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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/05/2026 has been entered.
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 limitation “analyzing a layout of pixel intensity to determine if there is a single pixel of intensity above a threshold or multiple pixels of intensity above the threshold” renders the claim indefinite for the following reasons:
i) The limitation is inconsistent with the applicant’s specification. The applicant’s specification indeed discloses that each individual pixel is analyzed to determine whether the fluorescence of individual pixel is above a threshold (see p5 line 23-24), however, does not disclose distinguishing a single pixel being above the threshold from multiple pixels being above the threshold. Rather, the applicant’s specification discloses and focuses on distinguishing a single EV spot (i.e., composed of multiple pixels) being above the threshold from multiple EV spots (i.e., each composed of multiple pixels) being above the threshold (see p5 line 23 through p6 line 4).
ii) It is further unclear and confusing how a “spot” is defined. For example, is a “spot” composed of bright pixels located within a pre-determined distance (i.e., is there any geometrical restraint)?
iii) The applicant’s specification then recites that multiple EV spots may be distinguished from a single EV spot by determining whether there are “dark pixels” or “points of no, low or lower fluorescence” separating the multiple EV spots, without providing a standard for ascertaining the requisite degrees of “dark” or “no, low or lower fluorescence”, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Consequently, the metes and bounds of the subject matter is not particularly pointed out and clearly and distinctively defined.
Similar reasons apply to claims 8 and 15.
Regarding claim 6, the limitation “detecting noise prior to classifying the input” renders the claim indefinite for the following reasons:
iv) It is unclear and confusing how the “detecting” is performed. For example, is the noise detected by the “one or more neural networks” or by a separate algorithm? Please amend the claim for clarification.
v) It is unclear and confusing when such “detecting” is performed with respect to the steps of claim 1. For example, is the noise detected before or after the neural networks receive the input? Please amend the claim for clarification.
Similar reasons apply to claims 13 and 19.
Regarding claim 7, the limitations “detecting one or more biomarkers by identifying fluorescence associated with the biomarkers” and “separating the input based on detection of biomarkers” render the claim indefinite for the following reasons:
vi) It is unclear and confusing when such “detecting”, “identifying”, and “separating” are each performed with respect to the steps of claim 1. For example, are each of these steps performed before, during, or after the machine learning classifying the input? Please amend the claim for clarification.
vii) It is unclear and confusing how such “detecting”, “identifying”, and “separating” are performed. For example, are each of these steps performed by the “one or more neural networks” or by a separate algorithm? Please amend the claim for clarification.
viii) It is unclear and confusing whether “separating the input into one or more categories” of the current claim is equivalent to “classifying the input” of claim 1 or is a separate process. For example, is there a primary classification performed by the neural network of claim 1 followed by a secondary separation of the input into categories as recited in claim 7, or are these two merely the same process? Please amend the claim for clarification.
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, 7-9, 12, 14, 15, 18, and 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 pixel of intensity above a threshold or multiple pixels of intensity above the threshold (see 6:58-62 and fig 8, the CNN classifier analyzes a layout pixels to determine that multiple pixels are bright enough to depict cells; and see 22:64-23:2, using an inherent brightness threshold that distinguishes cells and non-cells).
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 pixels with an intensity greater than a threshold (see 6:58-62 and fig 8, the CNN classifier determining whether the multiple pixels satisfying the inherent brightness threshold forms a single live cell (i.e., single spot) or cell aggregates (i.e., multiple spots)).
Regarding claim 7, Kimmerling further discloses:
detecting one or more biomarkers by identifying fluorescence associated with the biomarkers (see 6:58-62, 22:64-23:2, detecting that the multiple pixels satisfy the inherent threshold, which is a biomarker indicating presence of cells);
separating the input into one or more categories based on detection of the biomarkers (see 6:58-62 and fig 8, separating the input image into categories (e.g., single live cell and cell aggregates) based on the multiple pixels satisfying the inherent threshold).
Regarding claims 8, 9, 12, and 14, Kimmerling discloses everything claimed as applied above (see rejection of claims 1, 2, 5, and 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 pixel of intensity above a threshold or multiple pixels of intensity above the threshold (see 6:58-62 and fig 8, the CNN classifier analyzes a layout pixels to determine that multiple pixels are bright enough to depict cells; and see 22:64-23:2, using an inherent brightness threshold that distinguishes cells and non-cells).
Regarding claims 18 and 20, Kimmerling discloses everything claimed as applied above (see rejection of claims 5, 7, and 15).
Allowable Subject Matter
No prior art of record discloses the subject matter recited in claims 6, 13, and 19, however, these claims are rejected under 112(b) as recited above. These claims would be allowable if amended to overcome the 112(b) rejection and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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). However, Kimmerling does not disclose detecting noise prior to classifying the input. Similar reasons apply to claims 13 and 19.
Response to Arguments
Arguments regarding 112(b)
The states that the claim amendments overcome the 112(b) rejection, without any particular arguments. The examiner respectfully disagrees, as the 112(b) rejection is maintained, as stated above.
Arguments regarding prior art
The applicant argues that Kimmerling fails to disclose the subject matter recited in claim 1, specifically because Kimmerling does not disclose the limitation “machine learning implements image analysis comprising pixel analysis of intensity including analyzing a layout of pixel intensity to determine if there is a single pixel of intensity above a threshold or multiple pixels of intensity above the threshold”.
The examiner respectfully disagrees for the following reasons:
a) A portion of this limitation (i.e., “to determine if there is a single pixel of intensity above a threshold or multiple pixels of intensity above the threshold”) is currently rejected under 112(b) as being inconsistent with the specification.
b) Kimmerling clearly discloses analyzing an input that is a digital image (14:35-45, a digital image is well-understood as a two-dimensional layout of pixels with corresponding intensity values), which reads on the claimed “analyzing a layout of pixel intensity”. Kimmerling then discloses that an inherent brightness threshold for pixels is applied by the CNN for distinguishing cells and non-cells in the input image (22:64-23:2), which reads on the claimed “machine learning implements image analysis comprising pixel analysis of intensity”. Kimmerling also discloses that multiple pixels are determined as cells when the brightness threshold is met, which reads on the claimed “to determine if there is […] multiple pixels of intensity above the threshold”. The amended claim recites “determine if there is a single pixel of intensity above a threshold or multiple pixels of intensity above the threshold”, and Kimmerling discloses the latter of such limitation.
Similar reasons apply to claims 8 and 15.
Conclusion
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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