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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-8, 15, and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “a quality of the image meets the selected threshold.” The limitation renders the claim indefinite because it is not clear whether this quality corresponds to the earlier recited quality (evaluated based on characteristic features) or a new/different quality. For the purpose of further examination, the limitation has been interpreted as corresponding to the earlier recited quality, e.g., “the quality of the image.”
Claims 2-8 depend from claim 1 and therefore inherit all of the deficiencies of claim 1 discussed above.
Claim 2 further recites the limitation “dark sample.” The term ‘dark’ is a relative and/or subjective term which renders the claim indefinite. The term ‘dark’ is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It is not clear whether a dark sample corresponds to any sample having an intensity value, fall within certain color value range, or a value determined using some other metric.
A claim that requires the exercise of subjective judgment without restriction renders the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005)); see also Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1373, 112 USPQ2d 1188 (Fed. Cir. 2014).
For the purpose of further examination, the limitation has been interpreted as a non-blank sample.
Claim 15 recites the limitation “and other optical characteristics.” The limitation renders the claim indefinite because it is not clear what is included in the other characteristics. Neither the specification nor the claims provide a list of the possible optical characteristics. For the purpose of further examination, the claim has been interpreted as verifying any one of the optical characteristics of the image.
Claim 17 recites the limitation “features expected from a valid test cassette.” The terms ‘expected’ and ‘valid’ are relative and/or subjective terms which render the claim indefinite. The terms are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The validity of a test may be different in different diseases, test types, etc. and the claims and/or specification do not provide further details to determine the scope of the limitation.
A claim that requires the exercise of subjective judgment without restriction renders the claim indefinite. In re Musgrave, 431 F.2d 882, 893, 167 USPQ 280, 289 (CCPA 1970). Claim scope cannot depend solely on the unrestrained, subjective opinion of a particular individual purported to be practicing the invention. Datamize LLC v. Plumtree Software, Inc., 417 F.3d 1342, 1350, 75 USPQ2d 1801, 1807 (Fed. Cir. 2005)); see also Interval Licensing LLC v. AOL, Inc., 766 F.3d 1364, 1373, 112 USPQ2d 1188 (Fed. Cir. 2014).
For the purpose of further examination, the claim has been interpreted as verifying that the target region contains the same features as the ones stored in a database.
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.
Claim(s) 9-11, 13-18, 20-22, and 25-27 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Carrio et al. (“Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection,” Sensors 2015, 15, 29569-29593; doi:10.3390/s151129569), hereinafter referred to as Carrio.
Regarding claim 9, Carrio teaches a computer-implemented method, comprising:
receiving an image from an image-capturing device, the image comprising an area of interest in a test cartridge (Abstract: “Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results”; Carrio Fig. 2: shows the test strip);
providing a first identifier code identifying the image-capturing device to a processor that contains software designed to assess a subject diagnostics based on a digital analysis of the image (Carrio pg. 29576: “Three smartphone devices were selected for capturing and processing the test images, taking into account their technical specifications and the mobile phone market share: Apple iPhone 4, 4S, and 5”);
identifying a target region within the area of interest of the test cartridge (Carrio Fig. 6 & pg. 29579: “By applying the explained segmentation process, the colored region of each strip, which indicates the type of drug of each test strip, is finally localized within the ROI image”);
evaluating a quality of the image based on a characteristic feature of the target region and on the first identifier code (Carrio pg. 29576: “Images must meet certain requirements in terms of image quality (blur, contrast, illumination, etc.)”; Carrio pg. 29589: “interpreting results at the cutoff level in contrast”); and
when a quality of the image meets a selected threshold, providing the image to the processor (Carrio pg. 29576 discussed above; Carrio pg. 29573: “For the results and validation presented, the rapid oral fluid drug test DrugCheck SalivaScan [15], manufactured by Express Diagnostics Inc. (Minneapolis, MN, USA) was used”; Carrio Table 5: show the test results of the image processing algorithm and comparison with a human operator).
Regarding claim 10, Carrio teaches the computer-implemented method of claim 9, further comprising retrieving a calibration table from a remote server using the first identifier code, the calibration table associated with the image-capturing device, and indicative of a signal value that is a threshold for evaluating the quality of the image (Carrio pg. 29576, pg. 29576, & pg. 29589 discussed above).
Regarding claim 11, Carrio teaches the computer-implemented method of claim 9, wherein evaluating a quality of the image comprises selecting a threshold for a signal intensity in a process control area within the target region based on the first identifier code (Carrio pg. 29576, pg. 29576, & pg. 29589 discussed above).
