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 .
Election/Restrictions
Claim(s) 2-10 and 26-30 withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 03/18/2026.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/19/2024 has been considered by the examiner and made of record in the application file.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 11-12, 23, 25, 31-32, and 34-37 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng et al. (“Pointly-Supervised Instance Segmentation” - cited in IDS) in view of Yan et al. (“Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework With Spatio-Temporal Collaboration” (29 August 2022 date of publication))
Regarding Claim 1, Cheng et al. teaches a method, comprising: at a device: determining a point-level annotation defined for at least one object in a video; and using the point-level annotation for the at least one object to train a machine learning model with point-level supervision (Pg. 2607 – Abstract: teaches a method of training an instance segmentation model using point-level annotations. Specifically, Cheng discloses collecting point annotations for objects and training instance segmentation models using point-based supervision. Further, Cheng teaches that existing instance segmentation models may be trained using only annotated points associated with objects.)
Cheng et al. is silent on the remaining limitations of Claim 1. However, Yan et al. teaches performing video instance segmentation. (Pg. 393 – Abstract: teaches a weakly supervised framework for instance segmentation in videos, wherein deep networks are trained to segment and track multiple objects in video frames. Yan further teaches training segmentation networks using weak supervisory signals to perform video instance segmentation.)
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the point-supervised training framework of Cheng for use within the video instance segmentation framework of Yan because both references are directed to reducing annotation effort while training segmentation models. Cheng demonstrates that point annotations provide an effective supervisory signal for segmentation training, while Yan teaches applying weak supervision to video instance segmentation. The combination would have predictably reduced annotation costs while preserving segmentation performance.
Regarding Claim 11, Cheng et al. teaches wherein the machine learning model is pre- trained on a data set of images having mask annotations for objects in the images. (Pg. 2607 – Abstract: teaches training instance segmentation models based upon existing segmentation architectures trained on datasets containing object mask annotations.) The use of pre-trained segmentation models before fine-tuning with weaker forms of supervision was well known in the art.
Regarding Claim 12, Cheng et al. teaches wherein training the machine learning model with point-level supervision includes: processing the video by the pre-trained machine learning model to predict masks for one or more objects in the video, and fine-tuning the pre-trained machine learning model based on the point-level annotation defined for the at least one object in the video and based on the masks predicted for the one or more objects in the video. (Pg. 2607 – Abstract: teaches using a segmentation model to generate mask predications and training the model using point-based supervision)
Cheng et al. is silent on the remaining limitations of Claim 12. However. Yan et al. teaches generating segmentation masks for objects during video instance segmentation and refining segmentation performance through weakly supervised training. (Pg. 393 – Abstract)
It would have been obvious to one of ordinary skill in the art to process video data using a pre-trained segmentation model to predict masks and to fine-tune the model based upon point annotations and predicted masks in order to improve segmentation accuracy while reducing annotation burden.
Regarding Claim 23, Cheng et al. teaches wherein training the machine learning model with point-level supervision further includes using self-training of the fine-tuned machine learning model by: processing the video using the fine-tuned machine learning model to predict new masks for one or more objects in the video, and performing additional fine-tuning of the fine-tuned machine learning model based on the new masks predicted for the one or more objects in the video. (Pg. 2608 and 2612: teaches a self-training paradigm in which a Mask R-CNN model is initially trained using point supervision, after which pseudo-ground truth masks are generated by running inference using the trained mode. Further teaches retraining the Mask R-CNN model using the generated pseudo-ground truth masks, thereby improving segmentation performance.)
Cheng et al. is silent on the remaining limitations of performing foregoing operations in connection with video instance segmentation. (Pg. 393-Abstract: teaches a weakly supervised framework for instance segmentation networks to segment and track objects across video frames.)
It would have been obvious to one of ordinary skill in the art at the time of the invention to apply Cheg’s self-training technique to Yan’s video instance segmentation framework because both references seek to improve segmentation accuracy while minimizing annotation effort.
Claim 25 recites a system with elements corresponding to the steps recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Cheng et al. and Yan et al references, presented in rejection of Claim 1, apply to this claim.
The proposed teachings in Cheng et al. presented in Claim 11, apply to Claim 31 and Claim 36 and are incorporated herein by reference.
The proposed combination as well as the motivation for combining the Cheng et al. and Yan et al. references presented in the rejection of Claim 12, apply to Claim 32 and Claim 37 and are incorporated herein by reference.
The proposed combination as well as the motivation for combining the Cheng et al. and Yan et al. references presented in the rejection of Claim 23, apply to Claim 34 and are incorporated herein by reference.
Claim 35 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Additionally, the rationale and motivation to combine the Cheng et al. and Yan et al. references, presented in rejection of Claim 1, apply to this claim.
Allowable Subject Matter
Claim(s) 15-22, 24, 33, and 38 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAVEN S. JONES whose telephone number is (571)272-7759. The examiner can normally be reached M-Th 7:00a.m. - 5:00p.m..
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/RAVEN SIMONE JONES/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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