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 .
Information Disclosure Statement
The Information Disclosure Statement filed 09/24/2024 has been considered by examiner.
Drawings
The drawings are objected to because figures 1-3 features blank shape elements (see elements 102-106, 202, and 301-306) void of descriptive text labels . Corrected drawing sheets in compliance with 37 CFR 1.121(d) 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because it is directed towards software per se (See MPEP 2106.03 (I)). As claimed the “computer program product” is comprised of instructions and does not have a physical or tangible form. “As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories. Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719.” (See MPEP 2106.03 (I)).
Claim Rejections - 35 USC § 102
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 following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 8, and 13-15 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Neemuchwala (US 20100067769 A1).
With respect to claim 1, Neemuchwala teaches a method for refining annotations in images, the method comprising: obtaining an initially annotated image (“Tags, markers, and other background artifacts/obstructions may be removed by image operations unit 121 in step S310 or S311.” Paragraph 0062);
cropping said initially annotated image to obtain a cropped image, which retains only a part of the initially annotated image indicated by the annotation (“Image operations unit 121 may perform preprocessing and preparation operations on the mammography images (S304). Such preprocessing and preparation operations may include resizing, cropping, compression, etc., that change size and/or appearance of the mammography images” paragraph 0059);
analyzing pixel intensity distributions within the cropped image (“Fatty-dense tissue segmentation may also be performed using an intensity-based method for segmenting the dense tissue in the breast region. In mammography images, the dense tissue pixels concentrate at a higher intensity than fatty tissue pixels.” Paragraph 0075);
segmenting the cropped image based on the analysis of the pixel intensity distributions to obtain a segmented image (“Fatty-dense tissue segmentation may also be performed using an intensity-based method for segmenting the dense tissue in the breast region. In mammography images, the dense tissue pixels concentrate at a higher intensity than fatty tissue pixels.” Paragraph 0075);
refining the segmented image to obtain a refined segmented image (“To add an additional layer of protection to the dense tissue regions, the dense tissue results may be dilated to yield a final segmentation” paragraph 0075); and
performing similarity matching on the refined segmented image to obtain a delineation mask (“FIG. 10A illustrates an exemplary breast image, and FIG. 10B illustrates results of dense segmentation for the breast image of FIG. 10A according to an embodiment of the present invention illustrated in FIG. 9. FIG. 10B illustrates a breast mask that identifies dense tissue regions for the breast in FIG. 10A.” paragraph 0077).
With respect to claim 2, Neemuchwala teaches the method of claim 1, wherein the annotation in the initially annotated image is an image-level annotation (“Image operations unit 121 may perform preprocessing and preparation operations on the mammography images (S304). Such preprocessing and preparation operations may include resizing, cropping, compression, etc., that change size and/or appearance of the mammography images” paragraph 0059 and “Tags, markers, and other background artifacts/obstructions may be removed by image operations unit 121 in step S310 or S311.” Paragraph 0062).
With respect to claim 3, Neemuchwala teaches the method of claim 1, wherein the initially annotated image is a medical image (“Image operations unit 121 may perform preprocessing and preparation operations on the mammography images (S304). Such preprocessing and preparation operations may include resizing, cropping, compression, etc., that change size and/or appearance of the mammography images” paragraph 0059).
With respect to claim 8, Neemuchwala teaches the method of claim 1, wherein the refining the segmented image comprises: using an iterative Expectation Maximization algorithm (“Expectation maximization may be used for estimating the parameters based on these models…” paragraph 0075).
With respect to claim 13, Neemuchwala teaches a data processing system comprising: a processor configured to perform the method of claim 1 (see figure 1 element 37).
With respect to claim 14, Neemuchwala teaches a non-transitory computer program product comprising instructions (“In addition to performing temporal comparison of mammograms in accordance with embodiments of the present invention, the image processing unit 37 may perform additional image processing functions in accordance with commands received from the user input unit 77.” Paragraph 0029 and “Image operations unit 121, positional adjustment unit 131, segmentation unit 141, and selective registration unit 151 are software systems/applications.” Paragraph 0057), wherein when the instructions are executed by a computer, the instructions cause the computer to carry out the method of claim 1 (See figures 1 and 4).
With respect to claim 15, A non-transitory computer-readable storage medium (“Image operations unit 121, positional adjustment unit 131, segmentation unit 141, and selective registration unit 151 may also be purpose built hardware such as FPGA, ASIC, etc.” paragraph 0057) storing instructions that, when executed by a computer, cause the computer to carry out the method of claim 1 (see figures 1 and 4).
