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 .
Comments
The Preliminary Amendment filed on March 2, 2026 has been entered and made of record.
Election/Restrictions
Applicant’s election with traverse of Invention II (claims 2 and 3) in the reply filed on January 20, 2026 is acknowledged. The traversal is on the ground(s) that system claim 1 requires features in device claims 2 and/or 3. This is not found persuasive because claims 1, 2, and 3 were all independent from each other. However, Applicant has canceled claims 1 and 2. Claims 3-22 are pending.
The requirement is still deemed proper and is therefore made FINAL.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
Reference Numerals “801”, “802”, and “803” shown in Figure 14.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
FIG. C-1 mentioned in page 83 line 22;
FIG. E1-1 mentioned in page 91 line 5;
Figure E1-2 mentioned in page 92 line 14;
Figure E1-3 mentioned in page 92 line 21;
FIG. E1-3 mentioned in page 93 line 1; and
Figure E1-4 mentioned in page 93 line 34.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Page 84 lines 29-32: “For example, quantitatively, as shown in FIG. 1, at the boundary, the distance (X90%) in which the normalized intensity decreases from 90% to 10% should be less than 5% of the length of the object in the image by such optical system”. Figure 1 has been designated to illustrate both, a block diagram of improving assay accuracy (i.e., page 2 lines 7-9 and page 14 lines 7-9) and a sharp optical edge (i.e., page 84 lines 25-34).
Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 4, 17, 18 and 19 are objected to because of the following informalities:
Claim 4 line 7: “dimension of 300 µm or less;” should end with a period -- dimension of 300 µm or less. --
Claim 17 line 1: “wherein algorithm” should read -- wherein the algorithm --
Claim 18 line 1: “18, (New)” should read -- 18. (New) --
Claim 18 line 1: “wherein algorithm” should read -- wherein the algorithm --
Claim 19 line 1: “wherein histogram-based operation” should read -- wherein the histogram-based operation
Appropriate correction is required.
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.
Claim 15 is 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 15 recites the limitation “the algorithm” in line 1. There is insufficient antecedent basis for this limitation in the claim. It is unclear as to which algorithm the algorithm limitation is referring to (i.e., an algorithm that, when executed, improves quality of the image and/or performs a determination of trustworthy of an assay result by analyzing the operational variables displayed in the image of the portion of the sample, as recited in claim 3 lines 8-11, or an algorithm that utilizes the marks as a parameter for improving the quality of the image, as recited in claim 14 lines 1-2). However, it appears that the algorithm, claim 15 line 1, is referring to an algorithm that utilizes the marks as a parameter for improving the quality of the image, as recited in claim 14 lines 1-2, and has been treated as such. Affirmation of this is required by the appropriate amendment.
Appropriate correction is required.
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 3 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lalpuria et al. (U.S. Pub. No. 2009/0257632) in view of Honkanen et al. (U.S. Pub. No. 2013/0034284).
As to claim 3, Lalpuria et al. teaches an apparatus (i.e., “analysis device 44”, Paragraph [0025]) for assaying an analyte in a sample under one or more operational variables, comprising:
(a) a sample holder (i.e., “acceptable analysis chamber 10”, Paragraph [0021]) for receiving and compressing (i.e., “larger separators 26 are compressed to the point where most separators 26 are touching the interior surfaces of the panels 12, 16, thereby making the chamber height just slightly less than the mean separator 26 diameters”, Paragraph [0023]) at least a part of the sample into a thin layer (i.e., “analysis chamber that is operable to quiescently hold a biological fluid sample (e.g., a sample of substantially undiluted anticoagulated whole blood) for analysis”, Paragraph [0020]); and
(b) an imager (i.e., “image dissector 48”, Paragraph [0026]) configured to obtain an image of a portion of the thin layer (i.e., “The analysis of the sample quiescently disposed within the chamber 10 is performed using an analysis device that is operable to illuminate and image at least a portion of the sample and perform an analysis on the image. The image is produced in a manner that permits one or both of the light absorbance through, and fluorescent emissions from, at least a portion of the sample to be determined on a per image unit basis”, Paragraph [0024]).
However, Lalpuria et al. does not explicitly disclose a computing unit and a non-transitory computer readable medium having an algorithm that, when executed, improves quality of the image and/or performs a determination of trustworthy of an assay result by analyzing the operational variables displayed in the image of the portion of the sample.
