Detailed Action
This Office Action is in response to the remarks entered on 04/24/2026. Claims 1-20 are currently pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim Objections have been withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, it recites “inputting the training data, while maintained in the two-dimensional format and without first flattening the training data.” However, nowhere in the specification is it disclosed that the input data is pre-processed ‘without first flattening the training data’. Accordingly, the examiner concluded that the claim fails to comply with the written description requirement.
Claims 2-7 depend from claim 1 and inherits all its limitations. Therefore, claims 9 and 11-14 are rejected.
Claim 8 is a method claim which recites the same feature as claim 1, and is rejected for at least the same reasons. Claims 9-10 depend from claim 8 and inherit all its limitations.
Claim 11 is a system claim which recites the same feature as claim 1, and is rejected for at least the same reasons. Claims 12-20 depend from claim 11 and inherit all its limitations.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 rejections have been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 7-12, 14, and 17, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Abuadbba et al. (Abuadbba et al, “Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?”, 2020, hereinafter ‘Abuadbba’) in view of Mattioli et al. (Mattioli et al., “A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface”, 2021, hereinafter ‘Mattioli’).
Regarding claim 1, Abuadbba teaches:
A method comprising:
inputting the training data, a one-dimensional convolutional neural network configured to model sequential relationships by performing one-dimensional convolution along the first dimension; ([Abuadbba, page 310, right col, 4.3 Distance Correlation, lines 1-10] and [Abuadbba, page 311, left col, 4.4. Dynamic Time Warping (DTW), lines 1-13] disclose modeling sequential relationships between the channels by calculating distance correlation and Dynamic Time Warping. [Abuadbba, page 307, right col, 3.1.1 ECG Dataset and Preprocessing, line 9 – 11] discloses normalizing the sample (that includes at least one 1-dimensional data) and feeding them to the 1D CNN. [Abuadbba, page 309, left col, lines 6-7; and right col, 3.2.2 Server, line 1-9] discloses processing the received data from the client side (raw training data), and then calculating (predict) the activated output from the last layer, which is interpreted as the target value associated with the training data. The loss is calculated based on the label received from the client to train the 1DCNN)
collecting feature maps that result from previous layers in the one-dimensional convolutional neural network into a single layer; ([Abuadbba, page 307, right col, 3.1.1 ECG Dataset and Preprocessing, line 9 – 11] discloses normalizing the sample and feeding them to the 1D CNN. [Abuadbba, page 308, right col, 3.2 Splitting 1D CNN, line 5 – page 309, left col, line 11] discloses splitting the 1DCNN to a client side and a server side, and then propagating the output of the client to the first hidden layer of the server. [Abuadbba, page 309, left col, line 1 - right col, 3.2.2 Server, line 1-4] discloses forward propagating in i-th layer of client side layers, sending the activation a^l from the l-th layer of the client side to the server, and then the server continues forward propagation after receiving the activation. [Abuadbba, page 308, left col, Figure 3; page 3.2.3 Influence on Performance, line 1-7] discloses that a single layer in the server receives the activation)
inputting the output from the single layer into a fully connected network; and ([Abuadbba, page 307, right col, 3.1.2 1D CNN Model Architecture, line 1 – page 308, line 4; Figure 3] discloses that the 1D CNN model contains at least two Fully Connected layers and propagates input signal from 1D Convolution layer side and generates output from Softmax layer side. [Abuadbba, page 308, right col, 3.2.1 Client, line 1-6 and right col, 3.2.2 Server, line 1-4] discloses that the first l layers are hold by the client where other layers are hold by the server, which indicates that the last 2 Fully Connected layers that processes the output from the client side are hold by the server side. [Abuadbba, page 308, left col, Figure 3; page 3.2.3 Influence on Performance, line 1-7] discloses that a single layer in the server receives the activation)
