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 .
Status of Claims
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/5/24 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 are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 1004(A) and 1004(B) in Fig. 10C. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: 1082(E)-(H) in Para. [00170], line 5. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 17 recites the limitation "an autonomous or semi-autonomous machine" in line 3. It is unclear and indefinite if this is the same as the autonomous or semi-autonomous machine previously recited in line 2 of the claim.
Claim 20 recites the limitation "an autonomous or semi-autonomous machine" in line 4. It is unclear and indefinite if this is the same as the autonomous or semi-autonomous machine previously recited in line 4 of the claim.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method, system, and processor for determining/adjusting an image size. With respect to the analysis of claims 1, 10, and 18:
Step 1:
With regard to Step 1, claim 1 is directed to a method, claim 10 is directed to a system, and claim 18 is directed to a processor (i.e., machine); and therefore, the claims are directed to one of the statutory categories of inventions.
Step 2A, Prong One:
With regard to Step 2A, Prong One, the following limitations in claim 1 as drafted recite an abstract idea: “identifying one or more regions within one or more images; determining that a size associated with a region of the one or more regions is smaller than a threshold, the threshold corresponding to a resolution; in response to the determining that the size is smaller than the threshold, causing the region to be included in a subset of a frame; and applying the frame having the region included in the subset of the frame to determine one or more predictions associated with the region.” These limitations recite an abstract idea, such as a process, that under its broadest reasonable interpretation, covers performance of the limitation manually or in the mind by a human. That is, a person can identify regions in an image, compare the size of a region to the size of another image (i.e., threshold), crop the region (i.e., include the region in a subset of a frame), and classify the region (i.e., make a prediction). These are concepts that fall under the grouping of abstract idea mental processes, i.e., a concept performed in the human mind, evaluation, judgment, and/or opinion of a human.
With regard to Step 2A, Prong One, the following limitations in claim 10 as drafted recite an abstract idea: “determine that a size associated with an object represented in image data is smaller than a threshold; in response to the determination that the size is smaller than the threshold, cause at least a portion of the image data corresponding to the object to be incorporated into a frame at a first resolution that is less than a second resolution; and provide the frame to determine one or more predictions corresponding to the object.” These limitations recite an abstract idea, such as a process, that under its broadest reasonable interpretation, covers performance of the limitation manually or in the mind by a human. That is, a person can compare the size of a region to the size of another image (i.e., threshold), crop the region (i.e., include the region in a subset of a frame), and classify the region (i.e., make a prediction). These are concepts that fall under the grouping of abstract idea mental processes, i.e., a concept performed in the human mind, evaluation, judgment, and/or opinion of a human.
With regard to Step 2A, Prong One, the following limitations in claim 18 as drafted recite an abstract idea: “determine whether to adjust a size of an image identified based at least on evaluating the size of the image with respect to a threshold, the threshold related to an input resolution.” These limitations recite an abstract idea, such as a process, that under its broadest reasonable interpretation, covers performance of the limitation manually or in the mind by a human. That is, a person can determine whether to crop/change the size of an image by comparing the size of the image to the size of another image (i.e., threshold). These are concepts that fall under the grouping of abstract idea mental processes, i.e., a concept performed in the human mind, evaluation, judgment, and/or opinion of a human.
Step 2A, Prong Two:
The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception. In the instant case, there are no additional steps/elements/limitations in the claims, with the exception of the following in claim 1 (method claim), claim 10 (system claim), and claim 18 (processor claim): “using one or more first machine learning models,” “associated with one or more second machine learning models,” and “to the one
or more second machine learning models” in claim 1, “one or more processors to,” “associated with one or more machine learning models,” and “to the one or more machine learning models, the one or more machine learning models to” in claim 10, and “one or more circuits,” “using one or more first machine learning models,” “associated with one or more second machine learning models.” The one or more machine learning models, one or more first machine learning models, one or more second machine learning models, one or more processors, and one or more circuits are generic computer components. The machine learning models are generic computer components since specific details of the models are not recited. These are regarded as adding routine and conventional elements to perform the judicial exception, and do not apply it into a practical application. Accordingly, the above-mentioned additional elements/limitations do not integrate the abstract idea into a practical application; and therefore, the claims recite an abstract idea.
Step 2B:
Because the claims fail under Step 2A, the claims are further evaluated under Step 2B. The claims herein do not include additional elements that are sufficient to amount to significantly more than the judicial exception, because as discussed above with respect to integration of the abstract idea into practical application, the additional elements/limitations to perform the steps, amount to no more than insignificant routine and conventional elements. Mere instructions to apply an exception using generic components cannot provide an inventive concept. Therefore, claims 1, 10, and 18 are not patent eligible.
