DETAILED ACTION
1. This communication is in response to the amendments filed on March 25, 2026 for Application No. 18/084,140 in which Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
2. 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 Arguments
3. The amendments filed on March 25, 2026 have been considered. Claims 1, 10-13, 15, 17, and 20 have been amended. Thus, Claims 1-20 are pending and presented for examination.
4. Applicant’s arguments filed March 25, 2026 with respect to the 35 U.S.C. 112(b) rejection have been fully considered and are persuasive. Thus, the 35 U.S.C. 112(b) rejection has been withdrawn.
5. Applicant's arguments filed March 25, 2026 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant’s Arguments on Pgs. 8-10 of Arguments/Remarks state:
“Claim 1 has been amended to recite "applying, by the processing device, one or more image processing operations to the input image to generate a modified image, wherein the one or more image processing operations are selected based on the identified type of source." Without conceding the propriety of the rejection as applied to the original claims, Applicant submits that claim 1 as amended is patent eligible.
At Step 2A Prong 1, the Examiner has alleged that the "identifying" step recites a mental process. However, claim 1 as amended recites processing images through two separate neural networks executed by a processing device to obtain feature vectors, and then using those feature vectors to identify the source type. These steps cannot practically be performed in the human mind. As stated in MPEP § 2106.04(a)(2)(III)(A), "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." The USPTO's Example 39 (Method for Training a Neural Network for Facial Detection) is instructive. In Example 39, the USPTO found that a claim involving neural network processing of images "does not recite a mental process because the steps are not practically performed in the human mind." The Examiner's assertion that a human could observe an image and determine its source type ignores the specific claimed mechanism-using first and second neural networks to generate feature vectors, and then using those feature vectors to identify the source type. A human mind is not equipped to execute neural network computations on images.
Even assuming arguendo that the "identifying" step recites an abstract idea, claim 1, as amended, integrates the exception into a practical application at Step 2A Prong 2. The amended claim recites "applying, by the processing device, one or more image processing operations to the input image to generate a modified image, wherein the one or more image processing operations are selected based on the identified type of source." This reflects a concrete improvement to image processing technology. As described in Applicant's specification, "the techniques that work well for photographed images may be suboptimal for scanned documents (and vice versa), and applying such techniques to documents of a wrong source type may even be detrimental for successful content extraction." (Specification, par. [0019].) The amended claim uses the source type classification to select appropriate processing operations, thereby improving the quality of image processing-this is not merely outputting a classification result, but applying it to transform the input image.
This is analogous to USPTO Example 47, Claim 3 (Anomaly Detection), which was found eligible because the additional steps of dropping malicious packets and blocking traffic integrated the abstract idea of anomaly detection into a practical application by improving network security. Similarly, amended claim 1 uses the source type identification to select and apply processing operations that generate a modified image-a concrete improvement to image processing technology.
For similar reasons, the amended claim provides significantly more than any alleged abstract idea at Step 2B. The combination of neural network-based source type classification with source-type-dependent image processing operations represents a specific, unconventional approach to improving image processing quality.
The dependent claims recite additional features that further confirm eligibility. For example, claim 12 further specifies that the one or more image processing operations comprise one or more computer vision operations, demonstrating that the modified image is used for downstream computer vision processing.
Independent claims 13 and 17 are eligible for similar reasons. Claim 13 recites a metadata-based classification method using a trained metadata classifier-a specific technical process that cannot be performed in the human mind. Claim 17 recites a system with a memory and processing device that performs the same source type classification and is eligible for the same reasons as amended claim 1.
For at least these reasons, Applicant respectfully requests reconsideration and withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 101.”
