providing the first chemical spectra to the model”. Dependent claim 2 recites “wherein the predictive model is a partial least squares regression of the chemical spectra of the plurality of spent media and their associated known phenotypic”. Dependent claims 3 and 15 recite “wherein the predictive model is selected from the group consisting of partial least squares analysis (PLS), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares analysis (OPLS), or principal component analysis”. Dependent claims 4 and 16 recite “wherein the metabolite fingerprint variable comprises at least 5, 10, 25, 50, 75, 100, 150, 200, or 250 chemical spectra”. Dependent claims 7 and 19 recite “wherein the predicted phenotypic performance is production of a product of interest, said product of interest selected from the group consisting of: a small molecule, enzyme, protein, peptide, amino acid, organic acid, synthetic compound, fuel, alcohol, primary extracellular metabolite, secondary extracellular metabolite, intracellular component molecule, and combinations thereof”.
Povey teaches a Partial Least Squares Discriminant Analysis (PLS-DA) model to predict (i.e. predictive model) a cell line’s phenotypic characteristics using MALDI-ToF mass spectrometry fingerprinting method for mammalian cells (pg. 84 para. 1); via metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (pg. 84 para. 1); wherein preparation of cell pellets for spectrometry analysis was permed by resuspending cell pellets (pg. 86 col. 2 para. 1) for each of the 220 cell lines (i.e. plurality of spent media derived from the plurality of different host cells) (pg. 90 col. 2 para. 1); wherein data show that the approach can be used to predict the performance of recombinant cell lines expressing (i.e. phenotypic measurements) different monoclonal antibodies (i.e. reading on product being a protein) (pg. 92 col. 1 para. 1); wherein the approach is based around the development of a historical database of MALDI-ToF mass spectrometry spectra (i.e. chemical spectra) associated with subsequent productivity data at the 10 L scale to predict the performance of cell lines (pg. 91 col. 2 para. 3); wherein the PLS-DA model was generated using a training set comprising 29 cell lines from MALDI-ToF data (i.e. at least 5, 10, 25, 50, 75, 100, 150, 200, or 250 chemical spectra) (pg. 90 Fig. 5) and comprised 5 latent variables of the cell lines (i.e. fingerprinting variables) (pg. 89 col. 1 para. 2); anticipating claims 1-4, 7, 15-16 and 19.
Dependent claim 8 recites “wherein the metabolite fingerprint variable is based on the chemical spectra of a plurality of spent media from small lab-scale cultures, and wherein the phenotypic performance variable is based on the known phenotypic performance measurements of the plurality of different host cells in industrial cultures”. Dependent claim 9 recites “wherein the industrial cultures are at least 3 liter cultures, and wherein the small lab-scale cultures are less than 1000 microliter cultures”. Povey teaches the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data are used to predict the phenotype of mammalian cell lines at larger culture scale (i.e. industrial scale) using the PLS-DA model (pg. 84 para. 1); wherein PLS-DA was developed using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10 L bioreactor scale (i.e. small lab-scale cultures) (pg. 84 para. 1); anticipating claims 8-9.
Independent claim 14 recites “a computer-implemented method for predicting phenotypic performance of a host cell, said method comprising: a) providing a first chemical spectra for a first host cell, said first chemical spectra having been produced from an analysis of mass spectroscopy of a first spent media from a culture of the first host cell; b) providing a predictive model of phenotypic performance, said model comprising a metabolite fingerprint variable, and a phenotypic performance variable: i) wherein the metabolite fingerprint variable is based on chemical spectra of a plurality of spent media, each spent media having been derived from a plurality of different host cells; and ii) wherein the phenotypic performance variable is based on known phenotypic performance measurements associated with each of the plurality of different host cells of part (i); and c) utilizing the predictive model to predict the expected phenotypic performance of the first host cell by providing the first chemical spectra to the model; and d) growing the first host cell in an industrial culture in growth media wherein the industrial culture is at least a 02 liter culture; wherein first spent media of step (a) and the plurality of spent media of step (c)(i) were all derived from a lab-scale cultures of less than about 5 mL, and wherein the known phenotypic performance measurements of step (c)(ii) were obtained from industrial cultures of at least 0.25 L”. Povey teaches the recited steps (a) to (c) as described in claim 1; additionally Povey teaches the collection of cell cultures where the cell pellet was washed with 0.5 mL of PBS and centrifuged (i.e. less than 5 mL); wherein the growth and productivity of these cell lines were evaluated in a 10 L bioreactor model of Lonza’s large-scale (pg. 84 para. 1); anticipating claim 14.
