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 .
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.
Claim(s) 1-13 and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Miller et al. (US 2019/0339203) hereinafter known as Miller.
With regards to claim 1 and 18, Miller discloses multispectral imaging of biological samples by fluorescence microscopy [0002] comprising at least one processor ([0017][0070]; a controller 214 that includes a processing device 216.) and at least one memory that stores instructions to be executed by the at least one processor ([0017]; “…the disclosure features computer readable storage media that include instructions that, when executed by a processing device, cause the processing device to:...”), the method comprising:
acquiring a plurality of mixed images ([0094] ([0094]; background image that contains sample autofluorescence spectrum and fluorescence emission spectra of each of the non-endogenous spectral contributors in the sample)[0091] for a sample that contains a plurality of biomolecules ([0002]; biological samples) ([0069]; “…the prepared sample is imaged in step 104 to obtain one or more sample images.”); and
generating an unmixed image for at least one biomolecule among the plurality of biomolecules from the plurality of mixed images using an unmixing matrix ([0096]; “By performing decomposition step 110, the distribution of each of the applied dyes within the sample can be determined, and therefore, the distribution of the molecular targets associated with each of the dyes. Spectral unmixing can be used to perform the decomposition in step 110.”)([0109]; “…an S matrix can be created for each type and inverted to yield a set of S+ matrices to unmix pixels of each type of sample material.”)(see also [0110])
wherein a value of at least one element included in the unmixing matrix is determined based on training of an artificial neural network model. ([0109]; “…a trained machine classifier such as a neural network (e.g., implemented in processing device 216) can be used to perform the pixel region selections,…”)[0175]; “To train the machine learning algorithm, regions were manually drawn on the negative control autofluorescence image to identify pixels that were either positive or negative for red blood cells. Results from the trained algorithm for both sections are binary classification masks corresponding to images 902 in FIGS. 10A and 10B.”).
With regards to claim 2, Miller discloses the method of claim 1, wherein the unmixing matrix includes at least one element that determines a linear superposition ratio between the plurality of mixed images. [0147]
With regards to claim 3, Miller discloses the method of claim 1, wherein the plurality of mixed images are acquired in a consecutive and sequential manner by repeating a single round that includes a process of labeling a biomolecule in the sample with a fluorescent substance and acquiring a single mixed image. [0046][0062][0095]
With regards to claim 4, Miller discloses the method of claim 1, wherein the plurality of mixed images are two or more mixed images acquired in two or more wavelength ranges by labeling two or more biomolecules in the sample with two or more fluorescent substances of which emission spectra overlap, respectively. [0043][0083][0136]
With regards to claim 5, Miller discloses the method of claim 1, wherein the artificial neural network model is trained using two or more data sets generated based on a plurality of unmixed images generated using the unmixing matrix. [0105][0109][0135]
With regards to claim 6, Miller discloses the method of claim 5, wherein a first data set included in the two or more data sets includes at Least one piece of data that includes values of pixels corresponding to the same location in each of the plurality of unmixed images. [0053][0106][0109][0137]
With regards to claim 7, Miller discloses the method of claim 5, wherein a second data set included in the two or more data sets includes at least one piece of data that includes values of pixels corresponding to a random location from each of the plurality of unmixed images. [0011][0017][0051]
With regards to claim 8 and 19, Miller discloses the method of claim 1 and 18, wherein the artificial neural network model is trained to distinguish two or more data sets generated based on a plurality of unmixed images generated using the unmixing matrix. [0109][0110][0135] Table 3
With regards to claim 9, Miller discloses the method of claim 8, wherein the artificial neural network model generates a classification value for classifying input specific data into one of the two or more data sets. [0106][0109][0110]
With regards to claim 10, Miller discloses the method of claim 8, wherein the artificial neural network model [0109] receives first input data and second input data ([0174]; The Examiner considers the background image and autofluorescence image as 1st and 2nd input data), and generates a dependency evaluation value for the plurality of unmixed images based on the first input data and the second input data [0103].
With regards to claim 11, Miller discloses the method of claim 8, wherein the dependency evaluation value includes at least one of distinguishing capability between the first input data and the second input data. ([0041][0175]; binary classification)
With regards to claim 12 and 20, Miller discloses the method of claim 1 and 18, wherein the unmixing matrix and the artificial neural network model are trained through adversarial training. ([0041][0175]; binary classification mask)
With regards to claim 13, Miller discloses the method of claim 12, wherein the unmixing matrix is trained by additionally using a specific loss function that prevents a value of each pixel of an unmixed image from being negative. [0175][0176]
Allowable Subject Matter
Claims 14-17 are 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:
With regards to claim 14, Miller discloses the method of claim 1, wherein the unmixing matrix and the artificial neural network model are trained through adversarial training ([0041][0175]; binary classification mask),
a value of at least one element included in the unmixing matrix is updated to decrease accuracy of the classification value generated by the artificial neural network model [0106][0110], and
Miller do not disclose a value of at least one parameter included in the artificial neural network model is updated to increase the accuracy of the classification value.
With regards to claim 15, Miller do not disclose the method of claim 1, wherein the unmixing matrix and the artificial neural network model are trained through adversarial training,
a value of at least one element included in the unmixing matrix is updated to decrease the dependency evaluation value generated by the artificial neural network model, and
a value of at least one parameter included in the artificial neural network model is updated to increase the dependency evaluation value.
With regards to claim 16, Miller do not disclose the method of claim 1, further comprising:
performing pixel binning processing on each of the plurality of mixed images.
With regards to claim 17, Miller do not disclose the method of claim 1, wherein the artificial neural network model is trained based on a plurality of pixel binning processed unmixed images acquired by performing pixel binning processing on a plurality of unmixed images generated by the unmixing matrix, respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nova et al. (US 6,329,139)
Alsheimer (US 2024/0255497)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUGH H MAUPIN whose telephone number is (571)270-1495. The examiner can normally be reached M-F 7:30 - 5:00 pm.
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/HUGH MAUPIN/ Primary Examiner, Art Unit 2884
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