DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This action is responsive to the amendments and remarks received 09 April 2026. Claims 1 - 20 are currently pending.
Claim Objections
The objections to claims 4, 11, 14 and 18, due to minor informalities, are hereby withdrawn in view of the amendments and remarks received 09 April 2026.
Response to Arguments
Applicant's arguments filed 09 April 2026 have been fully considered but they are not persuasive.
On pages 14 - 17 of the remarks the Applicant’s Representative argues that the cited references, Tandon et al. and El-Zehiry et al., “fail to teach, disclose or suggest determining, by the processor, contours of each of the plurality of blood cells based on the edge detection of the plurality of blood-cells.” The Applicant’s Representative argues that the gradient-based techniques such as Sobel filtering in Tandon et al. “are used only as intermediate tools to assist segmentation and splicing of rectangular image patches” and that Tandon et al. never determine “contours of individual blood cells and identifying or storing a contour representation of each cell as a distinct analytical output.” Furthermore, the Applicant’s Representative argues that the closed boundaries referred to as part of the segmentation formulation in El-Zehiry et al. “arise from region-based segmentation masks, not from edge detection, and are not disclosed as contours that are determined based on edge detection techniques” and that EI-Zehiry et al. never extract “contours as a representation of each blood cell or describes using edge-detected contours as an intermediate or foundational structure for further analysis.” Therefore, the Applicant’s Representative argues that neither of the cited references, Tandon et al. and El-Zehiry et al., teach, suggest or disclose the aforementioned disputed claim limitation.
The Examiner respectfully disagrees.
The Examiner asserts that, at least, Tandon et al. disclose “determining, by the processor, contours of each of the plurality of blood cells based on the edge detection of the plurality of blood-cells”, see at least figures 12 and 25, page 6 paragraphs 0146 - 0147, page 11 paragraph 0200, page 15 paragraphs 0273 - 0277, page 16 paragraphs 0286 - 0290 and page 18 paragraphs 0330 - 0332 of Tandon et al. wherein it is disclosed that “segmentation may define boundaries in an image of the cellular artifacts” [0146], that typically “the collection of contiguous pixels is within or proximate to a boundary defined through segmentation. Often, a cellular artifact includes pixels of an identified boundary, all pixels within that boundary, and optionally some relatively small number of pixels surrounding the boundary” [0147], that “the segmentation process employs a gradient technique to identify cellular artifact edges” [0275], that “the image is then spliced based on the derived artifact dimensions from Sobel filtering elevation map generation given by equation 4. The Elevation map technique uses a Sobel operator to approximate the size of each artifact and conduct the cell extraction accordingly” [0286], that “the segmentation operation also involves applying a Sobel filter to the one or more images of the biological sample. In some implementations, the gray scale images are used. Data obtained through the Sobel filter accentuate edges of potential cellular artifacts” [0331] and that “segmentation further involves splicing the one or more images of the biological sample using the local maxima and data obtained from applying the Sobel filter, thereby obtaining a plurality of images of the cellular artifacts. In some applications, each spliced image includes a cellular artifact” [0332].
The Examiner asserts that, as shown herein above and in the cited portions, Tandon et al. disclose that a segmentation process is applied to the blood smear image, that the segmentation may define boundaries in an image of the cellular artifacts, that a boundary is defined through segmentation, and that the segmentation process employs a gradient technique to identify cellular artifact edges. Thus, the Examiner asserts that, at least, Tandon et al. disclose the aforementioned disputed claim limitation at least because Tandon et al. disclose that their segmentation process defines boundaries of cellular artifacts, i.e., contours of each of the plurality of blood cells, and that their segmentation process employs a gradient technique, Sobel filtering, i.e., edge detection, to identify cellular artifact edges.
Therefore, the Examiner asserts that the cited references, Tandon et al. and El-Zehiry et al., disclose the aforementioned disputed claim limitation.
On pages 14 and 17 - 21 of the remarks the Applicant’s Representative argues that the cited references, Tandon et al. and El-Zehiry et al., “fail to teach, disclose or suggest determining, by the processor, a bounding box for each of the plurality of blood-cells based on the contours of each of the plurality of blood-cells.” The Applicant’s Representative argues that Tandon et al. never determine “a bounding box based on contours of each blood cell.” Furthermore, the Applicant’s Representative argues that El-Zehiry et al. never generate “bounding boxes for blood cells at all” and that there is no teaching in EI-Zehiry et al. “for determining rectangular enclosures, image-space bounding boxes, or any box structures derived from contours.” The Applicant’s Representative argues that neither of the cited references, Tandon et al. and El-Zehiry et al., teach, disclose or suggest “using contours of individual blood cells as the basis for determining bounding boxes, as explicitly required by the claims.” Therefore, the Applicant’s Representative argues that Tandon et al. and El-Zehiry et al. fail to teach, disclose or suggest the aforementioned disputed claim limitation.
