DETAILED ACTION
In response to the amendment filed on 04/08/2026, all the amendments made to claims have been entered and the action follows:
Claim Objections
Claim 14 is objected to because of the following informalities: “based an detection” appears to include a grammatical error. Appropriate correction is required.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 2, 5-9, 12-15, and 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, the limitations “a single point of intensity above a threshold” and “multiple points of intensity above the threshold” render the claim indefinite, because it is unclear and confusing how the “point” and “pixel” relate to each other. For example, are multiple pixels above a threshold intensity within a pre-determined distance still considered a single point? Or is a single pixel above a threshold intensity considered a single point? Please amend the claim for clarifying the subject matter and maintaining consistency in claim language, as the metes and bounds of the claimed invention would not have been obvious to one of ordinary skill in the art. Similar reasons apply to claims 8 and 15.
Regarding claim 5, the limitation “determining a spot count of intensity greater than a threshold” renders the claim indefinite, because it is unclear and confusing how and when such determination is performed with respect to the steps of claim 1.
Claim 1 already recites determining if there is a “single point” or “multiple points”. Does the “spot count” of claim 5 refer to such limitations of claim 1 (i.e., spot is equivalent to a point), or is there a separate counting step? If there is a separate counting step, when is it performed with respect to the steps recited in claim 1? Please amend the claim for clarifying the subject matter and maintaining consistency in claim language, as the metes and bounds of the claimed invention would not have been obvious to one of ordinary skill in the art. Similar reasons apply to claims 12 and 18.
Regarding claim 6, the limitation “detecting noise” renders the claim indefinite, because it is unclear and confusing when such detection is performed with respect to the steps of claim 1. For example, is the noise detected before, during, or after the machine learning implementing the image analysis? Please amend the claim for clarifying the subject matter and maintaining consistency in claim language, as the metes and bounds of the claimed invention would not have been obvious to one of ordinary skill in the art. Similar reasons apply to claims 13 and 19.
Regarding claim 7, the limitation “separating the input based on detection of biomarkers” renders the claim indefinite, because it is unclear and confusing how and when such separation is performed with respect to the steps of claim 1.
Claim 1 does not recite any “detection of biomarkers”, and rather recites determining “a single point” or “multiple points” of intensity above threshold from fluorescence related to biomarkers (i.e., not biomarkers). Is the determination that “a single point” or “multiple points” of intensity above the threshold is present equivalent to the “detection of biomarkers”? Or is there a separate step of detecting biomarkers?
As a further example, do the separated inputs all enter a single neural network, each enter different neural networks (i.e., “one or more neural networks”), or selectively enter neural networks? Please amend the claim for clarification and maintaining consistency in claim language, as the metes and bounds of the claimed invention would not have been obvious to one of ordinary skill in the art. Similar reasons apply to claims 14 and 20.
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)(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.
Claims 1, 2, 5-9, 12-15, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kimmerling et al. (USPN 11,530,974).
Regarding claim 1, Kimmerling discloses:
receiving input at one or more neural networks (see 6:58-62 and fig 8, a convolutional neural network (CNN) classifier receiving an input image from a suspended microchannel resonators (SMR) platform; and see 7:20-25, wherein the input image is a fluorescent image); and
classifying the input into one or more classifications using machine learning based on fluorescence related to biomarkers in the input with the one or more neural networks (see 6:58-62, 7:20-25, and fig 8, the CNN classifier classifies the input image according to fluorescent markers in the image),
wherein the machine learning implements analysis comprising pixel analysis of intensity including analyzing a layout of pixel intensity to determine if there is a single point of intensity above a threshold or multiple points of intensity above the threshold (see 6:58-62 and fig 8, the CNN classifier is trained with training data such that a single point of bright intensity above an inherent threshold is classified as a single live cell, while multiple points of bright intensity above an inherent threshold are classified as cell aggregates).
Regarding claim 2, Kimmerling further discloses: wherein the input comprises fluorescence images (see rejection of claim 1, the input image is a fluorescent image).
Regarding claim 5, Kimmerling further discloses wherein classifying the input into one or more classifications includes determining a spot count of intensity greater than a threshold (see 6:58-62 and fig 8, the CNN classifier is trained with training data such that a single point of bright intensity above an inherent threshold is classified as a single live cell, while multiple points of bright intensity above an inherent threshold are classified as cell aggregates).
