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 .
Response to Amendment
The amendment was filed on 11/18/2025.
Claims 1-9, 11-13, and 15-22 are pending. Claim 10 and 14 are canceled.
Response to Arguments
Applicants’ arguments filed under Remarks on pages 13-17 on 11/18/2025 have been fully considered but they are not persuasive. Applicants state on page 14 as following:
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The Examiner respectfully disagrees. Suprem discloses providing a first predictive model having been configured to provide at least one first prediction task (page 7, right column, where it is disclosed that ODIN uses the YOLO object detection model as the baseline object detector. Paragraphs 3-5, YOLOv3); providing a second predictive model having been configured to provide a second prediction task, the second predictive model comprising one or more parameters, the second prediction task being derivable from the first prediction task (page 7 right column, paragraphs 1, 5 and second to last paragraph – “ODIN trains and deploys a lite model as soon as it detects a new cluster”). Section 6.3 of Suprem further describes specialized vs lite models where first and second models are disclosed.
Images are provided to the model as illustrated in Figures 3 and 6. Further, section 4.1 under KL Divergence section discloses that “DETECTOR continuously updates the parameters of the temporary cluster’s ∆-band based on the new data points in the input stream. It detects drift by using KL divergence to compare the posterior distribution of the temporary cluster’s ∆-band after a point is added against the prior distribution before a point is added.” On page 6, left column under “Training” section, Suprem discloses in “each iteration, the components of DA-GAN are updated sequentially. The image discriminator is trained to distinguish between real images and synthetic images generated by the decoder (Lines 5-7)… Finally, the autoencoder is updated to minimize the pixel-wise reconstruction loss (Lines 13).”
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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5, 11, 15-16, and 21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Suprem, Abhijit, et al. ("Odin: Automated drift detection and recovery in video analytics." arXiv preprint arXiv:2009.05440 (2020)) (hereafter, “Suprem”).
With regard to claim 1 Suprem discloses a computer-implemented method of machine-learning for on-the-fly adaptation of a predictive model configured for image processing (see Abstract on page 1), the method comprising: providing a first predictive model having been configured to provide at least one first prediction task (page 7, right column, paragraphs 3-5, YOLOv3 –“While YOLO is accurate on challenging datasets, it is computationally expensive and requires multiple GPUs for real-time operation (e.g.,40fps). Furthermore, the model is designed for dense, generalized object detection on the COCO dataset that contains a wide array of classes”); providing a second predictive model having been configured to provide a second prediction task, the second predictive model comprising one or more parameters, the second prediction task being derivable from the first prediction task (page 7 right column, paragraphs 1, 5 and second to last paragraph – “ODIN trains and deploys a lite model as soon as it detects a new cluster…The resultant model complexity is not justified when it is geared towards a particular domain with a narrow set of classes (e.g., dashboard camera videos in BDD)”); providing context-based images from a first stream of images obtained from an optical sensor to both the first and the second predictive models, each provided image being provided just once on-the-fly (algorithm 2 on page 7 left column); and performing an on-the-fly adaptation for the second predictive model, the on-the-fly adaptation comprising, for each provided image (algorithm 2 on page 7 left column): performing a respective first prediction by the first predictive model and a respective second prediction by the second predictive model (page 7 right column); computing a cost function of the respective first prediction and the respective second prediction (page 7 right column); and updating the one or more parameters of the second predictive model based on the computed cost function (page 7 left column, Model Generation and right column; YOLO-Lite, computing distance metric).
With regard to claim 2 Suprem discloses wherein performing the on-the-fly adaptation for the second predictive model further comprises: calibrating the second predictive model by performing the on-the-fly adaptation for the second predictive model using a few-shot learning method that is carried out with a limited number of the context-based images obtained, on-the-fly and just once, from the first stream of images (section 5.2 Types of Specialized models, student-teacher approach; temporary cluster throughout the reference starting at section 4.1).
With regard to claim 3 Suprem discloses further comprising prior to, performing, for each provided image, the respective first prediction and the respective second prediction: computing a prior condition from prior information of the provided image; and determining, based on the prior condition, whether the one or more images are to be provided to the first and the second model (KL Divergence in section 4.1; Model Generation under section 5.1).
With regard to claim 5, claim 5 is rejected same as claim 1 and the arguments similar to that presented above for claim 1 are equally applicable to claim 5. Suprem discloses providing a context-based image from a second stream of images; obtaining one or more predictions each obtained by applying one of the one or more predictive models to the provided image; computing one or more weights, each weight being computed for one of the one or more predictions; and computing a prediction from a combination of the one or more predictions and their respective one or more weights (section 5.3 page 8 left column, where “to pick k best-fit models to operate on xi. See also page 10 section 6.4 KNN-W), and all of the other limitations similar to claim 1 are not repeated herein, but incorporated by reference.
With regard to claim 11, claim 11 is rejected same as claims 1 and 5, and the arguments similar to that presented above for claims 1 and 5 are equally applicable to claim 11. Suprem discloses processing machine at section 6.1 under system setup, the system inherently includes a storage unit, and all of the other limitations similar to claims 1 and 5 are not repeated herein, but incorporated by reference.
With regard to claim 15, claim 15 is rejected same as claim 2 and the arguments similar to that presented above for claim 2 are equally applicable to claim 15, and all of the other limitations similar to claim 2 are not repeated herein, but incorporated by reference.
With regard to claim 16, claim 16 is rejected same as claim 3 and the arguments similar to that presented above for claim 3 are equally applicable to claim 16, and all of the other limitations similar to claim 3 are not repeated herein, but incorporated by reference.
With regard to claim 21, claim 21 is rejected same as claim 2 and the arguments similar to that presented above for claim 2 are equally applicable to claim 21, and all of the other limitations similar to claim 2 are not repeated herein, but incorporated by reference.
Allowable Subject Matter
Claims 4, 6-9, 12-13, 17-20 and 22 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, and overcoming stated 35 USC 112b rejection.
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 extension fee 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 SHEFALI D. GORADIA whose telephone number is (571)272-8958. The examiner can normally be reached on Monday-Thursday 8AM-6PM, Friday 8AM-12PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached on 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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SHEFALI D. GORADIA
Primary Patent Examiner
Art Unit 2676
/SHEFALI GORADIA/
Primary Examiner, Art Unit 2665
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