predicting, based on the fully connected network operating on the output, a target value associated with the training data. ([Abuadbba, page 308, left col, Figure 2 and 3] shows fully connected layers included in the 1DCNN. [Abuadbba, page 309, right col, 3.2.2 Server, line 1-9] discloses processing the received data from the client side, and then calculating (predict) the activated output from the last layer, which is interpreted as the target value associated with the training data. The loss is calculated based on the label received from the client. [Abuadbba, page 310, left col, 3.2.3 Influence on Performance, line 1-7] shows that the two conv layers OR three conv layers 1D CNN model were used to generate the target value)
However, Abuadbba does not specifically disclose:
organizing training data into a two-dimensional format comprising a first dimension and a second dimension;
inputting the training data, while maintained in the two-dimensional format and without first flattening the training data, into a one-dimensional convolutional neural network;
flattening an output of the single layer to obtain a flattened output;
inputting the flattened output from the single layer into a fully connected network;
Mattioli teaches:
organizing training data into a two-dimensional format comprising a first dimension and a second dimension; ([Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 1 – right col, line 12] discloses processing the 2D data using the 1D-CNN. [Mattioli, page 3, left col, 2.1. Dataset and ROIs, line 3 - bottom] and [Mattioli, page 4, Figure 1] collectively disclose that the ROIs are two-dimensional time-series data where each data point collected from different locations (channels) )
inputting the training data, while maintained in the two-dimensional format and without first flattening the training data, into a one-dimensional convolutional neural network; ([Mattioli, page 6, Table 4. Network architecture] and [Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 6-11]. The L1 receives the M x N input matrix, L6 Spatial Dropout Flatten layer flattens the input data, and L8-L10 Fully-connected Dropout layers follows the Flatten layer. Additionally, [page 5, left col, equation (2) and right col, line 1-2] “x is the two-dimensional input portion overlapping to the filter” clearly indicates that the input data x is the two-dimensional input and is flatten inside the neural network)
flattening an output of the single layer to obtain a flattened output; ([Mattioli, page 6, Table 4. Network architecture] and [Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 6-11]. The L1 receives the M x N input matrix, L6 Spatial Dropout Flatten layer flattens the input data, and L8-L10 Fully-connected Dropout layers follows the Flatten layer)
inputting the flattened output from the single layer into a fully connected network; ([Mattioli, page 6, Table 4. Network architecture] and [Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 6-11]. The L1 receives the M x N input matrix, L6 Spatial Dropout Flatten layer flattens the input data, and L8-L10 Fully-connected Dropout layers follows the Flatten layer)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Abuadbba and Mattioli to use the method of organizing training data into a two-dimensional format and processing the n-dimensional data using 1DCNN of Mattioli to implement the machine learning model prediction method of Abuadbba. The suggestion and/or motivation for doing so is to improve the performance of the prediction method by reducing the dimension of the input data and thus avoiding overfitting.
Regarding claim 2, Abuadbba in view of Mattioli teaches:
The method of claim 1, wherein the first dimension represents time and the second dimension represents a feature. ([Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 1 – right col, line 12] discloses processing the 2D data using the 1D-CNN. The input matrix dimension is M x N where M is the length of the time window considered (time) and N is the number of EEG channels (features) )
Regarding claim 4, Abuadbba teaches:
The method of claim 1, wherein the one-dimensional convolutional neural network comprises a Conv1D convolutional neural network. ([Abuadbba, page 307, right col, 3.1.1 ECG Dataset and Preprocessing, line 9 – 11] discloses normalizing the sample and feeding them to the 1D CNN. [Abuadbba, page 308, right col, 3.2 Splitting 1D CNN, line 5 – page 309, left col, line 11] discloses splitting the 1DCNN to a client side and a server side, and then propagating the output of the client to the first hidden layer of the server)
Regarding claim 7, Abuadbba teaches:
The method of claim 1, wherein the training data comprises time series data. ([Abuadbba, page 311, left col, 4.4 Dynamic Time Warping (DTW), line 7-10] and [Abuadbba, pave 315, right col, 8 CONCLUSION, line 1-3] collectively discloses that the training data comprises time series data)
Regarding claim 8, Abuadbba teaches:
