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 § 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2022/0084204) in view of Kumari (US 2024/0185588).
As to claim 1 Li discloses a method for labelling data, comprising:
at a computer system having one or more processors and memory (para. 0055):
obtaining a first image including an object, the first image associated with a physical environment (para. 0072);
applying a reference model to process the first image and generate a reference label (para. 0072);
applying an image generative model to generate a reference image based on the reference label (Fig. 3B item 365; para. 0067, 0076, 0080);
in accordance with a determination that the first image and the reference image satisfy a similarity criterion, labelling the first image with the reference label (para. 0072, 0081, 0087, e.g., “generated labels are associated with input image when a similarity between input image and version of input image reaches a certain threshold”; “if a determined difference is less than or equal to a difference threshold, process 350 proceeds to operation 375”; “operation is performed when processing logic can determine that a latest generated synthetic version and input image are approximately identical or at least have a threshold level of similarity. In at least one embodiment, processing logic can further determine that a set of labels corresponding to objects within synthetic version can also match objects within input image. Processing logic can then associate one or more labels of synthetic image with input image, resulting in a labelled version of input image”); and
adding the first image that is labelled with the reference label to a memory to be used to generate a target model for autonomously monitoring the physical environment (Fig. 3, item 375; para. 0070-0071, 0081, 0096, e.g., “process 400 can proceed to associate labels of synthetic image 419 that were generated during a most recent inverse optimization cycle to input image 410 without generating a new synthetic image and corresponding labels”).
Li is silent on using of a corpus of training data.
Kumari teaches using a corpus of training data (para. 0048).
It would have been obvious to one of ordinary skill in the art to incorporate Kumari’s teachings into Li since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve training data and performance.
As to claim 2, the combination of Li and Kumari discloses the method of claim 1, further comprising: generating the target model based on the first image and the reference label (Li, Fig. 4, 0096); generating a target output by the target model (Li, 0096); and applying the target output to at least partially automatically control a machine or vehicle to operate in the physical environment (Li, par. 0071, 0096, 0013).
As to claim 3, the combination of Li and Kumari discloses the method of claim 1, further comprising: applying the target model to process the first image and generate an intermediate output with a confidence score, wherein the reference model is applied in accordance with a determination that the confidence score does not satisfy a confidence threshold requirement (Li, 0078, 0103-0106).
As to claim 4, the combination of Li and Kumari discloses the method of claim 1, the target model including an image segmentation model, the method further comprising: applying the target model to process the first image and generate an intermediate output with an intersection over union (IOU) indicator, wherein the reference model is applied in accordance with a determination that the IOU indicator is lower than an IOU threshold (Li, para. 0072, 0081, 0087).
As to claim 5, the combination of Li and Kumari discloses the method of claim 1, further comprising: identifying one or more prior labels associated with image data previously captured for the physical environment (Li, para. 0071-0073, 0076-0078); and determining a semantic distance between the reference label and the one or more prior labels, wherein the image generative model is applied in accordance with the semantic distance satisfies a semantic proximity criterion (Li, para. 0071-0073, 0076-0078).
As to claim 6, the combination of Li and Kumari discloses the method of claim 1, wherein the reference label includes a first candidate label, the method further comprising: generating a second candidate label (Li, para. 0071-0073, 0076-0078); and selecting the first candidate label between the first candidate label and the second candidate label based on context information associated with the physical environment ( Li, para. 0071-0073, 0076-0078).
As to claim 7, the combination of Li and Kumari discloses the method of claim 6, wherein the context information associated with the physical environment includes a prior label associated with image data previously captured for the physical environment, the method further comprising: determining a first semantic distance between the first candidate label and the prior label (Li, para. 0071-0073, 0076-0078); and determining a second semantic distance between the second candidate label and the prior label, wherein the first candidate label is selected and included in the reference label in accordance with a determination that the first semantic distance is less than the second semantic distance ( Li, para. 0071-0073, 0076-0078).
As to claim 8, the combination of Li and Kumari discloses the method of claim 6, wherein the second candidate label is generated using the reference model ( Li, para. 0071-0073, 0076-0078).
