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 .
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.
[1] Claims 1, 3, 4, 7, 18, 37 and 38 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Arnold et al. (“Arnold”) [US 2022/0207730].
Regarding claim 1, Arnold teaches the claim limitations as follows:
A computer-implemented method of performing quality control prediction technique for a whole-slide image [fig. 9], comprising: receiving, via one or more processors, the whole-slide image, wherein the whole-slide image is subdivided into a grid including a plurality of image tiles (i.e. 904) [fig. 9; para. 0120]; processing, via one or more processors, the whole-slide image using a trained machine learning model (i.e. 908) to generate a prediction (i.e. 920, cancer presence indicator) based on the plurality of image tiles (i.e. 904) [fig. 9; paras. 0121 and 0124]; determining, based on an artificial neural network, a respective attention score for each of the plurality of image tiles (i.e. 916) [fig. 9; para. 0123]; generating a pass/fail indication (i.e. 940, cancer grade indicator) corresponding to the prediction by selecting a subset of the plurality of image tiles (i.e. 924) based on the respective attention score of each of the subset of the plurality of image tiles (i.e. clustering based attention scores) [fig. 9; para. 0125]; and processing the selected subset of image tiles (i.e. 928) to determine their biological relevance to the prediction (i.e. 940, cancer grade indicator) [fig. 9; paras. 0126 and 0129].
Regarding claim 3, Arnold teaches the claim limitations as follows:
The computer-implemented method of claim 1, wherein the prediction (i.e. 920, cancer presence indicator) passes quality control (i.e. 940, cancer grade indicator) when the subset of the plurality of image tiles pass one or more predetermined characteristics for inclusion (i.e. low/high grade cancer) [para. 0129].
Regarding claim 4, Arnold teaches the claim limitations as follows:
The computer-implemented method of claim 1, wherein the prediction (i.e. 920, cancer presence indicator) fails quality control (i.e. 940, cancer grade indicator) when the subset of the plurality of image tiles fail one or more predetermined characteristics for inclusion (i.e. no cancer) [para. 0129].
Regarding claim 7, Arnold teaches the claim limitations as follows:
The computer-implemented method of claim 1, further comprising: determining, via one or more processors, whether the plurality of image tiles include at least one of a biomarker, a tumor (i.e. cancer), or a specific cancer (i.e. low/high grade cancer), a cell type, density of stroma, presence of immune cells, stain characteristics (over staining or under staining), perineural invasion, or another biological characteristic that informs reliability of the prediction [para. 0129].
Regarding claim 18, Arnold teaches the claim limitations as follows:
The computer-implemented method of claim 1, further comprising: integrating the pass/fail indication (i.e. 940, cancer grade indicator) with a clinician workflow to inform subsequent diagnostic or treatment decisions (i.e. generate a report and associate the report to the patient), wherein the integration includes automatically updating patient records with the pass/fail indication and associated prediction details (i.e. 940, cancer grade indicator) [para. 0130].
Regarding claims 37 and 38, all limitations are rejected on the same basis as claim 1.
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 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.
[2] Claims 2, 22, 25-27 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Arnold et al. (“Arnold”) [US 2022/0207730] in view of Ramon et al. (“Ramon”) [WO 2023/042184].
Regarding claims 2, 22, 25-27 and 29, Arnold discloses the claim limitations as set forth in claim 1.
Arnold does not explicitly disclose the following claim limitations:
2. The computer-implemented method of claim 1, further comprising: processing the whole-slide image using a secondary machine learning model to generate an additional characteristic for each of the plurality of image tiles, wherein the additional characteristic provides a quantitative score or classification for each tile, and wherein the pass/fail indication is further based on a thresholding of the additional characteristic.
22. The computer-implemented method of claim 1, further comprising: re-running the prediction for the whole-slide image while excluding high-attention tiles identified as artifacts to enhance accuracy of the prediction.
25. The computer-implemented method of claim 1, further comprising: applying qualitative criteria to each of the image tiles reviewed to determine a presence of tumor tissue, artifacts, or other biological characteristics that inform a reliability of the prediction.
26. The computer-implemented method of claim 25, wherein the pass/fail indication fails when a predetermined percentage of the reviewed image tiles are removed based on the qualitative criteria indicating the presence of artifacts or lack of relevant biological content.
27. The computer-implemented method of claim 25, wherein the qualitative criteria include the presence of surgical ink, smudges, or other artifacts that could affect the reliability of the prediction.
29. The computer-implemented method of claim 27, wherein the artificial neural network is further trained to prioritize image tiles based on biological relevance over a presence of artifacts, thereby enhancing a specificity of the prediction.
However, in the same field of endeavor Ramon discloses the deficient claim limitations, as follows:
2. The computer-implemented method of claim 1, further comprising: processing the whole-slide image using a secondary machine learning model (i.e. 201/301 which is part of machine learning model 111) to generate an additional characteristic (i.e. QC score) for each of the plurality of image tiles [figs. 2 and 4; paras. 0042, 0082], wherein the additional characteristic provides a quantitative score or classification for each tile (i.e. QC score) [figs. 2 and 4; paras. 0042, 0082], and wherein the pass/fail indication is further based on a thresholding of the additional characteristic (i.e. tiles below threshold QC score are rejected) [para. 0082].
22. The computer-implemented method of claim 1, further comprising: re-running the prediction for the whole-slide image while excluding high-attention tiles identified as artifacts to enhance accuracy of the prediction (i.e. output of QC screening in 301 excludes artifact tiles and are fed to prediction process 307) [fig. 3; para. 0083].