Examiner respectfully disagrees. At Step 2A Prong 1, the limitation “identifying, using the first feature vector and the second feature vector, a type of source used to generate the IPO input” may still be practically performed by mental process – a human user is capable of observing/analyzing already obtained first and second feature vectors and accordingly using judgement/evaluation to identify a type of source used to generate the IPO input (input image). For example, a user may observe/analyze the already obtained first and second feature vectors (which may comprise visual features extracted from the input image) and the according input image itself and utilize judgement/evaluation to identify a type of source (camera, scanner, synthetic, etc.) that was used to generate the input image based on said analysis. There is no technical language presented by the currently drafted claim language which precludes the “identifying” step from being practically performed by mental process – a human user is clearly able to observe/analyze features of an image to identify whether said image was generated by a camera, scanner, or if the image was synthetically generated. Further, at Step 2A Prong 2 and Step 2B, the limitations “processing, by a processing device executing a first neural network, a first image associated with the IPO input to obtain a first feature vector” and “processing, by the processing device executing a second NN, a plurality of second images associated with the IPO input to obtain a second feature vector” amount to merely adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). The training/configuration/architecture of the first and second neural networks are not detailed by the currently drafted claim language – the models are simply “executed” without significantly more. This amounts to merely “applying” or utilizing black-box or pre-configured neural networks to perform the specific limitations of the claim without significantly more. This cannot provide an inventive concept.
Moreover, regarding the comparison of the instant claims to Example 39, Examiner asserts that the instant claims are not analogous to Example 39. Example 39 recites applying one or more transformations to digital facial images including mirroring, rotating, smoothing, or contrast reduction, alongside creating a first training set comprising digital facial images – a human user is not capable of applying transformations to digital facial images and creating a training set of digital facial images by mental process alone. On the contrary, the “identifying” step of the instant claims may be practically performed by mental process, as described by Examiner above.
Furthermore, the newly added limitation “applying, by the processing device, one or more image processing operations to the input image to generate a modified image, wherein the one or more image processing operations are selected based on the identified type of source” may similarly be considered mental process at Step 2A Prong 1. A human user is capable of observing/analyzing the input image and identified type of source and accordingly using judgement/evaluation to determine an image processing operation (cropping, resizing, etc.) to apply to the input image, with the aid of pen and paper, and accordingly generate a modified image. The currently drafted claim language is very broad/generic – the “image processing operations” are not further detailed/described and are not explicitly limited to technical processes other than the mere recitation of a generic “processing device” which carries out the operations without significantly more. Applicant states that the mere addition of this limitation provides a concrete improvement to image processing technology, however, this supposed improvement is not reflected by the currently drafted claim language, which is still very broad/generic, as mentioned above. Again, the instant claims are not comparable to the USPTO Example 47 – even though Applicant states that the claims provide an improvement, this improvement is not reflected into the claim language, as the claim merely “processes” a plurality of images by simply “executing” black-box neural networks without significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This applies to Claims 1-20.
Thus, the 35 U.S.C. 101 rejection is maintained.
6. Applicant’s arguments filed March 25, 2026 with respect to the 35 U.S.C. 103 rejection have been fully considered and are persuasive. Thus, the 35 U.S.C. 103 rejection has been withdrawn.
Claim Rejections - 35 USC § 101
7. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
8. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1:
Step 1: Claim 1 is a method type claim. Therefore, Claims 1-12 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
identifying, using the first feature vector and the second feature vector, a type of source used to generate the IPO input (mental process – identifying a type of source used to generate the image processing operation input may be performed manually by a user observing/analyzing the image processing operation input (an image, as supported by Applicant’s specification Par. [0005]) and the according first and second feature vectors and accordingly using judgement/evaluation to determine a type of source (camera-acquired image, scanning device-acquired image, or synthetic image, as supported by dependent claim 9) used to generate the IPO input/image. With reference to instant Figure 5A, for example, a human user is capable of observing/analyzing an image and identifying whether said image is generated synthetically or by a camera or scanning device)
applying, by the processing device, one or more image processing operations to the input image to generate a modified image, wherein the one or more image processing operations are selected based on the identified type of source (mental process – other than reciting “by the processing device”, applying one or more image processing operations to the input image to generate a modified image may be performed manually by a user observing/analyzing the input image and accordingly using judgement/evaluation to apply one or more image processing operations (cropping, resizing, segmentation – circling/separating regions of interest, etc.) to the input image (with the aid of pen and paper) to generate a modified image, where the user chooses the operation to apply based on the identified type of source (cropping – scanned images, segmentation – camera acquired images, etc.))