Regarding claim 13, Carrio teaches the computer-implemented method of claim 9, wherein evaluating a quality of the image comprises verifying that a reference line appears at a selected location (Carrio Fig. 1 & pg. 29575: “interpretation is based on the presence or absence of lines. Two differently-colored lines may appear in each test strip, a control line (C) and a test line (T)”; Carrio Figs. 5-6).
Regarding claim 14, Carrio teaches the computer-implemented method of claim 9, wherein evaluating a quality of the image comprises verifying that a signal intensity of a negative control is less than a selected threshold indicative of an assay interference (Carrio §3.2 described in pg. 29575 teaches interpreting the test results as positive, negative, or invalid; see “A negative result indicates that the analyte concentration is below the cutoff level”; also see Carrio Fig. 2).
Regarding claim 15, Carrio teaches the computer-implemented method of claim 9, wherein evaluating a quality of the image comprises verifying an exposure, a focus, and other optical characteristics of the image are satisfactory (Carrio pg. 29576: “Images must meet certain requirements in terms of image quality (blur, contrast, illumination, etc.)”).
Regarding claim 16, Carrio teaches the computer-implemented method of claim 9, wherein evaluating a quality of the image comprises assessing whether the image is appropriate to send to the AI model for inferencing (Carrio pg. 29576 discussed above teaches that the images must meet the quality requirement; Carrio pg. 29581: “Three different supervised machine learning classifiers based on artificial neural networks (ANN) have been implemented”).
Regarding claim 17, Carrio teaches the computer-implemented method of claim 9, wherein evaluating a quality of the image comprises verifying that a valid crop in the image contains features expected from a valid test cassette (Carrio Figs. 1-2, 5-6).
Regarding claim 18, Carrio teaches the computer-implemented method of claim 9, wherein evaluating a quality of the image comprises monitoring physical attributes of the image-capturing device (Carrio Fig. 4 & pg. 29576: teaches monitoring the smartphone light box set up to ensure that the relative positions are constant).
Regarding claim 20, Curio teaches a computer-implemented method, comprising:
retrieving a first image associated with an assay in a test cartridge carrying a biological sample from a user for a disease diagnostic (Carrio Abstract: “Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results”; Carrio Fig. 2: shows the test strip; Carrio pg. 29573: “human oral fluid samples”);
selecting a digital portion of the first image (Carrio Fig. 5 described in pg. 29578: “the selected four points in the template image that define the region of interest (ROI) of the strips (the area within the green border in Figure 5)”);
modifying, with a model, the digital portion of the first image to obtain a weighted value of the digital portion (Carrio pg. 29577: “the transformation that converts a point in the template image to the corresponding point in the current image”; Carrio Fig. 6 & pg. 29579: “By applying the explained segmentation process, the colored region of each strip, which indicates the type of drug of each test strip, is finally localized within the ROI image”);
determining, based on the weighted value of the digital portion and the model, a diagnostic value (Carrio §3.2 described in pg. 29575 teaches interpreting the test results as positive, negative, or invalid; see “A negative result indicates that the analyte concentration is below the cutoff level”); and
determining a certainty level for the diagnostic value based on a second weighted value from a second digital portion of the first image (Carrio Fig. 11: the classified results identified as very positive, positive, doubt, negative, and very negative).
Regarding claim 21, Carrio teaches the computer-implemented method of claim 20, wherein retrieving the first image comprises receiving an image from a client device via a remote network communication channel (Carrio Abstract: “smartphone-based automated reader”; Carrio pg. 29571: “easily stored and treated in a remote database by taking advantage of the smartphone connectivity capabilities”).
Regarding claim 22, Carrio teaches the computer-implemented method of claim 20, wherein retrieving the first image comprises accessing a database including multiple images of multiple assays including different biological samples from multiple users (Carrio Tables 2-4: shows that multiple samples were used).
Regarding claim 25, Carrio teaches the computer-implemented method of claim 20, wherein the assay is an immunoassay (Carrio pg. 29573: “DrugCheck SalivaScan is an immunoassay for rapid qualitative and presumptive detection of drugs-of-abuse in human oral fluid samples”).
Regarding claim 26, Carrio teaches the computer-implemented method of claim 20, wherein the assay is a lateral flow immunoassay (Carrio pg. 29591: “An innovative, low-cost, portable approach for the rapid interpretation of lateral flow saliva test results in drug-of-abuse detection based on the use of commonly-available smartphone devices has been presented and evaluated”).