Furthermore, with regard to claims 2-9, 11-17, and 19-20 when viewed individually, these additional steps, under their broadest reasonable interpretation, provide extra-solution activities to cover performance of the limitations as an abstract idea, and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Accordingly, they are not patent eligible.
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.
Claims 1-2, 4-10, 12, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jemaa et al. (WO 2023/092124 A1, see reference provided; hereinafter “Jemaa”).
Regarding claim 1, Jemaa teaches a method comprising (Jemaa, Abstract: “a method includes…”; Jemaa, Para. [2]: “methods for identification and segmentation of biological objects using medical images”):
identifying, using one or more first machine learning models, one or more regions within one or more images (Jemaa, Para. [7]: “the neural network processing system may determine, by one or more first machine-learning models based on the first downscaled image, one or more coarse segmentations corresponding to one or more organs portrayed in the first scan image, respectively. The neural network processing system may then extract one or more segments of the first scan image based on the one or more coarse segmentations, respectively”; Jemaa, Para. [083]: “The neural network processing system 120 may determine, by one or more first machine-learning models based on the first downscaled image, one or more coarse segmentations corresponding to one or more organs portrayed in the first scan image, respectively. In particular embodiments, at least one of the first machine-learning models may be based on a neural network model, at least one of the first machine-learning models may be based on an ensemble model of two or more neural network models, and at least one of the first machine-learning models may be based on a distillation model of the ensemble model. The neural network processing system 120 may then determine, for each of the one or more coarse segmentations, a region of interest associated with the corresponding organ. The neural network processing system 120 may then extract one or more segments of the first scan image based on the one or more coarse segmentations and their respective region of interests. Each extracted segment may be at the first resolution”);
determining that a size associated with a region of the one or more regions is smaller than a threshold, the threshold corresponding to a resolution associated with one or more second machine learning models (Jemaa, As shown in Fig. 5, CT scan image 510 is downscaled to CT image 520; Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520. As an example and not by way of limitation, the coarse resolution may be 144 (front to back) x 256 (left to right) x 192 (up to down). In particular embodiments, the neural network processing system 120 may load the downscaled full torso CT scan image 520 to a neural network, which may determine locations of the organs in the downscaled CT scan image 520. The neural network processing system 120 may additionally set a threshold for each organ's segmentation to define a sharp segmentation boundary, which is then used to define the organ's ROI. The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROI is in the original full-torso CT scan”; Jemaa, Para. [88]: the original resolution of the CT scan image fragment corresponding to the organ is 192 x 192 x 192; Note: the Examiner interprets the second resolution which is lower than the first resolution as a size smaller than a threshold);
in response to the determining that the size is smaller than the threshold, causing the region to be included in a subset of a frame (Jemaa, As shown in Fig. 5, CT scan image 510 is downscaled to CT image 520; Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution”; Jemaa, Para. [7]; Jemaa, Paras. [87]-[88]; Note: the Examiner interprets the segmentations of the scan image as a region of a subset of a frame);
and applying the frame having the region included in the subset of the frame to the one or more second machine learning models to determine one or more predictions associated with the region (Jemaa, Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments, and generating a segmented image of the first scan image based on the fine segmentations, wherein the segmented image comprises confirmed segmentations corresponding to the organs”; Jemaa, Para. [46]: a tumor classification is identified using one or more neural networks for each image region; Jemaa, Para. [59]; Jemaa, Para. [63]; Jemaa, Para. [84]: “At least one of the second machine-learning models may be based on an ensemble model of two or more neural network models… Another technical advantage may include aiding the radiologist's workflow by performing tumor/lesion detection and segmentation automation based on machine-learning models. Another technical advantage may include improved accuracy for segmentation as machine-learning models may be significantly more accurate in lesion segmentation if lesions appear as the only inhomogeneities in otherwise fairly homogeneous image texture”; Note: the Examiner interprets tumor classification, for example, as one or more predictions).
Jemaa discloses and teaches the above limitations in different embodiments. It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art, to combine the embodiments for identifying one or more regions using one or more first machine learning models, determining a size associated with a region is smaller than a threshold in which the threshold corresponds to a resolution associated with one or more second machine learning models, including the region to be included in a subset of a frame, and applying the frame to the one or more second machine learning models to determine one or more predictions, since the embodiments may include any combination or permutation of any of the components or steps described (Jemaa, Para. [125]) and in order to improve tracking of lesions to assist in medical analysis of certain diseases (Jemaa, Para. [7]). Therefore, one of ordinary skill in the art would be capable to have combined the embodiments as claimed by known methods, and that in combination, each element merely performs the same function as it does separately. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to claim 1.
