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 .
Response to Arguments
Applicant's arguments filed 10/09/2025 have been fully considered but they are not persuasive.
Applicants argument in page 10 argues that the limitation of FWHM does not set forth or describe any mathematical relationships, calculations, formulas, or equations using words or mathematical symbols, and therefore are not directed to an abstract idea. Examiner respectfully disagrees. Full width at half maximum (FWHM) is the difference between the two values of the independent variable at which the dependent variable is equal to half of its maximum value. In other words, it is the width of a spectrum curve measured between those points on the y-axis which are half the maximum amplitude. The definition is of a mathematical concept, therefore the claimed limitations are still found to be an abstract idea. Reporting the likelihood of failure score is not a description of a practical application since the scope of interpretations is so broad it includes simply processing data.
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 1-8, 12, 15-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 1, 17 and 22 recite receiving an image of beads in an array of tiles on a beadchip, calculating averages, predicting from the average values likelihood of failure and reporting the likelihood of failure score for the sample run. The claims recite mathematical concepts (e.g. mathematical relationships, formulas or equations, mathematical calculations) and mental process, i.e. concepts performed on the human mind (e.g. Observation, evaluation, judgement, opinion). The claims recite mathematical calculations that are used to calculate the averages and the likelihood of failure score. Thus, the claims recite a mathematical concept. If a claim limitation, under its broadest reasonable interpretation in light of the specification encompasses a mathematical calculation, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the additional elements of reporting the likelihood of failure score amounts to data-gathering steps which is considered to be insignificant extra-solution activity (See MPEP 2106.05(g)). The one or more processors and one or more storage devices in these steps are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the mathematical calculations and the insignificant extra-solution activity identified above, which includes the data gathering steps, is recognized by the courts as well-understood, routine, and conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP 2106.05(d)(II)(i) Receiving or transmitting data over a network, e.g., using the Internet to gather data, 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)). The claims are not patent eligible.
Claims 2, 18 and 23 are dependent on claims 1, 17 and 22 and includes all the limitations of claims 1, 17 and 22. Therefore, claims 2, 18 and 23 recites the same abstract idea of claims 1, 17 and 22. The claim recites the additional limitation of “wherein the image of beads in the array of tiles on the beadchip is a multi-channel image including at least a first and a second image channel”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claims 3, 19 and 24 are dependent on claims 1-2, 17-18 and 22-23 and includes all the limitations of claims 1-2, 17-18 and 22-23. Therefore, claims 3, 19 and 24 recites the same abstract idea of claims 1-2, 17-18 and 22-23. The claim recites the additional limitation of “channels are red and green channels resulting from colored illumination and/or colored filtering during collection of the multi-channel image”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claims 4 and 20 are dependent on claims 1 and 17 and includes all the limitations of claims 1 and 17. Therefore, claims 4 and 20 recites the same abstract idea of claim 1. The claim recites the additional limitation of “further including dividing the beadchip into regions of samples and predicting from the average FWHM values of the tiles within the samples, the likelihood of failure score prior to post-processing of individual samples”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claims 5, 21 and 25 are dependent on claims 1, 17 and 22 and includes all the limitations of claims 1, 17 and 22. Therefore, claims 5, 21 and 25 recites the same abstract idea of claims 1, 17 and 22. The claim recites the additional limitation of “trained classifier further predicts likelihood scores for alternative root causes of failure, further including reporting the likelihood score for at least one of the alternative root causes”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “including providing colorized images of the average FWHM values for the tiles to an operator to evaluate for root cause”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 7 is dependent on claim 1 and 2 and includes all the limitations of claim 1 and 2. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitation of “including dividing the beadchip into regions of samples comprising one or more swaths of tiles wherein a swath comprises at least two rows and at least eighteen columns of tiles”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 8 is dependent on claim 1-2 and 7 and includes all the limitations of claim 1-2 and 7. Therefore, claim 8 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the tile comprises at least ten thousand cores, arranged in at least one hundred rows, configured to hold one bead per core”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 12 is dependent on claim 1-2 and 7 and includes all the limitations of claim 1-2 and 7. Therefore, claim 12 recites the same abstract idea of claim 1. The claim recites the additional limitation of “calculating, over each swath in each image channel, a signal to noise ratio value of images of the beads, predicting from the signal to noise ratio values, using the trained classifier, a likelihood of failure score for the sample evaluation run”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 15 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 15 recites the same abstract idea of claim 1. The claim recites the additional limitation of “including providing an instrument identifier that distinguishes among instrument units as input to the trained classifier for predicting failure of the sample evaluation run”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claim 16 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 16 recites the same abstract idea of claim 1. The claim recites the additional limitation of “including providing a position of the tile on the beadchip along with the average FWHM value to the trained classifier for predicting failure of sample evaluation run”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
Claims 26, 27 and 28 are dependent on claims 1, 17 and 22 and includes all the limitations of claims 1, 17 and 22. Therefore, claims 26, 27 and 28 recites the same abstract idea of claims 1, 17 and 22. The claim recites the additional limitation of “the trained classifier is trained using average FWHM values of bead images in tiles from a genotyping instrument, wherein the average FWHM values that are used to train the classifier include FWHM values above a threshold value that indicate a lower focus quality of bead images and FWHM values below the threshold value that indicate a higher focus quality of the bead images”, which is merely elaborating on the abstract idea, by further specifying an additional mathematical calculation, therefore, does not amount to significantly more than the abstract idea.
