DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “preprocessing module comprises … image denoising, contrast enhancement, or image resizing” in claim 3, “preprocessing module comprises preprocessing,” in claims 4 and 13, “image alignment module comprises aligning,” in claims 5 and 14, “feature extraction module comprises processing an aligned image … to extract discriminative features,” in claims 6 and 15, “machine learning classifier module comprises discriminating,” in claims 8 and 16, “signal processing refinement module comprises using .. to enhance object boundaries,” in claims 10 and 18, “the post-processing module comprises eliminating duplicate detections and refining … and applying,” in claims 11 and 19, “applying a preprocessing module to the input image to result in a preprocessed image, applying an image alignment module to the preprocessed image to result in an aligned image, applying a feature extraction module to the aligned image to result in an extracted image, applying a machine learning classifier module to the extracted image to result in a classified image, applying a signal processing refinement module to the classified image to result in a refined image, and applying a post-processing module to the refined image to result in a final image,” in claim 12, and “the post-processing module comprises applying non-maximum suppression and geometric constraints,” in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 3–6, 8, 10–16, 18–20, and therefore claims 7 and 9 which depend from claims 6 and 8 respectively, 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. The claim limitations set forth above in the Claim Interpretation section, all invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. These limitations are interpreted as computer-implemented means plus function limitations, thus requiring that the corresponding structure, the algorithm that transforms the general purpose computer to a special purpose computer, be found in the Specification. However, the Specification lacks such corresponding structure. That is, the Specification lacks a finite sequence of steps for solving a logical or mathematical problem or performing a task. See MPEP §2181(I)(B). Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 3–6, 8, 10–12, 13–16, 18–20, and therefore claims 7 and 9 which depend from claims 6 and 8 respectively, are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As set forth above, given that the Specification fails to provide the algorithm for the above stated 112(f) invoking claim limitations, and given that a PHOSITA would not otherwise find the functionality claimed to be well-understood in the absence of a disclosed algorithm, then the claims are also deficient for lacking written description support.
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 1–6, 8, 10–14, 16, and 18–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without a practical application or significantly more.
Regarding claim 1, this claim recites the following limitations which are found to be abstract ideas not reciting a practical application or significantly more:
small object detection in images, apply a set of machine learning and signal processing techniques (abstract idea as a mental process (see MPEP §2106.04(a)(2)(III)) as a human mind is capable of small object detection images by using a machine learning technique (in their mind – such as supervised learning where a human would see positive examples and negative examples and learn that way) and where humans can also perform signal processing techniques such as discerning different colors in an image or edge boundaries of objects).
This judicial exception is not integrated into a practical application for the following reasons. Claim 1 recites the additional element of the step of receive an input image and transmit the input image, which while not necessarily being abstract ideas, are insignificant extra solution activity since they are merely data gathering and data output (see MPEP §2106.05(g)). Moreover, these elements amount to receiving and outputting data in a computer based system and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
Claim 1 further recites additional elements: claim 1 is directed towards a system comprising a memory storing instructions executed by a processor and a computer. While the processor and memory of claim 1 are additional elements, they are not sufficient to recite a practical application of the abstract ideas recited in claim 1 as they amount to mere generic computer elements and thus amount to no more than a recitation of the words "apply it" (or an equivalent) or are no more than mere instructions to implement an abstract idea or other exception on a computer. see MPEP §2106.05(f).
Further, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claim 1 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Therefore, independent claim 1 is directed towards an abstract idea without a practical application or significantly more.
Regarding claim 12, this claim recites the following limitations which are found to be abstract ideas not reciting a practical application or significantly more:
A method for using a system for small object detection in images using machine learning and signal processing techniques comprising: applying a preprocessing module to the input image to result in a preprocessed image, applying an image alignment module to the preprocessed image to result in an aligned image, applying a feature extraction module to the aligned image to result in an extracted image, applying a machine learning classifier module to the extracted image to result in a classified image, applying a signal processing refinement module to the classified image to result in a refined image, and applying a post-processing module to the refined image to result in a final image (abstract idea as a mental process (see MPEP §2106.04(a)(2)(III)) as a human mind is capable of small object detection images by using a machine learning technique (in their mind – such as supervised learning where a human would see positive examples and negative examples and learn that way) and where humans can also perform signal processing techniques such as discerning different colors in an image or edge boundaries of objects (feature extraction), taking an input image and focusing in on the center of the image (preprocessing), rotating the image if it is oriented upside-down (image alignment), classifying an object in the image based on past learning (applying a machine learning classifier module .. to the extracted image), using a physical aid such as bi-focal eyeglasses to bring image details into focus (applying a signal processing refinement module), cropping the image by folding it (applying a post-processing module to result in a final image)).
