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 Amendment
The Amendment filed 04/27/2026 has been entered. Claims 1-2, 4-5, 7, and 9-20 remain pending in the application. Claims 9-20 are withdrawn. New grounds of rejections necessitated by amendments are discussed below.
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 1-2, 4-5, and 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites the limitations “an image analysis system configured to determine a concentration…an image analytics module comprising computer program instructions configured to probabilistically determine from the 1D profile, the concentration…”.
In accordance with MPEP 2106, the claims are found to recite statutory subject matter (Step 1: YES) and are 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).
In the instant application, the limitations of “to determine a concentration…to probabilistically determine from the 1D profile, the concentration…” covers limitations of mental steps or mathematical concepts, i.e. mathematical calculations. Other than “an image analysis system” and “an image analytics module comprising computer program instructions”, if the claim limitations, under its broadest reasonable interpretation, covers performance of mathematical calculations but for the recitation of generic computer components, then the claim limitations fall within the “Mathematical Concepts” grouping of abstract ideas (MPEP 2106.05(f)). Accordingly, the claims recite abstract ideas (Step 2A: Prong 1: Yes).
This judicial exception is not integrated into a practical application because the claims do not recite any additional elements that reflects an improvement to technology or applies or uses the judicial exception in some other meaningful way (Step 2A, Prong 2: No). The image analysis system and image analytics module comprising computer program instructions are recited at a high-level of generality (i.e., as generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)). Additionally, the preceding steps and limitations are used for data gathering in the abstract idea; wherein, data gathering to be used in the abstract idea is insignificant extra-solution activity, and not a particular practical application. Additionally, the limitations of “a test result management module comprising computer program instructions configured to render a test result on the point of care device based on the concentration of the one or more target analytes in the sample” is directed to an additional abstract idea and is an insignificant extra-solution activity for data processing, i.e. “render a test result”. See MPEP 2106.05(g). Therefore, the claimed limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea that is not integrated into a practical application (Step 2A, Prong 2: No).
The claims 1-2, 4-5, and 7 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding the abstract idea, claim 1 merely recites the image analysis system and image analytics module comprising computer program instructions, wherein the claimed limitations of the image analysis system and image analytics module amount to no more than mere instructions to apply the exception using a generic computer component; wherein a general purpose computer is not a particular machine (MPEP 2106.05(b)). Claim 1 and dependent claims 2, 4-5, and 7 further recites limitations, however these limitations generally link the judicial exception to a particular field of use (MPEP 2106.05(h)) and are used for data gathering, wherein data gathering to be used in the abstract idea is an insignificant extra-solution activity, and not a practical application (see MPEP 2106.05(g)), which alone or in combination do not amount to significantly more. Additionally, the limitations of claims 1-2, 4-5, and 7 are well-understood, routine and conventional activities as evidenced by the prior art of Divaraniya et al. (WO 2019246361 A1; cited in the IDS filed 03/24/2023), Lawton (US 20030012439 A1), Lee et al. (WO 2008026881 A1), and Sothivelr (Sothivelr, Karthick, "Analysis of Sensor Signals and Quantification of Analytes Based on Estimation Theory" (2014). Master's Theses (2009 -). Paper 264). See MPEP 2106.05(d). The additional elements of the claims 1-2, 4-5, and 7 do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). The claims are not patent eligible.
Claim Rejections - 35 USC § 102/103
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Divaraniya et al. (WO 2019246361 A1; cited in the IDS filed 03/24/2023).