Regarding claim 2, Jemaa teaches the limitations as explained above in claim 1.
Jemaa further teaches, the method of claim 1 (see claim 1 above), wherein the causing the region to be included in the subset of the frame comprises causing the region to be included in the frame at a second resolution that includes at least one of fewer points or fewer pixels than the resolution associated with the one or more second machine learning models (Jemaa, As shown in Fig. 5, CT scan image 510 is downscaled to CT image 520, and 540 is the CT scan image fragment at the original resolution. The downscaled image 520 has fewer pixels (i.e., is smaller) since the resolution is reduced; Jemaa, Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution”; Jemaa, Para. [7]; Jemaa, Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520. As an example and not by way of limitation, the coarse resolution may be 144 (front to back) x 256 (left to right) x 192 (up to down). In particular embodiments, the neural network processing system 120 may load the downscaled full torso CT scan image 520 to a neural network, which may determine locations of the organs in the downscaled CT scan image 520. The neural network processing system 120 may additionally set a threshold for each organ's segmentation to define a sharp segmentation boundary, which is then used to define the organ's ROI. The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROI is in the original full-torso CT scan”; Jemaa, Para. [88]: the original resolution of the CT scan image fragment corresponding to the organ is 192 x 192 x 192. The neural network may perform fine segmentation based on its corresponding full resolution CT image).
Regarding claim 4, Jemaa teaches the limitations as explained above in claim 1.
Jemaa further teaches, the method of claim 1 (see claim 1 above), further comprising: evaluating a second size associated with a second region of the one or more regions with respect to the threshold (Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Para. [87]; Jemaa, Para. [88]: “As an example and not by way of limitation, the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution. The neural network processing system 120 may then perform fine segmentation for each coarsely segmented organ based on its corresponding full resolution CT image. More specifically, the neural network processing system 120 may focus on each coarsely segmented organ and segment the organ with full resolution to precisely identify boundaries. In particular embodiments, the neural network processing system 120 may run refinement models trained to segment specific organs in the ROIs, which may leave the largest volume component”; Note: the Examiner interprets the threshold as the original resolution);
determining, based at least on the evaluating, that the second size associated with the second region meets or exceeds the threshold (Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Jemaa, Para. [88]: “As an example and not by way of limitation, the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution. The neural network processing system 120 may then perform fine segmentation for each coarsely segmented organ based on its corresponding full resolution CT image. More specifically, the neural network processing system 120 may focus on each coarsely segmented organ and segment the organ with full resolution to precisely identify boundaries. In particular embodiments, the neural network processing system 120 may run refinement models trained to segment specific organs in the ROIs, which may leave the largest volume component”; Note: the Examiner interprets resampling the image fragment to the original resolution as meeting the threshold);
and based at least on the second size meeting or exceeding the threshold, causing the second region to be included in a second frame at the resolution associated with the one or more second machine learning models (Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Para. [87]; Jemaa, Para. [88]: “As an example and not by way of limitation, the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution. The neural network processing system 120 may then perform fine segmentation for each coarsely segmented organ based on its corresponding full resolution CT image. More specifically, the neural network processing system 120 may focus on each coarsely segmented organ and segment the organ with full resolution to precisely identify boundaries. In particular embodiments, the neural network processing system 120 may run refinement models trained to segment specific organs in the ROIs, which may leave the largest volume component”; Jemaa, Para. [108]: the one or more second machine-learning models use the one or more extracted segments to determine one or more fine segmentations corresponding to the one or more organs in the extracted segments).
Regarding claim 5, Jemaa teaches the limitations as explained above in claim 1.
Jemaa further teaches, the method of claim 1 (see claim 1 above), further comprising causing an increase of the size associated with the region from a first resolution to a second resolution, the second resolution including less points or pixels than the resolution associated with the one or more second machine learning models, wherein the causing of the region to be included in the subset of the frame comprises causing the region to be included in the subset of the frame at the second resolution (Jemaa, As shown in Fig. 5, image 520 is downscaled and then resampled to the original resolution (i.e., image 540). Image 540 is a more zoomed-in image (i.e., has less points) as a result of the image being resampled to the original resolution; Jemaa, Para. [88]: “the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution”; Jemaa, Para. [108]: the one or more second machine-learning models use the one or more extracted segments to determine one or more fine segmentations corresponding to the one or more organs in the extracted segments).
Regarding claim 6, Jemaa teaches the limitations as explained above in claim 1.