The Examiner respectfully disagrees.
The Examiner asserts that, at least, Tandon et al. disclose “determining, by the processor, a bounding box for each of the plurality of blood-cells based on the contours of each of the plurality of blood-cells”, see at least figures 5 and 23 - 25, page 6 paragraphs 0146 - 0147, page 11 paragraph 0200, page 15 paragraphs 0273 - 0275, page 16 paragraphs 0285 - 0290 and page 18 paragraphs 0330 - 0332, 0334 and 0338 of Tandon et al. wherein it is disclosed that “a cellular artifact represents a collection of contiguous pixels (with associated position and magnitude values) that are identified as likely belonging to a cell, parasite, or other sample feature of interest in a biological sample. Typically, the collection of contiguous pixels is within or proximate to a boundary defined through segmentation. Often, a cellular artifact includes pixels of an identified boundary, all pixels within that boundary, and optionally some relatively small number of pixels surrounding the boundary” [0147], that “the image is then spliced based on the derived artifact dimensions from Sobel filtering elevation map generation given by equation 4. The Elevation map technique uses a Sobel operator to approximate the size of each artifact and conduct the cell extraction accordingly” [0286], that the “spliced image is generated through the numpy sub array function passing the Euclidean and Sobel rectangular values as parameters, resulting in a dataset of segmented cells from the original smear image shot. The coordinates extracted from the Euclidean distance transformation and local maxima calculation are applied back to the original colored image to make the rectangular segmentation of cells on the original. The cells are then normalized to a 50×50 jpeg shot for identification” [0289] and that “segmentation further involves splicing the one or more images of the biological sample using the local maxima and data obtained from applying the Sobel filter, thereby obtaining a plurality of images of the cellular artifacts. In some applications, each spliced image includes a cellular artifact” [0332].
The Examiner asserts that, as shown herein above and in the cited portions, Tandon et al. disclose that a cellular artifact represents a collection of contiguous pixels that are identified as likely belonging to a cell, that the collection of contiguous pixels is within or proximate to a boundary defined through segmentation, that a Sobel operator is used to approximate the size of each artifact and conduct the cell extraction, that a spliced image is generated through the numpy sub array function passing the Euclidean and Sobel rectangular values as parameters, resulting in a dataset of segmented cells from the original smear image shot, and that their segmentation further involves splicing one or more images of the biological sample using the local maxima and data obtained from applying a Sobel filter to obtain a plurality of images of cellular artifacts. Thus, the Examiner asserts that, at least, Tandon et al. disclose the aforementioned disputed claim limitation at least because Tandon et al. disclose that the blood smear image is spliced based on data obtained from applying a Sobel filter, i.e., based on contours of each of the plurality of blood-cells, to obtain a plurality of rectangular images of cellular artifacts, i.e., bounding boxes for each of the plurality of blood-cells are determined.
Therefore, the Examiner asserts that the cited references, Tandon et al. and El-Zehiry et al., disclose the aforementioned disputed claim limitation.
Claim Rejections - 35 USC § 103
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3, 7, 8, 10, 14, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tandon et al. U.S. Publication No. 2018/0211380 A1 in view of El-Zehiry et al. U.S. Publication No. 2017/0132450 A1.
- The Examiner notes, with regards to claims 15 - 20, that claims 15 - 20 do not positively recite an interrelationship between the computer-executable instructions and an intended computer system for executing the computer-executable instructions and absent such a positively recited interrelationship the broadest reasonable interpretation of the limitations that the computer-executable instructions are intended to perform encompass interpretations wherein those limitations are non-functional because the claims do not limit the computer-executable instructions to an embodiment wherein the computer-executable instructions are executed by an intended computer system in order to perform its recited limitations.
