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 .
Response to Amendment
Claims 1-2, 5-9, 12-15, 18-20 remain pending. Claims 3-4, 10-11, and 16-17 have been canceled. Claims 1-2, 5-9, 12-15, and 18-19 have been amended. Amendments made to specifications and claims overcome all informality and drawing related objections. Amendments made to claims 1, 5, and 6 recite sufficient structure and thus no longer invoke 35 U.S.C. 112 112(f).
Specification
The use of the terms WiMax and WiGig, which are trade names or a marks used in commerce, have been noted in this application. The terms should be accompanied by the generic terminology; furthermore the terms should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the terms.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
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.
Claims 1-2, 5, 7-9, 12, 14-15, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Alam (Alam, Mahabubul, et al. "Quantum-classical hybrid machine learning for image classification (iccad special session paper)." 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD). IEEE, 2021.), in view of Grigoryan (US 20210150403 A1), Mowla (KR 20170095508 A), Harris (CN 112384748 A) , and Dang (Dang, Yijie, et al. "Image classification based on quantum K-Nearest-Neighbor algorithm." Quantum Information Processing 17 (2018): 1-18.).
With respect to claim 1, Alam teaches implementing a quantum neural network, wherein the quantum neural network comprises a plurality of quantum neural network clusters (“However, all these circuits are independent of each other. Hence, one can argue that all these computations can be done simultaneously if one has access to multiple quantum computing resources” page 4 Section 3 Part A Number of Circuit Executions lines 14-17, independent circuits as parallel network clusters and “For 3D RGB images, separate kernels are applied across the channels (2D planes), and they are collectively referred to as filters. For 2D images, filters and kernels are synonymous.” Page 3 Section 3 Part A Quantum Filters lines 5-8 And “Here, a 3-qubit quantum circuit is used as a filter. It encodes 3x3 image segments as a 3-qubit quantum state” page 3 Section 3 Part A lines 19-21, beams interpreted as qubits), and wherein a photonic quantum processor is configured to: receive the red-channel photon beam, the green-channel photon beam, and the blue-channel photon beam (“For 3D RGB images, separate kernels are applied across the channels (2D planes), and they are collectively referred to as filters. For 2D images, filters and kernels are synonymous.” Page 3 Section 3 Part A Quantum Filters lines 5-8 And “Here, a 3-qubit quantum circuit is used as a filter. It encodes 3x3 image segments as a 3-qubit quantum state” page 3 Section 3 Part A lines 19-21, beams interpreted as qubits); and for each of the red-channel photon beam, the green-channel photon beam, processing in parallel the blue-channel photon beam processing the red-channel photon beam, the green-channel photon beam, and the blue-channel photon beam, (“For 3D RGB images, separate kernels are applied across the channels (2D planes), and they are collectively referred to as filters. For 2D images, filters and kernels are synonymous.” Page 3 Section 3 Part A Quantum Filters lines 5-8 And “Here, a 3-qubit quantum circuit is used as a filter. It encodes 3x3 image segments as a 3-qubit quantum state” page 3 Section 3 Part A lines 19-21, beams interpreted as qubits) by a respective quantum neural network cluster of the plurality of quantum neural network clusters (“However, all these circuits are independent of each other. Hence, one can argue that all these computations can be done simultaneously if one has access to multiple quantum computing resources” page 4 Section 3 Part A Number of Circuit Executions lines 14-17, independent circuits as parallel network clusters and “For 3D RGB images, separate kernels are applied across the channels (2D planes), and they are collectively referred to as filters. For 2D images, filters and kernels are synonymous.” Page 3 Section 3 Part A Quantum Filters lines 5-8 And “Here, a 3-qubit quantum circuit is used as a filter. It encodes 3x3 image segments as a 3-qubit quantum state” page 3 Section 3 Part A lines 19-21, beams interpreted as qubits), and wherein processing the N-channel image data comprises: applying one or more filters to the image data to extract a plurality of features (“Similar to CNN, these quantum filters are moved across the 2D plane in finite steps (strides) to generate the complete output feature map of the image.” Page 3 Section 3 Part A Quantum Filters lines 9-11); applying a pooling process to the plurality of features to eliminate orientation effects (“One can also apply classical non-linear activation functions (for additional non-linearity) and maxpooling (downsampling) at the output of a Quanvolutional layer” page 4 section 3 Part A Network Design lines 5-8 ); identifying a plurality of features (“Similar to CNN, these quantum filters are moved across the 2D plane in finite steps (strides) to generate the complete output feature map of the image.” Page 3 Section 3 Part A Quantum Filters lines 9-11); and sending the plurality of identified features to the converter system as an N-channel photon beam, wherein the N-channel photon beam comprises a second red-channel photon beam, a second green- channel photon beam, or a second blue-channel photon beam (“The outputs from the final Quanvolutional layer can be fed to a an MLP (or a QNN)” page 4 Section 3 Part A Network Design lines 4-5).
