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 limitation(s) is/are: optical distortion element (in claims 1, 2, 3, 7, 8, 9), image sensor (in claims 1 and 7), intelligent classifier (in claim 4), distorted image learning unit (in claims 5 and 7), image playback unit (in claim 7), and a learning database in claims 6 and 7.
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 term “level” in claims 1, 5-7 is a relative term which renders the claim indefinite. The term “level” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The examiner suggests adding a definition or limitation of specifically stating what an irregular angle is.
The term “level” in claims 1, 5-7 is a relative term which renders the claim indefinite. The term “level” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The examiner suggests adding a definition or limitation of specifically stating what distorting an image to a level that cannot be recognized by humans is.
The term “irregular angle” in claims 2 and 8 is a relative term which renders the claim indefinite. The term “level” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The examiner suggests adding a definition or limitation of specifically stating what distorting an image to an irregular angle is.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 5-8 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Niebles (US 20240021018 A1).
Regarding claim 1, Niebles discloses an optical distortion element configured to distort light reflected from an object to a level that cannot be recognized by humans and to allow the distorted light to pass therethrough ("[0027] Systems in accordance with many embodiments can preserve privacy in captured images by incorporating aberrations to an optical component to generate distorted images.
[0030] Light passes through the privacy-preserving optic and strikes image sensor 220. The image sensor passes image data to the computer vision model 230 which may have been generated in concert with the privacy preserving optic. The computer vision model can produce an output which is passed to an input/output (I/O) interface for use in other parts of the system and/or in other systems.");
an image sensor configured to receive the distorted light from the optical distortion element, to convert the distorted light into an electrical image signal, and to output the electrical image signal (Figs. 3 and 4, [0034], [0040]-[0043] where light is first distorted by an optimized phase mask such that the object cannot be recognized, then captured by a camera sensor to create the image signal, before that image signal being intelligently processed by a HAR network");
an intelligent image processor configured to learn the distorted image signal and information about the object, to process the electrical image signal output from the image sensor, and to output computational results for optical features of the object ("[0019] FIG. 3 illustrates a privacy-preserving system architecture including an adversarial optimization framework that learns a lens' phase mask to encode human action features and perform human action recognition (HAR) while obscuring privacy-related attributes in accordance with an embodiment of the invention.
[0050] Systems in accordance with many embodiments can include two HAR CNN architectures (e.g., a C3D and Rubkisnet, among others) which can provide for improved efficiency for HAR. For a set of private videos, it can be assumed that an output of a classifier C is a set of action class labels custom-character.sub.C. Then, a system can use a standard cross-entropy function custom-character as the classifier's loss.").
Regarding claim 2, Niebles discloses wherein the optical distortion element is an optical scrambling element comprising a lens with a plurality of regions, each having an incident surface that is sloped at an irregular angle ("[0027] Systems in accordance with many embodiments can preserve privacy in captured images by incorporating aberrations to an optical component to generate distorted images. Many embodiments can include an optical component that includes a camera with two thin convex lenses and a phase mask between them.
[0034] Systems in accordance with many embodiments provide for privacy-preserving human action recognition using an adversarial framework that provides robust privacy protection at different stages along a computer vision processing pipeline. An adversarial framework system with privacy-preserving human action recognition (HAR) in accordance with an embodiment of the invention is illustrated in FIG. 3. As illustrated, the privacy-preserving HAR pipeline can include an optical component that captures images. In many embodiments, an optical component can include two thin convex lenses and a phase mask between to capture distorted images." (instant applicant’s spec states in paragraph [0030] the optical distortion element 50 may be a lens barrel including a plurality of lenses disposed so as to overlap each other.)).
Regarding claim 5, Niebles discloses An object detection learning apparatus for training an intelligent image processor, the object detection learning apparatus comprising ([0006] In a further embodiment, the machine learning includes: an optical component neural network trained to learn the set of camera parameters)
a distorted image learning unit configured to ([0014] In a further embodiment, the machine learning is trained to add aberrations to a lens surface of the optical element such that an acquired video is distorted to obscure at least one privacy attribute and to preserve features for HAR.)
sequentially output distorted image data obtained by distorting an original image to a level that cannot be recognized by humans ([0040] A simulated camera can take a video V.sub.x∈custom-character={X.sub.t}.sub.t=1.sup.T as input, which have T frames, and can output a corresponding distorted video V.sub.y∈custom-character. (sequentially outputting images is the same as outputting a video since a video is multiple successive frames))
and label data mapped to the original image in order to train the intelligent image processor ("[0008] In a further embodiment, the action recognition neural network branch includes training a convolutional neural network C to predicts class labels.