Jemaa further teaches, the method of claim 1 (see claim 1 above), wherein the resolution associated with the one or more second machine learning models corresponds to one or more resolutions associated with one or more frames of image data used to train the one or more second machine learning models (Jemaa, As shown in Fig. 1A, images 102 are input into the neural network processing system 120; Jemaa, Para. [41]: training images are included in a training data set to train one or more neural networks; Jemaa, Para. [59]; Jemaa, Paras. [60]-[61]: detection areas of a predefined size are input into the segmentation network; Jemaa, Paras. [69]-[70]; Jemaa, Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520… The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROIs in the original full-torso CT scan”; Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Jemaa, Abstract; Jemaa, Para. [88]).
Regarding claim 7, Jemaa teaches the limitations as explained above in claim 1.
Jemaa further teaches, the method of claim 1 (see claim 1 above), wherein the size associated with the region corresponds to a second resolution of the region, the second resolution including less points or pixels than the resolution associated with the one or more second machine learning models (Jemaa, As shown in Fig. 5, image 520 is downscaled and then resampled to the original resolution (i.e., image 540). Image 540 is a more zoomed-in image (i.e., has less points) as a result of the image being resampled to the original resolution; Jemaa, Para. [88]: “the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution”; Jemaa, Para. [108]: the one or more second machine-learning models use the one or more extracted segments to determine one or more fine segmentations corresponding to the one or more organs in the extracted segments).
Regarding claim 8, Jemaa teaches the limitations as explained above in claim 1.
Jemaa further teaches, the method of claim 1 (see claim 1 above), wherein the causing of the region to be included in the subset of the frame comprises causing one or more points of image data corresponding to the region to be mapped to one or more locations in the subset of the frame (Jemaa, Para. [109]: “At step 1670, the neural network processing system 120 may map the one or more fine segmentations to the first scan image, wherein the mapping comprises identifying one or more organ intersections, wherein each of the one or more organ intersections comprises a plurality of voxels, wherein each of the plurality of voxels is associated with two or more labels indicating two or more organs, respectively, and wherein each of the two or more labels is associated with a respective probability score, and resolving each of the one or more organ intersections by assigning, to each of the plurality of voxels ,within the organ intersection, a label indicating one of the two or more organs that is associated with the highest probability score”; Jemaa, Para. [52]: “For example, if a feature (e.g., a learned feature) is present in a similar location(s) throughout an entire image stack (e.g., a combination of a top virtual slice, a bottom virtual slice, and a central virtual slice), the bounding-box detection network may determine that the image region corresponding to (e.g., that includes) the feature represents a bounding box for a tumor”; Jemaa, Para. [55]: “It will be appreciated that a location of a boundary box in one image may relate to a location of a boundary box in another image”; Jemaa, Para. [58]).
Regarding claim 9, Jemaa teaches the limitations as explained above in claim 1.
Jemaa further teaches, the method of claim 1 (see claim 1 above), wherein the one or more regions within the one or more images correspond to one or more locations associated with one or more objects depicted in the one or more images (Jemaa, Para. [056]: location(s) of a biological object within the image; Jemaa, Para. [063]: “detect a location of an object”; Jemaa, Para. [87]: “the neural network processing system 120 may load the downscaled full torso CT scan image 520 to a neural network, which may determine locations of the organs in the downscaled CT scan image 520”).
Regarding claim 10, Jemaa teaches, a system comprising (Jemaa, Para. [2]: “a system and methods for identification and segmentation of biological objects using medical images”; Jemaa, Para. [113]: computer system 1700):
one or more processors to (Jemaa, Para. [113]: “computer system 1700 includes a processor”):
determine that a size associated with an object represented in image data is smaller than a threshold (Jemaa, As shown in Fig. 5, CT scan image 510 is downscaled to CT image 520; Jemaa, Para. [2]: “segmentation of biological objects using medical images”; Jemaa, Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Jemaa, Paras. [87]-[88]; Note: the Examiner interprets the second resolution which is lower than the first resolution as a size smaller than a threshold);
in response to the determination that the size is smaller than the threshold, cause at least a portion of the image data corresponding to the object to be incorporated into a frame at a first resolution that is less than a second resolution associated with one or more machine learning models (Jemaa, As shown in Fig. 5, CT scan image 510 is downscaled to CT image 520; Jemaa, Para. [2]: “segmentation of biological objects using medical images”; Jemaa, Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Jemaa, Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520. As an example and not by way of limitation, the coarse resolution may be 144 (front to back) x 256 (left to right) x 192 (up to down). In particular embodiments, the neural network processing system 120 may load the downscaled full torso CT scan image 520 to a neural network, which may determine locations of the organs in the downscaled CT scan image 520. The neural network processing system 120 may additionally set a threshold for each organ's segmentation to define a sharp segmentation boundary, which is then used to define the organ's ROI. The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROI is in the original full-torso CT scan”; Jemaa, Para. [88]: the original resolution of the CT scan image fragment corresponding to the organ is 192 x 192 x 192; Note: the Examiner interprets the segmentations of the scan image as a region of a subset of a frame. The Examiner also interprets the segmentations of the scan image as a portion of image data of a frame);
and provide the frame to the one or more machine learning models, the one or more machine learning models to determine one or more predictions corresponding to the object (Jemaa, Para. [2]: “segmentation of biological objects using medical images”; Jemaa, Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments, and generating a segmented image of the first scan image based on the fine segmentations, wherein the segmented image comprises confirmed segmentations corresponding to the organs”; Jemaa, Para. [46]: a tumor classification is identified using one or more neural networks for each image region; Jemaa, Para. [59]; Jemaa, Para. [63]; Jemaa, Para. [84]: “At least one of the second machine-learning models may be based on an ensemble model of two or more neural network models… Another technical advantage may include aiding the radiologist's workflow by performing tumor/lesion detection and segmentation automation based on machine-learning models. Another technical advantage may include improved accuracy for segmentation as machine-learning models may be significantly more accurate in lesion segmentation if lesions appear as the only inhomogeneities in otherwise fairly homogeneous image texture”; Note: the Examiner interprets tumor classification, for example, as one or more predictions).
