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
The Amendment filed July 20 2026 has been entered and considered. Claims 1 and 12 have been amended. Claims 2, 9-11, and 13 have been canceled. New claim 15 has been added. In light of the amendment the prior art rejections of claims 1 and 12 are withdrawn as moot. The new grounds of rejection set forth in the present action were necessitated by Applicants’ claim amendments; accordingly, this action is made final.
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.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Stumpe et al. (Previously cited) in view of Zeder et al. (Previously cited).
Regarding claim 15, Stumpe teaches a classification apparatus comprising an imaging section for capturing an image which includes a partial range of a pathological sample as a subject (Fig. 2B), and at least one processor, the at least one processor carrying out: an acquisition process of acquiring the image which has been captured by the imaging section (Para. 56, “The compute unit further includes various interfaces to other components of the system including an interface, not shown, to receive the digital images from the camera”); a classification process of classifying, as benign or malignant, a cell included as a subject in the image which has been acquired in the acquisition process (Para. 15, “the compute unit implements one or more machine learning pattern recognizers trained to identify individual tumor cells or areas of tumor cells which are classified in accordance with specific Gleason grade”); a prediction process of predicting a number of cells included as the subject in the image which has been acquired in the acquisition process (Para. 169, “To generate such statistics, two machine learning models are used which process the image data: 1) a machine learning model trained to detect individual cells, e.g., by finding individual cell nuclei, and counting all of such cells in the image”); and a control process of carrying out an automatic classification process of classifying, as benign or malignant, a cell which is included as the subject in the image (Para. 15 above).
Stumpe does not explicitly disclose classifying, as benign or malignant, a cell which is included as the subject in the image, in a case where the number of cells which has been predicted in the prediction process is equal to or greater than a predetermined number. However, they do identify individual cells and count them.
Zeder teaches that cell count is an important parameter to consider for quality when performing classification on medical imagery (Pg. 1, “Preparations subjected to autonomous imaging, often without prior manual inspection, may be of low quality for various reasons. Commonly encountered problems are fields of view (FOVs) with no cells, too high cell densities or an inhomogeneous distribution of cells.”).
Zeder does not explicitly disclose performing classification in a case where the number of cells which has been predicted in the prediction process is equal to or greater than a predetermined number. However, their method is towards quality control of medical images with cell count being a focus.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Stumpe to incorporate the teachings of Zeder to include classifying, as benign or malignant, a cell which is included as the subject in the image, in a case where the number of cells which has been predicted in the prediction process is equal to or greater than a predetermined number. Stumpe discloses an automated method for tumor cell classification, including an automatic cell counting process for improved identification, however, they do not explicitly disclose using a predetermined cell count threshold to proceed with classification. Zeder teaches a method for medical image quality control, with an emphasis on the role of cell count in image quality. They explicitly disclose that the density of cells in an image directly effects the quality of that image (Pg. 1). One of ordinary skill in the art would have recognized that cell count is an important variable to consider when analyzing medical images, as disclosed by Zeder, and looked for well-known techniques to factor it into the image acquisition and classification process. A threshold is a conventional method for guaranteeing that a specific value is met, lending itself particularly well to the problem of optimizing the cell count captured in the images. A person having ordinary skill in the art would have predictably supplemented the method of Stumpe with a cell count threshold to make sure that at least a certain cell count is present for classification, improving the quality of each captured medical image.
Allowable Subject Matter
Claims 1, 3-8, 12, and 14 are allowed.
Conclusion
Pertinent Prior Art: Chukka et al., US 20170103521 A1, “MEDICAL IMAGE ANALYSIS FOR IDENTIFYING BIOMARKER-POSITIVE TUMOR CELLS”, published 2017, Similar tumor cell analysis with automated cell counting and classification. Does not adjust magnification in response to a cell count threshold.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/DAVID ALEXANDER WAMBST/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698
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