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 Amendments
The amendments to the claims filed on 06/08/2026 have been acknowledged accepted and entered. Previously claims 1 – 20 were pending. Claims 21 – 23 have been added and now claims 1 – 23 are still currently pending.
Response to Arguments
Applicant’s arguments, see Remarks, filed 06/09/2026, with respect to independent claims 1, 8 and 15 have been fully considered and are persuasive. The 102(a)(1) claim rejections of claims 1, 8, 15 and all rejections and objects of their dependent claims have been withdrawn. Specifically the arguments regarding the aperiodic and periodic information processors being directed towards the processing of aperiodic and periodic image data rather than the processing being done in an aperiodic or periodic manner have been persuasive. Therefore, a second non-final with new rejections is being submitted.
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.
Claim 1, 6, 7, 21, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Quattrini; Fabio et al. (Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyri; hereinafter simply referred to as Quattrini) in view of Rana; Saadia et al. (Advance Approach to Estimate Fingerprint Images Quality Using Statistical & Graphical Methodology; hereinafter simply referred to as Rana) further in view of Yu; Jiachuan et al. (Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules; hereinafter simply referred to as Yu).
Regarding independent claim 1, Quattrini teaches:
A method of classifying an input dataset (See Page 1721 Left Column Paragraph 2, Figure 4, Page 1719 Right Column Paragraph 1, wherein the input dataset (scanned documents), is classified)
embedding the input dataset into a first embedding space (See Page 1720 Right Column Paragraph 2, wherein the input data set is embedded into a first embedding space, being tensor “X”)
inputting the first embedding space into a spectral module (See Page 1719 Left Column Paragraph 3, Page 1719 Right Column Final Paragraph, See Page 1720 Right Column Paragraph 2, and Figure 2, wherein the first embedding space, Tensor “X”, is input into a spectral module, Volumetric FFC (Fast Fourier Convolutions))
identifying global features in the input dataset based on a first subset of the first embedding space and identifying first local features in the input dataset, based on a second subset of the first embedding space, wherein the first subset and the second subset are different (See Page 1720 wherein a global branch and a local branch take in different separate “chunks” of the tensor (first and second subset of embedding space) and identify global and local features respectively (global and local volumetric information))
and combining the global features and the first local features into a dataset of classified features of the input dataset. (See Page 1721 Left Column Paragraph 1 and Page 1720 Right Column Final Paragraph wherein the global features and first local features (output of global and local branches) are combined into a dataset of classified features of the input dataset (final output vFFC Tensor)).
Quattrini does not explicitly disclose a spectral module including a periodic information processor and using the periodic information processor to identify global features.
However, Rana teaches of a spectral module including a periodic information processor and using the periodic information processor to identify global features. (See Page 3 Left Column Paragraph 1, wherein a spectral module uses a periodic information processor (method based on Fourier spectrum) which identifies a global measure (global feature) corresponding to a spectral measure which identifies or discriminates periodic texture/features).
As taught by Rana using periodic information to identify global features allows for the global features to indicate the layout of alternating periodic ridges and valley patterns used in the analysis of fingerprint images. (See Page 3 Right Column Paragraphs 2 and 3 wherein the global features create an effective indication for the layout of periodic features in a fingerprint image). As both the teachings of Quattrini and Rana deal with the technical field of image processing regarding the extraction of image features, 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 Quattrini with Rana to teach of a spectral module including a periodic information processor and using the periodic information processor to identify global features in order for effective analysis to be made on the fingerprint images.
Quattrini in view of Rana does not explicitly disclose a spectral module including an aperiodic information processor and using the aperiodic information processor to identify local features.
However, Yu teaches of a spectral module including an aperiodic information processor and using the aperiodic information processor to identify local features. (See Section 2 Paragraph 1, Section 2.1 Paragraph 3, Section 2.2.1 Second and third Paragraph, wherein Local features are detected in a method comprising the detection of defects/non-periodic features/information using Fourier spectrum filtering).
