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 .
Claim Objections
Claims 1-19, are objected to because of the following informalities: Currently, first independent claim numbered as claim 1, however second dependent claim which depends on claim 1, also numbered as claim 1, Examiner believes it is a typographic error and suggests following renumbering of claims. Appropriate correction is required. Further please note for Examination purposes Examiner relied on renumbering of the claims as being exaplined below.
(Original)1. A method, comprising:
utilizing, by a device, a first neural network model to identify objects in images to be labeled;
utilizing, by the device, the identified objects to generate annotated objects;
utilizing, by the device, a second neural network model to group the annotated objects into clusters;
utilizing, by the device, a third neural network model to associate labels with the clusters;
applying, by the device, the labels and manually-generated labels, for clusters for which labels are not determined by the third neural network model, to the images to generate labeled images;
generating, by the device, a dataset based on the labeled images; and
utilizing, by the device, the dataset to train a machine learning model.
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
utilize a first neural network model to identify objects in obtained images;
utilize the identified objects to generate annotated objects;
utilize a second neural network model to group the annotated objects into clusters;
utilize a third neural network model to associate labels with the clusters;
obtain manually-generated labels for clusters for which labels are not determined by the third neural network model;
apply the labels and the manually-generated labels to the obtained images to generate labeled images;
generate a dataset based on the labeled images; and
utilize the dataset to train a machine learning model.
one or more instructions that, when executed by one or more processors of a device, cause the device to:
utilize a first neural network model to identify objects in received images;
utilize the identified objects to generate annotated objects;
utilize a second neural network model to group the annotated objects into clusters;
utilize a third neural network model to associate labels with the clusters;
obtain manually-generated labels for clusters for which labels are not determined by the third neural network model;
apply the labels and the manually-generated labels to the received images to generate labeled images;
generate a dataset based on the labeled images; and
utilize the dataset to train a machine learning model.
labels associated with the clusters; and label the clusters based on the confidence scores.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-2, 4, 6, 7-10, 12, 14-17, 19, and 20, of the instant application are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-3, 8, 11, 14-15, and 19-20, of US Patent 12,094,181 B2, as being described below. The patent claims include all of the limitations of the instant application claims, respectively. The patent claims also include additional limitations. Hence, the instant application claims are generic to the species of invention covered by the respective patent claims. As such, the instant application claims are anticipated by the patent claims and are therefore not patentably distinct therefrom. (See Eli Lilly and Co. v. Barr Laboratories Inc., 58 USPQ2D 1869, "a later genus claim limitation is anticipated by, and therefore not patentably distinct from, an earlier species claim", In re Goodman, 29 USPQ2d 2010, "Thus, the generic invention is 'anticipated' by the species of the patented invention" and the instant “application claims are generic to species of invention covered by the patent claim, and since without terminal disclaimer, extant species claims preclude issuance of generic application claims”).
Instant Application 18/815,909.
US Patent 12,094,181 B2
Claims 1+2+7. A method, comprising: utilizing, by a device, a first neural network model to identify objects in images to be labeled; utilizing, by the device, the identified objects to generate annotated objects; utilizing, by the device, a second neural network model to group the annotated objects into clusters; utilizing, by the device, a third neural network model to associate labels with the clusters; applying, by the device, the labels and manually-generated labels, for clusters for which labels are not determined by the third neural network model, to the images to generate labeled images; generating, by the device, a dataset based on the labeled images; and utilizing, by the device, the dataset to train a machine learning model.
2.The method of claim 1, wherein the images are unprocessed images.
7. The method of claim 1, wherein the machine learning model is associated with computer vision.
4. The method of claim 1, further comprising: training the first neural network model using pre-annotated image datasets prior to utilizing the first neural network model to identify objects in the images.
6. The method of claim 1, further comprising: generating a validation dataset based on the labeled images; and validating the machine learning model using the validation dataset.
8+9+14. A device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to: utilize a first neural network model to identify objects in obtained images; utilize the identified objects to generate annotated objects; utilize a second neural network model to group the annotated objects into clusters; utilize a third neural network model to associate labels with the clusters; obtain manually-generated labels for clusters for which labels are not determined by the third neural network model; apply the labels and the manually-generated labels to the obtained images to generate labeled images; generate a dataset based on the labeled images; and utilize the dataset to train a machine learning model.
9. The device of claim 8, wherein the obtained images are unprocessed images.
14. The device of claim 8, wherein the machine learning model is associated with computer vision.
10. The device of claim 8, wherein the one or more processors are further configured to: utilize the first neural network model to identify segmentation boundaries for the objects.
12. The device of claim 8, wherein the one or more processors are further configured to: generate confidence scores for the labels associated with the clusters; and label the clusters based on the confidence scores.
