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 .
Claim Status
Claims 1-15 are currently pending and under examination herein.
Claim(s) 1-15 is rejected.
Priority
The instant application does not claim priority to any prior application. Therefore, the effective filing date of the instant application is 05/31/2023.
Drawings
The drawings filed on 05/31/2023 are objected to. In particular, figures 2B, 3B, 5A, and 6 are executed in color.
Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Specification
The specification filed on 05/31/2023 is accepted.
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.
Claim 14 is non-statutory as it recites "a computer-readable medium". The claim as instantly recited read on carrier waves, which are transitory propagating signals and therefore are not proper patentable subject matter because they do not fit within any of the four statutory categories of
invention (In re Nuijten, Federal Circuit, 2007). It is noted that the recitation of a "non-transitory
computer readable medium" would overcome the rejection with respect to claim 14 reading on signals. However, the amendment to only "non-transitory computer readable medium" would not overcome the rejection under 35 U.S.C. 101 since the claims would still be directed to a judicial exception without significantly more (see below).
In addition, claim 15 is non-statutory as it recites “a computer program product”. Of note, products that do not have a physical or tangible form, such as information (often referred to as “data per se”) or a computer program per se (often referred to as “software per se”) when claimed as a product without any structural recitations does not constitute patentable subject matter. As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories (see Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719). Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category (see MPEP 2106.03). If amended to fit within one of the four statutory categories of invention, the amendment would not overcome the rejection under 35 U.S.C. 101 since the claims would still be directed to a judicial exception without significantly more (see below).
Claims 1-15 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea and/or a natural phenomenon without significantly more.
In accordance with MPEP 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature, or natural phenomenon (Step 2A, Prong 1).
Claims 1, 14, and 15 recites a computer-implemented method (or computer-readable medium or computer program product, respectively) of: determining a measure of relative gene expression, comprising: computing a distribution of gene expression levels across the plurality of gene expression datasets; fitting a number of Gaussian components to the distribution of gene expression levels using a Gaussian mixture model; defining, based on the fitted Gaussian components, a set of relative gene expression thresholds for the plurality of gene expression datasets; and determining a measure of relative gene expression for each of a plurality of genes across the plurality of gene expression datasets based on the set of relative gene expression thresholds.
Claim 2 recites selecting a set of criteria based on the first transcriptomic platform used to measure the plurality of gene expression datasets; and evaluating the set of criteria based on the fitted Gaussian components to define the set of relative gene expression thresholds.
Claim 3 recites a method wherein the set of criteria are selected based on a first data structure comprising a set of transcriptomic platforms each having an associated set of criteria.
Claim 4 recites a method wherein the set of criteria comprise one or more of: a crossing point between Gaussian components; a mean of a Gaussian component; and a mean of a Gaussian component plus or minus an aspect of the standard deviation of the Gaussian component.
Claim 5 recites a method wherein the number of Gaussian components is selected based on the first transcriptomic platform used to measure the plurality of gene expression datasets.
Claim 6 recites a method wherein the number of Gaussian components is selected based on a second data structure comprising a set of transcriptomic platforms each having an associated number of Gaussian components.
Claim 8 recites computing a second distribution of gene expression levels across the second plurality of gene expression datasets; fitting a number of Gaussian components to the second distribution of expression levels using a Gaussian mixture model; defining, based on the fitted Gaussian components, a second set of relative gene expression thresholds for the second plurality of gene expression datasets, wherein the step of determining a measure of relative gene expression comprises: determining a measure of relative gene expression for each of a plurality of genes across the first and second pluralities of datasets based on the corresponding set of relative gene expression thresholds.
Claim 9 recites a computer-implemented method for identifying a drug target for a disease, comprising: determining, using the computer-implemented method of claim 1, a first measure of relative gene expression for a gene from a first sample associated with a disease of interest; and selecting the gene or associated biological entity as a drug target for the disease of interest based on the first measure of relative gene expression.
Claim 10 recites determining a second measure of relative gene expression for the same gene from a second sample; and selecting the gene or associated biological entity as the drug target for the disease of interest based on the first and second measures of relative gene expression.