- With regards to claims 1, 8 and 15, Tandon et al. disclose a method of analysing a blood smear image, (Tandon et al., Figs. 4A - 8, 10, 12, 18, 19, 24 & 25, Pg. 5 ¶ 0093 and 0096, Pg. 6 ¶ 0143 - 0147, Pg. 8 ¶ 0160 - Pg. 9 ¶ 0171, Pg. 10 ¶ 0193 - 0194, Pg. 11 ¶ 0205 - 0207, Pg. 12 ¶ 0210 - 0217) a system of analysing a blood smear image, (Tandon et al., Figs. 4A - 8, 10, 12, 18, 19, 24 & 25, Pg. 5 ¶ 0093 and 0096, Pg. 6 ¶ 0143 - 0147, Pg. 8 ¶ 0160 - Pg. 9 ¶ 0171, Pg. 10 ¶ 0193 - 0194, Pg. 11 ¶ 0205 - 0207, Pg. 12 ¶ 0210 - 0217, Pg. 22 ¶ 0380 - Pg. 23 ¶ 0388) comprising: a processor; (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) and a memory communicably coupled to the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) wherein the memory stores processor-executable instructions, which, on executing by the processor, cause the processor to [perform operations], (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) and a non-transitory computer-readable medium storing computer-executable instructions for analysing a blood smear image, (Tandon et al., Figs. 4A - 8, 10, 12, 18, 19, 24 & 25, Pg. 3 ¶ 0038, Pg. 5 ¶ 0093 and 0096, Pg. 6 ¶ 0143 - 0147, Pg. 8 ¶ 0160 - Pg. 9 ¶ 0171, Pg. 10 ¶ 0193 - 0194, Pg. 11 ¶ 0205 - 0207, Pg. 12 ¶ 0210 - 0217, Pg. 22 ¶ 0381) the computer-executable instructions configured for: detecting, by a processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) a plurality of blood cells in the blood smear image based on edge detection of the plurality of blood cells, (Tandon et al., Figs. 5, 12, 24 & 25, Pg. 6 ¶ 0143 and 0146 - 0147, Pg. 14 ¶ 0260 - 0263, Pg. 15 ¶ 0273 - 0277, Pg. 16 ¶ 0286 - 0290, Pg. 18 ¶ 0331 - 0332) wherein the edge detection of the plurality of blood cells in the blood smear image is based on a preprocessing of the blood smear image; (Tandon et al., Pg. 14 ¶ 0260 - 0268, Pg. 16 ¶ 0286 - 0290, Pg. 18 ¶ 0330 - 0332) determining, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) contours of each of the plurality of blood cells based on the edge detection of the plurality of blood-cells; (Tandon et al., Pg. 6 ¶ 0146 - 0147, Pg. 14 ¶ 0262 - 0263, Pg. 15 ¶ 0275 - 0276, Pg. 16 ¶ 0286 - 0290, Pg. 18 ¶ 0331 - 0332) determining, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) a bounding box for each of the plurality of blood-cells based on the contours of each of the plurality of blood-cells; (Tandon et al., Figs. 23 - 25, Pg. 6 ¶ 0146 - 0147, Pg. 14 ¶ 0262 - 0263, Pg. 16 ¶ 0285 - 0290, Pg. 18 ¶ 0330 - 0332, 0334 and 0338) classifying, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) each of the plurality of blood-cells as one of a white blood cell (WBC) or a red blood cell (RBC) using a deep learning model, (Tandon et al., Figs. 25, Pg. 7 ¶ 0150 - 0152, Pg. 11 ¶ 0206 - 0207, Pg. 12 ¶ 0210 - 0219, Pg. 13 ¶ 0221 - 0236, Pg. 16 ¶ 0296 - Pg. 17 ¶ 0313, Pg. 18 ¶ 0336) wherein the deep learning model is trained based on training data comprising a plurality of images of WBCs and RBCs; (Tandon et al., Figs. 7 & 8, Pg. 12 ¶ 0210 - 0219, Pg. 13 ¶ 0221 - 0236, Pg. 14 ¶ 0253 - 0256) determining, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) a count of WBCs, (Tandon et al., Pg. 11 ¶ 0205 - 0207, Pg. 12 ¶ 0210, Pg. 21 ¶ 0363 - 0370) and a count of RBCs (Tandon et al., Pg. 11 ¶ 0205 - 0207, Pg. 12 ¶ 0210 - 0211, Pg. 18 ¶ 0336, Pg. 21 ¶ 0365) based on the classification and the contours of each of the plurality of blood-cells; (Tandon et al., Pg. 7 ¶ 0150 - 