Alam does not teach an apparatus comprising: a converter system configured to: receive image data, wherein the image data comprises red-channel image data, green-channel image data, and blue-channel image data, and wherein the image data is represented by classical binary bits; and convert the image data to a photon beam comprising converted image data, wherein the converted image data comprises converted red-channel image data, converted green-channel image data and converted blue-channel image data, and wherein the converted image data is represented by photonic quantum bits; a photonic quantum computing system communicatively coupled the converter system, the photonic quantum computing system comprising: a beam splitter configured to: receive the photon beam; and split the photon beam into a red-channel photon beam, a green-channel photon beam, and a blue-channel photon beam, wherein the red-channel photon beam comprises the red-channel image data, the green-channel photon beam comprises the green-channel image data, and the blue-channel photon beam comprises to the blue-channel image data; and a photonic quantum processor coupled to the beam splitter wherein the processor is configured to a converted N-channel image data into grayscale image data; compare the plurality of features to a plurality of pre-trained features extracted from pre-training images; and matching respective pre-trained features to the plurality of features.
Grigoryan teaches an apparatus comprising: a processor of a converter system configured to: receive image data, wherein the image data comprises red-channel image data,green-channel image data, and blue-channel image data, and wherein the image data is represented by classical binary bits (see figure 16 and “An example of a combined classical/quantum computing system 1600 is shown in FIG. 16. In this example, a classical computer 1602 may be interconnected with a quantum computer 1604. In order to process an image 1606, image 1606 may be input to classical computer 1602 and processed, as described above,” paragraph 0201 and “In an embodiment, a method for the quantum representation of RGB color images may comprise receiving a discrete color image that may comprise a red component, a green component, and a blue component” paragraph 0022); and convert the image data, wherein the converted image data comprises converted red-channel image data, converted green-channel image data and converted blue-channel image data, and wherein the converted image data is represented by quantum bits (see figure 16 and “Quantum computer 1604 may perform processing starting with generating a quantum representation 1608 of image 1606.” Paragraph 0201);
Grigoryan is analogous art in the same field of endeavor as the claimed invention. Grigoryan is directed to quantum image processing (“The present invention relates generally to quantum computing systems and information systems, and more particularly, relates to quantum computing, qubit duplication, teleportation protocol, quantum image/signal representation, quantum signal processing, and to quaternion quantum image processing.” Paragraph 0002). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Grigoryan and Alam by utilizing Grigoryan’s multi-channel image representation creation and conversion process as input to Alam’s system, with the expectation that doing so would not limit the continued execution of its (Alam’s) processes.
Mowla teaches multi-channel image data (“A digital image represented in RGB colors can be represented as R, G, and B, where each pixel has 8 bits each. Fig. 1 (a) shows an example in which dark green is represented by RGB.” Page 3 lines 14-16) having a quantum representation using photonic qubits (photon beams) (“polarized photons are matched to bit values” and “The transmitter transmits any polarized photons (corresponding to any bit string) to the receiver” page 5 paragraph 3 lines 1-2).
Mowla is analogous art in the same field of endeavor as the claimed invention. Mowla is directed towards processing and analyzing images using photon representations (“The technique described below relates to a technique for transmitting constant information by combining steganography and quantum cryptography.” Page 3 lines 7-8 “Steganography is generally used to deliver specific information or messages using still images” page 3 lines 10-11). It would have been obvious before the effective filing date of the claimed invention, for a person of ordinary skill in the art to perform a simple substitution of the the photonic quantum image representation of Mowla in place of Grigoryan’s generalized quibit representation, within the combined system of Alam and Grigoryan, with the expectation that doing so would lead to the predictable result of a multi-channel photon beam based quantum image representation (“A digital image represented in RGB colors can be represented as R, G, and B, where each pixel has 8 bits each. Fig. 1 (a) shows an example in which dark green is represented by RGB.” Page 3 lines 14-16 and “polarized photons are matched to bit values” and “The transmitter transmits any polarized photons (corresponding to any bit string) to the receiver” page 5 paragraph 3 lines 1-2), due to the simple substitution of one known element for another or the mere application of a known technique to a piece of prior art ready for improvement (From Grigoryan 0222 “Other techniques being developed or investigated may include…wherein qubits may be implemented by processing states of different modes of light through both linear and nonlinear elements, linear optical quantum computers, wherein qubits may be implemented by processing states of different modes of light through linear elements, such as mirrors, beam splitters and phase shifters”) .
