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
Claims 1-10 and 12 are pending.
Claim 11 is cancelled.
Claims 1-6, 10 and 12 are withdrawn via amendment.
Claims 7-9 are hereby considered for examination.
Election/Restrictions
Claims 1-6 and 10, and 12 are withdrawn via amendment. Applicant timely traversed the
restriction (election) requirement in the reply filed on September 12, 2025. The amendment filed 11/11/2025 renders the restriction requirement in the last Office Action moot. Claims 7-9 are hereby examined accordingly.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over Gerlach et al. (US 20200300764 A1) in view of Garnavi et al. (US 20170270653 A1).
Regarding claim 7, Gerlach et al. teaches method for digital image processing (see para [0013]; “It is thus an object to provide a method using digital image processing for determining a binding of autoantibodies”), comprising - providing an immunofluorescence image (FB), which represents a staining of a biological cell substrate (S) by a fluorescence stain (see para [0093]; “to receive a first fluorescence image (SR) which represents a staining of a substrate, which in turn has multiple Crithidia luciliae cells (CR), by a first fluorescent dye”, see also para [0158]; “The fluorescence microscopy is especially a so-called indirect immunofluorescence microscopy (IIFT microscopy)”), - determining respective items of location information (LI), which indicate respective locations of respective relevant subsections of the cell substrate (S) in the fluorescence image (FB) (see para [0023]; “identification of respective first sub-images (ETB) in the first fluorescence image (SR) that each represent a respective Crithidia luciliae cell (CR), [0024] determination of respective second sub-images (ZTB) of the second fluorescence image (SG) that correspond to the respective first sub-images (ETB) of the first fluorescence image (SR), [0025] respective processing of at least one subset of the respective second sub-images (ZTB)”), - extracting respective image subsections (TB), which correspond to the respective subsections of the cell substrate, on the basis of the items of location information (LI) (see para [0009]; “An individual Crithidia cell CR from FIG. 1 is depicted again in FIG. 2 in more detail.. Such a sub-image TB of one Crithidia luciliae cell CR clearly shows a staining on the kinetoplast K”, see also claim 11; “to identify first sub-images (ETB) in the first fluorescence image (SR) that each represents at least one Crithidia luciliae cell (CR), to determine second sub-images (ZTB) of the second fluorescence image (SG) that correspond to the first sub-images (ETB) of the first fluorescence image (SR), to process at least one subset of the second sub-images (ZTB) by a pretrained convolutional neural network (CNN)”, Note: the “first sub-image” and second sub-images” are cropped subsections of the images identified from their locations, i.e., TB) - determining respective second partial confidence measures (ZTKM) of respective presences of the fluorescence pattern on the respective subsections using a second neural network (NN2) on the basis of the respective image subsections (TB) (see claim 1; “processing at least one subset of the second sub-images (ZTB) by a pretrained convolutional neural network (CNN) for determining binding measures (IBM1, IBM2) which indicate an extent of a binding of the autoantibody in a kinetoplast region (K)… and determining an overall binding measure (GBM) …. on the basis of the binding measures (IBM1, IBM2)”). Gerlach et al. additionally teach computing an overall image-level measure from the per-subimage binding measures (NN2 outputs), but does not specifically teach determining a confidence measure (KM) of the presence of the fluorescence pattern in the fluorescence image on the basis of the first partial confidence measures (ETKM) and the second partial confidence measures (ZTKM).
In the same field of endeavor, Garnavi et al. teaches and determining respective first partial confidence measures (ETKM) of respective presences of the fluorescence pattern on the respective subsections using a first neural network (NN1) on the basis of the overall fluorescence image (FB) (see para [0040]; “The method may further include predicting a first decision on gradability of the image with a first confidence score, by training a first classifier based on a combined set of unsupervised features, the set of supervised features and the complementary set of supervised features”), determining a confidence measure (KM) of the presence of the fluorescence pattern in the fluorescence image on the basis of the first partial confidence measures (ETKM) and the second partial confidence measures (ZTKM) (see para [0003]; “The method may further include predicting a first decision on gradability of the image with a first confidence score, by training a first classifier … The method may also include predicting a second decision on gradability of the image with a second confidence score, by training a second classifier based on the set of supervised features. The method may further include determining whether the image is gradable or ungradable based on a weighted combination of the first decision and the second decision, the first confidence score and the second confidence score representing respective weights for the first decision and the second decision”). Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filling date of the invention to incorporate a method useful for detecting a binding of autoantibodies from a patient sample to double-stranded deoxyribonucleic acid (DNA) using Crithidia luciliae cells by digital image processing of Gerlach et al. in view of the use of automatically determining image quality of a machine generated image of Garnavi et al. in order to improve robustness and accuracy of the final pattern decision (see para [0040]).
Regarding claim 8, the rejection of claim 7 is incorporated herein.
Gerlach et al. in the combination further teach computer program product (CPP), comprising commands which, upon the execution of the program by a computer, prompt it to carry out the method for digital image processing (see para [0111]; “15. Computer program product (CPP) [0112] comprising commands which, upon the execution of the program by a computer, prompt said computer to carry out the method for digital image processing”).
Regarding claim 9, the rejection of claim 7 is incorporated herein.
Gerlach et al. in the combination further teaches data carrier signal (SI2), which transmits the computer program product (CPP) (see also para [0113]; “Data carrier signal (SI2) which transmits the computer program product (CPP) according to embodiment 15”).
Conclusion
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/WINTA GEBRESLASSIE/Examiner, Art Unit 2677