0154, Pg. 8 ¶ 0160 - 0163, Pg. 11 ¶ 0205 - Pg. 12 ¶ 0211, Pg. 16 ¶ 0285 - Pg. 17 ¶ 0298, Pg. 18 ¶ 0329 - 0336, Pg. 21 ¶ 0363 - 0365) and outputting, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) a report comprising the count of WBC, the count of RBCs or the volumetric information of the RBCs and the WBCs. (Tandon et al., Figs. 4A, 6 & 7, Pg. 7 ¶ 0150 - 0151, Pg. 8 ¶ 0160 - 0165, Pg. 11 ¶ 0205 - Pg. 12 ¶ 0211, Pg. 18 ¶ 0334 - 0336, Pg. 21 ¶ 0362 - 0369) Tandon et al. fail to disclose explicitly determining volumetric information of the RBCs and the WBCs. Pertaining to analogous art, El-Zehiry et al. disclose analysing a blood smear image, (El-Zehiry et al., Abstract, Figs. 1, 2, 5 - 7 & 14, Pg. 2 ¶ 0030 - Pg. 3 ¶ 0032, Pg. 3 ¶ 0038 - 0042) comprising: detecting a plurality of blood cells in the blood smear image based on edge detection of the plurality of blood cells, (El-Zehiry et al., Figs. 1, 2, 5 & 6, Pg. 2 ¶ 0031 - Pg. 3 ¶ 0032, Pg. 3 ¶ 0039, Pg. 3 ¶ 0042 - Pg. 4 ¶ 0043) wherein the edge detection of the plurality of blood cells in the blood smear image is based on a preprocessing of the blood smear image; (El-Zehiry et al., Figs. 1, 2, 5 & 6, Pg. 2 ¶ 0031 - Pg. 3 ¶ 0032, Pg. 3 ¶ 0039, Pg. 3 ¶ 0042 - Pg. 4 ¶ 0043) determining contours of each of the plurality of blood cells based on the edge detection of the plurality of blood-cells; (El-Zehiry et al., Figs. 1, 2, 5 & 6, Pg. 2 ¶ 0031 - Pg. 3 ¶ 0032, Pg. 3 ¶ 0039, Pg. 3 ¶ 0042 - Pg. 4 ¶ 0043) determining a bounding box for each of the plurality of blood-cells based on the contours of each of the plurality of blood-cells; (El-Zehiry et al., Figs. 1, 2, 5 & 6, Pg. 2 ¶ 0031 - Pg. 3 ¶ 0032, Pg. 3 ¶ 0039, Pg. 3 ¶ 0042 - Pg. 4 ¶ 0043) determining a count of WBCs, a count of RBCs and volumetric information of the RBCs and the WBCs based on the classification and the contours of each of the plurality of blood-cells; (El-Zehiry et al., Figs. 1, 2, 5 & 6, Pg. 2 ¶ 0030 - 0031, Pg. 3 ¶ 0038 - 0042, Pg. 5 ¶ 0055 - 0059) and outputting a report comprising the count of WBCs, the count of RBCs or the volumetric information of the RBCs and the WBCs. (El-Zehiry et al., Fig. 14, Pg. 3 ¶ 0038 - 0042, Pg. 5 ¶ 0055 - 0059) Tandon et al. and El-Zehiry et al. are combinable because they are both directed towards image processing systems that process, classify and analyze blood cells. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Tandon et al. with the teachings of El-Zehiry et al. This modification would have been prompted in order to enhance the base device of Tandon et al. with the well-known and applicable technique El-Zehiry et al. applied to a comparable device. Determining volumetric information of the RBCs and the WBCs, as taught by El-Zehiry et al., would enhance the base device of Tandon et al. by providing it with additional information that it can use when classifying the health condition of a blood sample so as to improve the overall quality and reliability of any automated diagnoses it makes. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that volumetric information of the RBCs and the WBCs would be determined along with counts of the RBCs and the WBCs so as to provide the base device of Tandon et al. and its end-users with as much information as possible when evaluating the health condition of a blood sample. Therefore, it would have been obvious to combine Tandon et al. with El-Zehiry et al. to obtain the invention as specified in claims 1, 8 and 15.