Jemaa discloses and teaches the above limitations in different embodiments. It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art, to combine the embodiments for determining a size associated with an object in image data is smaller than a threshold, incorporating the portion of the image data into a frame as a first resolution less than a second resolution associated with one or more machine learning models, and determining one or more predictions corresponding to the object using the one or more machine learning models, since the embodiments may include any combination or permutation of any of the components or steps described (Jemaa, Para. [125]) and in order to improve tracking of lesions to assist in medical analysis of certain diseases (Jemaa, Para. [7]). Therefore, one of ordinary skill in the art would be capable to have combined the embodiments as claimed by known methods, and that in combination, each element merely performs the same function as it does separately. It is for at least the aforementioned reasons that the Examiner has reached a conclusion of obviousness with respect to claim 10.
Regarding claim 12, Jemaa teaches the limitations as explained above in claim 10.
Jemaa further teaches, the system of claim 10 (see claim 10 above), the one or more processors further to (Jemaa, Para. [113]: “computer system 1700 includes a processor”):
determine that a second size associated with a second object depicted in the image meets or exceeds the threshold (Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Jemaa, Para. [88]: “As an example and not by way of limitation, the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution. The neural network processing system 120 may then perform fine segmentation for each coarsely segmented organ based on its corresponding full resolution CT image. More specifically, the neural network processing system 120 may focus on each coarsely segmented organ and segment the organ with full resolution to precisely identify boundaries. In particular embodiments, the neural network processing system 120 may run refinement models trained to segment specific organs in the ROIs, which may leave the largest volume component”; Note: the Examiner interprets resampling the image fragment to the original resolution as meeting the threshold);
and in response to the determination that the second size meets or exceeds the threshold, cause at least a second portion of the image data corresponding to the second object to be incorporated into a second frame at the second resolution (Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Para. [87]; Jemaa, Para. [88]: “As an example and not by way of limitation, the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution. The neural network processing system 120 may then perform fine segmentation for each coarsely segmented organ based on its corresponding full resolution CT image. More specifically, the neural network processing system 120 may focus on each coarsely segmented organ and segment the organ with full resolution to precisely identify boundaries. In particular embodiments, the neural network processing system 120 may run refinement models trained to segment specific organs in the ROIs, which may leave the largest volume component”; Jemaa, Para. [108]: the one or more second machine-learning models use the one or more extracted segments to determine one or more fine segmentations corresponding to the one or more organs in the extracted segments).
Regarding claim 14, Jemaa teaches the limitations as explained above in claim 10.
Jemaa further teaches, the system of claim 10 (see claim 10 above), wherein the second resolution associated with the one or more machine learning models corresponds to one or more resolutions associated with one or more images used to train or update the one or more machine learning models (Jemaa, As shown in Fig. 1A, images 102 are input into the neural network processing system 120; Jemaa, Para. [41]: training images are included in a training data set to train one or more neural networks; Jemaa, Para. [59]; Jemaa, Paras. [60]-[61]: detection areas of a predefined size are input into the segmentation network; Jemaa, Paras. [69]-[70]; Jemaa, Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520… The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROIs in the original full-torso CT scan”; Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Jemaa, Abstract; Jemaa, Para. [88]).
Regarding claim 15, Jemaa teaches the limitations as explained above in claim 10.