Independent claim 20 recites “a method for selecting a host cell for industrial culture comprising the steps of: a) providing a plurality of test chemical spectra produced from mass spectroscopy analysis of spent media from lab-scale cultures of a plurality of test host cells; b) providing a predictive model of phenotypic performance, said model comprising a metabolite fingerprint variable, and a phenotypic performance variable: i) wherein the metabolite fingerprint variable comprises a ladder of chemical spectra, said ladder of chemical spectra having been produced from mass spectroscopy analysis of spent media from small lab-scale cultures of a plurality of host cells exhibiting a range of known phenotypic performance measurements in industrial culture; and ii) wherein the phenotypic performance variable comprises the known phenotypic performance measurement in industrial cultures associated with each the of chemical spectra of the ladder of chemical spectra of part (i); and c) utilizing the predictive model to predict the expected phenotypic performance of the test host cells in industrial culture by providing the test chemical spectra to the model; and d) selecting a test host cell for culture based, in part, on the predicted phenotypic performance of the test host cells in industrial culture”. Povey teaches a Partial Least Squares Discriminant Analysis (PLS-DA) model to predict (i.e. predictive model) a cell line’s phenotypic characteristics using MALDI-ToF mass spectrometry fingerprinting method for mammalian cells (pg. 84 para. 1); via metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (pg. 84 para. 1); wherein preparation of cell pellets for spectrometry analysis was permed by resuspending cell pellets (pg. 86 col. 2 para. 1) for each of the 220 cell lines (i.e. plurality of spent media derived from the plurality of different host cells) (pg. 90 col. 2 para. 1); wherein data show that the approach can be used to predict the performance of recombinant cell lines expressing (i.e. phenotypic measurements) different monoclonal antibodies (i.e. reading on product being a protein) (pg. 92 col. 1 para. 1); wherein the approach is based around the development of a historical database of MALDI-ToF mass spectrometry spectra (i.e. chemical spectra) associated with subsequent productivity data at the 10 L scale to predict the performance of cell lines (pg. 91 col. 2 para. 3); wherein the PLS-DA model was generated using a training set comprising 29 cell lines from MALDI-ToF data (i.e. at least 5, 10, 25, 50, 75, 100, 150, 200, or 250 chemical spectra) (pg. 90 Fig. 5) and comprised 5 latent variables of the cell lines (i.e. fingerprinting variables) (pg. 89 col. 1 para. 2); wherein following the initial development of the PLS-DA model using data to predict productivity of recombinant cell lines, an experiment was undertaken to both assess the ability of the existing model to predict the performance of recombinant cell lines expressing a different monoclonal antibody molecule (i.e. test host cell) and to increase the size of the training data set (pg. 89 col. 2 para. 2); anticipating claim 20.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 5, 12-13 and 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Povey as applied to claims 1 and 14 in the 102 rejection above further in view of Heinonen et. al. "Metabolite identification and molecular fingerprint prediction through machine learning." Bioinformatics 28(18):2333-2341 (2012) – referred to in the action as Heinonen.
Determination of the Scope and Content of the Prior Art
(MPEP §2141.01)
Dependent claims 5 and 18 recite “wherein the metabolite fingerprint variable and phenotypic performance variable comprise the chemical spectra and the known phenotypic performance measurements from spent media from host cell cultures that exhibit a range of phenotypic performance measurements, wherein the range of phenotypic performance measurements comprises at least a 10%, 20%, 30%, 40%, 50%, 60%,70%, 80%, or 90% relative difference between the lowest and highest known phenotypic performance measurements”. Dependent claim 12 recites “wherein the chemical spectra are based on positive ion mass spectroscopy”. Dependent claim 13 recites “wherein the chemical spectra are based on negative ion mass spectroscopy”.