Honkanen et al. teaches a computing unit (i.e., “processing system”, Paragraph [0050]) and a non-transitory computer readable medium (i.e., “computer readable medium”, Paragraph [0071]) having an algorithm that, when executed, improves quality of the image (See for example, “enhance the dynamic range of assay results such that biochemical analyses are more efficient, accurate, and reliable. In one embodiment, the systems and methods include capturing multiple exposures of an image, processing out-of-range values (for example, overexposed and underexposed portions of the image) based on a pixel evaluation, and generating a composite image with enhanced dynamic range. This composite image achieves a greater contrast range than the individual images used to form the composite image”, Paragraph [0042]) and/or performs a determination of trustworthy of an assay result by analyzing the operational variables displayed in the image of the portion of the sample.
Lalpuria et al. and Honkanen et al. are analogous art because they are from the field of digital image processing for assay analysis.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify Lalpuria et al. by incorporating the computing unit and the non-transitory computer readable medium having an algorithm that, when executed, improves quality of the image and/or performs a determination of trustworthy of an assay result by analyzing the operational variables displayed in the image of the portion of the sample, as taught by Honkanen et al.
The suggestion/motivation for doing so would have been to obtain more accurate, reliable, and efficient chemical and biological analyses.
Therefore, it would have been obvious to combine Honkanen et al. with Lalpuria et al. to obtain the invention as specified in claim 3.
As to claim 18, Honkanen et al. teaches wherein algorithm comprises at least one selected from the group consisting of a histogram-based operation, a mathematics-based operation, a convolution-based operation, a smoothing operation, derivative-based operation, a morphology-based operation, shading correction, image enhancement and/or restoration, segmentation, feature extraction and/or matching, object detection and/or classification and/or localization, image understanding, and any combination of thereof (i.e., “enhanced dynamic range technique”, Paragraph [0041]).
Claims 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lalpuria et al. in view of Honkanen et al. as applied to claim 3 above, and further in view of Shen et al. (U.S. Pub. No. 2020/0051217). The teachings of Lalpuria et al. and Honkanen et al. have been discussed above.
As to claim 16, Lalpuria et al. and Honkanen et al. do not explicitly disclose wherein the algorithm further comprises machine learning, artificial intelligence, statistical methods, or a combination of thereof.
Shen et al. teaches an algorithm that comprises machine learning, artificial intelligence, statistical methods, or a combination of thereof (See for example, “accesses an image to enhance. In some embodiments, the system may be configured to access an image captured by an imaging device (e.g., a digital camera or an imaging sensor thereof)”, Paragraph [0179]; “provides the image accessed at bock 802 to a trained machine learning model”, Paragraph [0180]; and “After providing the image as input to the machine learning model at block 804, process 800 proceeds to block 806 where the system obtains an enhanced image from the output of the machine learning model”, Paragraph [0183]).
Lalpuria et al., Honkanen et al. and Shen et al. are analogous art because they are from the field of digital image processing.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to further modify Lalpuria et al. and Honkanen et al. by incorporating the algorithm further comprises machine learning, artificial intelligence, statistical methods, or a combination of thereof, as taught by Shen et al.
The suggestion/motivation for doing so would have been to enhance poor quality images.
Therefore, it would have been obvious to combine Shen et al. with Lalpuria et al. and Honkanen et al. to obtain the invention as specified in claim 16.
As to claim 17, Lalpuria et al. and Honkanen et al. do not explicitly disclose wherein algorithm comprises at least one selected from the group consisting of denoising, image normalization, image sharpening, image scaling, alignment (e.g., for face detection), super resolution, deblurring, and any combination of thereof.
Shen et al. teaches an algorithm that comprises at least one selected from the group consisting of denoising, image normalization, image sharpening, image scaling, alignment (e.g., for face detection), super resolution, deblurring, and any combination of thereof (i.e., “the machine learning model can be used to perform denoising of input images”, Paragraph [0058]; “the system may be trained to remove noise artifacts corrupting the input image, such as brightness, contrast, blurring, and/or the like”, Paragraph [0067]; “the imaging device may perform further processing on the image (e.g., brightness, white, sharpness, contrast)”, Paragraph [0088]; and “the image enhancement system 111 may be configured to normalize pixel values”, Paragraph [0092]).