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
obtaining an input into an image processing operation (IPO input), wherein the IPO input comprises an input image (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
[…] by a processing device […] (recited at a high-level of generality (i.e., as a generic processing device) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
processing, by a processing device executing a first neural network (NN), a first image associated with the IPO input to obtain a first feature vector (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a generic black-box first neural network to a first image, in order to merely obtain a first feature vector without significantly more)
processing, by a processing device executing a second NN, a plurality of second images associated with the IPO input to obtain a second feature vector (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a generic black-box second neural network to a plurality of second images, in order to merely obtain a second feature vector without significantly more)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining an input into an image processing operation (IPO input), wherein the IPO input comprises an input image (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] by a processing device […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
processing, by a processing device executing a first neural network (NN), a first image associated with the IPO input to obtain a first feature vector (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a generic black-box first neural network to a first image, in order to merely obtain a first feature vector without significantly more. This cannot provide an inventive concept)
processing, by a processing device executing a second NN, a plurality of second images associated with the IPO input to obtain a second feature vector (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a generic black-box second neural network to a plurality of second images, in order to merely obtain a second feature vector without significantly more. This cannot provide an inventive concept)
For the reasons above, Claim 1 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 2-12. The additional limitations of the dependent claims are addressed below.
Regarding Claim 2:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 2 depends on.
identifying, using the plurality of probabilities, the type of source used to generate the IPO input (mental process – identifying a type of source used to generate the IPO input may be performed manually by a user observing/analyzing the image processing operation input (an image, as supported by Applicant’s specification Par. [0005]) and the plurality of probabilities and accordingly using judgement/evaluation to determine a type of source (camera-acquired image, scanning device-acquired image, or synthetic image, as supported by dependent claim 9) used to generate the IPO input/image. With reference to instant Figure 5A, for example, a human user is capable of observing/analyzing an image and identifying whether said image is generated synthetically or by a camera or scanning device)
Step 2A Prong 2 & Step 2B:
obtaining a combined feature vector comprising the first feature vector and second feature vector (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
processing, using a third NN, the combined feature vector to generate a plurality of probabilities, wherein each of the plurality of probabilities characterizes a likelihood that the IPO input is associated with a respective image source type of a plurality of image source types (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a generic black-box third neural network to a combined feature vector to generate a plurality of probabilities without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 3:
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 3 depends on.
Step 2A Prong 2 & Step 2B:
wherein the third NN comprises one or more fully-connected layers of neurons, and wherein each of the first NN and the second NN comprises one or more convolutional layers of neurons (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the third NN comprises one or more fully-connected layers of neurons and each of the first/second NN comprises one or more convolutional layers of neurons does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 4:
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 4 depends on.
Step 2A Prong 2 & Step 2B:
wherein at least one of the first NN, the second NN, or the third NN is trained using a neuron dropout technique (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a first/second/third neural network using a neuron dropout technique without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 5:
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 5 depends on.
Step 2A Prong 2 & Step 2B:
wherein at least one of the first NN, the second NN, or the third NN is trained using a variable learning rate (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a first/second/third neural network using a variable learning rate without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 6:
Step 2A Prong 1:
See the rejection of Claim 2 above, which Claim 6 depends on.
Step 2A Prong 2 & Step 2B:
wherein the first NN, the second NN, and the third NN are trained concurrently using a common set of training inputs (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a first/second/third neural network concurrently with a common set of training inputs without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 7:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 7 depends on.