As taught by Yu using the aperiodic information to identify local features allows for the image to be separated and categorized into periodic and non-periodic features. (See Section 2.2.1, second to last paragraph, Section 2.1 Paragraph 3, wherein the image can be split up into periodic and non-periodic image features after the capturing of local image features). As both the teachings of Quattrini in view of Rana and Yu deal with the technical field of image processing regarding the extraction of image features 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 Quattrini in view of Rana with Yu to teach of a spectral module including an aperiodic information processor and using the aperiodic information processor to identify local features in order for the image to be categorized into periodic and non-periodic image features.
Regarding dependent claim 6, Quattrini in view of Rana and Yu teaches:
The first subset and the second subset are mutually exclusive. (See Quattrini Page 1720 Right Column Paragraph 2, wherein the first subset and second subset (two separate chunks of tensor “X”) are mutually exclusive as they are separated and given to different branches being the global and local branch).
Regarding dependent claim 7, Quattrini in view of Rana and Yu teaches:
The first subset and the second subset combine to yield the input dataset. (See Quattrini Page 1720 Right Column Paragraph 2, wherein the first and second subset are two chunks of the tensor “X”, that when combined would provide the tensor “X” representing the input dataset).
Regarding dependent claim 21, Quattrini in view of Rana and Yu teaches:
The periodic information processor is configured to identify global features in the input dataset by processing periodic information in the first subset of the first embedding space, (See Rana Page 3 Left Column Paragraph 1, wherein a spectral module uses a periodic information processor (method based on Fourier spectrum) which identifies a global measure (global feature) corresponding to a spectral measure which identifies or discriminates periodic texture/features)
and the aperiodic information processor is configured to identify local features in the input dataset by processing aperiodic information in the second subset of the first embedding space. (See Yu Section 2 Paragraph 1, Section 2.1 Paragraph 3, Section 2.2.1 Second to last Paragraph wherein Local features are detected in a method comprising the detection of defects/non-periodic features/information using Fourier spectrum filtering, furthermore see Quattrini Page 1720 wherein the first and second subsets (two separate chucks) of the first embedding space (tensor ‘X’) are used for the detection of global and local features).
Regarding dependent claim 22, Quattrini in view of Rana and Yu teaches:
Aperiodic information processor is configured to identify local features based on spatially localized feature variations independent of periodic basis functions. (See Quattrini Page 1720 Right Column Paragraph 2, wherein the local features are identified based on 3D convolutions and spatially localized features variations (localized volumetric information) independently of periodic basis functions).
Regarding dependent claim 23, Quattrini in view of Rana and Yu teaches:
The periodic information processor is configured to translate at least a portion of the first embedding space into a frequency domain and identify global features based on frequency-domain representations and the aperiodic information processor is configured to identify local features without translating the first embedding space into the frequency domain. (See Quattrini Page 1720 Right Column Paragraph 2, wherein the input tensor is divided into chunks each of which is provided to a local and global branch respectively, wherein the global branch maps its input into the spectral domain to model global information and wherein the completely separate local branch uses 3D convolutions to model volumetric information without translating into the frequency domain).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Quattrini; Fabio et al. (Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyri; hereinafter simply referred to as Quattrini) in view of Rana; Saadia et al. (Advance Approach to Estimate Fingerprint Images Quality Using Statistical & Graphical Methodology; hereinafter simply referred to as Rana) further in view of Yu; Jiachuan et al. (Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules; hereinafter simply referred to as Yu) and further in view of Yin; Bangjie et al. (US 20230086552 A1; hereinafter simply referred to as Yin).
Regarding dependent claim 3, Quattrini in view of Rana and Yu 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 Quattrini in view of Rana and Yu 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 Quattrini in view of Rana and Yu 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.
Claims 8, 12, 13, 14, 15, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Quattrini; Fabio et al. (Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyri; hereinafter simply referred to as Quattrini) in view of Rana; Saadia et al. (Advance Approach to Estimate Fingerprint Images Quality Using Statistical & Graphical Methodology; hereinafter simply referred to as Rana) further in view of Yu; Jiachuan et al. (Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules; hereinafter simply referred to as Yu) and further in view of Xiao; Qiang et al. (US 20240242114 A1; hereinafter simply referred to as Xiao).