This judicial exception is not integrated into a practical application because claim 1 recites only abstract ideas with no additional elements. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements from claim 1 do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Therefore, independent claim 12 is directed towards an abstract idea without a practical application or significantly more.
Regarding claim 2, this claim further recites various “modules” without claiming what the modules do. To the extent these modules are considered additional elements and not purely abstract ideas, they are merely generic computer components recited at a high level of abstraction insufficient to integrate the abstract ideas recited into a practical application. Further, the recited modules are insufficient to amount to significantly more than the judicial exception because when considered separately and in combination, the above recited additional elements do not add significantly more (also known as an “inventive concept”) to the exception. Rather, the additional elements disclosed above perform well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Regarding claims 3–6, 8, 10–11, 13–14, 16, 18–20, the limitations are merely directed towards further abstract ideas, specifically mental processes per MPEP §2106.04(a)(2)(III), as the human mind is practically capable of image denoising/reduce noise – seeing shapes despite noise for example, using a pencil erasure to remove noise marks in an image (claims 3, 4, and 13), rotating an image (claims 5, 14), discerning (extracting) discriminative features (claim 6), discriminating between small objects and background clutter in an extracted feature (for example, using color as a feature)(claims 8, 16), edge detection to find object boundaries (claims 10, 18), eliminating duplicative objects detected and refining objects detected as well as applying geometric constraints (claims 11 and 19–20).
Finally, it is noted that as per MPEP 2106.04(d)(1), a practical application and thus subject matter eligibility can be found when the following two requirements are met per a two-step analysis: In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness
Claims 1–4, 6–13, and 15–20 are rejected under 35 U.S.C. 103 as being unpatentable over Hutchens, US Patent Application Publication No. US 2025/0078514 A1 (herein “Hutchens”).
Regarding claim 1, Hutchens teaches a system for small object detection in images using machine learning and signal processing techniques, the system comprising (Hutchens Abstract, AI based system to detect items (small object) within an image of items in a drawer, where ¶¶77 and 92 teach the images used to train a CNN in object detection (using machine learning) and ¶¶158–162 teaching various image processing steps to prepare the image data for the AI model (signal processing techniques)):
a computer, a processor (Hutchens fig. 1, ¶86, data processing unit 115 which is a processing unit including a processor), and a memory storing instructions, which when executed by processor, cause the processor to apply a set of machine learning and signal processing techniques (Hutchens ¶¶ 92 and 265–266, in a preferred arrangement, the AI based inventory control system utilizes an integrated circuit including a CPU, GPU, AI accelerator and memory controller, where the CPU/GPU performs image processing (signal processing techniques) and the AI accelerator executes the computer vision model (machine learning)), wherein the computer is configured to receive an input image and transmit the input image to the processor (Hutchens ¶¶85–87, data processing unit 115 receives images from the camera system, the data processing unit including an ARM-Cortex-M4 for image recognition and AI processing of the received camera images (thus transmitting to the processor for processing)).
While Hutchens teaches a data processing unit in fig. 1 shown as a computer that receives images, Hutchens teaches the SoC arrangement with a CPU/GPU/memory controller and AI processor in a separate embodiment. Therefore, Hutchens does not anticipate claim 1. However, it would have been obvious to a person having ordinary skill in the art (herein “PHOSITA”) to have modified the image processing unit (appears to be a computer) in the fig. 1 embodiment to include the System on a Chip disclosed in the preferred arrangement embodiment because such a modification would be simply combining the SoC into the image processing unit, with predictable results, thus such a modification would be combining prior art elements according to known methods to yield predictable results. See MPEP §2143(I)(A).
Regarding claim 2, Hutchens teaches wherein the set of machine learning and signal processing techniques comprises a preprocessing module (Hutchens ¶¶158–162, preprocessing steps by CPU), an image alignment module (Hutchens ¶385, system adjusts segmentation algorithm for alignment between segments and objects), a feature extraction module (Hutchens ¶176, data processed through a pre-trained CNN for feature extraction), a machine learning classifier module (Hutchens ¶¶254–258 and 294, AI recognition engine as a computer vision model is trained to output classifications of detected objects in the images), a signal processing refinement module (Hutchens ¶204, CNN provides refinement of extracted features from an image), and a post-processing module (Hutchens ¶372, post-processing steps on image segments including filtering and enhancement).