Step 2A Prong 2 & Step 2B:
wherein at least one of the first NN or the second NN comprises a MobileNetV3 neuron architecture (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that at least one of the first or second neural networks comprises a MobileNetV3 neuron architecture does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 8:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 8 depends on.
Step 2A Prong 2 & Step 2B:
wherein the second feature vector comprises a plurality of sub-vectors, wherein each of the plurality of sub-vectors is obtained by processing, using the second NN, a respective second image of the plurality of second images (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the second feature vector comprises a plurality of sub-vectors and wherein each of the plurality of sub-vectors is obtained by processing a second image using the second neural network does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 9:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 9 depends on.
Step 2A Prong 2 & Step 2B:
wherein the identified type of source used to generate the IPO input is selected from a set of classes, wherein the set of classes comprises at least two of: a camera-acquired image class, a scanning device-acquired image class, or a synthetic image class (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the identified type of source is selected from a set of classes comprising at least two of a camera-acquired image class, a scanning device-acquired image class, or a synthetic image class does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 10:
Step 2A Prong 1: See the rejection of Claim 1 above, which Claim 10 depends on.
generating, using the metadata, a metadata feature vector (mental process – generating a metadata feature vector may be performed manually by a user observing/analyzing the metadata and accordingly using judgement/evaluation to generate a feature vector comprising the observed metadata (with the aid of pen and paper))
[…] generate a plurality of probabilities, wherein each of the plurality of probabilities characterizes a likelihood that the IPO input is associated with a respective image source type of a plurality of image source types (mental process – generating a plurality of probabilities may be performed manually by a user observing/analyzing the metadata feature vector and accordingly using judgement/evaluation to generate a plurality of probabilities relating to a likelihood that the IPO input is associated with a respective image source type of a plurality. Alternatively, generating a plurality of probabilities may be performed by mathematical process utilizing simple formulas for generating an empirical or classical probabilities (i.e., a ratio of favorable outcomes to a total number of outcomes))
determining that the plurality of probabilities fails to satisfy a confidence criterion (mental process – determining that the plurality of probabilities fails to satisfy a confidence criterion may be performed manually by a user observing/analyzing the plurality of probabilities against a predetermined confidence criterion and accordingly using judgement/evaluation to determine that the plurality of probabilities fails to satisfy/meet said criterion)
Step 2A Prong 2 & Step 2B:
obtaining metadata associated with the IPO input (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
processing, using a metadata classifier, the metadata feature vector […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying an already configured/generically trained black box “metadata classifier” to a metadata feature vector to generate a plurality of probabilities without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 11:
Step 2A Prong 1:
See the rejection of Claim 1 above, which Claim 11 depends on.
Step 2A Prong 2 & Step 2B:
rescaling the input image to obtain the first image (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
cropping the plurality of second images from the input image (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 12:
Step 2A Prong 1:
See the rejection of Claim 11 above, which Claim 12 depends on.
Step 2A Prong 2 & Step 2B:
wherein the one or more image processing operations comprise: one or more computer vision operations (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the one or more image processing operations comprise one or more computer vision algorithms does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 1.
Regarding Claim 13:
Step 1: Claim 1 is a method claim. Therefore, Claims 13-16 are directed to either a process, machine, manufacture, or composition of matter.
2A Prong 1: If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by mathematical calculation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas.