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 4, 12, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Neemuchwala (US 20100067769 A1) in view of Abrol (US 12217417 B2).
With respect to claim 4, Neemuchwala teaches the method of claim 1,but does not explicitly teach any further limitations. Abrol teaches wherein the cropping comprises: retaining only the part of the initially annotated image to focus on a region of interest defined by the annotation (“In any case, the segmentation component can produce a cropped version of the medical image that includes one or more regions-of-interest and that excludes one or more regions-of-disinterest and/or one or more background regions.” Page 19 col 7 lines 1-6), the part of the initially annotated image being inside a perimeter defined by the annotation (“Accordingly, the segmentation component can crop out of the selected training image any portions of the selected training image that are not within the region-of-interest. Similarly, the segmentation component can crop out of the corresponding annotation image any portions of the corresponding annotation image that are not within the region-of-interest.” Page 20 col 9 lines 55-60), and
removing a part of the initially annotated image which is outside the perimeter defined by the annotation (“Accordingly, the segmentation component can crop out of the selected training image any portions of the selected training image that are not within the region-of-interest. Similarly, the segmentation component can crop out of the corresponding annotation image any portions of the corresponding annotation image that are not within the region-of-interest.” Page 20 col 9 lines 55-60).
Abrol is analogous art in the same field of the claimed invention. Abrol is directed towards refining medical based training images for AI usage (“The subject disclosure relates generally to medical images, and more specifically to learning-based domain transformation for medical images.” Page 16 column 1 lines 6-8). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Neemuchwala and Arbol by utilizing the explicit teachings of Arbol’s annotation aware cropping inside the cropping and segmentation system of Neemuchwala by substituting its preprocessing process for Arbol’s refinement strategy, with the expectation that doing so would help facilitate learning-based domain transformation for medical images (“In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatus and/or computer program products that facilitate learning-based domain transformation for medical images are described.” Page 16 col 1 lines 35-39).
With respect to claim 12, Neemuchwala teaches the method of claim 1, but does bot explicitly teach any further limitations. Abrol teaches wherein the delineation mask is a voxel-level annotation (“In any case, the pre-trained segmentation model can be configured to identify and/or mask the region-of-interest, and the segmentation component can electronically crop out of the medical image 104 any pixels/voxels that do not make up the region-of-interest.” Page 23 col 16 lines 48-53).
Abrol is analogous art in the same field of the claimed invention. Abrol is directed towards refining medical based training images for AI usage (“The subject disclosure relates generally to medical images, and more specifically to learning-based domain transformation for medical images.” Page 16 column 1 lines 6-8). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Neemuchwala and Arbol by utilizing the explicit teachings of Arbol’s annotation aware cropping inside the cropping and segmentation system of Neemuchwala by substituting its preprocessing process for Arbol’s refinement strategy, with the expectation that doing so would help facilitate learning-based domain transformation for medical images (“In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatus and/or computer program products that facilitate learning-based domain transformation for medical images are described.” Page 16 col 1 lines 35-39).
With respect to claim 16, Neemuchwala teaches the method of claim 2, but does not explicitly teach any further limitations. Abrol teaches wherein the cropping comprises: retaining only the part of the initially annotated image to focus on a region of interest defined by the annotation (“In any case, the segmentation component can produce a cropped version of the medical image that includes one or more regions-of-interest and that excludes one or more regions-of-disinterest and/or one or more background regions.” Page 19 col 7 lines 1-6), the part of the initially annotated image being inside a perimeter defined by the annotation (“Accordingly, the segmentation component can crop out of the selected training image any portions of the selected training image that are not within the region-of-interest. Similarly, the segmentation component can crop out of the corresponding annotation image any portions of the corresponding annotation image that are not within the region-of-interest.” Page 20 col 9 lines 55-60), and
removing a part of the initially annotated image which is outside the perimeter defined by the annotation (“Accordingly, the segmentation component can crop out of the selected training image any portions of the selected training image that are not within the region-of-interest. Similarly, the segmentation component can crop out of the corresponding annotation image any portions of the corresponding annotation image that are not within the region-of-interest.” Page 20 col 9 lines 55-60).