Therefore, in view of Shen et al., it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Lalpuria et al. and Honkanen et al. by incorporating the algorithm comprises at least one selected from the group consisting of denoising, image normalization, image sharpening, image scaling, alignment (e.g., for face detection), super resolution, deblurring, and any combination of thereof, as taught by Shen et al., in order to increase the signal to noise ratio of the image.
Allowable Subject Matter
Claims 4-14 and 19-22 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 15 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the closest prior art made of record fails to disclose, teach, and/or suggest, inter alia, the apparatus of claim 3, wherein the sample holder comprises a first plate, a second plate, and one or more marks, wherein each of the first and second plates comprises a sample contacting area for receiving and compressing at least a portion of the sample into a thin layer, and the marks have a sharp edge that (a) has predetermined and known shape and dimension, (b) is observable by the imager, and (c) is a microstructure having at least one lateral linear dimension of 300 µm or less; or the apparatus of claim 18, wherein histogram-based operation comprises at least one selected from the group consisting of contrast stretching, equalization, minimum filtering, median filtering, maximum filtering, and a combination thereof; the mathematics-based operation comprises at least one selected from the group consisting
of binary operation (e.g., NOT, OR, AND, XOR, and SUB) arithmetic-based operations (e.g., ADD, SUB, MUL, DIV, LOG, EXP, SQRT, TRIG, and INVERT), and any combination thereof; the convolution-based operation comprises at least one selected from the group consisting of an operation in the spatial domain, Fourier transform, DCT, integer transform, an operation in the frequency domain, and any combination thereof; the smoothing operation comprises at least one selected from the group consisting of a
linear filter, a uniform filter, a triangular filter, a Gaussian filter, a non-linear filter, a medial filter a kuwahara filter, and any combination thereof; the derivative-based operation comprises at least one selected from the group consisting of a first-derivative operation, a gradient filter, a basic derivative filter, a Prewitt gradient filters, a Sobel gradient filter, an alternative gradient filter, a Gaussian gradient filter, a second derivative filter, a basic second derivative filter, a frequency domain Laplacian, a Gaussian second derivative filter, an Alternative Laplacian filter, a Second-Derivative-in-the-Gradient-Direction (SDGD) filter, a third derivative filter, a higher derivative filter (e.g., a greater than third derivative filter), and any combination thereof; the morphology-based operation comprises at least one selected from the group consisting
of dilation, erosion, Boolean convolution, opening and/or closing, hit-and-miss operation, contour, skeleton, propagation, gray-value morphological processing, Gray-level dilation, gray-level erosion, gray-level opening, gray-level closing, morphological smoothing, morphological gradient, morphological Laplacian, and any combination thereof; the image enhancement and/or restoration comprises at least one selected from the group consisting of sharpening, unsharpening, noise suppression, distortion suppression, and any combination thereof; the segmentation comprises at least one selected from the group consisting of thresholding, fixed thresholding, Histogram-derived thresholding, Isodata algorithm, background-symmetry algorithm, Triangle algorithm, Edge finding, Gradient-based procedure, zero-crossing based procedure, PLUS-based procedure, Binary mathematical morphology, salt-or-pepper filtering,
Isolate objects with holes, filling holes in objects, removing border-touching objects, Exo-skeleton, Touching objects, Gray-value mathematical morphology, Top-hat transform, thresholding, Local contrast stretching, and any combination thereof; and the feature extraction and/or matching comprises at least one selected from the group consisting of Independent component analysis, Isomap, Kernel Principal Component Analysis, Latent semantic analysis, Partial least squares, Principal component analysis, Multifactor dimensionality reduction, Nonlinear dimensionality reduction, Multilinear principal component Analysis, Multilinear subspace learning, Semidefinite embedding, Autoencoder, and any combination thereof, as claimed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE M TORRES whose telephone number is (571)270-1356. The examiner can normally be reached Monday thru Friday; 10:00 AM to 6:00 PM EST.
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, Jennifer Mehmood can be reached at 571-272-2976. 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.
/JOSE M TORRES/Examiner, Art Unit 2664 05/14/2026
Prosecution Insights