generating, by a processing device and using the metadata, a metadata feature vector (mental process – other than reciting “by a processing device”, generating a metadata feature vector may be performed manually by a user observing/analyzing the metadata and accordingly using judgement/evaluation to generate a feature vector comprising the observed metadata (with the aid of pen and paper))
[…] generate a plurality of probabilities, wherein each of the plurality of probabilities characterizes a likelihood that the input image is associated with a respective image source type of a plurality of image source types (mental process – generating a plurality of probabilities may be performed manually by a user observing/analyzing the metadata feature vector and accordingly using judgement/evaluation to generate a plurality of probabilities relating to a likelihood that the input image is associated with a respective image source type of a plurality. Alternatively, generating a plurality of probabilities may be performed by mathematical process utilizing simple formulas for generating an empirical or classical probabilities (i.e., a ratio of favorable outcomes to a total number of outcomes))
identifying, using the plurality of probabilities, a type of source used to generate the input image (mental process – identifying a type of source used to generate the input image may be performed manually by a user observing/analyzing the input image and the plurality of probabilities and accordingly using judgement/evaluation to determine a type of source (camera-acquired image, scanning device-acquired image, or synthetic image, as supported by Applicant’s specification Par. [0077]) used to generate the input image. With reference to instant Figure 5A, for example, a human user is capable of observing/analyzing an image and identifying whether said image is generated synthetically or by a camera or scanning device)
applying, by the processing device, one or more image processing operations to the input image to generate a modified image, wherein the one or more image processing operations are selected based on the identified type of source (mental process – other than reciting “by the processing device”, applying one or more image processing operations to the input image to generate a modified image may be performed manually by a user observing/analyzing the input image and accordingly using judgement/evaluation to apply one or more image processing operations (cropping, resizing, segmentation – circling/separating regions of interest, etc.) to the input image (with the aid of pen and paper) to generate a modified image, where the user chooses the operation to apply based on the identified type of source (cropping – scanned images, segmentation – camera acquired images, etc.))
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
obtaining an input image and a metadata associated with the input image (Adding insignificant extra-solution activity to the judicial exception – see MPEP 2106.05(g))
[…] by a processing device […] (recited at a high-level of generality (i.e., as a generic processing device without significantly more) such that it amounts to no more than mere instructions to apply the exception using generic computer components)
processing, by the processing device and using a trained metadata classifier, the metadata feature vector […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying an already configured/generically trained black box “metadata classifier” to a metadata feature vector to generate a plurality of probabilities without significantly more)
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
obtaining an input image and a metadata associated with the input image (MPEP 2106.05(d)(II) indicates that merely “Receiving or transmitting data over a network” is a well-understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed limitation is well-understood, routine, conventional activity is supported under Berkheimer)
[…] by a processing device […] (mere instructions to apply the exception using generic computer components cannot provide an inventive concept)
processing, by the processing device and using a trained metadata classifier, the metadata feature vector […] (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying an already configured/generically trained black box “metadata classifier” to a metadata feature vector to generate a plurality of probabilities without significantly more. This cannot provide an inventive concept)
For the reasons above, Claim 13 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 14-16. The additional limitations of the dependent claims are addressed below.
Regarding Claim 14:
Step 2A Prong 1: See the rejection of Claim 13 above, which Claim 14 depends on.
responsive to the plurality of probabilities satisfying a confidence criterion, identifying, from the plurality of image source type, an image source type associated with a maximum probability of the plurality of probabilities (mental process – identifying an image source type associated with a maximum probability may be performed manually by a user observing/analyzing the plurality of probabilities and accordingly using judgement/evaluation to identify an image source type associated with a maximum probability of the plurality of probabilities)
Step 2A Prong 2 & Step 2B:
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 13.
Regarding Claim 15:
Step 2A Prong 1: See the rejection of Claim 13 above, which Claim 15 depends on.