Jemaa further teaches, the system of claim 10 (see claim 10 above), wherein a second size associated with at least one of the threshold or the frame corresponds to the second resolution associated with one or more machine learning models (Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Para. [87]; Jemaa, Para. [88]: “As an example and not by way of limitation, the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution. The neural network processing system 120 may then perform fine segmentation for each coarsely segmented organ based on its corresponding full resolution CT image. More specifically, the neural network processing system 120 may focus on each coarsely segmented organ and segment the organ with full resolution to precisely identify boundaries. In particular embodiments, the neural network processing system 120 may run refinement models trained to segment specific organs in the ROIs, which may leave the largest volume component”; Jemaa, Para. [108]: the one or more second machine-learning models use the one or more extracted segments to determine one or more fine segmentations corresponding to the one or more organs in the extracted segments).
Regarding claim 16, Jemaa teaches the limitations as explained above in claim 10.
Jemaa further teaches, the system of claim 10 (see claim 10 above), the one or more processors further to cause the one or more machine learning models to be updated using one or more frames corresponding to the second resolution (Jemaa, Para. [113]: “computer system 1700 includes a processor”; Jemaa, As shown in Fig. 1A, images 102 are input into the neural network processing system 120; Jemaa, Para. [41]: training images are included in a training data set to train one or more neural networks; Jemaa, Para. [59]; Jemaa, Paras. [60]-[61]: detection areas of a predefined size are input into the segmentation network; Jemaa, Paras. [69]-[70]; Jemaa, Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520… The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROIs in the original full-torso CT scan”; Jemaa, As shown in Fig. 5, image 540 is a CT image fragment that is resampled to the original resolution; Jemaa, Abstract; Jemaa, Para. [88]; Note: the Examiner interprets, for example, training one or more neural networks as updating the one or more machine learning models), the one or more frames including one or more padded portions at least partially surrounding one or more images (Jemaa, Para. [53]: “The bounding-box detection network may further process each detected bounding box 108, such that the margins of the bounding box include at least an amount of padding (e.g., 10px, 15px, or another suitable amount) from each edge of the region corresponding to the tumor. In some instances, the amount padding is predefined… In other instances, varying degrees of padding are added so as to maintain uniform bounding-box sizes”; Jemaa, Para. [61]), the one or more images having one or more resolutions that are less than the second resolution (Jemaa, As shown in Fig. 5, CT scan image 510 is downscaled to CT image 520; Jemaa, Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution…”; Jemaa, Para. [7]; Jemaa, Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520. As an example and not by way of limitation, the coarse resolution may be 144 (front to back) x 256 (left to right) x 192 (up to down). In particular embodiments, the neural network processing system 120 may load the downscaled full torso CT scan image 520 to a neural network, which may determine locations of the organs in the downscaled CT scan image 520. The neural network processing system 120 may additionally set a threshold for each organ's segmentation to define a sharp segmentation boundary, which is then used to define the organ's ROI. The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROI is in the original full-torso CT scan”; Jemaa, Para. [88]: the original resolution of the CT scan image fragment corresponding to the organ is 192 x 192 x 192).
Regarding claim 17, Jemaa teaches the limitations as explained above in claim 10.
Jemaa further teaches, the system of claim 10 (see claim 10 above), wherein the system is comprised in at least one of (Jemaa, Para. [2]: “a system and methods for identification and segmentation of biological objects using medical images”; Jemaa, Para. [113]: computer system 1700):
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing one or more simulation operations;
a system for performing one or more digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing one or more generative AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language model (VLMs);
a system for performing one or more conversational AI operations;
a system for generating synthetic data;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center;
or a system implemented at least partially using cloud computing resources (Jemaa, Para. [112]: “computer system 1700 may…reside in a cloud, which may include one or more cloud components in one or more networks”; Note: since the limitations are claimed in the alternative, the Examiner selects the cloud computing limitation).
