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 .
Election/Restrictions
Applicant’s election of Invention I (claims 1-5 and 16-20) in the reply filed on 07/21/2025 is acknowledged. Because applicant did not distinctly and specifically point out the supposed errors in the restriction requirement, the election has been treated as an election without traverse (MPEP § 818.01(a)).
Claims 6-15 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 07/21/2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2022 and 07/24/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-5 and 16-20 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.
Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.
When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may “express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure." Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340 (Fed. Cir. 2008) (internal citation omitted).It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015).
Claims 1 and 16 fails to sufficiently describe the usage of the heartbeat classification model in enough detail for one skilled in the art to understand how the inventor intended the function to be performed to show possession of the claimed invention. The mere statement and recitation of the usage of the model in claims 1 and 16, and in [0034]-[0046], [0057]-[0061], [0074]-[0078], [0083]-[0089] of the instant specification provides insufficient detail to the different classes and how they may be distinguished between the features of the ECG signal. Further, the instant specification fails to detail the class improvement between the classes, as disclosed in [0074]. The specification also fails to disclose how the features, such as RR interval, are classified as what class. Therefore, claims 1 and 16 do not provide sufficient detail for one to replicate and understand the intended function that’s being performed to show possession of the claimed invention.
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 1-5 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Each of independent claims 1 and 16 recites a step performing classification of the sample, which is a mental process. This judicial exception is not integrated into a practical application because the generically recited computer elements (ie. a memory and processor), determining values, and classifying samples do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The additional steps of “generating a feature map from the sample through multiple first layers of the heartbeat classification model; generating an attention mask based on an assistant feature generated from the feature map and the sample; generating a masked feature map by masking the feature map with the attention mask;” are data obtained from additional mathematical concepts as seen in the equations provided in [0064], [0067], [0070], [0073], [0071], and [0085] of the instant specification (i.e. abstract ideas).The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations are to receiving data, processing data, and classifying samples, which are all well-understood, routine, and conventional computer functions. See MPEP § 2106.05(d).
MPEP 2106(III) outlines steps for determining whether a claim is directed to statutory subject
matter. The stepwise analysis for the instant claim is provided here.
Step 1 – Statutory categories
Claim 16 is directed to a system (i.e. machine) and thus meets the step 1 requirements.
Claim 1 is directed to a method and thus meets the step 1 requirements.
Step 2A – Prong 1 – Judicial exception (j.e.)
Regarding claims 1 and 16, the following step is an abstract idea:
“performing classification of the sample”, which is a mental process when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(II), the mental process grouping includes observations, evaluations, judgements, and opinions. In this case, a human could analyze samples to classify the ECG samples for arrhythmias.
“converting the RGB signal to YCgCo and YCbCr signals… converting the color signal into a frequency domain… converting the extracted signal into a time domain”, which is a mathematical concept when given its broadest reasonable interpretation. As discussed in MPEP 2106.04(a)(2)(I), the mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. In this case, the conversion from RGB to any of the color spaces uses predetermined matrix equations. Further, the using of FFT and iFFT to convert from color to frequency and frequency to time domains are all mathematical equations.
Step 2A – Prong 2 – additional elements to integrate j.e. into a practical application
Regarding claim 1, the abstract idea is not integrated into a practical application.
The following claim elements do not add any meaningful limitation to the abstract idea:
- “sample” is data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)].
Regarding claim 16, the abstract idea is not integrated into a practical application.
The following claim elements do not add any meaningful limitation to the abstract idea:
- “memory”, “processor” are recited at a high level of generality amounting to generic computer components for implementing abstract idea [MPEP 2106.05(b)];
It is noted that the heart beat classification model is by definition automating the human thinking process with a computer.
- “sample” is data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)].
Step 2B – significantly more/inventive concept
Regarding claim 1, the abstract idea is not integrated into a practical application.
The following claim elements do not add any meaningful limitation to the abstract idea:
- “sample” is data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)].
Regarding claim 16, the abstract idea is not integrated into a practical application.
The following claim elements do not add any meaningful limitation to the abstract idea:
- “memory”, “processor” are recited at a high level of generality amounting to generic computer components for implementing abstract idea [MPEP 2106.05(b)];
It is noted that the heart beat classification model is by definition automating the human thinking process with a computer.