[0026] Systems can preserve temporal information in distorted images/videos using temporal similarity matrices (TSM) and can constrain the structure of the temporal embeddings from the private videos to match the TSM of the original video.
[0036] In many embodiments, a testbed can acquire distorted videos and their non-distorted version simultaneously and results in hardware can be matched with simulations.").
Regarding claim 6, Niebles discloses a learning database configured to ([0035] Systems in accordance with many embodiments can validate a training framework using one or more HAR backbone neural networks.)
store distorted image data obtained by distorting an original image to a level that cannot be recognized by humans and metadata regarding optical features of an object included in the original image in a state of being mapped to each other ("[0034] To preserve temporal information in distorted videos, systems in accordance with many embodiments can use temporal similarity matrices (TSM) and constrain the structure of the temporal embeddings from private videos to match the TSM of the original video.
[0035] Systems in accordance with many embodiments can validate a training framework using one or more HAR backbone neural networks. Systems in accordance with many embodiments can be tested using one or more available human action recognition backbone networks. In many embodiments, a testbed can acquire distorted videos and their non-distorted version simultaneously and results in hardware can be matched with simulations. (if there is a comparison between distorted and non-distorted video data, it would be obvious to one of ordinary skill in the art to say that they are both stored somewhere at some point in time).
[0051] To preserve temporal information, adversarial framework systems in accordance with many embodiments can use temporal similarity matrices (TSMs). TSMs can be useful representations for human action recognition and can be robust against dynamic view changes of a camera when paired with appropriate feature representation. Systems in accordance with many embodiments can use TSMs as a proxy to keep the temporal information (e.g., features) similar after distortion. A system can build a TSM for original and private videos and compare their structures. In particular, a system can use embeddings ê from a last convolutional layer of a HAR CNN architecture and compute the TSM values using the negative of the squared Euclidean distance").
Regarding claim 7, Niebles discloses a learning database configured to store image data and metadata related to optical features of an object included in an image of the image data in a state of being mapped to each other ("[0034] To preserve temporal information in distorted videos, systems in accordance with many embodiments can use temporal similarity matrices (TSM) and constrain the structure of the temporal embeddings from private videos to match the TSM of the original video.
[0035] Systems in accordance with many embodiments can validate a training framework using one or more HAR backbone neural networks. Systems in accordance with many embodiments can be tested using one or more available human action recognition backbone networks. In many embodiments, a testbed can acquire distorted videos and their non-distorted version simultaneously and results in hardware can be matched with simulations. (if there is a comparison between distorted and non-distorted video data, it would be obvious to one of ordinary skill in the art to say that they are both stored somewhere at some point in time).
[0051] To preserve temporal information, adversarial framework systems in accordance with many embodiments can use temporal similarity matrices (TSMs). TSMs can be useful representations for human action recognition and can be robust against dynamic view changes of a camera when paired with appropriate feature representation. Systems in accordance with many embodiments can use TSMs as a proxy to keep the temporal information (e.g., features) similar after distortion. A system can build a TSM for original and private videos and compare their structures. In particular, a system can use embeddings ê from a last convolutional layer of a HAR CNN architecture and compute the TSM values using the negative of the squared Euclidean distance");
an image playback unit configured to play and output the image data and to output the metadata related to the image data in a state of being synchronized at the time of output ("[0060] In many embodiments, a TSM can be built for original videos and private videos and their structures can be compared. In particular as described, a process can use embeddings e from a last convolutional layer of a HAR CNN architecture and compute the TSM values using the negative of the squared Euclidean distance, e.g., (T.sub.m′).sub.n.sub.1.sub.n.sub.2=−∥ê.sub.n.sub.1−ê.sub.n.sub.2∥.sup.2. Then, the mean square error (MSE) can be calculated between the T.sub.m′ and the TSM from the input video T.sub.m, which can be computed similarly using the last convolutional layer of the corresponding pretrained HAR CNN (non-privacy) network.
