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 .
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.
Claim Rejections - 35 USC § 102
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)(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 1-2 and 8-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Wang (11,715,203).
Regarding claims 1, 14 and 15 Wang discloses,
Receiving an image and displaying the image using a graphical user interface (note col. 4 lines 34-35, server obtains plurality of training images);
Receiving at least one first image annotation provided by a user via the graphical user interface (note col. 4 lines 34-36, server obtains plurality of labeled images);
Producing a first segmented image using a deep learning model, wherein the deep learning model uses the digital pathology image and the at least one first image annotation (note col. 4 lines 46-64, examiner interprets first segmentation model producing first segmented image it uses machine learning uses trained images and label images); and
displaying the first segmented image using the graphical user interface (note col. 4 lines 54, output segmentation results);
Receiving at least one second image annotation provided by the user via the graphical user interface (note col. 7 lines 25-38); producing a second segmented image using the deep learning model, wherein the deep learning model uses the digital pathology image, the at least one first image annotation, and the at least one second image annotation (note col. 7 lines 25-38, machine learning model and label images); and displaying the second segmented image using the graphical user interface (note col. 7 lines 34-36 second segmented image is outputted).
Regarding claims 2 and 16 Wang discloses,
Wherein the deep learning model is a three class model and the at least one first image annotation comprises at least one annotation for each class (note col. 8 lines 7-17 segmentation model, performs processing through the first segmentation model, and outputs a first segmented image with an initial target region labeled).
Regarding claim 8 Wang discloses,
Wherein the deep learning model is trained using a first dataset comprising training images with one or more annotations (note col. 13 lines 5-10, plurality of label images).
Regarding claim 9 Wang discloses,
Wherein the training images comprise images from a first image domain (note col. 4 lines 42-45, training images initial segmentation model).
Regarding claim 10 Wang discloses,
Wherein the training images further comprise images from a second image domain (note col. 7 lines 19-24, training images second segmentation model).
Regarding claim 11 Wang discloses,
Wherein the second image domain is different than the first image domain, and wherein the first image domain and the second image domain each comprise one of: natural scene images, digital pathology images, Immunohistochemistry images, x-ray images, and Hematoxylin and eosin images (note col. 13 lines 48-52, pathology image analysis scenario, and col. 13 lines 53- col. 14 lines 10).
Regarding claim 12 Wang discloses,
Wherein the one or more annotations comprise one or more simulated click annotations (note col. 9 lines 14-30 and col. 5 lines 33-42, selecting labeled data).
Regarding claim 13 Wang discloses,
Wherein the image is a whole-slide pathology image (note col. 13 lines 48-52, pathology image analysis).
Related Prior Art
Arbel et al (11,145,058) Receiving at least one first image annotation provided by a user via the graphical user interface (note col. 9 lines 20-35); producing a first segmented image using a deep learning model, wherein the deep learning model uses the digital pathology image and the at least one first image annotation (note col. 9 lines 20-35).
Fuchs et al (11,682,117) producing a first segmented image using a deep learning model, wherein the deep learning model uses the digital pathology image and the at least one first image annotation (note fig. 1, initial annotation and deep learning network).
Allowable Subject Matter
Claims 3-7 and 17-20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter for dependent claims 3 and 17. Prior art could not be found for the feature image annotation provided by the user is provided using a mouse click on the displayed image. These features in combination with other features could not be found in the prior art. Claims 4-7 and 18-20 depend on claims 3 and 17, respectively. Therefore are also objected.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am-3:00pm.
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G.D.
July 22, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676
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