Claim 11 recites the gene expression levels of the first and second samples have been measured using different transcriptomic platforms.
Claim 12 recites selecting a drug that incites a biological effect with respect to the drug target as a drug candidate for the disease of interest.
Claim 13 recites determining, using the computer-implemented method of claim 1, a measure of relative gene expression for the gene from at least one healthy sample of another cell type that is different to the particular cell type; and determining whether there is a safety risk associated with using the gene as a drug target based on the measure of relative gene expression.
The limitations of a computer-implemented method for determining a measure of relative gene expression, comprising: computing a distribution of gene expression levels across the plurality of gene expression datasets; fitting a number of Gaussian components to the distribution of gene expression levels using a Gaussian mixture model; defining, based on the fitted Gaussian components, a set of relative gene expression thresholds for the plurality of gene expression datasets; determining a measure of relative gene expression for each of a plurality of genes across the plurality of gene expression datasets based on the set of relative gene expression thresholds; the set of criteria comprise one or more of: a crossing point between Gaussian components; a mean of a Gaussian component; and a mean of a Gaussian component plus or minus an aspect of the standard deviation of the Gaussian component; computing a second distribution of gene expression levels across the second plurality of gene expression datasets; fitting a number of Gaussian components to the second distribution of expression levels using a Gaussian mixture model; defining, based on the fitted Gaussian components, a second set of relative gene expression thresholds for the second plurality of gene expression datasets, wherein the step of determining a measure of relative gene expression comprises: determining a measure of relative gene expression for each of a plurality of genes across the first and second pluralities of datasets based on the corresponding set of relative gene expression thresholds; determining, using the computer-implemented method of claim 1, a first measure of relative gene expression for a gene from a first sample associated with a disease of interest; and determining, using the computer-implemented method of claim 1, a measure of relative gene expression for the gene from at least one healthy sample of another cell type that is different to the particular cell type are verbal equivalents of mathematical calculations which fall under the “mathematical concept” grouping of ideas.
The limitations of selecting a set of criteria based on the first transcriptomic platform used to measure the plurality of gene expression datasets; evaluating the set of criteria based on the fitted Gaussian components to define the set of relative gene expression thresholds; the set of criteria are selected based on a first data structure comprising a set of transcriptomic platforms each having an associated set of criteria; the number of Gaussian components is selected based on the first transcriptomic platform used to measure the plurality of gene expression datasets; the number of Gaussian components is selected based on a second data structure comprising a set of transcriptomic platforms each having an associated number of Gaussian components; for identifying a drug target for a disease, comprising: selecting the gene or associated biological entity as a drug target for the disease of interest based on the first measure of relative gene expression; and selecting a drug that incites a biological effect with respect to the drug target as a drug candidate for the disease of interest; and determining whether there is a safety risk associated with using the gene as a drug target based on the measure of relative gene expression fall under the “mental processes” grouping of ideas. Selecting a variety of intermediates within the pipeline (i.e. drugs, criteria, or Gaussian components), evaluating the set of criteria or safety risk, and identifying a disease can all be practically performed in the human mind or with pen and paper. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 and Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016). In addition, the courts do not distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. Although the instant claims require the use of a computer or processor, the underlying, patent-ineligible invention can still be performed via pen and paper or in a person’s mind. (See Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015), Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016), and Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016)).
The limitation of the gene expression levels of the first and second samples have been measured using different transcriptomic platforms serve to merely further limit the judicial exception.
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). This judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflects an improvement to technology or applies or uses the recited judicial exception in some other meaningful way. Rather, the instant claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements:
Claim 1 recites receiving a plurality of gene expression datasets, wherein each gene expression dataset comprises gene expression levels for a respective sample, and wherein the plurality of gene expression datasets are all measured using a first transcriptomic platform.
Claim 7 recites a method wherein the first transcriptomic platform operates based on one of: RNA sequencing; or microarray analysis.
Claim 8 recites receiving a second plurality of gene expression datasets, wherein each of the second plurality of gene expression datasets comprises gene expression levels for a respective sample, and wherein the second plurality of gene expression datasets are all measured using a second transcriptomic platform that is different to the first transcriptomic platform.