- With regards to claims 3, 10 and 17, Tandon et al. in view of El-Zehiry et al. disclose the method, system and non-transitory computer-readable medium of claims 1, 8 and 15, respectively, wherein the edges of each of the plurality of blood cells are determined by determining a bimodal image of the blood smear image upon the preprocessing, (Tandon et al., Figs. 10 - 12, Pg. 6 ¶ 0146 - 0147, Pg. 8 ¶ 0166 - 0169, Pg. 14 ¶ 0262 - 0268, Pg. 15 ¶ 0274 - 0276, Pg. 16 ¶ 0284 - 0290) wherein the edges of each of the plurality of blood cells are determined based on the bimodal image by using an edge detection technique. (Tandon et al., Figs. 10 - 12, Pg. 6 ¶ 0146 - 0147, Pg. 8 ¶ 0166 - 0169, Pg. 14 ¶ 0262 - 0268, Pg. 15 ¶ 0274 - 0276, Pg. 16 ¶ 0284 - 0290)
- With regards to claims 7 and 14, Tandon et al. in view of El-Zehiry et al. disclose the method and system of claims 1 and 8, respectively, comprising: classifying, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) each of the plurality of blood-cells classified as the WBC as one of a plurality of WBC classes using the deep learning model, wherein each of the plurality of WBC classes correspond to a type of WBC from a plurality of WBC types. (Tandon et al., Fig. 25, Pg. 7 ¶ 0150 - 0152, Pg. 12 ¶ 0218 - 0219, Pg. 13 ¶ 0221 and 0231 - 0236, Pg. 16 ¶ 0296 - Pg. 17 ¶ 0298, Pg. 17 ¶ 0306 - 0313, Pg. 21 ¶ 0363 - 0368)
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tandon et al. U.S. Publication No. 2018/0211380 A1 in view of El-Zehiry et al. U.S. Publication No. 2017/0132450 A1 as applied to claims 1, 8 and 15 above, and further in view of Murphy et al. U.S. Publication No. 2018/0328848 A1.
- With regards to claims 2, 9 and 16, Tandon et al. in view of El-Zehiry et al. disclose the method, system and non-transitory computer-readable medium of claims 1, 8 and 15, respectively, wherein the preprocessing comprises: enhancing contrast, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) of the blood smear image; (Tandon et al., Figs. 10 - 12, Pg. 6 ¶ 0146 - 0147, Pg. 8 ¶ 0166 - 0169, Pg. 14 ¶ 0262 - 0268, Pg. 15 ¶ 0274 - 0276, Pg. 16 ¶ 0284 - 0290) and removing noise, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) in the blood smear image. (Tandon et al., Pg. 14 ¶ 0262 - 0268) Tandon et al. fail to disclose explicitly using a histogram equalization technique; and removing noise in the contrast enhanced blood smear image using a gaussian filter. Pertaining to analogous art, Murphy et al. disclose wherein the preprocessing comprises: removing noise in the contrast enhanced blood smear image using a gaussian filter. (Murphy et al., Figs. 4, 5 & 7A, Pg. 7 ¶ 0083 - 0086 and 0088 - 0089) Murphy et al. fail to disclose explicitly using a histogram equalization technique. However, the Examiner takes official notice of the fact that utilizing a histogram equalization technique when preprocessing digital images is notoriously well-known in the art. Tandon et al. in view of El-Zehiry et al. and Murphy et al. are combinable because they are all directed towards image processing systems that process, classify and analyze blood cells. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Tandon et al. in view of El-Zehiry et al. with the teachings of Murphy et al. This modification would have been prompted in order to enhance the combined base device of Tandon et al. in view of El-Zehiry et al. with the well-known and applicable technique Murphy et al. applied to a similar device. Removing noise in the contrast enhanced blood smear image using a gaussian filter, as taught by Murphy et al., would enhance the combined base device by improving its ability to accurately and reliably classify and analyze images of blood cells since as much erroneous image data as possible would be removed from the images and thus prevented from affecting classification and analysis results of the combined base device. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that a gaussian filter would be utilized to remove noise from the contrast enhanced blood smear image so as to improve the ability of the combined base device to accurately and reliably classify and analyze images of blood cells. In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Tandon et al. in view of El-Zehiry et al. in view of Murphy et al. to include a histogram equalization technique during image preprocessing. This modification would have been prompted in order to enhance the ability of the combined base device of Tandon et al. in view of El-Zehiry et al. in view of Murphy et al. with the notoriously well-known technique of utilizing histogram equalization to enhance the quality of images and improve discrimination between imaged objects. Utilizing a histogram equalization technique to enhance the contrast of the blood smear image would enhance the combined base device improving the contrast of the blood smear images such that imaged blood cells stand out more from the background thereby improving the ability of the combined base device to accurately and reliably detect, segment and analyze individual blood cells in the blood smear images. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that a histogram equalization technique would be utilized to enhance the quality of the blood smear images processed by the combined base device. Therefore, it would have been obvious to combine Tandon et al. in view of El-Zehiry et al. with Murphy et al. and the notoriously well-known technique of utilizing a histogram equalization technique to preprocess digital images to obtain the invention as specified in claims 2, 9 and 16.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Tandon et al. U.S. Publication No. 2018/0211380 A1 in view of El-Zehiry et al. U.S. Publication No. 2017/0132450 A1 as applied to claims 1, 8 and 15 above, and further in view of Jung et al., "WBC image classification and generative models based on convolutional neural network", BMC Medical Imaging, Vol. 22, No. 94, May 2022, pages 1 - 16 in view of Soni et al. U.S. Publication No. 2020/0311913 A1.
