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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/20/2024, 01/22/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Allowable Subject Matter
Claims 4 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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 11 – 19 rejected under 35 U.S.C. 101 because of the use of memory without “non-transitory” or indication that the memory is not transitory in some form.
Claim Rejections - 35 USC § 102
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.
(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.
Claim(s) 1-3, 5-13, 15-20 is/are rejected under 35 U.S.C. 102(a) as being taught by Bogomolny et al. (US Patent Number 11935222-B2, hereinafter “Bogomolny”).
Regarding claim 1, Bogomolny teaches: A computer-implemented method comprising: generating, by one or more processors, a cropped image from a production line image based on an outer circumference associated with a production line item; ([0049], "FIG. 3 illustrates an example of a wheel image and corresponding segments in accordance with certain embodiments of the presently disclosed subject matter. As shown, the exemplary wheel image 302 captures a wheel of a vehicle. The wheel image 302 is segmented into image segments 306 and 308 as illustrated in 304. The segmentation is performed such that the image segments 306 and 308 respectively correspond to a tire component and a rim component of the wheel. Taking the image segment 306 for example, in the current example, there is one segment 306 corresponding to the entire tire component. However, in other cases, there may be possibly one or more sub-segments corresponding to certain parts of the tire, such as, e.g., a sub-segment corresponding to the tread of the tire, and a sub-segment corresponding to the rest of the tire, etc.")
generating, by the one or more processors, a derivative cropped image from the cropped image based on an interior circumference associated with the production line item; ([0053], "FIG. 4 shows a tire image segment 402 which is obtained after the segmentation of an acquired tire image, as described above. The center of the tire 403 can be identified based on the tire image segment 402. The tire image segment 402 can then be unwrapped/straightened to a straight tire segment 404 according to the center of the tire 403. This can be done, for example, by converting the image coordinate systems as described above. As shown, in some cases, the straight tire segment 404 may not be completely straight, i.e., there may be some parts that still have small curves due to the unwrapping process. However, as long as the entire tire segment is in a mostly-straight form, it can be considered as a straight tire segment since such tire segment can be effectively used for the text identification process as will be described below.")
generating, by the one or more processors and by applying a geometric transformation function, a transformed input image from the derivative cropped image based on one or more model parameters of a machine learning detection model; ([0046], "In some embodiments, the segmentation deep learning model (e.g., the segmentation DNN) can be trained using a training dataset comprising a set of training wheel images each segmented and labeled according to one or more mechanical components comprised therein. The training wheel images and the labels corresponding to segments are provided to the segmentation DNN for training. The training process is to optimize the model so that it can correctly predict segmentation label (e.g., pixel-wise segmentation label) of a wheel image. In some cases, different training datasets covering images of various types of wheels (e.g., wheels of different types of vehicles) need to be provided so as to train the model to be able segment different types of incoming wheel images in runtime."; [0053], "FIG. 4 shows a tire image segment 402 which is obtained after the segmentation of an acquired tire image, as described above. The center of the tire 403 can be identified based on the tire image segment 402. The tire image segment 402 can then be unwrapped/straightened to a straight tire segment 404 according to the center of the tire 403. This can be done, for example, by converting the image coordinate systems as described above. As shown, in some cases, the straight tire segment 404 may not be completely straight, i.e., there may be some parts that still have small curves due to the unwrapping process. However, as long as the entire tire segment is in a mostly-straight form, it can be considered as a straight tire segment since such tire segment can be effectively used for the text identification process as will be described below.")
generating, by the one or more processors and using the machine learning detection model, a prediction output based on the transformed input image; ([0054], "Continuing with the description of FIG. 2, text marked on the tire can be identified (208) (e.g., by the text identification module 106) from the straight tire segment. Specifically, locations of a plurality of text portions can be detected (210) (e.g., by the text detection module 108) on the straight tire segment. Text content for each of the text portions can be recognized (212) (e.g., by the text recognition module 110 operatively connected to the text detection module)."; [0058], "In some embodiments, the text detection as described with reference to block 210 can be based on machine learning. By way of example, the text detection can be implemented using a text detection deep learning model, such as, e.g., a deep learning neural network (DNN). The text detection deep learning model can be deemed as being comprised in the text detection module 108 of PMC 102.")
and initiating, by the one or more processors, the performance of one or more prediction-based actions based on the prediction output. ([0064], "The one or more predefined rules are specifically configured in accordance with the special arrangement and positioning of text as marked on the tire, and can be used to analyze the recognized text so as to estimate the condition of the tire. ")
Regarding claim 2, Bogomolny teaches: The computer-implemented method of claim 1, wherein generating the cropped image of the production line image comprises: removing an exterior image portion of the production line image that is located outside the outer circumference associated with the production line item. (Fig. 3; [0053], "FIG. 4 shows a tire image segment 402 which is obtained after the segmentation of an acquired tire image, as described above. The center of the tire 403 can be identified based on the tire image segment 402. The tire image segment 402 can then be unwrapped/straightened to a straight tire segment 404 according to the center of the tire 403. This can be done, for example, by converting the image coordinate systems as described above. As shown, in some cases, the straight tire segment 404 may not be completely straight, i.e., there may be some parts that still have small curves due to the unwrapping process. However, as long as the entire tire segment is in a mostly-straight form, it can be considered as a straight tire segment since such tire segment can be effectively used for the text identification process as will be described below.")
Regarding claim 3, Bogomolny teaches: The computer-implemented method of claim 1, further comprising: identifying, using an edge detection model, the outer circumference of the production line item within the production line image. ([0071], "In some embodiments, tire pressure inspection can be performed by analyzing the tire image segment or the straight tire segment and computing a ratio between upper sidewall height (sidewall height refers to thickness of the tire rubber between the rim edge and the tire surface) and lower side-wall height of the tire. The ratio can be indicative of pressure condition of the tire. For instance, as illustrated in FIG. 3, the upper sidewall height 310 and lower sidewall height 312 can be measured from the tire image segment, and the ratio thereof can be calculated.")
