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 Arguments
Regarding the arguments against the rejection of claims under 35 USC 101, the Examiner respectfully disagrees. Applicant argues that the claims do not recite certain methods of organizing human activity, as the claims recite specific technical improvements to neural network architecture that integrates any abstract idea into a practical application. Examiner asserts that the use of the trained and “modified” BDNN as claimed is generated and implemented in a generic manner which recites mere computer implementation of the abstract idea as noted in the rejection. The limitations of modifying a deep learning architecture to generate a BDNN by inserting a Dense layer followed by a dropout layer still recites using a BDNN as a tool to carry out the abstract idea. There is no specific implementation or integration of this BDNN to carry out the abstract idea as noted in [0064] of the Applicant’s Specification as the use of the BDNN contributes only nominally to the execution of the abstract idea, see MPEP 2106.05(b).
As noted in [0004], [0064] and [0076] for the use of the BDNN to model uncertainty in predictions, this neural network is used merely as a tool that contributes nominally to perform this part of the abstract idea.
As noted in the rejection below, the data inputs for the BDNN recites generic training steps of the model, and where the processing and classification of the imaging data recites the abstract idea using the BDNN as a tool. Looking at [0021], there is no indication of a particular implementation of the BDNN to demonstrate the improvement of the interobserver agreement, high accuracy determinations of histologic assessments, and uncertainty determinations as the use of the BDNN contributes only nominally to the execution of the abstract idea.
Applicant further argues that the present claims are analogous to USPTO Example 47 in that both demonstrate improvements to deep learning. Examiner further asserts that Example 47 reciting an improvement of dropping malicious network packets and blocking future traffic recite additional elements that perform technology improvements and a practical application. The present claims however recite improvements to the assessments of the histological images, which is a part of the abstract idea and not a technology improvement. Again, the specific modification to the NN architecture contributes only nominally to the execution of this abstract idea.
Further, the use of the example of “Trading Technologies Int’l v. IBG” was meant to demonstrate that the use of generic computing components to make improvements to the abstract idea does not recite a technology improvement nor a practical application. In view of the current amendments, the rejection under 35 USC 101 is updated below.
Claim Rejections - 35 USC § 101
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.
Claims 1, 3-4, 6-7, 15, 17-18, and 20-31 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
It is appropriate for the Examiner to determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance to the Subject Matter Eligibility Test as recited in the following Steps: 1, 2A, and 2B, see MPEP 2106(III.).
Patent Subject Matter Eligibility Test: Step 1:
First, the Examiner is to establish whether the claim falls within any statutory category including a process, a machine, manufacture, or composition of matter, see MPEP 2106.03(II.) and MPEP 2106.03(I).
The claims recite a method, a system, and a “non-transitory” computer storage media. Accordingly, the claims are all within at least one of the four statutory categories.
Patent Subject Matter Eligibility Test: Step 2A- Prong One:
Step 2A of the Subject Matter Eligibility Test demonstrates whether a clam is directed to a judicial exception, see MPEP 2106.04(I.). Step 2A is a two-prong inquiry, where Prong One establishes the judicial exception. Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes, see MPEP 2106.04(II.)(A.)(1.) and 2106.04(a)(2).
Representative independent claim 1 includes limitations that recite at least one abstract idea as underlined in the following limitations. Specifically, independent claim 1 recites:
A system for histological grading predictions for inflammatory bowel disease (IBD), the system comprising:
at least one processor; and
one or more computer storage media storing computer executable instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising:
digitizing one or more slides to generate whole slide images from biopsy samples of one or more patients having IBD;
compiling a training data set of histopathology images from the digitized whole slide images;
accessing the training data set;
modifying a deep learning architecture by inserting a Dense layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), wherein the BDNN is configured to model uncertainty in predictions;
training the BDNN with the training data set to generate a trained BDNN;
processing, via the trained BDNN, a histopathology image of a target patient with IBD;
utilizing the trained BDNN to classify the histopathology image of the target patient with IBD; and
providing, based on the classification of the histopathology image of the target patient, a histologic assessment determination associated with an IBD severity for display via a user interface.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity”, more specifically managing interactions between people as the following abstract limitations recite histological grading predictions for inflammatory bowel disease for a patient:
“model” uncertainty in predictions, which is an abstract limitation related to analysis of predictions,
“processing” the histopathology images of a target patient with IBD, which is an abstract limitation related to an analysis of the image of the target patient,
“classify” the histopathology image of the target patient with IBD, which is an abstract limitation of analysis of the image for the histologic assessment,
“providing”, based on the classification of the histopathology image of the target patient, a histologic assessment determination associated with an IBD severity, which is an abstract limitation for performing histological grading predictions for inflammatory bowel disease and is related to the management of the care of the target patient.