Claim 13 recites receiving an indication of a gene that is differentially expressed between healthy and diseased samples of a particular cell type.
Claim 14 recites a computer-readable medium comprising data or instruction code, which when executed on a processor, causes the processor to implement the computer-implemented method of claim 1.
Claim 15 recites a computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of claim 1.
The limitations of receiving a plurality of gene expression datasets, wherein each gene expression dataset comprises gene expression levels for a respective sample, and wherein the plurality of gene expression datasets are all measured using a first transcriptomic platform; receiving a second plurality of gene expression datasets, wherein each of the second plurality of gene expression datasets comprises gene expression levels for a respective sample, and wherein the second plurality of gene expression datasets are all measured using a second transcriptomic platform that is different to the first transcriptomic platform; receiving an indication of a gene that is differentially expressed between healthy and diseased samples of a particular cell type amount to insignificant extra solution activity and comprise data gathering steps. Of note, the courts have ruled in Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) that the collection, analysis, and display of data are considered insignificant extra-solution activity and does not integrate the judicial exception into a practical application (see MPEP 2106.05(g)).
The limitations of a computer-readable medium comprising data or instruction code, which when executed on a processor, causes the processor to implement the computer-implemented method of claim 1 and a computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of claim 1 merely serve to implement the abstract idea on a generic computing system and there are no limitations that indicate that the claimed computer, processor, input device or computer-readable medium require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The limitations wherein the first transcriptomic platform operates based on one of: RNA sequencing; or microarray analysis merely serve to further limit the additional element that does not integrate into a practical application as the limitations do not change the additional element from being an insignificant data gathering activity. As such, claims 1-15 do not integrate the additional elements into a practical application.
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). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that amount to mere instructions to implement the abstract idea in a generic field-of-use and/or technological environment. The instant claims recite the following additional elements:
Claim 1 recites receiving a plurality of gene expression datasets, wherein each gene expression dataset comprises gene expression levels for a respective sample, and wherein the plurality of gene expression datasets are all measured using a first transcriptomic platform.
Claim 7 recites a method wherein the first transcriptomic platform operates based on one of: RNA sequencing; or microarray analysis.
Claim 8 recites receiving a second plurality of gene expression datasets, wherein each of the second plurality of gene expression datasets comprises gene expression levels for a respective sample, and wherein the second plurality of gene expression datasets are all measured using a second transcriptomic platform that is different to the first transcriptomic platform.
Claim 13 recites receiving an indication of a gene that is differentially expressed between healthy and diseased samples of a particular cell type.
Claim 14 recites a computer-readable medium comprising data or instruction code, which when executed on a processor, causes the processor to implement the computer-implemented method of claim 1.
Claim 15 recites a computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of claim 1.
The limitations of receiving a plurality of gene expression datasets, wherein each gene expression dataset comprises gene expression levels for a respective sample, and wherein the plurality of gene expression datasets are all measured using a first transcriptomic platform; receiving a second plurality of gene expression datasets, wherein each of the second plurality of gene expression datasets comprises gene expression levels for a respective sample, and wherein the second plurality of gene expression datasets are all measured using a second transcriptomic platform that is different to the first transcriptomic platform is a well-understood, routine, and conventional activity. Of note, generally obtaining datasets or having an input device for accessing data are conventional activities. Specifically, the courts have identified steps of receiving data over a network or storing and retrieving information in memory as conventional computer functions in Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC V. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., V. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. V. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); and Versata Dev. Group, Inc. V. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The limitations of receiving an indication of a gene that is differentially expressed between healthy and diseased samples of a particular cell type amounts to well-understood, routine, conventional activities as evidenced by Stanton (US6709855B1; see the variety of models and methods explicitly described in “A. Identification of Differentially Expressed Genes” in Col. 19-20).