- With regards to claims 4, 11 and 18, Tandon et al. in view of El-Zehiry et al. disclose the method, system and non-transitory computer-readable medium of claims 1, 8 and 15, respectively, wherein the training data is generated by: inputting, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) at least one training image to the deep learning model, (Tandon et al., Pg. 12 ¶ 0212 - 0219, Pg. 13 ¶ 0221, Pg. 14 ¶ 0253 - 0259, Pg. 16 ¶ 0289 - 0296) wherein the at least one training image comprises at least one WBC and/or at least one RBC; (Tandon et al., Pg. 12 ¶ 0212 - 0219, Pg. 13 ¶ 0221, Pg. 14 ¶ 0253 - 0259, Pg. 16 ¶ 0289 - 0296) and determining, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) the plurality of images of WBCs and RBCs (Tandon et al., Pg. 12 ¶ 0212 - 0219, Pg. 13 ¶ 0221, Pg. 14 ¶ 0253 - 0259, Pg. 16 ¶ 0289 - 0296) by: determining, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) bounding boxes for each of the at least one WBC and/or the at least one RBC based on detection of contours of the at least one WBC and the at least one RBC in the at least one training image; (Tandon et al., Figs. 23 - 25, Pg. 6 ¶ 0146 - 0147, Pg. 14 ¶ 0262 - 0263, Pg. 16 ¶ 0285 - 0290, Pg. 18 ¶ 0330 - 0332) and cropping, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) each of the bounding boxes to determine at least one WBC image and at least one RBC image. (Tandon et al., Figs. 23 - 25, Pg. 6 ¶ 0146 - 0147, Pg. 12 ¶ 0212 - 0219, Pg. 13 ¶ 0221, Pg. 14 ¶ 0253 - 0259, Pg. 16 ¶ 0285 - 0296, Pg. 18 ¶ 0330 - 0334) Tandon et al. fail to disclose explicitly generating a first set of samples corresponding to WBCs and a second set of samples corresponding to RBCs based on the at least one WBC image and the at least one RBC image respectively using a generator model of the deep learning model, wherein the first set of samples and the second set of samples are generated based on a classification of each sample as one of real sample or fake sample using a discriminator model of the deep learning model, wherein samples corresponding to the first set of samples and the second set of samples are generated until the first set of samples is balanced with respect to the second set of samples and each of the first set of samples and the second set of samples are classified as real by the discriminator model; and wherein the plurality of images of WBCs and RBCs are determined based on the first set of samples and the second set of samples, respectively. Pertaining to analogous art, Jung et al. disclose wherein the training data is generated by: inputting at least one training image to the deep learning model, wherein the at least one training image comprises at least one WBC and/or at least one RBC; (Jung et al., Pg. 1 Abstract, Pg. 2 Subsection “Contributions”, Pg. 5 Subsection “Pre-processing of WBC images”, Pg. 5 Figs. 1 & 2, Pg. 10 Subsection “Dataset sharing” - Pg. 12 Subsection “Conclusion”, Pg. 11 Fig. 4) and determining the plurality of images of WBCs and RBCs by: determining bounding boxes for each of the at least one WBC and/or the at least one RBC based on the at least one WBC and the at least one RBC in the at least one training image; (Jung et al., Pg. 1 Abstract, Pg. 2 Subsection “Contributions”, Pg. 5 Subsection “Pre-processing of WBC images”, Pg. 5 Figs. 1 & 2, Pg. 10 Subsection “Dataset sharing” - Pg. 12 Subsection “Conclusion”, Pg. 11 Fig. 4) cropping each of the bounding boxes to determine at least one WBC image and at least one RBC image; (Jung et al., Pg. 1 Abstract, Pg. 2 Subsection “Contributions”, Pg. 5 Subsection “Pre-processing of WBC images”, Pg. 5 Figs. 1 & 2, Pg. 10 Subsection “Dataset sharing” - Pg. 12 Subsection “Conclusion”, Pg. 11 Fig. 4) and generating a first set of samples corresponding to WBCs and a second set of samples corresponding to RBCs based on the at least one WBC image and the at least one RBC image respectively using a generator model of the deep learning model, (Jung et al., Pg. 1 Abstract, Pg. 2 Subsection “Contributions”, Pg. 5 Subsection “Pre-processing of WBC images”, Pg. 5 Figs. 1 & 2, Pg. 10 Subsection “Dataset sharing” - Pg. 12 Subsection “Conclusion”, Pg. 11 Fig. 4) wherein the first set of samples and the second set of samples are generated based on a classification of each sample as one of real sample or fake sample using a discriminator model of the deep learning model, (Jung et al., Pg. 1 Abstract, Pg. 2 Subsection “Contributions”, Pg. 5 Subsection “Pre-processing of WBC images”, Pg. 5 Figs. 1 & 2, Pg. 10 Subsection “Dataset sharing” - Pg. 12 Subsection “Conclusion”, Pg. 11 Fig. 4) wherein samples corresponding to the first set of samples and the second set of samples are generated until the first set of samples is balanced with respect to the second set of samples; (Jung et al., Pg. 1 Abstract, Pg. 2 Subsection “Contributions”, Pg. 