A method comprising: inputting the training data, ([Abuadbba, page 310, right col, 4.3 Distance Correlation, lines 1-10] and [Abuadbba, page 311, left col, 3.3 Dynamic Time Warping (DTW), lines 1-13] disclose modeling sequential relationships between the channels by calculating distance correlation and Dynamic Time Warping. [Abuadbba, page 307, right col, 3.1.1 ECG Dataset and Preprocessing, line 9 – 11] discloses normalizing the sample (that includes at least one 1-dimensional data) and feeding them to the 1D CNN. [Abuadbba, page 309, left col, lines 6-7; and right col, 3.2.2 Server, line 1-9] discloses processing the received data from the client side (raw training data), and then calculating (predict) the activated output from the last layer, which is interpreted as the target value associated with the training data. The loss is calculated based on the label received from the client to train the 1DCNN)
training the convolutional neural network on the training data to yield a trained convolutional neural network; and ([Abuadbba, page 307, right col, 3.1.1 ECG Dataset and Preprocessing, line 9 – 11] discloses normalizing the sample and feeding them to the 1D CNN. [Abuadbba, page 309, right col, 3.2.2 Server, line 1-9] discloses processing the received data from the client side, and then calculating (predict) the activated output from the last layer, which is interpreted as the target value associated with the training data. The loss is calculated based on the label received from the client to train the 1DCNN)
predicting, based on input data to the trained convolutional neural network, a target value associated with the training data. ([Abuadbba, page 308, left col, Figure 2 and 3] shows fully connected layers included in the 1DCNN. [Abuadbba, page 309, right col, 3.2.2 Server, line 1-9] discloses processing the received data from the client side, and then calculating (predict) the activated output from the last layer, which is interpreted as the target value associated with the training data. The loss is calculated based on the label received from the client. [Abuadbba, page 310, left col, 3.2.3 Influence on Performance, line 1-7] shows that the two conv layers OR three conv layers 1D CNN model were used to generate the target value)
Abuadbba does not specifically disclose:
organizing training data into a two-dimensional format comprising a first dimension and a second dimension;
inputting the training data, while maintained in the two-dimensional format and without first flattening the training data, into a one-dimensional convolutional neural network … performing one-dimensional convolution along the first dimension;
Mattioli teaches:
organizing training data into a two-dimensional format comprising a first dimension and a second dimension; ([Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 1 – right col, line 12] discloses processing the 2D data using the 1D-CNN. [Mattioli, page 3, left col, 2.1. Dataset and ROIs, line 3 - bottom] and [Mattioli, page 4, Figure 1] collectively disclose that the ROIs are two-dimensional time-series data where each data point collected from different locations (channels) )
inputting the training data, while maintained in the two-dimensional format and without first flattening the training data, into a one-dimensional convolutional neural network … performing one-dimensional convolution along the first dimension; ([Mattioli, page 6, Table 4. Network architecture] and [Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 6-11]. The L1 receives the M x N input matrix, L6 Spatial Dropout Flatten layer flattens the input data, and L8-L10 Fully-connected Dropout layers follows the Flatten layer. Additionally, [page 5, left col, equation (2) and right col, line 1-2] “x is the two-dimensional input portion overlapping to the filter” clearly indicates that the input data x is the two-dimensional input and is flatten inside the neural network)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Abuadbba and Mattioli to use the method of organizing training data into a two-dimensional format and processing the n-dimensional data using 1DCNN of Mattioli to implement the machine learning model prediction method of Abuadbba. The suggestion and/or motivation for doing so is to improve the performance of the prediction method by reducing the dimension of the input data and thus avoiding overfitting.
Regarding claim 9, Abuadbba teaches:
The method of claim 8, wherein the training data comprises time series data. ([Abuadbba, page 311, left col, 4.4 Dynamic Time Warping (DTW), line 7-10] and [Abuadbba, pave 315, right col, 8 CONCLUSION, line 1-3] collectively discloses that the training data comprises time series data)
Claim 10 is a method claim having similar limitation to claim 5. Therefore, claim 10 is rejected under the same rationale as of claim 5 above.