The limitations of a computer-readable medium comprising data or instruction code, which when executed on a processor, causes the processor to implement the computer-implemented method of claim, and a computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of claim 1 amount to well-understood, routine, and conventional activities. Of note, the courts have ruled that storing and retrieving information in memory is conventional. (see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93). As such, claims 1-15 are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4, 5, 7, 8, and 14-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liu et al (GMMchi: Gene Expression Clustering Using Gaussian Mixture Modeling, 16 February 2022).
Regarding claim 1, Liu teaches:
A computer-implemented method for determining a measure of relative gene expression (see "Abstract" on page 1 with description of the GMMchi tool), comprising:
receiving a plurality of gene expression datasets, wherein each gene expression dataset comprises gene expression levels for a respective sample, and wherein the plurality of gene expression datasets are all measured using a first transcriptomic platform (see "Introduction" on page 2 where gene expression data is produced as a continuous-valued dataset representing expression levels of individual genes via microarray and RNA-seq technologies);
computing a distribution of gene expression levels across the plurality of gene expression datasets (in “Introduction” on page 2 reciting that the field of computational gene expression analysis is an analysis of continuous distributions);
fitting a number of Gaussian components to the distribution of gene expression levels using a Gaussian mixture model (GMMchi utilizes Gaussian Mixture Modeling to detect and characterize gene expression; see "Abstract" on page 1 and "2.2. Gaussian Mixture Modeling" on page 3);
defining, based on the fitted Gaussian components, a set of relative gene expression thresholds for the plurality of gene expression datasets (thresholding described on page 6 for tail detection; additional background noise threshold considerations also disclosed on page 5 under "5.1 Background Noise Threshold");
and determining a measure of relative gene expression for each of a plurality of genes across the plurality of gene expression datasets based on the set of relative gene expression thresholds (tail detection on page 6 will identify non-normal data from the statistical analysis; background noise thresholding also plays a role in signifying lack of expression and no expression on page 5 under "5.1 Background Noise Threshold").
Regarding claim 2, Liu teaches:
The computer-implemented method of claim 1, wherein defining the set of relative gene expression thresholds based on the fitted Gaussian components comprises: selecting a set of criteria based on the first transcriptomic platform used to measure the plurality of gene expression datasets; and evaluating the set of criteria based on the fitted Gaussian components to define the set of relative gene expression thresholds (explicit recitation of platform-dependent selection as background noise threshold is a criteria required when using a fluoresence-based platform as recited in "5.1" on page 10). Under the broadest reasonable interpretation, the aformentioned microarray technique would require using the background threshold criterion as one of many stated in the paper (e,g. tail, BIC, etc.) required for exploring gene mRNA expression data. In addition, within the GitHub repository, the prior art references coding blocks including criteria such as single_tail_validation and chisquaremethod which are used in the gene expression measurement pipeline (see GMMchi.py file in the attached GitHub at the end of page 1).
Regarding claim 4,
Liu teaches the computer-implemented method of claim 2, wherein the set of criteria comprise one or more of: a crossing point between Gaussian components (see page 8 under "3.5" where the boundary between the high and low values of two Gaussian distributions is defined by their point of intersection; additionally referenced on the 3rd paragraph of page 10 in "5.1"); a mean of a Gaussian component; and a mean of a Gaussian component plus or minus an aspect of the standard deviation of the Gaussian component (see page 10 in "5.1", where threshold is derived from 2 standard deviations away from the mean).
Regarding claim 5,
The computer-implemented method of claim 1, wherein the number of Gaussian
components is selected based on the first transcriptomic platform used to measure the plurality of gene expression datasets (see “2.2 Gaussian Mixture Modeling” on page 3 wherein one or two Gaussian distributions are fitted to each distribution and are based on the dataset obtained from either microarrays or RNA-sequencing technologies as recited above in claim 1).
Regarding claim 7, Liu teaches:
The computer-implemented method of claim 1, wherein the first transcriptomic platform operates based on one of: RNA sequencing; or microarray analysis (see "Introduction" on page 2 where gene expression data is produced as a continuous-valued dataset representing expression levels of individual genes via microarray and RNA-seq technologies).