5 Subsection “Pre-processing of WBC images”, Pg. 5 Figs. 1 & 2, Pg. 10 Subsection “Dataset sharing” - Pg. 12 Subsection “Conclusion”, Pg. 11 Fig. 4) and wherein the plurality of images of WBCs and RBCs are determined based on the first set of samples and the second set of samples, respectively. (Jung et al., Pg. 1 Abstract, Pg. 2 Subsection “Contributions”, Pg. 5 Subsection “Pre-processing of WBC images”, Pg. 5 Figs. 1 & 2, Pg. 10 Subsection “Dataset sharing” - Pg. 12 Subsection “Conclusion”, Pg. 11 Fig. 4) Jung et al. fail to disclose expressly wherein each of the first set of samples and the second set of samples are classified as real by the discriminator model. Pertaining to analogous art, Soni et al. disclose wherein the training data is generated by: generating a first set of samples and a second set of samples using a generator model of the deep learning model, (Soni et al., Abstract, Figs. 1, 3 - 6 & 10, Pg. 2 ¶ 0019, Pg. 3 ¶ 0026, Pg. 4 ¶ 0040 - 0041, Pg. 6 ¶ 0058 - Pg. 7 ¶ 0060) wherein the first set of samples and the second set of samples are generated based on a classification of each sample as one of real sample or fake sample using a discriminator model of the deep learning model, (Soni et al., Abstract, Figs. 1, 3 - 6 & 10, Pg. 2 ¶ 0019, Pg. 3 ¶ 0026, Pg. 4 ¶ 0040 - 0041, Pg. 6 ¶ 0058 - Pg. 7 ¶ 0060) and wherein the plurality of images are determined based on the first set of samples and the second set of samples. (Soni et al., Abstract, Figs. 1, 3 - 6 & 10, Pg. 2 ¶ 0019, Pg. 3 ¶ 0026, Pg. 4 ¶ 0040 - 0041, Pg. 6 ¶ 0058 - Pg. 7 ¶ 0060) Tandon et al. in view of El-Zehiry et al. and Jung et al. are combinable because they are all directed towards image processing systems that process and classify blood cells. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Tandon et al. in view of El-Zehiry et al. with the teachings of Jung et al. This modification would have been prompted in order to enhance the combined base device of Tandon et al. in view of El-Zehiry et al. with the well-known technique Jung et al. applied to a comparable device. Utilizing a generator model of the deep learning model to generate training data until the first set of samples is balanced with respect to the second set of samples, as taught by Jung et al., would enhance the combined base device by improving its ability to produce a deep learning model this able to accurately and reliably classify blood cells since it would be able to easily obtain enough training images for each category of object to be classified to ensure that the deep learning model is properly trained. Furthermore, this modification would have been prompted by the teachings and suggestions of El-Zehiry et al. that the training set used to train their machine learning model had 100 images per category, see at least page 5 paragraph 0053 of El-Zehiry et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that a generator model of the deep learning model would be utilized to generate training data until the first set of samples is balanced with respect to the second set of samples so as to ensure that sufficient examples of each category of object to be classified by the deep learning model is readily and easily available to ensure that the deep learning model is properly trained to provide accurate and reliable classification of blood cell images. In addition, Tandon et al. in view of El-Zehiry et al. in view of Jung et al. and Soni et al. are combinable because they are all directed towards image processing systems that utilize machine learning models to evaluate medical images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Tandon et al. in view of El-Zehiry et al. in view of Jung et al. with the teachings of Soni et al. This modification would have been prompted in order to enhance the combined base device of Tandon et al. in view of El-Zehiry et al. in view of Jung et al. with the well-known technique Soni et al. applied to a similar device. Utilizing first and second sets of samples classified as real by the discriminator model as training data, as taught by Soni et al., would enhance the combined base device by improving its ability to accurately and reliably classify blood cells since only the most realistic and convincing looking synthetically generated images would be utilized when training the deep learning model. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that only first and second sets of samples classified as real by the discriminator model would be utilized as the training data so as to ensure that only the highest quality synthetically generated images are used to train the deep learning model of the combined base device. Therefore, it would have been obvious to combine Tandon et al. in view of El-Zehiry et al. with Jung et al. and Soni et al. to obtain the invention as specified in claims 4, 11 and 18.