Regarding claim 27, Carrio teaches the computer-implemented method of claim 20, wherein the assay is a molecular diagnostic assay (Carrio Table 1: the types of molecules being tested).
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.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carrio et al. (Sensors 2015, 15, 29569-29593; doi:10.3390/s151129569), in view of Cheng (US 2017/0254804 A1), hereinafter referred to as Carrio and Cheng, respectively.
Regarding claim 1, Carrio teaches a computer-implemented method, comprising:
receiving an image from an image-capturing device, the image comprising an area of interest in a test cartridge (Carrio Abstract: “Test images captured with the smartphone camera are processed in the device using computer vision and machine learning techniques to perform automatic extraction of the results”; Carrio Fig. 2: shows the test strip);
finding a border of the area of interest of the test cartridge and applying a geometrical transformation on an area delimited by the border of the test cartridge to bring the image of the area of interest in the test cartridge to a selected size and a selected shape (Carrio pg. 29577: “the transformation that converts a point in the template image to the corresponding point in the current image”; Carrio Fig. 5 described in pg. 29578: “the selected four points in the template image that define the region of interest (ROI) of the strips (the area within the green border in Figure 5)”);
identifying a target region within the area of interest of the test cartridge (Carrio Fig. 6 & pg. 29579: “By applying the explained segmentation process, the colored region of each strip, which indicates the type of drug of each test strip, is finally localized within the ROI image”);
evaluating a quality of the image based on a characteristic feature of the target region (Carrio pg. 29576: “Images must meet certain requirements in terms of image quality (blur, contrast, illumination, etc.)”; Carrio pg. 29589: “interpreting results at the cutoff level in contrast”); and
when a quality of image meets the selected threshold, providing the image to a processor that contains software designed to assess a subject diagnostics based on a digital analysis of the image (Carrio pg. 29576 discussed above; Carrio pg. 29573: “For the results and validation presented, the rapid oral fluid drug test DrugCheck SalivaScan [15], manufactured by Express Diagnostics Inc. (Minneapolis, MN, USA) was used”; Carrio Table 5: show the test results of the image processing algorithm and comparison with a human operator).
Carrio further teaches that the images must meet the quality requirement, e.g., blur, contrast, illumination, etc. (Carrio pg. 29576). However, Carrio does not appear to explicitly teach providing commands to adjust an optical coupling in the image-capturing device when the quality of the image is lower than a selected threshold.
Pertaining to the same field of endeavor, Cheng teaches providing commands to adjust an optical coupling in the image-capturing device when the quality of the image is lower than a selected threshold (The claims do not specify what the adjusting of optical coupling corresponds to. Using the BRI, the examiner has interpreted the limitation has adjusting camera parameters, e.g., focus, illumination, etc. Cheng ¶0026: “the user can adjust the intensity of the light emitted from the light source module 10 or selectively activate at least one of a plurality of LEDs”; Cheng ¶0031: “the image capturing module 30 can adjust an exposure value so as to capture the fluorescence image in a proper exposure value”; Cheng ¶0033: “The housing 40 further comprises a focus adjusting mechanism 45 to adjust the distance between the test unit 20 and the image capturing module 30”).
Carrio and Cheng are considered to be analogous art because they are directed to image processing. 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 method and system using an automated saliva test reader (as taught by Carrio) to adjust optical coupling (as taught by Cheng) because the combination ensures that the acquired images are proper for further analysis (Cheng ¶0031).
Regarding claim 2, Carrio, in view of Cheng, teaches the computer-implemented method of claim 1, wherein evaluating the quality of the image further comprises evaluating a second image, wherein the second image is one of a dark sample or a blank sample (Carrio Fig. 5: the template image is shown on the left containing multiple samples, including colored samples in columns 1-4 and a blank on the far right side).
Regarding claim 3, Carrio, in view of Cheng, teaches the computer-implemented method of claim 1, further comprising normalizing an intensity value for a pixel in the image of the area of interest relative to a selected intensity value from multiple pixels in the image of the area of interest (Carrio pg. 29581: “This normalization is done only with respect to the first 15 values of the lateral histogram, because this part of the histogram has proven to account well for illumination changes along the strip. The normalization with respect to the mean of these 15 values helps to minimize the influence of differences in illumination along the strip”).