Regarding claim 1, Divaraniya teaches a point of care device (abstract teaches methods, devices, and systems to quantify analytes; wherein “point of care” is interpreted as an intended use of the device, and the system of Divaraniya is interpreted as capable of being used at a point of care) comprising:
- a cassette holder (paragraphs [0125],[0127], “holder”) capable of accommodating a cassette carrying a sample therein (paragraphs [0125],[0127] teaches a biological sample is provided to a test cartridge and rest the cartridge in the holder; therefore, the holder is capable of accommodating a cassette carrying a sample; paragraph [0103] teaches lateral flow devices, i.e. cassette);
- a processor (paragraphs [0004]-[0007], “processor”); and
- a memory unit coupled to the processor (paragraphs [0004]-[0007] teaches a memory or computer-readable storage media executable by the processor; therefore a memory unit is coupled to the processor for executing instructions), comprising an image analysis system configured to determine a concentration of one or more target analytes in the sample (paragraph [0005] teaches an image processing algorithm to quantify an analyte), the image analysis system comprising:
- an image acquisition module comprising computer program instructions configured to obtain an image of the cassette (paragraph [0011] teaches a computer implemented method of capturing an image of a detection region for image processing; therefore, image analysis system includes computer program instructions to obtain or capture an image of the cassette);
- an image pre-processing module comprising computer program instructions (paragraph [0158] teaches a rule-based image processing algorithm; paragraphs [0244]-[0245] teaches image processing where regions of interests are programmatically detected, which implies a program; therefore, the computer program algorithms or instructions are interpreted as an image pre-processing module) configured to:
- identify a region of interest (ROI) from the image corresponding to a result viewing area of the cassette (paragraph [0158] “A rule-based image processing algorithm that first identifies a region of interest can be used to process the image”; paragraphs [0244]-[0245], teaches image processing first required identifying a region of interest and image processing captured lines of varying intensities using the image capture prototype, wherein the captured lines are interpreted as of a cassette);
- generate a unidimensional (1D) profile of the ROI (paragraphs [0161]-[0162] teaches image processing includes generating a single vector of pixel intensities from each image, i.e. 1D profile of the region of interest; paragraph [0246]-[0247] and Figs. 26-27 teach image processing of a signal into a single vector of pixel intensities) by averaging the ROI along a vertical axis (Figs. 26-27 and paragraphs [0246]-[0247] teaches processing the single vector of the ROI shown in Fig. 26 with a rolling mean along a vertical line, i.e. vertical axis, resulting in a processed signal of Fig. 27); and
- spatially filter the 1D profile for reducing distortions in the 1D profile (Figs. 26-27 and paragraphs [0246]-[0248] teaches the vector, i.e. 1D profile, was scrubbed, i.e. spatially filter, and condensed, which reduced distortions in the 1D profile as shown in Fig. 27; note that the instant application, specification, paragraph [0041], defines “spatial filter” as “an image processing technique employed for modifying or enhancing an image for smoothing by blurring and reducing noise in the image”; therefore, since the vector of Fig. 26 was scrubbed and shown to be modified for smoothing by blurring and reducing noise as shown in Fig. 27, the computer program instructions is interpreted as configured to spatially filter the 1D profile as claimed; additionally, paragraphs [0211],[0224] teaches the computer-implemented system can further comprise a topological and morphological transformation; paragraphs [0252],[0253] teaches removing noise from images of a region of interest using morphological transformations; therefore, the image pre-processing module is configured to or structurally capable of spatially filtering the 1D profile, e.g. region of interest, for reducing distortions in the 1D profile via topological and morphological transformation);
- an image analytics module comprising computer program instructions configured to probabilistically determine from the 1D profile, the concentration of the one or more target analytes in the sample based on a statistical model (paragraph [0011] teaches a quantification module processing pixel intensities of an image to quantify an amount of the analyte to determine a concentration of the analyte; paragraph [0162] teaches single vector of pixel intensities; paragraph [0119] teaches determining concentration of the analyte based on training set, i.e. statistical model, of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from, which would comprise the 1D profile; paragraph [0159] teaches quantifying the analyte from the vector; paragraph [0192] teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison; therefore, it appears that concentration of the analyte is probabilistically determined from the 1D profile of the ROI, based on a statistical model) which is based on 1D profiles of ROIs of images obtained for a plurality of cassettes (paragraph [0119] teaches determining concentration of the analyte based on training set, i.e. statistical model, of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from, which would inherently comprise 1D profiles of ROIs of images obtained for plurality of cassettes since training including a plurality of images would include imaging different cassettes with quantified concentrations that includes desired data such as a single vector of pixel intensities; paragraph [0159] teaches quantifying the analyte from the vector; paragraph [0162] teaches single vector of pixel intensities; paragraph [0192] teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison), wherein the statistical model comprises one or more parameters of the 1D profile varying with respect to one or more of time and the concentration of the one or more target analytes in the sample (paragraph [0119] teaches determining concentration of the analyte based on training set, i.e. statistical model, of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from, which would comprise the 1D profile for the different concentrations; paragraph [0192] teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison); and
- a test result management module comprising computer program instructions configured to render a test result on the point of care device based on the concentration of the one or more target analytes in the sample (paragraph [0011] teaches providing, by the processor of the mobile telecommunications device, a report on the health profile of the subject based on the concentration of the analyte, and providing by a visualization module, a report of the health profile of the subject based on the concentration of the analyte; therefore, the device includes computer program instructions configured to render the test result as claimed).