15+16+20. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: utilize a first neural network model to identify objects in received images; utilize the identified objects to generate annotated objects; utilize a second neural network model to group the annotated objects into clusters; utilize a third neural network model to associate labels with the clusters; obtain manually-generated labels for clusters for which labels are not determined by the third neural network model; apply the labels and the manually-generated labels to the received images to generate labeled images; generate a dataset based on the labeled images; and utilize the dataset to train a machine learning model.
16. The non-transitory computer-readable medium of claim 15, wherein the received images are unprocessed images.
20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: generate a validation dataset based on the labeled images; and validate the machine learning model using the validation dataset.
17. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: utilize the first neural network model to identify segmentation boundaries for the objects.
19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions further cause the device to: generate confidence scores for the labels associated with the clusters; and label the clusters based on the confidence scores.
Claim 1. A method, comprising: receiving, by a device, unprocessed images to be labeled; utilizing, by the device, a first neural network model to identify objects of interest in the unprocessed images and bounding boxes for the objects of interest; annotating, by the device, the objects of interest to generate annotated objects of interest; utilizing, by the device, a second neural network model to group the annotated objects of interest into clusters; utilizing, by the device, a third neural network model to determine labels for the clusters; requesting, by the device, manually-generated labels for clusters for which labels are not determined by the third neural network model; receiving, by the device, the manually-generated labels based on requesting the manually-generated labels; labeling, by the device, the unprocessed images with the labels and the manually-generated labels to generate labeled images; generating, by the device, a training dataset based on the labeled images; and training, by the device, a computer vision model with the training dataset to generate a trained computer vision model.
2. The method of claim 1, further comprising: receiving pre-annotated image datasets identifying existing annotated images; and training the first neural network model based on the pre-annotated image datasets, prior to utilizing the first neural network model.
3. The method of claim 1, further comprising: generating a validation dataset for the computer vision model based on the labeled images; and validating the computer vision model with the validation dataset.
8. A device, comprising: one or more processors configured to: receive unprocessed images to be labeled and pre-annotated image datasets identifying existing annotated images; train a first neural network model based on the pre-annotated image datasets; utilize the first neural network model to identify objects of interest in the unprocessed images and bounding boxes for the objects of interest; annotate the objects of interest to generate annotated objects of interest; utilize a second neural network model to group the annotated objects of interest into clusters; utilize a third neural network model to determine labels for the clusters; request manually-generated labels for clusters for which labels are not determined by the third neural network model; receive the manually-generated labels based on requesting the manually-generated labels; label the unprocessed images with the labels and the manually-generated labels to generate labeled images; generate a training dataset based on the labeled images; and train a computer vision model with the training dataset to generate a trained computer vision model.
11. The device of claim 8, wherein the one or more processors, to utilize the first neural network model to identify the objects of interest in the unprocessed images and the bounding boxes for the objects of interest, are configured to: identify segmented boundaries around the objects of interest in the unprocessed images; and predict coordinates of the bounding boxes for the objects of interest based on the segmented boundaries.
14. The device of claim 8, wherein the one or more processors, to utilize the third neural network model to determine the labels for the clusters, are configured to: classify the objects of interest in the unprocessed images; generate confidence scores for classifications of the objects of interest; and label the objects of interest based on the confidence scores.
15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to: receive unprocessed images to be labeled; utilize a first neural network model to identify objects of interest in the unprocessed images and bounding boxes for the objects of interest; annotate the objects of interest to generate annotated objects of interest; utilize a second neural network model to group the annotated objects of interest into clusters; utilize a third neural network model to determine labels for the clusters; request manually-generated labels for clusters for which labels are not determined by the third neural network model; receive the manually-generated labels based on requesting the manually-generated labels; label the unprocessed images with the labels and the manually-generated labels to generate labeled images; generate a training dataset based on the labeled images; train a computer vision model with the training dataset to generate a trained computer vision model; generate a validation dataset for the computer vision model based on the labeled images; and validate the computer vision model with the validation dataset.
19. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to utilize the first neural network model to identify the objects of interest in the unprocessed images and the bounding boxes for the objects of interest, cause the device to: identify segmented boundaries around the objects of interest in the unprocessed images; and predict coordinates of the bounding boxes for the objects of interest based on the segmented boundaries.
20. The non-transitory computer-readable medium of claim 15, wherein the one or more instructions, that cause the device to utilize the third neural network model to determine the labels for the clusters, cause the device to: classify the objects of interest in the unprocessed images; generate confidence scores for classifications of the objects of interest; and label the objects of interest based on the confidence scores.
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.
Renumbered claim(s) 1-20, is/are rejected under 35 U.S.C. 103 as being unpatentable over Hwang (US PGPUB 2022/0375090 A1) and further in view of Staudinger (US PGPUB 2021/0264300 A1).