Abrol is analogous art in the same field of the claimed invention. Abrol is directed towards refining medical based training images for AI usage (“The subject disclosure relates generally to medical images, and more specifically to learning-based domain transformation for medical images.” Page 16 column 1 lines 6-8). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Neemuchwala and Arbol by utilizing the explicit teachings of Arbol’s annotation aware cropping inside the cropping and segmentation system of Neemuchwala by substituting its preprocessing process for Arbol’s refinement strategy, with the expectation that doing so would help facilitate learning-based domain transformation for medical images (“In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatus and/or computer program products that facilitate learning-based domain transformation for medical images are described.” Page 16 col 1 lines 35-39).
With respect to claim 19, Neemuchwala and Arbol teach the method of claim 4, and Neemuchwala further teaches wherein the refining the segmented image comprises: using an iterative Expectation Maximization algorithm (“Expectation maximization may be used for estimating the parameters based on these models…” paragraph 0075).
Claims 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over Neemuchwala (US 20100067769 A1) in view of Wei (WO 2013075247 A1).
With respect to claim 5, Neemuchwala teaches the method of claim 1, but does not teach any further limitations. Wei teaches wherein the analyzing comprises: performing a histogram analysis on pixel intensities within the cropped image (see figure 2).
Wei is analogous art in the same field of endeavor as the claimed invention. Wei is directed to analyzing medical image intensities to identify anatomical structures (“In a first aspect, the present disclosure provides a histogram analysis method for automatically identifying a desired anatomic structure in a medical image.” Paragraph 00016). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to employ the strategy of Wei within the larger system of Neemuchwala by utilizing Wei’s histogram analysis process during Neemuchwala’s segmentation procedure, with the expectation that doing so would enable the system to automatically identify complex structures present in anatomical images (“Thus, there is still a need for a method of automatically identifying complex anatomical structures in a medical image.” Paragraph 00014 and “Herein is disclosed a histogram analysis method that combines both knowledge based intensity information and recursive subdivision (multi-scale analysis) to adaptively find thresholds in medical images.” Paragraph 00015)
With respect to claim 6, Neemuchwala and Wei teach the method of claim 5. Wei further teaches wherein the performing a histogram analysis on pixel intensities within the cropped image comprises: detecting modes (see figure 2 peak detection) and determining threshold values (see figure 2 knowledge base).
With respect to claim 7, Neemuchwala and Wei teach the method of claim 6. Wei further teaches wherein the segmenting comprises: segmenting the cropped image based on the threshold values (see figure 2 and “In a further aspect the method comprises segmenting the anatomic structure by thresholding the identified intensity values of the histogram segment associated with the anatomic structure to form a segmented image of the desired anatomic structure.” Paragraph 00017).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Neemuchwala in view of Ye (US 8675931 B2).
With respect to claim 9, Neemuchwala teach the method of claim 8, but does not explicitly teach and further limitation. Ye further teaches wherein the iterative Expectation Maximization algorithm uses the segmented image (“In step S1 the image to be segmented, or the required section of the image, is input as image data…” page 11 col 5. Lines 60-61) as an initial or prior guess.
Ye is analogous art in the same field of endeavor as the claimed invention. Ye is directed towards segmentation of medical images (“The present invention relates to the automated segmentation of medical images, and to the derivation of models for such automated segmentation.” Page 9 col 1 lines 12-15). A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine the teachings of Neemuchwala and Ye, by utilizing Ye’s segmentation teachings inside the broader annotation refinement scheme of Neemuchwala, with the expectation that doing so would enable the combined system to better isolate regions of interest from the background and other undesirable image parts (“Medical image segmentation is a difficult task because in most cases it is very hard to separate the object from the image background. This is due to the nature of the image acquisition process in which noise is inherent for all medical data, as well as the grey-value mappings of the objects themselves. The resolution of every acquisition device is limited, thus the value of each voxel in medical image represents an averaged value over some neighbouring region, called the partial volume effect. Moreover, the characteristics of the object such as low contrast, small size or location of the object within an area of complicated anatomy bring more critical challenges for automatic segmentation. For example, the intensities of lesions (e.g. juxta-vascular nodule, juxta-pleural nodule or colon polyp) are very similar to the adjacent tissues (e.g. blood vessel or pleural wall). In this case, traditional intensity-based or model-based methods might not properly segment the object.” Page 9 col 1. Lines 31-47).