In an alternative interpretation, if it is determined that Divaraniya fails to teach: the image pre-processing module comprising computer program instructions configured to: spatially filter the 1D profile for reducing distortions in the 1D profile; the image analytics module comprising computer program instructions configured to probabilistically determine from the 1D profile, the concentration of the one or more target analytes in the sample based on a statistical model which is based on 1D profiles of ROIs of images obtained for a plurality of cassettes, wherein the statistical model comprises one or more parameters of the 1D profile varying with respect to one or more of time and the concentration of the one or more target analytes in the sample, Divaraniya teaches a quantification module processing pixel intensities of an image to quantify an amount of the analyte to determine a concentration of the analyte (paragraph [0011]). Divaraniya teaches each image can be viewed as a matrix and the resulting matrix can then be deconstructed into a single vector of pixel intensities following a scan line (paragraph [0162]). Divaraniya teaches determining concentration of the analyte based on training set of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from(paragraph [0119]). Divaraniya teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison (paragraph [0192]). Divaraniya teaches image processing can include statistical tests (paragraphs [0165],[0255]), wherein statistical modeling and machine learning methods provide the tools necessary to utilize multiple feature types into a single analysis (paragraph [0313]). Divaraniya teaches once the region of interest was identified, results within the boundary were analyzed, which includes removing noise from the images using morphological transformations, i.e. spatial filtering (paragraph [0252]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the image analytics module of Divaraniya to incorporate Divaraniya’s teachings of a quantification module to process an image to determine concentration of an analyte, determining concentration based on training set of data of images that includes quantified concentrations of analytes, quality control processes that include statistical model used as a reference, and each image being deconstructed into a vector of pixel intensities (paragraphs [0011], [0119], [0165], [0162], [0192], [0255], [0313]) and removing noise with morphological transformations (paragraph [0252]) to provide: the image pre-processing module comprising computer program instructions configured to: spatially filter the 1D profile for reducing distortions in the 1D profile; the image analytics module comprising computer program instructions configured to probabilistically determine from the 1D profile, the concentration of the one or more target analytes in the sample based on a statistical model which is based on 1D profiles of ROIs of images obtained for a plurality of cassettes, wherein the statistical model comprises one or more parameters of the 1D profile varying with respect to one or more of time and the concentration of the one or more target analytes in the sample. Doing so would have a reasonable expectation of successfully improving minimum detection concentration, sensitivity, and accuracy of calculation of the concentration of analytes, by incorporating training set or statistical models to the measured 1D profile during calculations (Divaraniya, paragraph [0119]) and further reducing noise in analyzed image data (Divaraniya, paragraph [0252]).
Regarding claim 2, Divaraniya further teaches wherein the image acquisition module is configured to obtain the image of the cassette via an imaging unit of the point of care device at predefined time intervals based on one or more characteristics of the sample (paragraph [0132] teaches the device quantifies an analyte at two time points to determine a change in analyte over time, such as to determine a change in cyclical hormone, efficacy of treatment, and progression/regression of disease, i.e. characteristics of a sample; paragraph [0175] teaches the storage media quantifies the analyte at a first and second time point; therefore, the image acquisition module is configured to obtain images at time intervals based on characteristics of the sample).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Divaraniya as applied to claim 1 above, and further in view of Lawton (US 20030012439 A1).