fig. 7, ref 710 and 720");
an optical distortion element configured to distort the image played by the image playback unit to a level that cannot be recognized by humans and to allow the distorted image to pass therethrough ([0040] A simulated camera can take a video V.sub.x∈custom-character={X.sub.t}.sub.t=1.sup.T as input, which have T frames, and can output a corresponding distorted video V.sub.y∈custom-character. (sequentially outputting images is the same as outputting a video since a video is multiple successive frames));
an image sensor configured to receive the distorted image from the optical distortion element, to convert the distorted image into a distorted image signal, and to output the distorted image signal (Figs. 3 and 4, [0034], [0040]-[0043] where light is first distorted by an optimized phase mask such that the object cannot be recognized, then captured by a camera sensor to create the image signal, before that image signal being intelligently processed by a HAR network");
a distorted image learning unit configured to provide metadata related to original image data and the distorted image signal output from the image sensor to train the intelligent image processor ("[0008] In a further embodiment, the action recognition neural network branch includes training a convolutional neural network C to predicts class labels.
[0026] Systems can preserve temporal information in distorted images/videos using temporal similarity matrices (TSM) and can constrain the structure of the temporal embeddings from the private videos to match the TSM of the original video.
[0036] In many embodiments, a testbed can acquire distorted videos and their non-distorted version simultaneously and results in hardware can be matched with simulations.").
Regarding claim 8, Niebles discloses the optical distortion element is an optical scrambling element comprising a lens with a plurality of regions, each having an incident surface that is sloped at an irregular angle ("[0027] Systems in accordance with many embodiments can preserve privacy in captured images by incorporating aberrations to an optical component to generate distorted images. Many embodiments can include an optical component that includes a camera with two thin convex lenses and a phase mask between them.
[0034] Systems in accordance with many embodiments provide for privacy-preserving human action recognition using an adversarial framework that provides robust privacy protection at different stages along a computer vision processing pipeline. An adversarial framework system with privacy-preserving human action recognition (HAR) in accordance with an embodiment of the invention is illustrated in FIG. 3. As illustrated, the privacy-preserving HAR pipeline can include an optical component that captures images. In many embodiments, an optical component can include two thin convex lenses and a phase mask between to capture distorted images." (instant applicant’s spec states in paragraph [0030] the optical distortion element 50 may be a lens barrel including a plurality of lenses disposed so as to overlap each other.)).
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.
Claim(s) 3 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Niebles (US 20240021018 A1), and further in view of Salgar (US 20100149330 A1).
Regarding claims 3 and 9, Niebles does not disclose but in a similar field of endeavor of security systems with privacy zones, Salgar teaches wherein the optical distortion element is an element having an opaque planar shape ([0003] Privacy masking involves placing an obscuring visual element, such as an opaque rectangle, over areas designated as privacy zones during video capture.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Nieble’s disclosure of distorting light with Salgar’s teaching of shape classification in order provide a better balance between protecting privacy and providing surveillance of public spaces. ([0008]).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Niebles (US 20240021018 A1), and further in view of N (US 20190130214 A1).
Regarding claim 4, Niebles discloses wherein the intelligent image processor is an intelligent classifier configured to classify ("[0006] an adversarial neural network branch that tries to predict private information from the distorted video for a plurality of privacy categories.
[0007] In a further embodiment, the privacy categories include at least one category selected from the group consisting of a person's face, skin color, gender, relationship and nudity selection.").
Niebles does not explicitly disclose but in a similar field of endeavor of image recognition, N teaches, in better detail, configured to classify a shape of the object into one of a plurality of categories ([0049] This model approach first analyzes the extracted shape to decide the product category that the item falls into (e.g., bottles, cans, packets, tetra pack cuboids, etc.).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Niebles’s disclosure of distorting light with N’s teaching of shape classification in order to distinguish between one or more products and one or more unknown products in the image. ([abstract]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230394637 A1: claim 1 and 7 - [0036] In some embodiments of the present principles, a transform filter to transform/distort/scramble images in accordance with the present principles can include a Walsh-Hadamard transform (WHT) that can distort image data before it is digitized. In such embodiments, the WHT can be used to protect privacy with a camera-based edge device.
[0044] Referring back to FIG. 1, in some embodiments, the distorted images can then be digitized by the A/D converter 108 of the capture device 102. The capture device 102 can output a digital video signal, to the receiver/control unit 112, that is either RGB or monochrome using the output 110 of the capture device 102.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHMED A NASHER whose telephone number is (571)272-1885. The examiner can normally be reached Mon - Fri 0800 - 1700.
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, Andrew Moyer can be reached at (571) 272-9523. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AHMED A NASHER/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675