Harris teaches a photonic quantum computing system communicatively coupled a converter system (“receiving a digital representation of the input vector; encoding the input vector into a first plurality of optical signals using an optical encoder” page 4 lines 5-6), the photonic quantum computing system comprising: a beam splitter (“variable beam splitter” page 3 Contents of the Invention line 2) configured to: receive the photon beam (“comprising a first plurality of light input part” page 3 Contents of the Invention line 2); and split the photon beam (“a first plurality of light output part” page 3 Contents of the Invention lines 2-3) into a red-channel photon beam (“the variable beam splitter (VBS) is configured to transform n input light pulses from the input vector to the output vector” page 14 Photon Processor lines 5-6 and “The embodiments described herein are discussed in 2-dimensional convolution form, but can be popularized to any number of dimensions. for [Ih * Iw] input (referred to herein as "image",” page 46 lines 29-30 and “In some implementations, for example, in CNN, these operations can be generalized so that they can be applied to and/or generate multi-channel data. For example, the RGB image has three color channels.” Page 47 lines 28-29), a green-channel photon beam (“the variable beam splitter (VBS) is configured to transform n input light pulses from the input vector to the output vector” page 14 Photon Processor lines 5-6 and “The embodiments described herein are discussed in 2-dimensional convolution form, but can be popularized to any number of dimensions. for [Ih * Iw] input (referred to herein as "image",” page 46 lines 29-30 and “In some implementations, for example, in CNN, these operations can be generalized so that they can be applied to and/or generate multi-channel data. For example, the RGB image has three color channels.” Page 47 lines 28-29), and a blue-channel photon beam (“the variable beam splitter (VBS) is configured to transform n input light pulses from the input vector to the output vector” page 14 Photon Processor lines 5-6 and “The embodiments described herein are discussed in 2-dimensional convolution form, but can be popularized to any number of dimensions. for [Ih * Iw] input (referred to herein as "image",” page 46 lines 29-30 and “In some implementations, for example, in CNN, these operations can be generalized so that they can be applied to and/or generate multi-channel data. For example, the RGB image has three color channels.” Page 47 lines 28-29), wherein the red-channel photon beam comprises the red-channel image data (“the variable beam splitter (VBS) is configured to transform n input light pulses from the input vector to the output vector” page 14 Photon Processor lines 5-6 and “The embodiments described herein are discussed in 2-dimensional convolution form, but can be popularized to any number of dimensions. for [Ih * Iw] input (referred to herein as "image",” page 46 lines 29-30 and “In some implementations, for example, in CNN, these operations can be generalized so that they can be applied to and/or generate multi-channel data. For example, the RGB image has three color channels.” Page 47 lines 28-29), the green-channel photon beam comprises the green-channel image data(“the variable beam splitter (VBS) is configured to transform n input light pulses from the input vector to the output vector” page 14 Photon Processor lines 5-6 and “The embodiments described herein are discussed in 2-dimensional convolution form, but can be popularized to any number of dimensions. for [Ih * Iw] input (referred to herein as "image",” page 46 lines 29-30 and “In some implementations, for example, in CNN, these operations can be generalized so that they can be applied to and/or generate multi-channel data. For example, the RGB image has three color channels.” Page 47 lines 28-29), and the blue-channel photon beam comprises to the blue-channel image data (“the variable beam splitter (VBS) is configured to transform n input light pulses from the input vector to the output vector” page 14 Photon Processor lines 5-6 and “The embodiments described herein are discussed in 2-dimensional convolution form, but can be popularized to any number of dimensions. for [Ih * Iw] input (referred to herein as "image",” page 46 lines 29-30 and “In some implementations, for example, in CNN, these operations can be generalized so that they can be applied to and/or generate multi-channel data. For example, the RGB image has three color channels.” Page 47 lines 28-29); Harris also teaches a photonic quantum processor coupled to the beam splitter (“controlling the photon processor comprising a plurality of variable beam splitter (VBS)” page 4 lines 8-9).