Regarding claim 3, Hutchen teaches wherein the preprocessing module comprises at least one of image denoising, contrast enhancement, or image resizing (considering that the broadest reasonable interpretation (herein “BRI”) requires only one of the items in the list, Hutchens ¶¶393–396 at least teaches software performs resizing of an image segment (image resizing)).
Regarding claim 4, Hutchens teaches wherein the preprocessing module comprises preprocessing the input image to enhance its quality and reduce noise (Hutchens ¶¶158–162, preprocessing steps by CPU including data cleaning, normalization and augmentation, all which contribute towards enhancing quality and noise reduction).
Regarding claim 6, Hutchens teaches wherein the feature extraction module comprises processing an aligned image from the image alignment module to extract discriminative features (Hutchens ¶¶105–106 and preprocessing steps performed before CNN feature extraction on the images (extract discriminative features), where ¶290 teaches alignment as a preprocessing step before feature extraction).
Regarding claim 7, Hutchens teaches wherein the feature extraction module employs Convolutional Neural Networks (CNNs) to capture relevant spatial and contextual information from the aligned image (Hutchens ¶287, CNN used to determine segmentation which distinguishes multiple objects spatially from each other based on common characteristics (contextual information)).
Regarding claim 8, Hutchens teaches wherein the machine learning classifier module comprises discriminating between small objects and background clutter in a feature extracted image from the feature extraction module (Hutchens ¶¶197, 287, segmentation of an image into different regions of objects based on the size so that small objects are separated from large ones, and that only regions of interest such as the object regions are segmented, thus discriminating the objects from the background).
Regarding claims 9 and 17, Hutchens teaches wherein the machine learning classifier module comprises a machine learning classifier that is trained on a diverse dataset of images containing small objects and employs deep learning architectures to discriminate between small objects and background clutter (Hutchens ¶¶192, 197, segmentation performed by the CNN separates small objects from large objects and other image data (background), where ¶13 teaches the CNN (machine learning classifier) is trained using a learning data set of inventory items, and where ¶278 teaches the images of the objects used for the learning data set are taken from different angles, distances and perspectives (diverse dataset)).
Regarding claim 10, Hutchens teaches wherein the signal processing refinement module comprises using at least one of morphological operations, edge detection, and thresholding to enhance object boundaries and reduce false positives on an image from the machine learning classifier module (Hutchens ¶¶165, 180–184, where given that the BRI of the claim only requires one (due to “at least one” limitation where this BRI is consistent with the Specification disclosure), AI engine performing processing on the image (signal processing refinement) including a boundary generator and pixel checker performing thresholding, edge detection and template matching).
Regarding claim 11, Hutchens teaches wherein the post-processing module comprises eliminating duplicate detections and refining final object detections from an image from the signal processing refinement module, and wherein the post-processing module comprises applying non-maximum suppression and geometric constraints to the image from the signal processing refinement module (Hutchens ¶¶178–179, a non-maximum suppression algorithm is applied to the detected objects in the image to avoid duplicate detections of the same object, and a bounding box refinement is also applied to more accurately fit the object (geometric constraints)).
Regarding claim 12, Hutchens teaches a method for using a system for small object detection in images using machine learning and signal processing techniques, the method comprising (Hutchens Abstract, operations of an AI based system to detect items (small object) within an image of items in a drawer, where ¶¶77 and 92 teach the images used to train a CNN in object detection (using machine learning) and ¶¶158–162 teaching various image processing steps to prepare the image data for the AI model (signal processing techniques)):
receiving an input image (Hutchens ¶¶85–87, data processing unit 115 receives images from the camera system),
applying a preprocessing module to the input image to result in a preprocessed image (Hutchens ¶¶158–162, preprocessing steps by CPU),
applying an image alignment module to the preprocessed image to result in an aligned image (Hutchens ¶385, system adjusts segmentation algorithm for alignment between segments and objects),
applying a feature extraction module to the aligned image to result in an extracted image (Hutchens ¶176, data processed through a pre-trained CNN for feature extraction),
applying a machine learning classifier module to the extracted image to result in a classified image (Hutchens ¶¶254–258 and 294, AI recognition engine as a computer vision model is trained to output classifications of detected objects in the images),
applying a signal processing refinement module to the classified image to result in a refined image (Hutchens ¶204, CNN provides refinement of extracted features from an image), and
applying a post-processing module to the refined image to result in a final image (Hutchens ¶372, post-processing steps on image segments including filtering and enhancement).
