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 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 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.
Claims 1, 6 – 8, 12 – 15, 19 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang; Deng-yong et al. (CN 114677359 A; translated via Espacenet; hereinafter simply referred to as Zhang).
Regarding Independent claim 1, Zhang teaches of:
A method of classifying an input dataset, (See ¶ 12, 157 wherein a method of classifying an input dataset (image to be detected) via the use of a softmax classifier is disclosed)
embedding the input dataset into a first embedding space (See ¶ 15, 28 and 50 wherein the input dataset (image to be detected) is embedded into a first embedding space being the feature maps)
inputting the first embedding space into a spectral module including a periodic information processor and an aperiodic information processor (See ¶ 50, 30, 90 – 94 , 51, 110, 158, 165, 166, and figures 3 - 6 and 8 wherein the first embedding space being the feature maps, are used as input into a spectral module (The system comprising a local feature extraction module, 1100 in figure 3, and a global feature extraction module, 1200 in figure 8) wherein the local feature extraction module (aperiodic information processor, wherein the module is executed via a processor) is necessarily aperiodic as it comprises a convolutional neural network structure, as seen in figures 3 and 6, with attention modules, as seen in figures 4 and 5, which do not rely upon or assume repetition (being periodic), and wherein the global feature extraction module (periodic information processor, wherein the module is executed via a processor) comprises the use of Fast Fourier Convolution which relies on repeating/periodic functions making it periodic)
identifying global features in the input dataset using the periodic information processor based on a first subset of the first embedding space (See ¶ 28 – 30, 110 – 112, and figure 8 wherein global features (global feature map) is identified based on a first subset of the fist embedding space, being the previous global feature maps output by the fast Fourier convolutions)
identifying first local features in the input dataset using the aperiodic information processor based on a second subset of the first embedding space, wherein the first subset and the second subset are different (See ¶ 15 – 27, 90 – 108, and Figure 3 - 7 wherein local features (local feature map) is identified based on a second subset of the fist embedding space, being the previous local feature maps output by the previous local feature extraction stages, wherein the first and second subsets (global and local feature maps) are different)
and combining the global features and the first local features into a dataset of classified features of the input dataset (See ¶ 48 and 50 wherein the global features and local features (global and local feature maps) are concatenated/combined into a dataset of classified features of the input dataset being the concatenated feature image).
Regarding dependent claim 6, Zhang teaches:
The first subset and the second subset are mutually exclusive. (See ¶ 15 – 30, 50 wherein the first and second subsets being the local and global feature maps are mutually exclusive as they are obtained via the use of different modules which do not work together to make the local and global feature maps).
Regarding dependent claim 7, Zhang teaches:
The first subset and the second subset combine to yield the input dataset. (See ¶ 50 wherein the image to be detected (the input dataset) is processed to obtain the global and local feature maps (first and second subsets) and therefore, the global and local feature maps necessarily represent the input dataset (as the global and local feature maps in totality represent the image (input data set) they yield the image if combined)).
Regarding Independent claim 8, Zhang teaches of:
A computing system of classifying an input dataset, comprising one or more hardware processors; (See ¶ 12, 157 , 51, 171, and Figure 1 wherein a computing system of classifying an input dataset (image to be detected) via the use of a softmax classifier is disclosed comprising at least one processor)
an embedding processor executable by the one or more hardware processors (See ¶ 51, 171, and Figure 1 wherein the at least one processors executes the image detection method) configured for embedding the input dataset into a first embedding space (See ¶ 15, 28 and 50 wherein the input dataset (image to be detected) is embedded into a first embedding space being the feature maps)
a spectral processor executable by the one or more hardware processors (See ¶ 51, 171, and Figure 1 wherein the at least one processors executes the image detection method) configured for inputting the first embedding space into a spectral module including a periodic information processor and an aperiodic information processor (See ¶ 50, 30, 90 – 94 , 51, 110, 158, 165, 166, and figures 3 - 6 and 8 wherein the first embedding space being the feature maps, are used as input into a spectral module (The system comprising a local feature extraction module, 1100 in figure 3, and a global feature extraction module, 1200 in figure 8) wherein the local feature extraction module (aperiodic information processor, wherein the module is executed via a processor) is necessarily aperiodic as it comprises a convolutional neural network structure, as seen in figures 3 and 6, with attention modules, as seen in figures 4 and 5, which do not rely upon or assume repetition (being periodic), and wherein the global feature extraction module (periodic information processor, wherein the module is executed via a processor) comprises the use of Fast Fourier Convolution which relies on repeating/periodic functions making it periodic)
identifying global features in the input dataset using the periodic information processor based on a first subset of the first embedding space (See ¶ 28 – 30, 110 – 112, and figure 8 wherein global features (global feature map) is identified by the global feature extraction module (periodic information processor) based on a first subset of the fist embedding space, being the previous global feature maps output by the fast Fourier convolutions)
identifying first local features in the input dataset using the aperiodic information processor based on a second subset of the first embedding space, wherein the first subset and the second subset are different (See ¶ 15 – 27, 90 – 108, and Figure 3 - 7 wherein local features (local feature map) is identified by the local feature extraction module (aperiodic information processor) based on a second subset of the fist embedding space, being the previous local feature maps output by the previous local feature extraction stages, wherein the first and second subsets (global and local feature maps) are different)
an output interface executable by the one or more hardware processors (See ¶ 48, 51, 171, 161, feature concatenation module 1300/step 104, in figure 1, executed by the at least one processors) configured for combining the global features and the first local features into a dataset of classified features of the input dataset (See ¶ 48, 161, 157, and 50 wherein the global features and local features (global and local feature maps) are concatenated/combined into a dataset of classified features of the input dataset being the concatenated feature image).