Regarding claim 4, Divaraniya fails to teach: wherein the image analytics module is configured to apply a Gaussian filter to the 1D profile for determining a position of a test line and a position of a control line in the 1D profile.
Divaraniya teaches processing includes locating and normalizing the test area and the control area (paragraphs [0115],[0128], [0212], [0216],[0220]). Divaraniya teaches a colored line at the test location indicates a positive test and presence of the analyte; and a second line at the control location can serve as a positive control and indicate that the test was valid (paragraph [0111]). Divaraniya teaches processing can comprises processing pixel intensities over a vector (paragraph [0211]).
Lawton teaches a method, apparatus, and article of manufacture provides a zero-crossing region process for extracting data and related image elements from an input image (abstract). Lawton teaches zero-crossing region filtering in order to identify background and foreground areas of a scanned image for separate processing (paragraph [0001]). Lawton teaches applying a Gaussian filter to attempt to remove visual artifacts greater than a pre-determined spatial value in pixel (paragraph [0041]). Lawton teaches the application of the Gaussian and Laplacian filters locates the two areas of pixel locations that possess the greatest rate of change in pixel values (paragraph [0058]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the image analytics module of Divaraniya to incorporate Lawton’s teachings of applying a Gaussian filter to remove visual artifacts and locating pixel locations (Paragraphs [0041],[0058]) and Divaraniyas' teachings of locating a test and control line (paragraphs [0111], [0115],[0128], [0212], [0216],[0220]) to provide: wherein the image analytics module is configured to apply a Gaussian filter to the 1D profile for determining a position of a test line and a position of a control line in the 1D profile. Doing so would have a reasonable expectation of successfully improving image analysis of the 1D profile by removing visual artifacts before further image processing and determination of the position of the test and control lines.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Divaraniya as applied to claim 1 above, and further in view of Lee et al. (WO 2008026881 A1).
Regarding claim 5, Divaraniya fails to teach: wherein the image analytics module is configured to apply a Laplacian filter to the 1D profile for determining an intensity of a peak when present in the 1D profile.
Divaraniya teaches intensity of a test line is correlated to the amount of hormone present (paragraph [0104]) and signal intensity that is proportional to the amount of the analyte present in the sample (paragraph [0109]). Divaraniya teaches optical sensors interpret the intensity of the test area and the control area for a target analyte, and provide a quantitative measure of the amount of an analyte present in a sample (paragraph [0128]).
Lee teaches a method for detecting honeydew as a cause of sickness by analyzing a color reaction (abstract). Lee teaches an image analysis program and grading process (page 9), wherein brightness of color spots are extracted by a Laplace filter; accordingly, color spots located at a specific position of the web are distinguished from the background by the Laplace filter (pages 9-10, section 7). Lee teaches after processing with the Laplace filter, noise may be removed (page 10, lines 1-3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the image analytics module of Divaraniya to incorporate Lee’s teachings of an image analysis program that applies a Laplace filter to extract brightness and position of a spot (pages 9-10, section 7) and Divaraniya’s teachings of determining intensity of a test and control area (paragraphs [0104],[0109],[0128]) to provide: wherein the image analytics module is configured to apply a Laplacian filter to the 1D profile for determining an intensity of a peak when present in the 1D profile. Doing so would have a reasonable expectation of successfully improving image processing to allow for improved determination of an intensity of a peak for analyte quantification.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Divaraniya as applied to claim 1 above, and further in view of Sothivelr (Sothivelr, Karthick, "Analysis of Sensor Signals and Quantification of Analytes Based on Estimation Theory" (2014). Master's Theses (2009 -). Paper 264).
Regarding claim 7, Divaraniya fails to teach: wherein the image analytics module probabilistically determines the concentration of the of the one or more target analytes in the sample by applying one of a Bayesian filter and a Kalman filter.
Divaraniya teaches a machine learning algorithm that uses a supervised classification inference model that can use a probabilistic Bayesian model architecture (paragraph [0205]). Divaraniya teaches determining concentration of an analyte to report on the health profile of a subject (paragraph [0011]).