With respect to claim 10, Neemuchwala and Ye teach the method of claim 9. Ye teaches wherein the using of the iterative Expectation Maximization algorithm comprises: calculating a probability that each pixel belongs to a particular segment or structure (“Another embodiment uses a novel Mixture Gaussian model with expectation-maximization (EM) considering the spatial and shape information from the shape index mode map, which can be used on the intensity mode map M.sub.i. Based on Bayesian probability theory, for each mode, the probability of the mode belonging to one class is defined as…Each mode is assigned to the Gaussian component for which it gives the highest likelihood” page 13 col 10 lines 22-41), and
adjusting model parameters based on probabilities derived from the calculating (“It can be seen that, if a voxel under the consideration has the similar shape as that of the neighbourhood voxels, a high weighting is assigned to a voxel which gives a high probability that the voxel belongs to the same class as that of the neighbouring voxels. By combining the shape feature into equation (15), the prior probability not only takes into account the spatial information, but also the shape information. This may provide better segmentation compared to only considering the spatial information on the intensity mode map (M.sub.i)” page 14 col. 11 lines 15-24).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Neemuchwala in view of Schnorr (US 10646156 B1).
With respect to claim 11, Neemuchwala teaches the method of claim 1, but does not explicitly teach any further limitations. Schnorr teaches wherein the performing of the similarity matching on the refined segmented image comprises: using a Structural Similarity Index computation (“In various embodiments, the performance evaluation of the filter system and method comprises the use of, but not limited to, standard deviation (STD), peak signal-to-noise ratio (PNSR), equivalent looks (ENL), and edge preservation index (EPI), the structural similarity index measurement (SSIM), and an unassisted measure of the quality of the first-order and second-order descriptors of the denoised image ratio (UM)” page 27 col 20. Lines 8-15) and performing a fine-tuning where a structure from a training dataset (“In various non-limiting embodiments, the training of said US-DRN comprises the use of 100 to 500 images, optionally obtained from a US scanner or device, that are further resized, (e.g., 256×256). In various embodiments, one or more 2D channel can be assigned to a corresponding axial, coronal or sagittal slices in a Volume of Interest (VOI).” Page 27 col. 19 lines 42-48) with a highest Structural Similarity Index value is utilized to optimize the delineation mask (“The EPI value reflects the retentive ability of the boundary, and a bigger value is better.” Page 27 col. 20 lines 18-19).
Schnorr is analogous art in the same field of endeavor as the claimed invention. Schnorr is directed towards medical image processing (“The present disclosure relates to the field of digital image processing and digital data processing systems, and corresponding image data processing frameworks; in particular, an adaptive digital image processing framework for use in assisted reproductive technology (ART).” Page 18 col 1 lines 7-11). A person of ordinary skill in the art before the effective filing date of he claimed invention would have found it obvious to combine the system of Neemuchwala with Schnorr by utilizing Schnorr’s delineation strategy in place of Neemuchwala’s more general segmentation mask procedure, with the expectation that doing so would lead to less noisy (“A number of approaches have been proposed to suppress speckle while preserving relevant image features. Most of these approaches rely on detailed classical statistical models of signal and speckle, either in the original or in a transform domain. The need exists for alternative methods to improve US resolution…” page 19 col 3 lines 54-59) more refined image data (“When it comes to the medical image analysis domain, the data sets are often inadequate to reach the full potential of DL. In the computer vision domain, transfer learning and fine tuning are often used to solve the problem of a small data set. In general, DL algorithms recognize the important features of images and properly give weight to these features by modulating their inner parameters to make predictions for new data, thus accomplishing identification, segmentation, classification, or grading, and demonstrating strong processing ability and intact information retention” page 19 col 4 lines 8-17) for used with ml algorithms (“Through applied effort, ingenuity, and innovation, Applicant has identified a number of deficiencies of the conventional approach… Applicant has developed a solution that is embodied by the present invention, which is described in detail below.” Page 20 col 6 lines 35-46).
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Neemuchwala in view of Arbol and Wei.
With respect to claim 17, Neemuchwala and Arbol teach the method of claim 16, but do not explicitly teach the further limitations. Wei teaches wherein the analyzing comprises: performing a histogram analysis on pixel intensities within the cropped image (see figure 2).