The claim limitations as a whole recite steps for providing, based on processing and classifying the histopathology image of the target patient, a histologic assessment determination associated with an IBD severity, which recites social activity steps for the management of the health of the patient and therefore recite managing interactions between people and is a certain method of organizing human activity.
Additionally, claim 15 recites:
One or more non-transitory computer storage media having computer-executable instructions embodied thereon, that when executed by at least one processor, cause operations comprising:
digitizing whole slide images from biopsy samples of one or more patients having inflammatory bowel disease (IBD) and taken by one or more sensors;
compiling a training data set of histopathology images from the digitized whole slide images;
accessing the training data set;
modifying a deep learning architecture by inserting a Dense layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), wherein the BDNN is configured to model uncertainty in predictions;
training the BDNN with the training data set to generate a trained BDNN;
processing, via the trained BDNN, a histopathology image of a target patient with IBD;
utilizing the trained BDNN to determine a diagnosis of the target patient with IBD based on the histopathology image; and
providing, based on the diagnosis, a histologic assessment determination associated with an IBD severity for display via a user interface.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity”, more specifically managing interactions between people as the following abstract limitations recite histological assessment determinations for a patient’s inflammatory bowel disease:
“model” uncertainty in predictions, which is an abstract limitation related to analysis of predictions,
“processing” the histopathology images of a target patient with IBD, which is an abstract limitation related to an analysis of the image of the target patient,
“determine” a diagnosis of the target patient with IBD based on the histopathology image, which is an abstract limitation of analysis of the image for the assessment of the patient,
“providing”, based on the diagnosis, a histologic assessment determination associated with an IBD severity, which is an abstract limitation for performing histological assessment for inflammatory bowel disease severity and is related to the management of the care of the target patient.
The claim limitations as a whole recite steps for providing, based on processing and determinations from the histopathology image of the target patient, a histologic assessment determination associated with an IBD severity, which recites social activity steps for the management of the health of the patient and therefore recite managing interactions between people and is a certain method of organizing human activity.
Additionally, claim 27 recites:
A method for histological grading predictions for inflammatory bowel disease (IBD), the method comprising:
digitizing whole slide images from biopsy samples of one or more patients having inflammatory bowel disease (IBD) and taken by one or more sensors;
compiling a training data set of histopathology images from the digitized whole slide images;
modifying a deep learning architecture by inserting a Dense Layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), wherein the BDNN is configured to model uncertainty in predictions;
training the BDNN on the training data set to generate a trained BDNN;
utilizing the trained BDNN to classify a histopathology image of a target patient with IBD;
based on the classification of the histopathology image of the target patient, determining a histologic assessment associated with an IVD severity of the target patient; and
determining a response based on the IBD severity of the target patient, wherein the response is displayed via a user interface.
The Examiner submits that the foregoing underlined limitations constitute “certain methods of organizing human activity”, more specifically managing interactions between people as the following abstract limitations recite histological grading predictions for inflammatory bowel disease for a patient:
“model” uncertainty in predictions, which is an abstract limitation related to analysis of predictions,
“classifying” a histopathology image, which is an abstract limitation of analysis of the image,
“determining” a histologic assessment associated with an IBD severity of a target patient based on the classification of histopathology image of the target patient, which is an abstract limitation of analysis of the images to make assessments for the health of the patient,
“determining” a response based on the IBD severity of the target patient, which is an abstract limitation for the further management of the IBD severity of the patient.