Harris is analogous art in the same field of endeavor the claimed invention. Harris is directed towards a photon processing system that includes a beam splitter (“providing a photon processor. The photon processor may include: a first interconnection variable beam splitter (VBS) array” page 3 Contents of the Invention lines 1-2). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Grigoryan, Mowla, and Harris by using the structure of Harris (its processor, beam-splitter) to host the processing steps of the combined teachings of Alam, Grigoryan and Mowla, with the expectation that doing so would lead to faster processing when compared to conventional classical computing systems (“using some embodiments, estimating the speed of the matrix multiplication relative to conventional technology to accelerate two orders of magnitude. For example, the graphics processing unit (GPU) of the prior art can perform multiplication in about 10ns, can be executed by the photon processing system according to some embodiments in about 200ps” page 9 lines 12-16).
Dang teaches where a processor is converting a converted N-channel image data into grayscale image data, wherein the converted N- channel image data comprises the converted red-channel image data, the converted green-channel image data, or the converted blue-channel image data (“converted to a grayscale image” page 5 line 9); comparing the plurality of features to a plurality of pre- trained features extracted from pre-training images(“the feature vectors are stored in quantum superposition state which is used to achieve parallel computing of similarity. Next, the quantum minimum search algorithm is used to speed up the searching process for similarity” page 3 lines 10-13 and Fig 1 computing distance); and matching respective pre-trained features (Table 2 Ranking).
Dang is analogous art in the same field of endeavor as the claimed invention. Dang is directed towards quantum image processing (“how to apply the QKNN algorithm to image classification and describe our solution” page 3 Image classification lines 1-2). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Alam, Grigoryan, Mowla, Harris, and Dang by fusing the quantum image processing of Alam and Dang, incorporating Dang’s steps of greyscale conversion, and feature matching, would lead to improvements in efficiency, while maintaining the quality of the system’s quantum classification performance (“Moreover, our quantum scheme has a good classification performance while greatly improving the efficiency.” Page 16 lines 4-5).
With respect to claim 2, Alam, Grigoryan, Mowla, Harris and Dang teach the apparatus of Claim 1 Harris teaches the photonic quantum processor (“controlling the photon processor comprising a plurality of variable beam splitter (VBS)” page 4 lines 8-9). Alam further teaches applying one or more second filters to the plurality of features to refine the plurality of features (“a Quanvolutional layer can have many filters and multiple Quanvolutional layers can be stacked upon each other to develop a deep Quanvolutional Neural Network” page 4 Network Design lines 1-4); and applying a second pooling process to the plurality of features to further eliminate orientation effects (“a Quanvolutional layer can have many filters and multiple Quanvolutional layers can be stacked upon each other to develop a deep Quanvolutional Neural Network” page 4 Network Design lines 1-4 and “and maxpooling (downsampling) at the output of a Quanvolutional layer” page 4 Network Design lines 7-8).
With respect to claim 5, Alam, Grigoryan, Mowla, Harris and Dang teach the apparatus of Claim 1. Mowla further teaches wherein the converter system is further configured to: receive the second red-channel photon beam (“The transmitter transmits any polarized photons (corresponding to any bit string) to the receiver based on the quantum encryption described above. The receiver observes the received photon using any polarization filter” Page 5 paragraph 3 lines 1-3), the second green-channel photon beam (“The transmitter transmits any polarized photons (corresponding to any bit string) to the receiver based on the quantum encryption described above. The receiver observes the received photon using any polarization filter” Page 5 paragraph 3 lines 1-3), and the second blue-channel photon beam (“The transmitter transmits any polarized photons (corresponding to any bit string) to the receiver based on the quantum encryption described above. The receiver observes the received photon using any polarization filter” Page 5 paragraph 3 lines 1-3); convert the plurality of identified features to the plurality of converted identified features, wherein the plurality of converted identified features are represented by classical binary bits (“The transmitter and the receiver determine a bit stream having a polarization state that coincides with each other, and generate a secret key using at least a part of the bit stream.” Page 5 paragraph 3 lines 3-5 and “The receiver can receive the image, extract information hidden in the image, and decrypt the information using the secret key” page 6 paragraph 3 lines 3-4);
With respect to claim 7, Alam, Grigoryan, Mowla, Harris and Dang teach the apparatus of Claim 1, Alam further teaches wherein each quantum neural network cluster of the plurality of quantum neural network clusters (“However, all these circuits are independent of each other. Hence, one can argue that all these computations can be done simultaneously if one has access to multiple quantum computing resources” page 4 Section 3 Part A Number of Circuit Executions lines 14-17, independent circuits as parallel network clusters and “For 3D RGB images, separate kernels are applied across the channels (2D planes), and they are collectively referred to as filters. For 2D images, filters and kernels are synonymous.” Page 3 Section 3 Part A Quantum Filters lines 5-8 And “Here, a 3-qubit quantum circuit is used as a filter. It encodes 3x3 image segments as a 3-qubit quantum state” page 3 Section 3 Part A lines 19-21, beams interpreted as qubits) comprises a plurality of quantum neurons (“Similar to CNN, a Quanvolutional layer can have many filters and multiple Quanvolutional layers can be stacked upon each other to develop a deep Quanvolutional Neural Network” page 4 Network Design lines 1-4).
