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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1 and 10 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 8 and 15 of U.S. Patent No. 11,210,787. Although the claims at issue are not identical, they are not patentably distinct from each other because they contain the same subject matter by the current claim being inside of the claim referenced, see below.
Claim 1 of the current application, A computing device comprising: at least one memory; and at least one processor; wherein the at least one processor configured to: perform a first analysis on a plurality of digital pathology images using a machine learning model, generate first biomarker expression information about the digital pathology images based on the first analysis, determine prioritization values of the digital pathology images on which the first analysis was performed, and control a display device to output the digital pathology images based on the prioritization values.
The are both using generating a machine learning system by processing a plurality of training images. And a pathologist review outcome is equivalent to the display. Therefore, the subject matter of the current application is inside of claim 1 of the referenced patent.
Claim 1 of the referenced patent: An image processing method, comprising: receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient; generating a machine learning system by processing a plurality of training images, each training image comprising an image of human tissue and a diagnostic label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty; automatically identifying, using the machine learning system, an area of interest of the target image by analyzing microscopic features extracted from multiple image regions in the target image; determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability; determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image, the prioritization value comprising a first prioritization value determined based on preferences of a first user and a second prioritization value determined based on preferences of a second user; upon determining that the target feature comprises a feature in the target image indicating that further preparation is to be performed, then preparing a new slide for the target image prior to a user review; outputting, using the machine learning system, a plurality of digitized pathology images; and ordering, using the machine learning system, the digitized pathology images based on the plurality of prioritization values associated with the digitized pathology images, and a placement of the target image based on the prioritization value of the target image based on the target feature.
Claim 10 of the current application, A computing device comprising: at least one memory; and at least one processor; wherein the at least one processor configured to: perform a first analysis on a plurality of digital pathology images using a machine learning model, generate first biomarker expression information about the digital pathology images based on the first analysis, determine prioritization values of the digital pathology images on which the first analysis was performed, and control a display device to output the digital pathology images based on the prioritization values.
The are both using generating a machine learning system by processing a plurality of training images. And a pathologist review outcome is equivalent to the display. Therefore, the subject matter of the current application is inside of claim 1 of the referenced patent.
8 or 15, An image processing system, comprising: a memory storing instructions; and a processor configured to execute the instructions to perform operations comprising: receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient; generating a machine learning system by processing a plurality of training images, each training image comprising an image of human tissue and a diagnostic label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and an analytic difficulty; automatically identifying, using the machine learning system, an area of interest of the target image by analyzing microscopic features extracted from multiple image regions in the target image; determining, using the machine learning system, a probability of a target feature being present in the area of interest of the target image based on an average probability; determining, using the machine learning system, a prioritization value, of a plurality of prioritization values, of the target image based on the probability of the target feature being present in the target image, the prioritization value comprising a first prioritization value determined based on preferences of a first user and a second prioritization value determined based on preferences of a second user; upon determining that the target feature comprises a feature in the target image indicating that further preparation is to be performed, then preparing a new slide for the target image prior to a user review; outputting, using the machine learning system, a plurality of digitized pathology images; and ordering, using the machine learning system, the digitized pathology images based on the plurality of prioritization values associated with the digitized pathology images, and a placement of the target image based on the prioritization value of the target image based on the target feature.
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 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes (US 2017/0270666) in view of Jain (US 2021/0193323).
As per claims 1, 8, 19 a computing device, method and non-transitory computer-readable recording medium and comprising: at least one memory; and at least one processor; wherein the at least one processor configured to:
perform a first analysis on a plurality of images using a machine learning model (Barnes, ¶[0038] auto analyzed and scored and ¶[0046] and [0076] “tissue object-based machine learning system for automated scoring” ), generate first biomarker expression information about the digital pathology images based on the first analysis (Barnes, ¶[0044], [0054], [0059] tumor annotation represents biomarker, and these are pathology images since they have to do with disease ), determine prioritization values of the digital pathology images on which the first analysis was performed (Barnes, ¶[0110] threshold represents prioritization values. And in the current application’s specification ¶[0063] ““The prioritization value of the target image may include a first prioritization value of the target image for a first user and a second prioritization value of the target image for a second user, the first prioritization value may be determined based on the first user's preferences and the second prioritization value may be determined based on the second user's preferences. The label may include an artifact label corresponding to at least one of scanning lines, missing tissue, and/or blur.” No further guidance is provided. Since these prioritization values are based on “user preferences,” the Examiner understands that the values are the same. Further, neither the Specification nor the claim state that the “value ... fora ... user” is actually entered by the user.).
), and control a display device to output the digital pathology images based on the prioritization values (Barnes, ¶[0041] “Besides processor 105 and memory 110, computer 101 also includes user input and output devices such as a keyboard, mouse, stylus, and a display/touchscreen.” This represents the display).
Barnes mentions morphology however not in each slide and that they are pathology based.