Regarding claim 8,
Liu as modified teaches the computer-implemented method of claim 1, wherein the plurality of gene expression datasets is a first plurality of gene expression datasets all measured using the first transcriptomic platform, wherein the distribution of gene expression levels across the first plurality of gene expression datasets is a first distribution of gene expression levels, and wherein the set of relative gene expression thresholds for the first plurality of gene expression datasets is a first set of relative gene expression thresholds, the computer-implemented method (see rejection on claim 1) further comprising:
receiving a second plurality of gene expression datasets, wherein each of the second plurality of gene expression datasets comprises gene expression levels for a respective sample, and wherein the second plurality of gene expression datasets are all measured using a second transcriptomic platform that is different to the first transcriptomic platform (see "Introduction" on page 2 where gene expression data is produced as a continuous-valued dataset representing expression levels of individual genes via RNA-seq technologies);
computing a second distribution of gene expression levels across the second plurality of gene expression datasets (in “Introduction” on page 2 reciting that the field of computational gene expression analysis is an analysis of continuous distributions);
fitting a number of Gaussian components to the second distribution of expression levels using a Gaussian mixture model (GMMchi utilizes Gaussian Mixture Modeling to detect and characterize gene expression; see "Abstract" on page 1 and "2.2. Gaussian Mixture Modeling" on page 3);
defining, based on the fitted Gaussian components, a second set of relative gene expression thresholds for the second plurality of gene expression datasets, wherein the step of determining a measure of relative gene expression comprises (thresholding described on page 6 for tail detection; additional background noise threshold considerations also disclosed on page 5 under "5.1 Background Noise Threshold"):
determining a measure of relative gene expression for each of a plurality of genes across the first and second pluralities of datasets based on the corresponding set of relative gene expression thresholds (thresholding described on page 6 for tail detection; additional background noise threshold considerations also disclosed on page 5 under "5.1 Background Noise Threshold").
The Examiner notes that Liu already discloses the pipeline as recited as well as different ways of measuring gene expression through microarrays or RNA-sequencing technologies and one of ordinary skill in the art would merely make use of the microarray method for one pipeline and RNA-seq for the other to obtain a second set. In addition, the courts have held In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960), that mere duplication of parts has no patentable significance unless a new and unexpected result is produced (see MPEP 2144.04).
Regarding claim 14,
Liu as modified teaches a computer-readable medium comprising data or instruction code, which when executed on a processor, causes the processor to implement the computer-implemented method of claim 1 (see GMMchi.py file in the attached GitHub at the end of page 1; see https://github.com/jeffliu6068/GMMchi). Under the broadest reasonable interpretation, The Examiner notes that the implementation of the GitHub repository code would necessarily require the generic computer components recited.
Regarding claim 15,
Liu as modified teaches a computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the method of claim 1 (see GMMchi.py file in the attached GitHub at the end of page 1; see https://github.com/jeffliu6068/GMMchi).
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.
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 present rejection(s) reference specific passages from cited prior art. However,
Applicant is advised that the rejections are based on the entirety of each cited prior art. That is,
each cited prior art reference “must be considered in its entirety”. (See MPEP 2141.02(VI))
Therefore, Applicant is advised to review all portions of the cited prior art if traversing a
rejection based on the cited prior art.
Claim(s) 9-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (GMMchi: Gene Expression Clustering Using Gaussian Mixture Modeling, 16 February 2022), as applied in claim 1, in view of Stanton et al. (US6709855B1).
Regarding claim 9,
Liu as modified teaches determining, using the computer-implemented method of claim 1, a first measure of relative gene expression for a gene from a first sample. However, Liu as modified does not teach the subsequent step of associating with a disease of interest; and selecting the gene or associated biological entity as a drug target for the disease of interest based on the first measure of relative gene expression. Stanton teaches systemic analysis to assess gene and protein expression for various drugs to confirm the expression of a novel drug target in disease-relevant tissue (a strategy aimed at the identification of genes which are differentially expressed will require examining expression products that indicate a diseased state and indicate potential candidate targets for therapeutic modulation; see Col 3. lines 38-47). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Stanton's systemic analysis into Liu as modified's gene expression measuring pipeline in order to identify genes that are differentially expressed in relation to disease states (see "Introduction" in Col. 1 Lines 14-18). This could have been accomplished with reasonable expectation of success as identification of gene expression profiles is already present in Stanton's existing method.