Ascertainment of the Difference Between Scope the Prior Art and the Claims
(MPEP §2141.02)
Regarding claims 5 and 18; Povey does not explicitly teach the recited limitation above. However, Heinonen teaches a method for prediction of molecular characteristics and identification of metabolites from salient tandem mass spectral signals (i.e. chemical spectra) using machine learning with the support vector machine (i.e. computer-implemented method) (pg. 2333 col. 1 para. 1) for quantifying and qualifying chemical signals (pg. 2333 col. 1 para. 2); wherein three classes of mass spectral features and a probability product kernel over the spectral features (pg. 2334 col. 1 para. 2) was used in the model; wherein individual fingerprint performance was depicted in the form of predictive accuracy which varied from 0.5 to 1.0 (i.e. 50% to 100%) (pg. 2337 col. 2 para. 5 and Fig. 3); reading on claims 5 and 18
Regarding claims 12-13; Povey does not explicitly teach the recited limitation above. However, Heinonen teaches the use of ultra-high accuracy positive-mode for in predicting fingerprints and identifying metabolites for the Ltq dataset (pg. 2336 col. 2 para. 9) and ultra-high accuracy negative-mode for in predicting fingerprints and identifying metabolites for the Lipids dataset (pg. 2337 col. 1 para. 1); reading on claims 12-13.
Finding of Prima Facie Obviousness Rationale and Motivation
(MPEP §2142-2143)
Regarding claims 5, 12-13 and 18; it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings by Heinonen to the Partial Least Squares Discriminant Analysis (PLS-DA) model to predict (i.e. predictive model) a cell line’s phenotypic characteristics using MALDI-ToF mass spectrometry fingerprinting method for cells as taught by Povey to incorporate the metabolite fingerprint variable and phenotypic performance variable comprising the chemical spectra and the known phenotypic performance measurements from spent media from host cell cultures that exhibit a range of phenotypic performance measurements, wherein the range of phenotypic performance measurements comprises at least a 10%, 20%, 30%, 40%, 50%, 60%,70%, 80%, or 90% relative difference between the lowest and highest known phenotypic performance measurements; the chemical spectra based on positive and negative ion mass spectroscopy. One of ordinary skill in the art would have been motivated to combine the model by Heinonen with the teachings by Povey to use partial least squares discriminant analysis for handling large chromatographic time-of-flight mass spectrometry fingerprinting data (pg. 341 col. 1 para. 1 Heinonen). One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to computational methods for metabolite fingerprinting identification.
Claims 6 and 17 are rejected under 35 U.S.C. 103(a) as being unpatentable over Povey as applied to claims 1 and 14 in the 102 rejection above further in view of Roxas et. al. "Significance analysis of microarray for relative quantitation of LC/MS data in proteomics." BMC bioinformatics 9(1):187 (2008) – referred to in the action as Roxas.
Determination of the Scope and Content of the Prior Art
(MPEP §2141.01)
Dependent claims 6 and 17 recite “wherein the metabolite fingerprint variable and phenotypic performance variable comprise the chemical spectra and the known phenotypic performance measurements from spent media from host cell cultures that exhibit a range of phenotypic performance measurements, wherein the range of phenotypic performance measurements comprises at least a 2, 3, 4, 5, 6, 7, 8, 9, or 10- relative fold difference between the lowest and highest known phenotypic performance measurements”. Povey teaches a Partial Least Squares Discriminant Analysis (PLS-DA) model to predict (i.e. predictive model) a cell line’s phenotypic characteristics using MALDI-ToF mass spectrometry fingerprinting method for mammalian cells (pg. 84 para. 1); via metabolic based studies have now been undertaken and the subsequent data used to develop models to predict the phenotype of a given cell line (pg. 84 para. 1); wherein preparation of cell pellets for spectrometry analysis was permed by resuspending cell pellets (pg. 86 col. 2 para. 1) for each of the 220 cell lines (i.e. plurality of spent media derived from the plurality of different host cells) (pg. 90 col. 2 para. 1); wherein data show that the approach can be used to predict the performance of recombinant cell lines expressing (i.e. phenotypic measurements) different monoclonal antibodies (i.e. reading on product being a protein) (pg. 92 col. 1 para. 1); wherein the approach is based around the development of a historical database of MALDI-ToF mass spectrometry spectra (i.e. chemical spectra) associated with subsequent productivity data at the 10 L scale to predict the performance of cell lines (pg. 91 col. 2 para. 3); wherein the PLS-DA model was generated using a training set comprising 29 cell lines from MALDI-ToF data (i.e. at least 5, 10, 25, 50, 75, 100, 150, 200, or 250 chemical spectra) (pg. 90 Fig. 5) and comprised 5 latent variables of the cell lines (i.e. fingerprinting variables) (pg. 89 col. 1 para. 2); reading on “wherein the metabolite fingerprint variable and phenotypic performance variable comprise the chemical spectra and the known phenotypic performance measurements from spent media from host cell cultures that exhibit a range of phenotypic performance measurements”).