Regarding claim 4, Carrio, in view of Cheng, teaches the computer-implemented method of claim 1, wherein the target region comprises a process control area including at least one of a positive control area or a negative control area, and evaluating a quality of the image comprises evaluating a signal intensity in the process control area (Carrio pg. 29570: “it is possible to measure the color intensity of the test lines in order to determine the quantity of analyte in the sample”; Carrio Fig. 1 & pg. 29575: “interpretation is based on the presence or absence of lines. Two differently-colored lines may appear in each test strip, a control line (C) and a test line (T) … The intensity of the colored line in the test region may vary depending on the concentration of the analyte present in the specimen”).
Regarding claim 5, Carrio, in view of Cheng, teaches the computer-implemented method of claim 1, wherein identifying a target region within the area of interest comprises identifying at least a test line and a control line in the area of interest of the test cartridge within a field of view of the image (Carrio Figs. 5-6).
Regarding claim 6, Carrio, in view of Cheng, teaches the computer-implemented method of claim 1, further comprising displaying the image in a computer display, and including a viewing guide in the computer display, the viewing guide overlapping at least a portion of the digital analysis of the image (Carrio Fig. 6: shows the segmentation results highlighting the detected lines).
Regarding claim 7, Carrio, in view of Cheng, teaches the computer-implemented method of claim 1, further comprising displaying a test result in a computer display, and not displaying the image (Carrio Tables 5-7: the test results summary is shown but not the images).
Regarding claim 8, Carrio, in view of Cheng, teaches the computer-implemented method of claim 1, wherein evaluating a quality of the image comprises comparing a selected feature of the image with a value associated with selected features of multiple images having known quality values (Carrio Fig. 2 & pg. 29575: “the interpretation of the results should be made by comparing the color obtained in the reagent strip against a printed color pattern that is provided with the test”; Carrio pg. 29577: “this area is manually defined in a template image, which is stored in a database … calculating keypoints in the template image and extracting the corresponding descriptors” … When a new image is captured from the device, the first step consists of extracting the ORB keypoints and their descriptors and matching them with the ones extracted from the template”).
Claim(s) 12 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carrio et al. (Sensors 2015, 15, 29569-29593; doi:10.3390/s151129569), in view of Egan et al. (US 9,207,181 B2), hereinafter referred to as Carrio and Egan, respectively.
Regarding claim 12, Carrio teaches the computer-implemented method of claim 9, but does not appear to explicitly teach verifying that a signal intensity at the end of a test channel in the test cartridge is higher than a selected threshold indicative that the sample flowed to the end.
Pertaining to the same field of endeavor, Egan teaches verifying that a signal intensity at the end of a test channel in the test cartridge is higher than a selected threshold indicative that the sample flowed to the end (Egan col. 22 lines 14-47: “The procedural control zone is a region located between the last analyte test line and the absorbent pad. The analyzer scans this zone positioned at the downstream end of the test device, and determines whether adequate flow of the sample has occurred”).
Carrio and Egan are considered to be analogous art because they are directed to image processing. 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 method and system using an automated saliva test reader (as taught by Carrio) to check whether the sample flow is adequate (as taught by Egan) because the combination ensures that the sample is viable for further analysis.
Regarding claim 19, Carrio teaches the computer-implemented method of claim 18, but does not appear to explicitly teach that the physical attribute is internal temperature of the image-capturing device.
Pertaining to the same field of endeavor, Egan teaches that the physical attribute is internal temperature of the image-capturing device (Egan col. 9 lines 28-27: “includes a temperature sensing means, and in a preferred embodiment, includes at least two temperature sensing devices housed within the housing of the apparatus. A first internal temperature sensor is positioned to detect the temperature in the region associated with the optics system and a second internal temperature sensor is positioned elsewhere in the apparatus away from any internally generated heat source in order to detect ambient temperature of the environment in which the apparatus is operated”).
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 method and system using an automated saliva test reader (as taught by Carrio) to monitor the sensor temperature (as taught by Egan) because the combination can determine the difference between the ambient temperature and any internally generated heat (Egan col. 9 lines 28-27).
Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carrio et al. (Sensors 2015, 15, 29569-29593; doi:10.3390/s151129569), in view of Welch et al. (US 2010/0028870 A1), hereinafter referred to as Carrio and Welch, respectively.
Regarding claim 23, Carrio teaches the computer-implemented method of claim 20, further comprising comparing the certainty level for the diagnostic value to a predetermined value (Carrio pg. 29582: “The last step ensures that the algorithm gives only a trained output as a classification result if its confidence level is high enough, above a certain threshold”).