- “sample” is data (gathering, selecting, and displaying) that is necessary to implement the abstract idea on a computer amounting to insignificant extra-solution activity [MPEP 2106.05(g)].
The additional elements of claims 1 and 16, when considered separately and in combination, do not add significantly more (ie. an inventive concept) to the abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the memory and processor, along with their associated functions, are recited at a high level of generality and simply amount to implementing the abstract idea on a computer.
Dependent claims 2-5 and 17-20 do not integrate the abstract idea into a practical application
and do not add significantly more to the abstract idea of claim 1 and 16. The dependent claim limitations are directed to insignificant extra-solution activity (claims 2-5, and 17-20), which are insignificant extra-solution activity and do not amount to more than what is well-understood, routine, and conventional.
In summary, claims 1-5 and 16-20 are directed to an abstract idea without significantly more and, therefore, are patent ineligible.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-5, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20230397889)(Hereinafter Zhang) in view of Sellami et al. (“A robust deep convolutional neural network with batch-weighted loss for heartbeat classification” Expert Systems with Applications Volume 122, 15 May 2019, Pages 75-84)(IDS)(Hereinafter Sellami) .
Regarding claims 1, 5, 16 and 20, Zhang teaches A heartbeat classification method/apparatus (Abstract “Methods, apparatus and systems for robust and accurate detection of anomalies in medical images and electrocardiograms are disclosed. One example system for training a neural network engine includes a processor that is configured to receive a set of training electrocardiogram signals.”)
a memory storing at least one instruction ([0091] “The memory (e.g., main memory 1406, non-volatile memory 1410, machine-readable medium 1426) can be local, remote, or distributed.); and
a processor, wherein the processor executes the instruction/the heartbeat classification method comprising the steps of ([0006] “The processor is also configured to operate the neural network engine to identify the heart anomaly by classifying the set of training electrocardiogram signals and adjust the neural network engine based on the identified heart anomaly and the metadata.” And [0088]).
However, Zhang does not teach feature map generation from multiple convolution layers and a fully-connected layer for the performance of the classification. Sellami, in the same fiueld of endeavor, teaches the classification of heart beats using neural networks (Abstract), and further teaches
inputting a sample generated from an electrocardiogram signal to a heartbeat classification model (Page 78 right col. lines 30-33 “Input: our input data is composed of target heartbeats and their class labels. We can provide arbitrary number of neighboring beats with the target heartbeat for better classification performance.”);
generating a feature map from the sample through multiple first layers of the heartbeat classification model (Pg. 79 right col. lines 17-19 “Activation functions: We use ``Tanh” function after the first convolution while ``ReLU” is used after each of the next 8 convolutions.”);
generating an attention mask based on an assistant feature generated from the feature map and the sample (Pg. 79 right col. lines 5-7 and 14-19 “Batch normalization: Using a neural network with more layers are helpful in improving the classification performance of high dimensional time series data like ECG datasets…Activation functions: We use ``Tanh” function after the first convolution while ``ReLU” is used after each of the next 8 convolutions. If we use ``ReLU” after the first convolution, it is highly likely that important heartbeat features from the first convolutional layer are largely lost from the beginning of the network since it will convert all the negative values into 0.” Attention masking is a term of art which masks low values to 0 using weighting. Looking at Fig. 3, the ReLU function activation/convolution occurs following the first tanh activation/convolution, which is when the attention masking is done and the masked feature map is produced. Examiner interprets the assistant feature as weighting.);
generating a masked feature map by masking the feature map with the attention mask (Pg. 79 right col. lines 5-7 and 14-19 “Batch normalization: Using a neural network with more layers are helpful in improving the classification performance of high dimensional time series data like ECG datasets…Activation functions: We use ``Tanh” function after the first convolution while ``ReLU” is used after each of the next 8 convolutions. If we use ``ReLU” after the first convolution, it is highly likely that important heartbeat features from the first convolutional layer are largely lost from the beginning of the network since it will convert all the negative values into 0.” Attention masking is a term of art which masks low values to 0 using weighting. Looking at Fig. 3, the ReLU function activation/convolution occurs following the first tanh activation/convolution, which is when the attention masking is done and the masked feature map is produced. Examiner interprets the assistant feature as weighting.);
and performing classification of the sample from the masked feature map through a second layer of the heartbeat classification model (Pg. 79 right col. lines 39-42 and Fig. 3 the final step with the fully-connected layer [second layer] followed by the softmax function for the likelihood estimation.);
wherein the multiple first layers comprise multiple convolution layers and the second layer comprises at least one fully- connected layer (Pg. 79 right col. lines 1-4 and 38-41 “Convolution layers: our model contains 9 convolutional layers. Each convolution layer has 64k kernels with a length of 16 where k starts from 1 and increments by 1 per 2 convolutional layers. This choice allows the network to focus on small details of the input (heartbeats) and then construct the whole picture at the end. Let us denote as the output of the ith convolutional layer Ci…Likelihood estimation: after the last skip connection, we apply a fully connected layer followed by a softmax function. We do this to predict the probability of a class after minimizing the cross entropy weighted-loss given in Eq. (3).”) to increase accuracy and sensitivity of the model (Abstract). It would have been obvious to one skilled in the art, prior to the effective filing date of the invention, to modify the method and apparatus of Zhang, with the feature map generation from multiple convolution layers and a fully-connected layer for the performance of the classification of Sellami, because such a modification would allow to increase accuracy and sensitivity of the model.
