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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/18/2026 has been entered.
Response to Arguments
Applicant's arguments are moot in light of new art necessitated by amendment.
The 101 rejection is withdrawn because the claims are directed to a particular machine that amounts to significantly more than the abstract idea.
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 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over High-speed fluorescence image–enabled cell sorting by Schraivogel et al US20220156482A1 to Zordan et al.
Note on US20220156482A1, Applicant because the inventor Liyu Gong was an inventor on the US20220156482A1 application, but is not an inventor in the current application, applicant may submit an appropriate affidavit or declaration to disqualify US20220156482A1 as prior art by establishing that the US20220156482A1 disclosure was made by the inventor or a joint inventor, or the subject matter disclosed was obtained directly or indirectly from the inventor or a joint inventor.
Schraivogel teaches claims 1, 8 and 15. (currently amended) A method of image-based cell sorting comprising: (Schraivogel title “High-speed fluorescence image–enabled cell sorting”)
acquiring, by an imaging system of a flow cytometer, fluorescence images of individual cells while the cells are flowing through the flow cytometer; (Schraivogel p. 1 “For cell sorting, a set of intuitive spatial image parameters were extracted in real time from each image channel (Fig. 1B; for details of the image parameters, please see the mate-rials and methods and fig. S4A). Image parameters were treated identically to conventional pulse parameters (area, width, and height) by the sorting electronics, allowing the combination of spatial information and traditional flowcytometry features for analysis and sorting.” The images are from a flow cytometer, see fig. 1.)
extracting, one or more features from each fluorescence image, the features including spatial fluorescence distributions of a target protein and a nucleus; (Schraivogel p. 1 “For cell sorting, a set of intuitive spatial image parameters were extracted in real time from each image channel… Image parameters were treated identically to conventional pulse parameters (area, width, and height) by the sorting electronics, allowing the combination of spatial information and traditional flowcytometry features for analysis and sorting…. we demonstrate the advantage of multicolor fluorescence imaging for quantification of protein localization through spatial correlation of two signals.” Schraivogel teaches that it’s the target protein ReIA-mNG and a nucleus being extracted with the fluorescence distribution on p. 3, where ReIA-mNG is the target protein, “HeLa cells expressing RelA-mNG were treated with TNF a or left untreated and stained with the cell-permeable nuclear dye DRAQ5.” The fluorescence distribution is taught by “Correlation is the Pearson’s correlation score between the intensities of the pixel values from two imaging channels.” Schraivogel p. 3.)
(Schraivogel Fig. 2 description p. 3 “HeLa cells expressing RelA-mNG were treated with TNFa or left untreated and stained with the cell-permeable nuclear dye DRAQ5. Cells were then gated for singlets and live cells, and the correlation between RelA-mNG and DRAQ5 was used to differentiate between the treated (nuclear RelA) and untreated (cytoplasmic RelA) conditions.” Differentiating is detecting. DRAQ5 is the nuclear dye. The target protein dye is RelA-mNeonGreen (RelA-mNG).
sorting the cells in real-time based on the classification by physically separating the cells according to the classification, (Schraivogel abs “high-speed image-enabled cell sorting (ICS),which records multicolor fluorescence images and sorts cells based on measurements from image data at speeds up to 15,000 events per second.”)
Schraivogel doesn’t teach an encoder and sorting and fine-tuning on clusters.
However, Zordan teaches extracting, by a neural network-based feature encoder, one or more features … (Zordan fig. 2 encoder 200)
clustering one or more cells into one or more clusters based on the extracted features; (Zordan fig. 2 clustering 202)
selecting a cluster of the one or more clusters to sort; (Zordan para 27 “A user is able to look at the results of the unsupervised clustering to determine which population of cells to sort…. The user is able to label the clusters as “sort” or “do not sort,” for example.”)