While Hutchens teaches the above method steps, they are presented in separate embodiments. Therefore, Hutchens does not anticipate claim 12. However, it would have been obvious to a PHOSITA to have combined the image processing teachings from the embodiments because ¶493 teaches that various modifications of the various embodiments disclosed are contemplated and also such a modification would be combining prior art elements according to known methods to yield predictable results. See MPEP §2143(I)(A).
Regarding claim 13, Hutchens teaches wherein the preprocessing module comprises preprocessing the input image to enhance its quality and reduce noise (Hutchens ¶¶158–162, preprocessing steps by CPU including data cleaning, normalization and augmentation, all which contribute towards enhancing quality and noise reduction), and wherein the preprocessing module comprises at least one of image denoising, contrast enhancement, or image resizing (considering that the broadest reasonable interpretation (herein “BRI”) requires only one of the items in the list, Hutchens ¶¶393–396 at least teaches software performs resizing of an image segment (image resizing)).
Regarding claim 15, Hutchens teaches wherein the feature extraction module comprises processing the aligned image to extract discriminative features (Hutchens ¶¶105–106 and preprocessing steps performed before CNN feature extraction on the images (extract discriminative features), where ¶290 teaches alignment as a preprocessing step before feature extraction), and wherein the feature extraction module employs Convolutional Neural Networks (CNNs) to capture relevant spatial and contextual information from the aligned image (Hutchens ¶287, CNN used to determine segmentation which distinguishes multiple objects spatially from each other based on common characteristics (contextual information)).
Regarding claim 16, Hutchens teaches wherein the machine learning classifier module comprises discriminating between small objects and background clutter in the extracted image (Hutchens ¶¶197, 287, segmentation of an image into different regions of objects based on the size so that small objects are separated from large ones, and that only regions of interest such as the object regions are segmented, thus discriminating the objects from the background).
Regarding claim 18, Hutchens teaches wherein the signal processing refinement module comprises using at least one of morphological operations, edge detection, and thresholding to enhance object boundaries and reduce false positives on the classified image (Hutchens ¶¶165, 180–184, where given that the BRI of the claim only requires one (due to “at least one” limitation where this BRI is consistent with the Specification disclosure), AI engine performing processing on the image (signal processing refinement) including a boundary generator and pixel checker performing thresholding, edge detection and template matching).
Regarding claim 19, Hutchens teaches wherein the post-processing module comprises eliminating duplicate detections and refining final object detections from the refined image (Hutchens ¶¶178–179, a non-maximum suppression algorithm is applied to the detected objects in the image to avoid duplicate detections of the same object).
Regarding claim 20, Hutchens teaches wherein the post-processing module comprises applying non-maximum suppression and geometric constraints to the refined image (Hutchens ¶¶178–179, a non-maximum suppression algorithm is applied to the detected objects in the image to avoid duplicate detections of the same object, and a bounding box refinement is also applied to more accurately fit the object (geometric constraints)).
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Hutchens further in view of Zink, US Patent Application Publication No. US 2023/0154013 A1 (herein “Zink”).
Regarding claims 5 and 14, Hutchens teaches wherein the [image alignment module] comprises [aligning the input – claim 5 / preprocessed – claim 14] image to compensate for [translation], rotation, and scale variations (Hutchens ¶280, image processing including transformations of rotating and scaling).
Hutchens does not explicitly teach where Zink teaches the image alignment module comprising aligning the input/preprocessed image to compensate for translation (Zink ¶¶65–66, affine transformation performed on input image to apply relative translation, rotation and scaling).
Therefore, taking the teachings of Hutchens and Zink together as a whole, it would have been obvious to a PHOSITA before the effective filing date of the claimed invention to have modified the image processing of Hutchens to also include affine transformations of translation as disclosed by Zink at least because doing so would improve object recognition and tracking. Zink ¶76.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zhu et al., US Patent Application Publication No. US 2025/0054265 A1, directed towards image preprocessing and neural network based processing of images to detect, classify and track small objects in an image frame.
Quinton et al., US Patent Application Publication No. US 2022/0335334 A1, directed towards generating scale selecting training data in training a machine learning model for classification tasks.
Ely, US Patent Application Publication No. US 2025/0069241 A1, directed towards image processing including affine transformations of translation, scale, shear and rotation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE M KOETH whose telephone number is (571)272-5908. The examiner can normally be reached Monday-Thursday, 09:00-17:00, Friday 09:00-13:00, EDT/EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vincent Rudolph can be reached at 571-272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MICHELLE M. KOETH
Primary Examiner
Art Unit 2671
/MICHELLE M KOETH/Primary Examiner, Art Unit 2671