With respect to claim 8, Alam, Grigoryan, Mowla, Harris and Dang teach all limitations in consideration of claim 1, because claim 8 is the method version of claim 1. The additional limitation of “sending the plurality of identified first features as a second red-channel photon beam” is further taught by Alam (“The outputs from the final Quanvolutional layer can be fed to a an MLP (or a QNN)” page 4 Section 3 Part A Network Design lines 4-5).
With respect to claim 9, Alam, Grigoryan, Mowla, Harris and Dang teach the method of Claim 8 and all additional limitations, in consideration of claim 2 because claim 9 is the method version of claim 2.
With respect to claim 12, Alam, Grigoryan, Mowla, Harris and Dang teach the method of Claim 8 and all additional limitations, in consideration of claim 5 because claim 12 is the method version of claim 5.
With respect to claim 14, Alam, Grigoryan, Mowla, Harris and Dang teach the method of Claim 8 and all additional limitations, in consideration of claim 7 because claim 14 is the method version of claim 7.
With respect to claim 15, Alam, Grigoryan, Mowla, Harris and Dang teach all limitations in consideration of claim 1, because claim 15 is directed towards a non-transitory computer readable medium storing instructions that implements the processes of claim 1. Additional limitations are further taught by Harris. Harris teaches a non-transitory computer-readable medium storing instructions (“The memory 1-109) may also include executable instructions that cause the processor…” page 10 lines 45-46)
With respect to claim 18, Alam, Grigoryan, Mowla, Harris and Dang teach the non-transitory computer-readable medium of Claim 15. Alam, Grigoryan, Mowla, Harris and Dang also teach all further limitations in consideration of claim 5, because claim 18 is directed towards a non-transitory computer readable medium storing instructions that implements the processes of claim 5.
With respect to claim 20, Alam, Grigoryan, Mowla, Harris and Dang teach the non-transitory computer-readable medium of Claim 15. Harris further teaches wherein at least one of the one or more processors is a photonic quantum processor (“controlling the photon processor comprising a plurality of variable beam splitter (VBS)” page 4 lines 8-9).
Claims 6, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Alam, Grigoryan, Mowla, Harris, and Dang as applied to claims 5, 12, and 15, respectfully, and further in view of Butt (Butt, Khushbu Khalid, Guohui Li, Sajid Khan, and Sohaib Manzoor. 2020. "Fast and Efficient Image Encryption Algorithm Based on Modular Addition and SPD" Entropy 22, no. 1: 112.).
With respect to claim 6, Alam, Grigoryan, Mowla, Harris and Dang teach the apparatus of Claim 5, however they do not teach any further limitations. Butt teaches a data extraction system (“Simulation was carried out using the JetBrains PyCharm Edu 2019.1.1 × 64 software installed on a PC with 4 GB memory, an Intel Core I5 Processor, and the Windows 10 Enterprise operating system. For the histograms, Matlab R2017a was used” page 7 Simulation Results and Discussion lines 1-3) configured to: generate extracted data by combining the plurality of converted identified features, for each of the second red-channel photon beam, the second green-channel photon beam, and the second blue-channel photon beam (“Join all the bit-planes to the respective sequences” Page 7 Step 9 and Figure 1. Joining).