Sothivelr teaches analysis of samples containing multiple analytes by investigating a sensor signal processing approached based on estimation theory, specifically using a Kalman filter (abstract). Sothivelr teaches the system allows for accurate estimates of analyte concentration and sensor signal pre-processing techniques correct for linear baseline drift and outlier points in real-time (abstract). Sothivelr teaches in a sensor array, further signal processing is required to identify and quantify the analyte; wherein pattern recognition techniques include Bayesian analysis (section 1.2.4). Sothivelr teaches applications of Kalman filter includes image processing to filter out noise of measured images (section 2.4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the image analytics module of Divaraniya to incorporate the teachings of using a Kalman filter for pre-processing a signal and estimating analyte concentration, and use of Bayesian analysis, of Sothivelr (abstract; sections 1.2.4 and 2.4) to provide: wherein the image analytics module probabilistically determines the concentration of the of the one or more target analytes in the sample by applying one of a Bayesian filter and a Kalman filter. Doing so would have a reasonable expectation of successfully improving data pre-processing to therefore improve accurate determination of concentration of an analyte to report on the health profile of a subject. Additionally, doing so would have a reasonable expectation of successfully improving correcting of defects in a data, such as baseline drift and outlier points as taught by Sothivelr (abstract).
Response to Arguments
Applicant's arguments, see pages 6-8, filed 04/27/2026, with respect to the rejections under 35 U.S.C. 101, have been fully considered but they are not persuasive.
In response to applicant’s arguments that the claim as a whole is directed to an improved device that are not merely directed to abstract mathematical concepts, since amended claim 1 includes the limitation of “render a test result”, the examiner disagrees. The limitation of “render a test result on the point of care device based on the concentration of the one or more target analytes in the sample” is an additional abstract idea, e.g. mental step, and an insignificant post-solution activity for data processing. See MPEP 2106.05(g). Additionally, the limitation of “render a test result…” generally link the judicial exception to a particular field of use and therefore is not a particular practical application (MPEP 2106.05(h)). Therefore, the claimed limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea; and the claims do not amount to significantly more than the judicial exception itself.
Applicant’s arguments, see page 8, filed 04/27/2026, with respect to the claim interpretations under 35 U.S.C. 112(f), have been fully considered and are persuasive. The claim interpretations under 35 U.S.C. 112(f) of 12/29/2025 have been withdrawn.
Applicant's arguments, see pages 8-12, filed 04/27/2026, with respect to the rejections under 35 U.S.C. 102/103, have been fully considered but they are not persuasive.
In response to applicant’s argument that Divaraniya fails to disclose “an image pre-processing module” since paragraphs [0200],[0201],[0198], [0317] merely teaches a k-means algorithm (Remarks, pages 9-10), the examiner disagrees. Divaraniya teaches: an image pre-processing module comprising computer program instructions (paragraph [0158] teaches a rule-based image processing algorithm; paragraphs [0244]-[0245] teaches image processing where regions of interests are programmatically detected, which implies a program; therefore, the computer program algorithms or instructions are interpreted as an image pre-processing module). Note that the paragraphs applicant refers to are not cited as the image pre-processing module. Divaraniya’s paragraphs [0158],[0244]-[0245] teaches image processing algorithms, which are interpreted as an “image pre-processing module”, wherein “pre-processing” is interpreted as an intended use of the module. A recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim. Since Divaraniya’s algorithms or programs are capable of processing images, the algorithms or programs are capable of being used by a user for image pre-processing.
In response to applicant’s argument that Divaraniya fails to disclose “an image pre-processing module configured to identify a region of interest (ROI) from the image corresponding to a result viewing area of the cassette and generate a unidimensional (1D) profile of the ROI” and “spatially filter the 1D profile for reducing distortions in the 1D profile” (Remarks, page 10), the examiner disagrees.