The claim limitations as a whole recite steps for histological grading predictions for inflammatory bowel disease, which recites social activity steps for the management of the health of the patient and therefore recite managing interactions between people and is a certain method of organizing human activity.
Any limitations not identified above as part of the abstract idea are deemed “additional elements” (i.e., processor) and will be discussed in further detail below.
Accordingly, the claim as a whole recites at least one abstract idea.
Furthermore, dependent claims further define the at least one abstract idea, and thus fails to make the abstract idea any less abstract as noted below:
Claims 3 and 17 recite additional abstract limitations of “determining” that the target patient is at risk for dysplasia based on the historical image and assessment, thus further describing the abstract idea. Claims 6 and 20 recite further abstract limitations of “identifying” patterns, thus further describing the abstract idea. Claim 29 recites further abstract limitation of “processing” the one or more whole slide images, where patterns are identified, which further describes the abstract idea.
Patent Subject Matter Eligibility Test: Step 2A- Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrates the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exceptions into a “practical application,” see MPEP 2106.04(II.)(A.)(2.) and 2106.04(d)(I.).
In the present case, the additional limitations beyond the above-noted at least one abstract idea are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
Regarding claim 1:
A system for histological grading predictions for inflammatory bowel disease (IBD), the system comprising:
at least one processor; and
one or more computer storage media storing computer executable instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
digitizing one or more slides to generate whole slide images from biopsy samples of one or more patients having IBD (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
compiling a training data set of histopathology images from the digitized whole slide images;
accessing the training data set (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
modifying a deep learning architecture by inserting a Dense layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), wherein the BDNN is configured to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) model uncertainty in predictions;
training the BDNN with the training data set to generate a trained BDNN;
processing, via the trained BDNN, a histopathology image of a target patient with IBD;
utilizing the trained BDNN to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) classify the histopathology image of the target patient with IBD; and
providing, based on the classification of the histopathology image of the target patient, a histologic assessment determination associated with an IBD severity for display via a user interface (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of the overall computing system with at least one processor and one or more computer storage media storing computer executable instructions that when executed by the at least one processor, cause the at least one processor to perform operations, digitizing one or more slides to generate whole slide images from biopsy samples of one or more patients having IBD, compiling a training data set of histopathology images from the digitized whole slide images, accessing the training data set, modifying a deep learning architecture by inserting a Dense layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), training the BDNN with the training data to generate a trained BDNN, use of the trained BDNN, and the use of a user interface, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0030, 0080, 0068] of Applicant’s Specification recites the use of generic computing components of the processor and memory for the overall generic computing system. [0025, 0027] recites the use of slides to generate a training data set for generating the trained model, however the use of the slides to generate the training data does not recite a specific implementation of the training data set to be used for the training and modification of the BDNN to demonstrate a technology improvement. [0076] recites the step of accessing a training data set that is used to train the BDNN as further recited in [0064]. [0076] recites the modifying of a deep learning architecture by inserting the dense and dropout layers, however these additional elements do not recite an integration with the abstract idea that would demonstrate a technological improvement or a practical application, as the use of the BDNN is used merely a tool to perform the abstract idea. [0068, 0073] recites the use of the trained BDNN model to perform steps. [0064] recites the use of the BDNN for modeling uncertainty and is used merely as a tool. [0058, 0077] recites the use of a generically configured UI to perform actions related to providing the determination. The additional elements recite the use of generic computing components and generic steps of training a neural network with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Regarding claim 27:
A method for histological grading predictions for inflammatory bowel disease (IBD), the method comprising (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)):
digitizing whole slide images from biopsy samples of one or more patients having inflammatory bowel disease (IBD) and taken by one or more sensors;
compiling a training data set of histopathology images from the digitized whole slide images (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
modifying a deep learning architecture by inserting a Dense Layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), wherein the BDNN is configured to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) model uncertainty in predictions;
training the BDNN on the training data set to generate a trained BDNN (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f));
utilizing the trained BDNN to (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)) classify a histopathology image of a target patient with IBD;
based on the classification of the histopathology image of the target patient, determining a histologic assessment associated with an IVD severity of the target patient; and
determining a response based on the IBD severity of the target patient, wherein the response is displayed via a user interface (amounts to nothing more than an instruction to apply the abstract idea using a generic computer as noted below, see MPEP 2106.05(f)).