Butt is analogous art in the same field of endeavor as the claimed invention. Butt is directed towards RGB image processing involving splitting-colored images into their RGB channels, processing them, individually, then merging the channels back together (Figure 1). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine Alam, Grigoryan, Mowla, Harris, Dang, and Butt by adding Butt’s joining step as a post-processing step to the processing steps of combined system of Alam, Grigoryan, Mowla, Harris, and Dang, would lead to faster processing, simplifying the process by supporting the use and ultimately allowing the combination of modular image properties (color channels) (“To tackle the problems of complexity and encryption time, we have proposed a simple, fast, and secure image encryption algorithm which can ensure better security in less time, in comparison to some older algorithms” page 25 Conclusions lines 1-3 and “The use of modular
addition between the pixels of image blocks and bit-plane scrambling provides an advantage in the sense of fast processing” page 25 Conclusions lines 5-7).
With respect to claim 13, Alam, Grigoryan, Mowla, Harris and Dang teach the method of Claim 12. Alam, Grigoryan, Mowla, Harris, Dang and Butt teach all additional limitations in consideration of claim 6 because claim 13 is the method version of claim 6.
With respect to claim 19, Alam, Grigoryan, Mowla, Harris and Dang teach the non-transitory computer-readable medium of Claim 15. Alam, Grigoryan, Mowla, Harris, Dang and Butt teach all additional limitations in consideration of claim 6 because claim 19 is directed towards a non-transitory computer readable medium storing instructions that implements the processes of claim 6.
Response to Arguments
Applicant’s arguments, filed 09/30/2025, have been fully considered. Because of the below reasoning, and the amendments made to the independent claims, the rejections have been updated.
With respect to the Alam-Mowla-Harris-Dang combination, applicant argues on page 14 that such a combination is improper because there is no basis in the prior art to combine these references, specifically pointing to the inconsistencies of Alam and Mowla. With regard to the Alam-Mowla combination, the examiner disagrees that additional non-relevant limitations presented inside Mowla have a bearing on the combination itself (e.g. encoding images with secret keys, see page 15 paragraph 2). However, the examiner considers the additional arguments regarding Mowla as moot due to the updated rejections made in light of the amendments changing the scope of the independent claims, leading to a new grounds of rejection (See above claim mapping).
Applicant additionally argues that “Alam is bereft of any teaching of parallel processing.” (see page 17 paragraph 1). The examiner disagrees and recites Alam (“However, all these circuits are independent of each other. Hence, one can argue that all these computations can be done simultaneously if one has access to multiple quantum computing resources” page 4 Section 3 Part A Number of Circuit Executions lines 14-17 and “For 3D RGB images, separate kernels are applied across the channels (2D planes), and they are collectively referred to as filters. For 2D images, filters and kernels are synonymous.” Page 3 Section 3 Part A Quantum Filters lines 5-8 And “Here, a 3-qubit quantum circuit is used as a filter. It encodes 3x3 image segments as a 3-qubit quantum state” page 3 Section 3 Part A lines 19-21). The examiner interprets this as comprising the ability and structure necessary to process individual image channels simultaneously using multiple circuits. Based on this interpretation the examiner cannot come to the conclusion that Alam is bereft of any teaching of parallel processing.
Finally, the examiner also disagrees with the argument made on page 17 paragraph 2 and page 18, that no references teach multi-channel image processing. Applicant argues that Alam is deficient when it comes to such teaching and that no other references present in the combination sufficient teach such limitations. The examiner again recites Alam (“However, all these circuits are independent of each other. Hence, one can argue that all these computations can be done simultaneously if one has access to multiple quantum computing resources” page 4 Section 3 Part A Number of Circuit Executions lines 14-17 and “For 3D RGB images, separate kernels are applied across the channels (2D planes), and they are collectively referred to as filters. For 2D images, filters and kernels are synonymous.” Page 3 Section 3 Part A Quantum Filters lines 5-8 And “Here, a 3-qubit quantum circuit is used as a filter. It encodes 3x3 image segments as a 3-qubit quantum state” page 3 Section 3 Part A lines 19-21). Based on this recitation the examiner cannot conclude that Alam does not teach parallel processing “for each of a red-channel photon beam, a green-channel photon beam, and a blue channel photon beam”, when taken in combination with the other sources.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA C WILLIAMS whose telephone number is (571)272-7074. The examiner can normally be reached M-F 7:30am - 4:00pm.
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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