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 Arguments
Applicant's arguments filed May 7, 2026 have been fully considered but they are not persuasive.
Regarding claim 1,
applicant states that “Bera does not teach or suggest generating a dynamic particle classification algorithm by applying a predetermined order of quantified parameters of image filters”. Examiner disagrees with this statement. Bera teaches generating a dynamic classification algorithm by applying a predetermined order of the quantified parameters of image filters (The method may also include predicting, using the machine learning algorithm, optimal selection parameters for the image. The method may also include applying the optimal selection parameters to a filtering algorithm for the image. Abstract). To apply the quantified parameters of image filters in a predetermined order can be common knowledge when read in the broadest reasonable interpretation (BRI). For example, when the image filters are all sharpening filters, each having parameters to achieve different levels of sharpness, it is common knowledge that the filters will be placed in the order to gradually increase sharpness, with the filter targeting the lowest sharpness placed first and the one targeting the highest sharpness the last (i.e., a predetermined order). Since claim 1 does not sufficiently define “a predetermined order” nor “quantified parameters”, the aforementioned example based on BRI read against them.
Regarding claim 19,
applicant's arguments regarding the image filters being applied to images with the particle classification algorithm in a specified, particular order cited in claim 19 have been fully considered and are persuasive. Claim 19 is 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.
Response to Amendment
The Amendment of May 7, 2026 overcomes the following objection:
Objection to drawings
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 1-5, 20-22 and 24-29 are rejected under 35 U.S.C. 103 as being unpatentable over Bera (US Patent No.: US 11,487,967 B2), hereinafter Bera, in view of Kumar (PCT Patent Pub. No.: WO 2020/081582 A1), hereinafter Kumar.
Regarding claim 1, Bera teaches a method for determining image filters for classifying particles of a sample in a particle analyzer, the method comprising: inputting into a machine learning algorithm one or more training data sets comprising a plurality of images (In some embodiments, the image data and the environmental data may be inputted into the machine learning algorithm while it is still being trained (therefore the image data and the environmental data may be training data, in this instance). Column 6 line 57); [[and]] inputting quantified parameters (The classifier may help predict outputs of the machine learning algorithm, which in this instance may be the selection parameters to be used in the filtering algorithm. Column 3 line 47. To build a machine learning algorithm for predicting optimal selection parameters for the filtering algorithm (i.e., selection parameters that accurately and efficiently filter the image), the previously stored cloud environmental data and cloud selection parameters may be requested and received from the cloud on which they were stored. Column 4 line 46) of a plurality of image filters (To filter the image and capture its features, one or more image filters may be applied to the image in order to better learn and identify the various contents of the image. Column 4 line 23) into the machine learning algorithm (In some embodiments, building the machine learning algorithm includes receiving cloud environmental data and cloud selection parameters. Column 4 line 14); generating a dynamic (Method 100 includes operation 170 to predict optimal selection parameters. Column 7 line 4) classification algorithm (In some embodiments, the machine learning algorithm includes a classifier ( or a classification model). Column 5 line 37) based on the training data sets (In some embodiments, the image data and the environmental data may be inputted into the machine learning algorithm while it is still being trained (therefore the image data and the environmental data may be training data, in this instance). Column 6 line 57) and by applying a predetermined order (To apply the quantified parameters of image filters in a predetermined order can be a common knowledge. For example, when the image filters are all sharpening filters, each having parameters to achieve different levels of sharpness, it is common knowledge that the filters will be placed in the order to gradually increase sharpness, with the filter targeting the lowest sharpness placed first and the one targeting the highest sharpness the last (i.e., a predetermined order).) of the quantified parameters of the image filters (Once the machine learning algorithm begins training, the determined selection parameters (determined using the machine learning algorithm) should become more customized/tailored to different environmental data and image data. Column 5 line 63); and calculating an adjustment to one or more of the quantified parameters of the image filters (The method may also include predicting, using the machine learning algorithm, optimal selection parameters for the image. The method may also include applying the optimal selection parameters to a filtering algorithm for the image. Abstract).
Bera does not teach the following limitations as further recited, but Kumar further teaches inputting into a machine learning algorithm one or more training data sets comprising a plurality of images of particles (2D image data 1409 such as a lung X-ray or other X-ray or other 2D image data may be provided to a 2D convolutional neural network input 1410. The 2D CNN 1410 may be trained to recognize diagnostically useful features in x-rays, skin photographs, or other 2D image data. [0256]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bera to incorporate the teachings of Kumar to determine image filters for classifying particles of a sample in a particle analyzer by inputting into a machine learning algorithm one or more training data sets comprising a plurality of images of particles in order to identify target cells reliably, economically, and with the required specificity and sensitivity is needed.
Claims 2-5, 20-22 and 24-29, unamended and are rejected based on the combination of Bera, in view of Kumar as applied to claim 1 above. The grounds of rejection established in the last Office Action is fully incorporated herein.
Claims 6-7, unamended and are rejected based on the combination of Bera (US Patent No.: US 11,487,967 B2), hereinafter Bera, in view of Kumar (PCT Patent Pub. No.: WO 2020/081582 A1), hereinafter Kumar, further in view of Gorthi (Fluorescence imaging of flowing cells using a temporally coded excitation, Opt. Express, 2013, 21(4), 5164–5170), hereinafter Gorthi. The grounds of rejection established in the last Office Action is fully incorporated herein.
Claim 8, unamended and is rejected based on the combination of Bera (US Patent No.: US 11,487,967 B2), hereinafter Bera, in view of Kumar (PCT Patent Pub. No.: WO 2020/081582 A1), hereinafter Kumar, further in view of Walsh (Great Britain Patent Pub. No.: GB 2377349 A), hereinafter Walsh. The grounds of rejection established in the last Office Action is fully incorporated herein.
Claim 9, unamended and is rejected based on the combination of Bera (US Patent No.: US 11,487,967 B2), hereinafter Bera, in view of Kumar (PCT Patent Pub. No.: WO 2020/081582 A1), hereinafter Kumar, further in view of Setiawan (HISTOPATHOLOGY OF LUNG CANCER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH GAMMA CORRECTION, Commun. Math. Biol. Neurosci. 2022, 2022:81), hereinafter Setiawan. The grounds of rejection established in the last Office Action is fully incorporated herein.
Allowable Subject Matter
Claim 19 is 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: the closest prior arts of record teach the method according to claim [[18]] 1. However, none of them alone or in any combination teaches wherein the quantified parameters of the image filters are inputted into the machine learning algorithm in the order of: 1) enabled; 2) smooth; 3) sharpen; 4) blur; 5) threshold; 6) gamma correction; 7) edges; 8) invert and 9) intensity as specified in claim 19. Although the image filters prescribed are not new, the specified order that they are arranged is neither anticipated nor rendered obvious by the prior arts of record.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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