Regarding claims 2 and 17, Zhang teaches wherein the assistant feature comprises an RR interval of the electrocardiogram signal corresponding to the sample ([0079] “system 700 can be configured to highlight the difference in peaks in the ECG (e.g., gradient changes in the ECG signal indicating the important regions).”).
Regarding claims 3 and 18, claims 1 and 16 are obvious over Zhang and Sellami. However, Zhang does not teach normalizing statistical value through normalization and generating attention map from the normalized statistical value via an activation layer. Sellami, in the same field of endeavor, teaches the classification of heart beats using neural networks (Abstract), and further teaches wherein the step of generating an attention mask comprises:
calculating a statistical value of the feature map (Pg. 79 right col. line 13);
generating a normalized statistical value through normalization of the statistical value with the assistant feature (Pg. 79 right col. lines 5-12“learning the weights in a deep network such as our 9-layer CNN in Fig. 3 may require a long training time. To improve the training time, we perform batch normalization after each convolutional layer for faster training stage.”); and
generating the attention mask from the normalized statistical value through at least one activation layer (Pg. 79 right col. lines 14-19 “Activation functions: We use ``Tanh” function after the first convolution while ``ReLU” is used after each of the next 8 convolutions. If we use ``ReLU” after the first convolution, it is highly likely that important heartbeat features from the first convolutional layer are largely lost from the beginning of the network since it will convert all the negative values into 0.”) to increase accuracy and sensitivity of the model (Abstract). It would have been obvious to one skilled in the art, prior to the effective filing date of the invention, to modify the method and apparatus of Zhang, with the normalizing statistical value through normalization and generating attention map from the normalized statistical value via an activation layer of Sellami, because such a modification would allow to increase accuracy and sensitivity of the model.
Regarding claims 4 and 19, claims 1 and 16 are obvious over Zhang and Sellami. However, Zhang does not teach normalizing statistical value through normalization and generating attention map from the normalized statistical value via an activation layer. Sellami, in the same field of endeavor, teaches the classification of heart beats using neural networks (Abstract), and further teaches wherein the step of generating an attention mask further comprises inputting the normalized statistical value to the activation layer after multiplying the normalized statistical value by a weight value and shifting the normalized statistical value by a predetermined value (See Fig. 3 where BN (Batch Normalization) occurs prior to the activation layer ReLU where the normalized statistical values are inputted into the ReLU activation layer in the 3rd convolution layer. It is noted that the phrase “multiplying the normalized statistical value by a weight value and shifting the normalized statistical value by a predetermined value” occurs in the 2nd convolution layer prior to the 3rd.) to increase accuracy and sensitivity of the model (Abstract). It would have been obvious to one skilled in the art, prior to the effective filing date of the invention, to modify the method and apparatus of Zhang, with the normalizing statistical value through normalization and generating attention map from the normalized statistical value via an activation layer of Sellami, because such a modification would allow to increase accuracy and sensitivity of the model.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOUSSA M HADDAD whose telephone number is (571)272-6341. The examiner can normally be reached M-TH 8:00-6:00.
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/MOUSSA HADDAD/Examiner, Art Unit 3796
/ALLEN PORTER/Primary Examiner, Art Unit 3796