Regarding independent claim 8, Quattrini teaches:
Classifying an input dataset (See Page 1721 Left Column Paragraph 2, Figure 4, Page 1719 Right Column Paragraph 1, wherein the input dataset (scanned documents), is classified)
embedding the input dataset into a first embedding space (See Page 1720 Right Column Paragraph 2, wherein the input data set is embedded into a first embedding space, being tensor “X”)
inputting the first embedding space into a spectral module (See Page 1719 Left Column Paragraph 3, Page 1719 Right Column Final Paragraph, See Page 1720 Right Column Paragraph 2, and Figure 2, wherein the first embedding space, Tensor “X”, is input into a spectral module, Volumetric FFC (Fast Fourier Convolutions))
identifying global features in the input dataset based on a first subset of the first embedding space and identifying first local features in the input dataset, based on a second subset of the first embedding space, wherein the first subset and the second subset are different (See Page 1720 wherein a global branch and a local branch take in different separate “chunks” of the tensor (first and second subset of embedding space) and identify global and local features respectively (global and local volumetric information))
and combining the global features and the first local features into a dataset of classified features of the input dataset. (See Page 1721 Left Column Paragraph 1 and Page 1720 Right Column Final Paragraph wherein the global features and first local features (output of global and local branches) are combined into a dataset of classified features of the input dataset (final output vFFC Tensor)).
Quattrini does not explicitly disclose a spectral module including a periodic information processor and using the periodic information processor to identify global features.
However, Rana teaches of a spectral module including a periodic information processor and using the periodic information processor to identify global features. (See Page 3 Left Column Paragraph 1, wherein a spectral module uses a periodic information processor (method based on Fourier spectrum) which identifies a global measure (global feature) corresponding to a spectral measure which identifies or discriminates periodic texture/features).
As taught by Rana using periodic information to identify global features allows for the global features to indicate the layout of alternating periodic ridges and valley patterns used in the analysis of fingerprint images. (See Page 3 Right Column Paragraphs 2 and 3 wherein the global features create an effective indication for the layout of periodic features in a fingerprint image). As both the teachings of Quattrini and Rana deal with the technical field of image processing regarding the extraction of image features, 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 Quattrini with Rana to teach of a spectral module including a periodic information processor and using the periodic information processor to identify global features in order for effective analysis to be made on the fingerprint images.
Quattrini in view of Rana does not explicitly disclose a spectral module including an aperiodic information processor and using the aperiodic information processor to identify local features.
However, Yu teaches of a spectral module including an aperiodic information processor and using the aperiodic information processor to identify local features. (See Section 2 Paragraph 1, Section 2.1 Paragraph 3, Section 2.2.1 Second and third Paragraph, wherein Local features are detected in a method comprising the detection of defects/non-periodic features/information using Fourier spectrum filtering).
As taught by Yu using the aperiodic information to identify local features allows for the image to be separated and categorized into periodic and non-periodic features. (See Section 2.2.1, second to last paragraph, Section 2.1 Paragraph 3, wherein the image can be split up into periodic and non-periodic image features after the capturing of local image features). As both the teachings of Quattrini in view of Rana and Yu deal with the technical field of image processing regarding the extraction of image features 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 Quattrini in view of Rana with Yu to teach of a spectral module including an aperiodic information processor and using the aperiodic information processor to identify local features in order for the image to be categorized into periodic and non-periodic image features.
Quattrini in view of Rana and Yu does not explicitly disclose the use of multiple processors and an output interface used in the operations to execute the computing system for classifying an input dataset.
However, Xiao teaches of a computing system comprising a plurality of processors and an output interface used in the operations to execute the computing system for classifying an input dataset. (See ¶ 14 – 24, 29, 33, 40 – 43, 56, 90 – 93, 99, wherein a plurality of processors of the processing device ‘902’ in figure 9, are a part of a computing system ‘100’ in figure 1, used to perform operations such as embedding an input dataset, extracting global and local features from an image, inputting data into a neural network/spectral module, and an output interface for viewing outputs such as combined feature data).