Regarding claim 11, Abuadbba teaches:
A system comprising: a processor; and a computer-readable storage device storing instructions which, when executed by the processor, cause the processor to be configured to: ([Abuadbba, page 307, left col, 2.2 Split Learning, line 1-4 and right col, 3.1.1 ECG Dataset and Preprocessing, line 1-3] indicates that the training is performed on the client and the server using MIT-BIH dataset available online. The server is a computer that contains a computer-readable storage device storing instructions and using processor)
Claim 11 is a system claim having similar limitation to claim 1. Therefore, claim 11 is rejected under the same rationale as of claim 1 above.
Claim 12 is a system claim having similar limitation to claim 2. Therefore, claim 12 is rejected under the same rationale as of claim 2 above.
Claim 14 is a system claim having similar limitation to claim 4. Therefore, claim 14 is rejected under the same rationale as of claim 4 above.
Claim 17 is a system claim having similar limitation to claim 7. Therefore, claim 17 is rejected under the same rationale as of claim 7 above.
Regarding claim 19, Abuadbba teaches:
The system of claim 11, wherein a same padding is used to keep a size of the input data unchanged through the one-dimensional convolutional neural network. ([Abuadbba, page 307, right col, 3.1.2 1D CNN Model Architecture, line 4-5; page 311, right col, 5.1 Adding More Hidden Layers, line 4-6] discloses adding Zero padding to keep the size of the input data unchanged. The ‘same padding’ is achieved by adding zero to the original data which is the same as the ‘zero padding’)
Claims 3, 13, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Abuadbba in view of Mattioli and further in view of Lee (US 20200249651 A1, hereinafter ‘Lee’)
Regarding claim 3, Abuadbba in view of Mattioli teaches:
the method of claim 1, further comprising normalizing of the training data ([Mattioli, page 4, right col, line 12-22] discloses processing the training datasets for each ROI using a min-max normalization)
However, Abuadbba in view of Mattioli does not specifically disclose:
normalizing of the training data into a range between and including [-1, 1].
Lee teaches:
normalizing of the training data into a range between and including [-1, 1]. ([Lee, 0057] discloses normalizing the data sample into a range of (-1,1) )
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Abuadbba, Mattioli and Lee to use the method of normalizing the training data into a specific range of Lee to implement the machine learning model prediction method of Abuadbba. The suggestion and/or motivation for doing so is to improve the performance of the prediction method by organizing the input data and make it easier for the model to identify patterns in the input data.
Claim 13 is a system claim having similar limitation to claim 3. Therefore, claim 13 is rejected under the same rationale as of claim 3 above.
Regarding claim 18, Abuadbba in view of Mattioli and further in view of Lee teaches:
The system of claim 11, wherein the processor is configured to normalize the training data using min-max normalization. ([Lee, 0057] discloses normalizing the data sample into a range of (-1,1). [Lee, 0060] discloses pre-training the machine learning model using the big dataset 821)
Regarding claim 20, Abuadbba in view of Mattioli and further in view of Lee teaches:
The system of claim 11, wherein normalize the training data to a negative lower value and a positive higher value. ([Lee, 0057] discloses normalizing the data sample into a range of (-1,1) )
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Abuadbba in view of Mattioli and further in view of Unnikrishnan et al. (US 20220139556 A1, hereinafter ‘Unnikrishnan’).
Regarding claim 5, Abuadbba in view of Mattioli teaches:
The method of claim 1, further comprising: a time window over training data, wherein the time window covers a plurality of rows in the training data ([Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 1 – right col, line 12] discloses processing the 2D data using the 1D-CNN. The input matrix dimension is M x N where M is the length of the time window considered (time) and N is the number of EEG channels (features) )
However, Abuadbba in view of Mattioli does not specifically disclose:
further comprising: selecting a time window over the training data, wherein the time window covers a plurality of rows in the training data.
Unnikrishnan teaches:
further comprising: selecting a time window over the training data, wherein the time window covers a plurality of rows in the training data. ([Unnikrishnan, 0034] discloses selecting a particular time window that contains “w” number of rows from the training data)
Before the effective filing date of the invention to a person of ordinary skill in the art, it would have been obvious, having the teachings of Abuadbba, Mattioli and Unnikrishnan to use the method of selecting a time window over training data of Unnikrishnan to implement the machine learning model prediction method of Abuadbba. The suggestion and/or motivation for doing so is to improve the efficiency of the prediction method by reducing the size of the training data by selecting a subset of the training data.