Divaraniya teaches the image pre-processing module configured to:
identify a region of interest (ROI) from the image corresponding to a result viewing area of the cassette (paragraph [0158] “A rule-based image processing algorithm that first identifies a region of interest can be used to process the image”; paragraphs [0244]-[0245], teaches image processing first required identifying a region of interest and image processing captured lines of varying intensities using the image capture prototype, wherein the captured lines are interpreted as of a cassette);
generate a unidimensional (1D) profile of the ROI (paragraphs [0161]-[0162] teaches image processing includes generating a single vector of pixel intensities from each image, i.e. 1D profile of the region of interest; paragraph [0246]-[0247] and Figs. 26-27 teach image processing of a signal into a single vector of pixel intensities) by averaging the ROI along a vertical axis (Figs. 26-27 and paragraphs [0246]-[0247] teaches processing the single vector of the ROI shown in Fig. 26 with a rolling mean along a vertical line, i.e. vertical axis, resulting in a processed signal of Fig. 27); and
spatially filter the 1D profile for reducing distortions in the 1D profile (Figs. 26-27 and paragraphs [0246]-[0248] teaches the vector, i.e. 1D profile, was scrubbed, i.e. spatially filter, and condensed, which reduced distortions in the 1D profile as shown in Fig. 27; note that the instant application, specification, paragraph [0041], defines “spatial filter” as “an image processing technique employed for modifying or enhancing an image for smoothing by blurring and reducing noise in the image”; therefore, since the vector of Fig. 26 was scrubbed and shown to be modified for smoothing by blurring and reducing noise as shown in Fig. 27, the computer program instructions is interpreted as configured to spatially filter the 1D profile as claimed; additionally, paragraphs [0211],[0224] teaches the computer-implemented system can further comprise a topological and morphological transformation; paragraphs [0252],[0253] teaches removing noise from images of a region of interest using morphological transformations; therefore, the image pre-processing module is configured to or structurally capable of spatially filtering the 1D profile, e.g. region of interest, for reducing distortions in the 1D profile via topological and morphological transformation).
In response to applicant’s arguments that Divaraniya fails to teach an image analytics module comprising computer program instructions configured to probabilistically determine from the 1D profile, the concentration of the one or more target analytes in the sample based on a statistical model which is based on 1D profiles of ROIs of images obtained for a plurality of cassettes, wherein the statistical model comprises one or more parameters of the 1D profile varying with respect to one or more of time and the concentration of the one or more target analytes in the sample since Divaraniya merely teaches quantifying an amount of analyte to determine concentration based on pixel intensity as taught in paragraph [0011] (Remarks, pages 10-11), the examiner disagrees.
Divaraniya teaches: an image analytics module comprising computer program instructions configured to probabilistically determine from the 1D profile, the concentration of the one or more target analytes in the sample based on a statistical model (paragraph [0011] teaches a quantification module processing pixel intensities of an image to quantify an amount of the analyte to determine a concentration of the analyte; paragraph [0162] teaches single vector of pixel intensities; paragraph [0119] teaches determining concentration of the analyte based on training set, i.e. statistical model, of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from, which would comprise the 1D profile; paragraph [0192] teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison) which is based on 1D profiles of ROIs of images obtained for a plurality of cassettes (paragraph [0119] teaches determining concentration of the analyte based on training set, i.e. statistical model, of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from, which would inherently comprise 1D profiles of ROIs of images obtained for plurality of cassettes since training including a plurality of images would include imaging different cassettes with quantified concentrations that includes desired data such as a single vector of pixel intensities; paragraph [0162] teaches single vector of pixel intensities; paragraph [0192] teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison), wherein the statistical model comprises one or more parameters of the 1D profile varying with respect to one or more of time and the concentration of the one or more target analytes in the sample (paragraph [0119] teaches determining concentration of the analyte based on training set, i.e. statistical model, of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from, which would comprise the 1D profile for the different concentrations; paragraph [0192] teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison). Therefore, Divaraniya’s teachings in paragraphs [0011],[0119],[0162], [0192] together teach probabilistically determining from the 1D profile, the concentration of the analyte based on a statistical model as claimed. Additionally, note that applicant only refers to paragraph [0011] of Divaraniya and not the additional teachings found in paragraphs [0119],[0162], [0192].