Claims 5, 6, 12, 13, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tandon et al. U.S. Publication No. 2018/0211380 A1 in view of El-Zehiry et al. U.S. Publication No. 2017/0132450 A1 as applied to claims 1, 8 and 15 above, and further in view of Park et al. U.S. Publication No. 2023/0030787 A1.
- With regards to claims 5, 12 and 19, Tandon et al. in view of El-Zehiry et al. disclose the method, system and non-transitory computer-readable medium of claims 1, 8 and 15, respectively, comprises: displaying, by the processor, (Tandon et al., Fig. 4A, Pg. 3 ¶ 0038, Pg. 10 ¶ 0194 - 0197, Pg. 22 ¶ 0381 - Pg. 23 ¶ 0388) an analysis received from the system on a display screen. (Tandon et al., Pg. 7 ¶ 0150 - 0152, Pg. 8 ¶ 0162 - 0166, Pg. 11 ¶ 0205 - Pg. 12 ¶ 0211, Pg. 16 ¶ 0292 - 0293, Pg. 21 ¶ 0362 - 0365) Tandon et al. fail to disclose explicitly inputting the report as a query to a generative artificial intelligence-based query system; and an analysis received from the generative artificial intelligence-based query system. Pertaining to analogous art, Park et al. disclose inputting the report as a query to a generative artificial intelligence-based query system; (Park et al., Abstract, Figs. 2 - 5, Pg. 2 ¶ 0027 - 0028, Pg. 3 ¶ 0036 - 0044, Pg. 4 ¶ 0053 - 0060, Pg. 4 ¶ 0063 - Pg. 5 ¶ 0071) and displaying an analysis received from the generative artificial intelligence-based query system on a display screen. (Park et al., Abstract, Figs. 2 - 5, Pg. 2 ¶ 0027 - 0028, Pg. 3 ¶ 0036 - 0044, Pg. 4 ¶ 0053 - 0060, Pg. 4 ¶ 0063 - Pg. 5 ¶ 0071) Tandon et al. in view of El-Zehiry et al. and Park et al. are combinable because they are all directed towards systems and methods that utilize machine learning models and WBC counts to evaluate the health status of an individual. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined teachings of Tandon et al. in view of El-Zehiry et al. with the teachings of Park et al. This modification would have been prompted in order to enhance the combined base device of Tandon et al. in view of El-Zehiry et al. with the well-known technique Park et al. applied to a comparable device. Inputting the report as a query to a generative artificial intelligence-based query system, as taught by Park et al., would enhance the combined base device by allowing for end-users to utilize the WBC and RBC metrics it generated for various other purposes and in an increased number of applications, such as by inputting them into additional machine learning models, in order to improve the overall usefulness and appeal of the combined base device to potential end-users. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that the report generated by the combined base device would be input as a query to a generative artificial intelligence-based query system so as to allow for end-users to utilize the WBC and RBC metrics it generated for various other purposes and in an increased number of applications and thereby improve the overall usefulness and appeal of the combined base device to potential end-users. Therefore, it would have been obvious to combine Tandon et al. in view of El-Zehiry et al. with Park et al. to obtain the invention as specified in claims 5, 12 and 19.
- With regards to claims 6, 13 and 20, Tandon et al. in view of El-Zehiry et al. in view of Park et al. disclose the method, system and non-transitory computer-readable medium of claims 5, 12 and 19, respectively, wherein the analysis is based on the count of WBCs, the count of RBCs or the volumetric information of the RBCs and WBCs, (Tandon et al., Abstract, Pg. 21 ¶ 0363 - Pg. 22 ¶ 0379) wherein the analysis comprises one or more health conditions determined based on a comparison of the count of WBCs, the count of RBCs or the volumetric information of the RBCs and the WBCs with a corresponding predefined threshold count of WBCs, a predefined threshold count of RBCs or a predefined volumetric threshold of RBCs and WBCs. (Tandon et al., Abstract, Pg. 21 ¶ 0363 - Pg. 22 ¶ 0379)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC RUSH whose telephone number is (571) 270-3017. The examiner can normally be reached 9am - 5pm Monday - Friday.
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, Andrew Bee can be reached at (571) 270 - 5183. 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.
/ERIC RUSH/Primary Examiner, Art Unit 2677
Prosecution Insights