Regarding claim 6, Kimmerling further discloses wherein classifying the input into one or more classifications includes detecting noise (see fig 8, the CNN classifier is trained to classify debris).
Regarding claim 7, Kimmerling further discloses comprising separating the input based on detection of the biomarkers (see fig 8, binning the input image according to its classification result).
Regarding claims 8, 9, and 12-14, Kimmerling discloses everything claimed as applied above (see rejection of claims 1-7; and see Kimmerling fig 7, a computer).
Regarding claim 15, Kimmerling discloses:
a first computing device configured for sending one or more fluorescent images of extracellular vesicles to a second computing device (see 3:54-31:38 and fig 7, server 719); and the second computing device (see 3:54-31:38 and fig 7, computer 725) configured for:
receiving the one or more fluorescent images of extracellular vesicles at one or more neural networks (see 6:58-62 and fig 8, a CNN classifier receiving an input image from a SMR platform; and see 7:20-25, wherein the input image is a fluorescent image; and see 26:41-42, the input image is of extracellular vesicles); and
classifying the one or more fluorescent images of extracellular vesicles into one or more classifications using machine learning based on fluorescence related to biomarkers in the one or more fluorescent images of extracellular vesicles with the one or more neural networks (see 6:58-62, 7:20-25 and fig 8, the CNN classifier classifies the input image according to fluorescent markers in the image; and see 26:41-43, wherein one of the known classes is of extracellular vesicles),
wherein machine learning implements analysis comprising pixel analysis of intensity including analyzing a layout of pixel intensity to determine if there is a single point of intensity above a threshold or multiple points of intensity above the threshold (see 6:58-62 and fig 8, the CNN classifier is trained with training data such that a single point of bright intensity above an inherent threshold is classified as a single live cell, while multiple points of bright intensity above an inherent threshold are classified as cell aggregates).
Regarding claims 18-20, Kimmerling discloses everything claimed as applied above (see rejection of claims 5-7 and 15).
Response to Arguments
Arguments regarding rejection under 101
In view of the claim amendments and arguments made by the applicant, the rejection has been withdrawn.
Arguments regarding rejection under 112(b)
The applicant argues that the claim amendments overcome the rejection under 112(b). The examiner respectfully disagrees, as the newly added limitations resulted in further rejections under 112(b), as stated above.
Arguments regarding prior art rejection
Applicant's arguments have been fully considered but they are not persuasive. Applicant argues that Kimmerling does not disclose the subject matter of claim 1, specifically the limitation “machine learning implements image analysis comprising pixel analysis of intensity including analysing a layout of pixel intensity to determine if there is a single point of intensity above a threshold or multiple points of intensity above the threshold”.
The examiner respectfully disagrees. Kimmerling clearly discloses:
a machine learning algorithm employing a neural network for performing image analysis (see fig 8, an input image directed into the CNN classifier), which reads on the claimed “machine learning implements image analysis”;
that such image analysis includes the CNN classifier analyzing the input image, wherein an image is a layout of pixels with intensity values (see 22:64-23:4, “classifier based on a deep learning neural network […] identifying the brightness of each cell”; 23:31, “intensity values per pixel in the image”), which reads on the claimed “comprising pixel analysis of intensity including analysing a layout of pixel intensity”;
that such analysis includes determining whether pixels have intensity values satisfying inherent edge thresholds (see 23:1-2,31,50-56 and fig 8, “brightness” or “intensity” of the pixels is used as a “feature” to “obtain edges” (i.e., boundary) for each cell, which discloses an inherent threshold distinguishing an edge from a non-edge), leading to a determination of whether there is a single cell or multiple cells (see 6:58-62, “classify an individual cell vs […] cell aggregates”), which reads on the claimed “to determine if there is a single point of intensity above a threshold or multiple points of intensity above the threshold”.
Therefore, Kimmerling discloses the limitation specifically argued by the applicant, and the rejection under Kimmerling is maintained. Similar reasons apply to claims 8 and 15.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SJ PARK whose telephone number is (571)270-3569. The examiner can normally be reached M-F 8:00 AM - 5:00 PM.
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/SJ Park/Primary Examiner, Art Unit 2675
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