However, Carrio does not appear to explicitly teach updating the model.
Pertaining to the same field of endeavor, Welch teaches updating the model when the certainty level for the diagnostic value is less than a predetermined value (Welch ¶0181: “The initial set of data can be small, so models built from it can be inaccurate. Improving the modeled relationship further depends upon obtaining better values for weights whose confidence scores are low. To obtain this data, additional variants designed will provide additional data useful in establishing more precise sequence-expression relationship”; Welch ¶0184: “Additional analyses of the data may be used to further refine models”).
Carrio and Welch are considered to be analogous art because they are directed to assays. 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 method and system using an automated saliva test reader (as taught by Carrio) to update the model (as taught by Welch) because the combination produces more refined results.
Claim(s) 24 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Carrio et al. (Sensors 2015, 15, 29569-29593; doi:10.3390/s151129569), in view of Chou et al. (US 2020/0256856 A1), hereinafter referred to as Carrio and Chou, respectively.
Regarding claim 24, Carrio teaches the computer-implemented method of claim 20, but does not appear to explicitly teach that modifying the digital portion of the first image comprises convoluting a value of the digital portion of the first image with multiple values of adjacent digital portions of the first image according to a weighting coefficient in the model.
Pertaining to the same field of endeavor, Chou teaches that modifying the digital portion of the first image comprises convoluting a value of the digital portion of the first image with multiple values of adjacent digital portions of the first image according to a weighting coefficient in the model (Chou ¶0014: “The term ‘adaptative thresholding’ refers to the thresholding methods whose value at each pixel location depends on the neighboring pixel intensities. In image processing, adaptative thresholding typically takes a grayscale or color image as input and, in the simplest implementation, outputs a binary image representing the segmentation, wherein for each pixel in the image, a threshold has to be calculated …The term ‘detection model by convolution’ refers to a detection model that utilizes the convolution operations to detect and classify the input signal”; Chou ¶0037: “Convolution is a specialized kind of linear operation. Convolutional networks are simply neural networks that use convolution in place of general matrix multiplication in at least one of their layers”; Chou ¶0045: “fed into a convolutional neural network, and the output of the detection stage is a pixel-level prediction”).
Carrio and Chou are considered to be analogous art because they are directed to image-based assay systems. 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 method and system using an automated saliva test reader (as taught by Carrio) to perform convolution (as taught by Chou) because the combination produces pixel-level prediction results (Chou ¶0045).
Regarding claim 28, Carrio teaches the computer-implemented method of claim 27, but does not appear to explicitly teach that the molecular diagnostic assay comprises amplification of a molecular target and binding of the amplified molecular target to a surface for visualization.
Pertaining to the same field of endeavor, Chou teaches that the molecular diagnostic assay comprises amplification of a molecular target and binding of the amplified molecular target to a surface for visualization (Chou ¶0162: “one or both plate sample contact surfaces comprise one or a plurality of binding sites that each binds and immobilize a respective analyte; or ii. one or both plate sample contact surfaces comprise, one or a plurality of storage sites that each stores a reagent or reagents; wherein the reagent(s) dissolve and diffuse in the sample, and wherein the sample contains one or plurality of analytes; or iii. one or a plurality of amplification sites that are each capable of amplifying a signal from the analyte or a label of the analyte when the analyte or label is 500 nm from the amplification site; or iv. any combination of i to iii”).
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 method and system using an automated saliva test reader (as taught by Carrio) to amplify the molecular target to a surface (as taught by Chou) because the combination results in a better detection.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-28 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-28 of U.S. Patent No. 11,783,563 B2 and claims 1-27 of U.S. Patent No. 12,165,373 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because both the application and the patent are directed to a computer-implemented method comprising receiving one or more images from an image-capturing device, the one or more images comprising an area of interest in a test cartridge; finding a border of the area of interest of the test cartridge and applying a geometrical transformation on an area delimited by the border of the test cartridge to bring an image of the area of interest in the test cartridge to a selected size and a selected shape; identifying a target region within the area of interest of the test cartridge; evaluating a quality of the one or more images based on a characteristic feature of the target region; providing commands to adjust an optical coupling in the image-capturing device when the quality of the one or more images is lower than a selected threshold; and when the quality of the one or more images meets the selected threshold, providing the one or more images to a processor that contains software designed to assess a subject diagnostics based on a digital analysis of the one or more images. The dependent claims also recite the same subject matter.
Conclusion
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