As taught by Xiao using processors to implement a computing system allows for the computing system to have programmable instructions that can be executed by different types of processing devices. (See ¶ 90 and 99 wherein a processing device can carry out computer programs to carry out a computer implemented method). As both the teachings of Quattrini in view of Rana and Yu deal with the technical field of image processing regarding the extraction of global and local 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 Quattrini in view of Rana and Yu with Xiao to teach of a computing system comprising a plurality of processors and an output interface used in the operations to execute the computing system for classifying an input dataset in order to allow for the computing system to have programmable instructions that can be executed by different types of processing devices.
Regarding dependent claim 12, Quattrini in view of Rana, Yu and Xiao teaches:
The aperiodic information processor identifies local features in the input dataset using at least one convolutional operator. (See Quattrini Page 1720 wherein the local features in the input dataset are identified using convolutional layers (at least one convolutional operator)).
Regarding dependent claim 13, Quattrini in view of Rana, Yu and Xiao teaches:
The input dataset is an image. (See Quattrini Page 1719 Left Column Last 2 Paragraphs, and 1719 Right Column paragraphs 1 – 3 wherein the input dataset is an image).
Regarding dependent claim 14, Quattrini in view of Rana, Yu, and Xiao teaches:
The first subset and the second subset combine to yield the input dataset. (See Quattrini Page 1720 Right Column Paragraph 2, wherein the first and second subset are two chunks of the tensor “X”, that when combined would provide the tensor “X” representing the input dataset).
Regarding independent claim 15, claim 15 is a tangible processor-readable storage media claim corresponding to claim 8. Please see the discussion of claim 8 above. Furthermore, Xiao 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 ¶ 99 wherein a non-transitory machine readable storage medium contains executable programs/instructions executable by processors to implement the classifying process, furthermore please see the discussion of claim 8 above).
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, Xiao 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 ¶ 99 wherein a non-transitory machine readable storage medium contains executable programs/instructions executable by processors to implement the classifying process, furthermore please see the discussion of claim 12 above).
Regarding dependent claim 20, Quattrini in view of Rana, Yu, and Xiao teaches:
The first subset and the second subset are mutually exclusive (See Quattrini Page 1720 Right Column Paragraph 2, wherein the first subset and second subset (two separate chunks of tensor “X”) are mutually exclusive as they are separated and given to different branches being the global and local branch)
and combine to yield the input dataset. (See Quattrini Page 1720 Right Column Paragraph 2, wherein the first and second subset are two chunks of the tensor “X”, that when combined would provide the tensor “X” representing the input dataset)
Claims 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Quattrini; Fabio et al. (Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyri; hereinafter simply referred to as Quattrini) in view of Rana; Saadia et al. (Advance Approach to Estimate Fingerprint Images Quality Using Statistical & Graphical Methodology; hereinafter simply referred to as Rana) further in view of Yu; Jiachuan et al. (Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules; hereinafter simply referred to as Yu) and further in view of Xiao; Qiang et al. (US 20240242114 A1; hereinafter simply referred to as Xiao) and further in view of Yin; Bangjie et al. (US 20230086552 A1; hereinafter simply referred to as Yin).
Regarding dependent claim 10, Quattrini in view of Rana, Yu and Xiao 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 Quattrini in view of Rana, Yu and Xiao 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 Quattrini in view of Rana, Yu and Xiao 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 17, claim 17 is a tangible processor-readable storage media claim corresponding to claim 10. Please see the discussion of claim 10 above. Furthermore, Xiao 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 ¶ 99 wherein a non-transitory machine readable storage medium contains executable programs/instructions executable by processors to implement the classifying process, furthermore please see the discussion of claim 10 above).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEJANDRO HERNANDEZ whose telephone number is (703)756-1876. The examiner can normally be reached M-F 8 am - 5 pm ET.
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/ALEJANDRO HERNANDEZ/Examiner, Art Unit 2661
/AARON W CARTER/Primary Examiner, Art Unit 2661
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