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitation of the overall computer implemented method, digitizing whole slide images from biopsy samples of one or more patients having inflammatory bowel disease (IBD) and taken by one or more sensors, compiling a training data set of histopathology images from the digitized whole slide images, modifying a deep learning architecture by inserting a Dense Layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), training the BDNN with the training data set to generate a trained BDNN, use of the trained BDNN and use of a user interface, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f)). [0030, 0080, 0068] of Applicant’s Specification recites the use of generic computing components of the processor and memory for the overall computer implementation of the method. [0025, 0027] recites the use of slides to generate a training data set for generating the trained model. [0066] recites the use of sensors for generating the data for the training data set. However, the use of the slides and sensors to generate the training data does not recite a specific implementation of the training data set to be used for the training and modification of the BDNN to demonstrate a technology improvement. [0076] recites the step of accessing a training data set that is used to train the BDNN as further recited in [0064]. [0076] recites the modifying of a deep learning architecture by inserting the dense and dropout layers, however these additional elements do not recite an integration with the abstract idea that would demonstrate a technological improvement or a practical application, as the use of the BDNN is used merely a tool to perform the abstract idea. [0064] recites the use of the BDNN for modeling uncertainty and is used merely as a tool. [0068, 0073] recites the use of the trained BDNN model to perform steps. [0058, 0077] recites the use of a generically configured UI to perform actions related to providing the determination. The additional elements recite the use of generic computing components and generic steps of training a neural network with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer.
Looking at the additional limitations for each of the independent claims as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to providing a histologic assessment determination, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception, see MPEP 2106.04(d), 2106.05(a), 2106.05(b).
Claim 15 recites similar additional elements as claim 27 and is analyzed in a similar manner.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set below:
Claims 4 and 18 recite additional elements further describing the structure of the BDNN as inserting neural layers before the fully connected layer where the layers include a dense and dropout layer, however this is only being used as a tool where it is generically applied to the abstract idea without providing a technical improvement. Claims 6, 20, 21, 24, 29, 30 recite additional elements further describing the processing as performing patch-wise classification using Monte Carlo dropout, where this is a computer implemented algorithm that is only being used as a tool where it is generically applied to the abstract idea without providing a technical improvement. Claims 7 and 22 recites additional elements further describing the training of the BDNN as using a data augmentation technique on the historical images, where this is a computer implemented algorithm that is only being used as a tool where it is generically applied to the abstract idea without providing a technical improvement. Claims 23, 26, 28 recite the deep learning architecture as being ResNet, DenseNet, and EfficentNet, however these recite generic architectures that amount to nothing more than an instruction to apply the abstract idea using generic computing components. Claims 25 and 31 recite further the data augmentation technique using resized, random left, right flip and rotation, however these computing functions are generically recited and amount to nothing more than an instruction to apply the abstract idea using a generic computer.
Thus, taken alone and in ordered combination, the additional elements do not integrate the at least one abstract idea into a practical application.
Patent Subject Matter Eligibility Test: Step 2B:
Regarding Step 2B of the Subject Matter Eligibility Test, the independent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application, see MPEP 2106.05(II.). Further, it may need to be established, when determining whether a claim recites significantly more than a judicial exception, that the additional elements recite well understood, routine, and conventional activities, see MPEP 2106.05(d).