Regarding claim 18, Jemaa teaches, one or more processors comprising (Jemaa, Para. [113]: “computer system 1700 includes a processor”):
one or more circuits to determine whether to adjust a size of an image identified using one or more first machine learning models based at least on evaluating the size of the image with respect to a threshold, the threshold related to an input resolution associated with one or more second machine learning models (Jemaa, As shown in Fig. 5, CT scan image 510 is downscaled to CT image 520; Jemaa, Paras. [113]-[117]: discuss processor 1702 and the instructions it executes; Jemaa, Fig. 1A: neural network processing system 120; Jemaa, Abstract: “a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based, on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments”; Jemaa, Para. [7]; Jemaa, Para. [87]: “The neural network processing system 120 may then downscale the CT scan image 510 by resampling a full torso CT scan image 510 to coarse resolution to generate a downscaled CT scan image 520. As an example and not by way of limitation, the coarse resolution may be 144 (front to back) x 256 (left to right) x 192 (up to down). In particular embodiments, the neural network processing system 120 may load the downscaled full torso CT scan image 520 to a neural network, which may determine locations of the organs in the downscaled CT scan image 520. The neural network processing system 120 may additionally set a threshold for each organ's segmentation to define a sharp segmentation boundary, which is then used to define the organ's ROI. The neural network processing system 120 may then run separate models each trained to segment an organ based on their respective ROI is in the original full-torso CT scan”; Jemaa, Para. [88]: the original resolution of the CT scan image fragment corresponding to the organ is 192 x 192 x 192; Note: the Examiner interprets the second resolution which is lower than the first resolution as a size smaller than a threshold, and segmenting the image as adjusting the size of the image using machine learning models).
Regarding claim 19, Jemaa teaches the limitations as explained above in claim 18.
Jemaa further teaches, the one or more processors of claim 18 (see claim 18 above), the one or more circuits further to determine, based at least on the evaluating, to incorporate the image into a frame at a second resolution that includes less points or pixels than the input resolution associated with the one or more second machine learning models (Jemaa, As shown in Fig. 5, image 520 is downscaled and then resampled to the original resolution (i.e., image 540). Image 540 is a more zoomed-in image (i.e., has less points) as a result of the image being resampled to the original resolution; Jemaa, Para. [88]: “the fragments may be resampled to 192 x 192 x 192. FIG. 5 shows that the CT scan image fragment 540 corresponding to organ 515f is resampled to the original resolution”; Jemaa, Para. [108]: the one or more second machine-learning models use the one or more extracted segments to determine one or more fine segmentations corresponding to the one or more organs in the extracted segments).
Regarding claim 20, Jemaa teaches the limitations as explained above in claim 18.
Jemaa further teaches, the one or more processors of claim 18 (see claim 18 above), wherein the one or more processors are comprised in at least one of (Jemaa, Para. [113]: “computer system 1700 includes a processor”):
a control system for an autonomous or semi-autonomous machine;
a perception system for an autonomous or semi-autonomous machine;
a system for performing one or more simulation operations;
a system for performing one or more digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing one or more deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing one or more generative AI operations;
a system for performing operations using one or more large language models (LLMs);
a system for performing operations using one or more vision language models (VLMs);
a system for performing one or more conversational AI operations;
a system for generating synthetic data;
a system for presenting at least one of virtual reality content, augmented reality content, or mixed reality content;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center;
or a system implemented at least partially using cloud computing resources (Jemaa, Para. [112]: “computer system 1700 may…reside in a cloud, which may include one or more cloud components in one or more networks”; Note: since the limitations are claimed in the alternative, the Examiner selects the cloud computing limitation).
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Jemaa et al. (WO 2023/092124 A1, see reference provided; hereinafter “Jemaa”) in view of Kalu et al. (US 2021/0398333 A1, hereinafter “Kalu”).
Regarding claim 3, Jemaa teaches the limitations as explained above in claim 1.
Jemaa does not expressly disclose the following limitation: further comprising determining that a difference between a first aspect ratio associated with the region and a second aspect ratio associated with the resolution is greater than a second threshold, wherein the causing of the region to be included in the subset of the frame is further based at least on the difference being greater than the second threshold.
However, Kalu teaches, further comprising determining that a difference between a first aspect ratio associated with the region and a second aspect ratio associated with the resolution is greater than a second threshold, wherein the causing of the region to be included in the subset of the frame is further based at least on the difference being greater than the second threshold (Kalu, Para. [0045]: For example, if a determined cropped region had to be sized to a 600 pixel by 400 pixel region over the first image in order to meet the various ROI and/or focus region cropping criteria in place in a given crop request, the method may not suggest or recommend the determined crop to a device display screen or designated content area having a resolution greater than a predetermined multiple of one or more of the dimensions of the determined crop. For example, if the device display screen (or designated content area) that the crop was requested for had target dimensions of 1200 pixels by 800 pixels (or larger), i.e., a 3:2 aspect ratio landscape rectangular cropped region, then the determined cropped region of size 600 pixels by 400 pixels may simply be deemed too small for use as a background image (or within a designated content area) …”; Kalu, Para. [0058]: “if a rectangular aspect ratio cropping score is significantly higher (e.g., greater than some predetermined relative cropping score difference threshold) for one image when cropped as a single rectangular image, then it might be a better choice to display the one image as a single rectangular photo in the designated content area of the application or device's UI).