Regarding claim 5, Bogomolny teaches: The computer-implemented method of claim 1, further comprising: identifying, using an edge detection model, the outer circumference of the production line item within the production line image. ([0071], "In some embodiments, tire pressure inspection can be performed by analyzing the tire image segment or the straight tire segment and computing a ratio between upper sidewall height (sidewall height refers to thickness of the tire rubber between the rim edge and the tire surface) and lower side-wall height of the tire. The ratio can be indicative of pressure condition of the tire. For instance, as illustrated in FIG. 3, the upper sidewall height 310 and lower sidewall height 312 can be measured from the tire image segment, and the ratio thereof can be calculated.")
Regarding claim 6, Bogomolny teaches: The computer-implemented method of claim 5, further comprising: identifying the interior circumference of the production line item based on one or more item attributes of the production line item. ([0071], "In some embodiments, tire pressure inspection can be performed by analyzing the tire image segment or the straight tire segment and computing a ratio between upper sidewall height (sidewall height refers to thickness of the tire rubber between the rim edge and the tire surface) and lower side-wall height of the tire. The ratio can be indicative of pressure condition of the tire. For instance, as illustrated in FIG. 3, the upper sidewall height 310 and lower sidewall height 312 can be measured from the tire image segment, and the ratio thereof can be calculated.")
Regarding claim 7, Bogomolny teaches: The computer-implemented method of claim 6, wherein the one or more item attributes identify at least one of: (i) a curvature of a bottom portion of the production line item, (ii) a number of objects within the production line item, or (iii) a visual characteristic of the production line item or an object within the production line item. ([0071], "In some embodiments, tire pressure inspection can be performed by analyzing the tire image segment or the straight tire segment and computing a ratio between upper sidewall height (sidewall height refers to thickness of the tire rubber between the rim edge and the tire surface) and lower side-wall height of the tire. The ratio can be indicative of pressure condition of the tire. For instance, as illustrated in FIG. 3, the upper sidewall height 310 and lower sidewall height 312 can be measured from the tire image segment, and the ratio thereof can be calculated.")
Regarding claim 8, Bogomolny teaches: The computer-implemented method of claim 1, wherein the derivative cropped image comprises an at least partially circular boundary and the transformed input image comprises an at least partially rectangular boundary. (Fig. 4, 402; [0053], "FIG. 4 shows a tire image segment 402 which is obtained after the segmentation of an acquired tire image, as described above. The center of the tire 403 can be identified based on the tire image segment 402. The tire image segment 402 can then be unwrapped/straightened to a straight tire segment 404 according to the center of the tire 403. This can be done, for example, by converting the image coordinate systems as described above. As shown, in some cases, the straight tire segment 404 may not be completely straight, i.e., there may be some parts that still have small curves due to the unwrapping process. However, as long as the entire tire segment is in a mostly-straight form, it can be considered as a straight tire segment since such tire segment can be effectively used for the text identification process as will be described below.")
Regarding claim 9, Bogomolny teaches: The computer-implemented method of claim 8, wherein the at least partially rectangular boundary is scaled based on a model input size associated with the machine learning detection model. (Fig. 4, 402; [0053], "FIG. 4 shows a tire image segment 402 which is obtained after the segmentation of an acquired tire image, as described above. The center of the tire 403 can be identified based on the tire image segment 402. The tire image segment 402 can then be unwrapped/straightened to a straight tire segment 404 according to the center of the tire 403. This can be done, for example, by converting the image coordinate systems as described above. As shown, in some cases, the straight tire segment 404 may not be completely straight, i.e., there may be some parts that still have small curves due to the unwrapping process. However, as long as the entire tire segment is in a mostly-straight form, it can be considered as a straight tire segment since such tire segment can be effectively used for the text identification process as will be described below.")
Regarding claim 10, Bogomolny teaches: The computer-implemented method of claim 9, wherein the model input size is defined by a set of sliding window parameters. (Fig. 4, 402; [0041], "Semented using a segmentation deep learning model, such as, e.g., a deep learning neural network (also referred to as deep neural network, or DNN). The segmentation deep learning model can be deemed as being comprised in the Segmentation module 104 of PMC 102."; [0053], "FIG. 4 shows a tire image segment 402 which is obtained after the segmentation of an acquired tire image, as described above. The center of the tire 403 can be identified based on the tire image segment 402. The tire image segment 402 can then be unwrapped/straightened to a straight tire segment 404 according to the center of the tire 403. This can be done, for example, by converting the image coordinate systems as described above. As shown, in some cases, the straight tire segment 404 may not be completely straight, i.e., there may be some parts that still have small curves due to the unwrapping process. However, as long as the entire tire segment is in a mostly-straight form, it can be considered as a straight tire segment since such tire segment can be effectively used for the text identification process as will be described below.")
Regarding claim 11, claim 11 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 12, claim 12 has been analyzed with regard to claim 2 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 13, claim 13 has been analyzed with regard to claim 3 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 15, claim 15 has been analyzed with regard to claim 5 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 16, claim 16 has been analyzed with regard to claim 6 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 17, claim 17 has been analyzed with regard to claim 7 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 18, claim 18 has been analyzed with regard to claim 8 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 19, claim 19 has been analyzed with regard to claim 9 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: A computing system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to (Fig. 1)
Regarding claim 20, claim 20 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Bogomolny further teaching on: One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: (Fig. 1)
Conclusion
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/JINSU HWANG/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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