In response to applicant’s argument that there is no teaching, suggestion, or motivation to have arrived at the claims in the alternative interpretation of claim 1 since Divaraniya does not provide motivation for quantifying analyte levels to solve the objective problem of the present device to arrive at all of the limitations of claim 1 (Remarks, pages 11-12), the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
In this case, Divaraniya teaches a quantification module processing pixel intensities of an image to quantify an amount of the analyte to determine a concentration of the analyte (paragraph [0011]). Divaraniya teaches each image can be viewed as a matrix and the resulting matrix can then be deconstructed into a single vector of pixel intensities following a scan line (paragraph [0162]). Divaraniya teaches determining concentration of the analyte based on training set of quantified concentrations of analytes, wherein the training set includes images whose analyte concentration can be quantified from(paragraph [0119]). Divaraniya teaches quality control processes wherein signal intensities and parameters, which include a statistical model, are analyzed and stored as reference for later comparison (paragraph [0192]). Divaraniya teaches image processing can include statistical tests (paragraphs [0165],[0255]), wherein statistical modeling and machine learning methods provide the tools necessary to utilize multiple feature types into a single analysis (paragraph [0313]). Divaraniya teaches once the region of interest was identified, results within the boundary were analyzed, which includes removing noise from the images using morphological transformations, i.e. spatial filtering (paragraph [0252]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the image analytics module of Divaraniya to incorporate Divaraniya’s teachings of a quantification module to process an image to determine concentration of an analyte, determining concentration based on training set of data of images that includes quantified concentrations of analytes, quality control processes that include statistical model used as a reference, and each image being deconstructed into a vector of pixel intensities (paragraphs [0011], [0119], [0165], [0162], [0192], [0255], [0313]) and removing noise with morphological transformations (paragraph [0252]) to provide: the image pre-processing module comprising computer program instructions configured to: spatially filter the 1D profile for reducing distortions in the 1D profile; the image analytics module comprising computer program instructions configured to probabilistically determine from the 1D profile, the concentration of the one or more target analytes in the sample based on a statistical model which is based on 1D profiles of ROIs of images obtained for a plurality of cassettes, wherein the statistical model comprises one or more parameters of the 1D profile varying with respect to one or more of time and the concentration of the one or more target analytes in the sample. Doing so would have a reasonable expectation of successfully improving minimum detection concentration, sensitivity, and accuracy of calculation of the concentration of analytes, by incorporating training set or statistical models to the measured 1D profile during calculations (Divaraniya, paragraph [0119]) and further reducing noise in analyzed image data (Divaraniya, paragraph [0252]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fay et al. (US 5149972 A) teaches an imaging apparatus including a processor (abstract). Fay teaches during the image processing, a Gaussian filter is used to reduce the noise in the images before ratiometric calculations, this step improves signal to noise at the cost of spatial resolution (column 22, lines 51-56).
Monroe et al. (US 20190212766 A1) teaches optical imaging and advanced imaging processing techniques (abstract). Monroe teaches positions of the individual atoms may be determined by fitting the overall intensity distribution to a sum of a variable number of Gaussian functions; it is then possible to determine the peak positions by calculating the “Laplacian of Gaussians” (LoG) algorithm (paragraph [0045]). Monroe teaches the Laplacian is often applied to an image that has first been smoothed with something approximating a Gaussian smoothing filter in order to reduce its sensitivity to noise; and the LoG algorithm may also be referred to simply as Laplacian or Marr filter (paragraph [0045]).
Walls et al. (US 20160146738 A1) teaches a method for analyzing a sample by analyzing spectral measurements (abstract). Walls teaches pre-processing spectral measurements to make the raw data suitable for subsequent analysis, which can be performed by integrating of peak area associated with a given element to produce an intensity curve, and analyzing the peak maxima associated with an element paragraph [0041]). Walls teaches pre-processing may also include applying an inverse Laplace transform or other filter or function to the data, or removing data that do not meet quality controls standards (paragraph [0041]).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 HENRY H NGUYEN whose telephone number is (571)272-2338. The examiner can normally be reached M-F 7:30A-5:00P.
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/HENRY H NGUYEN/Primary Examiner, Art Unit 1758
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