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.
Claims 1-7, 15-28 are rejected under 35 U.S.C. 103 as being unpatentable over Dutta et al. (US 2019/0213473 A1) in view of Ho et al (US 2017/0037475 A1).
With respect to Claim 1, Dutta’473 shows a method of avoiding post-processing of an image [ ] during a sample evaluation run (paragraphs [0004], [0019], and [0055] describes predicting quality scores to avoid post processing (SBS) on predicted to fail runs acquired from images of samples), including:
receiving an image of objects in an array of tiles [ ] (paragraph [0026] producing four color channel images, paragraphs [0035]-[0036] the template generation process is replaced by a step where a hexagonal packed lattice of clusters is placed on x, y positions/array on an area corresponding to the size of the tile comprising patterned flow cells/beads), calculating averages, over each tile in image channel, of full width at half max (FWHM) values of images of the objects (paragraphs [0039]-[0040] calculating the average full width of clusters of molecules at half maximum (FWHM) representing their approximate size in pixels),
predicting from the average FWHM values of the tiles, using a trained classifier, a likelihood of failure score for the sample evaluation run (paragraph [0021] describes some runs fail do to quality which can be predicted as a final quality (likelihood of failure score), paragraph [0066] figure 9 predicting quality scores from a trained neural network/classifier),
wherein the trained classifier is trained to predict likelihood of failure scores from average FWHM values of images of objects [ ] in arrays of tiles [ ] (paragraph [0039]-[0040] focus score An includes FWHM correlated to quality scores Q30 in paragraphs [0066]-[0067]), and
reporting the likelihood of failure score for the sample evaluation run (paragraphs [0067] producing the scores for an operator).
Dutta’473 does not disclose an image from extension of probes on beads during a sample evaluation run, including: receiving an image of beads in an array of tiles on a beadchip.
Ho’475 shows an image from extension of probes on beads during a sample evaluation run (paragraph [0234] examples single probe sequence for imaged bead sample processed to generate a quality score), including: receiving an image of beads in an array of tiles on a beadchip (paragraph [0234] the imaged bead array on a beadchip).
It would have been obvious to one skilled in the art before the effective filing date of the current application to enable Dutta’473 image of objects in an array of tiles calculating averages, over each tile in image channel, of full width at half max (FWHM) values of images of the objects with the known technique of the image objects being from extension of probes on beads during a sample evaluation run including: receiving an image of beads in an array of tiles on a beadchip yielding the predictable results of generating data files regarding the images for genotype analysis as disclosed by Ho’475 (paragraph [0234]).
With respect to Claim 2, Dutta’473 and Ho’475 shows the method of claim 1, wherein the image of beads in the array of tiles on the beadchip (Ho’475: paragraph [0234]) is a multi-channel image including at least a first and a second image channel (Dutta’473: paragraph [0026] producing four color channel images, Ho’475: paragraph [0234] describes the images created from two wavelengths/color channels, paragraph [0109] examples the fluorescent labels include red and green).
With respect to Claim 3, Dutta’473 and Ho’475 shows the method of claim 2, wherein the channels are red and green channels resulting from colored illumination and/or colored filtering during collection of the multi-channel image (Dutta’473: paragraph [0026] producing four color channel images, Ho’475: paragraph [0234] describes the images created from two wavelengths/color channels, paragraph [0109] examples the fluorescent labels include red and green).
With respect to Claim 4, Dutta’473 and Ho’475 shows the method of claim 1, further including dividing the beadchip into regions of samples and predicting from the average FWHM values of the tiles within the samples, the likelihood of failure score prior to post-processing of individual samples (Dutta’473: paragraph [0039] describes clustering (divided regions of samples) the average FWHM values of the molecules.).
With respect to Claim 5, Dutta’473 and Ho’475 shows the method of claim 1, wherein the trained classifier further predicts likelihood scores for alternative root causes of failure, further including reporting the likelihood score for at least one of the alternative root causes (Dutta’473: Paragraph [0021] describes failure due to quality problems.).
With respect to Claim 6, Dutta’473 and Ho’475 shows the method of claim 1, further including providing colorized images (Dutta’473: paragraph [0026] producing four color channel images) of the average FWHM values for the tiles (Dutta’473: paragraphs [0039]-[0040] calculating the average full width of clusters of molecules at half maximum (FWHM) representing their approximate size in pixels) to an operator to evaluate for root cause (Dutta’473: paragraphs [0067] producing the scores for an operator).