fine-tuning a classification network using training data associated with the selected cluster including training the classification network to detect a first fluorescent dye in the target (Zordan para 28 “a classifier is implemented to fine-tune supervised classification.” Zordan teaches using multiple dyes in para 36 “The biological particles may be labeled with one or more labeling substances (such as a dye (particularly, a fluorescent dye) and a fluorochrome-labeled antibody)…”)
classifying incoming cells in real-time using the fine-tuned classification network; and (Zordan fig. 2 real-time classification 206)
sorting the cells in real-time based on the classification by physically separating the cells according to the classification, wherein the fine-tuning of the classification network is performed after selection of the cluster and before subsequent real-time sorting, and the fine-tuned classification network is immediately applied to classify and sort subsequently flowing cells. (Zordan para 7 “performing real-time classification of cells during active sorting using the classifier.” Zordan para 29 “real-time classification is performed during active sorting. The trained classifier is used to do the real-time classification based on the sorting classifications.”)
Zordan, Schraivogel and the claims are all directed to image based classification of cells in a cytometer. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to fine-tune on sorting data because Zordan’s “supervised classification network is very fast, allowing it to make real-time sort decisions as the cell travels through a device.” Zordan para 6.
Zordan teaches claims 2, 9 and 16. The method of claim 1 wherein the one or more features comprise a target (Zordan fig. 2 clustering 202 and para 36 “The biological particles may be labeled with one or more labeling substances (such as a dye (particularly, a fluorescent dye) and a fluorochrome-labeled antibody)…”)
Zordan doesn’t teach a protein.
However, Schraivogel teaches a protein. Schraivogel teaches that it’s the target protein ReIA-mNG and a nucleus being extracted with the fluorescence distribution on p. 3, where ReIA-mNG is the target protein, “HeLa cells expressing RelA-mNG were treated with TNF a or left untreated and stained with the cell-permeable nuclear dye DRAQ5.”
Zordan teaches claims 3, 10 and 17. The method of claim 2 wherein clustering the one or more cells is based on a location of the target (Zordan fig. 2 clustering 202 and para 36 “The biological particles may be labeled with one or more labeling substances (such as a dye (particularly, a fluorescent dye) and a fluorochrome-labeled antibody)…”)
Zordan doesn’t teach a protein.
However, Schraivogel teaches a protein. Schraivogel teaches that it’s the target protein ReIA-mNG and a nucleus being extracted with the fluorescence distribution on p. 3, where ReIA-mNG is the target protein, “HeLa cells expressing RelA-mNG were treated with TNF a or left untreated and stained with the cell-permeable nuclear dye DRAQ5.”
Schraivogel teaches claims 4, 11 and 18. The method of claim 3 wherein when the target protein is in the cytosol, (Schraivogel p. 5 “nuclear translocation of RelA upon NF-kB pathway activation… Cells were then treated with TNFa, and the 5% lower (cytoplasmicRelA) and upper (nuclear RelA) bins of the RelA-mNG/DRAQ5 correlation parameter were isolated…” The nuclear RelA is activated, and the cytosolic RelA is the non-actived dormant part.)
Schraivogel doesn’t teach activated cell sorting.
However, Zordan teaches the one or more cells are clustered as dormant cells, … the one or more cells are clustered as activated cells. (The clustering is taught by Zordan above. Zordan is an “Activated Cell Sorter (IACS).”)
Zordan teaches claims 5, 12 and 19. The method of claim 1 wherein identifying the cluster to sort is based on a user manually identifying the cluster. (Zordan para 27 “A user is able to look at the results of the unsupervised clustering to determine which population of cells to sort…. The user is able to label the clusters as “sort” or “do not sort,” for example.”)
Zordan teaches claims 6, 13 and 20. The method of claim 1 wherein identifying the cluster to sort is based on machine learning to identify the cluster. (Zordan para 27 “The user is able to label the clusters as “sort” or “do not sort,” for example. Multiple “sort” clusters are possible. In some embodiments, the labeling process is automated using machine learning, neural networks and artificial intelligence.”)
Zordan teaches claims 7, 14 and 21. The method of claim 1 wherein fine-tuning the classification network includes performing training with an additional dataset based on the cluster. (Zordan para 7 “Classifier results from the unsupervised clustering are used by the classifier to fine-tune a convolutional neural network. The classifier is configured to be re-trained for each experiment.” Retraining is training with an additional dataset, the fine-tuning/retraining is based on clusters.)
Conclusion
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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