As to claim 9, the combination of Li and Kumari discloses the method of claim 6, wherein the reference model comprises a first reference model, and the second candidate label is generated using a second reference model distinct from the first reference model ( Li, para. 0071-0073, 0076-0078).
As to claim 10, the combination of Li and Kumari discloses the method of claim 1, applying the reference model to process the first image and generate the reference label further comprising: applying a first model to process the first image and generate a first candidate label with a first weighing factor ( Li, para. 0071-0073, 0076-0078, 0111, 0112).; applying a second model to process the first image and generate a second candidate label with a second weighing factor ( Li, para. 0071-0073, 0076-0078, 0111, 0112).; and selecting the reference label from the first candidate label and the second candidate label based on the first weighing factor and the second weighing factor( Li, para. 0071-0073, 0076-0078, 0111, 0112).
As to claim 11, the combination of Li and Kumari discloses the method of claim 1, applying the reference model to process the first image and generate the reference label further comprising: generating a plurality of candidate labels; and consolidating the plurality of candidate labels to generate the reference label (para. 0071-0073, 0076-0078).
As to claim 12, the combination of Li and Kumari discloses the method of claim 1, further comprising: determining a similarity level between the first image and the reference image; and in accordance with a determination that the similarity level is greater than a similarity threshold, determining that the similarity criterion is satisfied (Li, para. 0072, 0081, 0083, 0087).
As to claim 13, the combination of Li and Kumari discloses the method of claim 1, further comprising: adding the reference image that is generated based on the reference label to the corpus of training data to be used to generate the target model (Li, para. 0070-0071, 0081, 0096; Kumari, para. 0048).
As to claim 14, the combination of Li and Kumari discloses the method of claim 1, further comprising: applying the image generative model to generate a second image based on the reference label (Li, para. 0070-0071, 0081, 0096); and adding the second image to the corpus of training data to be used to generate the target model (Li, para. 0070-0071, 0081, 0096; Kumari, para. 0048).
As to claim 15, the combination of Li and Kumari discloses the method of claim 1, further comprising: obtaining a test label corresponding to an object class (Li, para. 0071-0073, 0076-0078); applying the image generative model to generate a test image based on the test label (Li, para. 0067, 0076, 0080; and adding the test image and the test label to the corpus of training data to be used to generate the target model (Li, para. 0070-0071, 0081, 0096); Kumari, para. 0048)..
As to claim 16, the combination of Li and Kumari discloses the method of claim 15, wherein the first image has description information and metadata, obtaining the test label corresponding to the object class further comprising: extracting the test label from the description information or metadata of the first image (Li, para. 0071-0073, 0076-0078, 0148, 0488).
As to claim 17, the combination of Li and Kumari discloses the method of claim 1, further comprising: generating a first candidate label identifying the object in the first image (Li, para. 0071-0073, 0076-0078; generating a second candidate label identifying the object in the first image (Li, para. 0071-0073, 0076-0078; and combining keywords in the first candidate label and the second candidate label to generate the reference label applied by the image generative model to generate the reference image (Li, para. 0071-0073, 0076-0078; Kumari, para. 0109—0112).
As to claim 18, the combination of Li and Kumari discloses the method of claim 1, further comprising: applying the image generative model to generate one or more alternative images based on one or more alternative labels (Li, para. 0103-0106); and for each of the reference image and the one or more alternative images (Li, para. 0103-0106), determining a respective similarity level with the first image; wherein the reference label is selected in accordance with a determination that the respective similarity level of the reference image is higher than the respective similarity level of each alternative image (Li, para. (Li, para. 0072, 0081, 0083, 0087, 0103-0106).
As to claims 19-20, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sohn et al. disclose systems and methods for performing domain adaptation include collecting a labeled source image having a view of an object. Viewpoints of the object in the source image are synthesized to generate view augmented source images.
Zhang et al. discloses a method that includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair.
Chakraborty et al. disclose techniques that are generally described for machine learning exampled-based annotation of image data.
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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