Regarding dependent claim 12, Zhang teaches:
The aperiodic information processor identifies local features in the input dataset using at least one convolutional operator (See ¶ 16, 17, 23 – 27 wherein convolutional operations are used by the aperiodic information processor (local feature extraction module) to obtain local features (local feature maps)).
Regarding dependent claim 13, Zhang teaches:
The input dataset is an image (See ¶ 50 wherein the input dataset is an image (image to be detected)).
Regarding dependent claim 14, claim 14 is a computing system claim corresponding to claim 7. Please see the discussion of claim 7 above. Furthermore, Zhang teaches of a computing system comprising one or more processors. (See ¶ 51 and 171 wherein a computing system comprising one or more processors executes the image detection process).
Regarding independent claim 15, Zhang teaches:
One or more tangible processor-readable storage media embodies with instructions for executing one or more processors and circuits of a computing device (See ¶ 167, 168 wherein a non-transitory computer readable storage medium contains executable programs/instructions executable by processors to implement the image detection process) of classifying an input dataset, (See ¶ 12, 157 wherein a method of classifying an input dataset (image to be detected) via the use of a softmax classifier is disclosed)
embedding the input dataset into a first embedding space (See ¶ 15, 28 and 50 wherein the input dataset (image to be detected) is embedded into a first embedding space being the feature maps)
inputting the first embedding space into a spectral module including a periodic information processor and an aperiodic information processor (See ¶ 50, 30, 92, 51, 165, 166 wherein the first embedding space being the feature maps, are used as input into a spectral module (The system comprising a local feature extraction module and a global feature extraction module) wherein the local feature extraction module (aperiodic information processor, wherein the module is executed via a processor) is necessarily aperiodic as it comprises a convolutional neural network structure with attention modules which do not rely upon or assume repetition (being periodic), and wherein the global feature extraction module (periodic information processor, wherein the module is executed via a processor) comprises the use of Fast Fourier Convolution which relies on repeating/periodic functions making it periodic)
identifying global features in the input dataset using the periodic information processor based on a first subset of the first embedding space (See ¶ 28 – 30, wherein global features (global feature map) is identified based on a first subset of the fist embedding space, being the previous global feature maps output by the fast Fourier convolutions)
identifying first local features in the input dataset using the aperiodic information processor based on a second subset of the first embedding space, wherein the first subset and the second subset are different (See ¶ 15 – 27 wherein local features (local feature map) is identified based on a second subset of the fist embedding space, being the previous local feature maps output by the previous local feature extraction stages, wherein the first and second subsets (global and local feature maps) are different)
and combining the global features and the first local features into a dataset of classified features of the input dataset (See ¶ 48 and 50 wherein the global features and local features (global and local feature maps) are concatenated/combined into a dataset of classified features of the input dataset being the concatenated feature image).
Regarding dependent claim 19, claim 19 is a tangible processor-readable storage media claim corresponding to claim 12. Please see the discussion of claim 12 above. Furthermore, Zhang teaches of one or more tangible processor-readable storage media embodied with instructions for executing one or more processors and circuits of a computing device (See ¶ 167, 168 wherein a non-transitory computer readable storage medium contains executable programs/instructions executable by processors to implement the image detection process).