responsive to the plurality of probabilities not satisfying a confidence criterion, generating, using the input image, a first image and a plurality of second images (mental process – generating a first image and a plurality of second images may be performed manually by a user observing/analyzing the probabilities against the confidence criterion and the input image and accordingly using judgement/evaluation to generate a first image and plurality of second images, with the aid of pen and paper)
identifying, using the first feature vector and the second feature vector, the type of source used to generate the input image (mental process – identifying a type of source used to generate the image processing operation input may be performed manually by a user observing/analyzing the image processing operation input (an image, as supported by Applicant’s specification Par. [0005]) and the according first and second feature vectors and accordingly using judgement/evaluation to determine a type of source (camera-acquired image, scanning device-acquired image, or synthetic image, as supported by dependent claim 9) used to generate the IPO input/image. With reference to instant Figure 5A, for example, a human user is capable of observing/analyzing an image and identifying whether said image is generated synthetically or by a camera or scanning device)
Step 2A Prong 2 & Step 2B:
processing, using a first neural network (NN), the first image to obtain a first feature vector (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a generic black-box first neural network to a first image, in order to merely obtain a first feature vector without significantly more. This cannot provide an inventive concept)
processing, using a second NN, a plurality of second images to obtain a second feature vector (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of applying a generic black-box second neural network to a plurality of second images, in order to merely obtain a second feature vector without significantly more. This cannot provide an inventive concept)
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 13.
Regarding Claim 16:
Step 2A Prong 1:
See the rejection of Claim 13 above, which Claim 16 depends on.
Step 2A Prong 2 & Step 2B:
wherein the trained metadata classifier comprises one or more decision trees trained using a gradient boosting algorithm (Field of Use – limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application; in this case specifying that the trained metadata classifier comprises one or more decision trees trained using a gradient boosting algorithm does not integrate the exception into a practical application nor amount to significantly more – See MPEP 2106.05(h))
Accordingly, under Step 2A Prong 2 and Step 2B, these additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limits on practicing the abstract idea, as discussed above in the rejection of claim 13.
Independent Claim 17 recites substantially the same limitations as Claim 1, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
For the reasons above, Claim 17 is rejected as being directed to an abstract idea without significantly more. This rejection applies equally to dependent claims 18-20. The additional limitations of the dependent claims are addressed below.
Claim 18 recites substantially the same limitations as Claim 2, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 19 recites substantially the same limitations as Claim 9, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Claim 20 recites substantially the same limitations as Claim 10, in the form of a system, including generic computer components. The claim is also directed to performing mental processes without significantly more, therefore it is rejected under the same rationale.
Allowable Subject Matter
9. No prior art rejection is made for Claims 1-20. However, these claims are rejected under 35 U.S.C. 101 – abstract idea.
10. Alongside the previously disclosed Mayer reference of record (US PG-PUB 20220198711), Examiner has disclosed Cozzolino et al. (“Noiseprint: a CNN-based camera model fingerprint”), Bondi et al. (“First Steps Toward Camera Model Identification with Convolutional Neural Networks”), and Yang et al. (“Source Camera Identification Based on Content-Adaptive Fusion Network”). Cozzolino discloses the use of a Siamese network, trained with pairs of image patches coming from the same or different cameras, in order to extract a camera model fingerprint. Bondi discloses the use of convolutional neural networks which learn features characterizing camera models directly from acquired pictures. Yang discloses three parallel content-adaptive convolutional neural networks which may be utilized for source camera identification of small-size images. However, the aforementioned prior art references seemingly do not disclose the specific limitations of Independent Claims 1 and 17 including “processing, by the processing device executing a second NN, a plurality of second images associated with the IPO input to obtain a second feature vector; identifying, using the first feature vector and the second feature vector, a type of source used to generate the IPO input; and applying, by the processing device, one or more image processing operations to the input image to generate a modified image, wherein the one or more image processing operations are selected based on the identified type of source” and the specific limitations of Independent Claim 13 including “processing, by the processing device and using a trained metadata classifier, the metadata feature vector to generate a plurality of probabilities, wherein each of the plurality of probabilities characterizes a likelihood that the input image is associated with a respective image source type of a plurality of image source types; and identifying, using the plurality of probabilities, a type of source used to generate the input image; and applying, by the processing device, one or more image processing operations to the input image to generate a modified image, wherein the one or more image processing operations are selected based on the identified type of source” in combination with the remaining limitations of the Independent claims.
Conclusion
11. THIS ACTION IS MADE FINAL. 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.
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571)270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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