Regarding claim 10,
The combination of Liu as modified and Stanton teaches the computer-implemented method of claim 9, further comprising: determining a second measure of relative gene expression for the same gene from a second sample; and selecting the gene or associated biological entity as the drug target for the disease of interest based on the first and second measures of relative gene expression (see rejection on claim 9). Of note, the courts have held In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960), that mere duplication of parts has no patentable significance unless a new and unexpected result is produced (see MPEP 2144.04).
Regarding claim 11,
Liu as modified teaches the computer-implemented method of claim 10, wherein the gene expression levels of the first and second samples have been measured using different transcriptomic platforms (see "Introduction" where gene expression data is produced as a continuous-valued dataset representing expression levels of individual genes via microarray and RNA-seq technologies which are two different ways of measure gene expression).
Regarding claim 12,
Stanton teaches the computer-implemented method of claim 9, further comprising: selecting a drug that incites a biological effect with respect to the drug target as a drug candidate for the disease of interest (administering a differentially expressed gene modulator to the subject such that treatment occurs based on differential gene expression explicitly recited in Col. 67 Lines 16-26).
Regarding claim 13,
Stanton teaches a method for assessing safety of a potential drug target, comprising: receiving an indication of a gene that is differentially expressed between healthy and diseased samples of a particular cell type (see “Microarray analysis” in Col. 22 Lines 15-60, where microarrays are utilized within the methods of the invention to assess the expression profile of genes expressed in normal subjects and subjects suffering from a disease); determining, using the computer-implemented method of claim 1, a measure of relative gene expression for the gene from at least one healthy sample of another cell type that is different to the particular cell type (application of the microarray method allows for the assessment of differential gene expression in pairs of mRNA samples from two different tissues, or in the same tissue comparing normal versus disease states or time progression of the disease as recited in Col. 5 Lines 24-35); and determining whether there is a safety risk associated with using the gene as a drug target based on the measure of relative gene expression (see “XX. Cell-and Animal-based Model Systems” wherein the models can be used to identify drugs and therapies that are effective at treatment and efficacy).
Claim(s) 3 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (GMMchi: Gene Expression Clustering Using Gaussian Mixture Modeling, 16 February 2022), as claimed in claims 2 and 5, in view of Navone (Python Dictionaries 101: a Detailed Visual Introduction [Review of Python Dictionaries 101: a Detailed Visual Introduction]. freeCodeCamp.Org.)
Regarding claim 3,
Although Liu does teach the various criteria based on transcriptomic platforms, he does not explicitly teach the computer-implemented method of claim 2, wherein the set of criteria are selected based on a first data structure comprising a set of transcriptomic platforms each having an associated set of criteria. Navone teaches the data structure to store the related values between the transcriptomic platforms and criteria while preserving their relationship via a dictionary (see “Dictionaries in Context” on page 3 for explanation and “Syntax” on page 6-7 for implementation). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement Navonne’s dictionary into Liu as modified’s gene expression measuring pipeline. This could have been accomplished with reasonable expectation of success as the implementation of a dictionary allows for increased efficiency compared to standard methods (e.g. nested lists) as recited by Navone (see “Dictionaries in Context” on page 3).
Regarding claim 6,
Navone teaches the computer-implemented method of claim 5, wherein the number of Gaussian components is selected based on a second data structure comprising a set of transcriptomic platforms each having an associated number of Gaussian components (see rejection on claim 3). Of note, the courts have held In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960), that mere duplication of parts has no patentable significance unless a new and unexpected result is produced (see MPEP 2144.04).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Marczyk discusses adaptive filtering of microarray gene expression data based on Gaussian mixture decomposition (Adaptive filtering of microarray gene expression data based on Gaussian mixture decomposition. BMC Bioinformatics. 2013 Mar 2014).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER NGUYEN whose telephone number is (571)272-0127. The examiner can normally be reached Monday - Friday 7:30am - 5:00pm.
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 M. 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.
/P.N./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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