Regarding dependent claim 20, Zhang teaches:
The first subset and the second subset are mutually exclusive (See ¶ 15 – 30, 50 wherein the first and second subsets being the local and global feature maps are mutually exclusive as they are obtained via the use of different modules which do not work together to make the local and global feature maps)
and combine to yield the input dataset (See ¶ 50 wherein the image to be detected (the input dataset) is processed to obtain the global and local feature maps (first and second subsets) and therefore, the global and local feature maps necessarily represent the input dataset (as the global and local feature maps in totality represent the image (input data set) they would yield the image if combined)).
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 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 of this title, 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 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang; Deng-yong et al. (CN 114677359 A; translated via Espacenet; hereinafter simply referred to as Zhang) in view of Yin; Bangjie et al. (US 20230086552 A1; hereinafter simply referred to as Yin).
Regarding dependent claim 3, Zhang does not explicitly disclose:
The periodic information processor identifies global features in the input dataset using a frequency domain.
However, Yin teaches of the periodic information processor identifies global features in the input dataset using a frequency domain. (See ¶ 40, 41 wherein the global features (global frequency domain map) are identified in the input dataset (to be detected image) using a frequency domain).
As taught by Yin the periodic information processor identifying global features in the input dataset using a frequency domain allows for the detection of image information to be more accurate and allows for a plurality of different scenarios to be adapted. (See ¶ 50 wherein the periodic information processor identifies global features in the input dataset using a frequency domain which allows for the detection of image information to be more accurate). As both the teachings of Zhang and Yin deal with the technical field of image processing regarding global features of an image it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Zhang with Yin to teach of the periodic information processor identifies global features in the input dataset using a frequency domain in order for the detection of image information to be more accurate and allow for a plurality of different scenarios to be adapted.
Regarding dependent claim 10, claim 10 is a computing system claim corresponding to claim 3. Please see the discussion of claim 3 above. Furthermore, Zhang teaches of a computing system comprising one or more processors. (See ¶ 51 and 171 wherein a computing system comprising one or more processors executes the image detection process).
Regarding dependent claim 17, claim 17 is a tangible processor-readable storage media claim corresponding to claim 3. Please see the discussion of claim 3 above. Furthermore, Zhang teaches of one or more tangible processor-readable storage media embodied with instructions for executing one or more processors and circuits of a computing device (See ¶ 167, 168 wherein a non-transitory computer readable storage medium contains executable programs/instructions executable by processors to implement the image detection process).
Allowable Subject Matter
Claims 2, 4, 5, 9, 11, 16 and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indications of allowable subject matter:
Regarding clams 2 and 16, the reason of allowable subject matter is that the prior art fails to teach or reasonably suggest the limitations of claims 1 and 15 respectively, further comprising identifying second local features in the input dataset using an attention processor based on output of the spectral module; and combining the second local features with the global features and the first local features in the dataset of the classified features of the input dataset.
Regarding claims 4 and 18, the reason of allowable subject matter is that the prior art fails to teach or reasonably suggest the limitations of claims 3 and 17 respectively, further comprising the periodic information processor translates the first embedding space from a spatial domain into the frequency domain using a Hartley Transformation, identifies at least one of the global features using a spectral gating network, and applies an Inverse Hartley Transformation to translate the first embedding space from the frequency domain to the spatial domain.
Regarding claim 5, the reason of allowable subject matter is that the prior art fails to teach or reasonably suggest the limitations of claim 1, further comprising the aperiodic information processor identifies local features in the input dataset using at least one Hartley convolutional transformer.
Regarding claim 9, the reason of allowable subject matter is that the prior art fails to teach or reasonably suggest the limitations of claim 8, further comprising an attention processor executable by the one or more hardware processors and configured to identify second local features in the input dataset using attention processing based on the second embedding space, wherein the output interface is further configured to combine the second local features with the global features and the first local features in the dataset of the classified features of the input dataset.
Regarding claim 11, the reason of allowable subject matter is that the prior art fails to teach or reasonably suggest the limitations of claim 10, further comprising the periodic information processor is configured to translate the first embedding space from a spatial domain into the frequency domain using a neural operator, identify at least one of the global features using a spectral gating network, and apply an inverse transformation to translate the first embedding space from the frequency domain to the spatial domain.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892.
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/ALEJANDRO HERNANDEZ/Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661