Regarding claim 6, Abuadbba in view of Mattioli and further in view of Unnikrishnan teaches:
The method of claim 5, wherein the time window is one of static and dynamic. ([Unnikrishnan, 0034] discloses selecting a particular time window that contains “w” number of rows from the training data. It can be interpreted as ‘static’ as the selected time window does not change after the selection, but it can also be interpreted as ‘dynamic’ as it may be selected arbitrarily)
Regarding claim 15, Abuadbba in view of Mattioli teaches:
The system of claim 11, wherein the computer-readable storage device stores additional instructions which, when executed by the processor, cause the processor to be configured to: ([Abuadbba, page 307, left col, 2.2 Split Learning, line 1-4 and right col, 3.1.1 ECG Dataset and Preprocessing, line 1-3] indicates that the training is performed on the client and the server using MIT-BIH dataset available online. The server is a computer that contains a computer-readable storage device storing instructions and using processor)
Claim 15 is a system claim having similar limitation to claim 5. Therefore, claim 15 is rejected under the same rationale as of claim 5 above.
Claim 16 is a system claim having similar limitation to claim 6. Therefore, claim 16 is rejected under the same rationale as of claim 6 above.
Response to Arguments
Claim Objections
Applicant’s arguments, see [Remarks, page 6], filed 04/24/2026, with respect to Claim Objections have been fully considered and are persuasive. The Claim Objection of claim 10 has been withdrawn.
Response to Arguments under 35 U.S.C. 112(b)
Applicant’s arguments, see [Remarks, pages 6-8], filed 04/24/2026, with respect to 35 U.S.C. 112(b) have been fully considered and are persuasive. The Claim Objection of claims 1-20 have been withdrawn.
Response to Arguments under 35 U.S.C. 101
Applicant’s arguments, see [Remarks, pages 9-11], filed 04/24/2026, with respect to 35 U.S.C. 101 rejections have been fully considered and are persuasive. The 35 U.S.C. 101 rejections for Claims 1-20 have been withdrawn.
Response to Arguments under 35 U.S.C. 103
Arguments: Applicant asserts that the amended limitation of “inputting the training data, while maintained in the two-dimensional format and without first flattening the training data, into a one-dimensional convolutional neural network configured to model sequential relationships by performing one-dimensional convolution along the first dimension” is not taught or suggested by the cited combination [Remarks, page 12-13].
Examiner’s Response: Examiner respectfully disagrees. The Applicant alleges that Mattioli fails to disclose the features in claim 1 “a two-dimensional input matrix, multiple convolution layers operating over that structured input, a later flatten layer, then dense layer(s), and then the model head.” However, Mattioli teaches the exact structure in [Table 4. Network architecture] and [Mattioli, page 5, left col, 2.3. One-dimensional convolutional neural network (1D-CNN), line 6-11]. The L1 receives the M x N input matrix, L6 Spatial Dropout Flatten layer flattens the input data, and L8-L10 Fully-connected Dropout layers follows the Flatten layer. Additionally, [page 5, left col, equation (2) and right col, line 1-2] “x is the two-dimensional input portion overlapping to the filter” clearly indicates that the input data x is the two-dimensional input and is flatten inside the neural network.
Accordingly, arguments regarding claims 1, 8, and 11, and dependent claims, are not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jin et al., “FLATTENED CONVOLUTIONAL NEURAL NETWORKS FOR FEEDFORWARD ACCELERATION” (This prior art is pertinent because it discloses flattening the multi-dimensional input data using a flatten layer)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUN KWON whose telephone number is (571)272-2072. The examiner can normally be reached Monday – Friday 7:30AM – 4:30PM ET.
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/JUN KWON/Examiner, Art Unit 2127
/ABDULLAH AL KAWSAR/Supervisory Patent Examiner, Art Unit 2127
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