25. The computer-implemented method of claim 1, further comprising:
applying qualitative criteria (i.e. percentage of tissue) to each of the image tiles reviewed to determine a presence of tumor tissue (i.e. tissue), artifacts (i.e. pen marks), or other biological characteristics (i.e. tissue) that inform a reliability of the prediction [para. 0082].
26. The computer-implemented method of claim 25, wherein the pass/fail indication fails when a predetermined percentage of the reviewed image tiles are removed (i.e. insufficient tiles corresponding to tissue) based on the qualitative criteria (i.e. percentage of tissue) indicating the presence of artifacts or lack of relevant biological content [para. 0083].
27. The computer-implemented method of claim 25, wherein the qualitative criteria (i.e. percentage of tissue) include the presence of surgical ink, smudges, or other artifacts (i.e. pen marks) that could affect the reliability of the prediction [para. 0082].
29. The computer-implemented method of claim 27, wherein the artificial neural network is further trained to prioritize image tiles based on biological relevance (i.e. percentage of tissue) over a presence of artifacts (i.e. pen marks), thereby enhancing a specificity of the prediction [paras. 0082 and 0083].
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to modify the teachings of Arnold with Ramon and incorporate quality screening, the reasoning being to remove artifact tiles and reduce future processing of unwanted tiles [Ramon: para. 0082].
[3] Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Arnold et al. (“Arnold”) [US 2022/0207730] in view of Hu [WO 2022/192747].
Regarding claims 9 and 19, Arnold discloses the claim limitations as set forth in claim 1.
Arnold does not explicitly disclose the following claim limitations:
9. The computer-implemented method of claim 1, further comprising: training, via one or more processors, the artificial neural network using feedback from a reviewer.
19. The computer-implemented method of claim 1, further comprising: retraining or fine-tuning the trained machine learning model based on outcomes of the pass/fail indication to improve prediction accuracy for future whole-slide image analyses.
However, in the same field of endeavor Hu discloses the deficient claim limitations, as follows:
9. The computer-implemented method of claim 1, further comprising: training, via one or more processors, the artificial neural network using feedback from a reviewer (i.e. 760 and 765) [fig. 7].
19. The computer-implemented method of claim 1, further comprising: retraining or fine-tuning the trained machine learning model based on outcomes of the pass/fail indication to improve prediction accuracy for future whole-slide image analyses (i.e. 755, 760 and 765) [fig. 7].
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to modify the teachings of Arnold with Hu and implement the retraining of the machine learning model or artificial neural network, the reasoning being to improve future performance of the machine learning model or artificial neural network [Hu: para. 0087].
[4] Claims 31-34 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Arnold et al. (“Arnold”) [US 2022/0207730] in view of Aisaka et al. (“Aisaka”) [US 2022/0148714].
Regarding claims 31-34 and 36, Arnold discloses the claim limitations as set forth in claim 1.
Arnold does not explicitly disclose the following claim limitations:
31. The computer-implemented method of claim 1, further comprising: generating a report that includes a subset of tiles each having a high respective attention score without providing a pass/fail indication, to enable a clinician to make a determination based on the subset of tiles.
32. The computer-implemented method of claim 31, wherein the report includes qualitative and quantitative data associated with the subset of tiles to facilitate the clinician's determination.
33. The computer-implemented method of claim 31, further comprising: receiving the determination of the clinician; and retraining the artificial neural network to improve future predictions using the determination of the clinician.
34. The computer-implemented method of claim 33, wherein the retraining includes integrating feedback from the clinician's determination into the training of the artificial neural network to refine the selection and scoring of image tiles for quality control analysis.
36. The computer-implemented method of claim 31, further comprising: updating one or more patient records with details of the clinician's determination and associated highest attention tiles.
However, in the same field of endeavor Aisaka discloses the deficient claim limitations, as follows:
31. The computer-implemented method of claim 1, further comprising: generating a report that includes a subset of tiles each having a high respective attention score (i.e. attention areas that exceed threshold) without providing a pass/fail indication (i.e. only attention areas are provided to the display), to enable a clinician to make a determination based on the subset of tiles (i.e. pathologist is making a diagnosis) [para. 0129].
32. The computer-implemented method of claim 31, wherein the report includes qualitative and quantitative data associated with the subset of tiles to facilitate the clinician's determination (i.e. coordinates and tile images) [para. 0129].
33. The computer-implemented method of claim 31, further comprising: receiving the determination of the clinician (i.e. regions identified and saved by the pathologist); and retraining the artificial neural network to improve future predictions using the determination of the clinician (i.e. updating the learning model) [para. 0166].
34. The computer-implemented method of claim 33, wherein the retraining includes integrating feedback from the clinician's determination into the training of the artificial neural network to refine the selection and scoring of image tiles for quality control analysis (i.e. regions identified and saved by the pathologist are used in updating the learning model) [para. 0166].
36. The computer-implemented method of claim 31, further comprising: updating one or more patient records with details of the clinician's determination and associated highest attention tiles (i.e. viewing history, images and medical records are stored) [para. 0101].
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to modify the teachings of Arnold with Aisaka and incorporate a clinician's determination into the learning model the reasoning being generate a more accurate model [Aisaka: para. 0166].
Conclusion
[5] Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATH V PERUNGAVOOR whose telephone number is (571)272-7455. The examiner can normally be reached M-F, 8 am-5 pm.
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SATH V PERUNGAVOOR
Supervisory Patent Examiner
Art Unit 2488
/SATH V PERUNGAVOOR/Supervisory Patent Examiner, Art Unit 2488