Jain teaches, digital pathology images (Jain, ¶[009] “dividing the image slides into patches; receiving a plurality of labels correlated with a disease or lack thereof, wherein each label comprises of a morphological type;” ).
At the time of the effective filing date, it would have been obvious to one of ordinary skill in the art to modify Barnes with Jain’s ability to a label characterizing at least one of a slide of pathology.
The motivation would have been to improve image quality to be able to improve classification.
As per claims 2 and 11, Barnes in view Jain teaches, the computing device of claim 1, wherein the at least one processor is further configured to determine the prioritization values based on the first biomarker expression information (Barnes, ¶[0042] “Therefore, any other biomarker tissue slides (like immune markers or some other additional markers) will trigger slide image analysis and scoring specific to the particular marker and include those scores in the Cox model fitting process.” This represents determine the prioritization values based on the first biomarker expression information by developing the score).
As per claims 3 and 12, Barnes in view Jain teaches, the computing device of claim 1, wherein the first biomarker expression information comprises at least one of a biomarker type, a biomarker expression, or a biomarker expression class (Barnes, ¶[0058] “Image data is comprised of images of tissue sections from a plurality of patients in order to train the system, and may include color channels or frequency channels, tissue and biomarker data, as well as additional data or metadata identifying one or more clinical outcomes for the patients associated with the tissue sections.” Biomarker data would at least include biomarker type).
As per claims 4 and 13, Barnes in view Jain teaches, the computing device of claim 1, wherein the machine learning model is trained to identify cell information about cells in the digital pathology images, and wherein the first biomarker expression information is generated based on the cell information identified by the machine learning model (Jain, ¶[0019] “a morphology detector comprising an unsupervised machine learning model configured to cluster the labeled patch vectors, wherein each cluster corresponds to a morphological subtype expressed in the patch corresponding to the labeled patch vector and a patient outcome;” and ¶ [0085] FIG. 5 illustrates a sub-morphology detector (SMD) 212 according to an embodiment. The operations of the SMD 212 will be described in relation to FIG. 5 and previous figures. The SMD 212 receives labeled vectors and detects further morphological sub-patters within each label category, using unsupervised learning algorithms, such as clustering analysis, principal component and other unsupervised machine learning techniques.” This represents learning model trained to identify cell information in the digital pathology images as morphology would be that information , the biomarkers are the actual detector results in 212).
As per claims 5 and 14, Barnes in view of Jain teaches, the computing device of claim 1, wherein the at least one processor is further configured to: identify at least one stain expression level of cells in at least one of the digital pathology images (Barnes, ¶[0042] “ For instance, a tissue section may require staining by means of application of a staining assay containing one or more different biomarkers associated with chromogenic stains for brightfield imaging or fluorophores for fluorescence imaging. Staining assays can use chromogenic stains for brightfield imaging, organic fluorophores, quantum dots, or organic fluorophores together with quantum dots for fluorescence imaging, or any other combination of stains, biomarkers, and viewing or imaging devices.” This represents identify at least one stain expression level of cells in at least one of the digital pathology images, as this teaches “Staining assays can use chromogenic stains for brightfield imaging” ); calculate a biomarker expression grade based on the at least one identified stain expression level; and determine a high priority of at least one of the digital pathology image in which the biomarker expression grade is within a predetermined range based on a boundary for dividing biomarker expression grades ( Barnes, In the current application’s specification ¶[0063] ““The prioritization value of the target image
may include a first prioritization value of the target image for a first user and a second prioritization value of the target image for a second user, the first prioritization value may be determined based on the first user's preferences and the second prioritization value may be determined based on the second user's preferences. The label may include an artifact label corresponding to at least one of scanning lines, missing tissue, and/or blur.” No further guidance is provided. Since these prioritization values are based on “user preferences,” the Examiner understands that the values are the same. Further, neither the Specification nor the claim state that the “value ... fora ... user” is actually entered by the user. See claim 6 where the value is determined.” ).
As per claims 6, 8, 15 and 17 Barnes in view of Jain teaches, the computing device of claim 5, wherein the biomarker expression grade indicates a cancer grade (Barnes, ¶[0037] The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage breast cancer patients by providing a prognostic model to predict a recurrence risk of the patient and to categorize the patient into a high or low risk group. ).
As per claims 7 and 16, Barnes in view of Jain teaches, the computing device of claim 1, wherein the first biomarker expression information is a diagnostic feature (Barnes, ¶ [0054] “For instance, in a clinical or diagnostic workflow, when a new slide series comprising H&E and IHC slides from a new patient is input into system 100” ).
As per claims 9 and 18, Barnes in view of Jain teaches, the computing device of claim 3, wherein the biomarker expression comprises protein expression or gene expression (Barnes, ¶ [0057] “FIG. 2B shows an alternate means for FOV selection using representative regions or “hot spots” 231 on a Ki67 digitized whole slide 225. Hot spots are specific regions of the whole slide that contain relatively high and heterogeneous amounts of Ki67 protein.” ).
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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