It would have been obvious before the effective filing date of the claimed invention, to one of ordinary skill in the art, to combine determining that a difference between aspect ratios is greater than a threshold and including the region based on this difference as taught by Kalu with the method of Jemaa in order to identify parts of the image containing the most important content and ensure that such content is included in a determined cropped region from the image (Kalu, Abstract). Therefore, one of ordinary skill in the art would be capable to have combined the elements as claimed by known methods and that in combination, each element merely performs the same function as it does separately. It is for at least the aforementioned that the Examiner has reached a conclusion of obviousness with respect to claim 3.
Regarding claim 11, Jemaa teaches the limitations as explained above in claim 10.
Jemaa does not expressly disclose the following limitation: the one or more processors further to determine that a difference between a first aspect ratio associated with the first resolution and a second aspect ratio associated with the second resolution is greater than a second threshold, wherein the causing of the portion of the image data to be incorporated into the frame at the first resolution is further based at least on the difference being greater than the second threshold.
However, Kalu teaches, the one or more processors further to determine that a difference between a first aspect ratio associated with the first resolution and a second aspect ratio associated with the second resolution is greater than a second threshold, wherein the causing of the portion of the image data to be incorporated into the frame at the first resolution is further based at least on the difference being greater than the second threshold (Kalu, Para. [0070]: “Processor 605 may execute instructions necessary to carry out or control the operation of many functions performed by electronic device 600”; Kalu, Para. [0045]: For example, if a determined cropped region had to be sized to a 600 pixel by 400 pixel region over the first image in order to meet the various ROI and/or focus region cropping criteria in place in a given crop request, the method may not suggest or recommend the determined crop to a device display screen or designated content area having a resolution greater than a predetermined multiple of one or more of the dimensions of the determined crop. For example, if the device display screen (or designated content area) that the crop was requested for had target dimensions of 1200 pixels by 800 pixels (or larger), i.e., a 3:2 aspect ratio landscape rectangular cropped region, then the determined cropped region of size 600 pixels by 400 pixels may simply be deemed too small for use as a background image (or within a designated content area) …”; Kalu, Para. [0058]: “if a rectangular aspect ratio cropping score is significantly higher (e.g., greater than some predetermined relative cropping score difference threshold) for one image when cropped as a single rectangular image, then it might be a better choice to display the one image as a single rectangular photo in the designated content area of the application or device's UI; Kalu, Para. [0045]: resolution of determined cropped regions).
It would have been obvious before the effective filing date of the claimed invention, to one of ordinary skill in the art, to combine determining that a difference aspect ratios is greater than a threshold and including the region based on this difference as taught by Kalu with the system of Jemaa in order to identify parts of the image containing the most important content and ensure that such content is included in a determined cropped region from the image (Kalu, Abstract). Therefore, one of ordinary skill in the art would be capable to have combined the elements as claimed by known methods and that in combination, each element merely performs the same function as it does separately. It is for at least the aforementioned that the Examiner has reached a conclusion of obviousness with respect to claim 11.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Jemaa et al. (WO 2023/092124 A1, see reference provided; hereinafter “Jemaa”) in view of Park et al. (US 2021/0019544 A1, hereinafter “Park”).
Regarding claim 13, Jemaa teaches the limitations as explained above in claim 10.
Jemaa does not expressly disclose the following limitation: wherein a second size corresponding to the first resolution is larger than the size associated with the object.
However, Park teaches, wherein a second size corresponding to the first resolution is larger than the size associated with the object (Park, As shown in Fig. 9, the image of the car in F-Image 930 after crop/super-resolution is larger than the image of the car in peripheral image window 910; Park, Paras. [0111]-[0112]).
It would have been obvious before the effective filing date of the claimed invention, to one of ordinary skill in the art, to combine a size corresponding to a resolution being larger than the size associated with the object as taught by Park with the system of Jemaa in order to improve accuracy in detecting an object (Park, Para. [0066]). Therefore, one of ordinary skill in the art would be capable to have combined the elements as claimed by known methods and that in combination, each element merely performs the same function as it does separately. It is for at least the aforementioned that the Examiner has reached a conclusion of obviousness with respect to claim 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mustikovela et al. (US 2023/0004760 A1) teaches training object detection systems with generated images.
Koivisto et al. (US 2019/0258878 A1) teaches object detection for autonomous driving.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Daniella M. DiGuglielmo whose telephone number is (571)272-0183. The examiner can normally be reached Monday - Friday 8:00 AM - 4:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emily Terrell can be reached at (571)270-3717. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Daniella M. DiGuglielmo/Examiner, Art Unit 2666 /Molly Wilburn/Primary Examiner, Art Unit 2666
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