Regarding claim 1:
Regarding the additional limitation of the overall computing system with at least one processor and one or more computer storage media storing computer executable instructions that when executed by the at least one processor, cause the at least one processor to perform operations, digitizing one or more slides to generate whole slide images from biopsy samples of one or more patients having IBD, compiling a training data set of histopathology images from the digitized whole slide images, accessing the training data set, modifying a deep learning architecture by inserting a Dense layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), training the BDNN with the training data to generate a trained BDNN, use of the trained BDNN, and the use of a user interface, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”). [0030, 0080] of Applicant’s Specification recites the use of generic computing components of the processor and memory for the overall generic computing system. [0025, 0027, 0068] recites the use of slides to generate a training data set for generating the trained model, however the use of the slides to generate the training data does not recite a specific implementation of the training data set to be used for the training and modification of the BDNN to demonstrate a technology improvement. [0076] recites the step of accessing a training data set that is used to train the BDNN as further recited in [0064]. [0076] recites the modifying of a deep learning architecture by inserting the dense and dropout layers, however these additional elements do not recite an integration with the abstract idea that would demonstrate a technological improvement or a practical application, as the use of the BDNN is used merely a tool to perform the abstract idea. [0068, 0073] recites the use of the trained BDNN model to perform steps. [0064] recites the use of the BDNN for modeling uncertainty and is used merely as a tool. [0058, 0077] recites the use of a generically configured UI to perform actions related to providing the determination. The additional elements recite the use of generic computing components and generic steps of training a neural network with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception. Retrieving instructions stored in memory to perform steps of the system recites well understood, routine, and conventional activity.
Regarding claim 27:
Regarding the additional limitation of the overall computer implemented method, digitizing whole slide images from biopsy samples of one or more patients having inflammatory bowel disease (IBD) and taken by one or more sensors, compiling a training data set of histopathology images from the digitized whole slide images, modifying a deep learning architecture by inserting a Dense Layer followed by a Dropout layer before a final fully-connected layer to generate a Bayesian deep neural network (BDNN), training the BDNN with the training data set to generate a trained BDNN, use of the trained BDNN and use of a user interface, the Examiner submits that these limitations amount to nothing more than an instruction to apply the abstract idea using a generic computer and generic computing components (see MPEP § 2106.05(f) and MPEP § 2106.05(d)(II), specifically “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93”). [0030, 0080] of Applicant’s Specification recites the use of generic computing components of the processor and memory for the overall computer implementation of the method. [0025, 0027, 0068] recites the use of slides to generate a training data set for generating the trained model. [0066] recites the use of sensors for generating the data for the training data set. However, the use of the slides and sensors to generate the training data does not recite a specific implementation of the training data set to be used for the training and modification of the BDNN to demonstrate a technology improvement. [0076] recites the step of accessing a training data set that is used to train the BDNN as further recited in [0064]. [0076] recites the modifying of a deep learning architecture by inserting the dense and dropout layers, however these additional elements do not recite an integration with the abstract idea that would demonstrate a technological improvement or a practical application, as the use of the BDNN is used merely a tool to perform the abstract idea. [0068, 0073] recites the use of the trained BDNN model to perform steps. [0064] recites the use of the BDNN for modeling uncertainty and is used merely as a tool. [0058, 0077] recites the use of a generically configured UI to perform actions related to providing the determination. The additional elements recite the use of generic computing components and generic steps of training a neural network with a non-specific implementation to carry out steps of the abstract idea without showing an improvement to technology, computers or other technical fields, and thus recites mere instructions to implement the abstract idea on a computer and does not recite significantly more than the judicial exception. Retrieving instructions stored in memory to perform steps of the computer implemented method recites well understood, routine, and conventional activity.
Claim 15 is analyzed in a similar manner as claim 27.
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exceptions for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
For the reasons stated, the claims fail the Subject Matter Eligibility Test and therefore claims 1, 3-4, 6-7, 15, 17-18, and 20-31 are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Conclusion
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.
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/CONSTANTINE SIOZOPOULOS/
Examiner
Art Unit 3686
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