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 .
DETAILED ACTION
This action is responsive to patent application as filed on 2/20/2024
This action is made Non-Final.
Claims 1 – 7 are pending in the case. Claim 1 is the independent claim.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/26/2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 2/20/2024 have been accepted by the Examiner.
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 (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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jiang (USPUB 20200026287 A1).
Claim 1:
Jiang discloses An image analyzation method with an unrecognized object memory mechanism, the method comprising: reading a target image, wherein the target image includes a target object (0053-56: In the online learning and recognition system 308 shown in FIG. 4, the feature extraction component 400 computes features from input vehicle images 402); determining whether the target object is a known category object through an object recognition model; if the target object does not belong to a known category, executing an object memory mechanism, comprising: generating an image feature information of the target object through a feature extraction algorithm (0053-56: In the online learning and recognition system 308 shown in FIG. 4, the feature extraction component 400 computes features from input vehicle images 402); performing a feature classification process on the image feature information to produce an attention representative value (0053-56: the unsupervised learning component 404 automatically clusters the features into different categories; and the supervised learning component 406 labels the categories with a small set of training data (i.e., training data label 408), and determining a numerical range to which the target object belongs; storing the target image in a specified directory corresponding to the numerical range in an image database (0053-56: When the system finds new vehicles (untrained before), the system generates an unknown vehicle ID 414 signal; and then, the Retrain System Control 416 component issues a signal to save the images of the unknown vehicles); determining whether a number of at least one stored image in the specified directory exceeds a quantity threshold; and if the number of the at least one stored image exceeds the quantity threshold, assigning a new category label to the at least one stored image, and inputting the at least one stored image after labeling, as training data into the object recognition model in a retraining process; wherein the object recognition model recognizes the target object after the retraining process (0053-56: the online learning and recognition system 308 requires learning techniques that can learn from a small set of training samples. Because of the requirement of real-time computing, the online learning system can't afford to fully retrain the system when it needs to learn new vehicle classes; this requires that the system has the capability to remember the old vehicle classes when it is trained to learn new vehicle classes... The unsupervised learning component plays a role of clustering the online data into different categories, which makes the supervised learning process easier; the supervised learning component only needs to label the categories obtained from the unsupervised learning with a small set of training data. In addition, the online learning and recognition system 308 needs a memory component to remember the old classes when it is trained to learn new classes... The feature extraction component 400 extracts features/signatures from the input vehicle images 402; the unsupervised learning component 404 automatically clusters the features into different categories; and the supervised learning component 406 labels the categories with a small set of training data (i.e., training data label 408). The memory component 410 remembers both the categories and labels; when the system learns new classes, it doesn't need to retrain for the old classes... In the online learning and recognition system 308 shown in FIG. 4, the feature extraction component 400 computes features from input vehicle images 402. The features can be image edges or image local statistics computed with a local window. For vehicle identification 310, Speeded Up Robust Features (SURF) (see Literature Reference No. 4) and Histogram of Oriented Gradients (HOG) (see Literature Reference No. 5) have proven to have good and robust performance. In terms of machine learning, the most important component is the online learning component (i.e., unsupervised learning component 404 and supervised learning component 406). The online learning function is achieved by two-step learning: unsupervised learning and supervised learning. The two-step learning is controlled by the functional block of Learning Management 412. When the system finds new vehicles (untrained before), the system generates an unknown vehicle ID 414 signal; and then, the Retrain System Control 416 component issues a signal to save the images of the unknown vehicles (i.e., save unknown vehicle image 418). When the number of the saved images reaches a predetermined threshold, the Retrain System Control 416 outputs a signal for requesting new labels (i.e., new label request 420) of the unknown vehicles. A vehicle information center (element 422) transmits (such as through an Internet connection) new labels (element 424) for the unknown vehicles into the system, and the system is trained for the unknown vehicles in online operation/off-line operation).
Claim 7:
Jiang discloses when the object recognition model outputs an object category information as a result, the target object is a known category object (0081-83).
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.
Claim(s) 2 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Kalyuzhner (USPUB 20210365719 A1).
Claim 2:
Jiang discloses every feature of claim 1.
Jiang, by itself, does not seem to completely teach performing a semantic segmentation process on the target image to classify multiple pixels therein, producing at least one object mask; and defining a pixel collection within a range of the object mask in the target image as the target object.
The Examiner maintains that these features were previously well-known as taught by Kalyuzhner.
Kalyuzhner teaches performing a semantic segmentation process on the target image to classify multiple pixels therein, producing at least one object mask; and defining a pixel collection within a range of the object mask in the target image as the target object (0022: Training set 30 includes images 31 in which objects of a particular class are identified. For example, each image 31 may be associated with a corresponding mask image that flags the pixels belonging to a portion of the image containing an identified object—such as the pixels within a bounding box 33 for the object—and/or the pixels within a segmented boundary 35 of the object. Alternatively, the boundary of the portion of the image (e.g., bounding box 33) and/or boundary 35 may be marked in the image).
Jiang and Kalyuzhner are analogous art because they are from the same problem-solving area, panning (scrolling) through digital content displayed on a screen.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jiang and Kalyuzhner before him or her, to combine the teachings of Jiang and Kalyuzhner. The rationale for doing so would have been to more accurately identify a target object in an image.
Therefore, it would have been obvious to combine Jiang and Kalyuzhner to obtain the invention as specified in the instant claim(s).
Claim 3:
Jiang, by itself, does not seem to completely teach wherein the semantic segmentation process is performed by any one of the following: a segment anything model (SAM), a hybrid gene algorithm (HGA) model, and a mask region-based convolutional neural network (R-CNN) model.
The Examiner maintains that these features were previously well-known as taught by Kalyuzhner.
Kalyuzhner teaches wherein the semantic segmentation process is performed by any one of the following: a segment anything model (SAM), a hybrid gene algorithm (HGA) model, and a mask region-based convolutional neural network (R-CNN) model (0025: model 28 may include a Mask R-CNN).
Jiang and Kalyuzhner are analogous art because they are from the same problem-solving area, panning (scrolling) through digital content displayed on a screen.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jiang and Kalyuzhner before him or her, to combine the teachings of Jiang and Kalyuzhner. The rationale for doing so would have been to utilize the most effective neural network models.
Therefore, it would have been obvious to combine Jiang and Kalyuzhner to obtain the invention as specified in the instant claim(s).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiang in view of Kalyuzhner (USPUB 20210365719 A1) and further in view of Szegedy (USPUB 20150170002 A1).
Claim 4:
Jiang in view of Kalyuzhner teaches every feature of claim 2.
Jiang, by itself, does not seem to completely teach the object mask is an irregular mask.
The Examiner maintains that these features were previously well-known as taught by Szegedy.
Szegedy teaches the object mask is an irregular mask (0065).
Jiang and Szegedy are analogous art because they are from the same problem-solving area, panning (scrolling) through digital content displayed on a screen.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Jiang and Szegedy before him or her, to combine the teachings of Jiang and Szegedy. The rationale for doing so would have been to utilize the most effective neural network models.
Therefore, it would have been obvious to combine Jiang and Szegedy to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claims 5, 6 and 8 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.
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED-IBRAHIM ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached on M-Th 8-6 Fri: 7-12/OFF.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steph Hong can be reached on (571) 272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMMED H ZUBERI/Primary Examiner, Art Unit 2178