As per claim 1, Hwang discloses a method (Hwang, Figs. 1-10), comprising:
utilizing, by a device, a first neural network model to identify objects in images to be labeled (Hwang, paragraphs 20, and 117, discloses the panoptic segmentation neural network to learn to recognize object instances from the unknown object subclasses);
utilizing, by the device, the identified objects to generate annotated objects (Hwang, paragraphs 21, 41, 51, and 117, discloses the panoptic segmentation system determines a label for an unknown object subclass);
utilizing, by the device, a second neural network model to group the annotated objects into clusters (Hwang, paragraphs 18, 21, 50, 68, and 119);
utilizing, by the device, a third neural network model to associate labels with the clusters (Hwang, paragraph 187);
applying, by the device, the labels and manually-generated labels, for clusters for which labels are not determined by the third neural network model, to the images to generate labeled images (Hwang, paragraphs 36,136 and 169);
Hwang does not explicitly disclose generating, by the device, a dataset based on the labeled images; and utilizing, by the device, the dataset to train a machine learning model.
Staudinger discloses generating, by the device, a dataset based on the labeled images (Staudinger, paragraphs 35-37); and
utilizing, by the device, the dataset to train a machine learning model (Staudinger, paragraph 35, discloses a labeled training dataset may enable model improvement. That is, the training model may use a validation set of data to iterate over model parameters until the point where it arrives at a final set of parameters/weights to use in the model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Hwang teachings by training a model, as taught by Staudinger.
The motivation would be to improve the accuracy or completeness of model’s predictions with respect to input data (paragraph 35), as taught by Staudinger.
As per Renumbered claim 2, Hwang in view of Staudinger further discloses the method of claim 1, wherein the images are unprocessed images (Hwang, paragraph 2).
As per Renumbered claim 3, Hwang in view of Staudinger further discloses the method of claim 1, further comprising: utilizing the first neural network model to identify segmentation boundaries for the objects (Hwang, paragraphs 77 and 98).
As per Renumbered claim 4, Hwang in view of Staudinger further discloses the method of claim 1, further comprising:
training the first neural network model using pre-annotated image datasets prior to utilizing the first neural network model to identify objects in the images (Hwang, paragraphs 42, 53 and 64).
As per Renumbered claim 5, Hwang in view of Staudinger further discloses the method of claim 1, further comprising:
generating confidence scores for the labels associated with the clusters (Hwang, paragraphs 74-76); and
labeling the clusters based on the confidence scores (Hwang, paragraphs 134-135).
As per Renumbered claim 6, Hwang in view of Staudinger further discloses the method of claim 1, further comprising:
generating a validation dataset based on the labeled images (Staudinger, paragraph 35); and
validating the machine learning model using the validation dataset (Staudinger, paragraphs 35-37).
As per Renumbered claim 7, Hwang in view of Staudinger further discloses the method of claim 1, wherein the machine learning model is associated with computer vision (Staudinger, paragraph 66).
As per Renumbered claim 8, Hwang discloses a device (Hwang, Fig. 10:1000), comprising:
one or more memories (Hwang, Fig. 10:1004); and
one or more processors (Hwang, Fig. 10:1002), coupled to the one or more memories (Hwang, Fig. 10:1002:1004), configured to:
For rest of claim limitations please see the analysis of claim 1.
As per Renumbered claim 9, please see the analysis of Renumbered claim 2.
As per Renumbered claim 10, please see the analysis of Renumbered claim 3.
As per Renumbered claim 11, please see the analysis of Renumbered claim 4.
As per Renumbered claim 12, please see the analysis of Renumbered claim 5.
As per Renumbered claim 13, please see the analysis of Renumbered claim 6.
As per Renumbered claim 14, please see the analysis of Renumbered claim 7.
As per Renumbered claim 15, Hwang discloses a non-transitory computer-readable medium storing a set of instructions, the set of instructions (Hwang, paragraphs 2, 172-173) comprising:
one or more instructions that, when executed by one or more processors of a device (Hwang, paragraphs 2, 172-173), cause the device to:
For rest of claim limitations please see the analysis of claim 1.
As per Renumbered claim 16, please see the analysis of Renumbered claim 2.
As per Renumbered claim 17, please see the analysis of Renumbered claim 3.
As per Renumbered claim 18, please see the analysis of Renumbered claim 4.
As per Renumbered claim 19, please see the analysis of Renumbered claim 5.
As per Renumbered claim 20, please see the analysis of Renumbered claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SYED Z HAIDER whose telephone number is (571)270-5169. The examiner can normally be reached MONDAY-FRIDAY 9-5:30 EST.
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/SYED HAIDER/Primary Examiner, Art Unit 2633
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