Ascertainment of the Difference Between Scope the Prior Art and the Claims
(MPEP §2141.02)
Regarding claims 6 and 17; Povey does not explicitly teach the recited limitation above. However, Roxas teaches the fold change criterion for relative quantification of LC/MS data in proteomics (i.e. proteins expressed – reading on phenotypic measurements) (pg. 1 para. 1); wherein 95% of the total common peptides have intensities within a ~2-fold change (i.e. relative fold difference between the lowest and highest measurement) for a pair of cultures of human breast cancer cells (pg. 2 col. 1 para. 2); reading on claims 6 and 17.
Finding of Prima Facie Obviousness Rationale and Motivation
(MPEP §2142-2143)
Regarding claims 6 and 17; it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings by Roxas to the Partial Least Squares Discriminant Analysis (PLS-DA) model to predict (i.e. predictive model) a cell line’s phenotypic characteristics using MALDI-ToF mass spectrometry fingerprinting method for cells as taught by Povey to incorporate the range of phenotypic performance measurements comprising at least a 2, 3, 4, 5, 6, 7, 8, 9, or 10- relative fold difference between the lowest and highest known phenotypic performance measurements. One of ordinary skill in the art would have been motivated to combine the model by Roxas with the teachings by Povey to apply a commonly used method to evaluate protein expression (i.e. phenotypic measurements) level differences (pg. 2 col. 1 para. 1 Roxas). One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to computational methods for quantification of mass spectrometry data.
Claims 10-11 are rejected under 35 U.S.C. 103(a) as being unpatentable over Povey as applied to claims 1 and 14 in the 102 rejection above further in view of Nielsen et. al. "A pre-processing strategy for liquid chromatography time-of-flight mass spectrometry metabolic fingerprinting data." Metabolomics 6(3):341-352 (2010) – referred to in the action as Nielsen.
Determination of the Scope and Content of the Prior Art
(MPEP §2141.01)
Dependent claim 10 recites “wherein the mass spectroscopy is direct injection electrospray ionization mass spectrometry”. Dependent claim 11 recites “wherein the mass spectroscopy uses a time-of-flight spectrometer”.
Ascertainment of the Difference Between Scope the Prior Art and the Claims
(MPEP §2141.02)
Regarding claims 10-11; Povey does not explicitly teach the recited limitation above. However, Nielsen teaches a method that uses Matlab programming environment (pg. 344 col. 1 para. 1) for extraction of chemical information from large data sets and optimization in targeting small molecules from metabolomics data (pg. 341 col. 2 para. 1) via liquid chromatography high resolution time-of-flight mass spectrometry fingerprinting approach i.e. extracting and analyzing as large a part of the host-species metabolome as possible. (pg. 341 col. 2 para. 2); wherein all predictions were based on three-component models as the minimum suggested by cross validation classification error (pg. 347 col. 1 para. 1); wherein the system used a quadrupole/orthogonal acceleration time-of-flight mass spectrometer with electrospray ionization operated in positive ion mode (pg. 342 col. 2 para. 4); reading on claims 10-11.
Finding of Prima Facie Obviousness Rationale and Motivation
(MPEP §2142-2143)
Regarding claims 10-11; it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings by Nielsen to the Partial Least Squares Discriminant Analysis (PLS-DA) model to predict (i.e. predictive model) a cell line’s phenotypic characteristics using MALDI-ToF mass spectrometry fingerprinting method for cells as taught by Povey to incorporate direct injection electrospray ionization mass spectroscopy with a time-of-flight spectrometer. One of ordinary skill in the art would have been motivated to combine the model by Nielsen with the teachings by Povey to use partial least squares discriminant analysis for handling large chromatographic time-of-flight mass spectrometry fingerprinting data (pg. 341 col. 1 para. 1 Nielsen) One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to computational methods for metabolite fingerprinting identification.
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.
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/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685