With respect to Claim 7, Dutta’473 and Ho’475 shows the method of claim 2, further including dividing the beadchip into regions of samples comprising one or more swaths of tiles wherein a swath comprises at least two rows and at least eighteen columns of tiles (Figure 2 swath/template/sequencing quality data per cycle 219 depicted with Xn rows and y Rn volumns described in paragraphs [0034] and [0035] examples sequencing runs with identified locations in x and y positions with n equal to 25).
With respect to Claim 15, Dutta’473 and Ho’475 shows the method of claim 1, further including providing an instrument identifier that distinguishes among instrument units as input to the trained classifier for predicting failure of the sample evaluation run (Dutta’473: paragraph [0040] describes instrument specific models are utilized with pre-trained models, describing the training to include specific/identified instruments, for predicting quality scores).
With respect to Claim 16, Dutta’473 and Ho’475 shows the method of claim 1, further including providing a position of the tile on the beadchip (Dutta’473: paragraph [0035] describes identifying locations as x and y positions, Ho’475: paragraph [0234] the imaged bead array on a beadchip) along with the average FWHM value (Dutta’473: paragraphs [0039]-[0040] calculating the average full width of clusters of molecules at half maximum (FWHM) representing their approximate size in pixels) to the trained classifier for predicting failure of sample evaluation run (Dutta’473: paragraph [0066] figure 9 predicting quality scores from a trained neural network/classifier).
With respect to Claims 17 and 22, rejection analogous to those presented for claim 1, are applicable.
With respect to Claims 18 and 23, rejection analogous to those presented for claim 2, are applicable.
With respect to Claims 19 and 24, rejection analogous to those presented for claim 3, are applicable.
With respect to Claim 20, rejection analogous to those presented for claim 4, are applicable.
With respect to Claims 21 and 25, rejection analogous to those presented for claim 5, are applicable.
With respect to Claims 26-28, Dutta’473 and Ho’475 shows the method of claim 1, wherein the trained classifier is trained using average FWHM values of bead images in tiles from a genotyping instrument, wherein the average FWHM values that are used to train the classifier include FWHM values above a threshold value that indicate a lower focus quality of bead images and FWHM values below the threshold value that indicate a higher focus quality of the bead images (in Dutta’473: paragraph [0066] training model with quality scores, paragraphs [0039]-[0040] the quality scores including FWHM percentage/ thresholds indicating accuracy calls, and in Ho’475: paragraph [0178] using SNP genotyping).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Dutta et al. (US 2019/0213473 A1) in view of Ho et al (US 2017/0037475 A1) further in view of Kuhn (NPL “A novel, high performance random array platform for quantitative gene expression profiling”).
With respect to Claim 8, Dutta’473 and Ho’475 does not specifically show the method of claim 7, wherein the tile comprises at least ten thousand cores, arranged in at least one hundred rows, configured to hold one bead per core.
Kuhn (NPL) shows the method of claim 7, wherein the tile comprises at least ten thousand cores, arranged in at least one hundred rows, configured to hold one bead per core (figure 1 describes an array of 50,000 beads per array and a matrix of 96 arrays.).
At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Dutta’473 and Ho’475 to include the tile comprises at least ten thousand cores, arranged in at least one hundred rows, configured to hold one bead per core method taught by Kuhn. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to minimize the effects spatially localized artifacts (page 2 Design of a gene-expression probe array based on random assembly of beads in wells part).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Dutta et al. (US 2019/0213473 A1) in view of Ho et al (US 2017/0037475 A1) further in view of Segale et al. (US 2010/0157086 A1).
With respect to Claim 12, Dutta’473 and Ho’475 shows the method of claim 7, further including: calculating, over each swath in each image channel, a [signal to noise ratio] value of images of the beads, predicting from the [signal to noise ratio] values, using the trained classifier, a likelihood of failure score for the sample evaluation run (Dutta’473: [0066] figure 9 predicting quality scores from a trained neural network/classifier).
Segale’086 shows a signal to noise ratio value of images of the beads (paragraph [0058] describes the focus score to include image quality parameters include signal to noise ratio).
At the time of the invention, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claim invention to modify Dutta’473 and Ho’475 to include a signal to noise ratio value of images of the beads method taught by Segale’086. The suggestion/motivation for doing so would have been to improve the system’s ability to be able to improve focus (paragraph [0058]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ozcan et al. (US 2019/0294108 A1): paragraph [0037] tracking a particle's amplitude full-width-half-maximum (FWHM) as a function of the axial defocus distance.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IRIANA CRUZ whose telephone number is (571)270-3246. The examiner can normally be reached 10-6.
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, Akwasi M. Sarpong can be reached at (571) 270-3438. 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.
/IRIANA CRUZ/Primary Examiner, Art Unit 2681