Wei is analogous art in the same field of endeavor as the claimed invention. Wei is directed to analyzing medical image intensities to identify anatomical structures (“In a first aspect, the present disclosure provides a histogram analysis method for automatically identifying a desired anatomic structure in a medical image.” Paragraph 00016). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to employ the strategy of Wei within the larger system of Neemuchwala and Arbol by utilizing Wei’s histogram analysis process during Neemuchwala’s segmentation procedure, with the expectation that doing so would enable the system to automatically identify complex structures present in anatomical images (“Thus, there is still a need for a method of automatically identifying complex anatomical structures in a medical image.” Paragraph 00014 and “Herein is disclosed a histogram analysis method that combines both knowledge based intensity information and recursive subdivision (multi-scale analysis) to adaptively find thresholds in medical images.” Paragraph 00015)
With respect to claim 18, Neemuchwala and Arbol teach the method of claim 4, but do not explicitly teach any further limitations. Wei teaches wherein the analyzing comprises: performing a histogram analysis on pixel intensities within the cropped image (see figure 2).
Wei is analogous art in the same field of endeavor as the claimed invention. Wei is directed to analyzing medical image intensities to identify anatomical structures (“In a first aspect, the present disclosure provides a histogram analysis method for automatically identifying a desired anatomic structure in a medical image.” Paragraph 00016). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to employ the strategy of Wei within the larger system of Neemuchwala and Arbol by utilizing Wei’s histogram analysis process during Neemuchwala’s segmentation procedure, with the expectation that doing so would enable the system to automatically identify complex structures present in anatomical images (“Thus, there is still a need for a method of automatically identifying complex anatomical structures in a medical image.” Paragraph 00014 and “Herein is disclosed a histogram analysis method that combines both knowledge based intensity information and recursive subdivision (multi-scale analysis) to adaptively find thresholds in medical images.” Paragraph 00015)
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Neemuchwala, Arbol, and Schnorr.
With respect to claim 20, Neemuchwala and Arbol teach the method of claim 4, but do not teach the additional limitations. Schnorr teaches wherein the performing of the similarity matching on the refined segmented image comprises: using a Structural Similarity Index computation (“In various embodiments, the performance evaluation of the filter system and method comprises the use of, but not limited to, standard deviation (STD), peak signal-to-noise ratio (PNSR), equivalent looks (ENL), and edge preservation index (EPI), the structural similarity index measurement (SSIM), and an unassisted measure of the quality of the first-order and second-order descriptors of the denoised image ratio (UM)” page 27 col 20. Lines 8-15) and performing a fine-tuning where a structure from a training dataset (“In various non-limiting embodiments, the training of said US-DRN comprises the use of 100 to 500 images, optionally obtained from a US scanner or device, that are further resized, (e.g., 256×256). In various embodiments, one or more 2D channel can be assigned to a corresponding axial, coronal or sagittal slices in a Volume of Interest (VOI).” Page 27 col. 19 lines 42-48) with a highest Structural Similarity Index value is utilized to optimize the delineation mask (“The EPI value reflects the retentive ability of the boundary, and a bigger value is better.” Page 27 col. 20 lines 18-19).
Schnorr is analogous art in the same field of endeavor as the claimed invention. Schnorr is directed towards medical image processing (“The present disclosure relates to the field of digital image processing and digital data processing systems, and corresponding image data processing frameworks; in particular, an adaptive digital image processing framework for use in assisted reproductive technology (ART).” Page 18 col 1 lines 7-11). A person of ordinary skill in the art before the effective filing date of he claimed invention would have found it obvious to combine the system of Neemuchwala with Schnorr by utilizing Schnorr’s delineation strategy in place of Neemuchwala’s more general segmentation mask procedure, with the expectation that doing so would lead to less noisy (“A number of approaches have been proposed to suppress speckle while preserving relevant image features. Most of these approaches rely on detailed classical statistical models of signal and speckle, either in the original or in a transform domain. The need exists for alternative methods to improve US resolution…” page 19 col 3 lines 54-59) more refined image data (“When it comes to the medical image analysis domain, the data sets are often inadequate to reach the full potential of DL. In the computer vision domain, transfer learning and fine tuning are often used to solve the problem of a small data set. In general, DL algorithms recognize the important features of images and properly give weight to these features by modulating their inner parameters to make predictions for new data, thus accomplishing identification, segmentation, classification, or grading, and demonstrating strong processing ability and intact information retention” page 19 col 4 lines 8-17) for used with ml algorithms (“Through applied effort, ingenuity, and innovation, Applicant has identified a number of deficiencies of the conventional approach… Applicant has developed a solution that is embodied by the present invention, which is described in detail below.” Page 20 col 6 lines 35-46).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA C WILLIAMS whose telephone number is (571)272-7074. The examiner can normally be reached M-F 7:30am - 4:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew W 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.
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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